[ { "path": ".gitignore", "content": "/__pycache__\n/.ipynb_checkpoints\n*#\n*~\n/linalg-benchmark/.idea/linalg-benchmark.iml\n/linalg-benchmark/.idea/misc.xml\n/linalg-benchmark/.idea/modules.xml\n/linalg-benchmark/.idea/vcs.xml\n/linalg-benchmark/.idea/workspace.xml\n/linalg-benchmark/.idea\n.DS_Store\n__pycache__\n" }, { "path": "README.md", "content": "# stuff\n" }, { "path": "akaitsuki-slow/config.py", "content": "import argparse\n \n \ndef str2bool(v):\n return v.lower() in ('y', 'yes', 't', 'true', '1')\n \n \ndef get_args():\n parser = argparse.ArgumentParser()\n parser.register('type', 'bool', str2bool)\n \n parser.add_argument('--random_seed',\n type=int,\n default=1013,\n help='Random seed')\n \n parser.add_argument('--vocab_size',\n type=int,\n default=10000,\n help='Default embed size')\n \n parser.add_argument('--embed_size',\n type=int,\n default=128,\n help='Default embedding size if embedding_file is not given')\n \n parser.add_argument('--hidden_size',\n type=int,\n default=128,\n help='Hidden size of RNN units')\n \n parser.add_argument('--num_labels',\n type=int,\n default=96,\n help='num labels')\n \n parser.add_argument('--bidir',\n type='bool',\n default=True,\n help='bidir: whether to use a bidirectional RNN')\n \n parser.add_argument('--num_layers',\n type=int,\n default=1,\n help='Number of RNN layers')\n \n parser.add_argument('--rnn_type',\n type=str,\n default='gru',\n help='RNN type: lstm or gru (default)')\n \n parser.add_argument('--batch_size',\n type=int,\n default=32,\n help='Batch size')\n \n parser.add_argument('--dropout_rate',\n type=float,\n default=0.2,\n help='Dropout rate')\n \n parser.add_argument('--optimizer',\n type=str,\n default='sgd',\n help='Optimizer: sgd (default) or adam or rmsprop')\n \n parser.add_argument('--learning_rate', '-lr',\n type=float,\n default=0.1,\n help='Learning rate for SGD')\n \n return parser.parse_args()\n\n" }, { "path": "akaitsuki-slow/feed_dict.pbtxt", "content": "step_stats {\n dev_stats {\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n node_stats {\n node_name: \"_SOURCE\"\n all_start_micros: 1489437692909096\n op_start_rel_micros: 2\n op_end_rel_micros: 3\n all_end_rel_micros: 7\n memory {\n allocator_name: \"cpu\"\n }\n timeline_label: \"_SOURCE = NoOp()\"\n scheduled_micros: 1489437692909081\n }\n node_stats {\n node_name: \"mul/y\"\n all_start_micros: 1489437692909111\n op_end_rel_micros: 1\n all_end_rel_micros: 11\n memory {\n allocator_name: \"cpu\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n }\n allocation_description {\n requested_bytes: 4\n allocator_name: \"cpu\"\n ptr: 4588618432\n }\n }\n }\n timeline_label: \"mul/y = Const()\"\n scheduled_micros: 1489437692909103\n }\n node_stats {\n node_name: \"mul\"\n all_start_micros: 1489437692909144\n op_start_rel_micros: 2\n op_end_rel_micros: 12\n all_end_rel_micros: 16\n memory {\n allocator_name: \"cpu\"\n total_bytes: 4\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n }\n allocation_description {\n requested_bytes: 4\n allocator_name: \"cpu\"\n ptr: 4937671424\n }\n }\n }\n timeline_label: \"mul = Mul(_recv_Placeholder_0, mul/y)\"\n scheduled_micros: 1489437692909140\n }\n }\n}\npartition_graphs {\n node {\n name: \"_recv_Placeholder_0\"\n op: \"_Recv\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: true\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: -2824119418009608211\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"Placeholder:0\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"mul/y\"\n op: \"Const\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"dtype\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_FLOAT\n tensor_shape {\n }\n float_val: 2.0\n }\n }\n }\n }\n node {\n name: \"mul\"\n op: \"Mul\"\n input: \"_recv_Placeholder_0\"\n input: \"mul/y\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"_send_mul_0\"\n op: \"_Send\"\n input: \"mul\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: true\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: -2824119418009608211\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"mul:0\"\n }\n }\n }\n versions {\n producer: 21\n }\n}\n" }, { "path": "akaitsuki-slow/feed_dict.py", "content": "import numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.client import timeline \n \n\nsess = tf.Session()\na = tf.placeholder(tf.float32)\nb = a*2\nc0 = sess.run([b], feed_dict={a:2.})\n\nrun_metadata = tf.RunMetadata()\nrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\nrun_options.output_partition_graphs=True\n\nc0 = sess.run([b], feed_dict={a:2.}, options=run_options,\n run_metadata=run_metadata)\nwith open(\"feed_dict.pbtxt\", \"w\") as f:\n f.write(str(run_metadata))\n" }, { "path": "akaitsuki-slow/main.py", "content": "import logging\nimport time\nimport config\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.client import timeline \n \ndef retrieve_seq_length_op2(data):\n return tf.reduce_sum(tf.cast(tf.greater(data, tf.zeros_like(data)), tf.int32), 1)\n \n \ndef advanced_indexing_op(input, index):\n batch_size = tf.shape(input)[0]\n max_length = tf.shape(input)[1]\n dim_size = int(input.get_shape()[2])\n index = tf.range(0, batch_size) * max_length + (index - 1)\n flat = tf.reshape(input, [-1, dim_size])\n relevant = tf.gather(flat, index)\n return relevant\n \n \ndef bidirectional_dynamic_rnn(inputs, cell_fn, n_hidden, sequence_length=None, return_last=False, name='bidyrnn'):\n with tf.variable_scope(name):\n batch_size = array_ops.shape(inputs)[0]\n \n fw_cell = cell_fn(num_units=n_hidden)\n bw_cell = cell_fn(num_units=n_hidden)\n \n fw_initial_state = fw_cell.zero_state(batch_size, dtype=tf.float32)\n bw_initial_state = bw_cell.zero_state(batch_size, dtype=tf.float32)\n \n outputs, _ = tf.nn.bidirectional_dynamic_rnn(\n cell_fw=fw_cell,\n cell_bw=bw_cell,\n inputs=inputs,\n sequence_length=sequence_length,\n initial_state_fw=fw_initial_state,\n initial_state_bw=bw_initial_state,\n )\n outputs = tf.concat(outputs, 2)\n if return_last:\n outputs = advanced_indexing_op(outputs, sequence_length)\n return outputs\n \n \ndef BilinearAttention(inputs, n_hidden, mask=None,\n initializer=tf.random_uniform_initializer(-0.01, 0.01)):\n W = tf.get_variable('W', shape=(n_hidden, n_hidden),\n initializer=initializer)\n M = tf.matmul(inputs[1], W)\n M = tf.expand_dims(M, axis=1)\n alpha = tf.nn.softmax(tf.reduce_sum(inputs[0] * M, axis=2))\n if mask is not None:\n alpha *= mask\n alpha /= tf.reduce_sum(alpha, axis=1, keep_dims=True)\n alpha = tf.expand_dims(alpha, axis=2)\n outputs = tf.reduce_sum(inputs[0] * alpha, axis=1)\n \n return outputs\n \n \ndef inference(x1, x2, mask1, mask2, l, y,\n args, embeddings, reuse=False, training=False):\n with tf.variable_scope('model', reuse=reuse):\n embed = tf.get_variable('embed', shape=embeddings.shape,\n initializer=tf.constant_initializer(embeddings))\n embed1 = tf.nn.embedding_lookup(embed, x1)\n embed2 = tf.nn.embedding_lookup(embed, x2)\n \n keep = 1.0 - args.dropout_rate if training else 1.0\n dropout1 = tf.nn.dropout(embed1, keep)\n dropout2 = tf.nn.dropout(embed2, keep)\n \n rnn_cell = {'gru': tf.contrib.rnn.GRUCell,\n 'lstm': tf.contrib.rnn.LSTMCell}[args.rnn_type]\n rnn1 = bidirectional_dynamic_rnn(dropout1, cell_fn=rnn_cell,\n n_hidden=args.hidden_size,\n sequence_length=retrieve_seq_length_op2(mask1),\n name='rnn1')\n rnn2 = bidirectional_dynamic_rnn(dropout2, cell_fn=rnn_cell,\n n_hidden=args.hidden_size,\n sequence_length=retrieve_seq_length_op2(mask2),\n return_last=True,\n name='rnn2')\n \n args.rnn_output_size = 2 * args.hidden_size\n att = BilinearAttention([rnn1, rnn2], args.rnn_output_size, mask1)\n \n z = tf.layers.dense(att, units=args.num_labels,\n kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1),\n use_bias=False)\n \n prob = tf.nn.softmax(z)\n prob = prob * l\n prob /= tf.reduce_sum(prob, axis=1, keep_dims=True)\n \n pred = tf.to_int32(tf.arg_max(prob, dimension=1))\n acc = tf.reduce_mean(tf.to_float(tf.equal(pred, y)))\n \n if not training:\n return acc\n else:\n epsilon = 1e-7\n prob = tf.clip_by_value(prob, epsilon, 1 - epsilon)\n loss = tf.one_hot(y, depth=args.num_labels) * -tf.log(prob)\n loss = tf.reduce_sum(loss, axis=1)\n loss = tf.reduce_mean(loss)\n \n if args.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate=args.learning_rate)\n elif args.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer()\n elif args.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(learning_rate=args.learning_rate)\n else:\n raise NotImplementedError('optimizer = %s' % args.optimizer)\n train_op = optimizer.minimize(loss)\n return train_op, loss, acc\n \n \ndef main(args):\n logging.info('-' * 50)\n logging.info('Preparing data..')\n \n embeddings = np.random.uniform(-1, 1, (args.vocab_size, args.embed_size))\n x1 = np.random.choice(args.vocab_size, (4 * args.batch_size, 2000))\n x2 = np.random.choice(args.vocab_size, (4 * args.batch_size, 50))\n len1 = np.random.choice(np.arange(1000, 2000), 4 * args.batch_size)\n mask1 = np.ones((4 * args.batch_size, 2000)).astype('float32')\n for l, mask in zip(len1, mask1):\n mask[l:] = 0\n len2 = np.random.choice(np.arange(25, 50), 4 * args.batch_size)\n mask2 = np.ones((4 * args.batch_size, 50)).astype('float32')\n for l, mask in zip(len2, mask2):\n mask[l:] = 0\n l = np.ones((4 * args.batch_size, args.num_labels)).astype('float32')\n y = np.random.choice(args.num_labels, 4 * args.batch_size)\n \n in_x1 = tf.placeholder(tf.int32, [None, None])\n in_x2 = tf.placeholder(tf.int32, [None, None])\n in_mask1 = tf.placeholder(tf.float32, [None, None])\n in_mask2 = tf.placeholder(tf.float32, [None, None])\n in_l = tf.placeholder(tf.float32, [None, None])\n in_y = tf.placeholder(tf.int32, [None])\n feed_dict = {in_x1: x1, in_x2: x2,\n in_mask1: mask1, in_mask2: mask2,\n in_l: l, in_y: y}\n \n q = tf.RandomShuffleQueue(capacity=2000 * args.batch_size, min_after_dequeue=0,\n dtypes=[tf.int32, tf.int32, tf.float32, tf.float32, tf.float32, tf.int32],\n shapes=[x1.shape[1:], x2.shape[1:],\n mask1.shape[1:], mask2.shape[1:],\n l.shape[1:], y.shape[1:]])\n q_size = q.size()\n enqueue_op = q.enqueue_many([in_x1, in_x2, in_mask1, in_mask2, in_l, in_y])\n qr = tf.train.QueueRunner(q, [enqueue_op])\n all_data = q.dequeue_many(args.batch_size)\n \n logging.info('Building Computation Graph..')\n train_op, loss, ac = inference(*all_data, args, embeddings, reuse=False, training=True)\n light_op = tf.square(all_data[0])\n \n logging.info('-' * 50)\n logging.info('Create TensorFlow session..')\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n config.allow_soft_placement = True\n sess = tf.Session(config=config)\n \n logging.info('Initialize model parameters..')\n sess.run(tf.global_variables_initializer())\n \n logging.info('-' * 50)\n logging.info(args)\n \n logging.info('-' * 50)\n logging.info('Start training..')\n \n num_samples_in_queue = sess.run(q_size)\n while num_samples_in_queue < 1999 * args.batch_size:\n sess.run(qr.enqueue_ops, feed_dict)\n num_samples_in_queue = sess.run(q_size)\n print(\"Recharging queue, current size = %i\" % num_samples_in_queue)\n \n writer = tf.summary.FileWriter('summary', sess.graph)\n idx = 0\n while num_samples_in_queue > args.batch_size:\n idx += 1\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n begin_time = time.time()\n #sess.run(light_op, options=run_options, run_metadata=run_metadata)\n sess.run(train_op, options=run_options, run_metadata=run_metadata)\n end_time = time.time()\n writer.add_run_metadata(run_metadata, 'step = %d' % idx)\n writer.flush()\n num_samples_in_queue = sess.run(q_size)\n logging.info('%d left in queue' % num_samples_in_queue)\n logging.info('elapsed time %.2f(s)' % (end_time - begin_time))\n\n with open('stepstats-%d.json'%(idx,), 'w') as f:\n f.write(str(run_metadata))\n\n tl = timeline.Timeline(run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format()\n with open('timeline-%d.json'%(idx,), 'w') as f:\n f.write(ctf)\n \n writer.close()\n logging.info('-' * 50)\n logging.info('Close TensorFlow session..')\n sess.close()\n \n \nif __name__ == \"__main__\":\n args = config.get_args()\n np.random.seed(args.random_seed)\n tf.set_random_seed(args.random_seed)\n \n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(message)s', datefmt='%m-%d %H:%M')\n \n main(args)\n\n \n" }, { "path": "autotune/README.md", "content": "To run tests in this directory\n\n```\npytest\n```\n\nIf there's a slow test, you can run this file directly to see timings of individual tests, ie\n\n```\npython linesearch_test.py\n```\n" }, { "path": "autotune/autograd_lib.py", "content": "\"\"\"\nLibrary for extracting interesting quantites from autograd.\n\nNot thread-safe because of module-level variables affecting state of autograd\n\nNotation:\no: number of output classes (exact Hessian), number of Hessian samples (sampled Hessian)\nn: batch-size\ndo: output dimension (output channels for convolution)\ndi: input dimension (input channels for convolution)\ns: spatial dimension (Oh*Ow)\nOh, Ow: output height, output width (convolution)\nKh, Kw: kernel height, kernel width (convolution)\n\nHi: per-example Hessian\n Linear layer: shape [do*di, do*di]\n Conv2d layer: shape [do*di*Kh*Kw, do*di*Kh*Kw]\nHi_bias: per-example Hessian of bias\nH: mean Hessian of matmul\nH_bias: mean Hessian of bias\n\n\nJo: batch output Jacobian of matmul, gradient of output for each output,example pair, [o, n, ....]\nJo_bias: output Jacobian of bias\n\nA, activations: inputs into matmul\n Linear: [n, di]\n Conv2d: [n, di, Ih, Iw] -> (unfold) -> [n, di, Oh, Ow]\nB, backprops: backprop values (aka Lop aka Jacobian-vector product) for current layer\n Linear: [n, do]\n Conv2d: [n, do, Oh, Ow]\n\nweight: matmul part of layer, Linear [di, do], Conv [do, di, Kh, Kw]\n\nH, hess -- hessian\nS, sigma -- noise\nL, lyap -- lyapunov matrix\n\n\"\"\"\nimport math\nfrom typing import List, Optional, Callable, Tuple, Dict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport util as u\nimport globals as gl\nfrom attrdict import AttrDefault, AttrDict\n\n_supported_layers = ['Linear', 'Conv2d'] # Supported layer class types TODO(y): make non-private\n_supported_methods = ['exact', 'kron', 'mean_kron', 'experimental_kfac'] # supported approximation methods\n_supported_losses = ['LeastSquares', 'CrossEntropy']\n\n# module-level variables affecting state of autograd\n_global_hooks_disabled: bool = False # work-around for https://github.com/pytorch/pytorch/issues/25723\n_global_enforce_fresh_backprop: bool = False # global switch to catch double backprop errors on Hessian computation\n_global_backprops_prefix = '' # hooks save backprops to, ie param.{_backprops_prefix}backprops_list\n\n\nclass LayerStats:\n # Some notation/background from https://docs.google.com/document/d/19Jmh4spbSAnAGX_eq7WSFPgLzrpJEhiZRpjX1jSYObo/edit#heading=h.9fi55aowtmgy\n sparsity: torch.Tensor\n mean_activation: torch.Tensor\n mean_backprop: torch.Tensor\n\n sigma_l2: torch.Tensor # l2 norm of centered gradient covariance (singlconnected noise covariant matrix)\n sigma_erank: torch.Tensor # trace/l2 norm\n\n H_l2: torch.Tensor # spectral norm of H (largest curvature)\n H_fro: torch.Tensor # Frobenius norm of hessian\n grad_fro: torch.Tensor # Frobenius norm of gradient update\n param_fro: torch.Tensor # Frobenius norm of parameter tensor\n\n grad_curv: torch.Tensor # curvature in direction of gradient\n newton_curv: torch.Tensor # curvature in direction of Newton step\n\n step_openai: torch.Tensor # optimal step length using gradient direction and Hessian curvature estimate\n step_div_inf: torch.Tensor # divergent step size for infinite batch (2/spectral radius)\n step_div_1: torch.Tensor # (2/trace, a bad attempt to approximate Jain divergent lr, should be 2/R^2)\n newton_fro: torch.Tensor # Frobenius norm of newton step\n regret_gradient: torch.Tensor # expected improvement if we took optimal step-size in gradient direction\n reget_newton: torch.Tensor # expected improvement if we took Newton step\n batch_openai: torch.Tensor # optimal batch size from gradient noise stat (loss change from noise part over loss change from deterministic part)\n batch_jain_simple: torch.Tensor # optimal batch-size assuming well-specified model (trace/sigma)\n batch_jain_full: torch.Tensor # optimal batch size using Jain/Kakade approach\n noise_variance_pinv: torch.Tensor # asymptotic minimax rate (called noise variance in 1.1.4 of Jain/Kakade)\n\n # need: R^2 the largest Jacobian size\n # need: angle between gradient and newton step\n\n # 2 hessians, 2 covariance matrices, need l2, trace, rank, spectrum\n\n def __iter__(self):\n return iter(self.__dict__)\n\n def __init__(self):\n pass\n\n def __getitem__(self, item):\n return self.__dict__[item]\n\n def items(self):\n return self.__dict__.items()\n\n\ndef add_hooks(model: nn.Module) -> None:\n \"\"\"\n Adds hooks to model to save activations and backprop values.\n\n The hooks will\n 1. assign activations to layer.activations during forward pass\n 2. assign layer output to layer.output during forward pass\n 2. append backprops to layer.backprops_list during backward pass\n\n Call \"clear_backprops\" to clear backprops_list values for all parameters in the model\n Call \"remove_hooks(model)\" to undo this operation.\n\n\n Args:\n model:\n \"\"\"\n\n global _global_hooks_disabled\n _global_hooks_disabled = False\n\n handles = []\n for layer in model.modules():\n if _layer_type(layer) in _supported_layers:\n handles.append(layer.register_forward_hook(_capture_activations))\n handles.append(layer.register_forward_hook(_capture_output))\n handles.append(layer.register_backward_hook(_capture_backprops))\n\n model.__dict__.setdefault('autograd_hacks_hooks', []).extend(handles)\n\n\ndef remove_hooks(model: nn.Module) -> None:\n \"\"\"\n Remove hooks added by add_hooks. Provides Providesa hook for removing hooks.\n \"\"\"\n\n assert model == 0, \"not working, remove this after fix to https://github.com/pytorch/pytorch/issues/25723\"\n\n\n\n\n if not hasattr(model, 'autograd_hacks_hooks'):\n print(\"Warning, asked to remove hooks, but no hooks found\")\n else:\n for handle in model.autograd_hacks_hooks:\n handle\n del model.autograd_hacks_hooks\n\n\ndef disable_hooks() -> None:\n \"\"\"\n Globally disable all hooks installed by this library.\n \"\"\"\n\n global _global_hooks_disabled\n _global_hooks_disabled = True\n\n\ndef enable_hooks() -> None:\n \"\"\"The opposite of disable_hooks().\"\"\"\n\n global _global_hooks_disabled\n _global_hooks_disabled = False\n\n\ndef is_supported(layer: nn.Module) -> bool:\n \"\"\"Check if this layer is supported.\"\"\"\n\n return _layer_type(layer) in _supported_layers\n\n\ndef _layer_type(layer: nn.Module) -> str:\n return layer.__class__.__name__\n\n\ndef _capture_activations(layer: nn.Module, input: List[torch.Tensor], output: torch.Tensor):\n \"\"\"Save activations into layer.activations in forward pass\"\"\"\n\n if _global_hooks_disabled:\n return\n assert _layer_type(layer) in _supported_layers, \"Hook installed on unsupported layer, this shouldn't happen\"\n setattr(layer, \"activations\", input[0].detach())\n\n\ndef _capture_output(layer: nn.Module, input: List[torch.Tensor], output: torch.Tensor):\n \"\"\"Save activations into layer.activations in forward pass\"\"\"\n\n if _global_hooks_disabled:\n return\n assert _layer_type(layer) in _supported_layers, \"Hook installed on unsupported layer, this shouldn't happen\"\n setattr(layer, \"output\", output.detach())\n\n\ndef _capture_backprops(layer: nn.Module, _input, output):\n \"\"\"Append backprop to layer.backprops_list in backward pass.\"\"\"\n global _global_enforce_fresh_backprop\n\n if _global_hooks_disabled:\n return\n\n backprops_list_attr = _global_backprops_prefix + 'backprops_list'\n if _global_enforce_fresh_backprop:\n assert not hasattr(layer,\n backprops_list_attr), f\"Seeing result of previous backprop in {backprops_list_attr}, use {_global_backprops_prefix}clear_backprops(model) to clear\"\n _global_enforce_fresh_backprop = False\n\n if not hasattr(layer, backprops_list_attr):\n setattr(layer, backprops_list_attr, [])\n getattr(layer, backprops_list_attr).append(output[0].detach())\n\n\ndef clear_backprops(model: nn.Module) -> None:\n \"\"\"Delete layer.backprops_list in every layer.\"\"\"\n for layer in model.modules():\n if hasattr(layer, 'backprops_list'):\n del layer.backprops_list\n\n\ndef clear_hess_backprops(model: nn.Module) -> None:\n \"\"\"Delete layer.backprops_list in every layer.\"\"\"\n for layer in model.modules():\n if hasattr(layer, 'hess_backprops_list'):\n del layer.hess_backprops_list\n\n\ndef compute_grad1(model: nn.Module, loss_type: str = 'mean') -> None:\n \"\"\"\n Compute per-example gradients and save them under 'param.grad1'. Must be called after loss.backprop()\n\n Args:\n model:\n loss_type: either \"mean\" or \"sum\" depending whether backpropped loss was averaged or summed over batch\n \"\"\"\n\n assert loss_type in ('sum', 'mean')\n for layer in model.modules():\n if hasattr(layer, 'expensive'):\n continue\n\n layer_type = _layer_type(layer)\n if layer_type not in _supported_layers:\n continue\n assert hasattr(layer, 'activations'), \"No activations detected, run forward after add_hooks(model)\"\n assert hasattr(layer, 'backprops_list'), \"No backprops detected, run backward after add_hooks(model)\"\n assert len(layer.backprops_list) == 1, \"Multiple backprops detected, make sure to call clear_backprops(model)\"\n\n A = layer.activations\n n = A.shape[0]\n if loss_type == 'mean':\n B = layer.backprops_list[0] * n\n else: # loss_type == 'sum':\n B = layer.backprops_list[0]\n\n if layer_type == 'Linear':\n setattr(layer.weight, 'grad1', torch.einsum('ni,nj->nij', B, A))\n if layer.bias is not None:\n setattr(layer.bias, 'grad1', B)\n\n elif layer_type == 'Conv2d':\n Kh, Kw = layer.kernel_size\n di, do = layer.in_channels, layer.out_channels\n Oh, Ow = layer.backprops_list[0].shape[2:]\n weight_shape = [n] + list(layer.weight.shape) # n, do, di, Kh, Kw\n assert weight_shape == [n, do, di, Kh, Kw]\n A = torch.nn.functional.unfold(A, layer.kernel_size) # n, di * Kh * Kw, Oh * Ow\n\n assert A.shape == (n, di * Kh * Kw, Oh * Ow)\n assert layer.backprops_list[0].shape == (n, do, Oh, Ow)\n\n # B = B.reshape(n, -1, A.shape[-1])\n B = B.reshape(n, do, Oh * Ow)\n # noinspection PyTypeChecker\n grad1 = torch.einsum('ijk,ilk->ijl', B, A) # n, do, di * Kh * Kw\n assert grad1.shape == (n, do, di * Kh * Kw)\n\n setattr(layer.weight, 'grad1', grad1.reshape(weight_shape))\n if layer.bias is not None:\n setattr(layer.bias, 'grad1', torch.sum(B, dim=2))\n\n\ndef compute_hess(model: nn.Module, method='exact', attr_name=None, vecr_order=False, loss_aggregation='mean') -> None:\n \"\"\"Compute Hessian (torch.Tensor) for each parameter and save it under 'param.hess' by default.\n Hessian can be a Tensor or a tensor-like object like KronFactored.\n\n If attr_name is specified, saves it under 'param.{attr_name}'\n\n Must be called after backprop_hess().\n\n Args:\n model:\n method: which method to use for computing the Hessian\n kron: kronecker product\n mean_kron: mean of kronecker products, one kronecker product per datapoint\n experimental_kfac: experimental method for Conv2d\n attr_name: which attribute of Parameter object to use for storing the Hessian.\n vecr_order: determines whether Hessian computed with respect to vectorized parameters (math notation default) or row-vectorized parameters (more efficient in PyTorch)\n loss_aggregation: 'mean' or 'sum', determines whether final loss is sum or mean of per-example losses\n \"\"\"\n\n assert method in _supported_methods\n assert loss_aggregation in ['mean', 'sum']\n\n # TODO: get rid of hess_factored logic\n\n # legacy specification for factored version, remove\n if attr_name is None:\n hess_attr = 'hess' if (method == 'exact' or method == 'autograd') else 'hess_factored'\n else:\n hess_attr = attr_name\n\n li = 0\n for layer in model.modules():\n\n if hasattr(layer, 'expensive'):\n continue\n\n layer_type = _layer_type(layer)\n if layer_type not in _supported_layers:\n continue\n assert hasattr(layer, 'activations'), \"No activations detected, run forward after add_hooks(model)\"\n assert hasattr(layer, 'hess_backprops_list'), \"No backprops detected, run hess_backprop\"\n\n A = layer.activations\n n = A.shape[0]\n\n if layer_type == 'Linear':\n B = torch.stack(layer.hess_backprops_list)\n\n di = A.shape[1]\n do = layer.hess_backprops_list[0].shape[1]\n o = B.shape[0]\n\n original_A = A\n\n A = torch.stack([A] * o)\n\n if method == 'exact':\n Jo = torch.einsum(\"oni,onj->onij\", B, A).reshape(n * o, -1)\n H = torch.einsum('ni,nj->ij', Jo, Jo) / n\n\n # Alternative way\n # Jo = torch.einsum(\"oni,onj->onij\", B, A)\n # H = torch.einsum('onij,onkl->ijkl', Jo, Jo) / n\n # H = H.reshape(do*di, do*di)\n\n H_bias = torch.einsum('oni,onj->ij', B, B) / n\n else: # TODO(y): can optimize this case by not stacking A\n assert method == 'kron'\n # AA = torch.einsum(\"oni,onj->ij\", A, A) / (o * n) # # TODO(y): makes more sense to apply o factor to B\n # BB = torch.einsum(\"oni,onj->ij\", B, B) / n\n # H = u.Kron(AA, BB)\n H_bias = u.Kron(torch.eye(1), torch.einsum(\"oni,onj->ij\", B, B) / n) # TODO: reuse BB\n\n hess = u.KronFactoredCov(di, do)\n hess.add_samples(original_A, B)\n H = hess.value()\n\n\n elif layer_type == 'Conv2d':\n Kh, Kw = layer.kernel_size\n di, do = layer.in_channels, layer.out_channels\n n, do, Oh, Ow = layer.hess_backprops_list[0].shape\n o = len(layer.hess_backprops_list)\n\n A = torch.nn.functional.unfold(A, kernel_size=layer.kernel_size,\n stride=layer.stride,\n padding=layer.padding,\n dilation=layer.dilation) # n, di * Kh * Kw, Oh * Ow\n assert A.shape == (n, di * Kh * Kw, Oh * Ow)\n B = torch.stack([Bh.reshape(n, do, -1) for Bh in layer.hess_backprops_list]) # o, n, do, Oh*Ow\n\n A = torch.stack([A] * o) # o, n, di * Kh * Kw, Oh*Ow\n if gl.debug_dump_stats:\n print(f'layerA {li}', A)\n print(f'layerB {li}', B)\n\n if method == 'exact':\n Jo = torch.einsum('onis,onks->onik', B, A) # o, n, do, di * Kh * Kw\n Jo_bias = torch.einsum('onis->oni', B)\n\n Hi = torch.einsum('onij,onkl->nijkl', Jo, Jo) # n, do, di*Kh*Kw, do, di*Kh*Kw\n Hi = Hi.reshape(n, do * di * Kh * Kw, do * di * Kh * Kw) # n, do*di*Kh*Kw, do*di*Kh*Kw\n Hi_bias = torch.einsum('oni,onj->nij', Jo_bias, Jo_bias) # n, do, do\n H = Hi.mean(dim=0)\n H_bias = Hi_bias.mean(dim=0)\n elif method == 'kron':\n AA = torch.einsum(\"onis->oni\", A) / (Oh * Ow) # group input channels\n AA = torch.einsum(\"oni,onj->onij\", AA, AA) / (o * n) # remove factor of o because A is repeated o times\n\n AA = torch.einsum(\"onij->ij\", AA) # sum out outputs/classes\n\n BB = torch.einsum(\"onip->oni\", B) # group output channels\n BB = torch.einsum(\"oni,onj->ij\", BB, BB) / n\n elif method == 'mean_kron':\n AA = torch.einsum(\"onis->oni\", A) / (Oh * Ow) # group input channels\n AA = torch.einsum(\"oni,onj->onij\", AA, AA) / (o) # remove factor of o because A is repeated o times\n\n AA = torch.einsum(\"onij->nij\", AA) # sum out outputs/classes\n\n BB = torch.einsum(\"onip->oni\", B) # group output channels\n BB = torch.einsum(\"oni,onj->nij\", BB, BB)\n\n elif method == 'experimental_kfac':\n AA = torch.einsum(\"onis,onjs->onijs\", A, A)\n AA = torch.einsum(\"onijs->onij\", AA) / (Oh * Oh)\n AA = torch.einsum(\"onij->oij\", AA) / n\n AA = torch.einsum(\"oij->ij\", AA) / o\n\n BB = torch.einsum(\"onip,onjp->onijp\", B, B) / n\n BB = torch.einsum(\"onijp->onij\", BB)\n BB = torch.einsum(\"onij->nij\", BB)\n BB = torch.einsum(\"nij->ij\", BB)\n\n if method != 'exact':\n if method == 'mean_kron':\n H = u.MeanKronFactored(AA, BB)\n # H = u.KronFactored(AA[0,...], BB[0,...])\n else:\n H = u.Kron(AA, BB)\n\n BB_bias = torch.einsum(\"onip->oni\", B) # group output channels\n BB_bias = torch.einsum(\"oni,onj->onij\", BB_bias, BB_bias) / n # covariance\n BB_bias = torch.einsum(\"onij->ij\", BB_bias) # sum out outputs + examples\n H_bias = u.Kron(torch.eye(1), BB_bias)\n\n if loss_aggregation == 'sum':\n H = n * H\n H_bias = H * n\n\n if vecr_order:\n H = H.commute()\n H_bias = H_bias.commute()\n\n setattr(layer.weight, hess_attr, H)\n if layer.bias is not None:\n setattr(layer.bias, hess_attr, H_bias)\n li += 1\n\n\ndef backprop_hess(output: torch.Tensor, hess_type: str, model: Optional[nn.Module] = None) -> None:\n \"\"\"\n Call backprop 1 or more times to accumulate values needed for Hessian computation.\n\n Values are accumulated under .backprops_list attr of each layer and used by downstream functions like compute_hess\n\n Args:\n output: prediction of neural network (ie, input of nn.CrossEntropyLoss())\n hess_type: 'LeastSquares' or 'CrossEntropy'. Type of Hessian propagation, \"CrossEntropy\" results in exact Hessian for CrossEntropy\n model: optional model, used to freeze the parameters\n \"\"\"\n\n global _global_enforce_fresh_backprop, _global_hooks_disabled, _global_backprops_prefix\n\n assert not _global_hooks_disabled\n _global_enforce_fresh_backprop = True # enforce empty backprops_list on first backprop\n\n old_backprops_prefix = _global_backprops_prefix\n _global_backprops_prefix = 'hess_' # backprops go into hess_backprops_list\n\n valid_hess_types = ('LeastSquares', 'CrossEntropy', 'DebugLeastSquares')\n assert hess_type in valid_hess_types, f\"Unexpected hessian type: {hess_type}, valid types are {valid_hess_types}\"\n n, o = output.shape\n\n if hess_type == 'CrossEntropy':\n batch = F.softmax(output, dim=1)\n\n mask = torch.eye(o).to(gl.device).expand(n, o, o)\n diag_part = batch.unsqueeze(2).expand(n, o, o) * mask\n outer_prod_part = torch.einsum('ij,ik->ijk', batch, batch)\n hess = diag_part - outer_prod_part\n assert hess.shape == (n, o, o)\n\n with u.timeit(\"xent-symsqrt\"):\n for i in range(n):\n if torch.get_default_dtype() == torch.float64:\n hess[i, :, :] = u.symsqrt_svd(\n hess[i, :, :]) # more stable method since we don't care about speed with float64\n print('warning, slow method for cross-entropy')\n else:\n hess[i, :, :] = u.symsqrt(hess[i, :, :])\n u.nan_check(hess[i, :, :])\n hess = hess.transpose(0, 1)\n\n elif hess_type == 'LeastSquares':\n hess = []\n assert len(output.shape) == 2\n batch_size, output_size = output.shape\n\n id_mat = torch.eye(output_size).to(output.device).type(output.dtype)\n for out_idx in range(output_size):\n hess.append(torch.stack([id_mat[out_idx]] * batch_size))\n\n elif hess_type == 'DebugLeastSquares':\n hess = []\n assert len(output.shape) == 2\n batch_size, output_size = output.shape\n\n id_mat = torch.eye(output_size)\n id_mat[0, 0] = 10\n for out_idx in range(output_size):\n hess.append(torch.stack([id_mat[out_idx]] * batch_size))\n\n for o in range(o):\n output.backward(hess[o], retain_graph=True)\n\n # side-effect of Hessian backprop is that .grad buffers are updated.\n # Zero out those buffers to prevent accidental use\n if model is not None:\n model.zero_grad()\n\n _global_backprops_prefix = old_backprops_prefix\n\n\nclass LayerCov:\n \"\"\"Class representing second-order information associated with a layer.\"\"\"\n\n S: u.KronFactoredCov # expected gradient outer product: E[gg'] where g=gradient of loss\n J: u.KronFactoredCov # expected Jacobian outer product: E[hh'] where h=gradient of network output\n H: u.KronFactoredCov # expected hessian: E[H] where H is per-example hessian\n\n def __init__(self):\n self.S = None\n self.J = None\n self.H = None\n\n\ndef compute_cov(model: nn.Module, loss_fn: Callable, stats_iter, batch_size, steps, loss_type='CrossEntropy'):\n \"\"\"\n\n Augments model layers with their associated covariance matrices. At the end of the run, every layer will have an\n attribute 'cov' of type LayerCov\n\n Args:\n stats_iter: data iterator\n loss_type:\n model:\n loss_fn:\n batch_size: size of batch to use for estimation\n steps: number of steps to use to aggregate stats\n\n Returns:\n\n \"\"\"\n\n assert loss_type == 'CrossEntropy', 'only cross entropy is implemented'\n\n enable_hooks()\n\n clear_backprops(model)\n clear_hess_backprops(model)\n model.zero_grad()\n\n for i in range(steps):\n data, targets = next(stats_iter)\n output = model(data)\n loss = loss_fn(output, targets)\n assert len(data) == batch_size\n\n with u.timeit(\"backprop_J\"):\n backprop_hess(output, hess_type='LeastSquares')\n update_cov(model, 'activations', 'hess_backprops_list', 'J')\n clear_hess_backprops(model)\n\n with u.timeit(\"backprop_G\"):\n loss.backward(retain_graph=True)\n update_cov(model, 'activations', 'backprops_list', 'S')\n clear_backprops(model)\n model.zero_grad()\n\n # disable because super-slow\n if not gl.hacks_disable_hess:\n with u.timeit(\"backprop_H\"):\n backprop_hess(output, hess_type='CrossEntropy')\n update_cov(model, 'activations', 'hess_backprops_list', 'H')\n clear_hess_backprops(model)\n\n disable_hooks()\n\n\ndef update_cov(model, a_attr, b_attr, target_attr):\n \"\"\"Update Kronecker-factored layer covariance of a,b values.\n For every layer in the model, will perform\n a = layer.{a_attr}\n b = layer.{b_attr}\n layer.cov.{target_attr}.add_samples(a, b)\n \"\"\"\n\n for layer in model.modules():\n if not is_supported(layer):\n continue\n\n layer_type = _layer_type(layer)\n\n if hasattr(layer, 'cov'):\n layer_cov = layer.cov\n else:\n layer_cov = LayerCov()\n setattr(layer, 'cov', layer_cov)\n\n a_vals = getattr(layer, a_attr)\n a_dim = a_vals.shape[-1]\n b_vals = getattr(layer, b_attr)\n\n if layer_type == 'Conv2d':\n Kh, Kw = layer.kernel_size\n di, do = layer.in_channels, layer.out_channels\n n, do, Oh, Ow = a_vals.shape\n o = len(layer.hess_backprops_list)\n\n a_vals = torch.nn.functional.unfold(a_vals, kernel_size=layer.kernel_size,\n stride=layer.stride,\n padding=layer.padding,\n dilation=layer.dilation) # n, di * Kh * Kw, Oh * Ow\n assert a_vals.shape == (n, di * Kh * Kw, Oh * Ow)\n a_vals = torch.einsum(\"onis->oni\", a_vals) / (Oh * Ow) # group input channels\n b_vals = [b.reshape(n, do, -1) for b in b_vals]\n\n # backward vals are in a list\n # special handling for backprops, multiple vals are stacked into rank 3 tensor\n\n assert type(b_vals) == list\n if len(b_vals) > 1:\n b_vals = torch.stack(b_vals)\n\n else:\n b_vals = b_vals[0]\n\n b_dim = b_vals.shape[-1]\n\n covmat = getattr(layer_cov, target_attr)\n if covmat is None:\n covmat = u.KronFactoredCov(a_dim, b_dim)\n setattr(layer_cov, target_attr, covmat)\n covmat.add_samples(a_vals, b_vals)\n\n\ndef compute_stats(model, attr_name='stats', factored=False, sigma_centering=True):\n \"\"\"\n\n Combines activations and backprops to compute statistics for a model.\n Args:\n model:\n attr_name: stats are saved under this attribute name on corresponding Parameter\n\n \"\"\"\n\n # obtain n\n n = 0\n for param in model.modules():\n if hasattr(param, 'activations'):\n n = param.activations.shape[0]\n break\n assert n, \"Couldn't figure out size of activations\"\n\n for (i, layer) in enumerate(model.layers):\n\n if hasattr(layer, 'expensive'):\n continue\n\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n\n if param is None:\n continue\n\n s = LayerStats() # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n B_t = layer.backprops_list[0] * n\n\n s.sparsity = torch.sum(\n layer.output <= 0).float() / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n G = param.grad1.reshape((n, -1))\n g = G.mean(dim=0, keepdim=True)\n\n print(G)\n u.nan_check(G)\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n if sigma_centering:\n sigma = efisher - g.t() @ g\n else:\n sigma = efisher\n\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma) / s.sigma_l2\n\n H = param.hess\n\n u.nan_check(H)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = param.data.flatten().norm()\n\n # TODO(y): col vs row fix\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"\n\n Args:\n dd: direction, as a n-by-1 matrix\n eps: scalar length\n\n Returns:\n loss improvement if we take step eps in direction dd.\n \"\"\"\n assert u.is_row_matrix(dd)\n return (eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t()).squeeze()\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd (directional eigenvalue). \"\"\"\n assert u.is_row_matrix(dd)\n return (dd @ H @ dd.t() / (dd.flatten().norm() ** 2)).squeeze()\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = u.pinv(H)\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = curv_direction(g) # curvature (eigenvalue) in direction g\n ndir = g @ pinvH # newton direction (TODO(y): replace with lstsqsolve)\n s.newton_curv = curv_direction(ndir)\n\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 1234567\n s.step_div_inf = 2 / s.H_l2 # divegent step size for batch_size=infinity\n s.step_div_1 = torch.tensor(2) / torch.trace(H) # divergent step for batch_size=1\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2) # replace with \"quadratic_form\"\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n # todo: Lyapunov has to be redone\n with u.timeit(f'rho-{i}'):\n s.rho, lyap_erank, L_evals = u.truncated_lyapunov_rho(H, sigma)\n s.step_div_1_adjusted = s.step_div_1 / s.rho\n\n with u.timeit(f\"batch-{i}\"):\n # s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t()).squeeze()\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n print('original sigma: ', torch.trace(H @ sigma) / (g @ H @ g.t()))\n denom = (g @ H @ g.t())\n print('subtracted1:', torch.trace(H @ (sigma - g.t() @ g)) / denom)\n print('subtracted2:', torch.trace(H @ sigma) / denom - torch.trace(H @ g.t() @ g) / denom)\n print(\"left term: \", torch.trace(H @ sigma))\n print(\"right term: \", torch.trace(H @ g.t() @ g))\n print('denom: ', denom)\n\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2 / n # Gradient diversity / n\n s.noise_variance_pinv = torch.trace(pinvH @ sigma) # todo(y): replace with lsqtsolve\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n param_name = f\"{layer.name}={param_names[param]}\"\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n H_evals = u.symeig_pos_evals(H)\n S_evals = u.symeig_pos_evals(sigma)\n\n # s.H_evals = H_evals\n # s.S_evals = S_evals\n # s.L_evals = L_evals\n\n setattr(param, attr_name, s)\n\n # u.log_spectrum(f'{param_name}/hess', H_evals)\n # u.log_spectrum(f'{param_name}/sigma', S_evals)\n # u.log_spectrum(f'{param_name}/lyap', L_evals)\n\n return None\n\n\ndef compute_stats_factored(model, attr_name='stats', sigma_centering=True):\n \"\"\"Combines activations and backprops to compute statistics for a model. Assumes factored hessian was saved into 'hess2' of each param\"\"\"\n\n ein = torch.einsum\n n = 0\n for param in model.modules():\n if hasattr(param, 'activations'):\n n = param.activations.shape[0]\n break\n assert n, \"Couldn't figure out size of activations\"\n\n for (i, layer) in enumerate(model.layers):\n\n if hasattr(layer, 'expensive'):\n continue\n\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n\n if param is None:\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n do, di = layer.weight.shape\n H: u.Kron = param.hess2\n\n if param is layer.weight:\n assert H.shape == ((di, di), (do, do))\n else:\n assert H.shape == ((1, 1), (do, do))\n\n # TODO(y): fix stats for bias\n if param is layer.bias:\n continue\n\n G = param.grad1.reshape((n, -1))\n g = G.mean(dim=0, keepdim=True)\n\n if param is layer.weight:\n vecG = u.Vec(g, shape=(do, di))\n else: # bias\n vecG = u.Vec(g, shape=(do, 1))\n\n u.nan_check(G)\n\n A = layer.activations\n B = layer.backprops_list[0] * n\n\n AA = ein('ni,nj->ij', A, A)\n BB = ein('ni,nj->ij', B, B)\n Bc = B - torch.mean(B, dim=0)\n BBc = ein('ni,nj->ij', Bc, Bc)\n\n # subtracting mean breaks Kronecker factoring, so this is approximate\n if sigma_centering:\n sigma_k = u.Kron(AA, BBc) / n # only center backprops, centering both leads to underestimate of cov\n else:\n sigma_k = u.Kron(AA, BB) / n\n sigma_k = sigma_k / n # extra factor to average A's as well\n s.sparsity = torch.sum(\n layer.output <= 0).float() / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A)\n s.mean_backprop = torch.mean(B)\n\n with u.timeit(f'sigma-{i}'):\n s.sigma_l2 = sigma_k.sym_l2_norm()\n s.sigma_erank = sigma_k.trace() / s.sigma_l2\n\n # u.nan_check(param.hess)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = H.sym_l2_norm()\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.frobenius_norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = param.data.flatten().norm()\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = H.pinv()\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = u.matmul(vecG @ H, vecG) / (vecG @ vecG)\n newton_dir = vecG @ pinvH\n s.newton_curv = u.matmul(newton_dir @ H, newton_dir) / (newton_dir @ newton_dir)\n\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 1234567\n s.step_div_inf = 2 / s.H_l2 # divegent step size for batch_size=infinity\n s.step_div_1 = torch.tensor(2) / H.trace() # divergent step for batch_size=1\n s.newton_fro = newton_dir.norm() # frobenius norm of Newton update,\n\n def loss_direction(d: u.Vec, step): # improvement in loss if we go eps units in direction dir\n return step * (d @ vecG) - 0.5 * step ** 2 * (d @ H @ vecG)\n\n s.regret_gradient = loss_direction(vecG, s.step_openai)\n # can compute newton regret more efficiently by doing row-vectorized instead of col-vectorized\n vecG2 = u.Vecr(g, shape=(do, di))\n pinvH_rowvec = pinvH.commute() # original H was for col-vectorized order\n s.regret_newton = vecG2 @ pinvH_rowvec @ vecG2 / 2\n\n with u.timeit(f'rho-{i}'):\n # lyapunov matrix\n Xk = u.lyapunov_spectral(H.RR, sigma_k.RR) # compare backprops\n s.rho = u.erank(u.eye_like(Xk)) / u.erank(Xk)\n s.step_div_1_adjusted = s.step_div_1 / s.rho\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = (H @ sigma_k).trace() / (vecG @ H @ vecG)\n if not sigma_centering:\n s.batch_openai -= 1\n\n expected_grad_norm_sq = torch.norm(G, \"fro\") ** 2 / n # expected gradient norm squared\n s.diversity = expected_grad_norm_sq / torch.norm(g) ** 2 # Gradient diversity / n\n s.noise_variance_pinv = (pinvH @ sigma_k).trace()\n s.H_erank = H.trace() / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n param_name = f\"{layer.name}={param_names[param]}\"\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n setattr(param, attr_name, s)\n\n\n##########################################################################################\n### post-refactoring\n##########################################################################################\n\nimport torch.utils\n\n\n# second order covariance for two variables X,Y\nclass SecondOrderCov:\n\n # todo: add \"symmetric\" flag\n def __init__(self):\n self.mean_x = None\n self.mean_y = None\n self.cov_xy = None\n self.d_x = -1\n self.d_y = -1\n self.initialized = False\n self.n = 0\n\n def accumulate(self, data_x, data_y):\n assert u.is_matrix(data_x)\n assert u.is_matrix(data_y)\n if not self.initialized:\n self.d_x = data_x.shape[1]\n self.d_y = data_y.shape[1]\n self.cov_xy = torch.zeros(self.d_x, self.d_y).type(data_x.dtype).to(data_x.device)\n self.mean_x = torch.zeros(self.d_x).type(data_x.dtype).to(data_x.device)\n self.mean_y = torch.zeros(self.d_y).type(data_y.dtype).to(data_y.device)\n self.initialized = True\n n = data_x.shape[0]\n assert n == data_y.shape[0]\n self.cov_xy += torch.einsum(\"ni,nj->ij\", data_x, data_y)\n self.mean_x += torch.einsum(\"ni->i\", data_x)\n self.mean_y += torch.einsum(\"ni->i\", data_y)\n self.n += n\n\n def zero_(self):\n self.n = 0\n self.cov_xy.zero_()\n self.mean_x.zero_()\n self.mean_y.zero_()\n\n\nclass SymmetricFourthOrderCov:\n \"\"\"Fourth order generalized covariance.\n rank=4 gives exact stats\n rank=3 uses Isserlis Theorem to for compact storage\n rank=2 is equivalent to Kronecker factoring\n rank=1 (not implemented) analogous to batch normalization\n \"\"\"\n xx: SecondOrderCov\n yy: SecondOrderCov\n xy: SecondOrderCov\n xxyy: SecondOrderCov\n\n def __init__(self, rank=3):\n self.xx = SecondOrderCov() # rank2\n self.yy = SecondOrderCov() # rank2\n self.xy = SecondOrderCov() # rank3\n self.xxyy = SecondOrderCov() # rank4\n self.rank = rank\n\n def accumulate(self, data_x: torch.Tensor, data_y: torch.Tensor, cached_xx=None):\n assert u.is_matrix(data_x)\n assert u.is_matrix(data_y[0])\n n = data_x.shape[0]\n assert data_y.shape[0] == n\n\n if self.rank == 4:\n Jo = torch.einsum(\"oni,onj->onij\", data_y, data_x).reshape(n, -1)\n self.xxyy.accumulate(Jo, Jo)\n\n # Alternative way\n # Jo = torch.einsum(\"oni,onj->onij\", B, A)\n # H = torch.einsum('onij,onkl->ijkl', Jo, Jo) / n\n # H = H.reshape(do*di, do*di)\n\n else:\n if cached_xx is not None:\n self.xx.accumulate(data_x, data_x)\n self.yy.accumulate(data_y, data_y)\n\n if self.rank == 3:\n self.xy.accumulate(data_x, data_y)\n\n def zero_(self):\n self.xx.zero_()\n self.yy.zero_()\n self.xy.zero_()\n\n\nclass ModuleDict(dict):\n\n def __init__(self, defaultcreator=None, defaultvalue=None):\n assert (defaultcreator is None) or (defaultvalue is None), \"only one of defaultcreator/defaultvalue must be set\"\n self.defaultvalue = None\n self.defaultcreator = None\n if defaultcreator is not None:\n self.defaultcreator = defaultcreator\n elif defaultvalue is not None:\n self.defaultvalue = defaultvalue\n\n def __getitem__(self, item):\n if item not in self:\n if self.defaultcreator:\n self[item] = self.defaultcreator()\n elif self.defaultvalue:\n self[item] = self.defaultvalue\n else:\n assert False, f\"Requested value {item} which doesn't exist in ModuleDict and defaultcreator nor default value is set\"\n return self[item]\n\n\n# Namespace of global settings used by the library internally\n# Using module-level variable for settings means this library is not thread-safe.\n\nglobal_settings_initialized = False\n\n\nclass Settings(object):\n forward_hooks: List[Callable] # forward subhooks called by the global hook\n backward_hooks: List[Callable] # backward subhooks\n model: Optional[nn.Module]\n hook_handles: List[torch.utils.hooks.RemovableHandle] # removal handles of global hooks registered with PyTorch\n default_activations: Optional[ModuleDict]\n\n\n def __init__(self):\n assert global_settings_initialized is False, \"Reinitializing Settings seems like a bug.\"\n self.model = None\n self.hook_handles = []\n self.forward_hooks = []\n self.backward_hooks = []\n self.default_activations = None\n self.default_Acov = None\n\n # temporary settings to aggregate gradient norm squared globally\n self._hack_gradient_norm_sum = ModuleDict(defaultvalue=torch.zeros(()))\n self._hack_gradient_norm_count = ModuleDict(defaultvalue=torch.zeros(()))\n self._hack_activations_squared = None # initialized in set_default_activations\n\n # TODO(y): maybe remove all of this\n # store last activations captured for each layer. While this breaks encapsulation, this considerably simplifies the common use\n # case where the same set of activations is needed for several backward aggregation calls\n self.last_captured_activations = ModuleDict()\n\n # To prevent a mix of saved activations from different forward calls, keep counter which indicates in which\n # context each activation value was saved. This can be used to enforce that all activations were captured in the same context\n self.last_captured_activations_contextid = ModuleDict()\n self.activations_contextid = 0 # this gets incremented for each save_activations context\n\n\n\n\n\nglobal_settings = Settings()\n\n\ndef layer_cov_dict(model):\n \"\"\"Returns a dictionary of layer->KronFactoredCov for all supported layers in model.\"\"\"\n return {layer: u.KronFactoredCov() for layer in model.layers() if is_supported(layer)}\n\n\ndef _forward_hook(layer: nn.Module, input: List[torch.Tensor], output: torch.Tensor):\n for hook in global_settings.forward_hooks:\n hook(layer, input, output)\n\n\n# TODO(y): fix signature\ndef _backward_hook(layer: nn.Module, _input: torch.Tensor, output: torch.Tensor):\n for hook in global_settings.backward_hooks:\n hook(layer, _input, output)\n\n\ndef register(model: nn.Module):\n \"\"\"\n Registers given model with autograd_lib. This allows user to use decorators like save_activations(A) and module_hook\n \"\"\"\n global global_settings\n _global_hooks_disabled = False\n\n # TODO(y): make it work for multiple models and test. This needs check that hook list remains a singleton\n assert 'handles' not in vars(global_settings), \"Already called register in this thread\"\n\n global_settings.model = model\n\n layer: nn.Module\n for layer in model.modules():\n if _layer_type(layer) in _supported_layers:\n global_settings.hook_handles.append(layer.register_forward_hook(_forward_hook))\n layer.register_backward_hook(_backward_hook) # don't save handle, https://github.com/pytorch/pytorch/issues/25723\n\n\ndef _hack_zero_gradient_norms_squared():\n for layer in global_settings._hack_gradient_norm_count:\n global_settings._hack_gradient_norm_count[layer] = 0\n global_settings._hack_gradient_norm_sum[layer] = 0\n global_settings._hack_activations_squared[layer] = None\n\n\ndef _hack_update_gradient_norms_squared(layer: nn.Module, backprops: torch.Tensor):\n \"\"\"Trick from Ian Goodfellow https://arxiv.org/abs/1510.01799\n\n Add up gradient norm squared of gradients\n \"\"\"\n n = backprops.shape[0]\n A2 = global_settings._hack_activations_squared[layer] # initialized in \"set_default_activations\"\n assert n == A2.shape[0]\n sq_components = A2 * backprops * backprops\n global_settings._hack_gradient_norm_sum[layer] += torch.sum(sq_components)\n global_settings._hack_gradient_norm_count[layer] += n\n\n\ndef unregister():\n # TODO(y): switch to tensor backward hooks\n for handle in global_settings.hook_handles:\n handle.remove()\n\n\nfrom contextlib import contextmanager\n\n\n@contextmanager\ndef save_activations(storage: ModuleDict):\n \"\"\"Save activations to layer storage: storage[layer] = activations\n \"\"\"\n\n assert global_settings.hook_handles, \"No hooks have been registered.\"\n hook_called = [False]\n\n global_settings.activations_contextid += 1\n\n def hook(layer: nn.Module, input: List[torch.Tensor], _output: torch.Tensor):\n if layer in storage:\n print(\"warning, overwriting existing activation for layer \", layer)\n hook_called[0] = True\n activations = input[0].detach()\n storage[layer] = activations\n global_settings.last_captured_activations[layer] = activations\n global_settings.last_captured_activations_contextid[layer] = global_settings.activations_contextid\n assert len(global_settings.last_captured_activations) < 200, \"warning, possibly leaking activations, got more than 200\"\n\n global_settings.forward_hooks.append(hook)\n yield\n assert hook_called[0], \"Forward hook was never called.\"\n global_settings.forward_hooks.pop()\n\n\n@contextmanager\ndef extend_backprops(storage: ModuleDict):\n \"\"\"Extends list of backprops in storage with current backprops. storage[layer].extend([backprops])\n \"\"\"\n\n assert global_settings.hook_handles, \"No hooks have been registered.\"\n hook_called = [False]\n\n def hook(layer: nn.Module, _input, output):\n storage.setdefault(layer, []).extend([output[0].detach()])\n hook_called[0] = True\n\n global_settings.backward_hooks.append(hook)\n yield\n assert hook_called[0], \"Backward hook was never called.\"\n global_settings.backward_hooks.pop()\n\n\n@contextmanager\ndef module_hook(hook: Callable):\n \"\"\"Context manager for running given hook on forward or backward.\"\"\"\n\n # TODO(y): maybe add checking for arg types on hook to catch forward/backward hook mismatches\n # TODO(y): use weak ref for the hook handles so they are removed when model goes out of scope\n assert global_settings.hook_handles, \"Global hooks have not been registered. Make sure to call .register(model) on your model\"\n forward_hook_called = [False]\n backward_hook_called = [False]\n\n def forward_hook(layer: nn.Module, input: Tuple[torch.Tensor], output: torch.Tensor):\n assert len(input) == 1, \"Only support single input modules on forward.\"\n assert type(output) == torch.Tensor, \"Only support single output modules on forward.\"\n activations = input[0].detach()\n hook(layer, activations, output)\n forward_hook_called[0] = True\n\n def backward_hook(layer: nn.Module, input: Tuple[torch.Tensor], output: Tuple[torch.Tensor]):\n assert len(output) == 1, \"Only support single output modules on backward.\"\n backprops = output[0].detach()\n hook(layer, input, backprops)\n backward_hook_called[0] = True\n\n global_settings.forward_hooks.append(forward_hook)\n global_settings.backward_hooks.append(backward_hook)\n yield\n assert forward_hook_called[0] or backward_hook_called[0], \"Hook was called neither on forward nor backward pass, did you register your model?\"\n assert not (forward_hook_called[0] and backward_hook_called[0]), \"Hook was called both on forward and backward pass, did you register your model?\"\n global_settings.forward_hooks.pop()\n global_settings.backward_hooks.pop()\n\n\n@contextmanager\ndef save_activations2():\n \"\"\"Save activations to layer storage: storage[layer] = activations\n \"\"\"\n\n activations = {}\n def saveit(layer, A, _):\n activations[layer] = A\n with module_hook(saveit):\n yield activations\n\n\n\"\"\"\"\nConcept: backprop_func\n\nGiven output tensor, create a batch of matrices used for backprop.\n\nCan be used to compute per-example Hessians if this function returns X where XiXi'=Hi\nExamples: identity -- torch.eye for each example\nExamples: cross_entropy_hessian -- Hessian of cross-entropy function\nExamples: cross_entropy_rank1 -- Hessian of cross-entropy function, rank-1 approximation per example\nExamples: cross_entropy_average -- average Hessian, low rank approximation\n\"\"\"\n\n\ndef backward(tensor, backward_func, retain_graph=False):\n \"\"\"Custom backprop.\n \"\"\"\n\n vals = backward_func(tensor)\n o = len(vals)\n for idx, hess in enumerate(vals):\n tensor.backward(hess, retain_graph=(retain_graph or idx < o - 1))\n\n\ndef backward_accum(tensor, backward_func, storage: ModuleDict, retain_graph=False, update_gradient_norm=False):\n \"\"\"\n Backpropagates from given tensor and updates FourthOrderCov for each layer in storage.\n\n\n Args:\n tensor:\n backward_func: function used to generate backward values. See \"backward functions\" below. Special value of 1 indicates 1 backpropagation\n storage: layer->FourthOrderCov ModuleDict storage\n retain_graph: whether to release activations at the end of forward prop\n update_gradient_norm: whether to update gradient norm squared estimates\n\n \"\"\"\n\n if backward_func == 1:\n backward_func = backward_ones\n\n elif backward_func == 'identity':\n backward_func = backward_identity\n elif backward_func == 'xent':\n backward_func = backward_xent\n\n assert global_settings.hook_handles, \"No hooks have been registered.\"\n hook_called = [False]\n\n def hook(layer: nn.Module, _input, output):\n backprops = output[0].detach()\n A = global_settings.default_activations\n Acov = global_settings.default_Acov\n if update_gradient_norm:\n _hack_update_gradient_norms_squared(layer, backprops)\n storage[layer].accumulate(data_x=A[layer], data_y=backprops, cached_xx=Acov)\n hook_called[0] = True\n\n global_settings.backward_hooks.append(hook)\n backward(tensor, backward_func, retain_graph)\n assert hook_called[0], \"Backward hook was never called.\"\n global_settings.backward_hooks.pop()\n\n\n# def backward_kron(target, tensor, A, A_cov, gradient):\n# \"\"\"Calls backward, and aggrates covariance of backward values. If activations are provided, also updates cross covariance\"\"\"\n#\n# def hook(module: nn.Module, _input, output):\n# \"\"\"Appends all backprops (Jacobian Lops from upstream) to layer.backprops_list.\n# Using list in order to capture multiple backprop values for a single batch. Use util.clear_backprops(model)\n# to clear all saved values.\n# \"\"\"\n# backprops = output[0].detach()\n# buffer[module].cov = KronFactoredCov\n# if activations:\n# activations = activations[module]\n#\n# # compute covariance matrix\n#\n# with backward_hook(hook):\n# tensor.backwards(gradient)\n\n\ndef set_default_activations(A):\n global_settings.default_activations = A\n\n global_settings._hack_activations_squared = ModuleDict()\n for layer in A:\n global_settings._hack_activations_squared[layer] = A[layer] * A[layer]\n\n\ndef set_default_Acov(Acov):\n global_settings.default_Acov = Acov\n\n\n# Backward functions.\n# These accept output tensor and produce a list of matrices [...,mi,...] suitable for output.backward(mi)\ndef backward_xent(output):\n pass\n\n\ndef backward_identity(tensor):\n assert u.is_matrix(tensor), \"Only support rank-2 outputs.\"\"\"\n n, o = tensor.shape\n\n hess = []\n batch_size, output_size = tensor.shape\n id_mat = u.eye(output_size)\n for out_idx in range(output_size):\n hess.append(torch.stack([id_mat[out_idx]] * batch_size))\n\n return hess\n\n\ndef backprop_identity(output, retain_graph=False) -> None:\n \"\"\"\n Helper to find Jacobian with respect to given tensor. Backpropagates a row of identity matrix\n for each output of tensor. Rows are replicated across batch dimension.\n\n Args:\n output: target of backward\n retain_graph: same meaning as PyTorch retain_graph\n \"\"\"\n\n assert u.is_matrix(output), \"Only support rank-2 outputs.\"\"\"\n\n n, o = output.shape\n id_mat = u.eye(o)\n for idx in range(o):\n output.backward(torch.stack([id_mat[idx]] * n), retain_graph=(retain_graph or idx < o - 1))\n\n\n# TODO(y): rename to backward or backward_jacobian\n\n\ndef backward_ones(output):\n return [torch.ones_like(output)]\n\n# backward_jacobian(strategy='exact')\n# backward_jacobian(strategy='sampled')\n# backward_hessian(loss='cross_entropy', strategy='exact')\n# backward_hessian(loss='cross_entropy', strategy='sampled')\n\n\ndef backward_jacobian(output, sampled=False, retain_graph=False) -> None:\n \"\"\"\n Helper to find Jacobian with respect to given tensor. Backpropagates a row of identity matrix\n for each output of tensor. Rows are replicated across batch dimension.\n\n Args:\n output: target of backward\n retain_graph: same meaning as PyTorch retain_graph\n sampled:\n \"\"\"\n\n assert u.is_matrix(output), \"Only support rank-2 outputs.\"\"\"\n # assert strategy in ('exact', 'sampled')\n\n n, o = output.shape\n if not sampled:\n id_mat = torch.eye(o).to(gl.device)\n for idx in range(o):\n output.backward(torch.stack([id_mat[idx]] * n), retain_graph=(retain_graph or idx < o - 1))\n else:\n vals = torch.LongTensor(n, o).to(gl.device).random_(0, 2) * 2 - 1\n vals = vals.type(torch.get_default_dtype())\n vals /= o # factor to preserve magnitudes from exact case.\n # switching to subsampling, kfac_fro became 1000x smaller, diversity became 300x larger, kfac_l2 unaffected\n output.backward(vals, retain_graph=retain_graph)\n\n\ndef backward_hessian(output, loss='CrossEntropy', sampled=False, retain_graph=False) -> None:\n assert loss in ('CrossEntropy',), f\"Only CrossEntropy loss is supported, got {loss}\"\n assert u.is_matrix(output)\n\n # use Cholesky-like decomposition from https://www.wolframcloud.com/obj/yaroslavvb/newton/square-root-formulas.nb\n n, o = output.shape\n p = F.softmax(output, dim=1)\n\n mask = torch.eye(o).to(gl.device).expand(n, o, o)\n diag_part = p.sqrt().unsqueeze(2).expand(n, o, o) * mask\n hess_sqrt = diag_part - torch.einsum('ij,ik->ijk', p.sqrt(), p) # n, o, o\n\n if not sampled:\n for out_idx in range(o):\n output.backward(hess_sqrt[:, out_idx, :], retain_graph=(retain_graph or out_idx < o - 1))\n else:\n vals = torch.LongTensor(n, o).to(gl.device).random_(0, 2) * 2 - 1\n vals = vals.type(torch.get_default_dtype())/o\n mixed_vector = torch.einsum('nop,no->np', hess_sqrt, vals)\n output.backward(mixed_vector, retain_graph=retain_graph)\n\n\ndef grad_norms(A, B, m=None, approx='zero_order'):\n \"\"\"\n Compute gradient norms squared with respect to given metric.\n\n \"zero_order\" approximation uses Euclidian metric computation (standard gradient norms), otherwise try to recover\n metric tensor from one or more of the moments defined as follows on the population activation/backprop values A, B\n m.a = einsum('ni->i', A)\n m.b = einsum('nk->k', B)\n m.AA = einsum('nij->ij', A, A)\n m.BB = einsum('nkl->kl', B, B)\n m.BA = einsum('nik->ik', B, A)\n m.BABA = einsum('nkilj->kilj', B, A, B, A)\n\n Args:\n A: n, d1 tensor of activationsn\n B: n, d2 tensor of backprops\n m: expected moments of covariance/curvature tensor\n approx: which approximation to use to reconstruct metric tensor out of moments.\n \"zero_order\": ignore moments and use Euclidian metric\n \"kfac\": use m.AA, m.BB as Mijkl=Mij*Mkl\n \"isserlis: use m.AA, m.BB, m.BA, m.a, m.b as Mijkl=Mij*Mkl+Mik*Mjl+Mil*Mjk-2Mi*Mj*Mk*Ml\n \"full\": full 4th order moment, use m.BABA\n\n Returns:\n (n,) tensor of per-example gradient norms squared\n \"\"\"\n if approx == 'zero_order':\n norms = (A * A).sum(dim=1) * (B * B).sum(dim=1)\n elif approx == 'kfac':\n Am, Bm = A @ m.AA, B @ m.BB\n norms = (Am * A).sum(dim=1) * (Bm * B).sum(dim=1)\n # equivalent to torch.einsum('nk,ni,lk,ij,nl,nj->n', B, A, BB, AA, B, A)\n\n elif approx == 'isserlis':\n kfac = torch.einsum('nk,ni,lk,ij,nl,nj->n', B, A, m.BB, m.AA, B, A)\n cross1 = torch.einsum('nk,ni,ki,lj,nl,nj->n', B, A, m.BA, m.BA, B, A)\n cross2 = torch.einsum('nk,ni,li,kj,nl,nj->n', B, A, m.BA, m.BA, B, A)\n first_order = torch.einsum('nk,ni,i,j,k,l,nl,nj->n', B, A, m.a, m.a, m.b, m.b, B, A)\n norms = kfac + cross1 + cross2 - 2*first_order\n else:\n assert approx == 'full'\n norms = torch.einsum('ni,nk,nj,nl,likj->n', A, B, A, B, m.BABA)\n return norms\n\n\ndef offset_losses(A, B, alpha, offset, m, approx='zero_order'):\n \"\"\"\n Evaluates expected improvement in loss on example i after taking gradient step from loss on example i+offset\n\n If alpha is None, uses optimal learning rate for minimizing i example loss by using direction of i+offset gradient\n\n Returns:\n (n,) tensor of improvements\n \"\"\"\n if approx == 'zero_order':\n norms = (A * A).sum(dim=1) * (B * B).sum(dim=1)\n elif approx == 'kfac':\n Am, Bm = A @ m.AA, B @ m.BB\n norms = (Am * A).sum(dim=1) * (Bm * B).sum(dim=1)\n # equivalent to torch.einsum('nk,ni,lk,ij,nl,nj->n', B, A, BB, AA, B, A)\n elif approx == 'isserlis': # TODO(y): currently this runs out of memory, optimize the einsums below \n kfac = torch.einsum('nk,ni,lk,ij,nl,nj->n', B, A, m.BB, m.AA, B, A)\n cross1 = torch.einsum('nk,ni,ki,lj,nl,nj->n', B, A, m.BA, m.BA, B, A)\n cross2 = torch.einsum('nk,ni,li,kj,nl,nj->n', B, A, m.BA, m.BA, B, A)\n first_order = torch.einsum('nk,ni,i,j,k,l,nl,nj->n', B, A, m.a, m.a, m.b, m.b, B, A)\n norms = kfac + cross1 + cross2 - 2*first_order\n else:\n assert approx == 'full'\n norms = torch.einsum('ni,nk,nj,nl,likj->n', A, B, A, B, m.BABA)\n\n Ad = torch.roll(A, offset, 0)\n Bd = torch.roll(B, offset, 0)\n dot_prods = (A * Ad).sum(dim=1) * (B * Bd).sum(dim=1)\n if alpha is None: # use optimal step for given direction\n improvements = 1/2 * dot_prods*dot_prods / norms\n else:\n improvements = alpha*dot_prods - 1/2 * alpha**2 * norms\n\n return improvements\n\n\ndef offset_cosines(A, B, offset=1):\n \"\"\"\n Evaluates cosines between gradients\n\n If alpha is None, uses optimal learning rate for minimizing i example loss by using direction of i+offset gradient\n\n Returns:\n (n,) tensor of improvements\n \"\"\"\n assert offset != 0\n\n Ad = torch.roll(A, offset, 0)\n Bd = torch.roll(B, offset, 0)\n dot_products = (A * Ad).sum(dim=1) * (B * Bd).sum(dim=1)\n norms1 = (A * A).sum(dim=1) * (B * B).sum(dim=1)\n norms2 = (Ad * Ad).sum(dim=1) * (Bd * Bd).sum(dim=1)\n cosines_squared = dot_products*dot_products/(norms1 * norms2)\n cosines = torch.sqrt(cosines_squared)\n # print('max cosine float32', max(abs(cosines)).item())\n # dot_products = dot_products.type(torch.float64) # division by small numbers unstable, use higher precision\n # norms1 = norms1.type(torch.float64) # division by small numbers unstable, use higher precision\n # norms2 = norms2.type(torch.float64) # division by small numbers unstable, use higher precision\n # print('max cosine float64', max(abs(cosines)).item())\n return cosines\n\n\ndef offset_dotprod(A, B, offset=1):\n \"\"\"\n Evaluates cosines between gradients\n\n If alpha is None, uses optimal learning rate for minimizing i example loss by using direction of i+offset gradient\n\n Returns:\n (n,) tensor of improvements\n \"\"\"\n assert offset != 0\n\n Ad = torch.roll(A, offset, 0)\n Bd = torch.roll(B, offset, 0)\n dot_products = (A * Ad).sum(dim=1) * (B * Bd).sum(dim=1)\n return dot_products\n\n\ndef grad_curvs(A, B, metric):\n Am, Bm = A @ metric.AA, B @ metric.BB\n norms_before = (A * A).sum(dim=1) * (B * B).sum(dim=1)\n norms_after = (Am * A).sum(dim=1) * (Bm * B).sum(dim=1)\n return norms_after / norms_before\n\n" }, { "path": "autotune/autograd_lib_test.py", "content": "import sys\nfrom collections import namedtuple, defaultdict\n\nimport autograd_lib\nimport pytest\n\nimport util as u\n\n# Test exact Hessian computation\n\n# import torch\nfrom typing import Callable\n\nimport torch\nimport torch.nn as nn\n\nfrom attrdict import AttrDefault, AttrDict\n\n\ndef simple_model(d, num_layers):\n \"\"\"Creates simple linear neural network initialized to identity\"\"\"\n layers = []\n for i in range(num_layers):\n layer = nn.Linear(d, d, bias=False)\n layer.weight.data.copy_(torch.eye(d))\n layers.append(layer)\n return torch.nn.Sequential(*layers)\n\n\ndef test_hooks():\n d = 1\n model = simple_model(d, num_layers=5)\n autograd_lib.register(model)\n\n A1, A2, A3 = {}, {}, {}\n x = torch.ones(1, d)\n\n with autograd_lib.save_activations(A1):\n y = model(2 * x)\n\n with autograd_lib.save_activations(A2):\n with autograd_lib.save_activations(A3):\n y = model(x)\n\n B1 = {}\n B2 = {}\n with autograd_lib.extend_backprops(B1):\n y.backward(x, retain_graph=True)\n\n model[2].weight.requires_grad = False\n for layer in model:\n del layer.weight.grad\n\n # model.clear_grads()\n with autograd_lib.extend_backprops(B2):\n y.backward(2 * x)\n\n print(B2.values())\n for layer in model:\n print(layer.weight.grad)\n\n for layer in model:\n assert A1[layer] == 2 * x\n assert A2[layer] == x\n assert A3[layer] == x\n assert B1[layer] == [x]\n assert B2[layer] == [2 * x]\n\n autograd_lib.unregister()\n\n\ndef _test_activations_contextmanager():\n d = 5\n model = simple_model(d, num_layers=2)\n autograd_lib.register(model)\n\n A1, A2, A3 = {}, {}, {}\n x = torch.ones(1, d)\n\n with autograd_lib.save_activations(A1):\n y = model(x)\n with autograd_lib.save_activations(A2):\n z = model[1](x)\n\n context_ids = autograd_lib.global_settings.last_captured_activations_contextid\n assert context_ids[model[1]] == context_ids[model[0]] + 1\n\n\n# def _test_backprop():\n# d = 1\n# model = simple_model(d, num_layers=5)\n# autograd_lib.register(model)\n#\n# x = torch.ones(2, d)\n# y = model(x)\n#\n# # make sure buffers get freed, second call will cause a crash\n# autograd_lib.backward(y, kind='identity')\n# with pytest.raises(RuntimeError, match=r\".*retain_graph=True.*\"):\n# autograd_lib.backward(y, kind='identity')\n#\n# y = model(x)\n# B = {}\n# with autograd_lib.save_backprops(B):\n# autograd_lib.backward(y, kind='identity', retain_graph=True)\n# u.check_equal(B[model[0]], [x])\n#\n# with autograd_lib.save_backprops(B):\n# autograd_lib.backward(y, kind='identity', retain_graph=True)\n# u.check_equal(B[model[0]], [x, x])\n#\n# autograd_lib.unregister()\n\n\ndef test_jacobian():\n # ground truth for unit tests from\n # https://www.wolframcloud.com/obj/yaroslavvb/newton/linear-jacobians-and-hessians.nb\n\n def init_model(B, X, A):\n \"\"\" Initializes the model Y=B'XA\n \"\"\"\n B, X, A = u.to_pytorches(B, X, A)\n n = A.shape[1]\n d1, d2 = X.shape\n d3 = B.shape[1]\n\n # Do a test using Linear layers instead of matrix multiplies\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([d1, d2, d3], bias=False)\n model.layers[0].weight.data.copy_(X)\n model.layers[1].weight.data.copy_(B.t())\n\n def eval():\n return model(A.t())\n\n return eval, model.layers[0].weight\n\n # default Kronecker rules give result in vec order.\n # A*B=>(B*A)' gives scalar for vector or scalar jacobian in vecr order\n # For matrix/matrix Jacobian must also switch the first two dimensions\n\n # matrix variable, scalar output\n torch.set_default_dtype(torch.float64)\n B = torch.tensor([[-4.], [2]])\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n A = torch.tensor([[-1.], [3]])\n d_out, d_in = X.shape\n Y_func, X_var = init_model(B, X, A)\n Y = Y_func()\n u.check_equal(Y, [[-52]])\n\n J = u.jacobian(Y, X_var)\n assert J.shape == (1, 1, 2, 2)\n J = J.reshape(2, 2)\n\n u.check_equal(J, u.kron(B, A).T.reshape(d_out, d_in))\n u.check_equal(J, [[4, -12], [-2, 6]])\n\n # matrix variable, vector output, dvecr Y/dvecr X\n B = [[-4, 3], [2, 6]]\n B, X, A = u.to_pytorches(B, X, A)\n Y_func, X_var = init_model(B, X, A)\n Y = Y_func()\n u.check_equal(Y, [[-52, -81]])\n J = u.jacobian(Y, X_var)\n assert J.shape == (1, 2, 2, 2)\n J1 = u.kron(B, A).T\n assert J1.shape == (2, 4) # output and input directions are flattened\n u.check_equal(J, J1.reshape(J.shape))\n u.check_equal(J.reshape(J1.shape), J1)\n\n # matrix variable, matrix output, dvecr Y/dvecX\n A = torch.tensor([[-1., 4], [3, 0]])\n B, X, A = u.to_pytorches(B, X, A)\n Y_func, X_var = init_model(B, X, A)\n Y = Y_func()\n\n J = u.jacobian(Y, X_var)\n J = J.transpose(0, 1) # dvecrY/dvecr X -> dvecY/dvecr X\n assert J.shape == (2, 2, 2, 2)\n\n J1 = u.kron(B, A).T # this gives order where variable is row vectorized, but output is column vectorized\n assert J1.shape == (4, 4)\n u.check_equal(J, J1.reshape(J.shape))\n u.check_equal(J.reshape(J1.shape), J1)\n\n # Hessian of matrix variable, x output\n loss = (Y * Y).sum() / 2\n hess = u.hessian(loss, X_var)\n assert hess.shape == (2, 2, 2, 2)\n hess1 = u.kron(B @ B.t(), A @ A.t())\n assert hess1.shape == (4, 4)\n u.check_equal(hess1.reshape(hess.shape), hess)\n u.check_equal(hess1, hess.reshape(hess1.shape))\n\n\ndef create_toy_model():\n \"\"\"\n Create model from https://www.wolframcloud.com/obj/yaroslavvb/newton/linear-jacobians-and-hessians.nb\n PyTorch works on transposed representation, hence to obtain Y from notebook, do model(A.T).T\n \"\"\"\n\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([2, 2, 2], bias=False)\n autograd_lib.register(model)\n\n A = torch.tensor([[-1., 4], [3, 0]])\n B = torch.tensor([[-4., 3], [2, 6]])\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n\n model.layers[0].weight.data.copy_(X)\n model.layers[1].weight.data.copy_(B.t())\n return A, model\n\n\ndef test_gradient_norms():\n \"\"\"Per-example gradient norms.\"\"\"\n u.seed_random(1)\n A, model = create_toy_model()\n\n activations = {}\n\n def save_activations(layer, a, _):\n if layer != model.layers[0]:\n return\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n norms = {}\n\n def compute_norms(layer, _, b):\n if layer != model.layers[0]:\n return\n a = activations[layer]\n del activations[layer] # release memory kept by activations\n norms[layer] = (a * a).sum(dim=1) * (b * b).sum(dim=1)\n\n with autograd_lib.module_hook(compute_norms):\n loss.backward()\n\n u.check_equal(norms[model.layers[0]], [3493250, 9708800])\n\n\ndef test_full_hessian():\n u.seed_random(1)\n A, model = create_toy_model()\n data = A.t()\n # data = data.repeat(3, 1)\n activations = {}\n\n hess = defaultdict(float)\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n n = A.shape[0]\n\n di = A.shape[1]\n do = B.shape[1]\n\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backprop_identity(Y, retain_graph=True)\n\n # check against autograd\n hess_autograd = u.hessian(loss, model.layers[0].weight)\n hess0 = hess[model.layers[0]]\n u.check_equal(hess_autograd, hess0)\n\n # check against manual solution\n u.check_equal(hess0.reshape(4, 4),\n [[425, -75, 170, -30], [-75, 225, -30, 90], [170, -30, 680, -120], [-30, 90, -120, 360]])\n\n\ndef test_full_fisher():\n u.seed_random(1)\n A, model = create_toy_model()\n\n activations = {}\n\n def save_activations(layer, a, _):\n if layer != model.layers[0]:\n return\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n fisher = [0]\n\n def compute_fisher(layer, _, B):\n if layer != model.layers[0]:\n return\n A = activations[layer]\n n = A.shape[0]\n\n di = A.shape[1]\n do = B.shape[1]\n\n Jo = torch.einsum(\"ni,nj->nij\", B, A).reshape(n, -1)\n fisher[0] += torch.einsum('ni,nj->ij', Jo, Jo)\n\n with autograd_lib.module_hook(compute_fisher):\n loss.backward()\n\n result0 = torch.tensor([[5.383625e+06, -3.675000e+03, 4.846250e+06, -6.195000e+04],\n [-3.675000e+03, 1.102500e+04, -6.195000e+04, 1.858500e+05],\n [4.846250e+06, -6.195000e+04, 4.674500e+06, -1.044300e+06],\n [-6.195000e+04, 1.858500e+05, -1.044300e+06, 3.132900e+06]])\n u.check_close(fisher[0], result0)\n\n\ndef test_full_fisher_multibatch():\n torch.set_default_dtype(torch.float64)\n u.seed_random(1)\n A, model = create_toy_model()\n\n activations = {}\n\n def save_activations(layer, a, _):\n if layer != model.layers[0]:\n return\n activations[layer] = a\n\n fisher = [0]\n\n def compute_fisher(layer, _, B):\n if layer != model.layers[0]:\n return\n A = activations[layer]\n n = A.shape[0]\n\n di = A.shape[1]\n do = B.shape[1]\n\n Jo = torch.einsum(\"ni,nj->nij\", B, A).reshape(n, -1)\n fisher[0] += torch.einsum('ni,nj->ij', Jo, Jo)\n\n for x in A.t():\n with autograd_lib.module_hook(save_activations):\n y = model(x)\n loss = torch.sum(y * y) / 2\n\n with autograd_lib.module_hook(compute_fisher):\n loss.backward()\n\n # result computed using single step forward prop\n result0 = torch.tensor([[5.383625e+06, -3.675000e+03, 4.846250e+06, -6.195000e+04],\n [-3.675000e+03, 1.102500e+04, -6.195000e+04, 1.858500e+05],\n [4.846250e+06, -6.195000e+04, 4.674500e+06, -1.044300e+06],\n [-6.195000e+04, 1.858500e+05, -1.044300e+06, 3.132900e+06]])\n u.check_close(fisher[0], result0)\n # check against autograd\n # hess0 = u.hessian(loss, model.layers[0].weight).reshape([4, 4])\n # u.check_equal(hess[0], hess0)\n\n\ndef test_kfac_hessian():\n A, model = create_toy_model()\n data = A.t()\n data = data.repeat(7, 1)\n n = float(len(data))\n\n activations = {}\n hess = defaultdict(lambda: AttrDefault(float))\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n def compute_hessian(layer, _, B):\n A = activations[layer]\n hess[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n hess[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n\n for x in data:\n with autograd_lib.module_hook(save_activations):\n y = model(x)\n o = y.shape[1]\n loss = torch.sum(y * y) / 2\n\n with autograd_lib.module_hook(compute_hessian):\n autograd_lib.backprop_identity(y)\n\n hess0 = hess[model.layers[0]]\n result = u.kron(hess0.BB / n, hess0.AA / o)\n\n # check result against autograd\n loss = u.least_squares(model(data), aggregation='sum')\n hess0 = u.hessian(loss, model.layers[0].weight).reshape(4, 4)\n u.check_equal(hess0, result)\n\n\ndef test_full_hessian_multibatch():\n A, model = create_toy_model()\n data = A.t()\n data = data.repeat(3, 1)\n n = float(len(data))\n\n activations = {}\n hess = defaultdict(float)\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n def compute_hessian(layer, _, B):\n A = activations[layer]\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n\n for x in data:\n with autograd_lib.module_hook(save_activations):\n y = model(x)\n loss = torch.sum(y * y) / 2\n\n with autograd_lib.module_hook(compute_hessian):\n autograd_lib.backprop_identity(y)\n\n result = hess[model.layers[0]]\n\n # check result against autograd\n loss = u.least_squares(model(data), aggregation='sum')\n hess0 = u.hessian(loss, model.layers[0].weight)\n u.check_equal(hess0, result)\n\n\ndef test_diagonal_hessian():\n u.seed_random(1)\n A, model = create_toy_model()\n\n activations = {}\n\n def save_activations(layer, a, _):\n if layer != model.layers[0]:\n return\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n hess = [0]\n\n def compute_hess(layer, _, B):\n if layer != model.layers[0]:\n return\n A = activations[layer]\n hess[0] += torch.einsum(\"ni,nj->ij\", B * B, A * A).reshape(-1)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backprop_identity(Y, retain_graph=True)\n\n # check against autograd\n hess0 = u.hessian(loss, model.layers[0].weight).reshape([4, 4])\n u.check_equal(hess[0], torch.diag(hess0))\n\n # check against manual solution\n u.check_equal(hess[0], [425., 225., 680., 360.])\n\n\ndef test_full_hessian_xent():\n u.seed_random(1)\n torch.set_default_dtype(torch.float64)\n\n batch_size = 1\n d = [2, 2]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected(d, nonlin=True, bias=True)\n model.layers[0].weight.data.copy_(torch.eye(2))\n autograd_lib.register(model)\n loss_fn = torch.nn.CrossEntropyLoss()\n\n data = u.to_logits(torch.tensor([[0.7, 0.3]]))\n targets = torch.tensor([0])\n\n data = data.repeat([3, 1])\n targets = targets.repeat([3])\n n = len(data)\n\n activations = {}\n hess = defaultdict(float)\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n Y = model(data)\n loss = loss_fn(Y, targets)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(Y, loss='CrossEntropy', retain_graph=True)\n\n # check against autograd\n # 0.1459\n hess_autograd = u.hessian(loss, model.layers[0].weight)\n hess0 = hess[model.layers[0]] / n\n u.check_equal(hess_autograd, hess0)\n\n\ndef test_full_hessian_xent_multibatch():\n u.seed_random(1)\n torch.set_default_dtype(torch.float64)\n\n batch_size = 1\n d = [2, 2]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=True, bias=True)\n model.layers[0].weight.data.copy_(torch.eye(2))\n autograd_lib.register(model)\n loss_fn = torch.nn.CrossEntropyLoss()\n\n data = u.to_logits(torch.tensor([[0.7, 0.3]]))\n targets = torch.tensor([0])\n\n data = data.repeat([3, 1])\n targets = targets.repeat([3])\n n = len(data)\n\n activations = {}\n hess = defaultdict(float)\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n for i in range(n):\n with autograd_lib.module_hook(save_activations):\n data_batch = data[i: i + 1]\n targets_batch = targets[i: i + 1]\n Y = model(data_batch)\n loss = loss_fn(Y, targets_batch)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(Y, loss='CrossEntropy')\n\n # check against autograd\n # 0.1459\n Y = model(data)\n loss = loss_fn(Y, targets)\n hess_autograd = u.hessian(loss, model.layers[0].weight)\n hess0 = hess[model.layers[0]] / n\n u.check_equal(hess_autograd, hess0)\n\n\ndef test_full_hessian_xent_kfac():\n u.seed_random(1)\n torch.set_default_dtype(torch.float64)\n\n batch_size = 1\n d = [2, 2]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=True, bias=True)\n model.layers[0].weight.data.copy_(torch.eye(2))\n autograd_lib.register(model)\n loss_fn = torch.nn.CrossEntropyLoss()\n\n data = u.to_logits(torch.tensor([[0.7, 0.3]]))\n targets = torch.tensor([0])\n\n data = data.repeat([3, 1])\n targets = targets.repeat([3])\n n = len(data)\n\n activations = {}\n hess = defaultdict(lambda: AttrDefault(float))\n\n for i in range(n):\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n data_batch = data[i: i + 1]\n targets_batch = targets[i: i + 1]\n Y = model(data_batch)\n o = Y.shape[1]\n loss = loss_fn(Y, targets_batch)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n hess[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n hess[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(Y, loss='CrossEntropy')\n\n # expand\n hess_factored = hess[model.layers[0]]\n hess0 = torch.einsum('kl,ij->kilj', hess_factored.BB / n, hess_factored.AA / o) # hess for sum loss\n hess0 /= n # hess for mean loss\n\n # check against autograd\n # 0.1459\n Y = model(data)\n loss = loss_fn(Y, targets)\n hess_autograd = u.hessian(loss, model.layers[0].weight)\n u.check_equal(hess_autograd, hess0)\n\n # check diagonal hessian\n diag_autograd = torch.einsum('lili->li', hess_autograd)\n diag_kfac = torch.einsum('ll,ii->li', hess_factored.BB / n, hess_factored.AA / o / n)\n u.check_close(diag_autograd, diag_kfac)\n\n\ndef test_full_hessian_xent_kfac2():\n \"\"\"Test with uneven layers.\"\"\"\n u.seed_random(1)\n torch.set_default_dtype(torch.float64)\n\n batch_size = 1\n d = [3, 2]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=True, bias=False)\n autograd_lib.register(model)\n loss_fn = torch.nn.CrossEntropyLoss()\n\n data = u.to_logits(torch.tensor([[0.7, 0.2, 0.1]]))\n targets = torch.tensor([0])\n\n data = data.repeat([3, 1])\n targets = targets.repeat([3])\n n = len(data)\n\n activations = {}\n hess = defaultdict(lambda: AttrDefault(float))\n\n for i in range(n):\n def save_activations(layer, A, _):\n activations[layer] = A\n hess[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n\n with autograd_lib.module_hook(save_activations):\n data_batch = data[i: i + 1]\n targets_batch = targets[i: i + 1]\n Y = model(data_batch)\n o = Y.shape[1]\n loss = loss_fn(Y, targets_batch)\n\n def compute_hess(layer, _, B):\n hess[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(Y, loss='CrossEntropy')\n\n # expand\n hess_factored = hess[model.layers[0]]\n hess0 = torch.einsum('kl,ij->kilj', hess_factored.BB / n, hess_factored.AA / o) # hess for sum loss\n hess0 /= n # hess for mean loss\n\n # check against autograd\n # 0.1459\n Y = model(data)\n loss = loss_fn(Y, targets)\n hess_autograd = u.hessian(loss, model.layers[0].weight)\n u.check_equal(hess_autograd, hess0)\n\n\ndef test_full_hessian_xent_mnist():\n u.seed_random(1)\n\n data_width = 3\n batch_size = 2\n d = [data_width ** 2, 10]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n autograd_lib.register(model)\n dataset = u.TinyMNIST(dataset_size=batch_size, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n hess = defaultdict(float)\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n\n activations = {}\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n output = model(data)\n loss = loss_fn(output, targets)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(output, loss='CrossEntropy', retain_graph=True)\n\n # compute Hessian through autograd\n H_autograd = u.hessian(loss, model.layers[0].weight)\n u.check_close(hess[model.layers[0]] / n, H_autograd)\n\n\ndef test_full_hessian_xent_mnist_multilayer():\n \"\"\"Test regular and diagonal hessian computation.\"\"\"\n u.seed_random(1)\n\n data_width = 3\n batch_size = 2\n d = [data_width ** 2, 6, 10]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n autograd_lib.register(model)\n dataset = u.TinyMNIST(dataset_size=batch_size, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n hess = defaultdict(float)\n hess_diag = defaultdict(float)\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n\n activations = {}\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n output = model(data)\n loss = loss_fn(output, targets)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n BA = torch.einsum(\"nl,ni->nli\", B, A)\n hess[layer] += torch.einsum('nli,nkj->likj', BA, BA)\n hess_diag[layer] += torch.einsum(\"ni,nj->ij\", B * B, A * A)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(output, loss='CrossEntropy', retain_graph=True)\n\n # compute Hessian through autograd\n H_autograd = u.hessian(loss, model.layers[0].weight)\n u.check_close(hess[model.layers[0]] / batch_size, H_autograd)\n diag_autograd = torch.einsum('lili->li', H_autograd)\n u.check_close(diag_autograd, hess_diag[model.layers[0]] / batch_size)\n\n H_autograd = u.hessian(loss, model.layers[1].weight)\n u.check_close(hess[model.layers[1]] / batch_size, H_autograd)\n diag_autograd = torch.einsum('lili->li', H_autograd)\n u.check_close(diag_autograd, hess_diag[model.layers[1]] / batch_size)\n\n\ndef _test_kfac_hessian_xent_mnist():\n u.seed_random(1)\n\n data_width = 3\n batch_size = 2\n d = [data_width ** 2, 10]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n autograd_lib.register(model)\n dataset = u.TinyMNIST(dataset_size=batch_size, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n activations = {}\n hess = defaultdict(lambda: AttrDefault(float))\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n\n activations = {}\n\n def save_activations(layer, a, _):\n activations[layer] = a\n\n with autograd_lib.module_hook(save_activations):\n output = model(data)\n loss = loss_fn(output, targets)\n\n def compute_hess(layer, _, B):\n A = activations[layer]\n hess[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n hess[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n\n with autograd_lib.module_hook(compute_hess):\n autograd_lib.backward_hessian(output, loss='CrossEntropy', retain_graph=True)\n\n hess_factored = hess[model.layers[0]]\n hess0 = torch.einsum('kl,ij->kilj', hess_factored.BB / n, hess_factored.AA / o) # hess for sum loss\n hess0 /= n # hess for mean loss\n\n # compute Hessian through autograd\n H_autograd = u.hessian(loss, model.layers[0].weight)\n rel_error = torch.norm((hess0 - H_autograd).flatten()) / torch.norm(H_autograd.flatten())\n assert rel_error < 0.01 # 0.0057\n\n\ndef test_kfac_jacobian_mnist():\n u.seed_random(1)\n\n data_width = 3\n d = [data_width ** 2, 8, 10]\n model: u.SimpleMLP = u.SimpleMLP(d, nonlin=False)\n autograd_lib.register(model)\n\n batch_size = 4\n stats_steps = 2\n n = batch_size * stats_steps\n\n dataset = u.TinyMNIST(dataset_size=n, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n activations = {}\n jacobians = defaultdict(lambda: AttrDefault(float))\n total_data = []\n\n # sum up statistics over n examples\n for train_step in range(stats_steps):\n data, targets = next(train_iter)\n total_data.append(data)\n\n activations = {}\n\n def save_activations(layer, A, _):\n activations[layer] = A\n jacobians[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n\n with autograd_lib.module_hook(save_activations):\n output = model(data)\n loss = loss_fn(output, targets)\n\n def compute_jacobian(layer, _, B):\n A = activations[layer]\n jacobians[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n jacobians[layer].diag += torch.einsum(\"ni,nj->ij\", B * B, A * A)\n\n with autograd_lib.module_hook(compute_jacobian):\n autograd_lib.backward_jacobian(output)\n\n for layer in model.layers:\n jacobian0 = jacobians[layer]\n jacobian_full = torch.einsum('kl,ij->kilj', jacobian0.BB / n, jacobian0.AA / n)\n jacobian_diag = jacobian0.diag / n\n\n J = u.jacobian(model(torch.cat(total_data)), layer.weight)\n J_autograd = torch.einsum('noij,nokl->ijkl', J, J) / n\n u.check_equal(jacobian_full, J_autograd)\n\n u.check_equal(jacobian_diag, torch.einsum('ikik->ik', J_autograd))\n\n\ndef test_kfac_fisher_mnist():\n u.seed_random(1)\n\n data_width = 3\n d = [data_width ** 2, 8, 10]\n model: u.SimpleMLP = u.SimpleMLP(d, nonlin=False)\n autograd_lib.register(model)\n\n batch_size = 4\n stats_steps = 2\n n = batch_size * stats_steps\n\n dataset = u.TinyMNIST(dataset_size=n, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n activations = {}\n fishers = defaultdict(lambda: AttrDefault(float))\n total_data = []\n\n # sum up statistics over n examples\n for train_step in range(stats_steps):\n data, targets = next(train_iter)\n total_data.append(data)\n\n activations = {}\n\n def save_activations(layer, A, _):\n activations[layer] = A\n fishers[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n\n with autograd_lib.module_hook(save_activations):\n output = model(data)\n loss = loss_fn(output, targets) * len(data) # remove data normalization\n\n def compute_fisher(layer, _, B):\n A = activations[layer]\n fishers[layer].BB += torch.einsum(\"ni,nj->ij\", B, B)\n fishers[layer].diag += torch.einsum(\"ni,nj->ij\", B * B, A * A)\n\n with autograd_lib.module_hook(compute_fisher):\n autograd_lib.backward_jacobian(output)\n\n for layer in model.layers:\n fisher0 = fishers[layer]\n fisher_full = torch.einsum('kl,ij->kilj', fisher0.BB / n, fisher0.AA / n)\n fisher_diag = fisher0.diag / n\n\n u.check_equal(torch.einsum('ikik->ik', fisher_full), fisher_diag)\n\n\n# list replacement. Workaround for AttrDict automatically converting list objects to Tuple\nclass MyList:\n def __init__(self, *args, **kwargs):\n super(MyList, self).__init__(*args, **kwargs)\n self.storage = list()\n\n def __getattr__(self, *_args, **_kwargs):\n return self.storage.__getattribute__(*_args, **_kwargs)\n\n def normal_form(self):\n return self.value()\n\n def value(self):\n return self.storage\n\n\ndef test_grad_norms():\n \"\"\"Test computing gradient norms using various methods.\"\"\"\n\n u.seed_random(1)\n # torch.set_default_dtype(torch.float64)\n\n data_width = 3\n batch_size = 2\n d = [data_width ** 2, 6, 10]\n o = d[-1]\n stats_steps = 2\n num_samples = batch_size * stats_steps # number of samples used in computation of curvature stats\n\n model: u.SimpleModel = u.SimpleMLP(d, nonlin=True, bias=True)\n loss_fn = torch.nn.CrossEntropyLoss()\n autograd_lib.register(model)\n\n dataset = u.TinyMNIST(dataset_size=num_samples, data_width=data_width, original_targets=True)\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n stats_iter = iter(stats_loader)\n\n moments = defaultdict(lambda: AttrDefault(float))\n norms = defaultdict(lambda: AttrDefault(MyList))\n data_batches = []\n targets_batches = []\n for stats_step in range(stats_steps):\n data, targets = next(stats_iter)\n data_batches.append(data)\n targets_batches.append(targets)\n\n activations = {}\n def forward_aggregate(layer, A, _):\n activations[layer] = A\n moments[layer].AA += torch.einsum('ni,nj->ij', A, A)\n moments[layer].a += torch.einsum(\"ni->i\", A)\n\n with autograd_lib.module_hook(forward_aggregate):\n output = model(data)\n loss_fn(output, targets)\n\n def backward_aggregate(layer, _, B):\n A = activations[layer]\n moments[layer].b += torch.einsum(\"nk->k\", B)\n moments[layer].BA += torch.einsum(\"nl,ni->li\", B, A)\n moments[layer].BB += torch.einsum(\"nk,nl->kl\", B, B)\n moments[layer].BABA += torch.einsum('nl,ni,nk,nj->likj', B, A, B, A)\n\n with autograd_lib.module_hook(backward_aggregate):\n autograd_lib.backward_hessian(output, loss='CrossEntropy', retain_graph=True)\n\n # compare against results using autograd\n data = torch.cat(data_batches)\n targets = torch.cat(targets_batches)\n\n with autograd_lib.save_activations2() as activations:\n loss = loss_fn(model(data), targets)\n\n def normalize_moments(d, n):\n result = AttrDict()\n for val in d:\n if type(d[val]) == torch.Tensor:\n result[val] = d[val] / n\n return result\n\n def compute_norms(layer, _, B):\n A = activations[layer]\n for kind in ('zero_order', 'kfac', 'isserlis', 'full'):\n normalized_moments = normalize_moments(moments[layer], num_samples)\n norms_list = getattr(norms[layer], kind)\n norms_list.extend(autograd_lib.grad_norms(A, B, normalized_moments, approx=kind))\n\n with autograd_lib.module_hook(compute_norms):\n model.zero_grad()\n (len(data) * loss).backward(retain_graph=True)\n\n print(norms[model.layers[0]].zero_order.value())\n\n for layer in model.layers:\n output = model(data)\n losses = torch.stack([loss_fn(output[i:i + 1], targets[i:i + 1]) for i in range(len(data))])\n grads = u.jacobian(losses, layer.weight)\n grad_norms = torch.einsum('nij,nij->n', grads, grads)\n u.check_close(grad_norms, norms[layer].zero_order)\n\n # test gradient norms with custom metric\n kfac_norms, isserlis_norms, full_norms = [u.to_pytorch(getattr(norms[layer], k)) for k in ('kfac', 'isserlis', 'full')]\n error_kfac = max(abs(kfac_norms - full_norms))\n error_isserlis = max(abs(isserlis_norms - full_norms))\n assert error_isserlis < 1e-4\n assert error_kfac < 1e-4\n\n\nif __name__ == '__main__':\n # test_gradient_norms()\n # test_full_hessian()\n # test_diagonal_hessian()\n # test_full_fisher()\n # test_full_fisher_multibatch()\n # test_full_hessian_multibatch()\n # test_kfac_hessian()\n # test_full_hessian_xent()\n # test_full_hessian_xent_multibatch()\n test_full_hessian_xent_kfac()\n test_full_hessian_xent_kfac2()\n # test_full_hessian_xent_mnist()\n test_full_hessian_xent_mnist_multilayer()\n test_kfac_jacobian_mnist()\n # _test_kfac_jacobian_mnist()\n test_kfac_fisher_mnist()\n\n test_grad_norms()\n # test_hooks()\n # test_activations_contextmanager()\n # test_jacobian()\n # test_backprop()\n # u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/autograd_test.py", "content": "# Tests that compare manual computation of quantities against PyTorch autograd\n\nimport os\nimport sys\n\nimport globals as gl\nimport pytest\nimport torch\nfrom torch import nn as nn\nimport wandb\nfrom torch import optim\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport torch.nn.functional as F\n\nimport util as u\n\nimport numpy as np\n\nimport autograd_lib\n\nunfold = torch.nn.functional.unfold\nfold = torch.nn.functional.fold\n\n\ndef test_autoencoder_minimize():\n \"\"\"Minimize autoencoder for a few steps.\"\"\"\n u.seed_random(1)\n torch.set_default_dtype(torch.float32)\n data_width = 4\n targets_width = 2\n\n batch_size = 64\n dataset = u.TinyMNIST(data_width=data_width, targets_width=targets_width,\n dataset_size=batch_size)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n\n d1 = data_width ** 2\n d2 = 10\n d3 = targets_width ** 2\n model: u.SimpleModel = u.SimpleFullyConnected([d1, d2, d3], nonlin=True)\n model.disable_hooks()\n\n optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\n\n def loss_fn(data, targets):\n err = data - targets.view(-1, data.shape[1])\n assert len(data) == batch_size\n return torch.sum(err * err) / 2 / len(data)\n\n loss = 0\n for i in range(10):\n data, targets = next(iter(trainloader))\n optimizer.zero_grad()\n loss = loss_fn(model(data), targets)\n if i == 0:\n assert loss > 0.054\n pass\n loss.backward()\n optimizer.step()\n\n assert loss < 0.0398\n\n\ndef test_autoencoder_newton():\n \"\"\"Use Newton's method to train autoencoder.\"\"\"\n\n image_size = 3\n batch_size = 64\n dataset = u.TinyMNIST(data_width=image_size, targets_width=image_size,\n dataset_size=batch_size)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n\n d = image_size ** 2 # hidden layer size\n u.seed_random(1)\n model: u.SimpleModel = u.SimpleFullyConnected([d, d])\n model.disable_hooks()\n\n optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\n\n def loss_fn(data, targets):\n err = data - targets.view(-1, data.shape[1])\n assert len(data) == batch_size\n return torch.sum(err * err) / 2 / len(data)\n\n for i in range(10):\n data, targets = next(iter(trainloader))\n optimizer.zero_grad()\n loss = loss_fn(model(data), targets)\n if i > 0:\n assert loss < 1e-9\n\n loss.backward()\n W = model.layers[0].weight\n grad = u.tvec(W.grad)\n\n loss = loss_fn(model(data), targets)\n H = u.hessian(loss, W)\n\n # for col-major: H = H.transpose(0, 1).transpose(2, 3).reshape(d**2, d**2)\n H = H.reshape(d ** 2, d ** 2)\n\n # For col-major: W1 = u.unvec(u.vec(W) - u.pinv(H) @ grad, d)\n # W1 = u.untvec(u.tvec(W) - grad @ u.pinv(H), d)\n W1 = u.untvec(u.tvec(W) - grad @ H.pinverse(), d)\n W.data.copy_(W1)\n\n\ndef test_main_autograd():\n u.seed_random(1)\n log_wandb = False\n autograd_check = True\n use_double = False\n\n logdir = u.get_unique_logdir('/tmp/autoencoder_test/run')\n\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n\n batch_size = 5\n\n try:\n if log_wandb:\n wandb.init(project='test-autograd_test', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['batch'] = batch_size\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n data_width = 4\n targets_width = 2\n\n d1 = data_width ** 2\n d2 = 10\n d3 = targets_width ** 2\n o = d3\n n = batch_size\n d = [d1, d2, d3]\n model: u.SimpleModel = u.SimpleFullyConnected(d, nonlin=True, bias=True)\n if use_double:\n model = model.double()\n train_steps = 3\n\n dataset = u.TinyMNIST(data_width=data_width, targets_width=targets_width,\n dataset_size=batch_size * train_steps)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n def loss_fn(data, targets):\n err = data - targets.view(-1, data.shape[1])\n assert len(data) == batch_size\n return torch.sum(err * err) / 2 / len(data)\n\n loss_hessian = u.HessianExactSqrLoss()\n\n gl.token_count = 0\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n if use_double:\n data, targets = data.double(), targets.double()\n\n # get gradient values\n model.skip_backward_hooks = False\n model.skip_forward_hooks = False\n u.clear_backprops(model)\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n model.skip_forward_hooks = True\n\n output = model(data)\n for bval in loss_hessian(output):\n if use_double:\n bval = bval.double()\n output.backward(bval, retain_graph=True)\n\n model.skip_backward_hooks = True\n\n for (i, layer) in enumerate(model.layers):\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops_list[0] * n\n assert B_t.shape == (n, d[i + 1])\n\n # per example gradients\n G = u.khatri_rao_t(B_t, A_t)\n assert G.shape == (n, d[i+1] * d[i])\n Gbias = B_t\n assert Gbias.shape == (n, d[i + 1])\n\n # average gradient\n g = G.sum(dim=0, keepdim=True) / n\n gb = Gbias.sum(dim=0, keepdim=True) / n\n assert g.shape == (1, d[i] * d[i + 1])\n assert gb.shape == (1, d[i + 1])\n\n if autograd_check:\n u.check_close(B_t.t() @ A_t / n, layer.weight.saved_grad)\n u.check_close(g.reshape(d[i + 1], d[i]), layer.weight.saved_grad)\n u.check_close(torch.einsum('nj->j', B_t) / n, layer.bias.saved_grad)\n u.check_close(torch.mean(B_t, dim=0), layer.bias.saved_grad)\n u.check_close(torch.einsum('ni,nj->ij', B_t, A_t)/n, layer.weight.saved_grad)\n\n # empirical Fisher\n efisher = G.t() @ G / n\n _sigma = efisher - g.t() @ g\n\n #############################\n # Hessian stats\n #############################\n A_t = layer.activations\n Bh_t = [layer.backprops_list[out_idx + 1] for out_idx in range(o)]\n Amat_t = torch.cat([A_t] * o, dim=0) # todo: can instead replace with a khatri-rao loop\n Bmat_t = torch.cat(Bh_t, dim=0)\n Amat_t2 = torch.stack([A_t]*o, dim=0) # o, n, in_dim\n Bmat_t2 = torch.stack(Bh_t, dim=0) # o, n, out_dim\n\n assert Amat_t.shape == (n * o, d[i])\n assert Bmat_t.shape == (n * o, d[i + 1])\n\n Jb = u.khatri_rao_t(Bmat_t, Amat_t) # batch output Jacobian\n H = Jb.t() @ Jb / n\n Jb2 = torch.einsum('oni,onj->onij', Bmat_t2, Amat_t2)\n u.check_close(H.reshape(d[i+1], d[i], d[i+1], d[i]), torch.einsum('onij,onkl->ijkl', Jb2, Jb2)/n)\n\n Hbias = Bmat_t.t() @ Bmat_t / n\n u.check_close(Hbias, torch.einsum('ni,nj->ij', Bmat_t, Bmat_t) / n)\n\n if autograd_check:\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n Hbias_autograd = u.hessian(loss, layer.bias)\n u.check_close(H, H_autograd.reshape(d[i+1] * d[i], d[i+1] * d[i]))\n u.check_close(Hbias, Hbias_autograd)\n\n\ndef test_unfold():\n \"\"\"Reproduce convolution as a special case of matrix multiplication with unfolded input tensors\"\"\"\n gl.skip_backward_hooks = False\n gl.skip_forward_hooks = False\n gl.backward_idx = 0\n\n N, Xc, Xh, Xw = 1, 2, 3, 3\n model: u.SimpleModel = u.SimpleConvolutional([Xc, 2])\n\n weight_buffer = model.layers[0].weight.data\n weight_buffer.copy_(torch.ones_like(weight_buffer))\n dims = N, Xc, Xh, Xw\n\n size = np.prod(dims)\n X = torch.arange(0, size).reshape(*dims)\n\n def loss_fn(data):\n err = data.reshape(len(data), -1)\n return torch.sum(err * err) / 2 / len(data)\n\n layer = model.layers[0]\n output = model(X)\n loss = loss_fn(output)\n loss.backward()\n\n u.check_close(layer.activations, X)\n assert layer.backprops_list[0].shape == layer.output.shape\n\n unfold = torch.nn.functional.unfold\n fold = torch.nn.functional.fold\n out_unf = layer.weight.view(layer.weight.size(0), -1) @ unfold(layer.activations, (2, 2))\n u.check_close(fold(out_unf, layer.output.shape[2:], (1, 1)), output)\n\n\ndef test_cross_entropy_hessian_tiny():\n u.seed_random(1)\n\n batch_size = 1\n d = [2, 2]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected(d, nonlin=True, bias=True)\n model.layers[0].weight.data.copy_(torch.eye(2))\n\n loss_fn = torch.nn.CrossEntropyLoss()\n loss_hessian = u.HessianExactCrossEntropyLoss()\n\n data = u.to_logits(torch.tensor([[0.7, 0.3]]))\n targets = torch.tensor([0])\n\n # get gradient values\n u.clear_backprops(model)\n model.skip_forward_hooks = False\n model.skip_backward_hooks = False\n output = model(data)\n\n for bval in loss_hessian(output):\n output.backward(bval, retain_graph=True)\n i = 0\n layer = model.layers[i]\n H, Hbias = u.hessian_from_backprops(layer.activations,\n layer.backprops_list,\n bias=True)\n model.skip_forward_hooks = True\n model.skip_backward_hooks = True\n\n # compute Hessian through autograd\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n u.check_close(H, H_autograd.reshape(d[i] * d[i + 1], d[i] * d[i + 1]))\n Hbias_autograd = u.hessian(loss, layer.bias)\n u.check_close(Hbias, Hbias_autograd)\n\n\ndef test_cross_entropy_hessian_mnist():\n u.seed_random(1)\n\n data_width = 3\n batch_size = 2\n d = [data_width**2, 10]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n model: u.SimpleModel = u.SimpleFullyConnected(d, nonlin=False, bias=True)\n\n dataset = u.TinyMNIST(dataset_size=batch_size, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n loss_hessian = u.HessianExactCrossEntropyLoss()\n\n gl.token_count = 0\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n\n # get gradient values\n u.clear_backprops(model)\n model.skip_forward_hooks = False\n model.skip_backward_hooks = False\n output = model(data)\n for bval in loss_hessian(output):\n output.backward(bval, retain_graph=True)\n i = 0\n layer = model.layers[i]\n H, Hbias = u.hessian_from_backprops(layer.activations,\n layer.backprops_list,\n bias=True)\n model.skip_forward_hooks = True\n model.skip_backward_hooks = True\n\n # compute Hessian through autograd\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight).reshape(d[i] * d[i + 1], d[i] * d[i + 1])\n u.check_close(H, H_autograd)\n\n Hbias_autograd = u.hessian(loss, layer.bias)\n u.check_close(Hbias, Hbias_autograd)\n\n\ndef test_hessian():\n \"\"\"Tests of Hessian computation.\"\"\"\n u.seed_random(1)\n batch_size = 500\n\n data_width = 4\n targets_width = 4\n\n d1 = data_width ** 2\n d2 = 10\n d3 = targets_width ** 2\n o = d3\n N = batch_size\n d = [d1, d2, d3]\n\n dataset = u.TinyMNIST(data_width=data_width, targets_width=targets_width, dataset_size=batch_size)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n data, targets = next(train_iter)\n\n def loss_fn(data, targets):\n assert len(data) == len(targets)\n err = data - targets.view(-1, data.shape[1])\n return torch.sum(err * err) / 2 / len(data)\n\n u.seed_random(1)\n model: u.SimpleModel = u.SimpleFullyConnected(d, nonlin=False, bias=True)\n\n # backprop hessian and compare against autograd\n hessian_backprop = u.HessianExactSqrLoss()\n output = model(data)\n for bval in hessian_backprop(output):\n output.backward(bval, retain_graph=True)\n\n i, layer = next(enumerate(model.layers))\n A_t = layer.activations\n Bh_t = layer.backprops_list\n H, Hb = u.hessian_from_backprops(A_t, Bh_t, bias=True)\n\n model.disable_hooks()\n H_autograd = u.hessian(loss_fn(model(data), targets), layer.weight)\n u.check_close(H, H_autograd.reshape(d[i + 1] * d[i], d[i + 1] * d[i]),\n rtol=1e-4, atol=1e-7)\n Hb_autograd = u.hessian(loss_fn(model(data), targets), layer.bias)\n u.check_close(Hb, Hb_autograd, rtol=1e-4, atol=1e-7)\n\n # check first few per-example Hessians\n Hi, Hb_i = u.per_example_hess(A_t, Bh_t, bias=True)\n u.check_close(H, Hi.mean(dim=0))\n u.check_close(Hb, Hb_i.mean(dim=0), atol=2e-6, rtol=1e-5)\n\n for xi in range(5):\n loss = loss_fn(model(data[xi:xi + 1, ...]), targets[xi:xi + 1])\n H_autograd = u.hessian(loss, layer.weight)\n u.check_close(Hi[xi], H_autograd.reshape(d[i + 1] * d[i], d[i + 1] * d[i]))\n Hbias_autograd = u.hessian(loss, layer.bias)\n u.check_close(Hb_i[i], Hbias_autograd)\n\n # get subsampled Hessian\n u.seed_random(1)\n model = u.SimpleFullyConnected(d, nonlin=False)\n hessian_backprop = u.HessianSampledSqrLoss(num_samples=1)\n\n output = model(data)\n for bval in hessian_backprop(output):\n output.backward(bval, retain_graph=True)\n model.disable_hooks()\n i, layer = next(enumerate(model.layers))\n H_approx1 = u.hessian_from_backprops(layer.activations, layer.backprops_list)\n\n # get subsampled Hessian with more samples\n u.seed_random(1)\n model = u.SimpleFullyConnected(d, nonlin=False)\n\n hessian_backprop = u.HessianSampledSqrLoss(num_samples=o)\n output = model(data)\n for bval in hessian_backprop(output):\n output.backward(bval, retain_graph=True)\n model.disable_hooks()\n i, layer = next(enumerate(model.layers))\n H_approx2 = u.hessian_from_backprops(layer.activations, layer.backprops_list)\n\n assert abs(u.l2_norm(H) / u.l2_norm(H_approx1) - 1) < 0.08, abs(u.l2_norm(H) / u.l2_norm(H_approx1) - 1) # 0.0612\n assert abs(u.l2_norm(H) / u.l2_norm(H_approx2) - 1) < 0.03, abs(u.l2_norm(H) / u.l2_norm(H_approx2) - 1) # 0.0239\n assert u.kl_div_cov(H_approx1, H) < 0.3, u.kl_div_cov(H_approx1, H) # 0.222\n assert u.kl_div_cov(H_approx2, H) < 0.2, u.kl_div_cov(H_approx2, H) # 0.1233\n\n\ndef test_conv_grad():\n \"\"\"Test per-example gradient computation for conv layer.\n\n \"\"\"\n\n u.seed_random(1)\n N, Xc, Xh, Xw = 3, 2, 3, 7\n dd = [Xc, 2]\n\n Kh, Kw = 2, 3\n Oh, Ow = Xh - Kh + 1, Xw - Kw + 1\n model = u.SimpleConvolutional(dd, kernel_size=(Kh, Kw), bias=True).double()\n\n weight_buffer = model.layers[0].weight.data\n\n # output channels, input channels, height, width\n assert weight_buffer.shape == (dd[1], dd[0], Kh, Kw)\n\n input_dims = N, Xc, Xh, Xw\n size = int(np.prod(input_dims))\n X = torch.arange(0, size).reshape(*input_dims).double()\n\n def loss_fn(data):\n err = data.reshape(len(data), -1)\n return torch.sum(err * err) / 2 / len(data)\n\n layer = model.layers[0]\n output = model(X)\n loss = loss_fn(output)\n loss.backward()\n\n u.check_equal(layer.activations, X)\n\n assert layer.backprops_list[0].shape == layer.output.shape\n assert layer.output.shape == (N, dd[1], Oh, Ow)\n\n out_unf = layer.weight.view(layer.weight.size(0), -1) @ unfold(layer.activations, (Kh, Kw))\n assert out_unf.shape == (N, dd[1], Oh * Ow)\n reshaped_bias = layer.bias.reshape(1, dd[1], 1) # (Co,) -> (1, Co, 1)\n out_unf = out_unf + reshaped_bias\n\n u.check_equal(fold(out_unf, (Oh, Ow), (1, 1)), output) # two alternative ways of reshaping\n u.check_equal(out_unf.view(N, dd[1], Oh, Ow), output)\n\n # Unfold produces patches with output dimension merged, while in backprop they are not merged\n # Hence merge the output (width/height) dimension\n assert unfold(layer.activations, (Kh, Kw)).shape == (N, Xc * Kh * Kw, Oh * Ow)\n assert layer.backprops_list[0].shape == (N, dd[1], Oh, Ow)\n\n grads_bias = layer.backprops_list[0].sum(dim=(2, 3)) * N\n mean_grad_bias = grads_bias.sum(dim=0) / N\n u.check_equal(mean_grad_bias, layer.bias.grad)\n\n Bt = layer.backprops_list[0] * N # remove factor of N applied during loss batch averaging\n assert Bt.shape == (N, dd[1], Oh, Ow)\n Bt = Bt.reshape(N, dd[1], Oh*Ow)\n At = unfold(layer.activations, (Kh, Kw))\n assert At.shape == (N, dd[0] * Kh * Kw, Oh*Ow)\n\n grad_unf = torch.einsum('ijk,ilk->ijl', Bt, At)\n assert grad_unf.shape == (N, dd[1], dd[0] * Kh * Kw)\n\n grads = grad_unf.reshape((N, dd[1], dd[0], Kh, Kw))\n u.check_equal(grads.mean(dim=0), layer.weight.grad)\n\n # compute per-example gradients using autograd, compare against manual computation\n for i in range(N):\n u.clear_backprops(model)\n output = model(X[i:i + 1, ...])\n loss = loss_fn(output)\n loss.backward()\n u.check_equal(grads[i], layer.weight.grad)\n u.check_equal(grads_bias[i], layer.bias.grad)\n\n\ndef test_conv_hessian():\n \"\"\"Test per-example gradient computation for conv layer.\"\"\"\n u.seed_random(1)\n n, Xc, Xh, Xw = 3, 2, 3, 7\n dd = [Xc, 2]\n\n Kh, Kw = 2, 3\n Oh, Ow = Xh - Kh + 1, Xw - Kw + 1\n model: u.SimpleModel = u.ReshapedConvolutional(dd, kernel_size=(Kh, Kw), bias=True)\n weight_buffer = model.layers[0].weight.data\n\n assert (Kh, Kw) == model.layers[0].kernel_size\n\n data = torch.randn((n, Xc, Xh, Xw))\n\n # output channels, input channels, height, width\n assert weight_buffer.shape == (dd[1], dd[0], Kh, Kw)\n\n def loss_fn(data):\n err = data.reshape(len(data), -1)\n return torch.sum(err * err) / 2 / len(data)\n\n loss_hessian = u.HessianExactSqrLoss()\n # o = Oh * Ow * dd[1]\n\n output = model(data)\n o = output.shape[1]\n for bval in loss_hessian(output):\n output.backward(bval, retain_graph=True)\n assert loss_hessian.num_samples == o\n\n i, layer = next(enumerate(model.layers))\n\n At = unfold(layer.activations, (Kh, Kw)) # -> n, Xc * Kh * Kw, Oh * Ow\n assert At.shape == (n, dd[0] * Kh * Kw, Oh*Ow)\n\n # o, n, dd[1], Oh, Ow -> o, n, dd[1], Oh*Ow\n Bh_t = torch.stack([Bt.reshape(n, dd[1], Oh*Ow) for Bt in layer.backprops_list])\n assert Bh_t.shape == (o, n, dd[1], Oh*Ow)\n Ah_t = torch.stack([At]*o)\n assert Ah_t.shape == (o, n, dd[0] * Kh * Kw, Oh*Ow)\n\n # sum out the output patch dimension\n Jb = torch.einsum('onij,onkj->onik', Bh_t, Ah_t) # => o, n, dd[1], dd[0] * Kh * Kw\n Hi = torch.einsum('onij,onkl->nijkl', Jb, Jb) # => n, dd[1], dd[0]*Kh*Kw, dd[1], dd[0]*Kh*Kw\n Jb_bias = torch.einsum('onij->oni', Bh_t)\n Hb_i = torch.einsum('oni,onj->nij', Jb_bias, Jb_bias)\n H = Hi.mean(dim=0)\n Hb = Hb_i.mean(dim=0)\n\n model.disable_hooks()\n loss = loss_fn(model(data))\n H_autograd = u.hessian(loss, layer.weight)\n assert H_autograd.shape == (dd[1], dd[0], Kh, Kw, dd[1], dd[0], Kh, Kw)\n assert H.shape == (dd[1], dd[0]*Kh*Kw, dd[1], dd[0]*Kh*Kw)\n u.check_close(H, H_autograd.reshape(H.shape), rtol=1e-4, atol=1e-7)\n\n Hb_autograd = u.hessian(loss, layer.bias)\n assert Hb_autograd.shape == (dd[1], dd[1])\n u.check_close(Hb, Hb_autograd)\n\n assert len(Bh_t) == loss_hessian.num_samples == o\n for xi in range(n):\n loss = loss_fn(model(data[xi:xi + 1, ...]))\n H_autograd = u.hessian(loss, layer.weight)\n u.check_close(Hi[xi], H_autograd.reshape(H.shape))\n Hb_autograd = u.hessian(loss, layer.bias)\n u.check_close(Hb_i[xi], Hb_autograd)\n assert Hb_i[xi, 0, 0] == Oh*Ow # each output has curvature 1, bias term adds up Oh*Ow of them\n\n\n# Lenet-5 from https://github.com/pytorch/examples/blob/master/mnist/main.py\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(1, 20, 5, 1)\n self.conv2 = nn.Conv2d(20, 50, 5, 1)\n self.fc1 = nn.Linear(4 * 4 * 50, 500)\n self.fc2 = nn.Linear(500, 10)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n x = F.max_pool2d(x, 2, 2)\n x = F.relu(self.conv2(x))\n x = F.max_pool2d(x, 2, 2)\n x = x.view(-1, 4 * 4 * 50)\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\n\n# Tiny LeNet-5 for Hessian testing\nclass TinyNet(nn.Module):\n def __init__(self):\n super(TinyNet, self).__init__()\n self.conv1 = nn.Conv2d(1, 2, 2, 1)\n self.conv2 = nn.Conv2d(2, 2, 2, 1)\n self.fc1 = nn.Linear(2, 2)\n self.fc2 = nn.Linear(2, 10)\n\n def forward(self, x): # 28x28\n x = F.max_pool2d(x, 4, 4) # 7x7\n x = F.relu(self.conv1(x)) # 6x6\n x = F.max_pool2d(x, 2, 2) # 3x3\n x = F.relu(self.conv2(x)) # 2x2\n x = F.max_pool2d(x, 2, 2) # 1x1\n x = x.view(-1, 2 * 1 * 1) # C * W * H\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\n\ndef test_end2end_grad1():\n torch.manual_seed(1)\n model = Net()\n loss_fn = nn.CrossEntropyLoss()\n\n n = 4\n data = torch.rand(n, 1, 28, 28)\n targets = torch.LongTensor(n).random_(0, 10)\n\n autograd_lib.add_hooks(model)\n output = model(data)\n loss_fn(output, targets).backward(retain_graph=True)\n autograd_lib.compute_grad1(model)\n autograd_lib.disable_hooks()\n\n # Compare values against autograd\n losses = torch.stack([loss_fn(output[i:i+1], targets[i:i+1]) for i in range(len(data))])\n\n for layer in model.modules():\n if not autograd_lib.is_supported(layer):\n continue\n for param in layer.parameters():\n assert torch.allclose(param.grad, param.grad1.mean(dim=0))\n assert torch.allclose(u.jacobian(losses, param), param.grad1)\n\n\ndef test_end2end_hess():\n u.setup_logdir_and_event_writer('test')\n subtest_hess_type('CrossEntropy')\n subtest_hess_type('LeastSquares')\n\n\ndef subtest_hess_type(hess_type):\n torch.manual_seed(1)\n model = TinyNet()\n\n def least_squares_loss(data_, targets_):\n assert len(data_) == len(targets_)\n err = data_ - targets_\n return torch.sum(err * err) / 2 / len(data_)\n\n n = 3\n data = torch.rand(n, 1, 28, 28)\n\n autograd_lib.add_hooks(model)\n output = model(data)\n\n if hess_type == 'LeastSquares':\n targets = torch.rand(output.shape)\n loss_fn = least_squares_loss\n else: # hess_type == 'CrossEntropy':\n targets = torch.LongTensor(n).random_(0, 10)\n loss_fn = nn.CrossEntropyLoss()\n\n # Dummy backprop to make sure multiple backprops don't invalidate each other\n autograd_lib.backprop_hess(output, hess_type=hess_type)\n autograd_lib.clear_hess_backprops(model)\n\n autograd_lib.backprop_hess(output, hess_type=hess_type)\n\n autograd_lib.compute_hess(model)\n autograd_lib.disable_hooks()\n\n for layer in model.modules():\n if not autograd_lib.is_supported(layer):\n continue\n for param in layer.parameters():\n loss = loss_fn(output, targets)\n hess_autograd = u.hessian(loss, param)\n hess = param.hess\n assert torch.allclose(hess, hess_autograd.reshape(hess.shape))\n\n\ndef test_kron_nano():\n u.seed_random(1)\n\n d = [1, 2]\n n = 1\n # torch.set_default_dtype(torch.float32)\n\n loss_type = 'CrossEntropy'\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'DebugLeastSquares':\n loss_fn = u.debug_least_squares\n else:\n loss_fn = nn.CrossEntropyLoss()\n\n data = torch.randn(n, d[0])\n data = torch.ones(n, d[0])\n if loss_type.endswith('LeastSquares'):\n target = torch.randn(n, d[-1])\n elif loss_type == 'CrossEntropy':\n target = torch.LongTensor(n).random_(0, d[-1])\n target = torch.tensor([0])\n\n # Hessian computation, saves regular and Kronecker factored versions into .hess and .hess_factored attributes\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.compute_hess(model, method='kron')\n autograd_lib.compute_hess(model)\n autograd_lib.disable_hooks()\n\n for layer in model.layers:\n Hk: u.Kron = layer.weight.hess_factored\n Hk_bias: u.Kron = layer.bias.hess_factored\n Hk, Hk_bias = u.expand_hess(Hk, Hk_bias) # kronecker multiply the factors\n\n # old approach, using direct computation\n H2, H_bias2 = layer.weight.hess, layer.bias.hess\n\n # compute Hessian through autograd\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, target)\n H_autograd = u.hessian(loss, layer.weight)\n H_bias_autograd = u.hessian(loss, layer.bias)\n\n # compare autograd with direct approach\n u.check_close(H2, H_autograd.reshape(Hk.shape))\n u.check_close(H_bias2, H_bias_autograd)\n\n # compare factored with direct approach\n assert(u.symsqrt_dist(Hk, H2) < 1e-6)\n\n\ndef test_kron_tiny():\n u.seed_random(1)\n\n d = [2, 3, 3, 4, 5]\n n = 5\n # torch.set_default_dtype(torch.float32)\n\n loss_type = 'CrossEntropy'\n model: u.SimpleModel = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'DebugLeastSquares':\n loss_fn = u.debug_least_squares\n else:\n loss_fn = nn.CrossEntropyLoss()\n\n data = torch.randn(n, d[0])\n data = torch.ones(n, d[0])\n if loss_type.endswith('LeastSquares'):\n target = torch.randn(n, d[-1])\n elif loss_type == 'CrossEntropy':\n target = torch.LongTensor(n).random_(0, d[-1])\n\n # Hessian computation, saves regular and Kronecker factored versions into .hess and .hess_factored attributes\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.compute_hess(model, method='kron')\n autograd_lib.compute_hess(model)\n autograd_lib.disable_hooks()\n\n for layer in model.layers:\n H: u.Kron = layer.weight.hess_factored\n H_bias: u.Kron = layer.bias.hess_factored\n H, H_bias = u.expand_hess(H, H_bias) # kronecker multiply the factors\n\n # old approach, using direct computation\n H2, H_bias2 = layer.weight.hess, layer.bias.hess\n\n # compute Hessian through autograd\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, target)\n H_autograd = u.hessian(loss, layer.weight)\n H_bias_autograd = u.hessian(loss, layer.bias)\n\n # compare autograd with direct approach\n u.check_close(H2, H_autograd.reshape(H.shape))\n u.check_close(H_bias2, H_bias_autograd)\n\n # compare factored with direct approach\n assert(u.symsqrt_dist(H, H2) < 1e-6)\n\n\ndef test_kron_mnist():\n u.seed_random(1)\n\n data_width = 3\n batch_size = 3\n d = [data_width**2, 10]\n o = d[-1]\n n = batch_size\n train_steps = 1\n\n # torch.set_default_dtype(torch.float64)\n\n model: u.SimpleModel2 = u.SimpleFullyConnected2(d, nonlin=False, bias=True)\n autograd_lib.add_hooks(model)\n\n dataset = u.TinyMNIST(dataset_size=batch_size, data_width=data_width, original_targets=True)\n trainloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)\n train_iter = iter(trainloader)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n gl.token_count = 0\n for train_step in range(train_steps):\n data, targets = next(train_iter)\n\n # get gradient values\n u.clear_backprops(model)\n autograd_lib.enable_hooks()\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type='CrossEntropy')\n\n i = 0\n layer = model.layers[i]\n autograd_lib.compute_hess(model, method='kron')\n autograd_lib.compute_hess(model)\n autograd_lib.disable_hooks()\n\n # direct Hessian computation\n H = layer.weight.hess\n H_bias = layer.bias.hess\n\n # factored Hessian computation\n H2 = layer.weight.hess_factored\n H2_bias = layer.bias.hess_factored\n H2, H2_bias = u.expand_hess(H2, H2_bias)\n\n # autograd Hessian computation\n loss = loss_fn(output, targets) # TODO: change to d[i+1]*d[i]\n H_autograd = u.hessian(loss, layer.weight).reshape(d[i] * d[i + 1], d[i] * d[i + 1])\n H_bias_autograd = u.hessian(loss, layer.bias)\n\n # compare direct against autograd\n u.check_close(H, H_autograd)\n u.check_close(H_bias, H_bias_autograd)\n\n approx_error = u.symsqrt_dist(H, H2)\n assert approx_error < 1e-2, approx_error\n\n\ndef test_kron_conv_exact():\n \"\"\"Test per-example gradient computation for conv layer.\n\n\n Kronecker factoring is exact for 1x1 convolutions and linear activations.\n\n \"\"\"\n u.seed_random(1)\n\n n, Xh, Xw = 2, 2, 2\n Kh, Kw = 1, 1\n dd = [2, 2, 2]\n o = dd[-1]\n\n model: u.SimpleModel = u.PooledConvolutional2(dd, kernel_size=(Kh, Kw), nonlin=False, bias=True)\n data = torch.randn((n, dd[0], Xh, Xw))\n\n #print(model)\n #print(data)\n\n loss_type = 'CrossEntropy' # loss_type = 'LeastSquares'\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'DebugLeastSquares':\n loss_fn = u.debug_least_squares\n else: # CrossEntropy\n loss_fn = nn.CrossEntropyLoss()\n\n sample_output = model(data)\n\n if loss_type.endswith('LeastSquares'):\n targets = torch.randn(sample_output.shape)\n elif loss_type == 'CrossEntropy':\n targets = torch.LongTensor(n).random_(0, o)\n\n autograd_lib.clear_backprops(model)\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.compute_hess(model, method='mean_kron')\n autograd_lib.compute_hess(model, method='exact')\n autograd_lib.disable_hooks()\n\n for i in range(len(model.layers)):\n layer = model.layers[i]\n\n # direct Hessian computation\n H = layer.weight.hess\n H_bias = layer.bias.hess\n\n # factored Hessian computation\n Hk = layer.weight.hess_factored\n Hk_bias = layer.bias.hess_factored\n Hk = Hk.expand()\n Hk_bias = Hk_bias.expand()\n\n # autograd Hessian computation\n loss = loss_fn(output, targets)\n Ha = u.hessian(loss, layer.weight).reshape(H.shape)\n Ha_bias = u.hessian(loss, layer.bias)\n\n # compare direct against autograd\n Ha = Ha.reshape(H.shape)\n # rel_error = torch.max((H-Ha)/Ha)\n\n u.check_close(H, Ha, rtol=1e-5, atol=1e-7)\n u.check_close(Ha_bias, H_bias, rtol=1e-5, atol=1e-7)\n\n u.check_close(H_bias, Hk_bias)\n u.check_close(H, Hk)\n\n\ndef test_kron_1x2_conv():\n \"\"\"Minimal example of a 1x2 convolution whose Hessian/grad covariance doesn't factor as Kronecker.\n\n Two convolutional layers stacked on top of each other, followed by least squares loss.\n\n Outputs:\n 0 tensor([[[[0., 1., 1., 1.]]]])\n 1 tensor([[[[2., 3.]]]])\n 2 tensor([[[[8.]]]])\n\n Activations/backprops:\n layerA 0 tensor([[[[0., 1.],\n [1., 1.]]]])\n layerB 0 tensor([[[[1., 2.]]]])\n\n layerA 1 tensor([[[[2.],\n [3.]]]])\n layerB 1 tensor([[[[1.]]]])\n\n layer 0 discrepancy: 0.6597963571548462\n layer 1 discrepancy: 0.0\n\n \"\"\"\n u.seed_random(1)\n\n n, Xh, Xw = 1, 1, 4\n Kh, Kw = 1, 2\n dd = [1, 1, 1]\n o = dd[-1]\n\n model: u.SimpleModel = u.StridedConvolutional2(dd, kernel_size=(Kh, Kw), nonlin=False, bias=True)\n data = torch.tensor([0, 1., 1, 1]).reshape((n, dd[0], Xh, Xw))\n\n model.layers[0].bias.data.zero_()\n model.layers[0].weight.data.copy_(torch.tensor([1, 2]))\n\n model.layers[1].bias.data.zero_()\n model.layers[1].weight.data.copy_(torch.tensor([1, 2]))\n\n sample_output = model(data)\n\n autograd_lib.clear_backprops(model)\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type='LeastSquares')\n autograd_lib.compute_hess(model, method='kron', attr_name='hess_kron')\n autograd_lib.compute_hess(model, method='exact')\n autograd_lib.disable_hooks()\n\n for i in range(len(model.layers)):\n layer = model.layers[i]\n H = layer.weight.hess\n Hk = layer.weight.hess_kron\n Hk = Hk.expand()\n print(u.symsqrt_dist(H, Hk))\n\n\n@pytest.mark.skip(reason=\"need to update golden values\")\ndef _test_kron_conv_golden():\n \"\"\"Hardcoded error values to detect unexpected numeric changes.\"\"\"\n u.seed_random(1)\n\n n, Xh, Xw = 2, 8, 8\n Kh, Kw = 2, 2\n dd = [3, 3, 3, 3]\n o = dd[-1]\n\n model: u.SimpleModel = u.PooledConvolutional2(dd, kernel_size=(Kh, Kw), nonlin=False, bias=True)\n data = torch.randn((n, dd[0], Xh, Xw))\n\n # print(model)\n # print(data)\n\n loss_type = 'CrossEntropy' # loss_type = 'LeastSquares'\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'DebugLeastSquares':\n loss_fn = u.debug_least_squares\n else: # CrossEntropy\n loss_fn = nn.CrossEntropyLoss()\n\n sample_output = model(data)\n\n if loss_type.endswith('LeastSquares'):\n targets = torch.randn(sample_output.shape)\n elif loss_type == 'CrossEntropy':\n targets = torch.LongTensor(n).random_(0, o)\n\n autograd_lib.clear_backprops(model)\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.compute_hess(model, method='kron', attr_name='hess_kron')\n autograd_lib.compute_hess(model, method='mean_kron', attr_name='hess_mean_kron')\n autograd_lib.compute_hess(model, method='exact')\n autograd_lib.disable_hooks()\n\n errors1 = []\n errors2 = []\n for i in range(len(model.layers)):\n layer = model.layers[i]\n\n # direct Hessian computation\n H = layer.weight.hess\n H_bias = layer.bias.hess\n\n # factored Hessian computation\n Hk = layer.weight.hess_kron\n Hk_bias = layer.bias.hess_kron\n Hk = Hk.expand()\n Hk_bias = Hk_bias.expand()\n\n Hk2 = layer.weight.hess_mean_kron\n Hk2_bias = layer.bias.hess_mean_kron\n Hk2 = Hk2.expand()\n Hk2_bias = Hk2_bias.expand()\n\n # autograd Hessian computation\n loss = loss_fn(output, targets)\n Ha = u.hessian(loss, layer.weight).reshape(H.shape)\n Ha_bias = u.hessian(loss, layer.bias)\n\n # compare direct against autograd\n Ha = Ha.reshape(H.shape)\n # rel_error = torch.max((H-Ha)/Ha)\n\n u.check_close(H, Ha, rtol=1e-5, atol=1e-7)\n u.check_close(Ha_bias, H_bias, rtol=1e-5, atol=1e-7)\n\n errors1.extend([u.symsqrt_dist(H, Hk), u.symsqrt_dist(H_bias, Hk_bias)])\n errors2.extend([u.symsqrt_dist(H, Hk2), u.symsqrt_dist(H_bias, Hk2_bias)])\n\n errors1 = torch.tensor(errors1)\n errors2 = torch.tensor(errors2)\n golden_errors1 = torch.tensor([0.09458080679178238, 0.0, 0.13416489958763123, 0.0, 0.0003909761435352266, 0.0])\n golden_errors2 = torch.tensor([0.0945773795247078, 0.0, 0.13418318331241608, 0.0, 4.478318658129865e-07, 0.0])\n\n u.check_close(golden_errors1, errors1)\n u.check_close(golden_errors2, errors2)\n\n\nif __name__ == '__main__':\n # test_kron_conv_exact1()\n # test_kron_conv_exact2()\n # test_kron_conv_exact()\n # test_kron_1x2_conv()\n u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/ciresan_bench.py", "content": "import os\nimport sys\nimport time\nfrom typing import Optional, Tuple, Callable\n\n# import torch\nimport scipy\nimport torch\nfrom torchcurv.optim import SecondOrderOptimizer\n\n\nimport torch.nn as nn\n\nimport util as u\n\nimport numpy as np\n\n\"\"\"\nMKL version unknown\nPyTorch version 1.2.0\nScipy version: 1.2.1\nNumpy version: 1.16.4\n1024-by-1024 matrix\n 7079.93 linalg.solve_lyapunov\n 280.11 linalg.pinvh\n 1186.08 linalg.pinv\n 49.18 linalg.inv\n 118.23 qr\n 413.42 svd\n\"\"\"\n\nclass Net(nn.Module):\n def __init__(self, d):\n super().__init__()\n self.w = nn.Linear(d, 1, bias=False)\n\n def forward(self, x: torch.Tensor):\n result = self.w(x)\n return result\n\nlast_time = 0\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard. This function is\n \"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n global last_time\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n torch.cuda.synchronize()\n print(f\"{interval_ms:8.2f} {self.tag}\")\n last_time = interval_ms\n\n\ndef get_mkl_version():\n import ctypes\n import numpy as np\n\n # this recipe only works on Linux\n try:\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n except:\n return 'unknown'\n\n\ndef print_cpu_info():\n ver = 'unknown'\n try:\n for l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n ver = l\n break\n except:\n pass\n\n\ndef linalg_bench():\n if np.__config__.get_info(\"lapack_mkl_info\"):\n print(\"MKL version\", get_mkl_version())\n else:\n print(\"not using MKL\")\n\n print(\"PyTorch version\", torch.version.__version__)\n\n print(\"Scipy version: \", scipy.version.full_version)\n print(\"Numpy version: \", np.version.full_version)\n\n times = {}\n dimlist = [768, 2500, 2000, 1500, 1000, 500]\n for device in ['cpu', 'cuda']:\n for d in dimlist*2:\n print(f\"{d}-by-{d} matrix: \", device)\n n = 10000\n assert n > 2*d # to prevent singularity\n X = u.from_numpy(np.random.random((d, 10000)))\n Y = u.from_numpy(np.random.random((d, 10000)))\n H = X @ X.t()\n S = Y @ Y.t()\n if torch.cuda.is_available():\n H = H.to(device)\n S = S.to(device)\n\n with timeit(f\"symeig\"):\n result = torch.symeig(H, eigenvectors=False)\n times.setdefault(d, []).append(last_time)\n \n with timeit(f\"symeig\"):\n result = torch.symeig(H, eigenvectors=True)\n times.setdefault(d, []).append(last_time)\n\n with timeit(f\"svd\"):\n result = torch.svd(H)\n times.setdefault(d, []).append(last_time)\n for d in times:\n print(f\"{d}-by-{d}: {u.format_list(times[d][len(times[d])//2:])}\")\n\n\nif __name__ == '__main__':\n linalg_bench()\n" }, { "path": "autotune/curvature_test.py", "content": "# Prototype batch-size quantities from\n# Batch size formulas (https://docs.google.com/document/d/19Jmh4spbSAnAGX_eq7WSFPgLzrpJEhiZRpjX1jSYObo/edit)\nimport os\nimport sys\nfrom typing import Optional, Tuple, Callable\n\n# import torch\nimport torch.nn as nn\nfrom torchcurv.optim import SecondOrderOptimizer\n\nfrom util import *\n\n\nclass Net(nn.Module):\n def __init__(self, d):\n super().__init__()\n self.w = nn.Linear(d, 1, bias=False)\n\n def forward(self, x: torch.Tensor):\n result = self.w(x)\n return result\n\n\ndef test_singlelayer():\n # Reproduce Linear Regression example\n # https://www.wolframcloud.com/obj/yaroslavvb/newton/curvature-unit-tests.nb\n\n torch.set_default_dtype(torch.float32)\n\n d = 2\n n = 3\n model = Net(d)\n\n w0 = torch.tensor([[1, 2]]).float()\n assert w0.shape[1] == d\n model.w.weight.data.copy_(w0)\n\n X = torch.tensor([[-2, 0, 2], [-1, 1, 3]]).float()\n assert X.shape[0] == d\n assert X.shape[1] == n\n\n Y = torch.tensor([[0, 1, 2]]).float()\n assert Y.shape[1] == X.shape[1]\n\n data = X.t() # PyTorch expects batch dimension first\n target = Y.t()\n assert data.shape[0] == n\n\n output = model(data)\n # residuals, aka e\n residuals = output - Y.t()\n\n def compute_loss(residuals_):\n return torch.sum(residuals_ * residuals_) / (2 * n)\n\n loss = compute_loss(residuals)\n\n assert loss - 8.83333 < 1e-5, torch.norm(loss) - 8.83333\n\n # use learning rate 0 to avoid changing parameter vector\n optim_kwargs = dict(lr=0, momentum=0, weight_decay=0, l2_reg=0,\n bias_correction=False, acc_steps=1,\n curv_type=\"Cov\", curv_shapes={\"Linear\": \"Kron\"},\n momentum_type=\"preconditioned\", )\n curv_args = dict(damping=1, ema_decay=1) # todo: damping\n optimizer = SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=curv_args)\n\n def backward(last_layer: str) -> Callable:\n \"\"\"Creates closure that backpropagates either from output layer or from loss layer\"\"\"\n\n def closure() -> Tuple[Optional[torch.Tensor], torch.Tensor]:\n optimizer.zero_grad()\n output = model(data)\n if last_layer == \"output\":\n output.backward(torch.ones_like(target))\n return None, output\n elif last_layer == 'loss':\n loss = compute_loss(output - target)\n loss.backward()\n return loss, output\n else:\n assert False, 'last layer must be \"output\" or \"loss\"'\n\n return closure\n\n # loss = compute_loss(output - Y.t())\n # loss.backward()\n\n loss, output = optimizer.step(closure=backward('loss'))\n check_equal(output.t(), [[-4, 2, 8]])\n check_equal(residuals.t(), [[-4, 1, 6]])\n check_equal(loss, 8.833333)\n\n # batch output Jacobian\n J = X.t()\n check_close(J, [[-2, -1], [0, 1], [2, 3]])\n\n # matrix of activations, (n, d)\n A = model.w.data_input\n check_close(A, J)\n\n # matrix of backprops, add factor n to remove dependence on batch-size\n B = model.w.grad_output * n\n check_close(B, residuals)\n\n # gradients, n,d\n # method 1, manual computation\n G = residuals.repeat(1, d) * J\n check_close(G, [[8., 4.], [0., 1.], [12., 18.]])\n\n # method 2, get them of activation + backprop values\n check_close(G, khatri_rao_t(A, B))\n\n # method 3, PyTorch autograd\n # (n,) losses vector\n losses = torch.stack([compute_loss(r) for r in residuals])\n # batch-loss jacobian\n G2 = jacobian(losses, model.w.weight) * n\n # per-example gradients are row-matrices, squeeze to stack them into a single matrix\n G2 = G2.squeeze(1)\n check_close(G2, G)\n\n # mean gradient\n g = G.sum(dim=0) / n\n check_close(g, [6.66667, 7.66667])\n\n # empirical Fisher\n efisher = G.t() @ G / n\n check_close(efisher, [[69.3333, 82.6667], [82.6667, 113.667]])\n\n # centered empirical Fisher (Sigma in OpenAI paper, estimate of Sigma in Jain paper)\n sigma = efisher - outer(g, g)\n check_close(sigma, [[24.8889, 31.5556], [31.5556, 54.8889]])\n\n # loss\n loss2 = (residuals * residuals).sum() / (2 * n)\n check_close(to_python_scalar(loss2), 8.83333)\n\n ####################################################################\n # Hessian\n ####################################################################\n\n # method 1, manual computation\n H = J.t() @ J / n\n check_close(H, [[2.66667, 2.66667], [2.66667, 3.66667]])\n\n # method 2, using activation + backprop values\n check_close(A.t() @ torch.eye(n) @ A / n, H)\n\n # method 3, PyTorch backprop\n hess = hessian(compute_loss(residuals), model.w.weight)\n hess = hess.squeeze(2) # TODO(y): replace with transpose like in multilayer test\n hess = hess.squeeze(0)\n check_close(hess, H)\n\n sigma_norm = torch.norm(sigma)\n g_norm = torch.norm(g)\n\n g_ = g.unsqueeze(0) # turn g into row matrix\n\n # predicted drop in loss if we take a Newton step\n excess = to_python_scalar(g_ @ H.inverse() @ g_.t() / 2)\n check_close(excess, 8.83333)\n\n def loss_direction(direction, eps):\n \"\"\"loss improvement if we take step eps in direction dir\"\"\"\n return to_python_scalar(eps * (direction @ g.t()) - 0.5 * eps ** 2 * direction @ H @ direction.t())\n\n newtonImprovement = loss_direction(g_ @ H.inverse(), 1)\n check_close(newtonImprovement, 8.83333)\n\n ############################\n # OpenAI quantities\n grad_curvature = to_python_scalar(g_ @ H @ g_.t()) # curvature in direction of g\n stepOpenAI = to_python_scalar(g.norm() ** 2 / grad_curvature) if g_norm else 999\n check_close(stepOpenAI, 0.170157)\n batchOpenAI = to_python_scalar(torch.trace(H @ sigma) / grad_curvature) if g_norm else 999\n check_close(batchOpenAI, 0.718603)\n\n # improvement in loss when we take gradient step with optimal learning rate\n gradientImprovement = loss_direction(g_, stepOpenAI)\n assert newtonImprovement > gradientImprovement\n check_close(gradientImprovement, 8.78199)\n\n ############################\n # Gradient diversity quantities\n diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2\n check_close(diversity, 5.31862)\n\n ############################\n # Jain/Kakade quantities\n\n # noise scale (Jain, minimax rate of estimator)\n noise_variance = torch.trace(H.inverse() @ sigma)\n check_close(noise_variance, 26.)\n\n isqrtH = pinv_square_root(H)\n # measure of misspecification between model and actual noise (Jain, \\rho)\n # formula (3) of \"Parallelizing Stochastic Gradient Descent\"\n p_sigma = (kron(H, torch.eye(d)) + kron(torch.eye(d), H)).inverse() @ vec(sigma)\n p_sigma = unvec(p_sigma, d)\n rho = d / erank(p_sigma) if sigma_norm > 0 else 1\n check_close(rho, 1.21987)\n\n # use new method with Lyapunov factoring\n p_sigma2 = lyapunov_svd(H, sigma)\n rho2 = d / erank(p_sigma2)\n check_close(rho2, 1.21987)\n\n rhoSimple = (d / erank(isqrtH @ sigma @ isqrtH)) if sigma_norm > 0 else 1\n check_close(rhoSimple, 1.4221)\n assert 1 <= rho <= d, rho\n\n # divergent learning rate for batch-size 1 (Jain). Approximates max||x_i|| with avg.\n # For more accurate results may want to add stddev of ||x_i||\n # noinspection PyTypeChecker\n stepMin = 2 / torch.trace(H)\n check_close(stepMin, 0.315789)\n\n # divergent learning rate for batch-size infinity\n stepMax = 2 / l2_norm(H)\n check_close(stepMax, 0.340147)\n\n # divergent learning rate for batch-size 1, adjusted for misspecification\n check_close(stepMin / rhoSimple, 0.222058)\n check_close(stepMin / rho, 0.258871)\n\n # batch size that gives provides lr halfway between stepMin and stepMax\n batchJain = 1 + erank(H)\n check_close(batchJain, 2.07713)\n\n # batch size that provides halfway point after adjusting for misspecification\n check_close(1 + erank(H) * rhoSimple, 2.5318)\n check_close(1 + erank(H) * rho, 2.31397)\n\n\nclass Net2(nn.Module):\n def __init__(self, d1, d2):\n super().__init__()\n self.W = nn.Linear(d1, d2, bias=False)\n self.X2t = nn.Linear(d2, 1, bias=False)\n\n def forward(self, X1: torch.Tensor):\n result = self.W(X1)\n result = self.X2t(result)\n return result\n\n\ndef test_multilayer():\n # Reproduce multilayer example\n # https://www.wolframcloud.com/obj/yaroslavvb/newton/curvature-unit-tests.nb\n\n torch.set_default_dtype(torch.float64)\n\n d1 = 2\n d2 = 4\n n = 3\n model = Net2(d1, d2)\n\n W0 = u.to_pytorch([[3, 3], [0, 3], [1, -1], [-3, 1]])\n model.W.weight.data.copy_(W0)\n X2 = u.to_pytorch([[1], [-2], [-1], [3]])\n assert X2.shape == (d2, 1)\n model.X2t.weight.data.copy_(X2.t())\n\n X1 = u.to_pytorch([[2, -2, 3], [-3, 1, -3]])\n assert X1.shape == (d1, n)\n\n Y = u.to_pytorch([[-2, -3, 0]])\n assert Y.shape == (1, n)\n\n data = X1.t() # PyTorch expects batch dimension first\n target = Y.t()\n assert data.shape[0] == n\n\n output = model(data)\n # residuals, aka e\n residuals = output - Y.t()\n\n def compute_loss(residuals_):\n return torch.sum(residuals_ * residuals_) / (2 * n)\n\n loss = compute_loss(residuals)\n assert loss - 187.5 < 1e-5, torch.norm(loss) - 8.83333\n\n # use learning rate 0 to avoid changing parameter vector\n optim_kwargs = dict(lr=0, momentum=0, weight_decay=0, l2_reg=0,\n bias_correction=False, acc_steps=1,\n curv_type=\"Cov\", curv_shapes={\"Linear\": \"Kron\"},\n momentum_type=\"preconditioned\", update_inv=False, precondition_grad=False)\n curv_args = dict(damping=0, ema_decay=1)\n optimizer = SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=curv_args)\n\n # def set_requires_grad(v):\n # for p in model.parameters():\n # p.requires_grad = False\n #\n def backward(last_layer: str) -> Callable:\n \"\"\"Creates closure that backpropagates either from output layer or from loss layer\"\"\"\n\n def closure() -> Tuple[Optional[torch.Tensor], torch.Tensor]:\n optimizer.zero_grad()\n output = model(data)\n if last_layer == \"output\":\n output.backward(torch.ones_like(target))\n return None, output\n elif last_layer == 'loss':\n loss = compute_loss(output - target)\n loss.backward()\n return loss, output\n else:\n assert False, 'last layer must be \"output\" or \"loss\"'\n\n return closure\n\n # loss = compute_loss(output - Y.t())\n # loss.backward()\n\n loss, output = optimizer.step(closure=backward('loss'))\n check_close(output.t(), [[-17, 15, -24]])\n check_close(residuals.t(), [[-15, 18, -24]])\n check_close(loss, 187.5)\n\n # batch output Jacobian, n rows, i'th row gives sensitivity of i'th output example to parameters\n J = kron(X1, X2).t()\n assert J.shape == (n, d1 * d2)\n check_close(J, [[2, -4, -2, 6, -3, 6, 3, -9], [-2, 4, 2, -6, 1, -2, -1, 3], [3, -6, -3, 9, -3, 6, 3, -9]])\n\n # matrix of activations, (n, d1)\n At = model.W.data_input\n A = At.t()\n check_close(At, X1.t())\n\n # matrix of backprops, add factor n to remove dependence on batch-size\n Bt = model.W.grad_output * n\n check_close(Bt, [[-15, 30, 15, -45], [18, -36, -18, 54], [-24, 48, 24, -72]])\n\n # gradients, n,d\n # mean gradient, 1, d\n # method 1, manual computation\n G = khatri_rao_t(At, Bt)\n assert G.shape == (n, d1 * d2)\n check_close(G, [[-30, 60, 30, -90, 45, -90, -45, 135], [-36, 72, 36, -108, 18, -36, -18, 54],\n [-72, 144, 72, -216, 72, -144, -72, 216]])\n\n g = G.sum(dim=0, keepdim=True) / n\n check_close(g, [[-46, 92, 46, -138, 45, -90, -45, 135]])\n check_close(g, vec(get_param(model.W).grad).t())\n\n # method 2, explicit PyTorch autograd\n # (n,) losses vector\n losses = torch.stack([compute_loss(r) for r in residuals])\n # batch-loss jacobian\n G2 = jacobian(losses, model.W.weight) * n\n # per-example gradients are row-matrices, squeeze to stack them into a single matrix\n # each element of G2 is a matrix, vectorize+transpose to turn it into a row\n G2 = G2.transpose(1, 2).reshape(n, d1 * d2)\n check_close(G2, G)\n\n # Hessian\n # method 1, manual computation\n H = J.t() @ J / n\n check_close(H * n,\n [[17, -34, -17, 51, -17, 34, 17, -51],\n [-34, 68, 34, -102, 34, -68, -34, 102],\n [-17, 34, 17, -51, 17, -34, -17, 51],\n [51, -102, -51, 153, -51, 102, 51, -153],\n [-17, 34, 17, -51, 19, -38, -19, 57],\n [34, -68, -34, 102, -38, 76, 38, -114],\n [17, -34, -17, 51, -19, 38, 19, -57],\n [-51, 102, 51, -153, 57, -114, -57, 171]])\n\n # method 2, using activation + upstream matrices\n check_close(kron(A @ A.t(), X2 @ X2.t()) / n, H)\n\n # method 3, PyTorch autograd\n hess = hessian(compute_loss(residuals), model.W.weight)\n # Fix shape: vectorization flattens in column-major order, but PyTorch is row-major order\n # for reshape to flatten things correctly, transpose H_{ijkl} -> H_{jilk}\n hess = hess.transpose(2, 3).transpose(0, 1)\n hess = hess.reshape(d1 * d2, d1 * d2)\n check_close(hess, H)\n\n # method 4, get Jacobian + Hessian using backprop\n _loss, _output = optimizer.step(closure=backward('output'))\n B2t = model.W.grad_output\n\n # alternative way of getting batch Jacobian\n J2 = khatri_rao_t(At, B2t)\n check_close(J2, J)\n H2 = J2.t() @ J2 / n\n check_close(H2, H)\n\n # empirical Fisher\n efisher = G.t() @ G / n\n check_close(efisher, [[2460, -4920, -2460, 7380, -2394, 4788, 2394, -7182],\n [-4920, 9840, 4920, -14760, 4788, -9576, -4788, 14364],\n [-2460, 4920, 2460, -7380, 2394, -4788, -2394, 7182],\n [7380, -14760, -7380, 22140, -7182, 14364, 7182, -21546],\n [-2394, 4788, 2394, -7182, 2511, -5022, -2511, 7533],\n [4788, -9576, -4788, 14364, -5022, 10044, 5022, -15066],\n [2394, -4788, -2394, 7182, -2511, 5022, 2511, -7533],\n [-7182, 14364, 7182, -21546, 7533, -15066, -7533, 22599]])\n\n # centered empirical Fisher (Sigma in OpenAI paper, estimate of Sigma in Jain paper)\n sigma = efisher - g.t() @ g\n check_close(sigma, [[344, -688, -344, 1032, -324, 648, 324, -972], [-688, 1376, 688, -2064, 648, -1296, -648, 1944],\n [-344, 688, 344, -1032, 324, -648, -324, 972],\n [1032, -2064, -1032, 3096, -972, 1944, 972, -2916],\n [-324, 648, 324, -972, 486, -972, -486, 1458], [648, -1296, -648, 1944, -972, 1944, 972, -2916],\n [324, -648, -324, 972, -486, 972, 486, -1458],\n [-972, 1944, 972, -2916, 1458, -2916, -1458, 4374]])\n\n # loss\n loss2 = (residuals * residuals).sum() / (2 * n)\n check_close(to_python_scalar(loss2), 187.5)\n\n sigma_norm = torch.norm(sigma)\n g_norm = torch.norm(g)\n\n # predicted drop in loss if we take a Newton step\n excess = to_python_scalar(g @ H.pinverse() @ g.t() / 2)\n check_close(excess, 12747 / 68)\n\n def loss_direction(direction, eps):\n \"\"\"loss improvement if we take step eps in direction dir\"\"\"\n return to_python_scalar(eps * (direction @ g.t()) - 0.5 * eps ** 2 * direction @ H @ direction.t())\n\n newtonImprovement = loss_direction(g @ H.pinverse(), 1)\n check_close(newtonImprovement, 12747/68)\n\n ############################\n # OpenAI quantities\n ############################\n grad_curvature = to_python_scalar(g @ H @ g.t()) # curvature in direction of g\n stepOpenAI = to_python_scalar(g.flatten().norm() ** 2 / grad_curvature) if g_norm else 999\n check_close(stepOpenAI, 0.00571855)\n batchOpenAI = to_python_scalar(torch.trace(H @ sigma) / grad_curvature) if g_norm else 999\n check_close(batchOpenAI, 0.180201)\n\n # improvement in loss when we take gradient step with optimal learning rate\n gradientImprovement = loss_direction(g, stepOpenAI)\n assert newtonImprovement > gradientImprovement\n check_close(gradientImprovement, 177.604)\n\n ############################\n # Gradient diversity quantities\n ############################\n diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2\n check_close(diversity, 3.6013)\n\n ############################\n # Jain/Kakade quantities\n ############################\n\n # noise scale (Jain, minimax rate of estimator)\n noise_variance = torch.trace(H.pinverse() @ sigma)\n check_close(noise_variance, 333.706)\n\n isqrtH = pinv_square_root(H)\n # isqrtH = torch.tensor(isqrtH)\n # measure of misspecification between model and actual noise (Jain, \\rho)\n # formula (3) of \"Parallelizing Stochastic Gradient Descent\"\n p_sigma = torch.pinverse(kron(H, torch.eye(d1 * d2)) + kron(torch.eye(d1 * d2), H)) @ vec(sigma)\n p_sigma = unvec(p_sigma, d1 * d2)\n rho = d1 * d2 / erank(p_sigma) if sigma_norm > 0 else 1\n check_close(rho, 6.48399)\n\n rhoSimple = (d1 * d2 / erank(isqrtH @ sigma @ isqrtH)) if sigma_norm > 0 else 1\n check_close(rhoSimple, 6.55661)\n\n # divergent learning rate for batch-size 1 (Jain). Approximates max||x_i|| with avg.\n # For more accurate results may want to add stddev of ||x_i||\n # noinspection PyTypeChecker\n stepMin = 2 / torch.trace(H)\n check_close(stepMin, 0.0111111)\n\n # divergent learning rate for batch-size infinity\n stepMax = 2 / l2_norm(H)\n check_close(stepMax, 0.011419)\n\n # divergent learning rate for batch-size 1, adjusted for misspecification\n check_close(stepMin / rhoSimple, 0.00169464)\n check_close(stepMin / rho, 0.00171362)\n\n # batch size that gives provides lr halfway between stepMin and stepMax\n batchJain = 1 + erank(H)\n check_close(batchJain, 2.02771)\n\n # batch size that provides halfway point after adjusting for misspecification\n check_close(1 + erank(H) * rhoSimple, 7.73829)\n check_close(1 + erank(H) * rho, 7.66365)\n\n\nif __name__ == '__main__':\n run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/eval_conv2d_approx.py", "content": "\"\"\"Evaluate approximation quality of factoring on conv2d layers.\n\nEvaluates discrepancy in magnitude (l2 norm) and value (difference between normalized square roots), 0 means perfect agreement\n\nFor LeastSquares loss: kron and mean_kron are exact for all combinations with kernel_size=1\nFor CrossEntropy loss: kron is exact for all combinations with kernel_size=1, num_channels=1\n mean_kron is exact for all combinations with kernel_size=1\n\n\nIncreasing kernel_size>1, all methods over-estimate the Hessian magnitude, even in 1 channel/LeastSquares loss case\n\n========== mean_kron ========================================\nimage_size\n value: : [1.1446763892308809e-06, 9.212457143803476e-07, 1.4112242752162274e-06, 1.2591850691023865e-06, 1.194795800074644e-06]\n magnitude : 1.00, 1.00, 1.00, 1.00, 1.00\nnum_channels\n value: : [5.330160206540313e-07, 9.375009426548786e-07, 1.2972760714546894e-06, 1.1446763892308809e-06, 1.0141359780391213e-05, 9.359457180835307e-06, 6.479138392023742e-06, 6.426676463888725e-06, 8.470763532386627e-06, 5.907071681576781e-06]\n magnitude : 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00\nkernel_size\n value: : [1.1446763892308809e-06, 0.8146935105323792, 0.3585125207901001, 0.6153226494789124, 0.2633444368839264]\n magnitude : 1.00, 1.12, 1.13, 1.02, 1.00\n\n========== kron ========================================\nimage_size\n value: : [0.012090419419109821, 0.018052944913506508, 0.004046864341944456, 0.006037037819623947, 0.008428926579654217]\n magnitude : 1.00, 1.01, 1.00, 1.01, 1.01\nnum_channels\n value: : [0.004109012894332409, 0.029988422989845276, 0.0047996193170547485, 0.012090419419109821, 0.02191711962223053, 0.012329756282269955, 0.12856155633926392, 0.015723882243037224, 0.027835888788104057, 0.012887174263596535]\n magnitude : 1.00, 1.01, 1.00, 1.00, 1.03, 1.00, 1.02, 1.01, 1.00, 1.00\nkernel_size\n value: : [0.012090419419109821, 0.8148165941238403, 0.358644038438797, 0.6153406500816345, 0.263365775346756]\n magnitude : 1.00, 1.12, 1.13, 1.02, 1.00\n\"\"\"\n\n# Tests that compare manual computation of quantities against PyTorch autograd\n\nfrom typing import List\n\nimport autograd_lib\nimport torch\nimport util as u\nfrom attrdict import AttrDict\n\nunfold = torch.nn.functional.unfold\nfold = torch.nn.functional.fold\n\n\ndef compute_hess(n: int = 1, image_size: int = 1, kernel_size: int = 1, num_channels: int = 1, num_layers: int = 1,\n nonlin: bool = False,\n loss: str = 'CrossEntropy', method='exact', param_name='weight') -> List[torch.Tensor]:\n \"\"\"\n\n Compute Hessians for all layers for given architecture\n\n Args:\n param_name: which parameter to compute ('weight' or 'bias')\n n: number of examples\n image_size: width of image (square image)\n kernel_size: kernel size\n num_channels:\n num_layers:\n nonlin\n loss: LeastSquares or CrossEntropy\n method: 'kron', 'mean_kron'\n num_layers: number of layers in the network\n\n Returns:\n list of num_layers Hessian matrices.\n \"\"\"\n\n assert param_name in ['weight', 'bias']\n assert loss in autograd_lib._supported_losses\n assert method in autograd_lib._supported_methods\n\n u.seed_random(1)\n\n Xh, Xw = 1, image_size\n Kh, Kw = 1, kernel_size\n dd = [num_channels] * (num_layers + 1)\n\n model: u.SimpleModel2 = u.PooledConvolutional2(dd, kernel_size=(Kh, Kw), nonlin=nonlin, bias=True)\n # model: u.SimpleModel2 = u.StridedConvolutional2(dd, kernel_size=(Kh, Kw), nonlin=nonlin, bias=True)\n data = torch.randn((n, dd[0], Xh, Xw))\n\n autograd_lib.clear_backprops(model)\n autograd_lib.add_hooks(model)\n output = model(data)\n autograd_lib.backprop_hess(output, hess_type=loss)\n autograd_lib.compute_hess(model, method=method)\n autograd_lib.disable_hooks()\n\n result = []\n for i in range(len(model.layers)):\n param = getattr(model.layers[i], param_name)\n if method == 'exact' or method == 'autograd':\n result.append(param.hess)\n else:\n result.append(param.hess_factored.expand())\n return result\n\n\ndef main():\n # for kernel_size=1, mean kron factoring works for any image size\n main_vals = AttrDict(n=2, kernel_size=1, image_size=625, num_channels=5, num_layers=4, loss='CrossEntropy',\n nonlin=False)\n\n hess_list1 = compute_hess(method='exact', **main_vals)\n hess_list2 = compute_hess(method='kron', **main_vals)\n value_error = max([u.symsqrt_dist(h1, h2) for h1, h2 in zip(hess_list1, hess_list2)])\n magnitude_error = max([u.l2_norm(h2) / u.l2_norm(h1) for h1, h2 in zip(hess_list1, hess_list2)])\n print(value_error)\n print(magnitude_error)\n\n dimension_vals=dict(image_size=[2, 3, 4, 5, 6], num_channels=range(2, 12), kernel_size=[1, 2, 3, 4, 5])\n for method in ['mean_kron', 'kron']: # , 'experimental_kfac']:\n print()\n print('='*10, method, '='*40)\n for dimension in ['image_size', 'num_channels', 'kernel_size']:\n value_errors = []\n magnitude_errors = []\n for val in dimension_vals[dimension]:\n vals = AttrDict(main_vals.copy())\n vals.method = method\n vals[dimension] = val\n vals.image_size = max(vals.image_size, vals.kernel_size ** vals.num_layers)\n # print(vals)\n vals_exact = AttrDict(vals.copy())\n vals_exact.method = 'exact'\n hess_list1 = compute_hess(**vals_exact)\n hess_list2 = compute_hess(**vals)\n magnitude_error = max([u.l2_norm(h2) / u.l2_norm(h1) for h1, h2 in zip(hess_list1, hess_list2)])\n hess_list1 = [h/u.l2_norm(h) for h in hess_list1]\n hess_list2 = [h/u.l2_norm(h) for h in hess_list2]\n\n value_error = max([u.symsqrt_dist(h1, h2) for h1, h2 in zip(hess_list1, hess_list2)])\n value_errors.append(value_error)\n magnitude_errors.append(magnitude_error.item())\n print(dimension)\n print(' value: :', value_errors)\n print(' magnitude :', u.format_list(magnitude_errors))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/factored_test.py", "content": "\"\"\"Test factored implementation of stats\"\"\"\n\nimport argparse\nimport os\nimport sys\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch import nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndef test_factored_stats_golden_values():\n \"\"\"Test stats from values generated by non-factored version\"\"\"\n u.seed_random(1)\n u.install_pdb_handler()\n torch.set_default_dtype(torch.float32)\n\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n args = parser.parse_args()\n\n logdir = u.create_local_logdir('/temp/runs/factored_test')\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print('logging to ', logdir)\n\n loss_type = 'LeastSquares'\n\n args.data_width = 2\n args.dataset_size = 5\n args.stats_batch_size = 5\n d1 = args.data_width ** 2\n args.stats_batch_size = args.dataset_size\n args.stats_steps = 1\n\n n = args.stats_batch_size\n o = 10\n d = [d1, o]\n\n model = u.SimpleFullyConnected2(d, bias=False, nonlin=0)\n model = model.to(gl.device)\n print(model)\n\n dataset = u.TinyMNIST(data_width=args.data_width, dataset_size=args.dataset_size, loss_type=loss_type)\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False)\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n data, targets = stats_data, stats_targets\n\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model)\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2')\n\n autograd_lib.compute_stats_factored(model)\n\n params = list(model.parameters())\n assert len(params) == 1\n new_values = params[0].stats\n golden_values = torch.load('test/factored.pt')\n\n for valname in new_values:\n print(\"Checking \", valname)\n if valname == 'sigma_l2':\n u.check_close(new_values[valname], golden_values[valname], atol=1e-2) # sigma is approximate\n elif valname == 'sigma_erank':\n u.check_close(new_values[valname], golden_values[valname], atol=0.11) # 1.0 vs 1.1\n elif valname in ['rho', 'step_div_1_adjusted', 'batch_jain_full']:\n continue # lyapunov stats weren't computed correctly in golden set\n elif valname in ['batch_openai']:\n continue # batch sizes depend on sigma which is approximate\n elif valname in ['noise_variance_pinv']:\n pass # went from 0.22 to 0.014 after kron factoring (0.01 with full centering, 0.3 with no centering)\n elif valname in ['sparsity']:\n pass # had a bug in old calc (using integer arithmetic)\n else:\n u.check_close(new_values[valname], golden_values[valname], rtol=1e-4, atol=1e-6, label=valname)\n\n gl.event_writer.close()\n\n\ndef test_factored_vs_regular():\n \"\"\"Take simple network, compute values in two different ways, compare.\"\"\"\n\n u.seed_random(1)\n\n gl.project_name = 'test'\n gl.logdir_base = '/tmp/runs'\n u.setup_logdir_and_event_writer(run_name=sys._getframe().f_code.co_name)\n\n d = 3\n n = 3\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([d, d], bias=False, nonlin=False)\n param = model.layers[0].weight\n\n # param.data.copy_(torch.eye(d))\n #param.data.copy_(torch.arange(9).reshape(3, 3))\n # param.data.copy_(torch.zeros(d, d))\n\n # create simple matrix which is not quite symmetric\n source = 2*torch.eye(d)\n source[0, 0] = 3\n source[0, 1] = 4\n source[1, 0] = -2\n data = source.repeat([n, 1])\n noise = source.repeat_interleave(n, dim=0)\n\n autograd_lib.add_hooks(model)\n output = model(data)\n output.backward(retain_graph=True, gradient=noise)\n loss = u.least_squares(output)\n\n autograd_lib.backprop_hess(output, hess_type='LeastSquares', model=model)\n autograd_lib.compute_grad1(model)\n autograd_lib.compute_hess(model)\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2')\n autograd_lib.compute_stats(model, attr_name='stats_regular', sigma_centering=True)\n autograd_lib.compute_stats_factored(model, attr_name='stats_factored', sigma_centering=False)\n\n stats = param.stats_regular\n stats_factored = param.stats_factored\n for name in stats:\n print(name, stats[name], stats_factored[name])\n # u.check_close(stats[name], stats_factored[name], label=name)\n\n\nif __name__ == '__main__':\n test_factored_vs_regular()\n # u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/globals.py", "content": "# Module to hold global variables for curvature computation functions.\n# This is needed sincne functionality may be split over several modules\n\nfrom typing import Optional\n\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\n\nevent_writer: Optional[SummaryWriter] = None\nproject_name: Optional[str] = 'train_ciresan' # project name to use for wandb logging\nlogdir_base: str = '/ncluster/runs'\nrun_name: Optional[str] = None # run name to use, corresponds to logging dir and wandb run name\nlogdir: Optional[str] = None # logdir\ntoken_count: int = 0 # TODO(y): rename to global-step. Meaning is context-specific, in case of sequences it's number of tokens\n\nargs = None # global arg values\ndebug_dump_stats: bool = False # print activations/backprops to console\ndebug_linalg_crashes: bool = False # save matrices that cause linalg routines to crash\n\n\n# debug_hard_crashes_on_nans: bool = True # crash if encountering NaN\n\nhacks_disable_hess = False\n\n\nif torch.cuda.is_available():\n device = torch.device('cuda')\n print(\"Using GPU\")\nelse:\n device = torch.device('cpu')\n\n\ndef reset_global_step():\n global token_count\n token_count = 0\n\n\ndef increment_global_step(incr: int):\n global token_count\n token_count += incr\n\n\ndef get_global_step() -> int:\n return token_count\n\n\n" }, { "path": "autotune/hessian_test.py", "content": "# Test exact Hessian computation\n\n# import torch\nimport sys\nfrom typing import Callable\n\nimport torch\nimport torch.nn as nn\n\n\nclass Net(nn.Module):\n def __init__(self, d):\n super().__init__()\n self.w = nn.Linear(d, 1, bias=False)\n\n def forward(self, x: torch.Tensor):\n result = self.w(x)\n return result\n\n\ndef test_simple_hessian():\n # Compare against manual calculations in\n # https://www.wolframcloud.com/obj/yaroslavvb/newton/linear-jacobians-and-hessians.nb\n torch.set_default_dtype(torch.float32)\n\n d = [2, 3, 4, 2]\n n = d[0]\n c = d[-1]\n As = torch.tensor([[3, 1, -1], [1, -3, -2]]).float()\n Bs = torch.tensor([[[3, -3], [-3, -1], [-3, 3], [-3, 0]], [[2, -1], [-3, 0], [1, 1], [-2, 0]]]).float()\n\n # output Jacobian for first example\n Jo1 = u.kron(u.v2r(As[0]), Bs[0].t())\n u.check_equal(Jo1, [[9, -9, -9, -9, 3, -3, -3, -3, -3, 3, 3, 3], [-9, -3, 9, 0, -3, -1, 3, 0, 3, 1, -3, 0]])\n\n # batch output Jacobian\n Jb = torch.cat([u.kron(u.v2r(As[i]), Bs[i].t()) for i in range(n)])\n u.check_equal(Jb, [[9, -9, -9, -9, 3, -3, -3, -3, -3, 3, 3, 3], [-9, -3, 9, 0, -3, -1, 3, 0, 3, 1, -3, 0],\n [2, -3, 1, -2, -6, 9, -3, 6, -4, 6, -2, 4], [-1, 0, 1, 0, 3, 0, -3, 0, 2, 0, -2, 0]])\n\n W = torch.nn.Parameter(torch.ones((d[2], d[1])))\n\n def loss(i):\n residuals = Bs[i].t() @ W @ u.v2c(As[i])\n return 0.5 * torch.sum(residuals * residuals)\n\n u.check_equal(loss(0), 333 / 2)\n\n # check against PyTorch autograd\n i = 0\n outputs = Bs[i].t() @ W @ u.v2c(As[i])\n jac = u.jacobian(outputs, W)\n\n u.check_equal(Jo1, jac.transpose(0, 1).transpose(2, 3).reshape((c, -1)))\n\n Jb = torch.cat([u.kron(u.v2r(As[i]), Bs[i].t()) for i in range(n)])\n manualHess = Jb.t() @ Jb\n u.check_equal(manualHess, [[167, -60, -161, -85, 39, 0, -57, -15, -64, 30, 52, 35],\n [-60, 99, 51, 87, 0, 3, 27, 9, 30, -48, -12, -39],\n [-161, 51, 164, 79, -57, 27, 48, 33, 52, -12, -58, -23],\n [-85, 87, 79, 85, -15, 9, 33, 15, 35, -39, -23, -35],\n [39, 0, -57, -15, 63, -60, -9, -45, 12, -30, 24, -15],\n [0, 3, 27, 9, -60, 91, -21, 63, -30, 44, -24, 27],\n [-57, 27, 48, 33, -9, -21, 36, -9, 24, -24, -6, -21],\n [-15, 9, 33, 15, -45, 63, -9, 45, -15, 27, -21, 15],\n [-64, 30, 52, 35, 12, -30, 24, -15, 38, -30, -14, -25],\n [30, -48, -12, -39, -30, 44, -24, 27, -30, 46, -6, 33],\n [52, -12, -58, -23, 24, -24, -6, -21, -14, -6, 26, 1],\n [35, -39, -23, -35, -15, 27, -21, 15, -25, 33, 1, 25]])\n\n total_loss = torch.add(*[loss(i) for i in range(n)])\n u.check_equal(total_loss, 397 / 2)\n\n automaticHess = u.hessian(total_loss, W)\n automaticHess = automaticHess.transpose(0, 1).transpose(2, 3).reshape((d[1] * d[2], d[1] * d[2]))\n u.check_equal(automaticHess, manualHess)\n\n # Note: layers have dimensions (in, out), but the matrices have shape (out, in)\n layer = nn.Linear(d[1], d[2], bias=False)\n Blayer = nn.Linear(d[2], d[3], bias=False)\n model = torch.nn.Sequential(layer, nn.ReLU(), Blayer)\n layer.weight.data.copy_(torch.ones((d[2], d[1])))\n Blayer.weight.data.copy_(Bs[0].t())\n u.check_close(model(As[0]), [-18., -3.])\n\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport torch\nimport util as u\nfrom torch import nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndef test_explicit_hessian():\n \"\"\"Check computation of hessian of loss(B'WA) from https://github.com/yaroslavvb/kfac_pytorch/blob/master/derivation.pdf\n\n\n \"\"\"\n\n torch.set_default_dtype(torch.float64)\n A = torch.tensor([[-1., 4], [3, 0]])\n B = torch.tensor([[-4., 3], [2, 6]])\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n\n Y = B.t() @ X @ A\n u.check_equal(Y, [[-52, 64], [-81, -108]])\n loss = torch.sum(Y * Y) / 2\n hess0 = u.hessian(loss, X).reshape([4, 4])\n hess1 = u.Kron(A @ A.t(), B @ B.t())\n\n u.check_equal(loss, 12512.5)\n\n # PyTorch autograd computes Hessian with respect to row-vectorized parameters, whereas\n # autograd_lib uses math convention and does column-vectorized.\n # Commuting order of Kronecker product switches between two representations\n u.check_equal(hess1.commute(), hess0)\n\n # Do a test using Linear layers instead of matrix multiplies\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([2, 2, 2], bias=False)\n model.layers[0].weight.data.copy_(X)\n\n # Transpose to match previous results, layers treat dim0 as batch dimension\n u.check_equal(model.layers[0](A.t()).t(), [[5, -20], [-16, -8]]) # XA = (A'X0)'\n\n model.layers[1].weight.data.copy_(B.t())\n u.check_equal(model(A.t()).t(), Y)\n\n Y = model(A.t()).t() # transpose to data-dimension=columns\n loss = torch.sum(Y * Y) / 2\n loss.backward()\n\n u.check_equal(model.layers[0].weight.grad, [[-2285, -105], [-1490, -1770]])\n G = B @ Y @ A.t()\n u.check_equal(model.layers[0].weight.grad, G)\n\n u.check_equal(hess0, u.Kron(B @ B.t(), A @ A.t()))\n\n # compute newton step\n u.check_equal(u.Kron(A@A.t(), B@B.t()).pinv() @ u.vec(G), u.v2c([-5, -2, 0, -6]))\n\n # compute Newton step using factored representation\n autograd_lib.add_hooks(model)\n\n Y = model(A.t())\n n = 2\n loss = torch.sum(Y * Y) / 2\n autograd_lib.backprop_hess(Y, hess_type='LeastSquares')\n autograd_lib.compute_hess(model, method='kron', attr_name='hess_kron', vecr_order=False, loss_aggregation='sum')\n param = model.layers[0].weight\n\n hess2 = param.hess_kron\n print(hess2)\n\n u.check_equal(hess2, [[425, 170, -75, -30], [170, 680, -30, -120], [-75, -30, 225, 90], [-30, -120, 90, 360]])\n\n # Gradient test\n model.zero_grad()\n loss.backward()\n u.check_close(u.vec(G).flatten(), u.Vec(param.grad))\n\n # Newton step test\n # Method 0: PyTorch native autograd\n newton_step0 = param.grad.flatten() @ torch.pinverse(hess0)\n newton_step0 = newton_step0.reshape(param.shape)\n u.check_equal(newton_step0, [[-5, 0], [-2, -6]])\n\n # Method 1: colummn major order\n ihess2 = hess2.pinv()\n u.check_equal(ihess2.LL, [[1/16, 1/48], [1/48, 17/144]])\n u.check_equal(ihess2.RR, [[2/45, -(1/90)], [-(1/90), 1/36]])\n u.check_equal(torch.flatten(hess2.pinv() @ u.vec(G)), [-5, -2, 0, -6])\n newton_step1 = (ihess2 @ u.Vec(param.grad)).matrix_form()\n\n # Method2: row major order\n ihess2_rowmajor = ihess2.commute()\n newton_step2 = ihess2_rowmajor @ u.Vecr(param.grad)\n newton_step2 = newton_step2.matrix_form()\n\n u.check_equal(newton_step0, newton_step1)\n u.check_equal(newton_step0, newton_step2)\n\n\ndef test_factored_hessian():\n \"\"\"\"Simple test to ensure Hessian computation is working.\n\n In a linear neural network with squared loss, Newton step will converge in one step.\n Compute stats after minimizing, pass sanity checks.\n \"\"\"\n\n u.seed_random(1)\n loss_type = 'LeastSquares'\n\n data_width = 2\n n = 5\n d1 = data_width ** 2\n o = 10\n d = [d1, o]\n\n model = u.SimpleFullyConnected2(d, bias=False, nonlin=False)\n model = model.to(gl.device)\n print(model)\n\n dataset = u.TinyMNIST(data_width=data_width, dataset_size=n, loss_type=loss_type)\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=n, shuffle=False)\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n\n data, targets = stats_data, stats_targets\n\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n\n output = model(data)\n loss = loss_fn(output, targets)\n print(loss)\n loss.backward(retain_graph=True)\n layer = model.layers[0]\n\n autograd_lib.clear_hess_backprops(model)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks()\n\n # compute Hessian using direct method, compare against PyTorch autograd\n hess0 = u.hessian(loss, layer.weight)\n autograd_lib.compute_hess(model)\n hess1 = layer.weight.hess\n print(hess1)\n u.check_close(hess0.reshape(hess1.shape), hess1, atol=1e-9, rtol=1e-6)\n\n # compute Hessian using factored method\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2', vecr_order=True)\n # s.regret_newton = vecG.t() @ pinvH.commute() @ vecG.t() / 2 # TODO(y): figure out why needed transposes\n\n hess2 = layer.weight.hess2\n u.check_close(hess1, hess2, atol=1e-9, rtol=1e-6)\n\n # Newton step in regular notation\n g1 = layer.weight.grad.flatten()\n newton1 = hess1 @ g1\n\n g2 = u.Vecr(layer.weight.grad)\n newton2 = g2 @ hess2\n\n u.check_close(newton1, newton2, atol=1e-9, rtol=1e-6)\n\n # compute regret in factored notation, compare against actual drop in loss\n regret1 = g1 @ hess1.pinverse() @ g1 / 2\n regret2 = g2 @ hess2.pinv() @ g2 / 2\n u.check_close(regret1, regret2)\n\n current_weight = layer.weight.detach().clone()\n param: torch.nn.Parameter = layer.weight\n # param.data.sub_((hess1.pinverse() @ g1).reshape(param.shape))\n # output = model(data)\n # loss = loss_fn(output, targets)\n # print(\"result 1\", loss)\n\n # param.data.sub_((hess1.pinverse() @ u.vec(layer.weight.grad)).reshape(param.shape))\n # output = model(data)\n # loss = loss_fn(output, targets)\n # print(\"result 2\", loss)\n\n # param.data.sub_((u.vec(layer.weight.grad).t() @ hess1.pinverse()).reshape(param.shape))\n # output = model(data)\n # loss = loss_fn(output, targets)\n # print(\"result 3\", loss)\n #\n\n del layer.weight.grad\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n param.data.sub_(u.unvec(hess1.pinverse() @ u.vec(layer.weight.grad), layer.weight.shape[0]))\n output = model(data)\n loss = loss_fn(output, targets)\n print(\"result 4\", loss)\n\n # param.data.sub_((g1 @ hess1.pinverse() @ g1).reshape(param.shape))\n\n print(loss)\n\n\ndef test_hessian_multibatch():\n \"\"\"Test that Kronecker-factored computations still work when splitting work over batches.\"\"\"\n\n u.seed_random(1)\n\n # torch.set_default_dtype(torch.float64)\n\n gl.project_name = 'test'\n gl.logdir_base = '/tmp/runs'\n run_name = 'test_hessian_multibatch'\n u.setup_logdir_and_event_writer(run_name=run_name)\n\n loss_type = 'CrossEntropy'\n data_width = 2\n n = 4\n d1 = data_width ** 2\n o = 10\n d = [d1, o]\n\n model = u.SimpleFullyConnected2(d, bias=False, nonlin=False)\n model = model.to(gl.device)\n\n dataset = u.TinyMNIST(data_width=data_width, dataset_size=n, loss_type=loss_type)\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=n, shuffle=False)\n stats_iter = u.infinite_iter(stats_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n data, targets = stats_data, stats_targets\n\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n layer = model.layers[0]\n\n autograd_lib.clear_hess_backprops(model)\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks()\n\n # compute Hessian using direct method, compare against PyTorch autograd\n hess0 = u.hessian(loss, layer.weight)\n autograd_lib.compute_hess(model)\n hess1 = layer.weight.hess\n u.check_close(hess0.reshape(hess1.shape), hess1, atol=1e-8, rtol=1e-6)\n\n # compute Hessian using factored method. Because Hessian depends on examples for cross entropy, factoring is not exact, raise tolerance\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2', vecr_order=True)\n hess2 = layer.weight.hess2\n u.check_close(hess1, hess2, atol=1e-3, rtol=1e-1)\n\n # compute Hessian using multibatch\n # restart iterators\n dataset = u.TinyMNIST(data_width=data_width, dataset_size=n, loss_type=loss_type)\n assert n % 2 == 0\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=n//2, shuffle=False)\n stats_iter = u.infinite_iter(stats_loader)\n autograd_lib.compute_cov(model, loss_fn, stats_iter, batch_size=n//2, steps=2)\n\n cov: autograd_lib.LayerCov = layer.cov\n hess2: u.Kron = hess2.commute() # get back into AA x BB order\n u.check_close(cov.H.value(), hess2)\n\n\ndef _test_refactored_stats():\n gl.project_name = 'test'\n gl.logdir_base = '/tmp/runs'\n run_name = 'test_hessian_multibatch'\n u.setup_logdir_and_event_writer(run_name=run_name)\n\n loss_type = 'CrossEntropy'\n data_width = 2\n n = 4\n d1 = data_width ** 2\n o = 10\n d = [d1, o]\n\n model = u.SimpleFullyConnected2(d, bias=False, nonlin=False)\n model = model.to(gl.device)\n\n dataset = u.TinyMNIST(data_width=data_width, dataset_size=n, loss_type=loss_type)\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=n, shuffle=False)\n stats_iter = u.infinite_iter(stats_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n data, targets = stats_data, stats_targets\n\n covG = autograd_lib.layer_cov_dict()\n covH = autograd_lib.layer_cov_dict()\n covJ = autograd_lib.layer_cov_dict()\n\n autograd_lib.register(model)\n\n A = {}\n with autograd_lib.save_activations(A):\n output = model(data)\n loss = loss_fn(output, targets)\n\n Acov = autograd_lib.ModuleDict(autograd_lib.SecondOrder)\n for layer, activations in A.items():\n Acov[layer].accumulate(activations)\n\n autograd_lib.set_default_activations(A) # set activations to use by default when constructing cov matrices\n autograd_lib.set_default_Acov(Acov)\n\n # saves backprop covariances\n autograd_lib.backward_accum(loss, 1, covG)\n autograd_lib.backward_accum(output, autograd_lib.xent_bwd, covH)\n autograd_lib.backward_accum(output, autograd_lib.identity_bwd, covJ)\n\n #grad_cov = KronFactored(covA, covG.cov, covG.cross)\n #hess = KronFactored(covA, covH.cov, covH.cross)\n #grad_cov = KronFactored(covA, covJ.cov, covJ.cross)\n\n\ndef test_hessian_conv():\n \"\"\"Test conv hessian computation using factored and regular method.\"\"\"\n\n u.seed_random(1)\n unfold = torch.nn.functional.unfold\n fold = torch.nn.functional.fold\n\n import numpy as np\n\n u.seed_random(1)\n N, Xc, Xh, Xw = 3, 2, 3, 7\n dd = [Xc, 2]\n\n Kh, Kw = 2, 3\n Oh, Ow = Xh - Kh + 1, Xw - Kw + 1\n model = u.SimpleConvolutional(dd, kernel_size=(Kh, Kw), bias=True).double()\n\n weight_buffer = model.layers[0].weight.data\n\n # output channels, input channels, height, width\n assert weight_buffer.shape == (dd[1], dd[0], Kh, Kw)\n\n input_dims = N, Xc, Xh, Xw\n size = int(np.prod(input_dims))\n X = torch.arange(0, size).reshape(*input_dims).double()\n\n def loss_fn(data):\n err = data.reshape(len(data), -1)\n return torch.sum(err * err) / 2 / len(data)\n\n layer = model.layers[0]\n output = model(X)\n loss = loss_fn(output)\n loss.backward()\n\n u.check_equal(layer.activations, X)\n\n assert layer.backprops_list[0].shape == layer.output.shape\n assert layer.output.shape == (N, dd[1], Oh, Ow)\n\n out_unf = layer.weight.view(layer.weight.size(0), -1) @ unfold(layer.activations, (Kh, Kw))\n assert out_unf.shape == (N, dd[1], Oh * Ow)\n reshaped_bias = layer.bias.reshape(1, dd[1], 1) # (Co,) -> (1, Co, 1)\n out_unf = out_unf + reshaped_bias\n\n u.check_equal(fold(out_unf, (Oh, Ow), (1, 1)), output) # two alternative ways of reshaping\n u.check_equal(out_unf.view(N, dd[1], Oh, Ow), output)\n\n # Unfold produces patches with output dimension merged, while in backprop they are not merged\n # Hence merge the output (width/height) dimension\n assert unfold(layer.activations, (Kh, Kw)).shape == (N, Xc * Kh * Kw, Oh * Ow)\n assert layer.backprops_list[0].shape == (N, dd[1], Oh, Ow)\n\n grads_bias = layer.backprops_list[0].sum(dim=(2, 3)) * N\n mean_grad_bias = grads_bias.sum(dim=0) / N\n u.check_equal(mean_grad_bias, layer.bias.grad)\n\n Bt = layer.backprops_list[0] * N # remove factor of N applied during loss batch averaging\n assert Bt.shape == (N, dd[1], Oh, Ow)\n Bt = Bt.reshape(N, dd[1], Oh*Ow)\n At = unfold(layer.activations, (Kh, Kw))\n assert At.shape == (N, dd[0] * Kh * Kw, Oh*Ow)\n\n grad_unf = torch.einsum('ijk,ilk->ijl', Bt, At)\n assert grad_unf.shape == (N, dd[1], dd[0] * Kh * Kw)\n\n grads = grad_unf.reshape((N, dd[1], dd[0], Kh, Kw))\n u.check_equal(grads.mean(dim=0), layer.weight.grad)\n\n # compute per-example gradients using autograd, compare against manual computation\n for i in range(N):\n u.clear_backprops(model)\n output = model(X[i:i + 1, ...])\n loss = loss_fn(output)\n loss.backward()\n u.check_equal(grads[i], layer.weight.grad)\n u.check_equal(grads_bias[i], layer.bias.grad)\n\n\ndef _test_explicit_hessian_refactored():\n\n \"\"\"Check computation of hessian of loss(B'WA) from https://github.com/yaroslavvb/kfac_pytorch/blob/master/derivation.pdf\n\n\n \"\"\"\n\n torch.set_default_dtype(torch.float64)\n A = torch.tensor([[-1., 4], [3, 0]])\n B = torch.tensor([[-4., 3], [2, 6]])\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n\n Y = B.t() @ X @ A\n u.check_equal(Y, [[-52, 64], [-81, -108]])\n loss = torch.sum(Y * Y) / 2\n hess0 = u.hessian(loss, X).reshape([4, 4])\n hess1 = u.Kron(A @ A.t(), B @ B.t())\n\n u.check_equal(loss, 12512.5)\n\n # Do a test using Linear layers instead of matrix multiplies\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([2, 2, 2], bias=False)\n model.layers[0].weight.data.copy_(X)\n\n # Transpose to match previous results, layers treat dim0 as batch dimension\n u.check_equal(model.layers[0](A.t()).t(), [[5, -20], [-16, -8]]) # XA = (A'X0)'\n\n model.layers[1].weight.data.copy_(B.t())\n u.check_equal(model(A.t()).t(), Y)\n\n Y = model(A.t()).t() # transpose to data-dimension=columns\n loss = torch.sum(Y * Y) / 2\n loss.backward()\n\n u.check_equal(model.layers[0].weight.grad, [[-2285, -105], [-1490, -1770]])\n G = B @ Y @ A.t()\n u.check_equal(model.layers[0].weight.grad, G)\n\n autograd_lib.register(model)\n activations_dict = autograd_lib.ModuleDict() # todo(y): make save_activations ctx manager automatically create A\n with autograd_lib.save_activations(activations_dict):\n Y = model(A.t())\n\n Acov = autograd_lib.ModuleDict(autograd_lib.SecondOrderCov)\n for layer, activations in activations_dict.items():\n print(layer, activations)\n Acov[layer].accumulate(activations, activations)\n autograd_lib.set_default_activations(activations_dict)\n autograd_lib.set_default_Acov(Acov)\n\n B = autograd_lib.ModuleDict(autograd_lib.SymmetricFourthOrderCov)\n autograd_lib.backward_accum(Y, \"identity\", B, retain_graph=False)\n\n print(B[model.layers[0]])\n\n autograd_lib.backprop_hess(Y, hess_type='LeastSquares')\n autograd_lib.compute_hess(model, method='kron', attr_name='hess_kron', vecr_order=False, loss_aggregation='sum')\n param = model.layers[0].weight\n\n hess2 = param.hess_kron\n print(hess2)\n\n u.check_equal(hess2, [[425, 170, -75, -30], [170, 680, -30, -120], [-75, -30, 225, 90], [-30, -120, 90, 360]])\n\n # Gradient test\n model.zero_grad()\n loss.backward()\n u.check_close(u.vec(G).flatten(), u.Vec(param.grad))\n\n # Newton step test\n # Method 0: PyTorch native autograd\n newton_step0 = param.grad.flatten() @ torch.pinverse(hess0)\n newton_step0 = newton_step0.reshape(param.shape)\n u.check_equal(newton_step0, [[-5, 0], [-2, -6]])\n\n # Method 1: colummn major order\n ihess2 = hess2.pinv()\n u.check_equal(ihess2.LL, [[1/16, 1/48], [1/48, 17/144]])\n u.check_equal(ihess2.RR, [[2/45, -(1/90)], [-(1/90), 1/36]])\n u.check_equal(torch.flatten(hess2.pinv() @ u.vec(G)), [-5, -2, 0, -6])\n newton_step1 = (ihess2 @ u.Vec(param.grad)).matrix_form()\n\n # Method2: row major order\n ihess2_rowmajor = ihess2.commute()\n newton_step2 = ihess2_rowmajor @ u.Vecr(param.grad)\n newton_step2 = newton_step2.matrix_form()\n\n u.check_equal(newton_step0, newton_step1)\n u.check_equal(newton_step0, newton_step2)\n\n\ndef _test_new_setup():\n torch.set_default_dtype(torch.float64)\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n\n model: u.SimpleFullyConnected2 = u.SimpleFullyConnected2([2, 2, 2], bias=False)\n model.layers[0].weight.data.copy_(X)\n\n A = torch.tensor([[-1., 4], [3, 0]])\n B = torch.tensor([[-4., 3], [2, 6]])\n X = torch.tensor([[-5., 0], [-2, -6]], requires_grad=True)\n\n #########\n # computing per-example gradients\n #########\n activations = {}\n def save_activations(layer, a, _): activations[layer] = a\n with autograd_lib.module_hooks(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n norms = {}\n def compute_norms(layer, _, b):\n a = activations[layer]\n del activations[layer]\n norms[layer] = a*a.sum(dim=0)*(b*b).sum(dim=0)\n\n with autograd_lib.module_hooks(compute_norms):\n loss.backward()\n\n #########\n # Computing higher rank Hessian approximation\n #########\n activations = {}\n with autograd_lib.module_hooks(save_activations):\n Y = model(A.t())\n loss = torch.sum(Y * Y) / 2\n\n # kfac moments: 'ij', 'kl'\n # isserlis moments: 'ij', 'kl', 'il', 'ik'\n # first moments: 'i', 'j', 'k', 'l'\n # third moments: ...\n # forth moments: ...\n # moment_dict = {'Ai': Buffer, 'Bk': Buffer, 'AiAj': Buffer, 'BkBl': Buffer, 'AiBk': Buffer}\n # moments_dict = MomentsDict('A', 'AA', 'B', 'BB', 'AB', 'diag')\n # moments_dict = MomentsDict('i', 'ij', 'k', 'kl', 'jk', 'iikk')\n moment_dict = MomentsDict(['Ai', 'Bk', 'AiAj', 'BkBl', 'AiBk', 'AiAiBkBk', 'AiAjBkBl'])\n def accumulate_moments(layer, _, b):\n a = activations[layer]\n util.accumulate_moments(moment_dict, a, b)\n\n with autograd_lib.module_hooks(accumulate_moments):\n pass\n\n\n\n\nif __name__ == '__main__':\n # _test_factored_hessian()\n # test_hessian_multibatch()\n # test_hessian_conv()\n # test_explicit_hessian_refactored()\n # u.run_all_tests(sys.modules[__name__])\n\n u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/linalg_bench.py", "content": "import os\nimport sys\nimport time\nfrom typing import Optional, Tuple, Callable\n\n# import torch\nimport scipy\nimport torch\nfrom torchcurv.optim import SecondOrderOptimizer\n\n\nimport torch.nn as nn\n\nimport util as u\n\nimport numpy as np\n\n\"\"\"\nMKL version unknown\nPyTorch version 1.2.0\nScipy version: 1.2.1\nNumpy version: 1.16.4\n1024-by-1024 matrix\n 7079.93 linalg.solve_lyapunov\n 280.11 linalg.pinvh\n 1186.08 linalg.pinv\n 49.18 linalg.inv\n 118.23 qr\n 413.42 svd\n\"\"\"\n\nclass Net(nn.Module):\n def __init__(self, d):\n super().__init__()\n self.w = nn.Linear(d, 1, bias=False)\n\n def forward(self, x: torch.Tensor):\n result = self.w(x)\n return result\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard. This function is\n \"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n print(f\"{interval_ms:8.2f} {self.tag}\")\n\n\ndef get_mkl_version():\n import ctypes\n import numpy as np\n\n # this recipe only works on Linux\n try:\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n except:\n return 'unknown'\n\n\ndef print_cpu_info():\n ver = 'unknown'\n try:\n for l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n ver = l\n break\n except:\n pass\n\n\ndef linalg_bench():\n if np.__config__.get_info(\"lapack_mkl_info\"):\n print(\"MKL version\", get_mkl_version())\n else:\n print(\"not using MKL\")\n\n print(\"PyTorch version\", torch.version.__version__)\n\n print(\"Scipy version: \", scipy.version.full_version)\n print(\"Numpy version: \", np.version.full_version)\n\n for d in [1024]:\n print(f\"{d}-by-{d} matrix\")\n n = 10000\n assert n > 2*d # to prevent singularity\n X = np.random.random((d, 10000))\n Y = np.random.random((d, 10000))\n H = X @ X.T\n S = Y @ Y.T\n\n with timeit(f\"linalg.solve_lyapunov\"):\n result = scipy.linalg.solve_lyapunov(H, S)\n #print(result[0,0])\n\n with timeit(f\"linalg.pinvh\"):\n result = scipy.linalg.pinvh(H)\n #print(result[0, 0])\n\n with timeit(f\"linalg.pinv\"):\n result = scipy.linalg.pinv(H)\n #print(result[0, 0])\n\n\n with timeit(f\"linalg.inv\"):\n result = scipy.linalg.inv(H)\n #print(result[0, 0])\n\n with timeit(f\"qr\"):\n result = scipy.linalg.qr(H)\n #print(result[0, 0])\n\n with timeit(f\"qr-pivoting\"):\n result = scipy.linalg.qr(H, pivoting=True)\n #print(result[0, 0])\n\n with timeit(f\"svd\"):\n result = scipy.linalg.svd(H)\n #print(result[0, 0])\n\n\n\nif __name__ == '__main__':\n linalg_bench()\n" }, { "path": "autotune/linesearch_test_disabled.py", "content": "# Take simple MNIST model, test that line-search in Newton direction finds optimum\n# Additionally test Hessian manual vs autograd computation.\n# This functio\n\nimport argparse\nimport json\nimport os\nimport random\nimport shutil\nimport sys\n\nimport numpy as np\nimport torch.nn as nn\nfrom attrdict import AttrDefault\nfrom torch import optim\n\nimport util as u\n\nimport globals as gl\n\n\ntry:\n import wandb\nexcept Exception as e:\n pass\n # print(f\"wandb crash with {e}\")\n\nfrom torchvision import datasets, transforms\nimport torch\n\nfrom torchcurv.optim import SecondOrderOptimizer\nfrom torchcurv.utils import Logger\n\nDATASET_MNIST = 'MNIST'\nIMAGE_SIZE = 28\nNUM_CHANNELS = 1\n\nif torch.cuda.is_available():\n device = torch.device('cuda')\nelse:\n device = torch.device('cpu')\n\n\ndef install_pdb_handler():\n \"\"\"Automatically start pdb:\n 1. CTRL+\\\\ breaks into pdb.\n 2. pdb gets launched on exception.\n \"\"\"\n\n import signal\n import pdb\n\n def handler(_signum, _frame):\n pdb.set_trace()\n\n signal.signal(signal.SIGQUIT, handler)\n\n # Drop into PDB on exception\n # from https://stackoverflow.com/questions/13174412\n def info(type_, value, tb):\n if hasattr(sys, 'ps1') or not sys.stderr.isatty():\n # we are in interactive mode or we don't have a tty-like\n # device, so we call the default hook\n sys.__excepthook__(type_, value, tb)\n else:\n import traceback\n import pdb\n # we are NOT in interactive mode, print the exception...\n traceback.print_exception(type_, value, tb)\n print()\n # ...then start the debugger in post-mortem mode.\n pdb.pm()\n\n sys.excepthook = info\n\n\ninstall_pdb_handler()\n\n\nclass FastMNIST(datasets.MNIST):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n # self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\nclass FastBinaryMNIST(datasets.MNIST):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n # self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n self.targets = (self.targets > 0).float()\n self.targets.unsqueeze(1)\n assert len(self.targets.shape) == 2\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\nclass SimpleMNIST(datasets.MNIST):\n \"\"\"Simple dataset where goal is to predict sum of pixels.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n self.target = self.data.sum(dim=(2, 3)).squeeze(1)\n self.targets = self.targets.unsqueeze(1)\n assert len(self.targets.shape) == 2\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\ndef compute_loss(output, target):\n err = output - target.float()\n return torch.sum(err * err) / 2 / output.shape[0]\n\n\nlogger = None\n\n\ndef log(metrics, step):\n global logger\n # print(metrics)\n try:\n logger.write(metrics)\n except: # crashes with JSON conversion error sometimes\n pass\n\n try:\n wandb.log(metrics, step=step)\n except:\n pass\n\n\ndef test_lineasearch():\n \"\"\"Implement linesearch with sanity checks.\"\"\"\n global logger, stats_data, stats_targets, args\n run_name = 'default' # name of run in\n\n torch.set_default_dtype(torch.float32)\n\n # Copy this file & config to args.out\n out = '/tmp'\n if not os.path.isdir(out):\n os.makedirs(out)\n shutil.copy(os.path.realpath(__file__), out)\n\n # Setup logger\n log_file_name = 'log'\n logger = Logger(out, log_file_name)\n logger.start()\n\n # Set device\n use_cuda = False\n device = torch.device('cuda' if use_cuda else 'cpu')\n\n # Set random seed\n u.seed_random(1)\n\n # Setup data augmentation & data pre processing\n train_transforms, val_transforms = [], []\n\n train_transforms.append(transforms.ToTensor())\n val_transforms.append(transforms.ToTensor())\n\n train_transform = transforms.Compose(train_transforms)\n # val_transform = transforms.Compose(val_transforms)\n\n num_classes = 10\n dataset_class = SimpleMNIST\n\n class Net(nn.Module):\n def __init__(self, d, nonlin=True):\n super().__init__()\n self.layers = []\n self.all_layers = []\n self.d = d\n for i in range(len(d) - 1):\n linear = nn.Linear(d[i], d[i + 1], bias=False)\n self.layers.append(linear)\n self.all_layers.append(linear)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n return self.predict(x)\n\n stats_batch_size = 1\n def compute_layer_stats(layer):\n stats = AttrDefault(str, {})\n n = stats_batch_size\n param = u.get_param(layer)\n d = len(param.flatten())\n layer_idx = model.layers.index(layer)\n assert layer_idx >= 0\n assert stats_data.shape[0] == n\n\n def backprop_loss():\n model.zero_grad()\n output = model(stats_data) # use last saved data batch for backprop\n loss = compute_loss(output, stats_targets)\n loss.backward()\n return loss, output\n\n def backprop_output():\n model.zero_grad()\n output = model(stats_data)\n output.backward(gradient=torch.ones_like(output))\n return output\n\n # per-example gradients, n, d\n loss, output = backprop_loss()\n At = layer.data_input\n Bt = layer.grad_output * n\n G = u.khatri_rao_t(At, Bt)\n g = G.sum(dim=0, keepdim=True) / n\n u.check_close(g, u.vec(param.grad).t())\n\n stats.diversity = torch.norm(G, \"fro\") ** 2 / g.flatten().norm() ** 2\n\n stats.gradient_norm = g.flatten().norm()\n stats.parameter_norm = param.data.flatten().norm()\n pos_activations = torch.sum(layer.data_output > 0)\n neg_activations = torch.sum(layer.data_output <= 0)\n stats.sparsity = pos_activations.float()/(pos_activations+neg_activations)\n\n output = backprop_output()\n At2 = layer.data_input\n u.check_close(At, At2)\n B2t = layer.grad_output\n J = u.khatri_rao_t(At, B2t)\n H = J.t() @ J / n\n\n model.zero_grad()\n output = model(stats_data) # use last saved data batch for backprop\n loss = compute_loss(output, stats_targets)\n hess = u.hessian(loss, param)\n\n hess = hess.transpose(2, 3).transpose(0, 1).reshape(d, d)\n u.check_close(hess, H)\n u.check_close(hess, H)\n\n stats.hessian_norm = u.l2_norm(H)\n stats.jacobian_norm = u.l2_norm(J)\n Joutput = J.sum(dim=0) / n\n stats.jacobian_sensitivity = Joutput.norm()\n\n # newton decrement\n stats.loss_newton = u.to_python_scalar(g @ u.pinv(H) @ g.t() / 2)\n u.check_close(stats.loss_newton, loss)\n\n # do line-search to find optimal step\n def line_search(directionv, start, end, steps=10):\n \"\"\"Takes steps between start and end, returns steps+1 loss entries\"\"\"\n param0 = param.data.clone()\n param0v = u.vec(param0).t()\n losses = []\n for i in range(steps+1):\n output = model(stats_data) # use last saved data batch for backprop\n loss = compute_loss(output, stats_targets)\n losses.append(loss)\n offset = start+i*((end-start)/steps)\n param1v = param0v + offset*directionv\n\n param1 = u.unvec(param1v.t(), param.data.shape[0])\n param.data.copy_(param1)\n\n output = model(stats_data) # use last saved data batch for backprop\n loss = compute_loss(output, stats_targets)\n losses.append(loss)\n\n param.data.copy_(param0)\n return losses\n\n # try to take a newton step\n gradv = g\n line_losses = line_search(-gradv @ u.pinv(H), 0, 2, steps=10)\n u.check_equal(line_losses[0], loss)\n u.check_equal(line_losses[6], 0)\n assert line_losses[5] > line_losses[6]\n assert line_losses[7] > line_losses[6]\n return stats\n\n train_dataset = dataset_class(\n root='/tmp/data', train=True, download=True, transform=train_transform)\n\n batch_size = 32\n num_workers = 0\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n stats_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=stats_batch_size, shuffle=False, num_workers=num_workers)\n stats_data, stats_targets = next(iter(stats_loader))\n\n model = Net([NUM_CHANNELS * IMAGE_SIZE ** 2, 8, 8, 1], nonlin=False)\n setattr(model, 'num_classes', num_classes)\n model = model.to(device)\n u.freeze(model.layers[0])\n u.freeze(model.layers[2])\n\n # use learning rate 0 to avoid changing parameter vector\n optim_kwargs = dict(lr=0.001, momentum=0.9, weight_decay=0, l2_reg=0,\n bias_correction=False, acc_steps=1,\n curv_type=\"Cov\", curv_shapes={\"Linear\": \"Kron\"},\n momentum_type=\"preconditioned\", update_inv=False, precondition_grad=False)\n curv_args = dict(damping=0, ema_decay=1)\n SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=curv_args) # call optimizer to add backward hoooks\n optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n\n start_epoch = 1\n\n # Run training\n epochs = 100\n for epoch in range(start_epoch, epochs + 1):\n num_examples_processed = epoch * len(train_loader) * train_loader.batch_size\n layer_stats = compute_layer_stats(model.layers[1])\n\n\ndef train(model, device, train_loader, optimizer, epoch, args, logger):\n global global_data, global_target\n model.train()\n\n loss = None\n confidence = {'top1': 0, 'top1_true': 0, 'top1_false': 0, 'true': 0, 'false': 0}\n total_data_size = 0\n epoch_size = len(train_loader.dataset)\n num_iters_in_epoch = len(train_loader)\n base_num_iter = (epoch - 1) * num_iters_in_epoch\n\n last_elapsed_time = 0\n interval_ms = 0\n for batch_idx, (data, target) in enumerate(train_loader):\n data, target = data.to(device), target.to(device)\n global_data = data\n global_target = target\n\n for name, param in model.named_parameters():\n attr = 'p_pre_{}'.format(name)\n setattr(model, attr, param.detach().clone())\n\n # update params\n def closure():\n optimizer.zero_grad()\n output = model(data)\n loss = compute_loss(output, target)\n loss.backward(create_graph=False)\n return loss, output\n\n #loss, output = optimizer.step(closure=closure)\n loss, output = closure()\n optimizer.step()\n loss = loss.item()\n\n iteration = base_num_iter + batch_idx + 1\n total_data_size += len(data)\n\n batch_size = 128\n if batch_idx % 20 == 0:\n elapsed_time = logger.elapsed_time\n if last_elapsed_time:\n interval_ms = 1000 * (elapsed_time - last_elapsed_time)\n last_elapsed_time = elapsed_time\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, '\n 'Accuracy: {:.0f}/{} ({:.2f}%), '\n 'Elapsed Time: {:.1f}s'.format(epoch, total_data_size, epoch_size, 100. * (batch_idx + 1) / num_iters_in_epoch, loss, 0, total_data_size, 0, elapsed_time))\n\n # save log\n lr = optimizer.param_groups[0]['lr']\n metrics = {'epoch': epoch, 'iteration': iteration, 'elapsed_time': elapsed_time,\n 'accuracy': 0, 'loss': loss, 'lr': lr, 'step_ms': interval_ms}\n\n for name, param in model.named_parameters():\n attr = 'p_pre_{}'.format(name)\n p_pre = getattr(model, attr)\n p_norm = param.norm().item()\n p_shape = list(param.size())\n p_pre_norm = p_pre.norm().item()\n if param.grad is not None:\n g_norm = param.grad.norm().item()\n else:\n g_norm = 0\n upd_norm = param.sub(p_pre).norm().item()\n noise_scale = getattr(param, 'noise_scale', 0)\n\n p_log = {'p_shape': p_shape, 'p_norm': p_norm, 'p_pre_norm': p_pre_norm,\n 'g_norm': g_norm, 'upd_norm': upd_norm, 'noise_scale': noise_scale}\n # print(p_log)\n metrics[name] = p_log\n\n log(metrics, step=iteration * batch_size)\n\n return 0, loss, confidence\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n # Data\n parser.add_argument('--dataset', type=str,\n choices=[DATASET_MNIST], default=DATASET_MNIST,\n help='name of dataset')\n parser.add_argument('--root', type=str, default='./data',\n help='root of dataset')\n parser.add_argument('--epochs', type=int, default=100,\n help='number of epochs to train')\n parser.add_argument('--batch_size', type=int, default=128,\n help='input batch size for training')\n parser.add_argument('--stats_batch_size', type=int, default=1,\n help='size of batch to use for second order statistics')\n parser.add_argument('--val_batch_size', type=int, default=128,\n help='input batch size for valing')\n parser.add_argument('--normalizing_data', action='store_true',\n help='[data pre processing] normalizing data')\n parser.add_argument('--random_crop', action='store_true',\n help='[data augmentation] random crop')\n parser.add_argument('--random_horizontal_flip', action='store_true',\n help='[data augmentation] random horizontal flip')\n # Training Settings\n parser.add_argument('--arch_file', type=str, default=None,\n help='name of file which defines the architecture')\n parser.add_argument('--arch_name', type=str, default='LeNet5',\n help='name of the architecture')\n parser.add_argument('--arch_args', type=json.loads, default=None,\n help='[JSON] arguments for the architecture')\n parser.add_argument('--optim_name', type=str, default=SecondOrderOptimizer.__name__,\n help='name of the optimizer')\n parser.add_argument('--optim_args', type=json.loads, default=None,\n help='[JSON] arguments for the optimizer')\n parser.add_argument('--curv_args', type=json.loads, default=None,\n help='[JSON] arguments for the curvature')\n # Options\n parser.add_argument('--download', action='store_true', default=True,\n help='if True, downloads the dataset (CIFAR-10 or 100) from the internet')\n parser.add_argument('--create_graph', action='store_true', default=False,\n help='create graph of the derivative')\n parser.add_argument('--no_cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1,\n help='random seed')\n parser.add_argument('--num_workers', type=int, default=0,\n help='number of sub processes for data loading')\n parser.add_argument('--log_interval', type=int, default=50,\n help='how many batches to wait before logging training status')\n parser.add_argument('--log_file_name', type=str, default='log',\n help='log file name')\n parser.add_argument('--checkpoint_interval', type=int, default=50,\n help='how many epochs to wait before logging training status')\n parser.add_argument('--resume', type=str, default=None,\n help='checkpoint path for resume training')\n parser.add_argument('--out', type=str, default='/tmp',\n help='dir to save output files')\n parser.add_argument('--config', default=None,\n help='config file path')\n parser.add_argument('--fisher_mc_approx', action='store_true', default=False,\n help='if True, Fisher is estimated by MC sampling')\n parser.add_argument('--fisher_num_mc', type=int, default=1,\n help='number of MC samples for estimating Fisher')\n parser.add_argument('--log_wandb', type=int, default=0,\n help='log to wandb')\n\n args = parser.parse_args()\n\n u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/lyapunov_test.py", "content": "import os\nimport sys\nimport time\nfrom typing import Optional, Tuple, Callable\n\n# import torch\nimport scipy\nimport torch\nfrom torchcurv.optim import SecondOrderOptimizer\n\nimport torch.nn as nn\n\nimport util as u\n\nimport numpy as np\n\n\nclass Net(nn.Module):\n def __init__(self, d):\n super().__init__()\n self.w = nn.Linear(d, 1, bias=False)\n\n def forward(self, x: torch.Tensor):\n result = self.w(x)\n return result\n\n\n# Backward via iterative Lyapunov solver\n# from https://github.com/msubhransu/matrix-sqrt/blob/master/matrix_sqrt.py\ndef lyap_newton_schulz(z, dldz, numIters, dtype):\n batchSize = z.shape[0]\n dim = z.shape[1]\n normz = z.mul(z).sum(dim=1).sum(dim=1).sqrt()\n a = z.div(normz.view(batchSize, 1, 1).expand_as(z))\n I = torch.eye(dim, dim).view(1, dim, dim).repeat(batchSize, 1, 1).type(dtype)\n q = dldz.div(normz.view(batchSize, 1, 1).expand_as(z))\n for i in range(numIters):\n q = 0.5 * (q.bmm(3.0 * I - a.bmm(a)) - a.transpose(1, 2).bmm(a.transpose(1, 2).bmm(q) - q.bmm(a)))\n a = 0.5 * a.bmm(3.0 * I - a.bmm(a))\n dlda = 0.5 * q\n return dlda\n\n\ndef test_lyapunov():\n \"\"\"Test that scipy lyapunov solver works correctly.\"\"\"\n d = 2\n n = 3\n torch.set_default_dtype(torch.float32)\n\n model = Net(d)\n\n w0 = torch.tensor([[1, 2]]).float()\n assert w0.shape[1] == d\n model.w.weight.data.copy_(w0)\n\n X = torch.tensor([[-2, 0, 2], [-1, 1, 3]]).float()\n assert X.shape[0] == d\n assert X.shape[1] == n\n\n Y = torch.tensor([[0, 1, 2]]).float()\n assert Y.shape[1] == X.shape[1]\n\n data = X.t() # PyTorch expects batch dimension first\n target = Y.t()\n assert data.shape[0] == n\n\n output = model(data)\n # residuals, aka e\n residuals = output - Y.t()\n\n def compute_loss(residuals_):\n return torch.sum(residuals_ * residuals_) / (2 * n)\n\n loss = compute_loss(residuals)\n\n assert loss - 8.83333 < 1e-5, torch.norm(loss) - 8.83333\n\n # use learning rate 0 to avoid changing parameter vector\n optim_kwargs = dict(lr=0, momentum=0, weight_decay=0, l2_reg=0,\n bias_correction=False, acc_steps=1,\n curv_type=\"Cov\", curv_shapes={\"Linear\": \"Kron\"},\n momentum_type=\"preconditioned\", )\n curv_args = dict(damping=1, ema_decay=1) # todo: damping\n optimizer = SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=curv_args)\n\n def backward(last_layer: str) -> Callable:\n \"\"\"Creates closure that backpropagates either from output layer or from loss layer\"\"\"\n\n def closure() -> Tuple[Optional[torch.Tensor], torch.Tensor]:\n optimizer.zero_grad()\n output = model(data)\n if last_layer == \"output\":\n output.backward(torch.ones_like(target))\n return None, output\n elif last_layer == 'loss':\n loss = compute_loss(output - target)\n loss.backward()\n return loss, output\n else:\n assert False, 'last layer must be \"output\" or \"loss\"'\n\n return closure\n\n # loss = compute_loss(output - Y.t())\n # loss.backward()\n\n loss, output = optimizer.step(closure=backward('loss'))\n J = X.t()\n A = model.w.data_input\n B = model.w.grad_output * n\n G = residuals.repeat(1, d) * J\n losses = torch.stack([compute_loss(r) for r in residuals])\n g = G.sum(dim=0) / n\n efisher = G.t() @ G / n\n sigma = efisher - u.outer(g, g)\n loss2 = (residuals * residuals).sum() / (2 * n)\n H = J.t() @ J / n\n noise_variance = torch.trace(H.inverse() @ sigma)\n\n # H is not quite symmetric, make it so\n H = H + H.t()\n\n # Slow way\n p_sigma = u.lyapunov_lstsq(H, sigma)\n\n sigma0 = u.to_numpy(sigma)\n H0 = u.to_numpy(H)\n\n # Alternative faster way\n p_sigma2 = scipy.linalg.solve_lyapunov(H0, sigma0)\n print(f\"Error 1: {np.max(abs(H0 @ p_sigma2 + p_sigma2 @ H0 - sigma0))}\")\n u.check_close(p_sigma, p_sigma2)\n\n # alternative through SVD\n p_sigma3 = lyapunov_svd(torch.tensor(H0), torch.tensor(sigma0))\n u.check_close(p_sigma2, p_sigma3)\n\n # alternative through evals\n p_sigma4 = u.lyapunov_spectral(torch.tensor(H0), torch.tensor(sigma0))\n u.check_close(p_sigma2, p_sigma4)\n\n\ndef test_stability():\n bad_sigmas = torch.load('test/bad_sigmas.pt')\n H = bad_sigmas['H']\n sigma = bad_sigmas['sigma']\n\n X = u.lyapunov_spectral(H, sigma)\n discrepancy = torch.max(abs(X - X.t()) / X)\n assert discrepancy < 0.01\n\n\ndef compare_impl():\n \"\"\"\n lstsq : error 2.23668351395645e-07 erank 4.248441219329834 discrepancy 4760.62841796875\n scipy : error 4.3144709138687176e-07 erank 5.015336036682129 discrepancy 0.38698798418045044\n svd : error 2.6252257612213725e-07 erank 5.567287921905518 discrepancy 11.784327507019043\n spectral : error 3.1967988434189465e-07 erank 4.978618144989014 discrepancy 0.0020115238148719072\n \"\"\"\n\n bad_sigmas = torch.load('test/bad_sigmas.pt')\n H = bad_sigmas['H']\n sigma = bad_sigmas['sigma']\n\n # X = u.lyapunov_spectral(H, sigma)\n\n def print_stats(tag, X):\n error = torch.norm(H @ X + X @ H - sigma)\n discrepancy = torch.max(abs(X - X.t()) / X)\n print(tag, \": error\", error.item(), \"erank\", u.erank(X).item(), 'discrepancy', discrepancy.item())\n\n H0, sigma0 = u.to_numpys(H, sigma)\n X0 = scipy.linalg.solve_lyapunov(H0, sigma0)\n\n print_stats('lstsq', u.lyapunov_lstsq(H, sigma))\n print_stats('scipy', torch.tensor(X0))\n print_stats('svd', u.lyapunov_svd(H, sigma))\n print_stats('spectral', u.lyapunov_spectral(H, sigma))\n\n\ndef lyapunov_svd(A, C, rtol=1e-4, use_svd=False):\n \"\"\"Solve AX+XA=C\"\"\"\n\n assert A.shape[0] == A.shape[1]\n assert len(A.shape) == 2\n if use_svd:\n U, S, V = torch.svd(A)\n else:\n S, U = torch.symeig(A, eigenvectors=True)\n S = S.diag() @ torch.ones(A.shape)\n X = U @ ((U.t() @ C @ U) / (S + S.t())) @ U.t()\n error = A @ X + X @ A - C\n relative_error = torch.max(torch.abs(error)) / torch.max(torch.abs(A))\n if relative_error > rtol:\n print(f\"Warning, error {relative_error} encountered in lyapunov_svd\")\n\n return X\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n print(f\"{interval_ms:8.2f} {self.tag}\")\n\n\ndef get_mkl_version():\n import ctypes\n import numpy as np\n\n # this recipe only works on Linux\n try:\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n except:\n return 'unknown'\n\n\ndef print_cpu_info():\n ver = 'unknown'\n try:\n for l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n ver = l\n break\n except:\n pass\n\n\nif __name__ == '__main__':\n test_stability()\n # u.run_all_tests(sys.modules[__name__])\n" }, { "path": "autotune/mnist_end2end_test.py", "content": "import argparse\nimport os\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault, AttrDict\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom torch import nn as nn\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\ndef test_main():\n\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n help='learning rate (default: 0.01)')\n parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n help='SGD momentum (default: 0.5)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=1, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n\n parser.add_argument('--train_batch_size', type=int, default=100)\n parser.add_argument('--stats_batch_size', type=int, default=60000)\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=100, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--method', type=str, choices=['gradient', 'newton'], default='gradient',\n help=\"descent method, newton or gradient\")\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--lmb', type=float, default=1e-3)\n parser.add_argument('--hess_samples', type=int, default=1,\n help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=1, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--weight_decay', type=float, default=1e-4)\n\n #args = parser.parse_args()\n args = AttrDict()\n args.lmb = 1e-3\n args.compute_rho = 1\n args.weight_decay = 1e-4\n args.method = 'gradient'\n args.logdir = '/tmp'\n args.data_width = 2\n args.targets_width = 2\n args.train_batch_size = 10\n args.full_batch = False\n args.skip_stats = False\n args.autograd_check = False\n\n\n u.seed_random(1)\n logdir = u.create_local_logdir(args.logdir)\n run_name = os.path.basename(logdir)\n #gl.event_writer = SummaryWriter(logdir)\n gl.event_writer = u.NoOp()\n # print(f\"Logging to {run_name}\")\n\n\n # small values for debugging\n # loss_type = 'LeastSquares'\n loss_type = 'CrossEntropy'\n\n args.wandb = 0\n args.stats_steps = 10\n args.train_steps = 10\n args.stats_batch_size = 10\n args.data_width = 2\n args.targets_width = 2\n args.nonlin = False\n d1 = args.data_width ** 2\n d2 = 2\n d3 = args.targets_width ** 2\n\n d1 = args.data_width ** 2\n assert args.data_width == args.targets_width\n o = d1\n n = args.stats_batch_size\n d = [d1, 30, 30, 30, 20, 30, 30, 30, d1]\n\n if loss_type == 'CrossEntropy':\n d3 = 10\n o = d3\n n = args.stats_batch_size\n d = [d1, d2, d3]\n dsize = max(args.train_batch_size, args.stats_batch_size) + 1\n\n model = u.SimpleFullyConnected2(d, bias=True, nonlin=args.nonlin)\n model = model.to(gl.device)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['method'] = args.method\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n # optimizer = torch.optim.SGD(model.parameters(), lr=0.03, momentum=0.9)\n optimizer = torch.optim.Adam(model.parameters(), lr=0.03) # make 10x smaller for least-squares loss\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, dataset_size=dsize,\n original_targets=True)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_iter = None\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False,\n drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False,\n dataset_size=dsize, original_targets=True)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.train_batch_size, shuffle=False,\n drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.token_count = 0\n last_outer = 0\n val_losses = []\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000 * (time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n # print(\"val_loss\", val_loss.item())\n val_losses.append(val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n # compute stats\n if args.full_batch:\n data, targets = dataset.data, dataset.targets\n else:\n data, targets = next(stats_iter)\n\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model)\n\n for (i, layer) in enumerate(model.layers):\n\n # input/output layers are unreasonably expensive if not using Kronecker factoring\n if d[i] > 50 or d[i + 1] > 50:\n print(f'layer {i} is too big ({d[i], d[i + 1]}), skipping stats')\n continue\n\n if args.skip_stats:\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops_list[0] * n\n assert B_t.shape == (n, d[i + 1])\n\n with u.timeit(f\"khatri_g-{i}\"):\n G = u.khatri_rao_t(B_t, A_t) # batch loss Jacobian\n assert G.shape == (n, d[i] * d[i + 1])\n g = G.sum(dim=0, keepdim=True) / n # average gradient\n assert g.shape == (1, d[i] * d[i + 1])\n\n u.check_equal(G.reshape(layer.weight.grad1.shape), layer.weight.grad1)\n\n if args.autograd_check:\n u.check_close(B_t.t() @ A_t / n, layer.weight.saved_grad)\n u.check_close(g.reshape(d[i + 1], d[i]), layer.weight.saved_grad)\n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma) / s.sigma_l2\n\n lambda_regularizer = args.lmb * torch.eye(d[i + 1] * d[i]).to(gl.device)\n H = layer.weight.hess\n\n with u.timeit(f\"invH-{i}\"):\n invH = torch.cholesky_inverse(H + lambda_regularizer)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n s.iH_l2 = u.sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.iH_fro = invH.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = layer.weight.data.flatten().norm()\n\n u.nan_check(H)\n if args.autograd_check:\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n H_autograd = H_autograd.reshape(d[i] * d[i + 1], d[i] * d[i + 1])\n u.check_close(H, H_autograd)\n\n # u.dump(sigma, f'/tmp/sigmas/H-{step}-{i}')\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / (dd.flatten().norm() ** 2))\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = H.pinverse()\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = curv_direction(g)\n ndir = g @ pinvH # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = s.grad_fro ** 2 / s.grad_curv if s.grad_curv else 999\n s.step_max = 2 / s.H_l2\n s.step_min = torch.tensor(2) / torch.trace(H)\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2) # replace with \"quadratic_form\"\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n with u.timeit(f'rho-{i}'):\n p_sigma = u.lyapunov_spectral(H, sigma)\n\n discrepancy = torch.max(abs(p_sigma - p_sigma.t()) / p_sigma)\n\n s.psigma_erank = u.sym_erank(p_sigma)\n s.rho = H.shape[0] / s.psigma_erank\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2 / n\n\n # Faster approaches for noise variance computation\n # s.noise_variance = torch.trace(H.inverse() @ sigma)\n # try:\n # # this fails with singular sigma\n # s.noise_variance = torch.trace(torch.solve(sigma, H)[0])\n # # s.noise_variance = torch.trace(torch.lstsq(sigma, H)[0])\n # pass\n # except RuntimeError as _:\n s.noise_variance_pinv = torch.trace(pinvH @ sigma)\n\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n u.log_scalars(u.nest_stats(layer.name, s))\n\n # gradient steps\n with u.timeit('inner'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n # u.log_scalar(train_loss=loss.item())\n\n if args.method != 'newton':\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1 - args.weight_decay)\n else:\n for (layer_idx, layer) in enumerate(model.layers):\n param: torch.nn.Parameter = layer.weight\n param_data: torch.Tensor = param.data\n param_data.copy_(param_data - 0.1 * param.grad)\n if layer_idx != 1: # only update 1 layer with Newton, unstable otherwise\n continue\n u.nan_check(layer.weight.pre)\n u.nan_check(param.grad.flatten())\n u.nan_check(u.v2r(param.grad.flatten()) @ layer.weight.pre)\n param_new_flat = u.v2r(param_data.flatten()) - u.v2r(param.grad.flatten()) @ layer.weight.pre\n u.nan_check(param_new_flat)\n param_data.copy_(param_new_flat.reshape(param_data.shape))\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n assert val_losses[0] > 2.4 # 2.4828238487243652\n assert val_losses[-1] < 2.25 # 2.20609712600708\n\n\nif __name__ == '__main__':\n\n test_main()\n" }, { "path": "autotune/plotting_test.py", "content": "# Plot simple minimization problem in wandb\n\nimport argparse\nimport json\nimport os\nimport random\nimport shutil\nimport sys\nfrom typing import Any, Dict\n\nimport globals as g\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom attrdict import AttrDefault\n# from tensorboardX import SummaryWriter\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom torch import optim\n\n\n#def log_tb(tag, val):\n# \"\"\"Log value to tensorboard (relies on g.token_count rather than step count to give comparable graphs across\n# batch sizes)\"\"\"\n# g.event_writer.add_scalar(tag, val, g.token_count)\n\n\nimport util as u\n\n\ntry:\n import wandb\nexcept Exception as e:\n pass\n # print(f\"wandb crash with {e}\")\n\nfrom torchvision import datasets, transforms\nimport torch\n\nfrom torchcurv.optim import SecondOrderOptimizer, VIOptimizer\nfrom torchcurv.utils import Logger\n\nDATASET_MNIST = 'MNIST'\nIMAGE_SIZE = 28\nNUM_CHANNELS = 1\n\nif torch.cuda.is_available():\n device = torch.device('cuda')\nelse:\n device = torch.device('cpu')\n\n\ndef install_pdb_handler():\n \"\"\"Automatically start pdb:\n 1. CTRL+\\\\ breaks into pdb.\n 2. pdb gets launched on exception.\n \"\"\"\n\n import signal\n import pdb\n\n def handler(_signum, _frame):\n pdb.set_trace()\n\n signal.signal(signal.SIGQUIT, handler)\n\n # Drop into PDB on exception\n # from https://stackoverflow.com/questions/13174412\n def info(type_, value, tb):\n if hasattr(sys, 'ps1') or not sys.stderr.isatty():\n # we are in interactive mode or we don't have a tty-like\n # device, so we call the default hook\n sys.__excepthook__(type_, value, tb)\n else:\n import traceback\n import pdb\n # we are NOT in interactive mode, print the exception...\n traceback.print_exception(type_, value, tb)\n print()\n # ...then start the debugger in post-mortem mode.\n pdb.pm()\n\n sys.excepthook = info\n\n\ninstall_pdb_handler()\n\n\nclass FastMNIST(datasets.MNIST):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n # self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\nclass FastBinaryMNIST(datasets.MNIST):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n # self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n self.targets = (self.targets > 0).float()\n self.targets.unsqueeze(1)\n assert len(self.targets.shape) == 2\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\nclass SimpleMNIST(datasets.MNIST):\n \"\"\"Simple dataset where goal is to predict sum of pixels.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Scale data to [0,1]\n self.data = self.data.unsqueeze(1).float().div(255)\n\n # Normalize it with the usual MNIST mean and std\n self.data = self.data.sub_(0.1307).div_(0.3081)\n\n # Put both data and targets on GPU in advance\n self.data, self.targets = self.data.to(device), self.targets.to(device)\n self.targets = self.data.sum(dim=(2, 3))\n assert len(self.targets.shape) == 2\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\ndef compute_loss(output, target):\n err = output - target.float()\n return torch.sum(err * err) / 2 / output.shape[0]\n\n\nlogger = None\n\n\ndef log_scalars(metrics: Dict[str, Any], parent_tag: str = '') -> None:\n for tag in metrics:\n g.event_writer.add_scalar(tag=tag, scalar_value=metrics[tag], global_step=g.token_count)\n # g.event_writer.add_scalars(main_tag=tag, tag_scalar_dict=metrics, get_global_step=g.token_count)\n # try:\n # wandb.log(metrics, step=step)\n # except:\n # pass\n\n\ndef main():\n global logger, stats_data, stats_targets\n\n parser = argparse.ArgumentParser()\n # Data\n parser.add_argument('--dataset', type=str,\n choices=[DATASET_MNIST], default=DATASET_MNIST,\n help='name of dataset')\n parser.add_argument('--root', type=str, default='./data',\n help='root of dataset')\n parser.add_argument('--epochs', type=int, default=100,\n help='number of epochs to train')\n parser.add_argument('--batch_size', type=int, default=128,\n help='input batch size for training')\n parser.add_argument('--stats_batch_size', type=int, default=1,\n help='size of batch to use for second order statistics')\n parser.add_argument('--val_batch_size', type=int, default=128,\n help='input batch size for valing')\n parser.add_argument('--normalizing_data', action='store_true',\n help='[data pre processing] normalizing data')\n parser.add_argument('--random_crop', action='store_true',\n help='[data augmentation] random crop')\n parser.add_argument('--random_horizontal_flip', action='store_true',\n help='[data augmentation] random horizontal flip')\n # Training Settings\n parser.add_argument('--arch_file', type=str, default=None,\n help='name of file which defines the architecture')\n parser.add_argument('--arch_name', type=str, default='LeNet5',\n help='name of the architecture')\n parser.add_argument('--arch_args', type=json.loads, default=None,\n help='[JSON] arguments for the architecture')\n parser.add_argument('--optim_name', type=str, default=SecondOrderOptimizer.__name__,\n help='name of the optimizer')\n parser.add_argument('--optim_args', type=json.loads, default=None,\n help='[JSON] arguments for the optimizer')\n parser.add_argument('--curv_args', type=json.loads, default=None,\n help='[JSON] arguments for the curvature')\n # Options\n parser.add_argument('--download', action='store_true', default=True,\n help='if True, downloads the dataset (CIFAR-10 or 100) from the internet')\n parser.add_argument('--create_graph', action='store_true', default=False,\n help='create graph of the derivative')\n parser.add_argument('--no_cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1,\n help='random seed')\n parser.add_argument('--num_workers', type=int, default=0,\n help='number of sub processes for data loading')\n parser.add_argument('--log_interval', type=int, default=50,\n help='how many batches to wait before logging training status')\n parser.add_argument('--log_file_name', type=str, default='log',\n help='log file name')\n parser.add_argument('--checkpoint_interval', type=int, default=50,\n help='how many epochs to wait before logging training status')\n parser.add_argument('--resume', type=str, default=None,\n help='checkpoint path for resume training')\n parser.add_argument('--logdir', type=str, default='/temp/graph_test/run',\n help='dir to save output files')\n parser.add_argument('--out', type=str, default=None,\n help='dir to save output files')\n parser.add_argument('--config', default=None,\n help='config file path')\n parser.add_argument('--fisher_mc_approx', action='store_true', default=False,\n help='if True, Fisher is estimated by MC sampling')\n parser.add_argument('--fisher_num_mc', type=int, default=1,\n help='number of MC samples for estimating Fisher')\n parser.add_argument('--log_wandb', type=int, default=1,\n help='log to wandb')\n\n args = parser.parse_args()\n\n # get run name from logdir\n root_logdir=args.logdir\n count = 0\n while os.path.exists(f\"{root_logdir}{count:02d}\"):\n count += 1\n args.logdir = f\"{root_logdir}{count:02d}\"\n\n run_name = os.path.basename(args.logdir)\n\n assert args.out is None, \"Use args.logdir instead of args.out\"\n args.out = args.logdir\n\n # Copy this file & config to args.out\n if not os.path.isdir(args.out):\n os.makedirs(args.out)\n shutil.copy(os.path.realpath(__file__), args.out)\n\n # Load config file\n if args.config:\n with open(args.config) as f:\n config = json.load(f)\n dict_args = vars(args)\n dict_args.update(config)\n\n if args.config is not None:\n shutil.copy(args.config, args.out)\n if args.arch_file is not None:\n shutil.copy(args.arch_file, args.out)\n\n # Setup logger\n logger = Logger(args.out, args.log_file_name)\n logger.start()\n\n g.event_writer = SummaryWriter(args.logdir)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.log_wandb:\n wandb.init(project='test-graphs_test', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['config'] = args.config\n wandb.config['batch'] = args.batch_size\n wandb.config['optim'] = args.optim_name\n except Exception as e:\n if args.log_wandb:\n print(f\"wandb crash with {e}\")\n pass\n\n # Set device\n use_cuda = not args.no_cuda and torch.cuda.is_available()\n device = torch.device('cuda' if use_cuda else 'cpu')\n\n # Set random seed\n torch.manual_seed(args.seed)\n random.seed(args.seed)\n np.random.seed(args.seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(args.seed)\n\n # Setup data augmentation & data pre processing\n train_transforms, val_transforms = [], []\n if args.random_crop:\n train_transforms.append(transforms.RandomCrop(32, padding=4))\n\n if args.random_horizontal_flip:\n train_transforms.append(transforms.RandomHorizontalFlip())\n\n train_transforms.append(transforms.ToTensor())\n val_transforms.append(transforms.ToTensor())\n\n if args.normalizing_data:\n normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n train_transforms.append(normalize)\n val_transforms.append(normalize)\n\n train_transform = transforms.Compose(train_transforms)\n # val_transform = transforms.Compose(val_transforms)\n\n num_classes = 10\n dataset_class = SimpleMNIST\n\n class Net(nn.Module):\n def __init__(self, d, nonlin=True):\n super().__init__()\n self.layers = []\n self.all_layers = []\n self.d = d\n for i in range(len(d) - 1):\n linear = nn.Linear(d[i], d[i + 1], bias=False)\n self.layers.append(linear)\n self.all_layers.append(linear)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n return self.predict(x)\n\n def compute_layer_stats(layer):\n refreeze = False\n if hasattr(layer, 'frozen') and layer.frozen:\n u.unfreeze(layer)\n refreeze = True\n\n s = AttrDefault(str, {})\n n = args.stats_batch_size\n param = u.get_param(layer)\n _d = len(param.flatten()) # dimensionality of parameters\n layer_idx = model.layers.index(layer)\n # TODO: get layer type, include it in name\n assert layer_idx >= 0\n assert stats_data.shape[0] == n\n\n def backprop_loss():\n model.zero_grad()\n output = model(stats_data) # use last saved data batch for backprop\n loss = compute_loss(output, stats_targets)\n loss.backward()\n return loss, output\n\n def backprop_output():\n model.zero_grad()\n output = model(stats_data)\n output.backward(gradient=torch.ones_like(output))\n return output\n\n # per-example gradients, n, d\n _loss, _output = backprop_loss()\n At = layer.data_input\n Bt = layer.grad_output * n\n G = u.khatri_rao_t(At, Bt)\n g = G.sum(dim=0, keepdim=True) / n\n u.check_close(g, u.vec(param.grad).t())\n\n s.diversity = torch.norm(G, \"fro\") ** 2 / g.flatten().norm() ** 2\n s.grad_fro = g.flatten().norm()\n s.param_fro = param.data.flatten().norm()\n pos_activations = torch.sum(layer.data_output > 0)\n neg_activations = torch.sum(layer.data_output <= 0)\n s.a_sparsity = neg_activations.float()/(pos_activations+neg_activations) # 1 sparsity means all 0's\n activation_size = len(layer.data_output.flatten())\n s.a_magnitude = torch.sum(layer.data_output)/activation_size\n\n _output = backprop_output()\n B2t = layer.grad_output\n J = u.khatri_rao_t(At, B2t) # batch output Jacobian\n H = J.t() @ J / n\n\n s.hessian_l2 = u.l2_norm(H)\n s.jacobian_l2 = u.l2_norm(J)\n J1 = J.sum(dim=0) / n # single output Jacobian\n s.J1_l2 = J1.norm()\n\n # newton decrement\n def loss_direction(direction, eps):\n \"\"\"loss improvement if we take step eps in direction dir\"\"\"\n return u.to_python_scalar(eps * (direction @ g.t()) - 0.5 * eps ** 2 * direction @ H @ direction.t())\n\n s.regret_newton = u.to_python_scalar(g @ u.pinv(H) @ g.t() / 2)\n\n # TODO: gradient diversity is stuck at 1\n # TODO: newton/gradient angle\n # TODO: newton step magnitude\n s.grad_curvature = u.to_python_scalar(g @ H @ g.t()) # curvature in direction of g\n s.step_openai = u.to_python_scalar(s.grad_fro ** 2 / s.grad_curvature) if s.grad_curvature else 999\n\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n if refreeze:\n u.freeze(layer)\n return s\n\n train_dataset = dataset_class(\n root=gl.dataset, train=True, download=args.download, transform=train_transform)\n # val_dataset = t(root=args.root, train=False, download=args.download, transform=val_transform)\n\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)\n # val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.val_batch_size, shuffle=False, num_workers=args.num_workers)\n stats_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.stats_batch_size, shuffle=False, num_workers=args.num_workers)\n stats_data, stats_targets = next(iter(stats_loader))\n\n arch_kwargs = {} if args.arch_args is None else args.arch_args\n arch_kwargs['num_classes'] = num_classes\n\n model = Net([NUM_CHANNELS * IMAGE_SIZE ** 2, 8, 1], nonlin=False)\n setattr(model, 'num_classes', num_classes)\n model = model.to(device)\n param = u.get_param(model.layers[0])\n param.data.copy_(torch.ones_like(param)/len(param.data.flatten()))\n param = u.get_param(model.layers[1])\n param.data.copy_(torch.ones_like(param)/len(param.data.flatten()))\n\n u.freeze(model.layers[0])\n\n # use learning rate 0 to avoid changing parameter vector\n optim_kwargs = dict(lr=0.001, momentum=0.9, weight_decay=0, l2_reg=0,\n bias_correction=False, acc_steps=1,\n curv_type=\"Cov\", curv_shapes={\"Linear\": \"Kron\"},\n momentum_type=\"preconditioned\", update_inv=False, precondition_grad=False)\n curv_args = dict(damping=0, ema_decay=1)\n SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=curv_args) # call optimizer to add backward hoooks\n optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n\n start_epoch = 1\n\n # Run training\n for epoch in range(start_epoch, args.epochs + 1):\n num_examples_processed = epoch * len(train_loader) * train_loader.batch_size\n g.token_count = num_examples_processed\n for i in range(len(model.layers)):\n if i == 0:\n continue # skip initial expensive layer\n\n layer = model.layers[i]\n layer_stats = compute_layer_stats(layer)\n layer_name = f\"{i:02d}-{layer.__class__.__name__.lower()}\"\n log_scalars(u.nest_stats(f'stats/{layer_name}', layer_stats))\n\n # train\n accuracy, loss, confidence = train(model, device, train_loader, optimizer, epoch, args, logger)\n\n # save log\n iteration = epoch * len(train_loader)\n metrics = {'epoch': epoch, 'iteration': iteration,\n 'accuracy': accuracy, 'loss': loss,\n 'val_accuracy': 0, 'val_loss': 0,\n 'lr': optimizer.param_groups[0]['lr'],\n 'momentum': optimizer.param_groups[0].get('momentum', 0)}\n log_scalars(metrics)\n\n # save checkpoint\n if epoch % args.checkpoint_interval == 0 or epoch == args.epochs:\n path = os.path.join(args.out, 'epoch{}.ckpt'.format(epoch))\n data = {\n 'model': model.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'epoch': epoch\n }\n torch.save(data, path)\n\n\ndef train(model, device, train_loader, optimizer, epoch, args, logger):\n global global_data, global_target\n model.train()\n\n loss = None\n confidence = {'top1': 0, 'top1_true': 0, 'top1_false': 0, 'true': 0, 'false': 0}\n total_data_size = 0\n epoch_size = len(train_loader.dataset)\n num_iters_in_epoch = len(train_loader)\n base_num_iter = (epoch - 1) * num_iters_in_epoch\n\n last_elapsed_time = 0\n interval_ms = 0\n for batch_idx, (data, target) in enumerate(train_loader):\n data, target = data.to(device), target.to(device)\n global_data = data\n global_target = target\n\n for name, param in model.named_parameters():\n attr = 'p_pre_{}'.format(name)\n setattr(model, attr, param.detach().clone())\n\n # update params\n def closure():\n optimizer.zero_grad()\n output = model(data)\n loss = compute_loss(output, target)\n loss.backward(create_graph=args.create_graph)\n return loss, output\n\n loss, output = closure()\n optimizer.step()\n loss = loss.item()\n\n iteration = base_num_iter + batch_idx + 1\n total_data_size += len(data)\n\n if batch_idx % args.log_interval == 0:\n elapsed_time = logger.elapsed_time\n if last_elapsed_time:\n interval_ms = 1000 * (elapsed_time - last_elapsed_time)\n last_elapsed_time = elapsed_time\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}, '\n 'Accuracy: {:.0f}/{} ({:.2f}%), '\n 'Elapsed Time: {:.1f}s'.format(epoch, total_data_size, epoch_size, 100. * (batch_idx + 1) / num_iters_in_epoch, loss, 0, total_data_size, 0, elapsed_time))\n\n # save log\n lr = optimizer.param_groups[0]['lr']\n metrics = {'epoch': epoch, 'iteration': iteration, 'elapsed_time': elapsed_time,\n 'accuracy': 0, 'loss': loss, 'lr': lr, 'step_ms': interval_ms}\n\n log_scalars(metrics)\n for name, param in model.named_parameters():\n attr = 'p_pre_{}'.format(name)\n p_pre = getattr(model, attr)\n p_norm = param.norm().item()\n p_shape = list(param.size())\n p_pre_norm = p_pre.norm().item()\n if param.grad is not None:\n g_norm = param.grad.norm().item()\n else:\n g_norm = 0\n upd_norm = param.sub(p_pre).norm().item()\n noise_scale = getattr(param, 'noise_scale', 0)\n\n p_log = {'p_shape': p_shape[0], 'p_norm': p_norm, 'p_pre_norm': p_pre_norm,\n 'g_norm': g_norm, 'upd_norm': upd_norm, 'noise_scale': noise_scale}\n # print(p_log)\n metrics[name] = p_log\n log_scalars(u.nest_stats(f'kazuki/{name}', p_log))\n\n return 0, loss, confidence\n\n\ndef validate(model, device, val_loader, optimizer):\n model.eval()\n val_loss = 0\n correct = 0\n\n with torch.no_grad():\n for data, target in val_loader:\n\n data, target = data.to(device), target.to(device)\n\n if isinstance(optimizer, VIOptimizer):\n output = optimizer.prediction(data)\n else:\n output = model(data)\n\n val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n val_loss /= len(val_loader.dataset)\n val_accuracy = 100. * correct / len(val_loader.dataset)\n\n print('\\nEval: Average loss: {:.4f}, Accuracy: {:.0f}/{} ({:.2f}%)\\n'.format(\n val_loss, correct, len(val_loader.dataset), val_accuracy))\n\n return val_accuracy, val_loss\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/pytorch_benchmark.py", "content": "\"\"\"\n(pytorch_p36) [ec2-user@ip-172-31-6-232 cifar]$ python pytorch_benchmark.py\nMKL version b'Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications'\nPyTorch version 1.1.0\nScipy version: 1.3.0\nNumpy version: 1.16.4\nBenchmarking 1024-by-1024 matrix on cuda:0\n 882.84 svd\n 17.22 inv\n 227.04 pinv\n 452.77 eig\n 227.18 svd\n\n\nLaptop\n\nMKL version unknown\nPyTorch version 1.2.0\nScipy version: 1.2.1\nNumpy version: 1.16.4\nCPU version: unknown\nCPU logical cores: 8\nCPU physical cores: 4\nCPU physical sockets: 0\nBenchmarking 1024-by-1024 matrix on cpu\n 170.24 svd\n 22.41 inv\n 206.70 pinv\n 247.92 eig\n 180.16 pinverse\n 20.08 solve\n 124.89 svd\n 14.57 inv\n 197.24 pinv\n 221.06 eig\n 213.46 pinverse\n 21.75 solve\n\n\"\"\"\nimport os\nimport sys\nimport time\n\nimport numpy as np\n\nimport util as u\n\nimport torch\n\n# from @eamartin\ndef empty_aligned(n, align):\n \"\"\"Get n bytes of memory wih alignment align.\"\"\"\n a = np.empty(n + (align - 1), dtype=np.float32)\n data_align = a.ctypes.data % align\n offset = 0 if data_align == 0 else (align - data_align)\n return a[offset: offset + n]\n\n\ndef benchmark(method):\n\n start_time = time.time()\n times = []\n\n for i in range(1):\n if method == 'svd':\n _result = torch.svd(H)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'inv':\n _result = torch.inverse(H)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'pinv':\n _result = u.pinv(H)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'pinverse':\n _result = torch.pinverse(H)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'eig':\n _result = torch.symeig(H, eigenvectors=True)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'svd':\n _result = torch.svd(H)\n open('/dev/null', 'w').write(str(_result[0]))\n elif method == 'solve':\n _result = torch.solve(S, H)\n open('/dev/null', 'w').write(str(_result[0]))\n else:\n assert False\n new_time = time.time()\n elapsed_time = 1000 * (new_time - start_time)\n print(f\"{elapsed_time:8.2f} {method}\")\n start_time = new_time\n times.append(elapsed_time)\n\n\nif __name__ == '__main__':\n methods = ['svd', 'inv', 'pinv', 'eig', 'pinverse', 'solve']*2\n\n u.print_version_info()\n d = 1024\n\n x0 = torch.rand(d).reshape((d, 1)).float()\n\n X = torch.rand((d, 10000))\n Y = torch.rand((d, 10000))\n H = X @ X.t()\n S = Y @ Y.t()\n\n if torch.cuda.is_available():\n [x0, X, Y, H, S] = u.move_to_gpu([x0, X, Y, H, S])\n\n print(f\"Benchmarking {d}-by-{d} matrix on {x0.device}\")\n for method in methods:\n benchmark(method)\n\n# Other timings: svd\n# n=1000 Times: min: 126.04, median: 132.48\n# n=2000 Times: min: 573.03, median: 621.49\n# n=4096 Times: min: 5586.02, median: 6032.16\n# Other timings: inv\n# Times: min: 17.87, median: 23.41, mean: 27.90\n" }, { "path": "autotune/scipy_benchmark.py", "content": "\"\"\"\nMKL version b'Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications'\nPyTorch version 1.1.0\nScipy version: 1.3.0\nNumpy version: 1.16.4\nBenchmarking 1024-by-1024 matrix\n 246.61 gesdd\n 472.90 gesvd\n 160.03 eigh\n 22.65 inv\n 20.15 inv2\n 21.93 linsolve\n 1505.37 pinv\n 229.98 pinv2\n 173.93 pinvh\n 5800.83 lyapunov\n\"\"\"\n\nimport os\n\nfrom scipy import linalg # for svd\nimport numpy as np\nimport time\nimport sys\n\nimport util as u\n\n\n# from @eamartin\ndef empty_aligned(n, align):\n \"\"\"Get n bytes of memory wih alignment align.\"\"\"\n a = np.empty(n + (align - 1), dtype=np.float32)\n data_align = a.ctypes.data % align\n offset = 0 if data_align == 0 else (align - data_align)\n return a[offset: offset + n]\n\n\ndef benchmark(method, d):\n #x_old = np.random.randn(d * d).reshape((d, d)).astype(dtype=np.float32)\n #x = empty_aligned(d * d, 32).reshape((d, d))\n #x[:] = x_old\n #x = x @ x.T\n\n x0 = np.random.randn(d).reshape((d, 1)).astype(dtype=np.float32)\n\n X = np.random.random((d, 10000))\n Y = np.random.random((d, 10000))\n H = X @ X.T\n S = Y @ Y.T\n\n Xcov = H\n\n start_time = time.time()\n times = []\n\n for i in range(1):\n if method == 'gesdd':\n _result = linalg.svd(Xcov)\n elif method == 'gesvd':\n _result = linalg.svd(Xcov, lapack_driver='gesvd')\n elif method == 'eigh':\n _result = linalg.eigh(Xcov)\n elif method == 'inv':\n _result = linalg.inv(Xcov)\n elif method == 'inv2':\n _result = linalg.inv(Xcov, overwrite_a=True)\n elif method == 'linsolve':\n _result = linalg.solve(Xcov, x0)\n elif method == 'pinv':\n _result = linalg.pinv(Xcov)\n elif method == 'pinv2':\n _result = linalg.pinv2(Xcov)\n elif method == 'pinvh':\n _result = linalg.pinvh(Xcov)\n elif method == 'lyapunov':\n _result = linalg.solve_lyapunov(H, S)\n else:\n assert False\n new_time = time.time()\n elapsed_time = 1000 * (new_time - start_time)\n print(f\"{elapsed_time:8.2f} {method}\")\n start_time = new_time\n times.append(elapsed_time)\n\n\nif __name__ == '__main__':\n methods = ['gesdd', 'gesvd', 'eigh', 'inv', 'inv2', 'linsolve', 'pinv', 'pinv2', 'pinvh', 'lyapunov']\n\n u.print_version_info()\n d = 1024\n print(f\"Benchmarking {d}-by-{d} matrix\")\n\n for method in methods:\n benchmark(method, d)\n\n# Older timings:\n# Fastest way to compute eigenvectors for 4k matrix?\n#\n# Inverse on i3.metal\n# n=4096: 368 ms ± 1.51 ms per loop\n#\n# Xeon V3 benchmarks:\n# n=4096 eigs min: 27758.34, median: 28883.69\n# n=4096 gesdd min: 7241.70, median: 8477.95\n# n=4096 gesvd min=20487.48, median: 22057.64,\n# n=4096 inv min: 556.67, median: 579.25,\n# n=4096 linsolve: min: 534.40, median: 558.06, mean: 579.19\n#\n# Xeon V4:\n# n=4096 gesdd min: 5586.02, median: 6032.16\n#\n#\n# i7-5820K CPU @ 3.30GHz\n# n=4096 gesdd 7288.02, median: 7397.23, mean: 7478.78\n# n=4096 inv 520 msec\n#\n# after upgrading things\n# b'Intel(R) Math Kernel Library Version 2017.0.3 Product Build 20170413 for Intel(R) 64 architecture applications'\n# n=4096 inv 1427.54\n# Other timings: svd\n# n=1000 Times: min: 126.04, median: 132.48\n# n=2000 Times: min: 573.03, median: 621.49\n# n=4096 Times: min: 5586.02, median: 6032.16\n# Other timings: inv\n# Times: min: 17.87, median: 23.41, mean: 27.90\n" }, { "path": "autotune/svd_benchmark.py", "content": "# Fastest way to compute eigenvectors for 4k matrix?\n#\n# Inverse on i3.metal\n# n=4096: 368 ms ± 1.51 ms per loop\n# \n# Xeon V3 benchmarks:\n# n=4096 eigs min: 27758.34, median: 28883.69\n# n=4096 gesdd min: 7241.70, median: 8477.95\n# n=4096 gesvd min=20487.48, median: 22057.64,\n# n=4096 inv min: 556.67, median: 579.25,\n# n=4096 linsolve: min: 534.40, median: 558.06, mean: 579.19\n#\n# Xeon V4:\n# n=4096 gesdd min: 5586.02, median: 6032.16\n#\n#\n# i7-5820K CPU @ 3.30GHz\n# n=4096 gesdd 7288.02, median: 7397.23, mean: 7478.78\n# n=4096 inv 520 msec\n#\n# after upgrading things\n# b'Intel(R) Math Kernel Library Version 2017.0.3 Product Build 20170413 for Intel(R) 64 architecture applications'\n# n=4096 inv 1427.54\n\n\nfrom scipy import linalg # for svd\nimport numpy as np\nimport time\nimport sys\n\n\n# from @eamartin\ndef empty_aligned(n, align):\n \"\"\"Get n bytes of memory wih alignment align.\"\"\"\n a = np.empty(n + (align - 1), dtype=np.float32)\n data_align = a.ctypes.data % align\n offset = 0 if data_align == 0 else (align - data_align)\n return a[offset : offset + n]\n\n\ndef benchmark(method):\n n=1024\n x_old = np.random.randn(n*n).reshape((n,n)).astype(dtype=np.float32)\n x = empty_aligned(n*n, 32).reshape((n, n))\n x[:] = x_old\n x = x @ x.T\n\n x0 = np.random.randn(n).reshape((n,1)).astype(dtype=np.float32)\n\n start_time = time.time()\n times = []\n\n for i in range(1):\n if method == 'gesdd':\n result = linalg.svd(x)\n elif method == 'gesvd':\n result = linalg.svd(x, lapack_driver='gesvd')\n elif method == 'eigh':\n result = linalg.eigh(x)\n elif method == 'inv':\n result = linalg.inv(x)\n elif method == 'inv2':\n result = linalg.inv(x, overwrite_a=True)\n elif method == 'linsolve':\n result = linalg.solve(x, x0)\n else:\n assert False\n new_time = time.time()\n elapsed_time = 1000*(new_time - start_time)\n print(f\"elapsed_time:8.2f} {method}\")\n start_time = new_time\n times.append(elapsed_time)\n\n\nif __name__=='__main__':\n methods = ['gesdd', 'gesvd', 'eigh', 'inv', 'inv2', 'linsolve']\n\n for method in methods:\n benchmark(method)\n\n\n\n\n# Other timings: svd\n# n=1000 Times: min: 126.04, median: 132.48\n# n=2000 Times: min: 573.03, median: 621.49\n# n=4096 Times: min: 5586.02, median: 6032.16\n# Other timings: inv\n# Times: min: 17.87, median: 23.41, mean: 27.90\n" }, { "path": "autotune/test/gesvd_crash.txt", "content": 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}, { "path": "autotune/train_ciresan.py", "content": "# Train Ciresan's 6-layer deep MNIST network\n# (from http://yann.lecun.com/exdb/mnist/)\n\nimport argparse\nimport os\nimport sys\nimport time\nfrom typing import Callable, List\n\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport torch.nn as nn\nimport torchcontrib\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\nimport os\nimport argparse\nfrom importlib import import_module\nimport shutil\nimport json\n\nimport torch\nimport torch.nn.functional as F\nimport wandb\nfrom torchvision import datasets, transforms, models\nimport torchcurv\nfrom torchcurv.optim import SecondOrderOptimizer, VIOptimizer\nfrom torchcurv.utils import Logger\n\n\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=1, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/tmp/runs/curv_train_tiny/run')\n\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--bias', type=int, default=1, help=\"whether to add bias between layers\")\n\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--hess_samples', type=int, default=1, help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=1, help='skip all stats collection')\n\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=5, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--train_batch_size', type=int, default=64)\n parser.add_argument('--stats_batch_size', type=int, default=10000)\n parser.add_argument('--lr', type=float, default=1e-3)\n parser.add_argument('--weight_decay', type=float, default=1e-5)\n parser.add_argument('--momentum', type=float, default=0.9)\n parser.add_argument('--dropout', type=int, default=0)\n parser.add_argument('--swa', type=int, default=1)\n parser.add_argument('--lmb', type=float, default=1e-3)\n parser.add_argument('--uniform', type=int, default=0, help=\"all layers same size\")\n parser.add_argument('--redundancy', type=int, default=0, help=\"duplicate all layers this many times\")\n args = parser.parse_args()\n\n attemp_count = 0\n while os.path.exists(f\"{args.logdir}{attemp_count:02d}\"):\n attemp_count += 1\n logdir = f\"{args.logdir}{attemp_count:02d}\"\n\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {run_name}\")\n u.seed_random(1)\n\n d1 = 28*28\n if args.uniform:\n d = [784, 784, 784, 784, 784, 784, 10]\n else:\n d = [784, 2500, 2000, 1500, 1000, 500, 10]\n o = 10\n n = args.stats_batch_size\n if args.redundancy:\n model = u.RedundantFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout, redundancy=args.redundancy)\n else:\n model = u.SimpleFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout)\n model = model.to(gl.device)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.wandb:\n wandb.init(project='train_ciresan', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['redundancy'] = args.redundancy\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, original_targets=True,\n dataset_size=args.dataset_size)\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n assert not args.full_batch, \"fixme: validation still uses stats_iter\"\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n else:\n stats_iter = None\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False, original_targets=True,\n dataset_size=args.dataset_size)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=False)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n\n gl.token_count = 0\n last_outer = 0\n for step in range(args.stats_steps):\n epoch = gl.token_count // 60000\n print(gl.token_count)\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n # compute validation loss\n model.eval()\n if args.swa:\n with u.timeit('swa'):\n base_opt = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n opt = torchcontrib.optim.SWA(base_opt, swa_start=0, swa_freq=1, swa_lr=args.lr)\n for _ in range(100):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n opt.step()\n opt.swap_swa_sgd()\n\n with u.timeit(\"validate\"):\n val_accuracy, val_loss = validate(model, test_loader, f'test (epoch {epoch})')\n train_accuracy, train_loss = validate(model, stats_loader, f'train (epoch {epoch})')\n\n # save log\n metrics = {'epoch': epoch, 'val_accuracy': val_accuracy, 'val_loss': val_loss,\n 'train_loss': train_loss, 'train_accuracy': train_accuracy,\n 'lr': optimizer.param_groups[0]['lr'],\n 'momentum': optimizer.param_groups[0].get('momentum', 0)}\n u.log_scalars(metrics)\n\n # compute stats\n if args.full_batch:\n data, targets = dataset.data, dataset.targets\n else:\n data, targets = next(stats_iter)\n\n model.skip_forward_hooks = False\n model.skip_backward_hooks = False\n\n # get gradient values\n with u.timeit(\"backprop_g\"):\n gl.backward_idx = 0\n u.clear_backprops(model)\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n u.log_scalar(loss=loss.item())\n\n # get Hessian values\n hessian_activations = []\n hessian_backprops = []\n hessians = [] # list of Hessians in Kronecker form\n\n model.skip_forward_hooks = True\n for (i, layer) in enumerate(model.layers):\n if args.skip_stats:\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops[0] * n\n assert B_t.shape == (n, d[i + 1])\n\n G = (B_t, A_t)\n # g = G.sum(dim=0, keepdim=True) / n # average gradient\n g = u.kron_sum(G) / n\n assert g.shape == (1, d[i] * d[i + 1])\n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel()\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n with u.timeit(f'sigma-{i}'):\n # efisher = u.kron_cov(G) # G.t() @ G / n\n sigma = u.kron_sigma(G, g) # efisher - g.t() @ g\n s.sigma_l2 = u.kron_sym_l2_norm(sigma)\n s.sigma_erank = u.kron_trace(sigma)/s.sigma_l2 # torch.trace(sigma)/s.sigma_l2\n\n #############################\n # Hessian stats\n #############################\n\n # this is a pair of left/right Kronecker fctors\n H = hessians[i]\n\n with u.timeit(f\"invH-{i}\"):\n invH = u.kron_inverse(H)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.kron_sym_l2_norm(H)\n s.iH_l2 = u.kron_sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = u.kron_fro_norm(H)\n s.invH_fro = u.kron_fro_norm(invH)\n s.grad_fro = u.kron_fro_norm(g) # g.flatten().norm()\n s.param_fro = layer.weight.data.flatten().norm()\n\n u.kron_nan_check(H)\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = u.kron_pinv(H)\n\n def kron_curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd, using factored form\"\"\"\n # dd @ H @ dd.t(), computed by kron_quadratic_form(H, dd)\n return u.to_python_scalar(u.kron_quadratic_form(H, dd) / (dd.flatten().norm() ** 2))\n\n def kron_loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n\n # kron_matmul(dd, g) = dd @ g.t()\n return u.to_python_scalar(eps * (u.kron_matmul(dd, g)) - 0.5 * eps ** 2 * u.kron_quadratic_form(H, dd))\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = kron_curv_direction(g)\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 999\n s.step_max = 2 / s.H_l2\n s.step_min = torch.tensor(2) / u.kron_trace(H)\n\n s.regret_gradient = kron_loss_direction(g, s.step_openai)\n\n with u.timeit(f\"batch-{i}\"):\n # torch.trace(H @ sigma) # (g @ H @ g.t())\n s.batch_openai = u.kron_trace_matmul(H, sigma) / u.kron_quadratic_form(H, g)\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2\n\n # torch.trace(H)\n s.H_erank = u.kron_trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n\n u.log_scalars(u.nest_stats(layer.name, s))\n\n # gradient steps\n model.train()\n last_inner = 0\n for i in range(args.train_steps):\n if last_inner:\n u.log_scalars({\"time/inner\": 1000*(time.perf_counter() - last_inner)})\n last_inner = time.perf_counter()\n\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\ndef validate(model, val_loader, tag='validation'):\n model.eval()\n val_loss = 0\n correct = 0\n\n with torch.no_grad():\n for data, target in val_loader:\n\n data, target = data.to(gl.device), target.to(gl.device)\n\n output = model(data)\n val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n val_loss /= len(val_loader.dataset)\n val_accuracy = 100. * correct / len(val_loader.dataset)\n\n # TODO(y) log scalar here\n print(f'Eval: Average {tag} loss: {val_loss:.4f}, Accuracy: {correct:.0f}/{len(val_loader.dataset)} ({val_accuracy:.2f}%)')\n\n return val_accuracy, val_loss\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/train_ciresan_cca.py", "content": "# Train Ciresan's 6-layer deep MNIST network\n# (from http://yann.lecun.com/exdb/mnist/)\n\nimport argparse\nimport os\nimport sys\nimport time\nfrom typing import Callable, List\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport torch.nn as nn\nimport torchcontrib\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\nimport os\nimport argparse\nfrom importlib import import_module\nimport shutil\nimport json\n\nimport torch\nimport torch.nn.functional as F\nimport wandb\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/tmp/runs/curv_train_tiny/run')\n\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--bias', type=int, default=1, help=\"whether to add bias between layers\")\n\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--hess_samples', type=int, default=1, help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=1, help='skip all stats collection')\n\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=1000, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--lr', type=float, default=1e-3)\n parser.add_argument('--weight_decay', type=float, default=2e-5)\n parser.add_argument('--momentum', type=float, default=0.9)\n parser.add_argument('--dropout', type=int, default=0)\n parser.add_argument('--swa', type=int, default=0)\n parser.add_argument('--lmb', type=float, default=1e-3)\n\n parser.add_argument('--train_batch_size', type=int, default=64)\n parser.add_argument('--stats_batch_size', type=int, default=10000)\n parser.add_argument('--uniform', type=int, default=0, help='use uniform architecture (all layers same size)')\n parser.add_argument('--run_name', type=str, default='noname')\n\n gl.args = parser.parse_args()\n args = gl.args\n u.seed_random(1)\n\n gl.project_name = 'train_ciresan'\n u.setup_logdir_and_event_writer(args.run_name)\n print(f\"Logging to {gl.logdir}\")\n\n d1 = 28*28\n if args.uniform:\n d = [784, 784, 784, 784, 784, 784, 10]\n else:\n d = [784, 2500, 2000, 1500, 1000, 500, 10]\n o = 10\n n = args.stats_batch_size\n model = u.SimpleFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout)\n model = model.to(gl.device)\n\n\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, original_targets=True,\n dataset_size=args.dataset_size)\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n assert not args.full_batch, \"fixme: validation still uses stats_iter\"\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n else:\n stats_iter = None\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False, original_targets=True,\n dataset_size=args.dataset_size)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=False)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n autograd_lib.add_hooks(model)\n autograd_lib.disable_hooks()\n\n gl.token_count = 0\n last_outer = 0\n for step in range(args.stats_steps):\n epoch = gl.token_count // 60000\n print(gl.token_count)\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n # compute validation loss\n if args.swa:\n model.eval()\n with u.timeit('swa'):\n base_opt = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n opt = torchcontrib.optim.SWA(base_opt, swa_start=0, swa_freq=1, swa_lr=args.lr)\n for _ in range(100):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n opt.step()\n opt.swap_swa_sgd()\n\n with u.timeit(\"validate\"):\n val_accuracy, val_loss = validate(model, test_loader, f'test (epoch {epoch})')\n train_accuracy, train_loss = validate(model, stats_loader, f'train (epoch {epoch})')\n\n # save log\n metrics = {'epoch': epoch, 'val_accuracy': val_accuracy, 'val_loss': val_loss,\n 'train_loss': train_loss, 'train_accuracy': train_accuracy,\n 'lr': optimizer.param_groups[0]['lr'],\n 'momentum': optimizer.param_groups[0].get('momentum', 0)}\n u.log_scalars(metrics)\n\n # compute stats\n if args.full_batch:\n data, targets = dataset.data, dataset.targets\n else:\n data, targets = next(stats_iter)\n\n if not args.skip_stats:\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type='CrossEntropy')\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2')\n autograd_lib.compute_stats_factored(model)\n\n for (i, layer) in enumerate(model.layers):\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n\n if param is None:\n continue\n\n if not hasattr(param, 'stats'):\n continue\n s = param.stats\n param_name = param_names[param]\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n # gradient steps\n model.train()\n last_inner = 0\n for i in range(args.train_steps):\n if last_inner:\n u.log_scalars({\"time/inner\": 1000*(time.perf_counter() - last_inner)})\n last_inner = time.perf_counter()\n\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\ndef validate(model, val_loader, tag='validation'):\n model.eval()\n val_loss = 0\n correct = 0\n\n with torch.no_grad():\n for data, target in val_loader:\n\n data, target = data.to(gl.device), target.to(gl.device)\n\n output = model(data)\n val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n val_loss /= len(val_loader.dataset)\n val_accuracy = 100. * correct / len(val_loader.dataset)\n\n # TODO(y) log scalar here\n print(f'Eval: Average {tag} loss: {val_loss:.4f}, Accuracy: {correct:.0f}/{len(val_loader.dataset)} ({val_accuracy:.2f}%)')\n\n return val_accuracy, val_loss\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/train_ciresan_factored.py", "content": "# Train Ciresan's 6-layer deep MNIST network\n# (from http://yann.lecun.com/exdb/mnist/)\n\nimport argparse\nimport os\nimport sys\nimport time\nfrom typing import Callable, List\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport torch.nn as nn\nimport torchcontrib\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\nimport os\nimport argparse\nfrom importlib import import_module\nimport shutil\nimport json\n\nimport torch\nimport torch.nn.functional as F\nimport wandb\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/tmp/runs/curv_train_tiny/run')\n\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--bias', type=int, default=1, help=\"whether to add bias between layers\")\n\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--hess_samples', type=int, default=1, help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=1, help='skip all stats collection')\n\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=1000, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--lr', type=float, default=1e-3)\n parser.add_argument('--weight_decay', type=float, default=2e-5)\n parser.add_argument('--momentum', type=float, default=0.9)\n parser.add_argument('--dropout', type=int, default=0)\n parser.add_argument('--swa', type=int, default=0)\n parser.add_argument('--lmb', type=float, default=1e-3)\n\n parser.add_argument('--train_batch_size', type=int, default=64)\n parser.add_argument('--stats_batch_size', type=int, default=100)\n parser.add_argument('--stats_num_batches', type=int, default=10)\n parser.add_argument('--uniform', type=int, default=0, help='use uniform architecture (all layers same size)')\n parser.add_argument('--run_name', type=str, default='noname')\n parser.add_argument('--disable_hess', type=int, default=1, help='disable hessian because of sysmqrt slowness')\n\n gl.args = parser.parse_args()\n args = gl.args\n gl.hacks_disable_hess = args.disable_hess\n u.seed_random(1)\n\n gl.project_name = 'train_ciresan'\n u.setup_logdir_and_event_writer(args.run_name)\n print(f\"Logging to {gl.logdir}\")\n\n d1 = 28*28\n if args.uniform:\n d = [784, 784, 784, 784, 784, 784, 10]\n else:\n d = [784, 2500, 2000, 1500, 1000, 500, 10]\n o = 10\n n = args.stats_batch_size\n model = u.SimpleFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout)\n model = model.to(gl.device)\n\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, original_targets=True,\n dataset_size=args.dataset_size)\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n assert not args.full_batch, \"fixme: validation still uses stats_iter\"\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n else:\n stats_iter = None\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False, original_targets=True,\n dataset_size=args.dataset_size)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=False)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n autograd_lib.add_hooks(model)\n autograd_lib.disable_hooks()\n\n gl.token_count = 0\n last_outer = 0\n for step in range(args.stats_steps):\n epoch = gl.token_count // 60000\n print(gl.token_count)\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"validate\"):\n val_accuracy, val_loss = validate(model, test_loader, f'test (epoch {epoch})')\n train_accuracy, train_loss = validate(model, stats_loader, f'train (epoch {epoch})')\n\n # save log\n metrics = {'epoch': epoch, 'val_accuracy': val_accuracy, 'val_loss': val_loss,\n 'train_loss': train_loss, 'train_accuracy': train_accuracy,\n 'lr': optimizer.param_groups[0]['lr'],\n 'momentum': optimizer.param_groups[0].get('momentum', 0)}\n u.log_scalars(metrics)\n\n # compute stats\n if args.full_batch:\n data, targets = dataset.data, dataset.targets\n else:\n data, targets = next(stats_iter)\n\n if not args.skip_stats:\n autograd_lib.compute_cov(model, loss_fn, stats_iter, batch_size=args.stats_batch_size, steps=args.stats_num_batches)\n\n for (i, layer) in enumerate(model.layers):\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n cov: autograd_lib.LayerCov = layer.cov\n s = autograd_lib.LayerStats()\n # s.H_l2 = cov.H.value().sym_l2_norm()\n #s.J_trace = cov.J.value().trace()\n # setattr(s, 'J_trace', cov.J.value().trace())\n # print(f\"layer-{i}={s.J_trace}\")\n # print('log(prob(dep))', np.log(cov.J.prob_dep()))\n print('sigmas', cov.J.sigmas_indep())\n setattr(s, 'J_indep', cov.J.sigmas_indep())\n setattr(s, 'S_indep', cov.S.sigmas_indep())\n u.log_scalars(u.nest_stats(f\"layer-{i}\", s))\n\n # gradient steps\n model.train()\n last_inner = 0\n for i in range(args.train_steps):\n if last_inner:\n u.log_scalars({\"time/inner\": 1000*(time.perf_counter() - last_inner)})\n last_inner = time.perf_counter()\n\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\ndef validate(model, val_loader, tag='validation'):\n model.eval()\n val_loss = 0\n correct = 0\n\n with torch.no_grad():\n for data, target in val_loader:\n\n data, target = data.to(gl.device), target.to(gl.device)\n\n output = model(data)\n val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n val_loss /= len(val_loader.dataset)\n val_accuracy = 100. * correct / len(val_loader.dataset)\n\n # TODO(y) log scalar here\n print(f'Eval: Average {tag} loss: {val_loss:.4f}, Accuracy: {correct:.0f}/{len(val_loader.dataset)} ({val_accuracy:.2f}%)')\n\n return val_accuracy, val_loss\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/train_ciresan_new.py", "content": "# TODO(y): FRI -- go over all formulas and results from ciresan run\n# TODO(y): add angle historgam\n# Train Ciresan's 6-layer deep MNIST network.\n# (from http://yann.lecun.com/exdb/mnist/)\n\nimport argparse\nimport copy\nimport os\nimport sys\nimport time\nimport traceback\nfrom collections import defaultdict\nfrom typing import Callable, List\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport torch.nn as nn\nimport torchcontrib\nimport wandb\nfrom attrdict import AttrDefault, AttrDict\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\nimport os\nimport argparse\nfrom importlib import import_module\nimport shutil\nimport json\n\nimport torch\nimport torch.nn.functional as F\nimport wandb\n\nprint('asdfsdf')\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\nu.install_pdb_handler()\n\n\ndef skip_nans(t): return t[torch.isfinite(t)]\n\n\ndef erank(vals): return vals.sum() / vals.max()\n\ndef srank(vals): return (vals * vals).sum() / (vals.max() ** 2)\n\n\n@profile\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/tmp/runs/curv_train_tiny/run')\n\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--bias', type=int, default=1, help=\"whether to add bias between layers\")\n\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--hess_samples', type=int, default=1,\n help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=100, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--lr', type=float, default=1e-3)\n parser.add_argument('--weight_decay', type=float, default=0)\n parser.add_argument('--momentum', type=float, default=0.9)\n parser.add_argument('--dropout', type=int, default=0)\n parser.add_argument('--swa', type=int, default=0)\n parser.add_argument('--lmb', type=float, default=1e-3)\n\n parser.add_argument('--train_batch_size', type=int, default=64)\n parser.add_argument('--stats_batch_size', type=int, default=10000)\n parser.add_argument('--stats_num_batches', type=int, default=1)\n parser.add_argument('--run_name', type=str, default='noname')\n parser.add_argument('--launch_blocking', type=int, default=0)\n parser.add_argument('--sampled', type=int, default=0)\n parser.add_argument('--curv', type=str, default='kfac',\n help='decomposition to use for curvature estimates: zero_order, kfac, isserlis or full')\n parser.add_argument('--log_spectra', type=int, default=0)\n\n u.seed_random(1)\n gl.args = parser.parse_args()\n args = gl.args\n u.seed_random(1)\n\n gl.project_name = 'train_ciresan'\n u.setup_logdir_and_event_writer(args.run_name)\n print(f\"Logging to {gl.logdir}\")\n\n d1 = 28 * 28\n d = [784, 2500, 2000, 1500, 1000, 500, 10]\n\n # number of samples per datapoint. Used to normalize kfac\n model = u.SimpleFullyConnected2(d, nonlin=args.nonlin, bias=args.bias, dropout=args.dropout)\n model = model.to(gl.device)\n autograd_lib.register(model)\n\n assert args.dataset_size >= args.stats_batch_size\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, original_targets=True,\n dataset_size=args.dataset_size)\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n assert not args.full_batch, \"fixme: validation still uses stats_iter\"\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=True,\n drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n else:\n stats_iter = None\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False,\n original_targets=True,\n dataset_size=args.dataset_size)\n test_eval_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False,\n drop_last=False)\n train_eval_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False,\n drop_last=False)\n\n loss_fn = torch.nn.CrossEntropyLoss()\n autograd_lib.add_hooks(model)\n autograd_lib.disable_hooks()\n\n gl.token_count = 0\n last_outer = 0\n\n for step in range(args.stats_steps):\n epoch = gl.token_count // 60000\n lr = optimizer.param_groups[0]['lr']\n print('token_count', gl.token_count)\n if last_outer:\n u.log_scalars({\"time/outer\": 1000 * (time.perf_counter() - last_outer)})\n print(f'time: {time.perf_counter() - last_outer:.2f}')\n last_outer = time.perf_counter()\n\n with u.timeit(\"validate\"):\n val_accuracy, val_loss = validate(model, test_eval_loader, f'test (epoch {epoch})')\n train_accuracy, train_loss = validate(model, train_eval_loader, f'train (epoch {epoch})')\n\n # save log\n metrics = {'epoch': epoch, 'val_accuracy': val_accuracy, 'val_loss': val_loss,\n 'train_loss': train_loss, 'train_accuracy': train_accuracy,\n 'lr': optimizer.param_groups[0]['lr'],\n 'momentum': optimizer.param_groups[0].get('momentum', 0)}\n u.log_scalars(metrics)\n\n def mom_update(buffer, val):\n buffer *= 0.9\n buffer += val * 0.1\n\n if not args.skip_stats:\n # number of samples passed through\n n = args.stats_batch_size * args.stats_num_batches\n\n # quanti\n forward_stats = defaultdict(lambda: AttrDefault(float))\n\n hessians = defaultdict(lambda: AttrDefault(float))\n jacobians = defaultdict(lambda: AttrDefault(float))\n fishers = defaultdict(lambda: AttrDefault(float)) # empirical fisher/gradient\n quad_fishers = defaultdict(lambda: AttrDefault(float)) # gradient statistics that depend on fisher (4th order moments)\n train_regrets = defaultdict(list)\n test_regrets1 = defaultdict(list)\n test_regrets2 = defaultdict(list)\n train_regrets_opt = defaultdict(list)\n test_regrets_opt = defaultdict(list)\n cosines = defaultdict(list)\n dot_products = defaultdict(list)\n hessians_histograms = defaultdict(lambda: AttrDefault(u.MyList))\n jacobians_histograms = defaultdict(lambda: AttrDefault(u.MyList))\n fishers_histograms = defaultdict(lambda: AttrDefault(u.MyList))\n quad_fishers_histograms = defaultdict(lambda: AttrDefault(u.MyList))\n\n current = None\n current_histograms = None\n\n for i in range(args.stats_num_batches):\n activations = {}\n backprops = {}\n\n def save_activations(layer, A, _):\n activations[layer] = A\n forward_stats[layer].AA += torch.einsum(\"ni,nj->ij\", A, A)\n\n print('forward')\n with u.timeit(\"stats_forward\"):\n with autograd_lib.module_hook(save_activations):\n data, targets = next(stats_iter)\n output = model(data)\n loss = loss_fn(output, targets) * len(output)\n\n def compute_stats(layer, _, B):\n A = activations[layer]\n if current == fishers:\n backprops[layer] = B\n\n # about 27ms per layer\n with u.timeit('compute_stats'):\n current[layer].BB += torch.einsum(\"ni,nj->ij\", B, B) # TODO(y): index consistency\n current[layer].diag += torch.einsum(\"ni,nj->ij\", B * B, A * A)\n current[layer].BA += torch.einsum(\"ni,nj->ij\", B, A)\n current[layer].a += torch.einsum(\"ni->i\", A)\n current[layer].b += torch.einsum(\"nk->k\", B)\n current[layer].norm2 += ((A * A).sum(dim=1) * (B * B).sum(dim=1)).sum()\n\n # compute curvatures in direction of all gradiennts\n if current is fishers:\n assert args.stats_num_batches == 1, \"not tested on more than one stats step, currently reusing aggregated moments\"\n hess = hessians[layer]\n jac = jacobians[layer]\n Bh, Ah = B @ hess.BB / n, A @ forward_stats[layer].AA / n\n Bj, Aj = B @ jac.BB / n, A @ forward_stats[layer].AA / n\n norms = ((A * A).sum(dim=1) * (B * B).sum(dim=1))\n\n current[layer].min_norm2 = min(norms)\n current[layer].median_norm2 = torch.median(norms)\n current[layer].max_norm2 = max(norms)\n\n norms2_hess = ((Ah * A).sum(dim=1) * (Bh * B).sum(dim=1))\n norms2_jac = ((Aj * A).sum(dim=1) * (Bj * B).sum(dim=1))\n\n current[layer].norm += norms.sum()\n current_histograms[layer].norms.extend(torch.sqrt(norms))\n current[layer].curv_hess += (skip_nans(norms2_hess / norms)).sum()\n current_histograms[layer].curv_hess.extend(skip_nans(norms2_hess / norms))\n current[layer].curv_hess_max += (skip_nans(norms2_hess / norms)).max()\n current[layer].curv_hess_median += (skip_nans(norms2_hess / norms)).median()\n\n current_histograms[layer].curv_jac.extend(skip_nans(norms2_jac / norms))\n current[layer].curv_jac += (skip_nans(norms2_jac / norms)).sum()\n current[layer].curv_jac_max += (skip_nans(norms2_jac / norms)).max()\n current[layer].curv_jac_median += (skip_nans(norms2_jac / norms)).median()\n\n current[layer].a_sparsity += torch.sum(A <= 0).float() / A.numel()\n current[layer].b_sparsity += torch.sum(B <= 0).float() / B.numel()\n\n current[layer].mean_activation += torch.mean(A)\n current[layer].mean_activation2 += torch.mean(A*A)\n current[layer].mean_backprop = torch.mean(B)\n current[layer].mean_backprop2 = torch.mean(B*B)\n\n current[layer].norms_hess += torch.sqrt(norms2_hess).sum()\n current_histograms[layer].norms_hess.extend(torch.sqrt(norms2_hess))\n current[layer].norms_jac += norms2_jac.sum()\n current_histograms[layer].norms_jac.extend(torch.sqrt(norms2_jac))\n\n normalized_moments = copy.copy(hessians[layer])\n normalized_moments.AA = forward_stats[layer].AA\n normalized_moments = u.divide_attributes(normalized_moments, n)\n\n train_regrets_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=0, m=normalized_moments,\n approx=args.curv)\n test_regrets1_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=1, m=normalized_moments,\n approx=args.curv)\n test_regrets2_ = autograd_lib.offset_losses(A, B, alpha=lr, offset=2, m=normalized_moments,\n approx=args.curv)\n test_regrets_opt_ = autograd_lib.offset_losses(A, B, alpha=None, offset=2,\n m=normalized_moments, approx=args.curv)\n train_regrets_opt_ = autograd_lib.offset_losses(A, B, alpha=None, offset=0,\n m=normalized_moments, approx=args.curv)\n cosines_ = autograd_lib.offset_cosines(A, B)\n train_regrets[layer].extend(train_regrets_)\n test_regrets1[layer].extend(test_regrets1_)\n test_regrets2[layer].extend(test_regrets2_)\n train_regrets_opt[layer].extend(train_regrets_opt_)\n test_regrets_opt[layer].extend(test_regrets_opt_)\n cosines[layer].extend(cosines_)\n dot_products[layer].extend(autograd_lib.offset_dotprod(A, B))\n\n # statistics of the form g.Sigma.g\n elif current == quad_fishers:\n hess = hessians[layer]\n sigma = fishers[layer]\n jac = jacobians[layer]\n Bs, As = B @ sigma.BB / n, A @ forward_stats[layer].AA / n\n Bh, Ah = B @ hess.BB / n, A @ forward_stats[layer].AA / n\n Bj, Aj = B @ jac.BB / n, A @ forward_stats[layer].AA / n\n\n norms = ((A * A).sum(dim=1) * (B * B).sum(dim=1))\n norms2_hess = ((Ah * A).sum(dim=1) * (Bh * B).sum(dim=1))\n norms2_jac = ((Aj * A).sum(dim=1) * (Bj * B).sum(dim=1))\n norms_sigma = ((As * A).sum(dim=1) * (Bs * B).sum(dim=1))\n\n current[layer].norm += norms.sum() # TODO(y) remove, redundant with norm2 above\n current[layer].curv_sigma += (skip_nans(norms_sigma / norms)).sum()\n current[layer].curv_sigma_max = skip_nans(norms_sigma / norms).max()\n current[layer].curv_sigma_median = skip_nans(norms_sigma / norms).median()\n current[layer].curv_hess += skip_nans(norms2_hess / norms).sum()\n current[layer].curv_hess_max += skip_nans(norms2_hess / norms).max()\n current[layer].lyap_hess_mean += skip_nans(norms_sigma / norms2_hess).mean()\n current[layer].lyap_hess_max = max(skip_nans(norms_sigma/norms2_hess))\n current[layer].lyap_jac_mean += skip_nans(norms_sigma / norms2_jac).mean()\n current[layer].lyap_jac_max = max(skip_nans(norms_sigma/norms2_jac))\n\n print('backward')\n with u.timeit(\"backprop_H\"):\n with autograd_lib.module_hook(compute_stats):\n current = hessians\n current_histograms = hessians_histograms\n autograd_lib.backward_hessian(output, loss='CrossEntropy', sampled=args.sampled,\n retain_graph=True) # 600 ms\n current = jacobians\n current_histograms = jacobians_histograms\n autograd_lib.backward_jacobian(output, sampled=args.sampled, retain_graph=True) # 600 ms\n current = fishers\n current_histograms = fishers_histograms\n model.zero_grad()\n loss.backward(retain_graph=True) # 60 ms\n current = quad_fishers\n current_histograms = quad_fishers_histograms\n model.zero_grad()\n loss.backward() # 60 ms\n\n print('summarize')\n for (i, layer) in enumerate(model.layers):\n stats_dict = {'hessian': hessians, 'jacobian': jacobians, 'fisher': fishers}\n\n # evaluate stats from\n # https://app.wandb.ai/yaroslavvb/train_ciresan/runs/425pu650?workspace=user-yaroslavvb\n for stats_name in stats_dict:\n s = AttrDict()\n stats = stats_dict[stats_name][layer]\n\n for key in forward_stats[layer]:\n # print(f'copying {key} in {stats_name}, {layer}')\n try:\n assert stats[key] == float()\n except:\n f\"Trying to overwrite {key} in {stats_name}, {layer}\"\n stats[key] = forward_stats[layer][key]\n\n diag: torch.Tensor = stats.diag / n\n\n # jacobian:\n # curv in direction of gradient goes down to roughly 0.3-1\n # maximum curvature goes up to 1000-2000\n #\n # Hessian:\n # max curv goes down to 1, in direction of gradient 0.0001\n\n s.diag_l2 = torch.max(diag) # 40 - 3000 smaller than kfac l2 for jac\n s.diag_fro = torch.norm(\n diag) # jacobian grows to 0.5-1.5, rest falls, layer-5 has phase transition, layer-4 also\n s.diag_trace = diag.sum() # jacobian grows 0-1000 (first), 0-150 (last). Almost same as kfac_trace (771 vs 810 kfac). Jacobian has up/down phase transition\n s.diag_average = diag.mean()\n\n # normalize for mean loss\n BB = stats.BB / n\n AA = stats.AA / n\n # A_evals, _ = torch.symeig(AA) # averaging 120ms per hit, 90 hits\n # B_evals, _ = torch.symeig(BB)\n\n # s.kfac_l2 = torch.max(A_evals) * torch.max(B_evals) # 60x larger than diag_l2. layer0/hess has down/up phase transition. layer5/jacobian has up/down phase transition\n s.kfac_trace = torch.trace(AA) * torch.trace(BB) # 0/hess down/up tr, 5/jac sharp phase transition\n s.kfac_fro = torch.norm(stats.AA) * torch.norm(\n stats.BB) # 0/hess has down/up tr, 5/jac up/down transition\n # s.kfac_erank = s.kfac_trace / s.kfac_l2 # first layer has 25, rest 15, all layers go down except last, last noisy\n # s.kfac_erank_fro = s.kfac_trace / s.kfac_fro / max(stats.BA.shape)\n\n s.diversity = (stats.norm2 / n) / u.norm_squared(\n stats.BA / n) # gradient diversity. Goes up 3x. Bottom layer has most diversity. Jacobian diversity much less noisy than everythingelse\n\n # discrepancy of KFAC based on exact values of diagonal approximation\n # average difference normalized by average diagonal magnitude\n diag_kfac = torch.einsum('ll,ii->li', BB, AA)\n s.kfac_error = (torch.abs(diag_kfac - diag)).mean() / torch.mean(diag.abs())\n u.log_scalars(u.nest_stats(f'layer-{i}/{stats_name}', s))\n\n # openai batch size stat\n s = AttrDict()\n hess = hessians[layer]\n jac = jacobians[layer]\n fish = fishers[layer]\n quad_fish = quad_fishers[layer]\n\n # the following check passes, but is expensive\n # if args.stats_num_batches == 1:\n # u.check_close(fisher[layer].BA, layer.weight.grad)\n\n def trsum(A, B):\n return (A * B).sum() # computes tr(AB')\n\n grad = fishers[layer].BA / n\n s.grad_fro = torch.norm(grad)\n\n # get norms\n s.lyap_hess_max = quad_fish.lyap_hess_max\n s.lyap_hess_ave = quad_fish.lyap_hess_sum / n\n s.lyap_jac_max = quad_fish.lyap_jac_max\n s.lyap_jac_ave = quad_fish.lyap_jac_sum / n\n s.hess_trace = hess.diag.sum() / n\n s.jac_trace = jac.diag.sum() / n\n\n # Version 1 of Jain stochastic rates, use Hessian for curvature\n b = args.train_batch_size\n\n s.hess_curv = trsum((hess.BB / n) @ grad @ (hess.AA / n), grad) / trsum(grad, grad)\n s.jac_curv = trsum((jac.BB / n) @ grad @ (jac.AA / n), grad) / trsum(grad, grad)\n\n # compute gradient noise statistics\n # fish.BB has /n factor twice, hence don't need extra /n on fish.AA\n # after sampling, hess_noise,jac_noise became 100x smaller, but normalized is unaffected\n s.hess_noise = (trsum(hess.AA / n, fish.AA / n) * trsum(hess.BB / n, fish.BB / n))\n s.jac_noise = (trsum(jac.AA / n, fish.AA / n) * trsum(jac.BB / n, fish.BB / n))\n s.hess_noise_centered = s.hess_noise - trsum(hess.BB / n @ grad, grad @ hess.AA / n)\n s.jac_noise_centered = s.jac_noise - trsum(jac.BB / n @ grad, grad @ jac.AA / n)\n s.openai_gradient_noise = (fish.norms_hess / n) / trsum(hess.BB / n @ grad, grad @ hess.AA / n)\n\n s.mean_norm = torch.sqrt(fish.norm2) / n\n s.min_norm = torch.sqrt(fish.min_norm2)\n s.median_norm = torch.sqrt(fish.median_norm2)\n s.max_norm = torch.sqrt(fish.max_norm2)\n s.enorms = u.norm_squared(grad)\n s.a_sparsity = fish.a_sparsity\n s.b_sparsity = fish.b_sparsity\n s.mean_activation = fish.mean_activation\n s.msr_activation = torch.sqrt(fish.mean_activation2)\n s.mean_backprop = fish.mean_backprop\n s.msr_backprop = torch.sqrt(fish.mean_backprop2)\n\n s.norms_centered = fish.norm2 / n - u.norm_squared(grad)\n s.norms_hess = fish.norms_hess / n\n s.norms_jac = fish.norms_jac / n\n\n s.hess_curv_grad = fish.curv_hess / n # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.\n s.hess_curv_grad_max = fish.curv_hess_max # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.\n s.hess_curv_grad_median = fish.curv_hess_median # phase transition, hits minimum loss in layer 1, then starts going up. Other layers take longer to reach minimum. Decreases with depth.\n s.sigma_curv_grad = quad_fish.curv_sigma / n\n s.sigma_curv_grad_max = quad_fish.curv_sigma_max\n s.sigma_curv_grad_median = quad_fish.curv_sigma_median\n s.band_bottou = 0.5 * lr * s.sigma_curv_grad / s.hess_curv_grad\n s.band_bottou_stoch = 0.5 * lr * quad_fish.curv_ratio / n\n s.band_yaida = 0.25 * lr * s.mean_norm**2\n s.band_yaida_centered = 0.25 * lr * s.norms_centered\n\n s.jac_curv_grad = fish.curv_jac / n # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.\n s.jac_curv_grad_max = fish.curv_jac_max # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.\n s.jac_curv_grad_median = fish.curv_jac_median # this one has much lower variance than jac_curv. Reaches peak at 10k steps, also kfac error reaches peak there. Decreases with depth except for last layer.\n\n # OpenAI gradient noise statistics\n s.hess_noise_normalized = s.hess_noise_centered / (fish.norms_hess / n)\n s.jac_noise_normalized = s.jac_noise / (fish.norms_jac / n)\n\n train_regrets_, test_regrets1_, test_regrets2_, train_regrets_opt_, test_regrets_opt_, cosines_, dot_products_ = (torch.stack(r[layer]) for r in (train_regrets, test_regrets1, test_regrets2, train_regrets_opt, test_regrets_opt, cosines, dot_products))\n s.train_regret = train_regrets_.median() # use median because outliers make it hard to see the trend\n s.test_regret1 = test_regrets1_.median()\n s.test_regret2 = test_regrets2_.median()\n s.test_regret_opt = test_regrets_opt_.median()\n s.train_regret_opt = train_regrets_opt_.median()\n s.mean_dot_product = torch.mean(dot_products_)\n s.median_dot_product = torch.median(dot_products_)\n a = [1, 2, 3]\n\n s.median_cosine = cosines_.median()\n s.mean_cosine = cosines_.mean()\n\n # get learning rates\n L1 = s.hess_curv_grad / n\n L2 = s.jac_curv_grad / n\n diversity = (fish.norm2 / n) / u.norm_squared(grad)\n robust_diversity = (fish.norm2 / n) / fish.median_norm2\n dotprod_diversity = fish.median_norm2 / s.median_dot_product\n s.lr1 = 2 / (L1 * diversity)\n s.lr2 = 2 / (L2 * diversity)\n s.lr3 = 2 / (L2 * robust_diversity)\n s.lr4 = 2 / (L2 * dotprod_diversity)\n\n hess_A = u.symeig_pos_evals(hess.AA / n)\n hess_B = u.symeig_pos_evals(hess.BB / n)\n fish_A = u.symeig_pos_evals(fish.AA / n)\n fish_B = u.symeig_pos_evals(fish.BB / n)\n jac_A = u.symeig_pos_evals(jac.AA / n)\n jac_B = u.symeig_pos_evals(jac.BB / n)\n u.log_scalars({f'layer-{i}/hessA_erank': erank(hess_A)})\n u.log_scalars({f'layer-{i}/hessB_erank': erank(hess_B)})\n u.log_scalars({f'layer-{i}/fishA_erank': erank(fish_A)})\n u.log_scalars({f'layer-{i}/fishB_erank': erank(fish_B)})\n u.log_scalars({f'layer-{i}/jacA_erank': erank(jac_A)})\n u.log_scalars({f'layer-{i}/jacB_erank': erank(jac_B)})\n gl.event_writer.add_histogram(f'layer-{i}/hist_hess_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_fish_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_jac_eig', u.outer(hess_A, hess_B).flatten(), gl.get_global_step())\n\n s.hess_l2 = max(hess_A) * max(hess_B)\n s.jac_l2 = max(jac_A) * max(jac_B)\n s.fish_l2 = max(fish_A) * max(fish_B)\n s.hess_trace = hess.diag.sum() / n\n\n s.jain1_sto = 1/(s.hess_trace + 2 * s.hess_l2)\n s.jain1_det = 1/s.hess_l2\n\n s.jain1_lr = (1 / b) * (1/s.jain1_sto) + (b - 1) / b * (1/s.jain1_det)\n s.jain1_lr = 2 / s.jain1_lr\n\n s.regret_ratio = (\n train_regrets_opt_ / test_regrets_opt_).median() # ratio between train and test regret, large means overfitting\n u.log_scalars(u.nest_stats(f'layer-{i}', s))\n\n # compute stats that would let you bound rho\n if i == 0: # only compute this once, for output layer\n hhh = hessians[model.layers[-1]].BB / n\n fff = fishers[model.layers[-1]].BB / n\n d = fff.shape[0]\n L = u.lyapunov_spectral(hhh, 2 * fff, cond=1e-8)\n L_evals = u.symeig_pos_evals(L)\n Lcheap = fff @ u.pinv(hhh, cond=1e-8)\n Lcheap_evals = u.eig_real(Lcheap)\n\n u.log_scalars({f'mismatch/rho': d/erank(L_evals)})\n u.log_scalars({f'mismatch/rho_cheap': d/erank(Lcheap_evals)})\n u.log_scalars({f'mismatch/diagonalizability': erank(L_evals)/erank(Lcheap_evals)}) # 1 means diagonalizable\n u.log_spectrum(f'mismatch/sigma', u.symeig_pos_evals(fff), loglog=False)\n u.log_spectrum(f'mismatch/hess', u.symeig_pos_evals(hhh), loglog=False)\n u.log_spectrum(f'mismatch/lyapunov', L_evals, loglog=True)\n u.log_spectrum(f'mismatch/lyapunov_cheap', Lcheap_evals, loglog=True)\n\n gl.event_writer.add_histogram(f'layer-{i}/hist_grad_norms', u.to_numpy(fishers_histograms[layer].norms.value()), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_grad_norms_hess', u.to_numpy(fishers_histograms[layer].norms_hess.value()), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_curv_jac', u.to_numpy(fishers_histograms[layer].curv_jac.value()), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_curv_hess', u.to_numpy(fishers_histograms[layer].curv_hess.value()), gl.get_global_step())\n gl.event_writer.add_histogram(f'layer-{i}/hist_cosines', u.to_numpy(cosines[layer]), gl.get_global_step())\n\n if args.log_spectra:\n with u.timeit('spectrum'):\n # 2/alpha\n # s.jain1_lr = (1 / b) * s.jain1_sto + (b - 1) / b * s.jain1_det\n # s.jain1_lr = 1 / s.jain1_lr\n\n # hess.diag_trace, jac.diag_trace\n\n # Version 2 of Jain stochastic rates, use Jacobian squared for curvature\n s.jain2_sto = s.lyap_jac_max * s.jac_trace / s.lyap_jac_ave\n s.jain2_det = s.jac_l2\n s.jain2_lr = (1 / b) * s.jain2_sto + (b - 1) / b * s.jain2_det\n s.jain2_lr = 1 / s.jain2_lr\n\n u.log_spectrum(f'layer-{i}/hess_A', hess_A)\n u.log_spectrum(f'layer-{i}/hess_B', hess_B)\n u.log_spectrum(f'layer-{i}/hess_AB', u.outer(hess_A, hess_B).flatten())\n u.log_spectrum(f'layer-{i}/jac_A', jac_A)\n u.log_spectrum(f'layer-{i}/jac_B', jac_B)\n u.log_spectrum(f'layer-{i}/fish_A', fish_A)\n u.log_spectrum(f'layer-{i}/fish_B', fish_B)\n\n u.log_scalars({f'layer-{i}/trace_ratio': fish_B.sum()/hess_B.sum()})\n\n L = torch.eig(u.lyapunov_spectral(hess.BB, 2*fish.BB, cond=1e-8))[0]\n L = L[:, 0] # extract real part\n L = L.sort()[0]\n L = torch.flip(L, [0])\n\n L_cheap = torch.eig(fish.BB @ u.pinv(hess.BB, cond=1e-8))[0]\n L_cheap = L_cheap[:, 0] # extract real part\n L_cheap = L_cheap.sort()[0]\n L_cheap = torch.flip(L_cheap, [0])\n\n d = len(hess_B)\n u.log_spectrum(f'layer-{i}/Lyap', L)\n u.log_spectrum(f'layer-{i}/Lyap_cheap', L_cheap)\n\n u.log_scalars({f'layer-{i}/dims': d})\n u.log_scalars({f'layer-{i}/L_erank': erank(L)})\n u.log_scalars({f'layer-{i}/L_cheap_erank': erank(L_cheap)})\n\n u.log_scalars({f'layer-{i}/rho': d/erank(L)})\n u.log_scalars({f'layer-{i}/rho_cheap': d/erank(L_cheap)})\n\n model.train()\n with u.timeit('train'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1 - args.weight_decay)\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\ndef validate(model, val_loader, tag='validation'):\n model.eval()\n val_loss = 0\n correct = 0\n\n with torch.no_grad():\n for data, target in val_loader:\n data, target = data.to(gl.device), target.to(gl.device)\n\n output = model(data)\n val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n val_loss /= len(val_loader.dataset)\n val_accuracy = 100. * correct / len(val_loader.dataset)\n\n # TODO(y) log scalar here\n print(\n f'Eval: Average {tag} loss: {val_loss:.4f}, Accuracy: {correct:.0f}/{len(val_loader.dataset)} ({val_accuracy:.2f}%)')\n\n return val_accuracy, val_loss\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "autotune/train_medium.py", "content": "import argparse\nimport os\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom torch import nn as nn\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\n@profile\ndef main():\n\n u.seed_random(1)\n logdir = u.create_local_logdir(args.logdir)\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {run_name}\")\n\n d1 = args.data_width ** 2\n assert args.data_width == args.targets_width\n o = d1\n n = args.stats_batch_size\n d = [d1, 30, 30, 30, 20, 30, 30, 30, d1]\n\n # small values for debugging\n # loss_type = 'LeastSquares'\n loss_type = 'CrossEntropy'\n\n args.wandb = 0\n args.stats_steps = 10\n args.train_steps = 10\n args.stats_batch_size = 10\n args.data_width = 2\n args.targets_width = 2\n args.nonlin = False\n d1 = args.data_width ** 2\n d2 = 2\n d3 = args.targets_width ** 2\n\n if loss_type == 'CrossEntropy':\n d3 = 10\n o = d3\n n = args.stats_batch_size\n d = [d1, d2, d3]\n dsize = max(args.train_batch_size, args.stats_batch_size)+1\n\n model = u.SimpleFullyConnected2(d, bias=True, nonlin=args.nonlin)\n model = model.to(gl.device)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['method'] = args.method\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n #optimizer = torch.optim.SGD(model.parameters(), lr=0.03, momentum=0.9)\n optimizer = torch.optim.Adam(model.parameters(), lr=0.03) # make 10x smaller for least-squares loss\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, dataset_size=dsize, original_targets=True)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_iter = None\n if not args.full_batch:\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width, train=False, dataset_size=dsize, original_targets=True)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n elif loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.token_count = 0\n last_outer = 0\n val_losses = []\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n print(\"val_loss\", val_loss.item())\n val_losses.append(val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n # compute stats\n if args.full_batch:\n data, targets = dataset.data, dataset.targets\n else:\n data, targets = next(stats_iter)\n\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model)\n\n for (i, layer) in enumerate(model.layers):\n\n # input/output layers are unreasonably expensive if not using Kronecker factoring\n if d[i]>50 or d[i+1]>50:\n print(f'layer {i} is too big ({d[i],d[i+1]}), skipping stats')\n continue\n\n if args.skip_stats:\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops_list[0] * n\n assert B_t.shape == (n, d[i + 1])\n\n with u.timeit(f\"khatri_g-{i}\"):\n G = u.khatri_rao_t(B_t, A_t) # batch loss Jacobian\n assert G.shape == (n, d[i] * d[i + 1])\n g = G.sum(dim=0, keepdim=True) / n # average gradient\n assert g.shape == (1, d[i] * d[i + 1])\n\n u.check_equal(G.reshape(layer.weight.grad1.shape), layer.weight.grad1)\n\n if args.autograd_check:\n u.check_close(B_t.t() @ A_t / n, layer.weight.saved_grad)\n u.check_close(g.reshape(d[i + 1], d[i]), layer.weight.saved_grad)\n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma)/s.sigma_l2\n\n lambda_regularizer = args.lmb * torch.eye(d[i + 1]*d[i]).to(gl.device)\n H = layer.weight.hess\n\n with u.timeit(f\"invH-{i}\"):\n invH = torch.cholesky_inverse(H+lambda_regularizer)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n s.iH_l2 = u.sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.iH_fro = invH.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = layer.weight.data.flatten().norm()\n\n u.nan_check(H)\n if args.autograd_check:\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n H_autograd = H_autograd.reshape(d[i] * d[i + 1], d[i] * d[i + 1])\n u.check_close(H, H_autograd)\n\n # u.dump(sigma, f'/tmp/sigmas/H-{step}-{i}')\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / (dd.flatten().norm() ** 2))\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = u.pinv(H)\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = curv_direction(g)\n ndir = g @ pinvH # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = s.grad_fro**2 / s.grad_curv if s.grad_curv else 999\n s.step_max = 2 / s.H_l2\n s.step_min = torch.tensor(2) / torch.trace(H)\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2) # replace with \"quadratic_form\"\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n with u.timeit(f'rho-{i}'):\n p_sigma = u.lyapunov_svd(H, sigma)\n if u.has_nan(p_sigma) and args.compute_rho: # use expensive method\n print('using expensive method')\n import pdb; pdb.set_trace()\n H0, sigma0 = u.to_numpys(H, sigma)\n p_sigma = scipy.linalg.solve_lyapunov(H0, sigma0)\n p_sigma = torch.tensor(p_sigma).to(gl.device)\n\n if u.has_nan(p_sigma):\n # import pdb; pdb.set_trace()\n s.psigma_erank = H.shape[0]\n s.rho = 1\n else:\n s.psigma_erank = u.sym_erank(p_sigma)\n s.rho = H.shape[0] / s.psigma_erank\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2 / n\n\n # Faster approaches for noise variance computation\n # s.noise_variance = torch.trace(H.inverse() @ sigma)\n # try:\n # # this fails with singular sigma\n # s.noise_variance = torch.trace(torch.solve(sigma, H)[0])\n # # s.noise_variance = torch.trace(torch.lstsq(sigma, H)[0])\n # pass\n # except RuntimeError as _:\n s.noise_variance_pinv = torch.trace(pinvH @ sigma)\n\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n u.log_scalars(u.nest_stats(layer.name, s))\n\n # gradient steps\n with u.timeit('inner'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n # u.log_scalar(train_loss=loss.item())\n\n if args.method != 'newton':\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n else:\n for (layer_idx, layer) in enumerate(model.layers):\n param: torch.nn.Parameter = layer.weight\n param_data: torch.Tensor = param.data\n param_data.copy_(param_data - 0.1 * param.grad)\n if layer_idx != 1: # only update 1 layer with Newton, unstable otherwise\n continue\n u.nan_check(layer.weight.pre)\n u.nan_check(param.grad.flatten())\n u.nan_check(u.v2r(param.grad.flatten()) @ layer.weight.pre)\n param_new_flat = u.v2r(param_data.flatten()) - u.v2r(param.grad.flatten()) @ layer.weight.pre\n u.nan_check(param_new_flat)\n param_data.copy_(param_new_flat.reshape(param_data.shape))\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n help='learning rate (default: 0.01)')\n parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n help='SGD momentum (default: 0.5)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=1, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n\n parser.add_argument('--train_batch_size', type=int, default=100)\n parser.add_argument('--stats_batch_size', type=int, default=60000)\n parser.add_argument('--dataset_size', type=int, default=60000)\n parser.add_argument('--train_steps', type=int, default=100, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--method', type=str, choices=['gradient', 'newton'], default='gradient',\n help=\"descent method, newton or gradient\")\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=28)\n parser.add_argument('--targets_width', type=int, default=28)\n parser.add_argument('--lmb', type=float, default=1e-3)\n parser.add_argument('--hess_samples', type=int, default=1, help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=1, help='use expensive method to compute rho')\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n parser.add_argument('--full_batch', type=int, default=0, help='do stats on the whole dataset')\n parser.add_argument('--weight_decay', type=float, default=1e-4)\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/train_small.py", "content": "# To verify Newton convergence in 1 step\n# python train_tiny.py --wandb=0 --method=newton --nonlin=0 --layer=0\n\nimport argparse\nimport os\nimport sys\nimport time\nfrom typing import Callable, List\n\nimport globals as gl\n# import torch\nimport scipy\nimport torch\nimport torch.nn as nn\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\n\ndef main():\n attemp_count = 0\n while os.path.exists(f\"{args.logdir}{attemp_count:02d}\"):\n attemp_count += 1\n logdir = f\"{args.logdir}{attemp_count:02d}\"\n\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {run_name}\")\n u.seed_random(1)\n\n d1 = args.data_width ** 2\n d2 = 10\n d3 = args.targets_width ** 2\n o = d3\n n = args.stats_batch_size\n d = [d1, d2, d3]\n model = u.SimpleFullyConnected(d, nonlin=args.nonlin)\n model = model.to(gl.device)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['method'] = args.method\n wandb.config['d1'] = d1\n wandb.config['d2'] = d2\n wandb.config['d3'] = d3\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n optimizer = torch.optim.SGD(model.parameters(), lr=0.03, momentum=0.9)\n\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width,\n dataset_size=args.dataset_size)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width,\n dataset_size=args.dataset_size, train=False)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=True, drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n skip_forward_hooks = False\n skip_backward_hooks = False\n\n def capture_activations(module: nn.Module, input: List[torch.Tensor], output: torch.Tensor):\n if skip_forward_hooks:\n return\n assert not hasattr(module, 'activations'), \"Seeing results of previous autograd, call util.zero_grad to clear\"\n assert len(input) == 1, \"this was tested for single input layers only\"\n setattr(module, \"activations\", input[0].detach())\n setattr(module, \"output\", output.detach())\n\n def capture_backprops(module: nn.Module, _input, output):\n if skip_backward_hooks:\n return\n assert len(output) == 1, \"this works for single variable layers only\"\n if gl.backward_idx == 0:\n assert not hasattr(module, 'backprops'), \"Seeing results of previous autograd, call util.zero_grad to clear\"\n setattr(module, 'backprops', [])\n assert gl.backward_idx == len(module.backprops)\n module.backprops.append(output[0])\n\n def save_grad(param: nn.Parameter) -> Callable[[torch.Tensor], None]:\n \"\"\"Hook to save gradient into 'param.saved_grad', so it can be accessed after model.zero_grad(). Only stores gradient\n if the value has not been set, call util.zero_grad to clear it.\"\"\"\n\n def save_grad_fn(grad):\n if not hasattr(param, 'saved_grad'):\n setattr(param, 'saved_grad', grad)\n\n return save_grad_fn\n\n for layer in model.layers:\n layer.register_forward_hook(capture_activations)\n layer.register_backward_hook(capture_backprops)\n layer.weight.register_hook(save_grad(layer.weight))\n\n def loss_fn(data, targets):\n err = data - targets.view(-1, data.shape[1])\n assert len(data) == len(targets)\n return torch.sum(err * err) / 2 / len(data)\n\n gl.token_count = 0\n last_outer = 0\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n # compute validation loss\n skip_forward_hooks = True\n skip_backward_hooks = True\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n print(\"val_loss\", val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n # compute stats\n data, targets = next(stats_iter)\n skip_forward_hooks = False\n skip_backward_hooks = False\n\n # get gradient values\n with u.timeit(\"backprop_g\"):\n gl.backward_idx = 0\n u.zero_grad(model)\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n\n # get Hessian values\n skip_forward_hooks = True\n id_mat = torch.eye(o).to(gl.device)\n\n u.log_scalar(loss=loss.item())\n\n with u.timeit(\"backprop_H\"):\n # optionally use randomized low-rank approximation of Hessian\n hess_rank = args.hess_samples if args.hess_samples else o\n\n for out_idx in range(hess_rank):\n model.zero_grad()\n # backprop to get section of batch output jacobian for output at position out_idx\n output = model(data) # opt: using autograd.grad means I don't have to zero_grad\n if args.hess_samples:\n bval = torch.LongTensor(n, o).to(gl.device).random_(0, 2) * 2 - 1\n bval = bval.float()\n else:\n ei = id_mat[out_idx]\n bval = torch.stack([ei] * n)\n gl.backward_idx = out_idx + 1\n output.backward(bval)\n skip_backward_hooks = True #\n\n for (i, layer) in enumerate(model.layers):\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops[0] * n\n assert B_t.shape == (n, d[i + 1])\n\n with u.timeit(f\"khatri_g-{i}\"):\n G = u.khatri_rao_t(B_t, A_t) # batch loss Jacobian\n assert G.shape == (n, d[i] * d[i + 1])\n g = G.sum(dim=0, keepdim=True) / n # average gradient\n assert g.shape == (1, d[i] * d[i + 1])\n\n if args.autograd_check:\n u.check_close(B_t.t() @ A_t / n, layer.weight.saved_grad)\n u.check_close(g.reshape(d[i + 1], d[i]), layer.weight.saved_grad)\n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel()\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma)/s.sigma_l2\n\n #############################\n # Hessian stats\n #############################\n A_t = layer.activations\n Bh_t = [layer.backprops[out_idx + 1] for out_idx in range(hess_rank)]\n Amat_t = torch.cat([A_t] * hess_rank, dim=0)\n Bmat_t = torch.cat(Bh_t, dim=0)\n\n assert Amat_t.shape == (n * hess_rank, d[i])\n assert Bmat_t.shape == (n * hess_rank, d[i + 1])\n\n lambda_regularizer = args.lmb * torch.eye(d[i] * d[i + 1]).to(gl.device)\n with u.timeit(f\"khatri_H-{i}\"):\n Jb = u.khatri_rao_t(Bmat_t, Amat_t) # batch Jacobian, in row-vec format\n\n with u.timeit(f\"H-{i}\"):\n H = Jb.t() @ Jb / n\n\n with u.timeit(f\"invH-{i}\"):\n invH = torch.cholesky_inverse(H+lambda_regularizer)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n s.iH_l2 = u.sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.iH_fro = invH.flatten().norm()\n s.jacobian_fro = Jb.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = layer.weight.data.flatten().norm()\n\n u.nan_check(H)\n if args.autograd_check:\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n H_autograd = H_autograd.reshape(d[i] * d[i + 1], d[i] * d[i + 1])\n u.check_close(H, H_autograd)\n\n # u.dump(sigma, f'/tmp/sigmas/H-{step}-{i}')\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / (dd.flatten().norm() ** 2))\n\n with u.timeit(\"pinvH\"):\n pinvH = u.pinv(H)\n\n with u.timeit(f'curv-{i}'):\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2)\n s.grad_curv = curv_direction(g)\n ndir = g @ pinvH # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 999\n s.step_max = 2 / u.sym_l2_norm(H)\n s.step_min = torch.tensor(2) / torch.trace(H)\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n with u.timeit(f'rho-{i}'):\n p_sigma = u.lyapunov_svd(H, sigma)\n if u.has_nan(p_sigma) and args.compute_rho: # use expensive method\n H0 = H.cpu().detach().numpy()\n sigma0 = sigma.cpu().detach().numpy()\n p_sigma = scipy.linalg.solve_lyapunov(H0, sigma0)\n p_sigma = torch.tensor(p_sigma).to(gl.device)\n\n if u.has_nan(p_sigma):\n s.psigma_erank = H.shape[0]\n s.rho = 1\n else:\n s.psigma_erank = u.sym_erank(p_sigma)\n s.rho = H.shape[0] / s.psigma_erank\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n print('openai batch', s.batch_openai)\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2\n\n # s.noise_variance = torch.trace(H.inverse() @ sigma)\n # try:\n # # this fails with singular sigma\n # s.noise_variance = torch.trace(torch.solve(sigma, H)[0])\n # # s.noise_variance = torch.trace(torch.lstsq(sigma, H)[0])\n # pass\n # except RuntimeError as _:\n s.noise_variance_pinv = torch.trace(pinvH @ sigma)\n\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n u.log_scalars(u.nest_stats(layer.name, s))\n\n # gradient steps\n last_inner = 0\n for i in range(args.train_steps):\n if last_inner:\n u.log_scalars({\"time/inner\": 1000*(time.perf_counter() - last_inner)})\n last_inner = time.perf_counter()\n\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n u.log_scalar(train_loss=loss.item())\n\n if args.method != 'newton':\n optimizer.step()\n else:\n for (layer_idx, layer) in enumerate(model.layers):\n param: torch.nn.Parameter = layer.weight\n param_data: torch.Tensor = param.data\n param_data.copy_(param_data - 0.1 * param.grad)\n if layer_idx != 1: # only update 1 layer with Newton, unstable otherwise\n continue\n u.nan_check(layer.weight.pre)\n u.nan_check(param.grad.flatten())\n u.nan_check(u.v2r(param.grad.flatten()) @ layer.weight.pre)\n param_new_flat = u.v2r(param_data.flatten()) - u.v2r(param.grad.flatten()) @ layer.weight.pre\n u.nan_check(param_new_flat)\n param_data.copy_(param_new_flat.reshape(param_data.shape))\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n help='learning rate (default: 0.01)')\n parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n help='SGD momentum (default: 0.5)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=1, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n\n parser.add_argument('--train_batch_size', type=int, default=3)\n parser.add_argument('--stats_batch_size', type=int, default=1000)\n parser.add_argument('--dataset_size', type=int, default=10000)\n parser.add_argument('--train_steps', type=int, default=10, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--method', type=str, choices=['gradient', 'newton'], default='gradient',\n help=\"descent method, newton or gradient\")\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=8)\n parser.add_argument('--targets_width', type=int, default=4)\n parser.add_argument('--lmb', type=float, default=1e-3)\n parser.add_argument('--hess_samples', type=int, default=1, help='number of samples when sub-sampling outputs, 0 for exact hessian')\n parser.add_argument('--hess_kfac', type=int, default=0, help='whether to use KFAC approximation for hessian')\n parser.add_argument('--compute_rho', type=int, default=0, help='use expensive method to compute rho')\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/train_small_xent.py", "content": "\"\"\"Train small network on MNIST with Cross-Entropy loss\"\"\"\n\nimport argparse\nimport os\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch import nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\nfrom train_ciresan import validate\n\n\n@profile\ndef main():\n\n u.install_pdb_handler()\n u.seed_random(1)\n logdir = u.create_local_logdir(args.logdir)\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {logdir}\")\n\n loss_type = 'CrossEntropy'\n\n d1 = args.data_width ** 2\n args.stats_batch_size = min(args.stats_batch_size, args.dataset_size)\n args.train_batch_size = min(args.train_batch_size, args.dataset_size)\n n = args.stats_batch_size\n o = 10\n d = [d1, 60, 60, 60, o]\n # dataset_size = args.dataset_size\n\n model = u.SimpleFullyConnected2(d, bias=True, nonlin=args.nonlin, last_layer_linear=True)\n model = model.to(gl.device)\n u.mark_expensive(model.layers[0]) # to stop grad1/hess calculations on this layer\n print(model)\n\n try:\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name, dir='/tmp/wandb.runs')\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)\n # optimizer = torch.optim.Adam(model.parameters(), lr=0.03) # make 10x smaller for least-squares loss\n dataset = u.TinyMNIST(data_width=args.data_width, dataset_size=args.dataset_size, loss_type=loss_type)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, train=False, dataset_size=args.dataset_size, loss_type=loss_type)\n test_batch_size = min(args.dataset_size, 1000)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False, drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n val_losses = []\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n print(\"val_loss\", val_loss.item())\n val_losses.append(val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n with u.timeit(\"validate\"):\n if loss_type == 'CrossEntropy':\n val_accuracy, val_loss = validate(model, test_loader, f'test (stats_step {step})')\n # train_accuracy, train_loss = validate(model, train_loader, f'train (stats_step {step})')\n\n metrics = {'stats_step': step, 'val_accuracy': val_accuracy, 'val_loss': val_loss}\n u.log_scalars(metrics)\n\n data, targets = stats_data, stats_targets\n\n if not args.skip_stats:\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model)\n\n for (i, layer) in enumerate(model.layers):\n\n if hasattr(layer, 'expensive'):\n continue\n\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n # input/output layers are unreasonably expensive if not using Kronecker factoring\n if d[i]*d[i+1] > 8000:\n print(f'layer {i} is too big ({d[i],d[i+1]}), skipping stats')\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n B_t = layer.backprops_list[0] * n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n G = param.grad1.reshape((n, -1))\n g = G.mean(dim=0, keepdim=True)\n\n u.nan_check(G)\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n # sigma_spectrum =\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma)/s.sigma_l2\n\n H = param.hess\n lambda_regularizer = args.lmb * torch.eye(H.shape[0]).to(gl.device)\n u.nan_check(H)\n\n with u.timeit(f\"invH-{i}\"):\n invH = torch.cholesky_inverse(H+lambda_regularizer)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n s.iH_l2 = u.sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.iH_fro = invH.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = param.data.flatten().norm()\n\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / (dd.flatten().norm() ** 2))\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = u.pinv(H)\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = curv_direction(g) # curvature (eigenvalue) in direction g\n ndir = g @ pinvH # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 1234567\n s.step_div_inf = 2 / s.H_l2 # divegent step size for batch_size=infinity\n s.step_div_1 = torch.tensor(2) / torch.trace(H) # divergent step for batch_size=1\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2) # replace with \"quadratic_form\"\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n with u.timeit(f'rho-{i}'):\n s.rho, s.lyap_erank, lyap_evals = u.truncated_lyapunov_rho(H, sigma)\n s.step_div_1_adjusted = s.step_div_1/s.rho\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2 / n # Gradient diversity / n\n s.noise_variance_pinv = torch.trace(pinvH @ sigma)\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n param_name = f\"{layer.name}={param_names[param]}\"\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n H_evals = u.symeig_pos_evals(H)\n sigma_evals = u.symeig_pos_evals(sigma)\n u.log_spectrum(f'{param_name}/hess', H_evals)\n u.log_spectrum(f'{param_name}/sigma', sigma_evals)\n u.log_spectrum(f'{param_name}/lyap', lyap_evals)\n\n # gradient steps\n with u.timeit('inner'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.increment_global_step(data.shape[0])\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--lr', type=float, default=0.03, help='learning rate')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n parser.add_argument('--train_batch_size', type=int, default=10)\n parser.add_argument('--stats_batch_size', type=int, default=10)\n parser.add_argument('--dataset_size', type=int, default=500)\n parser.add_argument('--data_width', type=int, default=8)\n parser.add_argument('--train_steps', type=int, default=10, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n parser.add_argument('--weight_decay', type=float, default=1e-3)\n parser.add_argument('--lmb', type=float, default=1e-3, help=\"lambda regularizer for inverting H\")\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/train_small_xent_factored.py", "content": "\"\"\"Train small network on MNIST with Cross-Entropy loss\"\"\"\n\nimport argparse\nimport os\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch import nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\nfrom train_ciresan import validate\n\n\n@profile\ndef main():\n u.install_pdb_handler()\n u.seed_random(1)\n logdir = u.create_local_logdir(args.logdir)\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {logdir}\")\n\n loss_type = 'CrossEntropy'\n\n d1 = args.data_width ** 2\n args.stats_batch_size = min(args.stats_batch_size, args.dataset_size)\n args.train_batch_size = min(args.train_batch_size, args.dataset_size)\n n = args.stats_batch_size\n o = 10\n d = [d1, 60, 60, 60, o]\n # dataset_size = args.dataset_size\n\n model = u.SimpleFullyConnected2(d, bias=True, nonlin=args.nonlin, last_layer_linear=True)\n model = model.to(gl.device)\n u.mark_expensive(model.layers[0]) # to stop grad1/hess calculations on this layer\n print(model)\n\n try:\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name, dir='/tmp/wandb.runs')\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)\n # optimizer = torch.optim.Adam(model.parameters(), lr=0.03) # make 10x smaller for least-squares loss\n dataset = u.TinyMNIST(data_width=args.data_width, dataset_size=args.dataset_size, loss_type=loss_type)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n stats_data, stats_targets = next(stats_iter)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, train=False, dataset_size=args.dataset_size, loss_type=loss_type)\n test_batch_size = min(args.dataset_size, 1000)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False, drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n val_losses = []\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n print(\"val_loss\", val_loss.item())\n val_losses.append(val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n with u.timeit(\"validate\"):\n if loss_type == 'CrossEntropy':\n val_accuracy, val_loss = validate(model, test_loader, f'test (stats_step {step})')\n # train_accuracy, train_loss = validate(model, train_loader, f'train (stats_step {step})')\n\n metrics = {'stats_step': step, 'val_accuracy': val_accuracy, 'val_loss': val_loss}\n u.log_scalars(metrics)\n\n data, targets = stats_data, stats_targets\n\n if not args.skip_stats:\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type='CrossEntropy')\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model, method='kron', attr_name='hess2')\n\n autograd_lib.compute_stats_factored(model)\n\n for (i, layer) in enumerate(model.layers):\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n\n if param is None:\n continue\n\n if not hasattr(param, 'stats'):\n continue\n s = param.stats\n param_name = param_names[param]\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n # gradient steps\n with u.timeit('inner'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.increment_global_step(data.shape[0])\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--lr', type=float, default=0.03, help='learning rate')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n parser.add_argument('--train_batch_size', type=int, default=10)\n parser.add_argument('--stats_batch_size', type=int, default=10)\n parser.add_argument('--dataset_size', type=int, default=500)\n parser.add_argument('--data_width', type=int, default=8)\n parser.add_argument('--train_steps', type=int, default=10, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n parser.add_argument('--weight_decay', type=float, default=1e-3)\n parser.add_argument('--lmb', type=float, default=1e-3, help=\"lambda regularizer for inverting H\")\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/train_tiny.py", "content": "# To verify Newton convergence in 1 step\n# python train_tiny.py --wandb=0 --method=newton --nonlin=0 --layer=0\n\nimport argparse\nimport os\nimport sys\nfrom typing import Callable\n\nimport globals as gl\n# import torch\nimport torch\nimport torch.nn as nn\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport util as u\n\n\ndef main():\n attemp_count = 0\n while os.path.exists(f\"{args.logdir}{attemp_count:02d}\"):\n attemp_count += 1\n logdir = f\"{args.logdir}{attemp_count:02d}\"\n\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {run_name}\")\n u.seed_random(1)\n\n try:\n # os.environ['WANDB_SILENT'] = 'true'\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['method'] = args.method\n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n # data_width = 4\n # targets_width = 2\n\n d1 = args.data_width ** 2\n d2 = 10\n d3 = args.targets_width ** 2\n o = d3\n n = args.stats_batch_size\n d = [d1, d2, d3]\n model = u.SimpleFullyConnected(d, nonlin=args.nonlin)\n optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)\n\n dataset = u.TinyMNIST(data_width=args.data_width, targets_width=args.targets_width,\n dataset_size=args.dataset_size)\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n\n def capture_activations(module, input, _output):\n if skip_forward_hooks:\n return\n assert gl.backward_idx == 0 # no need to forward-prop on Hessian computation\n assert not hasattr(module, 'activations'), \"Seeing activations from previous forward, call util.zero_grad to clear\"\n assert len(input) == 1, \"this works for single input layers only\"\n setattr(module, \"activations\", input[0].detach())\n\n def capture_backprops(module: nn.Module, _input, output):\n if skip_backward_hooks:\n return\n assert len(output) == 1, \"this works for single variable layers only\"\n if gl.backward_idx == 0:\n assert not hasattr(module, 'backprops'), \"Seeing results of previous autograd, call util.zero_grad to clear\"\n setattr(module, 'backprops', [])\n assert gl.backward_idx == len(module.backprops)\n module.backprops.append(output[0])\n\n def save_grad(param: nn.Parameter) -> Callable[[torch.Tensor], None]:\n \"\"\"Hook to save gradient into 'param.saved_grad', so it can be accessed after model.zero_grad(). Only stores gradient\n if the value has not been set, call util.zero_grad to clear it.\"\"\"\n def save_grad_fn(grad):\n if not hasattr(param, 'saved_grad'):\n setattr(param, 'saved_grad', grad)\n return save_grad_fn\n\n for layer in model.layers:\n layer.register_forward_hook(capture_activations)\n layer.register_backward_hook(capture_backprops)\n layer.weight.register_hook(save_grad(layer.weight))\n\n def loss_fn(data, targets):\n err = data - targets.view(-1, data.shape[1])\n assert len(data) == len(targets)\n return torch.sum(err * err) / 2 / len(data)\n\n gl.token_count = 0\n for step in range(args.stats_steps):\n data, targets = next(stats_iter)\n skip_forward_hooks = False\n skip_backward_hooks = False\n\n # get gradient values\n gl.backward_idx = 0\n u.zero_grad(model)\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n\n print(\"loss\", loss.item())\n\n # get Hessian values\n skip_forward_hooks = True\n id_mat = torch.eye(o)\n\n u.log_scalars({'loss': loss.item()})\n\n # o = 0\n for out_idx in range(o):\n model.zero_grad()\n # backprop to get section of batch output jacobian for output at position out_idx\n output = model(data) # opt: using autograd.grad means I don't have to zero_grad\n ei = id_mat[out_idx]\n bval = torch.stack([ei] * n)\n gl.backward_idx = out_idx+1\n output.backward(bval)\n skip_backward_hooks = True #\n\n for (i, layer) in enumerate(model.layers):\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n assert A_t.shape == (n, d[i])\n\n # add factor of n because backprop takes loss averaged over batch, while we need per-example loss\n B_t = layer.backprops[0] * n\n assert B_t.shape == (n, d[i+1])\n\n G = u.khatri_rao_t(B_t, A_t) # batch loss Jacobian\n assert G.shape == (n, d[i]*d[i+1])\n g = G.sum(dim=0, keepdim=True) / n # average gradient\n assert g.shape == (1, d[i]*d[i+1])\n\n if args.autograd_check:\n u.check_close(B_t.t() @ A_t / n, layer.weight.saved_grad)\n u.check_close(g.reshape(d[i+1], d[i]), layer.weight.saved_grad)\n\n # empirical Fisher\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n # u.dump(sigma, f'/tmp/sigmas/{step}-{i}')\n s.sigma_l2 = u.l2_norm(sigma)\n\n #############################\n # Hessian stats\n #############################\n A_t = layer.activations\n Bh_t = [layer.backprops[out_idx+1] for out_idx in range(o)]\n Amat_t = torch.cat([A_t] * o, dim=0)\n Bmat_t = torch.cat(Bh_t, dim=0)\n\n assert Amat_t.shape == (n*o, d[i])\n assert Bmat_t.shape == (n*o, d[i+1])\n\n Jb = u.khatri_rao_t(Bmat_t, Amat_t) # batch Jacobian, in row-vec format\n H = Jb.t() @ Jb / n\n pinvH = u.pinv(H)\n\n s.hess_l2 = u.l2_norm(H)\n s.invhess_l2 = u.l2_norm(pinvH)\n\n s.hess_fro = H.flatten().norm()\n s.invhess_fro = pinvH.flatten().norm()\n\n s.jacobian_l2 = u.l2_norm(Jb)\n s.grad_fro = g.flatten().norm()\n s.param_fro = layer.weight.data.flatten().norm()\n\n u.nan_check(H)\n if args.autograd_check:\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n H_autograd = u.hessian(loss, layer.weight)\n H_autograd = H_autograd.reshape(d[i] * d[i+1], d[i] * d[i+1])\n u.check_close(H, H_autograd)\n\n # u.dump(sigma, f'/tmp/sigmas/H-{step}-{i}')\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / dd.flatten().norm() ** 2)\n\n s.regret_newton = u.to_python_scalar(g @ u.pinv(H) @ g.t() / 2)\n s.grad_curv = curv_direction(g)\n ndir = g @ u.pinv(H) # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', u.pinv(H)) # save Newton preconditioner\n s.step_openai = 1/s.grad_curv if s.grad_curv else 999\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n u.log_scalars(u.nest_stats(layer.name, s))\n\n # gradient steps\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n u.log_scalar(train_loss=loss.item())\n\n if args.method != 'newton':\n optimizer.step()\n else:\n for (layer_idx, layer) in enumerate(model.layers):\n param: torch.nn.Parameter = layer.weight\n param_data: torch.Tensor = param.data\n param_data.copy_(param_data - 0.1*param.grad)\n if layer_idx != 1: # only update 1 layer with Newton, unstable otherwise\n continue\n u.nan_check(layer.weight.pre)\n u.nan_check(param.grad.flatten())\n u.nan_check(u.v2r(param.grad.flatten()) @ layer.weight.pre)\n param_new_flat = u.v2r(param_data.flatten()) - u.v2r(param.grad.flatten()) @ layer.weight.pre\n u.nan_check(param_new_flat)\n param_data.copy_(param_new_flat.reshape(param_data.shape))\n\n gl.token_count += data.shape[0]\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n help='input batch size for testing (default: 1000)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n help='learning rate (default: 0.01)')\n parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n help='SGD momentum (default: 0.5)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status')\n parser.add_argument('--save-model', action='store_true', default=False,\n help='For Saving the current Model')\n\n parser.add_argument('--wandb', type=int, default=1, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=1, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n\n parser.add_argument('--train_batch_size', type=int, default=3)\n parser.add_argument('--stats_batch_size', type=int, default=100)\n parser.add_argument('--dataset_size', type=int, default=100)\n parser.add_argument('--train_steps', type=int, default=300, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=10, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--method', type=str, choices=['gradient', 'newton'], default='gradient',\n help=\"descent method, newton or gradient\")\n parser.add_argument('--layer', type=int, default=-1, help=\"restrict updates to this layer\")\n parser.add_argument('--data_width', type=int, default=4)\n parser.add_argument('--targets_width', type=int, default=2)\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/train_tiny_xent.py", "content": "\"\"\"Train small network on MNIST with Cross-Entropy loss\"\"\"\n\nimport argparse\nimport os\nimport time\n\nimport autograd_lib\nimport globals as gl\n# import torch\nimport torch\nimport util as u\nimport wandb\nfrom attrdict import AttrDefault\nfrom torch import nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\nfrom train_ciresan import validate\n\n\n@profile\ndef main():\n\n u.install_pdb_handler()\n u.seed_random(1)\n logdir = u.create_local_logdir(args.logdir)\n run_name = os.path.basename(logdir)\n gl.event_writer = SummaryWriter(logdir)\n print(f\"Logging to {logdir}\")\n\n loss_type = 'CrossEntropy'\n\n d1 = args.data_width ** 2\n n = args.stats_batch_size\n o = 10\n d = [d1, o]\n dataset_size = 10000\n\n model = u.SimpleFullyConnected2(d, bias=True, nonlin=args.nonlin, last_layer_linear=True)\n model = model.to(gl.device)\n print(model)\n\n try:\n if args.wandb:\n wandb.init(project='curv_train_tiny', name=run_name, dir='/tmp/wandb.runs')\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config['train_batch'] = args.train_batch_size\n wandb.config['stats_batch'] = args.stats_batch_size\n wandb.config['n'] = n\n except Exception as e:\n print(f\"wandb crash with {e}\")\n\n optimizer = torch.optim.SGD(model.parameters(), lr=0.03, momentum=0.9)\n # optimizer = torch.optim.Adam(model.parameters(), lr=0.03) # make 10x smaller for least-squares loss\n dataset = u.TinyMNIST(data_width=args.data_width, dataset_size=dataset_size, loss_type=loss_type)\n\n train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=False, drop_last=True)\n train_iter = u.infinite_iter(train_loader)\n\n stats_loader = torch.utils.data.DataLoader(dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n stats_iter = u.infinite_iter(stats_loader)\n\n test_dataset = u.TinyMNIST(data_width=args.data_width, train=False, dataset_size=dataset_size, loss_type=loss_type)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.stats_batch_size, shuffle=False, drop_last=True)\n test_iter = u.infinite_iter(test_loader)\n\n if loss_type == 'LeastSquares':\n loss_fn = u.least_squares\n else: # loss_type == 'CrossEntropy':\n loss_fn = nn.CrossEntropyLoss()\n\n autograd_lib.add_hooks(model)\n gl.reset_global_step()\n last_outer = 0\n val_losses = []\n for step in range(args.stats_steps):\n if last_outer:\n u.log_scalars({\"time/outer\": 1000*(time.perf_counter() - last_outer)})\n last_outer = time.perf_counter()\n\n with u.timeit(\"val_loss\"):\n test_data, test_targets = next(test_iter)\n test_output = model(test_data)\n val_loss = loss_fn(test_output, test_targets)\n print(\"val_loss\", val_loss.item())\n val_losses.append(val_loss.item())\n u.log_scalar(val_loss=val_loss.item())\n\n with u.timeit(\"validate\"):\n if loss_type == 'CrossEntropy':\n val_accuracy, val_loss = validate(model, test_loader, f'test (stats_step {step})')\n train_accuracy, train_loss = validate(model, stats_loader, f'train (stats_step {step})')\n\n metrics = {'stats_step': step, 'val_accuracy': val_accuracy, 'val_loss': val_loss,\n 'train_loss': train_loss, 'train_accuracy': train_accuracy}\n u.log_scalars(metrics)\n\n data, targets = next(stats_iter)\n\n if not args.skip_stats:\n # Capture Hessian and gradient stats\n autograd_lib.enable_hooks()\n autograd_lib.clear_backprops(model)\n autograd_lib.clear_hess_backprops(model)\n with u.timeit(\"backprop_g\"):\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward(retain_graph=True)\n with u.timeit(\"backprop_H\"):\n autograd_lib.backprop_hess(output, hess_type=loss_type)\n autograd_lib.disable_hooks() # TODO(y): use remove_hooks\n\n with u.timeit(\"compute_grad1\"):\n autograd_lib.compute_grad1(model)\n with u.timeit(\"compute_hess\"):\n autograd_lib.compute_hess(model)\n\n for (i, layer) in enumerate(model.layers):\n if args.skip_stats:\n continue\n\n param_names = {layer.weight: \"weight\", layer.bias: \"bias\"}\n for param in [layer.weight, layer.bias]:\n # input/output layers are unreasonably expensive if not using Kronecker factoring\n if d[i]*d[i+1] > 2000:\n print(f'layer {i} is too big ({d[i],d[i+1]}), skipping stats')\n continue\n\n s = AttrDefault(str, {}) # dictionary-like object for layer stats\n\n #############################\n # Gradient stats\n #############################\n A_t = layer.activations\n B_t = layer.backprops_list[0] * n\n s.sparsity = torch.sum(layer.output <= 0) / layer.output.numel() # proportion of activations that are zero\n s.mean_activation = torch.mean(A_t)\n s.mean_backprop = torch.mean(B_t)\n\n # empirical Fisher\n G = param.grad1.reshape((n, -1))\n g = G.mean(dim=0, keepdim=True)\n\n u.nan_check(G)\n with u.timeit(f'sigma-{i}'):\n efisher = G.t() @ G / n\n sigma = efisher - g.t() @ g\n # sigma_spectrum =\n s.sigma_l2 = u.sym_l2_norm(sigma)\n s.sigma_erank = torch.trace(sigma)/s.sigma_l2\n\n H = param.hess\n lambda_regularizer = args.lmb * torch.eye(H.shape[0]).to(gl.device)\n u.nan_check(H)\n\n with u.timeit(f\"invH-{i}\"):\n invH = torch.cholesky_inverse(H+lambda_regularizer)\n\n with u.timeit(f\"H_l2-{i}\"):\n s.H_l2 = u.sym_l2_norm(H)\n s.iH_l2 = u.sym_l2_norm(invH)\n\n with u.timeit(f\"norms-{i}\"):\n s.H_fro = H.flatten().norm()\n s.iH_fro = invH.flatten().norm()\n s.grad_fro = g.flatten().norm()\n s.param_fro = param.data.flatten().norm()\n\n def loss_direction(dd: torch.Tensor, eps):\n \"\"\"loss improvement if we take step eps in direction dd\"\"\"\n return u.to_python_scalar(eps * (dd @ g.t()) - 0.5 * eps ** 2 * dd @ H @ dd.t())\n\n def curv_direction(dd: torch.Tensor):\n \"\"\"Curvature in direction dd\"\"\"\n return u.to_python_scalar(dd @ H @ dd.t() / (dd.flatten().norm() ** 2))\n\n with u.timeit(f\"pinvH-{i}\"):\n pinvH = u.pinv(H)\n\n with u.timeit(f'curv-{i}'):\n s.grad_curv = curv_direction(g) # curvature (eigenvalue) in direction g\n ndir = g @ pinvH # newton direction\n s.newton_curv = curv_direction(ndir)\n setattr(layer.weight, 'pre', pinvH) # save Newton preconditioner\n s.step_openai = 1 / s.grad_curv if s.grad_curv else 1234567\n s.step_div_inf = 2 / s.H_l2 # divegent step size for batch_size=infinity\n s.step_div_1 = torch.tensor(2) / torch.trace(H) # divergent step for batch_size=1\n\n s.newton_fro = ndir.flatten().norm() # frobenius norm of Newton update\n s.regret_newton = u.to_python_scalar(g @ pinvH @ g.t() / 2) # replace with \"quadratic_form\"\n s.regret_gradient = loss_direction(g, s.step_openai)\n\n with u.timeit(f'rho-{i}'):\n s.rho, s.lyap_erank, lyap_evals = u.truncated_lyapunov_rho(H, sigma)\n s.step_div_1_adjusted = s.step_div_1/s.rho\n\n with u.timeit(f\"batch-{i}\"):\n s.batch_openai = torch.trace(H @ sigma) / (g @ H @ g.t())\n s.diversity = torch.norm(G, \"fro\") ** 2 / torch.norm(g) ** 2 / n # Gradient diversity / n\n s.noise_variance_pinv = torch.trace(pinvH @ sigma)\n s.H_erank = torch.trace(H) / s.H_l2\n s.batch_jain_simple = 1 + s.H_erank\n s.batch_jain_full = 1 + s.rho * s.H_erank\n\n param_name = f\"{layer.name}={param_names[param]}\"\n u.log_scalars(u.nest_stats(f\"{param_name}\", s))\n\n H_evals = u.symeig_pos_evals(H)\n sigma_evals = u.symeig_pos_evals(sigma)\n u.log_spectrum(f'{param_name}/hess', H_evals)\n u.log_spectrum(f'{param_name}/sigma', sigma_evals)\n u.log_spectrum(f'{param_name}/lyap', lyap_evals)\n\n # gradient steps\n with u.timeit('inner'):\n for i in range(args.train_steps):\n optimizer.zero_grad()\n data, targets = next(train_iter)\n model.zero_grad()\n output = model(data)\n loss = loss_fn(output, targets)\n loss.backward()\n\n optimizer.step()\n if args.weight_decay:\n for group in optimizer.param_groups:\n for param in group['params']:\n param.data.mul_(1-args.weight_decay)\n\n gl.increment_global_step(data.shape[0])\n\n gl.event_writer.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n parser.add_argument('--wandb', type=int, default=0, help='log to weights and biases')\n parser.add_argument('--autograd_check', type=int, default=0, help='autograd correctness checks')\n parser.add_argument('--logdir', type=str, default='/temp/runs/curv_train_tiny/run')\n parser.add_argument('--train_batch_size', type=int, default=10)\n parser.add_argument('--stats_batch_size', type=int, default=10)\n parser.add_argument('--data_width', type=int, default=8)\n parser.add_argument('--train_steps', type=int, default=10, help=\"this many train steps between stat collection\")\n parser.add_argument('--stats_steps', type=int, default=1000000, help=\"total number of curvature stats collections\")\n parser.add_argument('--nonlin', type=int, default=1, help=\"whether to add ReLU nonlinearity between layers\")\n parser.add_argument('--skip_stats', type=int, default=0, help='skip all stats collection')\n parser.add_argument('--weight_decay', type=float, default=1e-3)\n parser.add_argument('--lmb', type=float, default=1e-3, help=\"lambda regularizer for inverting H\")\n\n args = parser.parse_args()\n\n main()\n" }, { "path": "autotune/util.py", "content": "# Take simple example, plot per-layer stats over time\n# This function allows you to visualize the statistics of a layer.\nimport inspect\nimport math\nimport os\nimport random\nimport re\nimport sys\nimport time\nfrom typing import Any, Dict, Callable, Optional, Tuple, Union, Sequence, Iterable\nfrom typing import List\n\nimport six\nimport wandb\nfrom attrdict import AttrDict\nfrom torch.utils import tensorboard\n\nimport globals as gl\nimport numpy as np\nimport scipy\nimport scipy.linalg as linalg\nimport torch\nimport torch.nn as nn\nimport torchvision.datasets as datasets\nfrom PIL import Image\n\nimport platform\n\nimport torch.nn.functional as F\n\n# to enable referring to functions in its own module as u.func\nu = sys.modules[__name__]\n\n\n# numerical noise cutoff for eigenvalues, from scipy.linalg.pinv2\n# max float32 condition number: 8.4k (8388.61)\n# max float64 condition number: 4.5B\n\ndef get_condition(dtype):\n \"\"\"Return number x such that values below max(eigenval)*x are indistinguishable from noise\"\"\"\n assert 'float' in str(dtype)\n if str(dtype).endswith('float32') or str(dtype).endswith('float16'):\n return 1e3 * 1.1920929e-07\n else: # assume float64\n return 1e6 * 2.220446049250313e-16\n\n\ndef v2c(vec):\n \"\"\"Convert vector to column matrix.\"\"\"\n vec = to_pytorch(vec)\n assert len(vec.shape) == 1\n return torch.unsqueeze(vec, 1)\n\n\ndef v2c_np(vec):\n \"\"\"Convert vector to column matrix.\"\"\"\n assert len(vec.shape) == 1\n return np.expand_dims(vec, 1)\n\n\ndef v2r(vec: torch.Tensor) -> torch.Tensor:\n \"\"\"Converts rank-1 tensor to row matrix\"\"\"\n vec = to_pytorch(vec)\n assert len(vec.shape) == 1\n return vec.unsqueeze(0)\n\n\ndef c2v(col: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert vector into row matrix.\"\"\"\n vec = to_pytorch(col)\n assert len(col.shape) == 2\n assert col.shape[1] == 1\n return torch.reshape(col, [-1])\n\n\ndef vec(mat):\n \"\"\"vec operator, stack columns of the matrix into single column matrix.\"\"\"\n vec = to_pytorch(mat)\n assert len(mat.shape) == 2\n return mat.t().reshape(-1, 1)\n\n\ndef test_vec():\n mat = torch.tensor([[1, 3, 5], [2, 4, 6]])\n check_equal(c2v(vec(mat)), [1, 2, 3, 4, 5, 6])\n\n\ndef test_kron_trace():\n n = 5\n m = 4\n A = torch.rand((m, m))\n B = torch.rand((n, n))\n C = kron(A, B)\n u.check_close(torch.trace(C), u.kron_trace((A, B)))\n\n\ndef tvec(mat):\n \"\"\"transposed vec operator concatenates rows into single row matrix\"\"\"\n assert len(mat.shape) == 2\n return mat.reshape(1, -1)\n\n\ndef test_tvec():\n mat = torch.tensor([[1, 3, 5], [2, 4, 6]])\n check_equal(tvec(mat), [[1, 3, 5, 2, 4, 6]])\n\n\ndef unvec(a, rows):\n \"\"\"reverse of vec, rows specifies number of rows in the final matrix.\"\"\"\n assert len(a.shape) == 2\n assert a.shape[1] == 1, f\"argument expected to be a column matrix, instead got shape {a.shape}\"\n assert a.shape[0] % rows == 0\n cols = a.shape[0] // rows\n return a.reshape(cols, -1).t()\n\n\ndef untvec(a, rows):\n \"\"\"reverse of tvec, rows specifies number of rows in the final matrix.\"\"\"\n assert len(a.shape) == 2\n assert a.shape[0] == 1\n assert a.shape[1] % rows == 0\n return a.reshape(rows, -1)\n\n\ndef kron(a: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], b: Optional[torch.Tensor] = None):\n \"\"\"Kronecker product a otimes b.\"\"\"\n\n if isinstance(a, Tuple):\n assert b is None\n a, b = a\n\n if is_vector(a) and is_vector(b):\n return torch.einsum('i,j->ij', a, b).flatten()\n\n # print('inside a', a)\n # print('inside b', b)\n result = torch.einsum(\"ab,cd->acbd\", a, b)\n # print('kron', result)\n # TODO: use tensor.continuous\n\n if result.is_contiguous():\n return result.view(a.size(0) * b.size(0), a.size(1) * b.size(1))\n else:\n print(\"Warning kronecker product not contiguous, using reshape\")\n return result.reshape(a.size(0) * b.size(0), a.size(1) * b.size(1))\n\n\ndef stable_kron(a, b):\n a_norm, b_norm = torch.max(a), torch.max(b)\n return kron(a / a_norm, b / b_norm) * a_norm * b_norm\n\n\nclass SpecialForm:\n def normal_form(self):\n raise NotImplemented\n\n\nclass Vec(SpecialForm):\n \"\"\"Helper class representing x=Vec(X) and associated kronecker products\n\n x = vec(X)\n vec(AXB)' = x'(B*A')\n vec(AXB) = (B'*A)x\n\n xx = dot-product\n \"\"\"\n\n mat: torch.Tensor\n shape: Tuple\n numel: int\n rank: int\n\n def __init__(self, mat, shape: Tuple = None):\n mat = to_pytorch(mat)\n if shape is not None:\n mat = mat.reshape(shape)\n else:\n shape = mat.shape\n self.mat = mat\n self.shape = shape\n\n assert np.prod(shape) == mat.numel()\n self.rank = len(shape)\n self.numel = self.mat.numel()\n\n assert self.rank >= 0\n assert self.rank <= 2\n\n def vec_form(self):\n return u.vec(self.mat).flatten()\n\n def matrix_form(self):\n return self.mat\n\n def normal_form(self):\n return self.vec_form()\n\n def __matmul__(self, other):\n if type(other) == Vec:\n return torch.sum(self.mat * other.mat)\n elif type(other) != torch.Tensor:\n return NotImplemented\n\n return self.vec_form() @ other\n\n def __rmatmul__(self, other):\n if type(other) == Vec:\n return other.__matmul__(self)\n elif type(other) != torch.Tensor:\n return NotImplemented\n return other @ self.vec_form()\n\n def __truediv__(self, other):\n return Vec(self.mat / other)\n\n def norm(self):\n return self.mat.flatten().norm()\n\n def commute(self):\n \"\"\"Transpose matrix inside of vec operation.\n Equivalent to left multiplication by the commutation matrix.\"\"\"\n\n return Vecr(self.mat)\n\n def __str__(self):\n return str(to_numpy(self.normal_form()))\n\n\nclass Vecr(SpecialForm):\n \"\"\"Helper class representing row vectorization Vecr(X)=Vec(X') and associated kronecker products\n\n x=vecr(X)\n (A*B')x = vecr(AXB)\n x'(A'*B) = vecr(AXB)'\n xx = dotproduct\n \"\"\"\n\n mat: torch.Tensor\n shape: Tuple\n numel: int\n rank: int\n\n def __init__(self, mat, shape: Tuple = None):\n mat = to_pytorch(mat)\n if shape is not None:\n mat = mat.reshape(shape)\n else:\n shape = mat.shape\n self.mat = mat\n self.shape = shape\n\n assert np.prod(shape) == mat.numel()\n self.rank = len(shape)\n self.numel = self.mat.numel()\n\n assert self.rank >= 0\n assert self.rank <= 2\n\n def vec_form(self):\n return self.mat.flatten()\n\n def matrix_form(self):\n return self.mat\n\n def normal_form(self):\n return self.vec_form()\n\n def __matmul__(self, other):\n if type(other) == Vecr:\n return torch.sum(self.mat * other.mat)\n elif type(other) != torch.Tensor:\n return NotImplemented\n\n return self.vec_form() @ other\n\n def __rmatmul__(self, other):\n if type(other) == Vecr:\n return other.__matmul__(self)\n elif type(other) != torch.Tensor:\n return NotImplemented\n return other @ self.vec_form()\n\n def __truediv__(self, other):\n return Vecr(self.mat / other)\n\n def norm(self):\n return self.mat.flatten().norm()\n\n def commute(self):\n \"\"\"Transpose matrix inside of vec operation.\n Equivalent to left multiplication by the commutation matrix.\"\"\"\n\n return Vec(self.mat)\n\n def __str__(self):\n return str(to_numpy(self.normal_form()))\n\n\nclass Cov(SpecialForm):\n pass\n\n\nclass FactoredCov(SpecialForm):\n pass\n\n\nclass KronFactoredCov(SpecialForm):\n \"\"\"Kronecker factored covariance matrix. Covariance matrix of random variable ba' constructed from paired\n samples of a and b. Each sample of a can correspond to multiple samples of b, reprented by an extra batch dimension in b sample matrix.\"\"\"\n\n a_num: int # number of a samples\n b_num: int # number of b samples\n ab_num: int # number of samples used for AB cross-covariance estimate\n a_dim: int # dimension of a samples\n b_dim: int # dimension of b samples\n AA: torch.Tensor # sum of a covariances\n BB: torch.Tensor # sum of b covariances\n AB: torch.Tensor # sum of a,b cross covariances\n\n def __init__(self, a_dim, b_dim):\n self.a_dim = a_dim\n self.b_dim = b_dim\n self.AA = torch.zeros(a_dim, a_dim).to(gl.device)\n self.BB = torch.zeros(b_dim, b_dim).to(gl.device)\n self.AB = torch.zeros(a_dim, b_dim).to(gl.device)\n\n self.a_num = 0\n self.b_num = 0\n self.ab_num = 0\n\n def add_samples(self, A: torch.Tensor, B: torch.Tensor):\n \"\"\"\n\n Args:\n A: (*, d1) matrix of samples of a where * is zero or more batch dimensions\n B: (*, d2) matrix of samples of b where * is zero or more batch dimensions\n\n For variable batch dimensions, currently only supports A having 1 batch dimension, and B having 1 or 2 batch dimensions\n \"\"\"\n\n if is_matrix(A) and is_matrix(B):\n n = A.shape[0]\n assert B.shape[0] == n, f\"Number of samples do not match, got {A.shape}, {B.shape}\"\n assert A.shape[1] == self.a_dim\n assert B.shape[1] == self.b_dim\n\n self.AA += torch.einsum(\"ni,nj->ij\", A, A)\n self.BB += torch.einsum(\"ni,nj->ij\", B, B)\n self.AB += torch.einsum(\"ni,nj->ij\", A, B)\n self.a_num += n\n self.b_num += n\n self.ab_num += n\n\n elif len(A.shape) == 2 and len(B.shape) == 3:\n # TODO(y): this can be done more efficiently without stacking A\n n = A.shape[0]\n assert n == B.shape[1], f\"Number of samples do not match, got {A.shape}, {B.shape}\"\n assert A.shape[1] == self.a_dim\n assert B.shape[2] == self.b_dim\n o = B.shape[0]\n A = torch.stack([A] * o)\n\n self.AA += torch.einsum(\"oni,onj->ij\", A, A)\n self.BB += torch.einsum(\"oni,onj->ij\", B, B)\n self.AB += torch.einsum(\"oni,onj->ij\", A, B)\n # TODO(y): fix inconsistent counts, current version is what was needed to make Hessians match autograd\n self.a_num += (o * n)\n self.b_num += n\n self.ab_num += o * n\n else:\n assert False, f\"Broadcasting not implemented for shapes {A.shape} and {B.shape}\"\n\n def value(self) -> \"Kron\":\n return Kron(self.AA / self.a_num, self.BB / self.b_num)\n\n def cross(self) -> torch.Tensor:\n \"\"\"Return cross covariance matrix AB'\"\"\"\n return self.AB / self.ab_num\n\n def wilks(self) -> torch.Tensor:\n \"\"\"Returns Wilk's statistic for the test of independence of two terms\"\"\"\n\n covA, covB = self.value()\n covAB = self.cross()\n\n with u.timeit('wilks'):\n K = isymsqrt(covA) @ covAB @ isymsqrt(covB)\n U, S, V = robust_svd(K)\n vals = 1. - square(torch.diag(S))\n return torch.prod(vals)\n\n def bartlett(self):\n \"\"\"Returns Bartlett statistic for the test of independence.\"\"\"\n q = self.a_dim\n p = self.b_dim\n n = self.ab_num\n val = -(n - (p + q + 3.) / 2.) * torch.log(self.wilks())\n print('bartlett, ', val, 'mean', q * p)\n return val\n\n def prob_dep(self):\n \"\"\"Returns probability of independence hypothesis holding.\"\"\"\n from scipy.stats import chi2\n rv = chi2(self.a_dim * self.b_dim)\n return rv.sf(to_numpy(self.bartlett()))\n\n def sigmas_indep(self):\n \"\"\"Returns number of standard deviations away from independence.\"\"\"\n from scipy.stats import chi2\n df = self.a_dim * self.b_dim\n return (self.bartlett() - df) / (4 * np.sqrt(to_numpy(df)))\n\n def __str__(self):\n return f\"KronFactoredCov(AA=\\n{self.AA},\\n BB={self.BB})\"\n\n\ndef square(a: torch.Tensor):\n return a * a\n\n\n# TODO(y): rename into sym-kron\nclass Kron(SpecialForm):\n \"\"\"Represents kronecker product of two symmetric matrices.\n\n\n\n \"\"\"\n LL: torch.Tensor # left factor\n RR: torch.Tensor # right factor\n\n def __init__(self, LL, RR):\n LL = to_pytorch(LL)\n RR = to_pytorch(RR)\n\n assert is_matrix(LL), f\"shape check fail with {LL.shape}\"\n assert is_matrix(RR), f\"shape check fail with {RR.shape}\"\n self.LL = LL\n self.RR = RR\n\n # todo(y): remove this check onces it's split into KronFactored and SymmetricKronFactored\n assert LL.shape[0] == LL.shape[1], f\"shape check fail with {LL.shape}\"\n assert RR.shape[0] == RR.shape[1], f\"shape check fail with {RR.shape}\"\n\n self.lsize = LL.shape[0]\n self.rsize = RR.shape[0]\n\n def commute(self):\n \"\"\"Commutes order of operation: A kron B -> B kron A \"\"\"\n\n return Kron(LL=self.RR, RR=self.LL)\n\n def normal_form(self):\n return self.expand()\n\n def expand(self):\n \"\"\"Returns expanded representation (row-major form)\"\"\"\n return kron(self.LL, self.RR)\n\n def expand_vec(self):\n \"\"\"Returns expanded representation (col-major form, to match vec order in literature)\"\"\"\n return kron(self.RR, self.LL)\n\n def sym_l2_norm(self):\n return sym_l2_norm(self.LL) * sym_l2_norm(self.RR)\n\n def symsqrt(self, cond=None, return_rank=False):\n a = symsqrt(self.LL, cond, return_rank)\n b = symsqrt(self.RR, cond, return_rank)\n if not return_rank:\n return Kron(a, b)\n else:\n a, rank_a = a\n b, rank_b = b\n return Kron(a, b), rank_a * rank_b\n\n def trace(self):\n return torch.trace(self.LL) * torch.trace(self.RR)\n\n def frobenius_norm(self):\n return torch.norm(self.LL.flatten()) * torch.norm(self.RR.flatten())\n\n def pinv(self):\n return Kron(torch.pinverse(self.LL), torch.pinverse(self.RR))\n\n def inv(self):\n return Kron(torch.inverse(self.LL), torch.inverse(self.RR))\n\n @property\n def shape(self):\n return self.LL.shape, self.RR.shape\n\n def qf(self, G):\n \"\"\"Returns quadratic form g @ H @ g' \"\"\"\n assert G.shape[1] == self.RR.shape[0]\n assert G.shape[0] == self.LL.shape[0]\n return torch.sum(G * (self.LL @ G @ self.RR))\n\n def qf_vec(self, G):\n \"\"\"Returns quadratic form g' @ H @ g\"\"\"\n assert G.shape[0] == self.RR.shape[0]\n assert G.shape[1] == self.LL.shape[0]\n return torch.sum(G * (self.RR @ G @ self.LL))\n\n # TODO(y): implement in-place ops\n def __truediv__(self, other):\n return Kron(self.LL, self.RR / other)\n\n def __add__(self, other):\n other = to_python_scalar(other)\n return Kron(self.LL, self.RR + other)\n\n def __radd__(self, other):\n other = to_python_scalar(other)\n return Kron(self.LL + other, self.RR)\n\n def __mul__(self, other):\n other = to_python_scalar(other)\n return Kron(self.LL, self.RR * other)\n\n def __rmul__(self, other):\n other = to_python_scalar(other)\n return Kron(self.LL * other, self.RR)\n\n # remove, scalar addition doesn't make sense because it breaks factoring\n # def __add__(self, other):\n # if u.is_scalar(other):\n # return Kron(self.LL, self.RR+to_pytorch(other))\n # else:\n # return NotImplemented\n #\n # def __radd__(self, other):\n # if u.is_scalar(other):\n # return Kron(self.LL+to_pytorch(other), self.RR)\n # else:\n # return NotImplemented\n\n def __matmul__(self, x):\n if type(x) == Kron:\n return Kron(self.LL @ x.LL, self.RR @ x.RR)\n elif type(x) in [Vec, Vecr]:\n\n X = x.matrix_form()\n if type(x) == Vec: # kron @ vec(mat)\n assert X.shape == (self.rsize, self.lsize), f\"Dimension mismatch, {X.shape}, {self.lsize}, {self.rsize}\"\n return Vec(self.RR @ X @ self.LL.t())\n elif type(x) == Vecr:\n assert X.shape == (self.lsize, self.rsize), f\"Dimension mismatch, {X.shape}, {self.lsize}, {self.rsize}\"\n return Vecr(self.LL @ X @ self.RR.t())\n elif type(x) is torch.Tensor:\n return self.normal_form() @ x\n else:\n return NotImplemented\n\n def __rmatmul__(self, x):\n if type(x) in [Vec, Vecr]:\n X = x.matrix_form()\n if type(x) == Vec:\n assert X.shape == (self.rsize, self.lsize), f\"Dimension mismatch, {X.shape}, {self.lsize}, {self.rsize}\"\n return Vec(self.RR.t() @ X @ self.LL)\n elif type(x) == Vecr:\n assert X.shape == (self.lsize, self.rsize), f\"Dimension mismatch, {X.shape}, {self.lsize}, {self.rsize}\"\n return Vecr(self.LL.t() @ X @ self.RR)\n elif type(x) is torch.Tensor:\n return x @ self.normal_form()\n else:\n return NotImplemented\n\n def __str__(self):\n return f\"Kron(\\n{self.LL},\\n {self.RR})\"\n\n def __iter__(self):\n return iter([self.LL, self.RR])\n\n\nclass MeanKronFactored(SpecialForm):\n \"\"\"Factored representation as a mean of kronecker products\"\"\"\n AA: torch.Tensor # stacked forward factors\n BB: torch.Tensor # stacked backward factor\n\n def __init__(self, AA: torch.Tensor, BB: torch.Tensor):\n # AA: n, di, di\n # BB: n, do, do\n\n assert AA.shape[0] == BB.shape[0]\n assert AA.shape[1] == AA.shape[2]\n assert BB.shape[1] == BB.shape[2]\n n, di, _ = AA.shape\n n, do, _ = BB.shape\n self.AA = AA\n self.BB = BB\n self.n = n\n self.di = di\n self.do = do\n\n def expand(self):\n result = torch.einsum('nij,nkl->nikjl', self.BB, self.AA)\n # print(result)\n # result = kron(self.BB[0,...], self.AA[0,...]).unsqueeze(0) # torch.einsum(\"ab,cd->acbd\", a, b)\n # result = torch.einsum(\"ab,cd->acbd\", a, b)\n # print('outside left', self.BB[0,...])\n # print('outside right', self.AA[0,...])\n # print('outside', torch.einsum('ab,cd->abcd', self.BB[0,...], self.AA[0,...]))\n # result = kron(self.BB[0,...], self.AA[0,...]) # torch.einsum(\"ab,cd->acbd\", a, b)\n # if not result.is_contiguous():\n # print(\"Warning, using contiguous\")\n # result = result.contiguous() # needed for .view\n\n result = result.sum(dim=0) / self.n\n return result.view(self.do * self.di, self.do * self.di)\n\n\ndef expand_hess(*v) -> Union[torch.Tensor, List[torch.Tensor]]:\n \"\"\"Expands Hessian represented in Kronecker factored form.\n\n Note: For consistency with PyTorch autograd, we use row-major order. This means the order of Kronecker\n multiplication needs to be reversed compared to literature which uses column-major order (implied by vec).\"\"\"\n result = [kron(a.RR, a.LL) for a in v]\n\n if len(result) == 1:\n return result[0]\n else:\n return result\n\n\ndef test_kron():\n A = torch.tensor([[1, 2], [3, 4]])\n B = torch.tensor([[6, 7], [8, 9]])\n C = kron(A, B)\n Cnp = np.kron(to_numpy(A), to_numpy(B))\n check_equal(C, [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]])\n check_equal(C, Cnp)\n\n\ndef nan_check(mat):\n nan_mask = torch.isnan(mat).float()\n nans = torch.sum(nan_mask).item()\n not_nans = torch.sum(torch.tensor(1) - nan_mask).item()\n\n assert nans == 0, f\"matrix of shape {mat.shape} has {nans}/{nans + not_nans} nans\"\n\n\ndef has_nan(mat):\n return torch.sum(torch.isnan(mat)) > 0\n\n\ndef fro_norm(mat: torch.Tensor):\n return torch.norm(mat.flatten())\n\n\nfrobenius_norm = fro_norm\n\n\ndef l2_norm(mat: torch.Tensor):\n \"\"\"Largest eigenvalue.\"\"\"\n try:\n u, s, v = robust_svd(mat)\n except RuntimeError as e:\n if gl.debug_linalg_crashes:\n print(e)\n dump(mat, '/tmp/l2_norm.txt')\n assert False, f\"svd failed with {e}\"\n else:\n return -1\n return torch.max(s)\n\n\ndef sym_l2_norm(mat: torch.Tensor):\n \"\"\"Largest eigenvalue assuming that matrix is symmetric.\"\"\"\n\n u.check_symmetric(mat)\n\n if gl.debug_linalg_crashes:\n try:\n evals, _evecs = torch.symeig(mat)\n except RuntimeError as e:\n print(e)\n dump(mat, '/tmp/sym_l2_norm.txt')\n sys.exit()\n else:\n evals, _evecs = torch.symeig(mat)\n\n return torch.max(evals)\n\n\ndef inv_square_root_numpy(mat):\n assert type(mat) == np.ndarray\n return scipy.linalg.inv(scipy.linalg.sqrtm(mat))\n\n\ndef pinv_square_root_numpy(mat):\n assert type(mat) == np.ndarray\n result = scipy.linalg.inv(scipy.linalg.sqrtm(mat))\n return result\n\n\ndef erank(mat):\n \"\"\"Effective rank of matrix.\"\"\"\n return torch.trace(mat) / l2_norm(mat)\n\n\ndef rank(A):\n \"\"\"Rank of a matrix\"\"\"\n U, S, V = torch.svd(A)\n cond = get_condition(A.dtype)\n cutoff = torch.max(S) * cond\n return torch.sum(S > cutoff).type(torch.get_default_dtype())\n\n\ndef sym_erank(mat):\n \"\"\"Effective rank of symmetric matrix.\"\"\"\n return torch.trace(mat) / sym_l2_norm(mat)\n\n\ndef lyapunov_spectral(A, B, cond=None):\n u.check_symmetric(A)\n u.check_symmetric(B)\n\n s, U = torch.symeig(A, eigenvectors=True)\n if cond is None:\n cond = get_condition(s.dtype)\n cutoff = cond * max(s)\n s = torch.where(s > cutoff, s, torch.tensor(0.).to(s.device))\n\n C = U.t() @ B @ U # TODO(y): throw away eigenvectors corresponding to discarded evals. U=U[:num_eigs]\n s = s.unsqueeze(1) + s.unsqueeze(0)\n si = torch.where(s > 0, 1 / s, s)\n Y = C * si\n X = U @ Y @ U.t()\n\n # cancel small asymetries introduces by multiplication by small numbers\n X = (X + X.t()) / 2\n return X\n\n\ndef lyapunov_svd(A, C, rtol=1e-4, eps=1e-7, use_svd=False):\n \"\"\"Solve AX+XA=C using SVD\"\"\"\n\n # This method doesn't work for singular matrices, so regularize it\n # TODO: can optimize performance by reusing eigenvalues from regularization computations\n A = regularize_mat(A, eps)\n C = regularize_mat(C, eps)\n\n assert A.shape[0] == A.shape[1]\n assert len(A.shape) == 2\n if use_svd:\n U, S, V = robust_svd(A)\n else:\n S, U = torch.symeig(A, eigenvectors=True)\n S = S.diag() @ torch.ones_like(A)\n X = U @ ((U.t() @ C @ U) / (S + S.t())) @ U.t()\n error = A @ X + X @ A - C\n relative_error = torch.max(torch.abs(error)) / torch.max(torch.abs(A))\n if relative_error > rtol:\n # TODO(y): currently spams with errors, implement another method based on Newton iteration\n pass\n print(f\"Warning, error {relative_error} encountered in lyapunov_svd\")\n\n return X\n\n\ndef deleteme():\n t = torch.ones(3, dtype=torch.float64)\n a = t / 2.\n\n\ndef lyapunov_svd2(A, C, rtol=1e-4, eps=1e-7, use_svd=False):\n \"\"\"Solve AX+XA=C using SVD\"\"\"\n\n # This method doesn't work for singular matrices, so regularize it\n # TODO: can optimize performance by reusing eigenvalues from regularization computations\n # A = regularize_mat(A, eps)\n # C = regularize_mat(C, eps)\n\n assert A.shape[0] == A.shape[1]\n assert len(A.shape) == 2\n if use_svd:\n U, S, V = robust_svd(A)\n else:\n S, U = torch.symeig(A, eigenvectors=True)\n S = S.diag() @ torch.ones_like(A)\n factor = (S + S.t())\n cutoff = max(S) * get_condition(S)\n factor = torch.where(factor > cutoff, 1 / factor, factor)\n X = U @ ((U.t() @ C @ U) * factor) @ U.t()\n error = A @ X + X @ A - C\n relative_error = torch.max(torch.abs(error)) / torch.max(torch.abs(A))\n if relative_error > rtol:\n # TODO(y): currently spams with errors, implement another method based on Newton iteration\n pass\n print(f\"Warning, error {relative_error} encountered in lyapunov_svd\")\n\n return X\n\n\ndef lyapunov_truncated(A, C, use_svd=False, top_k=None, check_error=False):\n \"\"\"Truncated solution to AX+XA=C. top_k specified how many dimensions of A to use. If None, use threshold for\n acceptable condition.\"\"\"\n\n rankA = u.rank(A)\n rankC = u.rank(C)\n if rankA < rankC:\n print(\"Warning, losing precision, rank A: {rankA}, rank C: {rankC}\")\n\n assert A.shape[0] == A.shape[1]\n assert len(A.shape) == 2\n cond = get_condition(A.dtype)\n\n if use_svd:\n U, S, V = torch.svd(A)\n else:\n # flip to be in decreasing order like for SVD\n # TODO: can optimize this case by skipping flip\n S, U = torch.symeig(A, eigenvectors=True)\n S = torch.flip(S, [0])\n U = torch.flip(U, [1])\n cutoff = torch.max(S) * cond\n rank = torch.sum(S > cutoff)\n if top_k is not None:\n top_k = rank\n\n S = torch.where(S > cutoff, S, torch.zeros_like(S))\n S = S.diag() @ torch.ones_like(A)\n U = U[:, :top_k]\n projected = U.t() @ C @ U\n divider = S + S.t()\n divider = divider[:top_k, :top_k]\n\n divided = torch.where(S > 0, projected / divider, torch.zeros_like(projected))\n X = U @ divided @ U.t()\n if check_error:\n error = A @ X + X @ A - C\n relative_error = torch.max(torch.abs(error)) / torch.max(torch.abs(A))\n if relative_error > 1e-3:\n print('rel error', relative_error)\n return X\n\n\ndef lyapunov_lstsq(A, C):\n \"\"\"Slow explicit solution to least squares Lyapunov in kronecker expanded form.\"\"\"\n n, n = A.shape\n ii = torch.eye(n)\n sol = torch.lstsq(u.vec(C), kron(ii, A) + kron(A.t(), ii))[0]\n return unvec(sol, n)\n\n\n# TODO(y): reuse logic from above\ndef truncated_lyapunov_rho(A, C):\n \"\"\"Returns quantities related to spectrum of solution to AX+XA=2C\n\n rho: measure of misfit\n erank: effective rank of X\n .\"\"\"\n\n C = 2 * C # to center spectrum at 1\n assert A.shape[0] == A.shape[1]\n assert len(A.shape) == 2\n cond = get_condition(A.dtype)\n\n S, U = torch.symeig(A, eigenvectors=True)\n S = torch.flip(S, [0])\n U = torch.flip(U, [1])\n cutoff = torch.max(S) * cond\n rank = torch.sum(S > cutoff)\n top_k = rank\n\n S = S[:top_k].diag()\n S = S @ torch.ones_like(S)\n\n U = U[:, :top_k]\n projected = U.t() @ C @ U\n divider = S + S.t()\n divider = divider[:top_k, :top_k]\n\n divided = torch.where(S > 0, projected / divider, torch.zeros_like(projected))\n X = U @ divided @ U.t()\n nan_check(X)\n\n U, S, V = torch.svd(X) # can we use symeig here?\n # S = torch.symeig(X).eigenvalues\n # S = u.filter_evals(S)\n\n erank = torch.sum(S) / torch.max(S)\n rho = A.shape[0] / erank\n spectrum = filter_evals(S)\n\n return rho, erank, spectrum\n\n\ndef outer(x, y=None):\n \"\"\"Outer product of xy', treating x,y as column vectors. If y is not specified, compute xx'\"\"\"\n if y is None:\n y = x\n return x.unsqueeze(1) @ y.unsqueeze(0)\n\n\ndef to_python_scalar(x):\n \"\"\"Convert object to Python scalar.\"\"\"\n if hasattr(x, 'item'):\n return x.item()\n x = to_numpy(x).flatten()\n assert len(x) == 1\n return x[0]\n\n\ndef is_scalar(x):\n try:\n x = to_python_scalar(x)\n except:\n return False\n return True\n\n\ndef from_numpy(x) -> torch.Tensor:\n if isinstance(x, torch.Tensor):\n return x\n else:\n return torch.tensor(x)\n\n\n_pytorch_floating_point_types = (torch.float16, torch.float32, torch.float64)\n\n_numpy_type_map = {\n 'float64': torch.DoubleTensor,\n 'float32': torch.FloatTensor,\n 'float16': torch.HalfTensor,\n 'int64': torch.LongTensor,\n 'int32': torch.IntTensor,\n 'int16': torch.ShortTensor,\n 'int8': torch.CharTensor,\n 'uint8': torch.ByteTensor,\n}\n\n\ndef pytorch_dtype_to_floating_numpy_dtype(dtype):\n \"\"\"Converts PyTorch dtype to numpy floating point dtype, defaulting to np.float32 for non-floating point types.\"\"\"\n if dtype == torch.float64:\n dtype = np.float64\n elif dtype == torch.float32:\n dtype = np.float32\n elif dtype == torch.float16:\n dtype = np.float16\n else:\n dtype = np.float32\n return dtype\n\n\ndef to_normal_form(x):\n \"\"\"Convert object to a normal expression, ie FactoredMatrix->Tensor, identity op for objects not in special form.\"\"\"\n if hasattr(x, 'normal_form'):\n x = x.normal_form()\n assert not hasattr(x, 'normal_form'), 'infinite loop detected while expanding normal form'\n return x\n\n\ndef to_pytorch(x) -> torch.Tensor:\n \"\"\"Convert numeric object to floating point PyTorch tensor.\"\"\"\n x = to_normal_form(x)\n if type(x) == torch.Tensor:\n if x.dtype not in _pytorch_floating_point_types:\n x = x.type(torch.get_default_dtype())\n return x\n else:\n return from_numpy(to_numpy(x))\n\n\ndef to_pytorches(*xs) -> Tuple[torch.Tensor, ...]:\n return (to_pytorch(x) for x in xs)\n\n\ndef to_numpy(x, dtype: np.dtype = None) -> np.ndarray:\n \"\"\"\n Convert numeric object to floating point numpy array. If dtype is not specified, use PyTorch default dtype.\n\n Args:\n x: numeric object\n dtype: numpy dtype, must be floating point\n\n Returns:\n floating point numpy array\n \"\"\"\n\n assert np.issubdtype(dtype, np.floating), \"dtype must be real-valued floating point\"\n\n # Convert to normal_form expression from a special form (https://reference.wolfram.com/language/ref/Normal.html)\n if hasattr(x, 'normal_form'):\n x = x.normal_form()\n\n if type(x) == np.ndarray:\n assert np.issubdtype(x.dtype, np.floating), f\"numpy type promotion not implemented for {x.dtype}\"\n\n if type(x) == torch.Tensor:\n dtype = pytorch_dtype_to_floating_numpy_dtype(x.dtype)\n return x.detach().cpu().numpy().astype(dtype)\n\n # list or tuple, iterate inside to convert PyTorch arrrays\n if type(x) in [list, tuple]:\n x = [to_numpy(r) for r in x]\n\n # Some Python type, use numpy conversion\n result = np.array(x, dtype=dtype)\n assert np.issubdtype(result.dtype, np.number), f\"Provided object ({result}) is not numeric, has type {result.dtype}\"\n if dtype is None:\n return result.astype(pytorch_dtype_to_floating_numpy_dtype(torch.get_default_dtype()))\n return result\n\n\ndef to_numpys(*xs, dtype=np.float32):\n return (to_numpy(x, dtype) for x in xs)\n\n\ndef khatri_rao(A: torch.Tensor, B: torch.Tensor):\n \"\"\"Khatri-Rao product.\n i'th column of result C_i is a Kronecker product of A_i and B_i\n\n Section 2.6 of Kolda, Tamara G., and Brett W. Bader. \"Tensor decompositions and applications.\" SIAM review 51.3\n (2009): 455-500\"\"\"\n assert A.shape[1] == B.shape[1]\n # noinspection PyTypeChecker\n return torch.einsum(\"ik,jk->ijk\", A, B).reshape(A.shape[0] * B.shape[0], A.shape[1])\n\n\ndef khatri_rao_t(A: torch.Tensor, B: torch.Tensor):\n \"\"\"Transposed Khatri-Rao, inputs and outputs are transposed.\n\n i'th row of result C_i is a Kronecker product of corresponding rows of A and B\"\"\"\n\n assert A.shape[0] == B.shape[0]\n # noinspection PyTypeChecker\n return torch.einsum(\"ki,kj->kij\", A, B).reshape(A.shape[0], A.shape[1] * B.shape[1])\n\n\n# Autograd functions, from https://gist.github.com/apaszke/226abdf867c4e9d6698bd198f3b45fb7\n# noinspection PyTypeChecker\ndef jacobian(y: torch.Tensor, x: torch.Tensor, create_graph=False):\n jac = []\n flat_y = y.reshape(-1)\n grad_y = torch.zeros_like(flat_y)\n for i in range(len(flat_y)):\n grad_y[i] = 1.\n grad_x, = torch.autograd.grad(flat_y, x, grad_y, retain_graph=True, create_graph=create_graph)\n jac.append(grad_x.reshape(x.shape))\n grad_y[i] = 0.\n return torch.stack(jac).reshape(y.shape + x.shape)\n\n\ndef hessian(y: torch.Tensor, x: torch.Tensor):\n return jacobian(jacobian(y, x, create_graph=True), x)\n\n\ndef pinv(mat: torch.Tensor, cond=None) -> torch.Tensor:\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\n\n cond : float or None\n Cutoff for 'small' singular values. If omitted, singular values smaller\n than ``max(M,N)*largest_singular_value*eps`` are considered zero where\n ``eps`` is the machine precision.\n \"\"\"\n\n # Take cut-off logic from scipy\n # https://github.com/ilayn/scipy/blob/0f4c793601ecdd74fc9826ac02c9b953de99403a/scipy/linalg/basic.py#L1307\n\n # assert False, \"Disabled due to numerical instability, see test_pinverse\"\n nan_check(mat)\n u, s, v = robust_svd(mat)\n if cond in [None, -1]:\n cond = torch.max(s) * max(mat.shape) * np.finfo(np.dtype('float32')).eps\n rank = torch.sum(s > cond)\n\n u = u[:, :rank]\n u /= s[:rank]\n return u @ v.t()[:rank]\n\n\ndef eig_real(mat: torch.Tensor) -> torch.Tensor:\n \"\"\"Wrapper around torch.eig which discards imaginary values and returns result in descending order.\n\n Prints warning when non-zero imaginary parts detected, see \"Criteria for the reality of matrix eigenvalues\"\n Products of symmetric matrices are not symmetric but have eigenvalues for no imaginary parts.\n https://link.springer.com/article/10.1007%2FBF01195188\n \"\"\"\n\n evals = torch.eig(mat).eigenvalues\n re_part = evals[:, 0] # extract real part\n im_part = evals[:, 1] # extract real part\n if im_part.sum() / evals.max() > 1e-7:\n print(\"Warning, eig_real is discarding non-zero imaginary parts\")\n re_part = re_part.sort(descending=True).values\n return re_part\n\n\ndef pinv_square_root(mat: torch.Tensor, eps=1e-4) -> torch.Tensor:\n nan_check(mat)\n u, s, v = robust_svd(mat)\n one = torch.from_numpy(np.array(1))\n ivals: torch.Tensor = one / torch.sqrt(s)\n si = torch.where(s > eps, ivals, s)\n return u @ torch.diag(si) @ v.t()\n\n\ndef symeig_pos_evals(mat: torch.Tensor) -> torch.Tensor:\n \"\"\"Returns positive eigenvalues from symeig in decreasing order (to match order of .svd())\"\"\"\n\n s, u = torch.symeig(mat, eigenvectors=False)\n return torch.flip(filter_evals(s, remove_negative=True), dims=[0])\n\n\ndef svd_pos_svals(mat):\n \"\"\"Returns positive singular values of a matrix.\"\"\"\n\n U, S, V = robust_svd(mat)\n return filter_evals(S)\n\n\ndef filter_evals(vals, cond=None, remove_small=True, remove_negative=True):\n \"\"\"Given list of eigenvalues or singular values, remove values indistinguishable from noise and/or small values.\"\"\"\n orig_vals = vals\n if cond is None:\n cond = get_condition(vals.dtype)\n above_cutoff = (abs(vals) > cond * torch.max(abs(vals)))\n if remove_small:\n vals = vals[above_cutoff]\n if remove_negative:\n vals = vals[vals > 0]\n # if len(vals) == 0:\n # print(\"Warning, got empty eigenvalue list\")\n # return orig_vals\n return vals\n\n\ndef isymsqrt(mat, *args):\n return symsqrt(mat, inverse=True, *args)\n\n\ndef symsqrt(mat, cond=None, return_rank=False, inverse=False):\n \"\"\"Computes the symmetric square root of a symmetric matrix. Throws away small and negative eigenvalues.\"\"\"\n\n nan_check(mat)\n s, u = torch.symeig(mat, eigenvectors=True)\n\n # check_symmetric(mat)\n\n # todo(y): dedupe with getcond\n cond_dict = {torch.float32: 1e3 * 1.1920929e-07, torch.float64: 1E6 * 2.220446049250313e-16}\n\n if cond in [None, -1]:\n cond = cond_dict[mat.dtype]\n\n # Note, this can include negative values, see https://github.com/pytorch/pytorch/issues/25972\n above_cutoff = (s > cond * torch.max(abs(s)))\n\n if torch.sum(above_cutoff) == 0:\n return torch.zeros_like(mat)\n\n sigma_diag = torch.sqrt(s[above_cutoff])\n if inverse:\n sigma_diag = 1 / sigma_diag\n u = u[:, above_cutoff]\n\n B = u @ torch.diag(sigma_diag) @ u.t()\n\n if torch.sum(torch.isnan(B)) > 0:\n if gl.debug_linalg_crashes:\n dump(mat, '/tmp/symsqrt.txt')\n assert False\n\n if return_rank:\n return B, len(sigma_diag)\n else:\n return B\n\n\ndef symsqrt_svd(mat: torch.Tensor):\n \"\"\"Like symsqrt, but uses SVD.\"\"\"\n\n u, s, v = robust_svd(mat)\n svals: torch.Tensor = torch.sqrt(s)\n eps = get_condition(mat.dtype) * torch.max(abs(s))\n si = torch.where(s > eps, svals, s)\n if len(si) == 0:\n return torch.zeros_like(mat)\n return u @ torch.diag(si) @ v.t()\n\n\ndef robust_svd(mat: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Try to perform SVD and handle errors.\n \"\"\"\n\n assert is_matrix(mat), f\"shape {mat.shape}\"\n try:\n U, S, V = torch.svd(mat)\n except Exception as e: # this can fail, see https://github.com/pytorch/pytorch/issues/25978\n if is_square_matrix(mat):\n s = torch.symeig(mat).eigenvalues\n eps = get_condition(mat.dtype) * torch.max(abs(s))\n else:\n eps = get_condition(mat.dtype) * u.frobenius_norm(mat) / mat.shape[0]\n print(f\"Warning, SVD diverged with {e}, regularizing with {eps}\")\n mat = regularize_mat2(mat, eps * 2)\n U, S, V = torch.svd(mat)\n return U, S, V\n\n\ndef regularize_mat(mat, eps):\n rtol = l2_norm(mat) * eps\n atol = 1e-12\n return mat + torch.eye(mat.shape[0]) * (rtol + atol)\n\n\ndef regularize_mat2(mat, eps):\n \"\"\"Adds a multiple of identity to matrix.\"\"\"\n assert is_matrix(mat), f\"{mat.shape}\"\n if mat.shape[0] == mat.shape[1]:\n return mat + torch.eye(mat.shape[0]).to(gl.device) * eps\n if mat.shape[0] > mat.shape[1]:\n transpose = True\n mat = mat.T\n else:\n transpose = False\n reg = torch.cat([torch.eye(mat.shape[0]), torch.zeros(mat.shape[0], mat.shape[1] - mat.shape[0])], dim=1)\n mat = mat + reg.to(gl.device) * eps\n if transpose:\n return mat.T\n else:\n return mat\n\n\ndef symsqrt_dist(cov1: torch.Tensor, cov2: torch.Tensor) -> float:\n \"\"\"Distance between square roots of matrices\"\"\"\n\n cov1 = to_pytorch(cov1)\n cov2 = to_pytorch(cov2)\n cov1 = symsqrt_svd(cov1)\n cov2 = symsqrt_svd(cov2)\n return torch.norm(cov1 - cov2).item()\n\n\ndef check_symmetric(mat):\n try:\n u.check_close(mat, mat.t())\n except:\n discrepancy = torch.max(abs(mat - mat.t()) / mat)\n print(f\"warning, matrix not symmetric: {discrepancy}\")\n\n\ndef check_close(a0, b0, rtol=1e-5, atol=1e-8, label: str = '') -> None:\n \"\"\"Convenience method for check_equal with tolerances defaulting to typical errors observed in neural network\n ops in float32 precision.\"\"\"\n return check_equal(a0, b0, rtol=rtol, atol=atol, label=label)\n\n\ndef check_equal(observed, truth, rtol=1e-9, atol=1e-12, label: str = '') -> None:\n \"\"\"\n Assert fail any entries in two arrays are not close to each to desired tolerance. See np.allclose for meaning of rtol, atol\n\n \"\"\"\n\n # special handling for lists, which could contain\n # if type(observed) == List and type(truth) == List:\n # for a, b in zip(observed, truth):\n # check_equal(a, b)\n\n truth = to_numpy(truth)\n observed = to_numpy(observed)\n\n # broadcast to match shapes if necessary\n if observed.shape != truth.shape:\n # common_shape = (np.zeros_like(observed) + np.zeros_like(truth)).shape\n truth = truth + np.zeros_like(observed)\n observed = observed + np.zeros_like(truth)\n\n assert truth.shape == observed.shape, f\"Observed shape {observed.shape}, expected shape {truth.shape}\"\n # run np.testing.assert_allclose for extra info on discrepancies\n if not np.allclose(observed, truth, rtol=rtol, atol=atol, equal_nan=True):\n print(f'Numerical testing failed for {label}')\n np.testing.assert_allclose(truth, observed, rtol=rtol, atol=atol, equal_nan=True)\n\n\ndef get_param(layer): # TODO(y): deprecate?\n \"\"\"Extract parameter out of layer, assumes there's just one parameter in a layer.\"\"\"\n named_params = [(name, param) for (name, param) in layer.named_parameters()]\n assert len(named_params) == 1, named_params\n return named_params[0][1]\n\n\nglobal_timeit_dict = {}\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n # print(f\"{interval_ms:8.2f} {self.tag}\")\n log_scalars({'time/' + self.tag: interval_ms})\n\n\ndef run_all_tests(module: nn.Module):\n class local_timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(f\"{interval_ms:8.2f} {self.tag}\")\n\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name, func in all_functions:\n if name.startswith(\"test_\"):\n with local_timeit(name):\n func()\n print(module.__name__ + \" tests passed.\")\n\n\ndef freeze(layer: nn.Module):\n for param in layer.parameters():\n param.requires_grad = False\n setattr(layer, \"frozen\", True)\n\n\ndef unfreeze(layer: nn.Module):\n for param in layer.parameters():\n param.requires_grad = True\n setattr(layer, \"frozen\", False)\n\n\ndef mark_expensive(layer: nn.Module):\n setattr(layer, 'expensive', True)\n\n\ndef nest_stats(tag: str, stats) -> Dict:\n \"\"\"Nest given dict of stats under tag using TensorBoard syntax /nest1/tag\"\"\"\n result = {}\n for key, value in stats.items():\n result[f\"{tag}/{key}\"] = value\n return result\n\n\ndef seed_random(seed: int) -> None:\n \"\"\"Manually set seed to seed for configurable random number generators in current process.\"\"\"\n torch.manual_seed(seed)\n random.seed(seed)\n np.random.seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(seed)\n\n\nclass TinyMNIST(datasets.MNIST):\n \"\"\"Custom-size MNIST autoencoder dataset for debugging. Generates data/target images with reduced resolution and 0\n channels. When provided with original 28, 28 resolution, generates standard 1 channel MNIST dataset.\n\n Use original_targets kwarg to get original MNIST labels instead of autoencoder targets.\n\n\n \"\"\"\n\n def __init__(self, dataset_root='/tmp/data', data_width=4, targets_width=4, dataset_size=0,\n train=True, original_targets=None, loss_type=None):\n \"\"\"\n\n Args:\n data_width: dimension of input images\n targets_width: dimension of target images\n dataset_size: number of examples, use for smaller subsets and running locally\n original_targets: if False, replaces original classification targets with image reconstruction targets\n loss_type: if LeastSquares, then convert classes to one-hot format\n \"\"\"\n super().__init__(dataset_root, download=True, train=train)\n\n assert loss_type is None or original_targets is None # can't specify both loss type and original targets\n assert loss_type in [None, 'LeastSquares', 'CrossEntropy']\n\n if loss_type is None and original_targets is None:\n original_targets = False # default to LeastSquares targets\n if loss_type is not None:\n original_targets = True\n\n if dataset_size > 0:\n # assert dataset_size <= self.data.shape[0]\n self.data = self.data[:dataset_size, :, :]\n self.targets = self.targets[:dataset_size]\n\n if data_width != 28 or targets_width != 28:\n new_data = np.zeros((self.data.shape[0], data_width, data_width))\n new_targets = np.zeros((self.data.shape[0], targets_width, targets_width))\n for i in range(self.data.shape[0]):\n arr = self.data[i, :].numpy().astype(np.uint8)\n im = Image.fromarray(arr)\n im.thumbnail((data_width, data_width), Image.ANTIALIAS)\n new_data[i, :, :] = np.array(im) / 255\n im = Image.fromarray(arr)\n im.thumbnail((targets_width, targets_width), Image.ANTIALIAS)\n new_targets[i, :, :] = np.array(im) / 255\n self.data = torch.from_numpy(new_data).type(torch.get_default_dtype())\n if not original_targets:\n self.targets = torch.from_numpy(new_targets).type(torch.get_default_dtype())\n else:\n self.data = self.data.type(torch.get_default_dtype()).unsqueeze(1)\n if not original_targets:\n self.targets = self.data\n\n # self.data = self.data.type(torch.get_default_dtype())\n # if not original_targets: # don't cast original int labels\n # self.targets = self.targets.type(u.dtype)\n if loss_type == 'LeastSquares': # convert to one-hot format\n new_targets = torch.zeros((self.targets.shape[0], 10))\n new_targets.scatter(1, self.targets.unsqueeze(1), 1)\n self.targets = new_targets\n\n self.data, self.targets = self.data.to(gl.device), self.targets.to(gl.device)\n\n def __getitem__(self, index):\n \"\"\"\n Args:\n index (int): Index\n\n Returns:\n tuple: (image, target) where target is index of the target class.\n \"\"\"\n img, target = self.data[index], self.targets[index]\n\n return img, target\n\n\nmodel_layer_map = {}\nmodel_param_map = {}\n\n\nclass SimpleModel(nn.Module):\n \"\"\"Simple sequential model. Adds layers[] attribute, flags to turn on/off hooks, and lookup mechanism from layer to parent\n model.\"\"\"\n\n layers: List[nn.Module]\n all_layers: List[nn.Module]\n skip_forward_hooks: bool\n skip_backward_hooks: bool\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n self.skip_backward_hooks = False\n self.skip_forward_hooks = False\n\n def disable_hooks(self):\n self.skip_forward_hooks = True\n self.skip_backward_hooks = True\n\n def enable_hooks(self):\n self.skip_forward_hooks = False\n self.skip_backward_hooks = False\n\n # TODO(y): make public method\n def _finalize(self):\n \"\"\"Extra logic shared across all SimpleModel instances.\"\"\"\n # self.type(u.dtype)\n\n global model_layer_map\n for module in self.modules():\n model_layer_map[module] = self\n for param in self.parameters():\n model_layer_map[param] = self\n\n u.register_hooks(self)\n\n\n# TODO(y): rename to LeastSquaresLoss\ndef least_squares(data, targets=None, aggregation='mean'):\n \"\"\"Least squares loss (like MSELoss, but an extra 1/2 factor.\"\"\"\n assert is_matrix(data), f\"Expected matrix, got {data.shape}\"\n assert aggregation in ('mean', 'sum')\n if targets is None:\n targets = torch.zeros_like(data)\n err = data - targets.view(-1, data.shape[1])\n normalizer = len(data) if aggregation == 'mean' else 1\n return torch.sum(err * err) / 2 / normalizer\n\n\ndef debug_least_squares(data, targets=None):\n \"\"\"Least squares loss which weights one of the coordinates (for testing).\"\"\"\n if targets is None:\n targets = torch.zeros_like(data)\n err = data - targets.view(-1, data.shape[1])\n\n err[:, 0] *= 10\n\n return torch.sum(err * err) / 2 / len(data)\n\n\n# Fork of SimpleModel that doesn't automatically register hooks, for autograd_lib.py refactoring\nclass SimpleModel2(nn.Module):\n \"\"\"Simple sequential model. Adds layers[] attribute, flags to turn on/off hooks, and lookup mechanism from layer to parent\n model.\"\"\"\n\n layers: List[nn.Module]\n all_layers: List[nn.Module]\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n\n # TODO(y): make public method\n def _finalize(self):\n \"\"\"Extra logic shared across all SimpleModel instances.\"\"\"\n # self.type(u.dtype)\n\n global model_layer_map\n for module in self.modules():\n model_layer_map[module] = self\n for param in self.parameters():\n model_layer_map[param] = self\n\n\ndef get_parent_model(module_or_param) -> Optional[nn.Module]:\n \"\"\"Returns root model for given parameter.\"\"\"\n global model_layer_map\n global model_param_map\n if module_or_param in model_layer_map:\n assert module_or_param not in model_param_map\n return model_layer_map[module_or_param]\n if module_or_param in model_param_map:\n return model_param_map[module_or_param]\n\n\n# Functions to capture backprops/activations and save them on the layer\n\n# layer.register_forward_hook(capture_activations) -> saves activations/output as layer.activations/layer.output\n# layer.register_backward_hook(capture_backprops) -> appends each backprop to layer.backprops_list\n# layer.weight.register_hook(save_grad(layer.weight)) -> saves grad under layer.weight.saved_grad\n# util.clear_backprops(model) -> delete all values above\n\ndef capture_activations(module: nn.Module, input: List[torch.Tensor], output: torch.Tensor):\n \"\"\"Saves activations (layer input) into layer.activations. \"\"\"\n\n model = get_parent_model(module)\n if getattr(model, 'skip_forward_hooks', False):\n return\n assert not hasattr(module,\n 'activations'), \"Seeing results of previous forward, call util.clear_backprops(model) to clear or do 'model.disable_hooks()'\"\n assert len(input) == 1, \"this was tested for single input layers only\"\n setattr(module, \"activations\", input[0].detach())\n setattr(module, \"output\", output.detach())\n\n\ndef capture_backprops(module: nn.Module, _input, output):\n \"\"\"Appends all backprops (Jacobian Lops from upstream) to layer.backprops_list.\n Using list in order to capture multiple backprop values for a single batch. Use util.clear_backprops(model)\n to clear all saved values.\n \"\"\"\n model = get_parent_model(module)\n if getattr(model, 'skip_backward_hooks', False):\n return\n assert len(output) == 1, \"this works for single variable layers only\"\n if not hasattr(module, 'backprops_list'):\n setattr(module, 'backprops_list', [])\n assert len(\n module.backprops_list) < 100, \"Possible memory leak, captured more than 100 backprops, comment this assert \" \\\n \"out if this is intended.\"\"\"\n\n module.backprops_list.append(output[0].detach())\n\n\ndef save_grad(param: nn.Parameter) -> Callable[[torch.Tensor], None]:\n \"\"\"Hook to save gradient into 'param.saved_grad', so it can be accessed after model.zero_grad(). Only stores gradient\n if the value has not been set, call util.clear_backprops to clear it.\"\"\"\n\n def save_grad_fn(grad):\n if not hasattr(param, 'saved_grad'):\n setattr(param, 'saved_grad', grad)\n\n return save_grad_fn\n\n\ndef clear_backprops(model: nn.Module) -> None:\n \"\"\"model.zero_grad + delete all backprops/activations/saved_grad values\"\"\"\n model.zero_grad()\n for m in model.modules():\n if hasattr(m, 'backprops_list'):\n del m.backprops_list\n if hasattr(m, 'activations'):\n del m.activations\n for p in model.parameters():\n if hasattr(p, 'saved_grad'):\n del p.saved_grad\n\n\n# TODO: remove?\ndef register_hooks(model: SimpleModel):\n # TODO(y): remove hardcoding of parameter name\n for layer in model.layers:\n assert not layer._forward_hooks, f\"Some hooks already registered, bug? {layer._forward_hooks}\"\n assert not layer._backward_hooks, f\"Some hooks already registered, bug? {layer._backward_hooks}\"\n\n layer.register_forward_hook(u.capture_activations)\n layer.register_backward_hook(u.capture_backprops)\n\n for param in model.parameters():\n assert not param._backward_hooks, f\"Some param hooks already registered, bug? {param._backward_hooks}\"\n param.register_hook(u.save_grad(param))\n\n\nclass SimpleFullyConnected(SimpleModel):\n \"\"\"Simple feedforward network that works on images.\"\"\"\n\n def __init__(self, d: List[int], nonlin=False, bias=False, dropout=False):\n \"\"\"\n Feedfoward network of linear layers with optional ReLU nonlinearity. Stores layers in \"layers\" attr, ie\n model.layers[0] refers to first linear layer.\n\n Args:\n d: list of layer dimensions, ie [768, 20, 10] for MNIST 10-output with hidden layer of 20\n nonlin: whether to include ReLU nonlinearity\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n for i in range(len(d) - 1):\n linear = nn.Linear(d[i], d[i + 1], bias=bias)\n setattr(linear, 'name', f'{i:02d}-linear')\n self.layers.append(linear)\n self.all_layers.append(linear)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n if i <= len(d) - 3 and dropout:\n self.all_layers.append(nn.Dropout(p=0.5))\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n super()._finalize()\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n return self.predict(x)\n\n\nclass SimpleFullyConnected2(SimpleModel2):\n \"\"\"Simple feedforward network that works on images.\"\"\"\n\n def __init__(self, d: List[int], nonlin=False, bias=False, last_layer_linear=False, dropout=False):\n \"\"\"\n Feedfoward network of linear layers with optional ReLU nonlinearity. Stores layers in \"layers\" attr, ie\n model.layers[0] refers to first linear layer.\n\n Args:\n d: list of layer dimensions, ie [768, 20, 10] for MNIST 10-output with hidden layer of 20\n nonlin: whether to include ReLU nonlinearity\n last_layer_linear: don't apply nonlinearity to loast layer\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n for i in range(len(d) - 1):\n linear = nn.Linear(d[i], d[i + 1], bias=bias)\n setattr(linear, 'name', f'{i:02d}-linear')\n self.layers.append(linear)\n self.all_layers.append(linear)\n if nonlin:\n if not last_layer_linear or i < len(d) - 2:\n self.all_layers.append(nn.ReLU())\n if i <= len(d) - 3 and dropout:\n self.all_layers.append(nn.Dropout(p=0.5))\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n super()._finalize()\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n return self.predict(x)\n\n\nclass SimpleMLP(nn.Module):\n \"\"\"Simple feedforward network that works on images.\"\"\"\n\n layers: List[nn.Module]\n all_layers: List[nn.Module]\n\n def __init__(self, d: List[int], nonlin=False, bias=False):\n \"\"\"\n Feedfoward network of linear layers with optional ReLU nonlinearity. Stores layers in \"layers\" attr, ie\n model.layers[0] refers to first linear layer.\n\n Args:\n d: list of layer dimensions, ie [768, 20, 10] for MNIST 10-output with hidden layer of 20\n nonlin: whether to include ReLU nonlinearity\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n for i in range(len(d) - 1):\n linear = nn.Linear(d[i], d[i + 1], bias=bias)\n setattr(linear, 'name', f'{i:02d}-linear')\n self.layers.append(linear)\n self.all_layers.append(linear)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n return self.predict(x)\n\n\nclass RedundantFullyConnected2(SimpleModel2):\n \"\"\"Simple feedforward network that works on images.\"\"\"\n\n def __init__(self, d: List[int], nonlin=False, bias=False, last_layer_linear=False, dropout=False, redundancy=1):\n \"\"\"\n Feedfoward network of linear layers with optional ReLU nonlinearity. Stores layers in \"layers\" attr, ie\n model.layers[0] refers to first linear layer.\n\n Args:\n d: list of layer dimensions, ie [768, 20, 10] for MNIST 10-output with hidden layer of 20\n nonlin: whether to include ReLU nonlinearity\n last_layer_linear: don't apply nonlinearity to loast layer\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n self.linear_groups = []\n self.dropout = dropout\n for i in range(len(d) - 1):\n group = []\n for l in range(redundancy):\n layer = nn.Linear(d[i], d[i + 1], bias=bias)\n group.append(layer)\n layer.weight.data.copy_(layer.weight.data / redundancy)\n if hasattr(layer, 'bias'):\n layer.bias.data.copy_(layer.bias.data / redundancy)\n layer_name = f'layer%02d' % (i * redundancy + l,)\n setattr(self, layer_name, group[-1]) # needed to make params discoverable by optimizers\n # print(\"adding layer \", layer_name, getattr(self, layer_name))\n self.linear_groups.append(group)\n if nonlin:\n if not last_layer_linear or i < len(d) - 2:\n self.all_layers.append(nn.ReLU())\n if i <= len(d) - 3 and dropout:\n self.all_layers.append(nn.Dropout(p=0.5))\n super()._finalize()\n\n def forward(self, x: torch.Tensor):\n x = x.reshape((-1, self.d[0]))\n for layer_group in self.linear_groups:\n y = 0.\n for layer in layer_group:\n y0 = layer(x)\n if self.dropout:\n y0 = F.dropout(y0, 0.01)\n y += y0\n\n y = F.relu(y)\n x = y\n return x\n\n\nclass SimpleConvolutional(SimpleModel):\n \"\"\"Simple conv network.\"\"\"\n\n def __init__(self, d: List[int], kernel_size=(2, 2), nonlin=False, bias=False):\n \"\"\"\n\n Args:log\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n for i in range(len(d) - 1):\n conv = nn.Conv2d(d[i], d[i + 1], kernel_size, bias=bias)\n setattr(conv, 'name', f'{i:02d}-conv')\n self.layers.append(conv)\n self.all_layers.append(conv)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n self._finalize()\n\n def forward(self, x: torch.Tensor):\n return self.predict(x)\n\n\nclass SimpleConvolutional2(SimpleModel2):\n \"\"\"Simple conv network.\"\"\"\n\n def __init__(self, d: List[int], kernel_size=(2, 2), nonlin=False, bias=False):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n assert len(d) >= 2\n for di in d:\n assert di > 0\n\n for i in range(len(d) - 1):\n conv = nn.Conv2d(d[i], d[i + 1], kernel_size, bias=bias)\n setattr(conv, 'name', f'{i:02d}-conv')\n\n self.layers.append(conv)\n self.all_layers.append(conv)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n self._finalize()\n\n def forward(self, x: torch.Tensor):\n return self.predict(x)\n\n\nclass ReshapedConvolutional2(SimpleConvolutional2):\n \"\"\"Simple conv network, output is flattened\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels\n \"\"\"\n super().__init__(*args, **kwargs)\n\n def forward(self, x: torch.Tensor):\n output = self.predict(x)\n return output.reshape(output.shape[0], -1)\n\n\nclass PooledConvolutional2(SimpleConvolutional2):\n \"\"\"Simple conv network, output is pooled across spatial dimension. Num-channels = num_outputs\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2, 2] to have 2 conv layers with 2 channels\n \"\"\"\n super().__init__(*args, **kwargs)\n\n def forward(self, x: torch.Tensor):\n x = self.predict(x)\n x = F.adaptive_avg_pool2d(x, [1, 1])\n return x.reshape(x.shape[0], -1)\n\n\nclass StridedConvolutional2(SimpleModel2):\n \"\"\"Convolutional net without overlapping, single output, squeezes singleton spatial dimensions, rank-2 result\"\"\"\n\n def __init__(self, d: List[int], kernel_size=(2, 2), nonlin=False, bias=False):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels in each group\n o: number of output classes used in final classification layer\n input channels of input must by d[0]*o\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n for i in range(len(d) - 1):\n\n # each group considers o filters independently\n conv = nn.Conv2d(d[i], d[i + 1], kernel_size=kernel_size, stride=kernel_size, bias=bias)\n setattr(conv, 'name', f'{i:02d}-conv')\n self.layers.append(conv)\n self.all_layers.append(conv)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n # average out all groups of o filters\n\n self.final_chan = d[-1]\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n self._finalize()\n\n def forward(self, x: torch.Tensor):\n for i, layer in enumerate(self.all_layers):\n x = layer(x)\n\n assert x.shape[2] == 1 and x.shape[3] == 1\n return x.reshape(x.shape[0], 1)\n\n\nclass GroupedConvolutional2(SimpleModel2):\n \"\"\"Conv network without mixing of output dimension, applies convolution to o independent groups of d channels\n Each group is only affected by 1 output\n \"\"\"\n\n def __init__(self, d: List[int], kernel_size=(2, 2), o=None, nonlin=False, bias=False):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels in each group\n o: number of output classes used in final classification layer\n input channels of input must by d[0]*o\n \"\"\"\n super().__init__()\n self.layers: List[nn.Module] = []\n self.all_layers: List[nn.Module] = []\n self.d: List[int] = d\n self.o = o\n for i in range(len(d) - 1):\n\n # each group considers o filters independently\n conv = nn.Conv2d(d[i] * o, d[i + 1] * o, kernel_size, bias=bias, groups=o)\n setattr(conv, 'name', f'{i:02d}-conv')\n self.layers.append(conv)\n self.all_layers.append(conv)\n if nonlin:\n self.all_layers.append(nn.ReLU())\n # average out all groups of o filters\n\n self.final_chan = d[-1]\n self.predict = torch.nn.Sequential(*self.all_layers)\n\n self._finalize()\n\n def forward(self, x: torch.Tensor):\n x = self.predict(x)\n x = F.adaptive_avg_pool2d(x, (1, 1)) # n, o*do, 1, 1\n n, out_dim, Oh, Ow = x.shape\n assert (Oh, Ow) == (1, 1)\n assert out_dim == self.final_chan * self.o\n x = x.reshape(n, self.o, self.final_chan)\n x = torch.einsum('noc->no', x) # average across groups\n assert x.shape == (n, self.o)\n return x\n\n\nclass ReshapedConvolutional(SimpleConvolutional):\n \"\"\"Simple conv network, output is flattened\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n\n Args:\n d: list of channels, ie [2, 2] to have 2 conv layers with 2 channels\n \"\"\"\n super().__init__(*args, **kwargs)\n\n def forward(self, x: torch.Tensor):\n output = self.predict(x)\n return output.reshape(output.shape[0], -1)\n\n\ndef log_scalars(metrics: Dict[str, Any]) -> None:\n assert gl.event_writer is not None, \"initialize event_writer as gl.event_writer = SummaryWriter(logdir)\"\n for tag in metrics:\n gl.event_writer.add_scalar(tag=tag, scalar_value=metrics[tag], global_step=gl.get_global_step())\n # gl.event_writer.add_s\n if 'epoch' in metrics:\n print('logging at ', gl.get_global_step(), metrics.get('epoch', -1))\n\n\ndef log_scalar(**metrics) -> None:\n for tag in metrics:\n gl.event_writer.add_scalar(tag=tag, scalar_value=metrics[tag], global_step=gl.get_global_step())\n\n\n# for line profiling\ntry:\n # noinspection PyUnboundLocalVariable\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\n@profile\ndef log_spectrum(tag, vals: torch.Tensor, loglog=True, discard_tiny=False):\n \"\"\"Given eigenvalues or singular values in decreasing order, log this plg.\"\"\"\n\n if 'darwin' in platform.system().lower():\n import matplotlib\n matplotlib.use('PS')\n\n import matplotlib.pyplot as plt\n\n if discard_tiny:\n vals = filter_evals(vals)\n\n y = vals\n x = torch.arange(len(vals), dtype=vals.dtype) + 1.\n if loglog:\n y = torch.log10(y)\n x = torch.log10(x)\n fig, ax = plt.subplots()\n x, y = to_numpys(x, y)\n markerline, stemlines, baseline = ax.stem(x, y, markerfmt='bo', basefmt='r-', bottom=min(y))\n plt.setp(baseline, color='r', linewidth=2)\n gl.event_writer.add_figure(tag=tag, figure=fig, global_step=gl.get_global_step())\n\n\ndef get_events(fname, x_axis='step'):\n \"\"\"Returns event dictionary for given run, has form\n {tag1: {step1: val1}, tag2: ..}\n\n If x_axis is set to \"time\", step is replaced by timestamp\n \"\"\"\n\n from tensorflow.python.summary import summary_iterator # local import because TF is heavy dep and only used here\n\n result = {}\n\n events = summary_iterator.summary_iterator(fname)\n\n try:\n for event in events:\n if x_axis == 'step':\n x_val = event.step\n elif x_axis == 'time':\n x_val = event.wall_time\n else:\n assert False, f\"Unknown x_axis ({x_axis})\"\n\n vals = {val.tag: val.simple_value for val in event.summary.value}\n # step_time: valuelayer\n for tag in vals:\n event_dict = result.setdefault(tag, {})\n if x_val in event_dict:\n print(f\"Warning, overwriting {tag} for {x_axis}={x_val}\")\n print(f\"old val={event_dict[x_val]}\")\n print(f\"new val={vals[tag]}\")\n\n event_dict[x_val] = vals[tag]\n except Exception as e:\n print(e)\n pass\n\n return result\n\n\ndef infinite_iter(obj):\n \"\"\"Wraps iterable object to restart on last iteration.\"\"\"\n while True:\n for result in iter(obj):\n yield result\n\n\n# noinspection PyTypeChecker\ndef dump(result, fname):\n \"\"\"Save result to file. Load as np.genfromtxt(fname). \"\"\"\n result = to_numpy(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n location = fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, X=result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, X=result, delimiter=',')\n print(\"Dumping to\", location)\n\n\ndef print_version_info():\n \"\"\"Print version numbers of numerical packages in current env.\"\"\"\n\n def get_mkl_version():\n import ctypes\n import numpy as np\n\n # this recipe only works on Linux\n try:\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n except:\n return 'unknown'\n\n if np.__config__.get_info(\"lapack_mkl_info\"):\n print(\"MKL version\", get_mkl_version())\n else:\n print(\"not using MKL\")\n\n print(\"PyTorch version\", torch.version.__version__)\n\n print(\"Scipy version: \", scipy.version.full_version)\n print(\"Numpy version: \", np.version.full_version)\n print(\"Python version: \", sys.version, sys.platform)\n print_cpu_info()\n\n\ndef print_cpu_info():\n ver = 'unknown'\n try:\n for l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n ver = l\n break\n except:\n pass\n\n # core counts from https://stackoverflow.com/a/23378780/419116\n print(\"CPU version: \", ver)\n sys.stdout.write(\"CPU logical cores: \")\n sys.stdout.flush()\n os.system(\n \"echo $([ $(uname) = 'Darwin' ] && sysctl -n hw.logicalcpu_max || lscpu -p | egrep -v '^#' | wc -l)\")\n sys.stdout.write(\"CPU physical cores: \")\n sys.stdout.flush()\n os.system(\n \"echo $([ $(uname) = 'Darwin' ] && sysctl -n hw.physicalcpu_max || lscpu -p | egrep -v '^#' | sort -u -t, -k 2,4 | wc -l)\")\n\n # get mapping of logical cores to physical sockets\n import re\n socket_re = re.compile(\n \"\"\".*?processor.*?(?P\\\\d+).*?physical id.*?(?P\\\\d+).*?power\"\"\",\n flags=re.S)\n from collections import defaultdict\n socket_dict = defaultdict(list)\n try:\n for cpu, socket in socket_re.findall(open('/proc/cpuinfo').read()):\n socket_dict[socket].append(cpu)\n except FileNotFoundError:\n pass\n print(\"CPU physical sockets: \", len(socket_dict))\n\n\ndef move_to_gpu(tensors):\n return [tensor.cuda() for tensor in tensors]\n\n\ndef fmt(a):\n \"\"\"Helper function for converting copy-pasted Mathematica matrices into Python.\"\"\"\n\n a = a.replace('\\n', '')\n print(a.replace(\"{\", \"[\").replace(\"}\", \"]\"))\n\n\ndef to_logits(p: torch.Tensor) -> torch.Tensor:\n \"\"\"Inverse of F.softmax\"\"\"\n if len(p.shape) == 1:\n batch = torch.unsqueeze(p, 0)\n else:\n assert len(p.shape) == 2\n batch = p\n\n batch = torch.log(batch) - torch.log(batch[:, -1])\n return batch.reshape(p.shape)\n\n\nclass CrossEntropySoft(nn.Module):\n \"\"\"Like torch.nn.CrossEntropyLoss but instead of class index it accepts a\n probability distribution.\n\n The `input` is expected to contain raw, unnormalized scores for each class.\n The `target` is expected to contain empirical probabilities for each class (positive and adding up to 1)\n\n \"\"\"\n\n def __init__(self):\n super(CrossEntropySoft, self).__init__()\n return\n\n def forward(self, inputs, target):\n \"\"\"\n :param inputs: predictions\n :param target: target labels\n :return: loss\n \"\"\"\n\n # check that targets are positive and add up to 1\n assert (target >= 0).sum() == target.numel()\n sums = target.sum(dim=1)\n assert np.allclose(sums, torch.ones_like(sums))\n\n assert len(target.shape) == 2\n n = target.shape[0]\n log_likelihood = -F.log_softmax(inputs, dim=1)\n loss = torch.sum(torch.mul(target, log_likelihood)) / n\n\n return loss\n\n\ndef get_unique_logdir(root_logdir: str) -> str:\n \"\"\"Increments suffix at the end of root_logdir until getting directory that doesn't exist locally, return that.\"\"\"\n count = 0\n while os.path.exists(f\"{root_logdir}{count:02d}\"):\n count += 1\n\n return f\"{root_logdir}{count:02d}\"\n\n\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndef setup_logdir_and_event_writer(run_name: str, init_wandb=False):\n \"\"\"Creates unique logdir like project/runname02, sets up wandb if necessary\"\"\"\n assert gl.project_name is not None\n\n gl.logdir = u.get_unique_logdir(f'{gl.logdir_base}/{gl.project_name}/{run_name}')\n gl.run_name = os.path.basename(gl.logdir)\n gl.event_writer = SummaryWriter(gl.logdir)\n\n if init_wandb or (gl.args and hasattr(gl.args, 'wandb') and gl.args.wandb):\n wandb.init(project=gl.project_name, name=gl.run_name)\n wandb.tensorboard.patch(tensorboardX=False)\n wandb.config.update(vars(gl.args))\n\n\n######################################################\n# Hessian backward samplers\n#\n# A sampler provides a representation of hessian of the loss layer\n#\n# For a batch of size n,o, Hessian backward sampler will produce k backward values\n# (k between 1 and o) where each value can be fed as model.backward(value)\n#\n# The covariance of gradients corresponding to these k backward will be summed up to form an estimate of Hessian of the network\n# sum_i gg' \\approx H\n#\n# sampler = HessianSamplerMyLoss\n# for bval in sampler(model(batch)):\n# model.zero_grad()\n# model.backward(bval)\n\nclass HessianBackprop:\n num_samples: int # number of samples\n\n\nclass HessianExactSqrLoss(HessianBackprop):\n \"\"\"Sampler for loss err*err/2/len(batch), produces exact Hessian.\"\"\"\n\n def __init__(self):\n super().__init__()\n\n def __call__(self, output: torch.Tensor):\n assert len(output.shape) == 2\n batch_size, output_size = output.shape\n self.num_samples = output_size\n\n id_mat = torch.eye(output_size)\n for out_idx in range(output_size):\n yield torch.stack([id_mat[out_idx]] * batch_size)\n\n\nclass HessianSampledSqrLoss(HessianBackprop):\n \"\"\"Sampler for loss err*err/2/len(batch), produces exact Hessian.\"\"\"\n\n def __init__(self, num_samples):\n super().__init__()\n self.num_samples = num_samples\n\n def __call__(self, output: torch.Tensor):\n assert len(output.shape) == 2\n batch_size, output_size = output.shape\n assert self.num_samples <= output_size, f\"Requesting more samples than needed for exact Hessian computation \" \\\n f\"({self.num_samples}>{output_size})\"\n\n # exact sampler provides n samples whose outer products add up to Identity\n # here the sum is num_samples*identity in expectation\n # therefore must divide by sqrt(num_samples)\n\n for out_idx in range(self.num_samples):\n # sample random vectors of +1/-1's\n bval = torch.LongTensor(batch_size, output_size).to(gl.device).random_(0, 2) * 2 - 1\n yield bval.float() / math.sqrt(self.num_samples)\n\n\nclass HessianExactCrossEntropyLoss(HessianBackprop):\n \"\"\"Sampler for nn.CrossEntropyLoss, produces exact Hessian.\"\"\"\n\n def __init__(self):\n super().__init__()\n\n def __call__(self, logits: torch.Tensor):\n assert len(logits.shape) == 2\n\n n, d = logits.shape\n batch = F.softmax(logits, dim=1)\n\n mask = torch.eye(d).expand(n, d, d)\n diag_part = batch.unsqueeze(2).expand(n, d, d) * mask\n outer_prod_part = torch.einsum('ij,ik->ijk', batch, batch)\n hess = diag_part - outer_prod_part\n assert hess.shape == (n, d, d)\n\n for i in range(n):\n hess[i, :, :] = u.symsqrt(hess[i, :, :])\n\n for out_idx in range(d):\n sample = hess[:, out_idx, :]\n assert sample.shape == (n, d)\n yield sample\n\n\ndef hessian_from_backprops(A_t, Bh_t, bias=False):\n \"\"\"Computes Hessian from a batch of forward and backward values.\n\n See documentation on HessianSampler for assumptions on how backprop values are generated\n\n For batch size n\n Forward values have shape n,layer_inputs\n Backward values is a list of length c of tensors of shape n,layer_outputs\n\n For exact Hessian computation, c is number of classes.\n\n Args:\n bias: if True, also return Hessian of the bias parameter\n \"\"\"\n n = A_t.shape[0]\n Amat_t = torch.cat([A_t] * len(Bh_t), dim=0) # todo: can instead replace with a khatri-rao loop\n Bmat_t = torch.cat(Bh_t, dim=0)\n Jb = u.khatri_rao_t(Bmat_t, Amat_t) # batch Jacobian\n H = Jb.t() @ Jb / n\n\n if not bias:\n return H\n else:\n Hbias = Bmat_t.t() @ Bmat_t / n\n return H, Hbias\n\n\n# TODO: rename to \"mean_hess\"\ndef per_example_hess(A_t, Bh_t, bias=False):\n \"\"\"Computes Hessian from a batch of forward and backward values.\n\n\n Args:\n bias: if True, also return Hessian of the bias parameter\n \"\"\"\n n = A_t.shape[0]\n in_dim = A_t.shape[1]\n out_dim = Bh_t[0].shape[1]\n o = len(Bh_t)\n assert Bh_t[0].shape[0] == n\n\n Amat_t = torch.stack([A_t] * len(Bh_t), dim=0)\n Bmat_t = torch.stack(Bh_t, dim=0)\n assert Amat_t.shape == (o, n, in_dim)\n assert Bmat_t.shape == (o, n, out_dim)\n\n # sum out output classes, get batch of per-example jacobians\n Ji = torch.einsum('oni,onj->nij', Bmat_t, Amat_t)\n assert Ji.shape == (n, out_dim, in_dim)\n Ji = Ji.reshape((n, out_dim * in_dim)) # individual jacobians\n\n # original Hessian computation\n Jb = u.khatri_rao_t(Bmat_t.reshape(o * n, -1), Amat_t.reshape(o * n, -1))\n Hmean = Jb.t() @ Jb / n\n\n # method 2: einsum-only version for mean hessian\n # o,n -> o,n,i,j\n Jb2 = torch.einsum('oni,onj->onij', Bmat_t, Amat_t)\n check_close(Jb2.reshape((o * n, out_dim * in_dim)), Jb)\n Hmean2 = torch.einsum('onij,onkl->ijkl', Jb2, Jb2).reshape((out_dim * in_dim,\n out_dim * in_dim)) / n\n check_close(Hmean, Hmean2)\n\n # method 3: einsum-only for individual hessians\n # sum over classes, \n Hi = torch.einsum('onij,onkl->nijkl', Jb2, Jb2)\n Hmean3 = Hi.mean(dim=0)\n Hmean3 = Hmean3.reshape((out_dim * in_dim, out_dim * in_dim))\n check_close(Hmean, Hmean3)\n\n # flatten last two pairs of dimensions for form d^2/dvec dvec\n Hi = Hi.reshape(n, out_dim * in_dim, out_dim * in_dim)\n if not bias:\n return Hi\n else:\n # assert False, \"not tested\"\n Hb_i = torch.einsum('oni,onj->nij', Bmat_t, Bmat_t)\n # Hbias = Bmat_t.t() @ Bmat_t / n\n return Hi, Hb_i\n\n\ndef kl_div_cov(mat1, mat2, eps=1e-3):\n \"\"\"KL divergence between two zero centered Gaussian's with given covariance matrices.\"\"\"\n\n evals1 = torch.symeig(mat1).eigenvalues\n evals2 = torch.symeig(mat2).eigenvalues\n k = mat1.shape[0]\n # scale regularizer in proportion to achievable numerical precision (taken from scipy.pinv2)\n l1 = torch.max(evals1) * k\n l2 = torch.max(evals2) * k\n l = max(l1, l2)\n reg = torch.eye(mat1.shape[0]) * l * eps\n mat1 = mat1 + reg\n mat2 = mat2 + reg\n\n div = torch.trace(mat1 @ torch.inverse(mat2)) - (torch.logdet(mat1) - torch.logdet(mat2)) - k\n return div\n\n\n# Functions for kronecker factored representation. Matrix is given as tuple of two matrices\ndef kron_quadratic_form(H, dd):\n \"\"\"dd @ H @ dd.t(),\"\"\"\n pass\n\n\ndef kron_trace(H: Tuple[torch.Tensor, torch.Tensor]):\n \"\"\"trace(H)\"\"\"\n A, B = H\n return torch.trace(A) * torch.trace(B)\n\n\ndef kron_trace_matmul(H, sigma):\n \"\"\"\n tr(H@sigma)\n \"\"\"\n H = u.kron(H)\n sigma = u.kron(sigma)\n return torch.trace(H @ sigma)\n\n\ndef kron_pinv(H: Tuple):\n A, B = H\n return u.pinv(A), u.pinv(B)\n\n\ndef kron_nan_check(H):\n u.nan_check(H[0])\n u.nan_check(H[1])\n\n\ndef kron_fro_norm(H):\n return H[0].norm() * H[1].norm()\n\n\ndef kron_sym_l2_norm(H):\n return u.sym_l2_norm(H[0]) * u.sym_l2_norm(H[1])\n\n\ndef kron_inv(H):\n return torch.inverse(H[0]), torch.inverse(H[1])\n\n\ndef kron_sigma(G):\n Bt, At = G\n grad = torch.einsum('nij,nkl->ijkl', Bt, At)\n cov = torch.einsum('ij,kl->ijkl', grad, grad)\n\n\ndef kron_batch_sum(G: Tuple):\n \"\"\"The format of gradient is G={Bt, At} where Bt is (n,do) and At is (n,di)\"\"\"\n Bt, At = G\n return torch.einsum('ni,nj->ij', Bt, At)\n\n\ndef chop(mat: torch.Tensor, eps=1e-10) -> torch.Tensor:\n \"\"\"Set values below max(mat)*eps to zero\"\"\"\n\n mat = to_normal_form(mat)\n zeros = torch.zeros(mat.shape)\n return torch.where(abs(mat) < eps, zeros, mat)\n\n\ndef format_list(ll: List) -> str:\n formatted = [\"%.2f\" % (d,) for d in ll]\n return ', '.join(formatted)\n\n\ndef create_local_logdir(logdir) -> str:\n \"\"\"Dedupes logdir by appending a number to avoid conflict with existing folder at logdir, ie logdir, logdir01\"\"\"\n attemp_count = 0\n while os.path.exists(f\"{logdir}{attemp_count:02d}\"):\n attemp_count += 1\n logdir = f\"{logdir}{attemp_count:02d}\"\n return logdir\n\n\nclass NoOp:\n \"\"\"Dummy callable that accepts every signature\"\"\"\n\n def __getattr__(self, *_args, **_kwargs):\n def no_op(*_args, **_kwargs): pass\n\n return no_op\n\n\ndef install_pdb_handler():\n \"\"\"Signals to automatically start pdb:\n 1. CTRL+\\\\ breaks into pdb.\n 2. pdb gets launched on exception.\n \"\"\"\n\n import signal\n import pdb\n\n def handler(_signum, _frame):\n pdb.set_trace()\n\n signal.signal(signal.SIGQUIT, handler)\n\n # Drop into PDB on exception\n # from https://stackoverflow.com/questions/13174412\n def info(type_, value, tb):\n if hasattr(sys, 'ps1') or not sys.stderr.isatty():\n # we are in interactive mode or we don't have a tty-like\n # device, so we call the default hook\n sys.__excepthook__(type_, value, tb)\n else:\n import traceback\n import pdb\n # we are NOT in interactive mode, print the exception...\n traceback.print_exception(type_, value, tb)\n print()\n # ...then start the debugger in post-mortem mode.\n pdb.pm()\n\n sys.excepthook = info\n\n\ndef randomly_rotate(X: torch.Tensor) -> torch.Tensor:\n \"\"\"Randomly rotate d,n data matrix X\"\"\"\n\n d, n = X.shape\n z = torch.randn((d, d), dtype=X.dtype)\n q, r = torch.qr(z)\n d = torch.diag(r)\n ph = d / abs(d)\n rot_mat = q * ph\n return rot_mat @ X\n\n\ndef random_cov(rank, d=None, n=20) -> torch.Tensor:\n \"\"\"\n\n Args:\n rank: dimensionality of space spanned by data\n d: embedding dimension\n n: number of examples to generate covariance matrix\n\n Returns:\n covariance matrix of size d and rank rank\n \"\"\"\n if d is None:\n d = rank\n assert d >= rank\n X = torch.randn((rank, n))\n X = torch.cat([X, torch.zeros(d - rank, n)])\n X = randomly_rotate(X)\n return X @ X.t() / n\n\n\ndef _to_mathematica(x):\n x = to_numpy(x)\n x = np.array2string(x, separator=',')\n x = x.replace('[', '{')\n x = x.replace(']', '}')\n x = x.replace('(', '')\n x = x.replace(')', '')\n x = x.replace('tensor', '')\n return x\n\n\ndef _from_mathematica(x):\n x = x.replace('\\n', '')\n x = x.replace('{', '[')\n x = x.replace('}', ']')\n return x\n\n\ndef _dim_check(d, rank=0):\n assert d > 0\n assert d < 1e6\n assert 0 <= rank <= d\n\n\ndef random_cov_pair(shared_rank, independent_rank, d, n=20, strength=1):\n \"\"\"Generate pair of covariance matrices which share covariance in subspace of dimension shared_rank,\n and have independent covariances of dimension independent_rank. Strength determines relative scale of covariance\n in the independent subspace.\"\"\"\n _dim_check(d, shared_rank + independent_rank)\n _dim_check(d, shared_rank)\n _dim_check(d, independent_rank)\n print(shared_rank, independent_rank, d)\n shared = random_cov(shared_rank, d, n)\n if independent_rank == 0 or strength == 0:\n return shared, shared\n A = shared + strength * random_cov(independent_rank, d, n)\n B = shared + strength * random_cov(independent_rank, d, n)\n return A, B\n\n\ndef is_row_matrix(dd):\n return len(dd.shape) == 2 and dd.shape[0] == 1\n\n\ndef is_col_matrix(dd):\n return len(dd.shape) == 2 and dd.shape[1] == 1\n\n\ndef is_square_matrix(dd):\n return len(dd.shape) == 2 and dd.shape[0] == dd.shape[1] and dd.shape[0] >= 1\n\n\ndef is_vector(dd) -> bool:\n shape = dd.shape\n return len(shape) == 1 and shape[0] >= 1\n\n\ndef is_matrix(dd) -> bool:\n shape = dd.shape\n return len(shape) == 2 and shape[0] >= 1 and shape[1] >= 1\n\n\ndef eye(d: int) -> torch.Tensor:\n return torch.eye(d).to(gl.device)\n\n\ndef eye_like(X: torch.Tensor) -> torch.Tensor:\n \"\"\"Create identity matrix of same shape as X.\"\"\"\n # TODO(y): dedup with regularize to support rectangular matrices\n assert is_square_matrix(X)\n d = X.shape[0]\n\n return torch.eye(d).type(X.dtype).to(X.device)\n\n\ndef rmul(a: torch.Tensor, b):\n # https://github.com/pytorch/pytorch/issues/26333\n return b.__rmul__(a)\n\n\ndef matmul(a, b):\n try:\n return a @ b\n except TypeError:\n return rmatmul(a, b)\n\n\ndef rmatmul(a: torch.Tensor, b):\n return b.__rmatmul__(a)\n\n\n# helper util for norm squared, usual norm is slow https://discuss.pytorch.org/t/torch-norm-3-6x-slower-than-manually-calculating-sum-of-squares/14684\ndef norm_squared(param):\n return (param * param).sum()\n\n\ndef dot_product(A, B):\n return (A * B).sum() # computes tr(AB')\n\n\nif __name__ == '__main__':\n run_all_tests(sys.modules[__name__])\n\n\n# import matplotlib.pyplot as plt\n#\n#\n# def spectral_plot(vals: torch.Tensor, loglog=True):\n# fig, ax = plt.subplots()\n# y = vals\n# x = torch.arange(len(vals), dtype=y.dtype) + 1.\n# if loglog:\n# y = torch.log10(y)\n# x = torch.log10(x)\n#\n# markerline, stemlines, baseline = ax.stem(x, y, markerfmt='bo', basefmt='r-', bottom=min(y))\n# plt.setp(baseline, color='r', linewidth=2)\n#\n# plt.show()\n\n\ndef copy_stats(shared_stats, stats):\n for key in shared_stats:\n assert key not in stats, f\"Trying to overwrite {key}\"\n stats[key] = shared_stats[key]\n return None\n\n\ndef skip_nans(t): return t[torch.isfinite(t)]\n\n\n# list replacement. Workaround for AttrDict automatically converting list objects to Tuple\nclass MyList:\n def __init__(self, *args, **kwargs):\n super(MyList, self).__init__(*args, **kwargs)\n self.storage = list()\n\n def __getattr__(self, *_args, **_kwargs):\n return self.storage.__getattribute__(*_args, **_kwargs)\n\n def normal_form(self):\n return self.value()\n\n def value(self):\n return self.storage\n\n\ndef divide_attributes(d, n):\n \"\"\"Helper util to divide all tensor attributes of d by n, return result as new AttrDict\"\"\"\n\n result = AttrDict()\n for val in d:\n if type(d[val]) == torch.Tensor:\n result[val] = d[val] / n\n return result\n" }, { "path": "autotune/util_test.py", "content": "import math\nimport os\nimport sys\n\n# import torch\nimport pytest\nimport scipy\nfrom scipy import linalg\nimport torch\n\nimport numpy as np\nimport util as u\n\nimport torch.nn.functional as F\n\n\ndef test_khatri_rao():\n A = torch.tensor([[1, 2], [3, 4]])\n B = torch.tensor([[5, 6], [7, 8]])\n C = torch.tensor([[5, 12], [7, 16],\n [15, 24], [21, 32]])\n u.check_equal(u.khatri_rao(A, B), C)\n\n\ndef test_khatri_rao_t():\n A = torch.tensor([[-2., -1.],\n [0., 1.],\n [2., 3.]])\n B = torch.tensor([[-4.],\n [1.],\n [6.]])\n C = torch.tensor([[8., 4.],\n [0., 1.],\n [12., 18.]])\n u.check_equal(u.khatri_rao_t(A, B), C)\n\n\ndef test_to_logits():\n torch.set_default_dtype(torch.float32)\n\n p = torch.tensor([0.2, 0.5, 0.3])\n u.check_close(p, F.softmax(u.to_logits(p), dim=0))\n u.check_close(p.unsqueeze(0), F.softmax(u.to_logits(p.unsqueeze(0)), dim=1))\n\n\ndef test_cross_entropy_soft():\n torch.set_default_dtype(torch.float32)\n\n q = torch.tensor([0.4, 0.6]).unsqueeze(0).float()\n p = torch.tensor([0.7, 0.3]).unsqueeze(0).float()\n observed_logit = u.to_logits(p)\n\n # Compare against other loss functions\n # https://www.wolframcloud.com/obj/user-eac9ee2d-7714-42da-8f84-bec1603944d5/newton/logistic-hessian.nb\n\n loss1 = F.binary_cross_entropy(p[0], q[0])\n u.check_close(loss1, 0.865054)\n\n loss_fn = u.CrossEntropySoft()\n loss2 = loss_fn(observed_logit, q)\n u.check_close(loss2, loss1)\n\n loss3 = F.cross_entropy(observed_logit, torch.tensor([0]))\n u.check_close(loss3, loss_fn(observed_logit, torch.tensor([[1, 0.]])))\n\n # check gradient\n observed_logit.requires_grad = True\n grad = torch.autograd.grad(loss_fn(observed_logit, target=q), observed_logit)\n u.check_close(p - q, grad[0])\n\n # check Hessian\n observed_logit = u.to_logits(p)\n observed_logit.zero_()\n observed_logit.requires_grad = True\n hessian_autograd = u.hessian(loss_fn(observed_logit, target=q), observed_logit)\n hessian_autograd = hessian_autograd.reshape((p.numel(), p.numel()))\n p = F.softmax(observed_logit, dim=1)\n hessian_manual = torch.diag(p[0]) - p.t() @ p\n u.check_close(hessian_autograd, hessian_manual)\n\n\ndef test_symsqrt():\n u.seed_random(1)\n torch.set_default_dtype(torch.float32)\n\n mat = torch.reshape(torch.arange(9) + 1, (3, 3)).float() + torch.eye(3) * 5\n mat = mat + mat.t() # make symmetric\n smat = u.symsqrt(mat)\n u.check_close(mat, smat @ smat.t())\n u.check_close(mat, smat @ smat)\n\n def randomly_rotate(X):\n \"\"\"Randomly rotate d,n data matrix X\"\"\"\n d, n = X.shape\n z = torch.randn((d, d), dtype=X.dtype)\n q, r = torch.qr(z)\n d = torch.diag(r)\n ph = d / abs(d)\n rot_mat = q * ph\n return rot_mat @ X\n\n n = 20\n d = 10\n X = torch.randn((d, n))\n\n # embed in a larger space\n X = torch.cat([X, torch.zeros_like(X)])\n X = randomly_rotate(X)\n cov = X @ X.t() / n\n sqrt, rank = u.symsqrt(cov, return_rank=True)\n assert rank == d\n assert torch.allclose(sqrt @ sqrt, cov, atol=1e-5)\n\n Y = torch.randn((d, n))\n Y = torch.cat([Y, torch.zeros_like(X)])\n Y = randomly_rotate(X)\n cov = u.Kron(X @ X.t(), Y @ Y.t())\n sqrt, rank = cov.symsqrt(return_rank=True)\n assert rank == d * d\n u.check_close(sqrt @ sqrt, cov, rtol=1e-4)\n\n X = torch.tensor([[7., 0, 0, 0, 0]]).t()\n X = randomly_rotate(X)\n cov = X @ X.t()\n u.check_close(u.sym_l2_norm(cov), 7 * 7)\n\n Y = torch.tensor([[8., 0, 0, 0, 0]]).t()\n Y = randomly_rotate(Y)\n cov = u.Kron(X @ X.t(), Y @ Y.t())\n u.check_close(cov.sym_l2_norm(), 7 * 7 * 8 * 8)\n\n\n@pytest.mark.skip(reason=\"fails, need to redo pinv implementation\")\ndef atest_pinv():\n a = torch.tensor([[2., 7, 9], [1, 9, 8], [2, 7, 5]])\n b = torch.tensor([[6., 6, 1], [10, 7, 7], [7, 10, 10]])\n C = u.Kron(a, b)\n u.check_close(a.flatten().norm() * b.flatten().norm(), C.frobenius_norm())\n\n u.check_close(C.frobenius_norm(), 4 * math.sqrt(11635.))\n\n Ci = [[0, 5 / 102, -(7 / 204), 0, -(70 / 561), 49 / 561, 0, 125 / 1122, -(175 / 2244)],\n [1 / 20, -(53 / 1020), 8 / 255, -(7 / 55), 371 / 2805, -(224 / 2805), 5 / 44, -(265 / 2244), 40 / 561],\n [-(1 / 20), 3 / 170, 3 / 170, 7 / 55, -(42 / 935), -(42 / 935), -(5 / 44), 15 / 374, 15 / 374],\n [0, -(5 / 102), 7 / 204, 0, 20 / 561, -(14 / 561), 0, 35 / 1122, -(49 / 2244)],\n [-(1 / 20), 53 / 1020, -(8 / 255), 2 / 55, -(106 / 2805), 64 / 2805, 7 / 220, -(371 / 11220), 56 / 2805],\n [1 / 20, -(3 / 170), -(3 / 170), -(2 / 55), 12 / 935, 12 / 935, -(7 / 220), 21 / 1870, 21 / 1870],\n [0, 5 / 102, -(7 / 204), 0, 0, 0, 0, -(5 / 102), 7 / 204],\n [1 / 20, -(53 / 1020), 8 / 255, 0, 0, 0, -(1 / 20), 53 / 1020, -(8 / 255)],\n [-(1 / 20), 3 / 170, 3 / 170, 0, 0, 0, 1 / 20, -(3 / 170), -(3 / 170)]]\n C = C.expand_vec()\n C0 = u.to_numpy(C)\n Ci = torch.tensor(Ci)\n u.check_close(C @ Ci @ C, C)\n\n u.check_close(linalg.pinv(C0), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(torch.pinverse(C), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(u.pinv(C), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(C.pinv(), Ci, rtol=1e-5, atol=1e-6)\n\n\ndef test_pinverse():\n\n def subtest(dtype):\n # {{11041, 13359, 15023, 18177}, {13359, 16165, 18177, 21995}, {15023, 18177, 20453, 24747}, {18177, 21995, 24747, 29945}}\n x = [[11041, 13359, 15023, 18177], [13359, 16165, 18177, 21995], [15023, 18177, 20453, 24747], [18177, 21995, 24747, 29945]]\n x = u.to_pytorch(x).type(dtype)\n # {{29945, -24747, -21995, 18177}, {-24747, 20453, 18177, -15023}, {-21995, 18177, 16165, -13359}, {18177, -15023, -13359, 11041}}\n y0 = [[29945, -24747, -21995, 18177], [-24747, 20453, 18177, -15023], [-21995, 18177, 16165, -13359], [18177, -15023, -13359, 11041]]\n y0 = u.to_pytorch(y0)/16 # ground-truth\n y0 = u.to_pytorch(y0).type(dtype)\n\n # print('discrepancy1 truth', torch.norm(x @ y0 @ x - x))\n # print('discrepancy2 truth', torch.norm(y0 @ x @ y0 - y0))\n y1 = torch.pinverse(x)\n # print('torch error', torch.norm(y0-y1))\n if dtype == torch.float64:\n assert torch.norm(y0-y1) < 2e-5 # 1.8943e-05\n y2 = scipy.linalg.pinv(x)\n # print('scipy error', torch.norm(y0-u.from_numpy(y2))) # 2.5631e-05\n\n subtest(torch.float64)\n subtest(torch.float32)\n\n\ndef test_l2_norm():\n mat = torch.tensor([[1, 1], [0, 1]]).float()\n u.check_equal(u.l2_norm(mat), 0.5 * (1 + math.sqrt(5)))\n ii = torch.eye(5)\n u.check_equal(u.l2_norm(ii), 1)\n\n\ndef test_symsqrt_neg():\n \"\"\"Test robustness to small negative eigenvalues.\"\"\"\n u.seed_random(1)\n torch.set_default_dtype(torch.float32)\n\n mat = torch.tensor([[1.704692840576171875e-05, -9.693153669044357601e-15, -4.637238930627063382e-07,\n -5.784777457051859528e-08, -7.958237541183521557e-11, -9.898678399622440338e-06,\n -2.152719247305867611e-07, -1.635662982835128787e-08, -6.400216989277396351e-06,\n -1.906904145698717912e-08],\n [-9.693153669044357601e-15, 9.693318840469072190e-15, -4.495100185314538545e-21,\n -5.607465147510056466e-22, -7.714305304820877864e-25, -9.595268609986125189e-20,\n -2.086734953246380139e-21, -1.585527368218132061e-22, -6.204040063550678436e-20,\n -1.848454455492805625e-22],\n [-4.637238930627063382e-07, -4.495100185314538545e-21, 4.637315669242525473e-07,\n -2.682631101578927119e-14, -3.690551006200058401e-17, -4.590410516980281130e-12,\n -9.983013764605641605e-14, -7.585219006177833234e-15, -2.968034672201635971e-12,\n -8.843071403179941781e-15],\n [-5.784777457051859528e-08, -5.607465147510056466e-22, -2.682631101578927119e-14,\n 5.784875867220762302e-08, -4.603820523722603687e-18, -5.726360683376563454e-13,\n -1.245342652107404510e-14, -9.462269640040017228e-16, -3.702509490405986314e-13,\n -1.103139288087268827e-15],\n [-7.958237541183521557e-11, -7.714305304820877864e-25, -3.690551006200058401e-17,\n -4.603820523722603687e-18, 7.958373543504038139e-11, -7.877872806981575052e-16,\n -1.713243580702275093e-17, -1.301743849346897150e-18, -5.093618864028342207e-16,\n -1.517611416046059107e-18],\n [-9.898678399622440338e-06, -9.595268609986125189e-20, -4.590410516980281130e-12,\n -5.726360683376563454e-13, -7.877872806981575052e-16, 9.898749340209178627e-06,\n -2.130980288062023220e-12, -1.619145491510778911e-13, -6.335585528427500890e-11,\n -1.887647481900456281e-13],\n [-2.152719247305867611e-07, -2.086734953246380139e-21, -9.983013764605641605e-14,\n -1.245342652107404510e-14, -1.713243580702275093e-17, -2.130980288062023220e-12,\n 2.152755484985391377e-07, -3.521243228436451468e-15, -1.377834044774539635e-12,\n -4.105169319306963341e-15],\n [-1.635662982835128787e-08, -1.585527368218132061e-22, -7.585219006177833234e-15,\n -9.462269640040017228e-16, -1.301743849346897150e-18, -1.619145491510778911e-13,\n -3.521243228436451468e-15, 1.635690871637507371e-08, -1.046895521119271810e-13,\n -3.119158858896762436e-16],\n [-6.400216989277396351e-06, -6.204040063550678436e-20, -2.968034672201635971e-12,\n -3.702509490405986314e-13, -5.093618864028342207e-16, -6.335585528427500890e-11,\n -1.377834044774539635e-12, -1.046895521119271810e-13, 6.400285201380029321e-06,\n -1.220501699922618699e-13],\n [-1.906904145698717912e-08, -1.848454455492805625e-22, -8.843071403179941781e-15,\n -1.103139288087268827e-15, -1.517611416046059107e-18, -1.887647481900456281e-13,\n -4.105169319306963341e-15, -3.119158858896762436e-16, -1.220501699922618699e-13,\n 1.906936653028878936e-08]])\n evals = torch.eig(mat).eigenvalues\n assert torch.min(evals) < 0\n smat = u.symsqrt(mat)\n u.check_close(mat, smat @ smat.t())\n u.check_close(mat, smat @ smat)\n\n\ndef test_truncated_lyapunov():\n d = 100\n n = 1000\n shared_rank = 2\n independent_rank = 1\n A, C = u.random_cov_pair(shared_rank=shared_rank, independent_rank=independent_rank, strength=0.1, d=d, n=n)\n X = u.lyapunov_truncated(A, C)\n\n # effective rank of X captures dimensionality of shared subspace\n u.check_close(u.rank(X), shared_rank + independent_rank, rtol=1e-4)\n u.check_close(u.erank(X), shared_rank, rtol=1e-2)\n\n\ndef test_lyapunov_lstsq():\n torch.manual_seed(1)\n torch.set_default_dtype(torch.float64)\n # A = torch.tensor([1., 2, 3, 4]).reshape(2, 2)\n # C = torch.tensor([5., 6, 7, 8]).reshape(2, 2)\n # X = u.lyapunov_lstsq(A, C)\n # u.check_close(X, [[0.1, 1.1], [1.3, 0.1]])\n #\n # X = u.lyapunov_lstsq(A, A)\n # u.check_close(X, [[0.5, -.1], [.1, .5]])\n #\n A = u.random_cov(1, 3, n=100)\n # print('A=', u._to_mathematica(A))\n X = u.lyapunov_lstsq(A, 2 * A)\n # print('X=', u._to_mathematica(X))\n # print(torch.svd(X)[1])\n X = u.lyapunov_svd(A, 2 * A)\n # print(X)\n # print(torch.svd(X)[1])\n # torch.set_default_dtype(torch.float32)\n\n\ndef test_robust_svd():\n mat = np.genfromtxt('test/gesvd_crash.txt', delimiter=\",\")\n mat = torch.tensor(mat).type(torch.get_default_dtype())\n U, S, V = u.robust_svd(mat)\n mat2 = U @ torch.diag(S) @ V.T\n u.check_close(mat, mat2)\n\n\ndef test_misc():\n d = 3\n a = u.eye_like(torch.ones((d, d)))\n assert u.erank(a) == d\n\n\ndef test_kron():\n \"\"\"Test kron, vec and vecr identities\"\"\"\n torch.set_default_dtype(torch.float64)\n a = torch.tensor([1, 2, 3, 4]).reshape(2, 2)\n b = torch.tensor([5, 6, 7, 8]).reshape(2, 2)\n u.check_close(u.Kron(a, b).trace(), 65)\n\n a = torch.tensor([[2., 7, 9], [1, 9, 8], [2, 7, 5]])\n b = torch.tensor([[6., 6, 1], [10, 7, 7], [7, 10, 10]])\n Ck = u.Kron(a, b)\n u.check_close(a.flatten().norm() * b.flatten().norm(), Ck.frobenius_norm())\n\n u.check_close(Ck.frobenius_norm(), 4 * math.sqrt(11635.))\n\n Ci = [[0, 5 / 102, -(7 / 204), 0, -(70 / 561), 49 / 561, 0, 125 / 1122, -(175 / 2244)],\n [1 / 20, -(53 / 1020), 8 / 255, -(7 / 55), 371 / 2805, -(224 / 2805), 5 / 44, -(265 / 2244), 40 / 561],\n [-(1 / 20), 3 / 170, 3 / 170, 7 / 55, -(42 / 935), -(42 / 935), -(5 / 44), 15 / 374, 15 / 374],\n [0, -(5 / 102), 7 / 204, 0, 20 / 561, -(14 / 561), 0, 35 / 1122, -(49 / 2244)],\n [-(1 / 20), 53 / 1020, -(8 / 255), 2 / 55, -(106 / 2805), 64 / 2805, 7 / 220, -(371 / 11220), 56 / 2805],\n [1 / 20, -(3 / 170), -(3 / 170), -(2 / 55), 12 / 935, 12 / 935, -(7 / 220), 21 / 1870, 21 / 1870],\n [0, 5 / 102, -(7 / 204), 0, 0, 0, 0, -(5 / 102), 7 / 204],\n [1 / 20, -(53 / 1020), 8 / 255, 0, 0, 0, -(1 / 20), 53 / 1020, -(8 / 255)],\n [-(1 / 20), 3 / 170, 3 / 170, 0, 0, 0, 1 / 20, -(3 / 170), -(3 / 170)]]\n C = Ck.expand()\n C0 = u.to_numpy(C)\n Ci = torch.tensor(Ci)\n u.check_close(C @ Ci @ C, C)\n\n u.check_close(Ck.inv().expand(), torch.inverse(Ck.expand()))\n u.check_close(Ck.inv().expand_vec(), torch.inverse(Ck.expand_vec()))\n u.check_close(Ck.pinv().expand(), torch.pinverse(Ck.expand()))\n\n u.check_close(linalg.pinv(C0), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(torch.pinverse(C), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(Ck.inv().expand(), Ci, rtol=1e-5, atol=1e-6)\n u.check_close(Ck.pinv().expand(), Ci, rtol=1e-5, atol=1e-6)\n\n Ck2 = u.Kron(b, 2 * a)\n u.check_close((Ck @ Ck2).expand(), Ck.expand() @ Ck2.expand())\n u.check_close((Ck @ Ck2).expand_vec(), Ck.expand_vec() @ Ck2.expand_vec())\n\n d2 = 3\n d1 = 2\n G = torch.randn(d2, d1)\n g = u.vec(G)\n H = u.Kron(u.random_cov(d1), u.random_cov(d2))\n\n Gt = G.t()\n gt = g.reshape(1, -1)\n\n vecX = u.Vec([1, 2, 3, 4], shape=(2, 2))\n K = u.Kron([[5, 6], [7, 8]], [[9, 10], [11, 12]])\n\n u.check_equal(vecX @ K, [644, 706, 748, 820])\n u.check_equal(K @ vecX, [543, 655, 737, 889])\n\n u.check_equal(u.matmul(vecX @ K, vecX), 7538)\n u.check_equal(vecX @ (vecX @ K), 7538)\n u.check_equal(vecX @ vecX, 30)\n\n vecX = u.Vec([1, 2], shape=(1, 2))\n K = u.Kron([[5]], [[9, 10], [11, 12]])\n\n u.check_equal(vecX.norm()**2, 5)\n\n # check kronecker rules\n X = torch.tensor([[1., 2], [3, 4]])\n A = torch.tensor([[5., 6], [7, 8]])\n B = torch.tensor([[9., 10], [11, 12]])\n x = u.Vec(X)\n\n # kron/vec/vecr identities\n u.check_equal(u.Vec(A @ X @ B), x @ u.Kron(B, A.t()))\n u.check_equal(u.Vec(A @ X @ B), u.Kron(B.t(), A) @ x)\n u.check_equal(u.Vecr(A @ X @ B), u.Kron(A, B.t()) @ u.Vecr(X))\n u.check_equal(u.Vecr(A @ X @ B), u.Vecr(X) @ u.Kron(A.t(), B))\n\n def extra_checks(A, X, B):\n x = u.Vec(X)\n u.check_equal(u.Vec(A @ X @ B), x @ u.Kron(B, A.t()))\n u.check_equal(u.Vec(A @ X @ B), u.Kron(B.t(), A) @ x)\n u.check_equal(u.Vecr(A @ X @ B), u.Kron(A, B.t()) @ u.Vecr(X))\n u.check_equal(u.Vecr(A @ X @ B), u.Vecr(X) @ u.Kron(A.t(), B))\n u.check_equal(u.Vecr(A @ X @ B), u.Vecr(X) @ u.Kron(A.t(), B).normal_form())\n u.check_equal(u.Vecr(A @ X @ B), u.matmul(u.Kron(A, B.t()).normal_form(), u.Vecr(X)))\n u.check_equal(u.Vec(A @ X @ B), u.matmul(u.Kron(B.t(), A).normal_form(), x))\n u.check_equal(u.Vec(A @ X @ B), x @ u.Kron(B, A.t()).normal_form())\n u.check_equal(u.Vec(A @ X @ B), x.normal_form() @ u.Kron(B, A.t()).normal_form())\n u.check_equal(u.Vec(A @ X @ B), u.Kron(B.t(), A).normal_form() @ x.normal_form())\n u.check_equal(u.Vecr(A @ X @ B), u.Kron(A, B.t()).normal_form() @ u.Vecr(X).normal_form())\n u.check_equal(u.Vecr(A @ X @ B), u.Vecr(X).normal_form() @ u.Kron(A.t(), B).normal_form())\n\n # shape checks\n d1, d2 = 3, 4\n extra_checks(torch.ones((d1, d1)), torch.ones((d1, d2)), torch.ones((d2, d2)))\n\n A = torch.rand(d1, d1)\n B = torch.rand(d2, d2)\n #x = torch.rand((d1*d2))\n #X = x.t().reshape(d1, d2)\n # X = torch.rand((d1, d2))\n # x = u.vec(X)\n x = torch.rand((d1*d2))\n # print((u.vec(A@X@B)-u.kron(B.t(), A) @ x).norm())\n\n\ndef test_contiguous():\n\n d = 5\n A = torch.rand((d, d))\n B = torch.rand((d, d))\n result = torch.einsum(\"ab,cd->acbd\", A, B)\n assert(result.is_contiguous())\n result = torch.einsum(\"ab,cd->acbd\", B, A)\n assert(result.is_contiguous())\n result = torch.einsum(\"ab,cd->acbd\", B, A.t())\n assert(not result.is_contiguous())\n\n\nif __name__ == '__main__':\n # test_truncated_lyapunov()\n # test_lyapunov_lstsq()\n # test_robust_svd()\n # test_contiguous()\n # test_kron()\n test_pinverse()\n# u.run_all_tests(sys.modules[__name__])\n" }, { "path": "aws-recipes.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Init\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ncluster version 0.1.73\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import time\\n\",\n \"import boto3\\n\",\n \"import ncluster\\n\",\n \"import boto3\\n\",\n \"import base64\\n\",\n \"import pickle\\n\",\n \"import shlex\\n\",\n \"from ncluster import aws_util as u\\n\",\n \"from ncluster import util as util\\n\",\n \"import subprocess\\n\",\n \"\\n\",\n \"from pprint import pprint as pprint\\n\",\n \"import importlib\\n\",\n \"\\n\",\n \"sys.path.insert(0, os.environ['HOME'])\\n\",\n \"from credentials import * # go_yaro, go_mind, go_andrew\\n\",\n \"\\n\",\n \"\\n\",\n \"def ossystem(cmd, shell=True):\\n\",\n \" \\\"\\\"\\\"Like os.system, but returns output of command as string.\\\"\\\"\\\"\\n\",\n \" p = subprocess.Popen(cmd, shell=shell, stdout=subprocess.PIPE,\\n\",\n \" stderr=subprocess.STDOUT)\\n\",\n \" (stdout, stderr) = p.communicate()\\n\",\n \" return stdout.decode('ascii')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Filters=[{'Name': 'instance-state-name', 'Values': ['stopped']}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def easy_filter()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Instances\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Launching EFA enabled instance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import importlib\\n\",\n \"client = u.get_ec2_client()\\n\",\n \"ec2 = u.get_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" image = u.lookup_image('dlami23-efa')\\n\",\n \" keypair = u.get_keypair()\\n\",\n \" security_group = u.get_security_group()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ['NCLUSTER_ZONE']='us-east-1d'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"subnet=u.get_subnet()\\n\",\n \"placement_group = ''\\n\",\n \"name='efa-programmatic'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instance_type='p3dn.24xlarge'\\n\",\n \"disk_size=500\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ami-03147512dd1ffee88'\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ncluster-yaroslavvb'\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keypair.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-01494dff7a2a16363'\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'subnet-047707529f095f614'\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ami-03147512dd1ffee88'\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ncluster-yaroslavvb'\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keypair.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"boto3.resources.factory.ec2.SecurityGroup\"\n ]\n },\n \"execution_count\": 71,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(security_group)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import boto3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"keypair_name='ncluster-yaroslavvb'\\n\",\n \"instance_type='p3dn.24xlarge'\\n\",\n \"instance_type='c5n.18xlarge'\\n\",\n \"security_group_id='sg-0139018179276c4ba'\\n\",\n \"subnet_id='subnet-047707529f095f614'\\n\",\n \"image_id='ami-03147512dd1ffee88'\\n\",\n \"zone='us-east-1d'\\n\",\n \"\\n\",\n \"args = {'ImageId': image_id,\\n\",\n \" 'InstanceType': instance_type,\\n\",\n \" 'MinCount': 1,\\n\",\n \" 'MaxCount': 1,\\n\",\n \" 'KeyName': keypair_name}\\n\",\n \"\\n\",\n \"args['TagSpecifications'] = [{\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Tags': [{\\n\",\n \" 'Key': 'Name',\\n\",\n \" 'Value': name\\n\",\n \" }]\\n\",\n \"}]\\n\",\n \"\\n\",\n \"subnet = u.get_subnet()\\n\",\n \"args['NetworkInterfaces'] = [{'SubnetId': subnet_id,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'InterfaceType':'efa',\\n\",\n \" 'Groups': [security_group_id]}]\\n\",\n \"placement_specs = {'AvailabilityZone': zone}\\n\",\n \"\\n\",\n \"args['Placement'] = placement_specs\\n\",\n \"args['Monitoring'] = {'Enabled': True}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instances = ec2.create_instances(**args)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'1.9.122'\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"boto3.__version__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting boto3\\n\",\n \"\\u001b[?25l Downloading https://files.pythonhosted.org/packages/8e/5e/d109e3ec9a6b43887ed9de1bd16141ad8e4442aaecff6f90fca05429c43a/boto3-1.9.159-py2.py3-none-any.whl (128kB)\\n\",\n \"\\u001b[K 100% |████████████████████████████████| 133kB 240kB/s ta 0:00:01\\n\",\n \"\\u001b[?25hCollecting botocore<1.13.0,>=1.12.159 (from boto3)\\n\",\n \"\\u001b[?25l Downloading https://files.pythonhosted.org/packages/52/bb/113f5083f145c41750d5b6f171a76c20f2e2b55a7bd9d02f55e849559619/botocore-1.12.159-py2.py3-none-any.whl (5.5MB)\\n\",\n \"\\u001b[K 100% |████████████████████████████████| 5.5MB 1.3MB/s ta 0:00:011\\n\",\n \"\\u001b[?25hRequirement already satisfied, skipping upgrade: jmespath<1.0.0,>=0.7.1 in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from boto3) (0.9.4)\\n\",\n \"Requirement already satisfied, skipping upgrade: s3transfer<0.3.0,>=0.2.0 in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from boto3) (0.2.0)\\n\",\n \"Requirement already satisfied, skipping upgrade: docutils>=0.10 in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from botocore<1.13.0,>=1.12.159->boto3) (0.14)\\n\",\n \"Requirement already satisfied, skipping upgrade: urllib3<1.26,>=1.20; python_version >= \\\"3.4\\\" in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from botocore<1.13.0,>=1.12.159->boto3) (1.24.1)\\n\",\n \"Requirement already satisfied, skipping upgrade: python-dateutil<3.0.0,>=2.1; python_version >= \\\"2.7\\\" in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from botocore<1.13.0,>=1.12.159->boto3) (2.8.0)\\n\",\n \"Requirement already satisfied, skipping upgrade: six>=1.5 in /Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages (from python-dateutil<3.0.0,>=2.1; python_version >= \\\"2.7\\\"->botocore<1.13.0,>=1.12.159->boto3) (1.12.0)\\n\",\n \"Installing collected packages: botocore, boto3\\n\",\n \" Found existing installation: botocore 1.12.122\\n\",\n \" Uninstalling botocore-1.12.122:\\n\",\n \" Successfully uninstalled botocore-1.12.122\\n\",\n \" Found existing installation: boto3 1.9.122\\n\",\n \" Uninstalling boto3-1.9.122:\\n\",\n \" Successfully uninstalled boto3-1.9.122\\n\",\n \"Successfully installed boto3-1.9.159 botocore-1.12.159\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install -U boto3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Launching EFA2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidParameterCombination) when calling the RunInstances operation: Network interfaces and an instance-level security groups may not be specified on the same request\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 22\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 23\\u001b[0m \\u001b[0mec2\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mboto3\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mSession\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mresource\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'ec2'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 24\\u001b[0;31m \\u001b[0minstances\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcreate_instances\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m~/anaconda3/envs/main/lib/python3.6/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/envs/main/lib/python3.6/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/envs/main/lib/python3.6/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 355\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 356\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 357\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 358\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 359\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/envs/main/lib/python3.6/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 659\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 660\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 661\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 662\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 663\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidParameterCombination) when calling the RunInstances operation: Network interfaces and an instance-level security groups may not be specified on the same request\"\n ]\n }\n ],\n \"source\": [\n \"import boto3\\n\",\n \"instance_type='p3dn.24xlarge'\\n\",\n \"\\n\",\n \"subnet_id='subnet-07e59d5b61d434fd4'\\n\",\n \"name='efa-programmatic'\\n\",\n \"image_id='ami-03147512dd1ffee88'\\n\",\n \"security_group_id='sg-01494dff7a2a16363'\\n\",\n \"\\n\",\n \"args = {'ImageId': image_id,\\n\",\n \" 'InstanceType': instance_type,\\n\",\n \" 'MinCount': 1,\\n\",\n \" 'MaxCount': 1,\\n\",\n \" 'SecurityGroupIds': [security_group_id],\\n\",\n \" 'KeyName': 'ncluster-yaroslavvb'}\\n\",\n \"\\n\",\n \"args['NetworkInterfaces'] = [{'SubnetId': subnet_id,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'InterfaceType':'efa',\\n\",\n \" 'Groups': [security_group_id]}]\\n\",\n \"placement_specs = {'AvailabilityZone': 'us-east-1b'}\\n\",\n \"\\n\",\n \"ec2 = boto3.Session().resource('ec2')\\n\",\n \"instances = ec2.create_instances(**args)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Checking disks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using region us-east-1\\n\"\n ]\n }\n ],\n \"source\": [\n \"instance=u.lookup_instance('demo')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volumes = list(instance.volumes.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volume = volumes[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'AttachTime': datetime.datetime(2019, 6, 23, 22, 34, 54, tzinfo=tzutc()),\\n\",\n \" 'Device': '/dev/sda1',\\n\",\n \" 'InstanceId': 'i-048f463c7019b821e',\\n\",\n \" 'State': 'attached',\\n\",\n \" 'VolumeId': 'vol-00af8a22453ab79f0',\\n\",\n \" 'DeleteOnTermination': True}]\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.attachments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"500\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.size\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vol-00af8a22453ab79f0'\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'gp2'\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.volume_type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Volume.snapshotsCollectionManager(ec2.Volume(id='vol-00af8a22453ab79f0'), ec2.Snapshot)\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.snapshots\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/dev/sda1'\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume.attachments[0]['Device']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" /dev/sda1 500 gp2 vol-00af8a22453ab79f0 \\n\",\n \" /dev/xvda 500 gp2 vol-063f8a82cc1ebc300 \\n\"\n ]\n }\n ],\n \"source\": [\n \"instance = u.lookup_instances('efa-image11')\\n\",\n \"for volume in instance.volumes.all():\\n\",\n \" device = volume.attachments[0]['Device']\\n\",\n \" print(f\\\"{device:>10s} {volume.size:>10d} {volume.volume_type:>10s} {volume.id:>30s} \\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Checking security group\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using region us-east-1\\n\"\n ]\n },\n {\n \"ename\": \"AssertionError\",\n \"evalue\": \"Didn't find any instances matching 'pair'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAssertionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0minstance\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mu\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlookup_instance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'pair'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m~/Dropbox/git0/ncluster/ncluster/aws_util.py\\u001b[0m in \\u001b[0;36mlookup_instance\\u001b[0;34m(fragment)\\u001b[0m\\n\\u001b[1;32m 460\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 461\\u001b[0m \\u001b[0minstances\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlookup_instances\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfragment\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 462\\u001b[0;31m \\u001b[0;32massert\\u001b[0m \\u001b[0minstances\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34mf\\\"Didn't find any instances matching '{fragment}'\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 463\\u001b[0m \\u001b[0mnames\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mget_name\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0minstances\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 464\\u001b[0m \\u001b[0;32massert\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0minstances\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34mf\\\"Found multiple instances matching fragment {fragment}: {','.join(names)}\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAssertionError\\u001b[0m: Didn't find any instances matching 'pair'\"\n ]\n }\n ],\n \"source\": [\n \"instance=u.lookup_instance('pair')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"''\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.placement_group.name\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Filtering instances by state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['MindscribeWeb-env',\\n\",\n \" 'launch_interact-gpt1-ryanmckinney',\\n\",\n \" 'voice-server-auto',\\n\",\n \" 'voice-server-dev-server',\\n\",\n \" 'voice-server-auto']\"\n ]\n },\n \"execution_count\": 69,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[u.get_name(i) for i in ec2.instances.filter(Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Getting default VPC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from ncluster import aws_util as u\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Vpc(id='vpc-0a007bc3954ed0c9d')\"\n ]\n },\n \"execution_count\": 85,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_vpc()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = u.get_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'Vpcs': [{'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'DhcpOptionsId': 'dopt-086557b289697d4b4',\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-d5c91aaf',\\n\",\n \" 'OwnerId': '331439827203',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-f6a70b9a',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'IsDefault': True},\\n\",\n \" {'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'DhcpOptionsId': 'dopt-086557b289697d4b4',\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'OwnerId': '331439827203',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-0a30b722e61658fc8',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'ncluster'}]}],\\n\",\n \" 'ResponseMetadata': {'RequestId': 'c0cb05a1-8ee4-4e30-a137-4f074ccbd48a',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'content-length': '1902',\\n\",\n \" 'date': 'Mon, 03 Jun 2019 20:46:11 GMT',\\n\",\n \" 'server': 'AmazonEC2'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_vpcs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Vpc(id='vpc-d5c91aaf')\"\n ]\n },\n \"execution_count\": 88,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_default_vpc()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create EFA-friendly security group\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 1. check existing security group that works\\n\",\n \"# 2. create new security group, double check that it looks the same\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-east-1'\\n\",\n \"importlib.reload(boto3)\\n\",\n \"d = u.get_security_group_dict()\\n\",\n \"good_group=d['ncluster']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'FromPort': 6006,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6016,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 0,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': 8888,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8899,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'}]},\\n\",\n \" {'FromPort': 60000,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 61000,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6379,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6379,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 2049,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'NFS-udp'}],\\n\",\n \" 'Ipv6Ranges': [{'CidrIpv6': '::/0', 'Description': 'NFS-udp'}],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 2049,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 0,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': -1,\\n\",\n \" 'IpProtocol': 'icmp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': -1,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': 2049,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'NFS'}],\\n\",\n \" 'Ipv6Ranges': [{'CidrIpv6': '::/0', 'Description': 'NFS'}],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 2049,\\n\",\n \" 'UserIdGroupPairs': []}]\"\n ]\n },\n \"execution_count\": 84,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.ip_permissions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-west-2'\\n\",\n \"importlib.reload(boto3)\\n\",\n \"d = u.get_security_group_dict()\\n\",\n \"bad_group=d['ncluster']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-01494dff7a2a16363'\"\n ]\n },\n \"execution_count\": 86,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"good_group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-0481f4230bc91ab83'\"\n ]\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bad_group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-01494dff7a2a16363'\"\n ]\n },\n \"execution_count\": 90,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"good_group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'FromPort': 6006,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6016,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 0,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': 8888,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8899,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'}]},\\n\",\n \" {'FromPort': 60000,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 61000,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6379,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6379,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 2049,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'NFS-udp'}],\\n\",\n \" 'Ipv6Ranges': [{'CidrIpv6': '::/0', 'Description': 'NFS-udp'}],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 2049,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 0,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': -1,\\n\",\n \" 'IpProtocol': 'icmp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': -1,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'},\\n\",\n \" {'GroupId': 'sg-09a65a86d87d54560',\\n\",\n \" 'PeeringStatus': 'active',\\n\",\n \" 'UserId': '331439827203',\\n\",\n \" 'VpcId': 'vpc-0a007bc3954ed0c9d',\\n\",\n \" 'VpcPeeringConnectionId': 'pcx-087644c72415b62e1'}]},\\n\",\n \" {'FromPort': 2049,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'NFS'}],\\n\",\n \" 'Ipv6Ranges': [{'CidrIpv6': '::/0', 'Description': 'NFS'}],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 2049,\\n\",\n \" 'UserIdGroupPairs': []}]\"\n ]\n },\n \"execution_count\": 88,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"good_group.ip_permissions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What correct rule looks like\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'}]},\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-069bb8d7ea3950cfc'\"\n ]\n },\n \"execution_count\": 73,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\r\\n\",\n \" \\\"GroupId\\\": \\\"sg-0d9123fbedb51266f\\\"\\r\\n\",\n \"}\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!aws ec2 create-security-group --group-name MySecurityGroup --description \\\"My security group\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'}]}]\"\n ]\n },\n \"execution_count\": 92,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"good_group.ip_permissions_egress\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-01494dff7a2a16363',\\n\",\n \" 'UserId': '331439827203'}]},\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}]\"\n ]\n },\n \"execution_count\": 93,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bad_group.ip_permissions_egress\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': 'sg-0481f4230bc91ab83',\\n\",\n \" 'UserId': '331439827203'}]}]\"\n ]\n },\n \"execution_count\": 109,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bad_group.reload()\\n\",\n \"bad_group.ip_permissions_egress\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"other_security_group=bad_group\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rule = {\\n\",\n \"\\\"IpProtocol\\\": \\\"-1\\\",\\n\",\n \"\\\"PrefixListIds\\\": [],\\n\",\n \"'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': other_security_group.id,\\n\",\n \" 'UserId': u.get_account_number()}]\\n\",\n \"}\\n\",\n \"bad_group.authorize_egress(IpPermissions=[rule])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'RequestId': 'c537d59e-f7ca-432b-b6f7-434e0eef6715',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'content-length': '257',\\n\",\n \" 'date': 'Fri, 07 Jun 2019 22:34:25 GMT',\\n\",\n \" 'server': 'AmazonEC2'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 108,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bad_group.authorize_egress(IpPermissions=[rule])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!aws ec2 authorize-security-group-ingress --group-id sg-0d9123fbedb51266f --protocol all --port all --cidr 0.0.0.0/0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\r\\n\",\n \" \\\"SecurityGroups\\\": [\\r\\n\",\n \" {\\r\\n\",\n \" \\\"Description\\\": \\\"My security group\\\",\\r\\n\",\n \" \\\"GroupName\\\": \\\"MySecurityGroup\\\",\\r\\n\",\n \" \\\"IpPermissions\\\": [\\r\\n\",\n \" {\\r\\n\",\n \" \\\"IpProtocol\\\": \\\"-1\\\",\\r\\n\",\n \" \\\"IpRanges\\\": [\\r\\n\",\n \" {\\r\\n\",\n \" \\\"CidrIp\\\": \\\"0.0.0.0/0\\\"\\r\\n\",\n \" }\\r\\n\",\n \" ],\\r\\n\",\n \" \\\"Ipv6Ranges\\\": [],\\r\\n\",\n \" \\\"PrefixListIds\\\": [],\\r\\n\",\n \" \\\"UserIdGroupPairs\\\": []\\r\\n\",\n \" }\\r\\n\",\n \" ],\\r\\n\",\n \" \\\"OwnerId\\\": \\\"331439827203\\\",\\r\\n\",\n \" \\\"GroupId\\\": \\\"sg-0d9123fbedb51266f\\\",\\r\\n\",\n \" \\\"IpPermissionsEgress\\\": [\\r\\n\",\n \" {\\r\\n\",\n \" \\\"IpProtocol\\\": \\\"-1\\\",\\r\\n\",\n \" \\\"IpRanges\\\": [\\r\\n\",\n \" {\\r\\n\",\n \" \\\"CidrIp\\\": \\\"0.0.0.0/0\\\"\\r\\n\",\n \" }\\r\\n\",\n \" ],\\r\\n\",\n \" \\\"Ipv6Ranges\\\": [],\\r\\n\",\n \" \\\"PrefixListIds\\\": [],\\r\\n\",\n \" \\\"UserIdGroupPairs\\\": []\\r\\n\",\n \" }\\r\\n\",\n \" ],\\r\\n\",\n \" \\\"VpcId\\\": \\\"vpc-d5c91aaf\\\"\\r\\n\",\n \" }\\r\\n\",\n \" ]\\r\\n\",\n \"}\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!aws ec2 describe-security-groups --group-names MySecurityGroup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create one through boto3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vpc = u.get_default_vpc()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"security_group_name='MySecurityGroup3'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.get_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"group2=ec2.create_security_group(GroupName=security_group_name,\\n\",\n \" Description=security_group_name,\\n\",\n \" VpcId=vpc.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-0f8536aa45d3ab3ee'\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group2.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rule={'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': group2.id,\\n\",\n \" 'UserId': u.get_account_number()}]\\n\",\n \" },\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rule2= {\\n\",\n \" \\\"IpProtocol\\\": \\\"-1\\\",\\n\",\n \" \\\"IpRanges\\\": [\\n\",\n \" {\\n\",\n \" \\\"CidrIp\\\": \\\"0.0.0.0/0\\\"\\n\",\n \" }\\n\",\n \" ],\\n\",\n \" \\\"Ipv6Ranges\\\": [],\\n\",\n \" \\\"PrefixListIds\\\": [],\\n\",\n \" 'UserIdGroupPairs': [{'Description': 'efa',\\n\",\n \" 'GroupId': group2.id,\\n\",\n \" 'UserId': u.get_account_number()}]\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ncluster'\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.description\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.is_default\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'1.9.159'\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"boto3.__version__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pip install\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"response = group2.authorize_ingress(IpPermissions=[rule2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'RequestId': 'b99465f3-fc28-41c7-99b0-b0e73be00641',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'content-length': '259',\\n\",\n \" 'date': 'Fri, 07 Jun 2019 00:52:03 GMT',\\n\",\n \" 'server': 'AmazonEC2'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Account-level\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Limits\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = u.get_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AccountAttributes': [{'AttributeName': 'supported-platforms',\\n\",\n \" 'AttributeValues': [{'AttributeValue': 'VPC'}]},\\n\",\n \" {'AttributeName': 'vpc-max-security-groups-per-interface',\\n\",\n \" 'AttributeValues': [{'AttributeValue': '5'}]},\\n\",\n \" {'AttributeName': 'max-elastic-ips',\\n\",\n \" 'AttributeValues': [{'AttributeValue': '5'}]},\\n\",\n \" {'AttributeName': 'max-instances',\\n\",\n \" 'AttributeValues': [{'AttributeValue': '300'}]},\\n\",\n \" {'AttributeName': 'vpc-max-elastic-ips',\\n\",\n \" 'AttributeValues': [{'AttributeValue': '5'}]},\\n\",\n \" {'AttributeName': 'default-vpc',\\n\",\n \" 'AttributeValues': [{'AttributeValue': 'vpc-d5c91aaf'}]}],\\n\",\n \" 'ResponseMetadata': {'RequestId': 'c576523f-f2e6-436f-ba84-f96edadac907',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'content-length': '1858',\\n\",\n \" 'date': 'Fri, 21 Jun 2019 15:46:24 GMT',\\n\",\n \" 'server': 'AmazonEC2'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_account_attributes()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Account name\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- https://docs.aws.amazon.com/IAM/latest/APIReference/API_ListAccountAliases.html \\n\",\n \"- https://docs.aws.amazon.com/cli/latest/reference/iam/list-account-aliases.html \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['keyapp']\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"go_mind()\\n\",\n \"importlib.reload(boto3)\\n\",\n \"iam_client = boto3.client('iam')\\n\",\n \"iam_client.list_account_aliases()\\n\",\n \"iam_client.list_account_aliases()['AccountAliases']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'yaroslavvb'\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import getpass\\n\",\n \"getpass.getuser()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'yaroslavvb'\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"iam_client.get_user()['User']['UserName']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AccountAliases': ['yacht'],\\n\",\n \" 'IsTruncated': False,\\n\",\n \" 'ResponseMetadata': {'RequestId': '8901298c-8a1d-11e9-a205-2d1e7619d282',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'x-amzn-requestid': '8901298c-8a1d-11e9-a205-2d1e7619d282',\\n\",\n \" 'content-type': 'text/xml',\\n\",\n \" 'content-length': '384',\\n\",\n \" 'date': 'Sat, 08 Jun 2019 18:45:06 GMT'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# yaro\\n\",\n \"importlib.reload(boto3)\\n\",\n \"go_yaro()\\n\",\n \"iam_client = boto3.client('iam')\\n\",\n \"iam_client.list_account_aliases()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AccountAliases': ['antics'],\\n\",\n \" 'IsTruncated': False,\\n\",\n \" 'ResponseMetadata': {'RequestId': '8e8fe37b-8a1d-11e9-831f-2d10654543da',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'x-amzn-requestid': '8e8fe37b-8a1d-11e9-831f-2d10654543da',\\n\",\n \" 'content-type': 'text/xml',\\n\",\n \" 'content-length': '385',\\n\",\n \" 'date': 'Sat, 08 Jun 2019 18:45:15 GMT'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# andrew\\n\",\n \"go_andrew()\\n\",\n \"importlib.reload(boto3)\\n\",\n \"iam_client = boto3.client('iam')\\n\",\n \"iam_client.list_account_aliases()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'Account': '979440106638',\\n\",\n \" 'Arn': 'arn:aws:iam::979440106638:user/yaroslavvb',\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-length': '407',\\n\",\n \" 'content-type': 'text/xml',\\n\",\n \" 'date': 'Sat, 08 Jun 2019 18:45:44 GMT',\\n\",\n \" 'x-amzn-requestid': '9fc0fc18-8a1d-11e9-8f18-af3e36405676'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '9fc0fc18-8a1d-11e9-8f18-af3e36405676',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'UserId': 'AIDA6ICZN7SHO54UXNLS3'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# mindscribe\\n\",\n \"go_mind()\\n\",\n \"importlib.reload(boto3)\\n\",\n \"sts_client = boto3.client('sts')\\n\",\n \"pprint(sts_client.get_caller_identity())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Valid zones for p3dn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Valid zones for yacht p3dn.24xlarge\\n\",\n \"eu-west-1a: 9.739100\\n\",\n \"eu-west-1b: 9.739100\\n\",\n \"ap-northeast-1b: 42.783000\\n\",\n \"us-east-1b: 9.363600\\n\",\n \"us-east-1d: 9.363600\\n\",\n \"us-west-2b: 9.363600\\n\",\n \"us-west-2c: 9.363600\\n\",\n \"{'eu-west-1a': '9.739100', 'eu-west-1b': '9.739100', 'ap-northeast-1b': '42.783000', 'us-east-1b': '9.363600', 'us-east-1d': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"import boto3\\n\",\n \"importlib.reload(boto3)\\n\",\n \"\\n\",\n \"def valid_zones(prefix, init_func, instanceType):\\n\",\n \" product = 'Linux/UNIX (Amazon VPC)'\\n\",\n \" result = {}\\n\",\n \" print(\\\"Valid zones for \\\", prefix, instanceType)\\n\",\n \" for region in boto3.client('ec2').describe_regions()['Regions']:\\n\",\n \" importlib.reload(boto3)\\n\",\n \" client = boto3.client('ec2', region_name=region['RegionName'])\\n\",\n \" for zone in [z['ZoneName'] for z in client.describe_availability_zones()['AvailabilityZones'] if z['State'] == 'available']:\\n\",\n \" try:\\n\",\n \" price = client.describe_spot_price_history(InstanceTypes=[instanceType],\\n\",\n \" MaxResults=1,\\n\",\n \" ProductDescriptions=[product],\\n\",\n \" AvailabilityZone=zone)['SpotPriceHistory'][0]['SpotPrice']\\n\",\n \" print(\\\"%s: %s\\\" % (zone, price))\\n\",\n \" result[zone] = price\\n\",\n \" except IndexError: pass\\n\",\n \" return result\\n\",\n \"\\n\",\n \"print(valid_zones('yacht', go_yacht, 'p3dn.24xlarge'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Valid zones for yacht p3.16xlarge\\n\",\n \"eu-west-2a: 8.613600\\n\",\n \"eu-west-2b: 8.613600\\n\",\n \"eu-west-1a: 7.932000\\n\",\n \"eu-west-1b: 7.932000\\n\",\n \"eu-west-1c: 26.440000\\n\",\n \"ap-northeast-2a: 11.954400\\n\",\n \"ap-northeast-2c: 11.954400\\n\",\n \"ap-northeast-1a: 10.065600\\n\",\n \"ap-northeast-1c: 10.065600\\n\",\n \"ca-central-1b: 8.078400\\n\",\n \"ap-southeast-1a: 10.161600\\n\",\n \"ap-southeast-1b: 10.161600\\n\",\n \"ap-southeast-2b: 10.161600\\n\",\n \"eu-central-1a: 9.175200\\n\",\n \"eu-central-1b: 9.175200\\n\",\n \"us-east-1a: 24.480000\\n\",\n \"us-east-1b: 7.344000\\n\",\n \"us-east-1c: 7.344000\\n\",\n \"us-east-1d: 7.344000\\n\",\n \"us-east-1f: 7.344000\\n\",\n \"us-east-2a: 7.344000\\n\",\n \"us-east-2b: 7.344000\\n\",\n \"us-west-2a: 7.344000\\n\",\n \"us-west-2b: 7.344000\\n\",\n \"us-west-2c: 7.344000\\n\",\n \"Valid zones for yacht p3dn.24xlarge\\n\",\n \"eu-west-1a: 9.739100\\n\",\n \"eu-west-1c: 9.739100\\n\",\n \"ap-northeast-1a: 12.834900\\n\",\n \"us-east-1b: 9.363600\\n\",\n \"us-east-1c: 9.363600\\n\",\n \"us-west-2b: 9.363600\\n\",\n \"us-west-2c: 9.363600\\n\",\n \"Valid zones for antics p3.16xlarge\\n\",\n \"eu-west-2a: 8.613600\\n\",\n \"eu-west-2b: 8.613600\\n\",\n \"eu-west-1a: 7.932000\\n\",\n \"eu-west-1b: 7.932000\\n\",\n \"eu-west-1c: 26.440000\\n\",\n \"ap-northeast-2a: 11.954400\\n\",\n \"ap-northeast-2c: 11.954400\\n\",\n \"ap-northeast-1a: 10.065600\\n\",\n \"ap-northeast-1c: 10.065600\\n\",\n \"ca-central-1b: 8.078400\\n\",\n \"ap-southeast-1a: 10.161600\\n\",\n \"ap-southeast-1b: 10.161600\\n\",\n \"ap-southeast-2b: 10.161600\\n\",\n \"eu-central-1a: 9.175200\\n\",\n \"eu-central-1b: 9.175200\\n\",\n \"us-east-1a: 24.480000\\n\",\n \"us-east-1b: 7.344000\\n\",\n \"us-east-1c: 7.344000\\n\",\n \"us-east-1d: 7.344000\\n\",\n \"us-east-1f: 7.344000\\n\",\n \"us-east-2a: 7.344000\\n\",\n \"us-east-2b: 7.344000\\n\",\n \"us-west-2a: 7.344000\\n\",\n \"us-west-2b: 7.344000\\n\",\n \"us-west-2c: 7.344000\\n\",\n \"Valid zones for antics p3dn.24xlarge\\n\",\n \"eu-west-1a: 9.739100\\n\",\n \"eu-west-1c: 9.739100\\n\",\n \"ap-northeast-1a: 12.834900\\n\",\n \"us-east-1b: 9.363600\\n\",\n \"us-east-1c: 9.363600\\n\",\n \"us-west-2b: 9.363600\\n\",\n \"us-west-2c: 9.363600\\n\",\n \"Valid zones for keyapp p3.16xlarge\\n\",\n \"eu-west-2a: 8.613600\\n\",\n \"eu-west-2b: 8.613600\\n\",\n \"eu-west-1a: 7.932000\\n\",\n \"eu-west-1b: 7.932000\\n\",\n \"eu-west-1c: 26.440000\\n\",\n \"ap-northeast-2a: 11.954400\\n\",\n \"ap-northeast-2c: 11.954400\\n\",\n \"ap-northeast-1a: 10.065600\\n\",\n \"ap-northeast-1c: 10.065600\\n\",\n \"ca-central-1b: 8.078400\\n\",\n \"ap-southeast-1a: 10.161600\\n\",\n \"ap-southeast-1b: 10.161600\\n\",\n \"ap-southeast-2b: 10.161600\\n\",\n \"eu-central-1a: 9.175200\\n\",\n \"eu-central-1b: 9.175200\\n\",\n \"us-east-1a: 24.480000\\n\",\n \"us-east-1b: 7.344000\\n\",\n \"us-east-1c: 7.344000\\n\",\n \"us-east-1d: 7.344000\\n\",\n \"us-east-1f: 7.344000\\n\",\n \"us-east-2a: 7.344000\\n\",\n \"us-east-2b: 7.344000\\n\",\n \"us-west-2a: 7.344000\\n\",\n \"us-west-2b: 7.344000\\n\",\n \"us-west-2c: 7.344000\\n\",\n \"Valid zones for keyapp p3dn.24xlarge\\n\",\n \"eu-west-1a: 9.739100\\n\",\n \"eu-west-1c: 9.739100\\n\",\n \"ap-northeast-1a: 12.834900\\n\",\n \"us-east-1b: 9.363600\\n\",\n \"us-east-1c: 9.363600\\n\",\n \"us-west-2b: 9.363600\\n\",\n \"us-west-2c: 9.363600\\n\",\n \"{('yacht', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '7.932000', 'eu-west-1b': '7.932000', 'eu-west-1c': '26.440000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1a': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2b': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1c': '7.344000', 'us-east-1d': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.344000', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('yacht', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1c': '9.739100', 'ap-northeast-1a': '12.834900', 'us-east-1b': '9.363600', 'us-east-1c': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}, ('antics', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '7.932000', 'eu-west-1b': '7.932000', 'eu-west-1c': '26.440000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1a': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2b': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1c': '7.344000', 'us-east-1d': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.344000', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('antics', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1c': '9.739100', 'ap-northeast-1a': '12.834900', 'us-east-1b': '9.363600', 'us-east-1c': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}, ('keyapp', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '7.932000', 'eu-west-1b': '7.932000', 'eu-west-1c': '26.440000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1a': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2b': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1c': '7.344000', 'us-east-1d': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.344000', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('keyapp', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1c': '9.739100', 'ap-northeast-1a': '12.834900', 'us-east-1b': '9.363600', 'us-east-1c': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get valid zones for accounts\\n\",\n \"valid_zone_dict = {}\\n\",\n \"for (prefix, init) in [('yacht', go_yacht), ('antics', go_antics), ('keyapp', go_keyapp)]:\\n\",\n \" importlib.reload(boto3)\\n\",\n \"\\n\",\n \" for instance_type in ['p3.16xlarge', 'p3dn.24xlarge']:\\n\",\n \" valid_zone_dict[(prefix, instance_type)] = valid_zones(prefix, init, instance_type)\\n\",\n \"print(valid_zone_dict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = {('yacht', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '26.440000', 'eu-west-1b': '7.932000', 'eu-west-1c': '7.932000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1b': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2a': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1d': '7.344000', 'us-east-1e': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.471400', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('yacht', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1b': '9.739100', 'ap-northeast-1b': '12.834900', 'us-east-1b': '9.363600', 'us-east-1d': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}, ('antics', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '26.440000', 'eu-west-1b': '7.932000', 'eu-west-1c': '7.932000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1b': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2a': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1d': '7.344000', 'us-east-1e': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.471400', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('antics', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1b': '9.739100', 'ap-northeast-1b': '12.834900', 'us-east-1b': '9.363600', 'us-east-1d': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}, ('keyapp', 'p3.16xlarge'): {'eu-west-2a': '8.613600', 'eu-west-2b': '8.613600', 'eu-west-1a': '26.440000', 'eu-west-1b': '7.932000', 'eu-west-1c': '7.932000', 'ap-northeast-2a': '11.954400', 'ap-northeast-2c': '11.954400', 'ap-northeast-1b': '10.065600', 'ap-northeast-1c': '10.065600', 'ca-central-1b': '8.078400', 'ap-southeast-1a': '10.161600', 'ap-southeast-1b': '10.161600', 'ap-southeast-2a': '10.161600', 'eu-central-1a': '9.175200', 'eu-central-1b': '9.175200', 'us-east-1a': '24.480000', 'us-east-1b': '7.344000', 'us-east-1d': '7.344000', 'us-east-1e': '7.344000', 'us-east-1f': '7.344000', 'us-east-2a': '7.471400', 'us-east-2b': '7.344000', 'us-west-2a': '7.344000', 'us-west-2b': '7.344000', 'us-west-2c': '7.344000'}, ('keyapp', 'p3dn.24xlarge'): {'eu-west-1a': '9.739100', 'eu-west-1b': '9.739100', 'ap-northeast-1b': '12.834900', 'us-east-1b': '9.363600', 'us-east-1d': '9.363600', 'us-west-2b': '9.363600', 'us-west-2c': '9.363600'}}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'eu-west-2a': '8.613600',\\n\",\n \" 'eu-west-2b': '8.613600',\\n\",\n \" 'eu-west-1a': '26.440000',\\n\",\n \" 'eu-west-1b': '7.932000',\\n\",\n \" 'eu-west-1c': '7.932000',\\n\",\n \" 'ap-northeast-2a': '11.954400',\\n\",\n \" 'ap-northeast-2c': '11.954400',\\n\",\n \" 'ap-northeast-1b': '10.065600',\\n\",\n \" 'ap-northeast-1c': '10.065600',\\n\",\n \" 'ca-central-1b': '8.078400',\\n\",\n \" 'ap-southeast-1a': '10.161600',\\n\",\n \" 'ap-southeast-1b': '10.161600',\\n\",\n \" 'ap-southeast-2a': '10.161600',\\n\",\n \" 'eu-central-1a': '9.175200',\\n\",\n \" 'eu-central-1b': '9.175200',\\n\",\n \" 'us-east-1a': '24.480000',\\n\",\n \" 'us-east-1b': '7.344000',\\n\",\n \" 'us-east-1d': '7.344000',\\n\",\n \" 'us-east-1e': '7.344000',\\n\",\n \" 'us-east-1f': '7.344000',\\n\",\n \" 'us-east-2a': '7.471400',\\n\",\n \" 'us-east-2b': '7.344000',\\n\",\n \" 'us-west-2a': '7.344000',\\n\",\n \" 'us-west-2b': '7.344000',\\n\",\n \" 'us-west-2c': '7.344000'}\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d[('yacht', 'p3.16xlarge')]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'eu-west-1a': '9.739100',\\n\",\n \" 'eu-west-1b': '9.739100',\\n\",\n \" 'ap-northeast-1b': '12.834900',\\n\",\n \" 'us-east-1b': '9.363600',\\n\",\n \" 'us-east-1d': '9.363600',\\n\",\n \" 'us-west-2b': '9.363600',\\n\",\n \" 'us-west-2c': '9.363600'}\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d[('keyapp', 'p3dn.24xlarge')]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'eu-west-1a': '9.739100',\\n\",\n \" 'eu-west-1b': '9.739100',\\n\",\n \" 'ap-northeast-1b': '12.834900',\\n\",\n \" 'us-east-1b': '9.363600',\\n\",\n \" 'us-east-1d': '9.363600',\\n\",\n \" 'us-west-2b': '9.363600',\\n\",\n \" 'us-west-2c': '9.363600'}\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d[('antics', 'p3dn.24xlarge')]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'eu-west-1a': '9.739100',\\n\",\n \" 'eu-west-1b': '9.739100',\\n\",\n \" 'ap-northeast-1b': '12.834900',\\n\",\n \" 'us-east-1b': '9.363600',\\n\",\n \" 'us-east-1d': '9.363600',\\n\",\n \" 'us-west-2b': '9.363600',\\n\",\n \" 'us-west-2c': '9.363600'}\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d[('yacht', 'p3dn.24xlarge')]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Images\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mimages\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mimages\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mall\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0;34m[\\u001b[0m\\u001b[0mim\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mim\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mimages\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mim\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mid\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;34m'ami-a9d09ed1'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"images = list(ec2.images.all())\\n\",\n \"[im for im in images if im.id == 'ami-a9d09ed1'][0].name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"amazon_ami='ami-0cc96feef8c6bbff3'\\n\",\n \"ec2 = u.get_ec2_resource()\\n\",\n \"images = list(ec2.images.all())\\n\",\n \"[im for im in images if im.id == amazon_ami][0].name\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Get root device name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"images = list(ec2.images.filter(ImageIds=['ami-0cc96feef8c6bbff3']));\\n\",\n \"device_name = images[0].root_device_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = images[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Amazon Linux 2 AMI 2.0.20190612 x86_64 HVM gp2'\"\n ]\n },\n \"execution_count\": 90,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.description\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/dev/xvda'\"\n ]\n },\n \"execution_count\": 83,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.root_device_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = u.get_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'Images': [{'Architecture': 'x86_64',\\n\",\n \" 'CreationDate': '2019-06-11T07:15:13.000Z',\\n\",\n \" 'ImageId': 'ami-0cc96feef8c6bbff3',\\n\",\n \" 'ImageLocation': 'amazon/amzn2-ami-hvm-2.0.20190612-x86_64-gp2',\\n\",\n \" 'ImageType': 'machine',\\n\",\n \" 'Public': True,\\n\",\n \" 'OwnerId': '137112412989',\\n\",\n \" 'State': 'available',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/xvda',\\n\",\n \" 'Ebs': {'DeleteOnTermination': True,\\n\",\n \" 'SnapshotId': 'snap-08f742c6ac4f80dda',\\n\",\n \" 'VolumeSize': 8,\\n\",\n \" 'VolumeType': 'gp2',\\n\",\n \" 'Encrypted': False}}],\\n\",\n \" 'Description': 'Amazon Linux 2 AMI 2.0.20190612 x86_64 HVM gp2',\\n\",\n \" 'EnaSupport': True,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'ImageOwnerAlias': 'amazon',\\n\",\n \" 'Name': 'amzn2-ami-hvm-2.0.20190612-x86_64-gp2',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SriovNetSupport': 'simple',\\n\",\n \" 'VirtualizationType': 'hvm'}],\\n\",\n \" 'ResponseMetadata': {'RequestId': '5b4677b0-8357-4c84-bfca-4a6914874746',\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'content-length': '1727',\\n\",\n \" 'date': 'Tue, 09 Jul 2019 17:25:01 GMT',\\n\",\n \" 'server': 'AmazonEC2'},\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 89,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_images(ImageIds=['ami-0cc96feef8c6bbff3'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ebs'\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.root_device_type\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Volumes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.get_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'noname'\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_name(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'gp2'\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v.volume_type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volumes = list(ec2.volumes.all())\\n\",\n \"for v in volumes:\\n\",\n \" print(v.id, u.get_name(v), v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" volume_names = set(u.get_name(v) for v in ec2.volumes.all())\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'2prepare-image', 'imagenet18', 'noname'}\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"volume_names\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'05'\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"f\\\"{5:02d}\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"vol-0d0f0b89221e5e821 noname 0.one_small_machine_wiki_state\\n\",\n \"vol-0417b338eca4208dd 2prepare-image 2prepare-image\\n\",\n \"vol-0caa9c257d63d99bc noname 4prepare\\n\",\n \"vol-053292dec5fff7965 noname tensorboard-east\\n\",\n \"vol-0b3370c415f9dc7cd noname 0.one-imagenet\\n\",\n \"vol-0e7e0928ccc431214 noname shell\\n\",\n \"vol-0f2293283ba7a254e noname pytorch_aws\\n\",\n \"vol-021050b054b05c6a4 noname sshtest\\n\",\n \"vol-0fef8436b3b5c0e99 noname mnist\\n\",\n \"vol-0ff25f669b4da1a84 noname ncluster_launch\\n\",\n \"vol-07f0daec1852ab6f1 noname efs_test\\n\",\n \"vol-0bddd83e9f307b0c3 noname shell\\n\",\n \"vol-0cd4da6eabe52cebb noname shell\\n\",\n \"vol-09fe7efe1b43e8e6b noname mnist\\n\",\n \"vol-04191ce2971c80d14 noname 1.mnist_distributed\\n\",\n \"vol-096daf0d410040175 noname 0.mnist_distributed\\n\",\n \"vol-0b7341b3dbe9da443 noname 0.mnist_distributed2\\n\",\n \"vol-09e4b8c3b7b590f57 noname 1.mnist_distributed2\\n\",\n \"vol-083dd0297aab919ae noname 2test\\n\",\n \"vol-01dd504f31802f2ab noname test\\n\",\n \"vol-05acff587a1a279b8 noname test2\\n\",\n \"vol-045366b5c3a90e53c noname imagenet_prep\\n\",\n \"vol-02c4b1c5ebe4db908 noname imagenet-prep\\n\",\n \"vol-0fd20d716517c942d imagenet18 \\n\",\n \"vol-0ddc6ef3d0f57fe6d noname grover-checkpoints\\n\",\n \"vol-06e70db102aa4d4cc noname 0.2imagenet\\n\",\n \"vol-001ac894268e0a891 noname tensorboard\\n\",\n \"vol-01c48e1cc0676ba08 noname 1.multi-imagenet00\\n\",\n \"vol-0925c55a1dd96fbc4 noname 3.multi-imagenet00\\n\",\n \"vol-08ffffce4f3738a77 noname 2.multi-imagenet00\\n\",\n \"vol-0fb2e947428404acc noname 0.multi-imagenet00\\n\",\n \"vol-02c5e2b7bfd164f49 noname imagenet-p3dn\\n\",\n \"vol-09459ca540d4a43d1 noname 1.two-imagenet\\n\",\n \"vol-0a74c9ada4c644db3 noname 0.two-imagenet\\n\",\n \"vol-0695b61a472256744 noname 1.four-imagenet\\n\",\n \"vol-069d252f9079ced1e noname 2.four-imagenet\\n\",\n \"vol-09b24a5bfe5f0beb1 noname 3.four-imagenet\\n\",\n \"vol-0fd9c9d205170c755 noname 0.four-imagenet\\n\"\n ]\n }\n ],\n \"source\": [\n \"volumes = list(ec2.volumes.all())\\n\",\n \"for v in volumes:\\n\",\n \" if v.attachments:\\n\",\n \" machine = u.get_name(ec2.Instance(v.attachments[0]['InstanceId']))\\n\",\n \" else:\\n\",\n \" machine = ''\\n\",\n \" print(v.id, u.get_name(v), machine)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ec2.volumes.filter()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using region us-east-1\\n\"\n ]\n }\n ],\n \"source\": [\n \"instance = u.lookup_instance('imagenet-prep')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instance.attach_volume(Device='/dev/xvdh')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ec2.create_volume?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"v =volumes[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1000\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v.size\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Keypairs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Delete security group\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem'\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_keypair_fn()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem'\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_keypair_fn()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pairs = u.get_keypair_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'dnew': ec2.KeyPairInfo(name='dnew'),\\n\",\n \" 'ncluster-ben': ec2.KeyPairInfo(name='ncluster-ben'),\\n\",\n \" 'ncluster-darius': ec2.KeyPairInfo(name='ncluster-darius'),\\n\",\n \" 'ncluster-dlam': ec2.KeyPairInfo(name='ncluster-dlam'),\\n\",\n \" 'ncluster-fakeben': ec2.KeyPairInfo(name='ncluster-fakeben'),\\n\",\n \" 'ncluster-ubuntu': ec2.KeyPairInfo(name='ncluster-ubuntu'),\\n\",\n \" 'ncluster-ubuntu2': ec2.KeyPairInfo(name='ncluster-ubuntu2'),\\n\",\n \" 'ncluster-yaroslav': ec2.KeyPairInfo(name='ncluster-yaroslav'),\\n\",\n \" 'ncluster-yaroslavvb': ec2.KeyPairInfo(name='ncluster-yaroslavvb')}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pairs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ncluster-yaroslavvb'\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_keypair_name()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'224u-ec2': ec2.KeyPairInfo(name='224u-ec2'),\\n\",\n \" 'aws-eb': ec2.KeyPairInfo(name='aws-eb'),\\n\",\n \" 'dlam': ec2.KeyPairInfo(name='dlam'),\\n\",\n \" 'ec2-test': ec2.KeyPairInfo(name='ec2-test'),\\n\",\n \" 'ncluster-darius': ec2.KeyPairInfo(name='ncluster-darius'),\\n\",\n \" 'ncluster-ryanmckinney': ec2.KeyPairInfo(name='ncluster-ryanmckinney'),\\n\",\n \" 'ncluster-ubuntu': ec2.KeyPairInfo(name='ncluster-ubuntu')}\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pairs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fingerprinting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem\\n\",\n \"50:b9:04:03:b9:79:53:1d:cd:d2:1f:82:7b:ef:20:63:cb:19:26:26\\n\"\n ]\n }\n ],\n \"source\": [\n \"pair = pairs['ncluster-yaroslavvb']\\n\",\n \"print(u.get_keypair_fn())\\n\",\n \"print(pair.key_fingerprint)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"openssl pkcs8 -in /Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem -inform PEM -outform DER -topk8 -nocrypt | openssl sha1 -c\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f'openssl pkcs8 -in {u.get_keypair_fn()} -inform PEM -outform DER -topk8 -nocrypt | openssl sha1 -c')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/bin/sh: openssl pkcs8 -in /Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem -inform PEM -outform DER -topk8 -nocrypt | openssl sha1 -c: No such file or directory\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!'openssl pkcs8 -in /Users/yaroslavvb/.ncluster/ncluster-yaroslavvb-979440106638-us-east-1.pem -inform PEM -outform DER -topk8 -nocrypt | openssl sha1 -c'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'(stdin)= f2:8c:2a:ea:a3:da:ca:b0:7e:a0:d5:54:d3:5a:86:19:82:1e:b1:40\\\\n'\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ossystem(f'openssl pkcs8 -in {u.get_keypair_fn()} -inform PEM -outform DER -topk8 -nocrypt | openssl sha1 -c')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not the same :(\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Deleting keypairs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Deleting keypair ncluster-yaroslavvb\\n\",\n \"Warning, the following instances will be no longer accessible:\\n\",\n \"gpubox i-00a15a75a001b9fab\\n\",\n \"train_squad-large-yaroslavvb i-038ab3bf2c5feeafc\\n\",\n \"Proceed? (y/N)y\\n\"\n ]\n }\n ],\n \"source\": [\n \"key_name = 'ncluster-yaroslavvb'\\n\",\n \"keypair = pairs[key_name]\\n\",\n \"print(f\\\"Deleting keypair {key_name}\\\")\\n\",\n \"ec2 = u.get_ec2_resource()\\n\",\n \"instance_list = []\\n\",\n \"for instance in ec2.instances.all():\\n\",\n \" if instance.key_name == key_name:\\n\",\n \" instance_list.append(instance)\\n\",\n \"if instance_list:\\n\",\n \" print(\\\"Warning, after deleting keypair, the following instances will be no longer accessible:\\\")\\n\",\n \" for i in instance_list:\\n\",\n \" print(u.get_name(i), i.id)\\n\",\n \" answer = input(\\\"Proceed? (y/N)\\\")\\n\",\n \"else:\\n\",\n \" answer = \\\"y\\\"\\n\",\n \"if answer.lower() == 'y':\\n\",\n \" keypair.delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'i-038ab3bf2c5feeafc'\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.id\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Scratch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "aws-scratch.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Init\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#import aws\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"import time\\n\",\n \"#import tensorflow as tf\\n\",\n \"import boto3\\n\",\n \"\\n\",\n \"sys.path.insert(0, os.environ['HOME']+'/git0/cluster')\\n\",\n \"import util as u\\n\",\n \"\\n\",\n \"import boto3\\n\",\n \"from collections import OrderedDict, defaultdict\\n\",\n \"ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \"client = u.create_ec2_client()\\n\",\n \"ec2 = u.create_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# G3 Instance peak performance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" 8192 x 8192 matmul took: 0.29 sec, 3792.15 G ops/sec\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"import time\\n\",\n \"\\n\",\n \"n = 8192\\n\",\n \"dtype = tf.float32\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" matrix1 = tf.Variable(tf.ones((n, n), dtype=dtype))\\n\",\n \" matrix2 = tf.Variable(tf.ones((n, n), dtype=dtype))\\n\",\n \" product = tf.matmul(matrix1, matrix2)\\n\",\n \"\\n\",\n \"\\n\",\n \"# avoid optimizing away redundant nodes\\n\",\n \"config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \"sess = tf.Session(config=config)\\n\",\n \"\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"iters = 10\\n\",\n \"\\n\",\n \"# pre-warming\\n\",\n \"sess.run(product.op)\\n\",\n \"\\n\",\n \"start = time.time()\\n\",\n \"for i in range(iters):\\n\",\n \" sess.run(product.op)\\n\",\n \"end = time.time()\\n\",\n \"ops = n**3 + (n-1)*n**2 # n^2*(n-1) additions, n^3 multiplications\\n\",\n \"elapsed = (end - start)\\n\",\n \"rate = iters*ops/elapsed/10**9\\n\",\n \"print('\\\\n %d x %d matmul took: %.2f sec, %.2f G ops/sec' % (n, n,\\n\",\n \" elapsed/iters,\\n\",\n \" rate,))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Enable eager mode\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"Do not call tfe.enable_eager_execution more than once in the same process. Note eager-mode methods such as tfe.run() also call tfe.enable_eager_execution.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0mtensorflow\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcontrib\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0meager\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpython\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mtfe\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mtfe\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0menable_eager_execution\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m~/anaconda3/envs/tf/lib/python3.5/site-packages/tensorflow/python/eager/context.py\\u001b[0m in \\u001b[0;36menable_eager_execution\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 439\\u001b[0m raise ValueError(\\n\\u001b[1;32m 440\\u001b[0m \\u001b[0;34m\\\"Do not call %s more than once in the same process. Note eager-mode \\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 441\\u001b[0;31m \\\"methods such as tfe.run() also call %s.\\\" % (func_name, func_name))\\n\\u001b[0m\\u001b[1;32m 442\\u001b[0m \\u001b[0m_default_mode\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mEAGER_MODE\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 443\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: Do not call tfe.enable_eager_execution more than once in the same process. Note eager-mode methods such as tfe.run() also call tfe.enable_eager_execution.\"\n ]\n }\n ],\n \"source\": [\n \"from tensorflow.contrib.eager.python import tfe\\n\",\n \"tfe.enable_eager_execution()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Instance control\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#instance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 325,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import sys, os\\n\",\n \"sys.path.insert(0, '/Users/yaroslav/git0/cluster')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import cluster_aws as toby_aws\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances = cluster_aws.LookupAwsInstances(instance_tag=\\\"whisper\\\", ssh_key=os.environ['HOME']+'/d/yaroslav.pem')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"toby_aws.LookupAwsInstances(\\\"testjob\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 134,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \"instances = ec2.instances.filter(Filters=[{'Name': 'instance-state-name', 'Values': ['terminated']}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances=list(instances)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 137,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Instance(id='i-08353b6df1352063d'),\\n\",\n \" ec2.Instance(id='i-01fb7721c2de6de49'),\\n\",\n \" ec2.Instance(id='i-0358efb3e93ea2c62'),\\n\",\n \" ec2.Instance(id='i-01cef8d3f8c5abd08')]\"\n ]\n },\n \"execution_count\": 137,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.instancesCollection(ec2.ServiceResource(), ec2.Instance)\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.instances.filter(InstanceIds=[]).all()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'ec2.Instance' object has no attribute 'name'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0minstance\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'ec2.Instance' object has no attribute 'name'\"\n ]\n }\n ],\n \"source\": [\n \"instance.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'Key': 'Name', 'Value': 'testjob'}]\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 133,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('i-06a4a0d8e2236ae1c', 'testjob'),\\n\",\n \" ('i-0b18d5c2bf06afa87', 'testjob'),\\n\",\n \" ('i-0a279259ebf8e1c0b', 'testjob'),\\n\",\n \" ('i-06977aebb6a40ce75', 'testjob')]\"\n ]\n },\n \"execution_count\": 133,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[(i.id, tag['Value']) for i in instances for tag in i.tags if tag['Key'] == 'Name']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating new instances with userdata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.create_instances?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Getting instance hostnames\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 148,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances=list(ec2.instances.filter(InstanceIds=['i-0be3150cdca115d44']))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 151,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'ec2.Instance' object has no attribute 'hostname'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0minstance\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhostname\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'ec2.Instance' object has no attribute 'hostname'\"\n ]\n }\n ],\n \"source\": [\n \"instance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 150,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'34.212.254.42'\"\n ]\n },\n \"execution_count\": 150,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance=instances[0]\\n\",\n \"instance.public_ip_address\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 156,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"i-0be3150cdca115d44\\n\"\n ]\n }\n ],\n \"source\": [\n \"instances=ec2.instances.filter(InstanceIds=['i-0be3150cdca115d44'])\\n\",\n \"for i in instances:\\n\",\n \" print(i.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 159,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"i-0be3150cdca115d44\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in instances:\\n\",\n \" print(i.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 152,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"34.209.255.229 i-047e3cc351ce00468 nodes.0726aa4c-pachydermcluster.kubernetes.com\\n\",\n \"52.36.167.177 i-0fd359996572dd7c6 testjob\\n\",\n \"52.40.109.190 i-0267a30cf58b2dc75 master-us-west-2a.masters.0726aa4c-pachydermcluster.kubernetes.com\\n\",\n \"52.89.53.239 i-0941a8cf4f1824c3f nodes.90c444a2-pachydermcluster.kubernetes.com\\n\",\n \"52.37.131.117 i-06d8bd4450bf17b06 master-us-west-2a.masters.90c444a2-pachydermcluster.kubernetes.com\\n\",\n \"52.37.61.251 i-0b735ac755913f501 nodes.0726aa4c-pachydermcluster.kubernetes.com\\n\",\n \"52.27.162.71 i-07f875dcda4f055ed testjob\\n\",\n \"34.212.254.42 i-0be3150cdca115d44 testjob\\n\",\n \"34.215.225.82 i-068c23912bcf71bfc nodes.90c444a2-pachydermcluster.kubernetes.com\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('i-047e3cc351ce00468', '34.209.255.229'),\\n\",\n \" ('i-0fd359996572dd7c6', '52.36.167.177'),\\n\",\n \" ('i-0267a30cf58b2dc75', '52.40.109.190'),\\n\",\n \" ('i-0941a8cf4f1824c3f', '52.89.53.239'),\\n\",\n \" ('i-06d8bd4450bf17b06', '52.37.131.117'),\\n\",\n \" ('i-0b735ac755913f501', '52.37.61.251'),\\n\",\n \" ('i-07f875dcda4f055ed', '52.27.162.71'),\\n\",\n \" ('i-0be3150cdca115d44', '34.212.254.42'),\\n\",\n \" ('i-068c23912bcf71bfc', '34.215.225.82')])\"\n ]\n },\n \"execution_count\": 152,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import boto3\\n\",\n \"from collections import OrderedDict, defaultdict\\n\",\n \"ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_private_instance_ip_map():\\n\",\n \" \\\"\\\"\\\"Return instance_id->private_ip map for all running instances.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \" # Get information for all running instances\\n\",\n \" running_instances = ec2.instances.filter(Filters=[{\\n\",\n \" 'Name': 'instance-state-name',\\n\",\n \" 'Values': ['running']}])\\n\",\n \"\\n\",\n \" ec2info = OrderedDict()\\n\",\n \" for instance in running_instances:\\n\",\n \" name = ''\\n\",\n \" for tag in instance.tags or []:\\n\",\n \" if 'Name' in tag['Key']:\\n\",\n \" name = tag['Value']\\n\",\n \" print(\\\"%-15s %-20s %s\\\"%(instance.private_ip_address, instance.id, name))\\n\",\n \" ec2info[instance.id] = instance.private_ip_address\\n\",\n \" \\n\",\n \" return ec2info\\n\",\n \"\\n\",\n \"def get_public_instance_ip_map():\\n\",\n \" \\\"\\\"\\\"Return instance_id->private_ip map for all running instances.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \" # Get information for all running instances\\n\",\n \" running_instances = ec2.instances.filter(Filters=[{\\n\",\n \" 'Name': 'instance-state-name',\\n\",\n \" 'Values': ['running']}])\\n\",\n \"\\n\",\n \" ec2info = OrderedDict()\\n\",\n \" for instance in running_instances:\\n\",\n \" name = ''\\n\",\n \" for tag in instance.tags or []:\\n\",\n \" if 'Name' in tag['Key']:\\n\",\n \" name = tag['Value']\\n\",\n \" print(\\\"%-15s %-20s %s\\\"%(instance.public_ip_address, instance.id, name))\\n\",\n \" ec2info[instance.id] = instance.public_ip_address\\n\",\n \" \\n\",\n \" return ec2info\\n\",\n \"\\n\",\n \"get_public_instance_ip_map()\\n\",\n \"#from pprint import pprint as pp\\n\",\n \"#pp(get_instance_ip_map())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Sending files\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 125,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import paramiko\\n\",\n \"def SshToHost(hostname,\\n\",\n \" ssh_key=None,\\n\",\n \" username='ubuntu',\\n\",\n \" retry=10):\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"Create ssh connection to host\\n\",\n \"\\n\",\n \" Creates and returns and ssh connection to the host passed in. \\n\",\n \"\\n\",\n \" Args:\\n\",\n \" hostname: host name or ip address of the system to connect to.\\n\",\n \" retry: number of time to retry.\\n\",\n \" ssh_key: full path to the ssk hey to use to connect.\\n\",\n \" username: username to connect with.\\n\",\n \"\\n\",\n \" returns SSH client connected to host.\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" k = paramiko.RSAKey.from_private_key_file(ssh_key)\\n\",\n \" \\n\",\n \" ssh_client = paramiko.SSHClient()\\n\",\n \" ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n\",\n \"\\n\",\n \" counter = retry\\n\",\n \" while counter > 0:\\n\",\n \" try:\\n\",\n \" ssh_client.connect(hostname=hostname, username=username, pkey=k)\\n\",\n \" break\\n\",\n \" except Exception as e:\\n\",\n \" counter = counter - 1\\n\",\n \" print('Exception connecting to host via ssh (could be a timeout):'.format(e))\\n\",\n \" if counter == 0:\\n\",\n \" print('Got impatient with retrying ssh to host. Time to give up.')\\n\",\n \" return None\\n\",\n \"\\n\",\n \" return ssh_client\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 126,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"username='ubuntu'\\n\",\n \"KEY_PATH = os.environ['HOME']+'/d/yaroslav.pem'\\n\",\n \"ssh_key=KEY_PATH\\n\",\n \"hostname = \\\"52.27.162.71\\\"\\n\",\n \"ssh_client = SshToHost(hostname, ssh_key)\\n\",\n \"sftp = ssh_client.open_sftp()\\n\",\n \"sftp.put('/temp/hi.txt', 'hi.txt')\\n\",\n \"stdin,stdout,stderr=ssh_client.exec_command('cat hi.txt')\\n\",\n \"print(stdout.read().decode(\\\"ascii\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 154,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'yaroslav.pem'\"\n ]\n },\n \"execution_count\": 154,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.path.basename(KEY_PATH)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"hi\\n\",\n \"testing\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 165,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1510529639.275616\"\n ]\n },\n \"execution_count\": 165,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"time.time()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 180,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import struct\\n\",\n \"value = time.time()\\n\",\n \"ba = bytearray(struct.pack(\\\"d\\\", value)) \\n\",\n \"import base64\\n\",\n \"b16=base64.b16encode(ba)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 181,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1510530098.287752\"\n ]\n },\n \"execution_count\": 181,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"value\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 182,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"b'876A920C3782D641'\"\n ]\n },\n \"execution_count\": 182,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b16\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 183,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"bytearray(b'\\\\x87j\\\\x92\\\\x0c7\\\\x82\\\\xd6A')\"\n ]\n },\n \"execution_count\": 183,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 186,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"bytearray(b'\\\\x87j\\\\x92\\\\x0c7\\\\x82\\\\xd6A')\"\n ]\n },\n \"execution_count\": 186,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 189,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"b16s = b16.decode('ascii')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 187,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1510530098.287752,)\"\n ]\n },\n \"execution_count\": 187,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"struct.unpack('d', base64.b16decode(b16))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 190,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1510530098.287752,)\"\n ]\n },\n \"execution_count\": 190,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"struct.unpack('d', base64.b16decode(b16s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 192,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['asdfasdf-asdf', 'asdf']\"\n ]\n },\n \"execution_count\": 192,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'asdfasdf-asdf-asdf'.rsplit('-',1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"( >>,\\n\",\n \" >>,\\n\",\n \" >>)\"\n ]\n },\n \"execution_count\": 127,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ssh_client.exec_command('ls')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"( >>,\\n\",\n \" >>,\\n\",\n \" >>)\"\n ]\n },\n \"execution_count\": 99,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ssh_client.exec_command('cd efs', get_pty=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 121,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# from https://stackoverflow.com/questions/35821184/implement-an-interactive-shell-over-ssh-in-python-using-paramiko\\n\",\n \"import paramiko\\n\",\n \"import re\\n\",\n \"\\n\",\n \"\\n\",\n \"class Shell:\\n\",\n \" def __init__(self, host, ssh_key, username='ubuntu', retry=10):\\n\",\n \" k = paramiko.RSAKey.from_private_key_file(ssh_key)\\n\",\n \"\\n\",\n \" self.ssh = paramiko.SSHClient()\\n\",\n \" self.ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n\",\n \" \\n\",\n \" counter = retry\\n\",\n \" while counter > 0:\\n\",\n \" try:\\n\",\n \" self.ssh.connect(hostname=hostname, username=username, pkey=k)\\n\",\n \" break\\n\",\n \" except Exception as e:\\n\",\n \" counter = counter - 1\\n\",\n \" print('Exception connecting to host via ssh (could be a timeout):'.format(e))\\n\",\n \" if counter == 0:\\n\",\n \" print('Got impatient with retrying ssh to host. Time to give up.')\\n\",\n \" return None\\n\",\n \"\\n\",\n \" shell = self.ssh.invoke_shell()\\n\",\n \"\\n\",\n \"\\n\",\n \" def __del__(self):\\n\",\n \" self.ssh.close()\\n\",\n \"\\n\",\n \" def cmd(self, cmd):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" :param cmd: the command to be executed on the remote computer\\n\",\n \" :examples: execute('ls')\\n\",\n \" execute('finger')\\n\",\n \" execute('cd folder_name')\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" cmd = cmd.strip('\\\\n')\\n\",\n \" shell.send(cmd+'\\\\n')\\n\",\n \" \\n\",\n \" self.stdin.write(cmd + '\\\\n')\\n\",\n \" self.stdin.flush()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 122,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"shell = Shell(hostname, ssh_key)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 123,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"shell.cmd('ls')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 124,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mss\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mshell\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/file.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, size)\\u001b[0m\\n\\u001b[1;32m 191\\u001b[0m \\u001b[0;32mwhile\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 192\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 193\\u001b[0;31m \\u001b[0mnew_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_DEFAULT_BUFSIZE\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 194\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mEOFError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 195\\u001b[0m \\u001b[0mnew_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/channel.py\\u001b[0m in \\u001b[0;36m_read\\u001b[0;34m(self, size)\\u001b[0m\\n\\u001b[1;32m 1328\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1329\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1330\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mchannel\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrecv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msize\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1331\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1332\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_write\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/channel.py\\u001b[0m in \\u001b[0;36mrecv\\u001b[0;34m(self, nbytes)\\u001b[0m\\n\\u001b[1;32m 681\\u001b[0m \\\"\\\"\\\"\\n\\u001b[1;32m 682\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 683\\u001b[0;31m \\u001b[0mout\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0min_buffer\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnbytes\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 684\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mPipeTimeout\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 685\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0msocket\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/buffered_pipe.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, nbytes, timeout)\\u001b[0m\\n\\u001b[1;32m 154\\u001b[0m \\u001b[0;32mwhile\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_buffer\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_closed\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 155\\u001b[0m \\u001b[0mthen\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtime\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtime\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 156\\u001b[0;31m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_cv\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mwait\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 157\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 158\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;34m-=\\u001b[0m \\u001b[0mtime\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtime\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m-\\u001b[0m \\u001b[0mthen\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/threading.py\\u001b[0m in \\u001b[0;36mwait\\u001b[0;34m(self, timeout)\\u001b[0m\\n\\u001b[1;32m 291\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 292\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 293\\u001b[0;31m \\u001b[0mwaiter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0macquire\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 294\\u001b[0m \\u001b[0mgotit\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 295\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"ss=shell.stdout.read()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 116,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mshell\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcmd\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'ls'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mshell\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdecode\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"ascii\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/file.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, size)\\u001b[0m\\n\\u001b[1;32m 191\\u001b[0m \\u001b[0;32mwhile\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 192\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 193\\u001b[0;31m \\u001b[0mnew_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_DEFAULT_BUFSIZE\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 194\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mEOFError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 195\\u001b[0m \\u001b[0mnew_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/channel.py\\u001b[0m in \\u001b[0;36m_read\\u001b[0;34m(self, size)\\u001b[0m\\n\\u001b[1;32m 1328\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1329\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1330\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mchannel\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrecv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msize\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1331\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1332\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_write\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/channel.py\\u001b[0m in \\u001b[0;36mrecv\\u001b[0;34m(self, nbytes)\\u001b[0m\\n\\u001b[1;32m 681\\u001b[0m \\\"\\\"\\\"\\n\\u001b[1;32m 682\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 683\\u001b[0;31m \\u001b[0mout\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0min_buffer\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnbytes\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 684\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mPipeTimeout\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 685\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0msocket\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/paramiko/buffered_pipe.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, nbytes, timeout)\\u001b[0m\\n\\u001b[1;32m 154\\u001b[0m \\u001b[0;32mwhile\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_buffer\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_closed\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 155\\u001b[0m \\u001b[0mthen\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtime\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtime\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 156\\u001b[0;31m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_cv\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mwait\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtimeout\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 157\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 158\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;34m-=\\u001b[0m \\u001b[0mtime\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtime\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m-\\u001b[0m \\u001b[0mthen\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/threading.py\\u001b[0m in \\u001b[0;36mwait\\u001b[0;34m(self, timeout)\\u001b[0m\\n\\u001b[1;32m 291\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 292\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 293\\u001b[0;31m \\u001b[0mwaiter\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0macquire\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 294\\u001b[0m \\u001b[0mgotit\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 295\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"shell.cmd('ls')\\n\",\n \"print(shell.stdout.read().decode(\\\"ascii\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"shell.stdout\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 113,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['anaconda3 hi.txt\\\\n',\\n\",\n \" 'Anaconda3-4.4.0-Linux-x86_64.sh home\\\\n',\\n\",\n \" 'cuda-repo-ubuntu1604_8.0.61-1_amd64.deb libcudnn5_5.1.10-1_cuda8.0_amd64.deb\\\\n',\\n\",\n \" 'd libcudnn6_6.0.21-1_cuda8.0_amd64.deb\\\\n',\\n\",\n \" 'Dropbox pytorch_example\\\\n',\\n\",\n \" 'early-eager pytorch_example.tar\\\\n',\\n\",\n \" 'efs #test.txt#\\\\n',\\n\",\n \" 'efs2 whisper\\\\n',\\n\",\n \" 'git0\\\\n',\\n\",\n \" 'ubuntu@ip-172-31-37-145:~$ echo end of stdOUT buffer. finished with exit status $?\\\\n']\"\n ]\n },\n \"execution_count\": 113,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"stdout\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"channel = ssh_client.invoke_shell()\\n\",\n \"stdin = channel.makefile('wb')\\n\",\n \"stdout = channel.makefile('r')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"stdin,stdout,stderr=ssh_client.exec_command('cd efs', get_pty=True)\\n\",\n \"print(stdout.read().decode(\\\"ascii\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"anaconda3\\t\\t\\t\\t hi.txt\\r\\n\",\n \"Anaconda3-4.4.0-Linux-x86_64.sh\\t\\t home\\r\\n\",\n \"cuda-repo-ubuntu1604_8.0.61-1_amd64.deb libcudnn5_5.1.10-1_cuda8.0_amd64.deb\\r\\n\",\n \"d\\t\\t\\t\\t\\t libcudnn6_6.0.21-1_cuda8.0_amd64.deb\\r\\n\",\n \"Dropbox\\t\\t\\t\\t\\t pytorch_example\\r\\n\",\n \"early-eager\\t\\t\\t\\t pytorch_example.tar\\r\\n\",\n \"efs\\t\\t\\t\\t\\t #test.txt#\\r\\n\",\n \"efs2\\t\\t\\t\\t\\t whisper\\r\\n\",\n \"git0\\r\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"stdin,stdout,stderr=ssh_client.exec_command('ls', get_pty=True)\\n\",\n \"print(stdout.read().decode(\\\"ascii\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 92,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sftp = ssh_client.open_sftp()\\n\",\n \"sftp.put('/temp/hi.txt', 'hi.txt')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Placement groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 129,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"res = client.create_placement_group(GroupName=\\\"test3\\\", Strategy='cluster')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 130,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 19:49:22 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '4f745b13-6567-4272-8197-fe4a6acf5ef7',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 130,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 153,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"def _create_ec2_client():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.client('ec2', region_name=REGION)\\n\",\n \"\\n\",\n \"def get_placement_group_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionary of {placement_group_name: state}\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" client = _create_ec2_client()\\n\",\n \" response = client.describe_placement_groups()\\n\",\n \" assert _is_good_response(response)\\n\",\n \"\\n\",\n \" result = OrderedDict()\\n\",\n \" for placement_group_response in response['PlacementGroups']:\\n\",\n \" key = placement_group_response['GroupName']\\n\",\n \" assert key not in result, (\\\"Duplicate placement group \\\" +\\n\",\n \" placement_group_name)\\n\",\n \" result[key] = placement_group_response['State']\\n\",\n \" return response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 154,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'inet3',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon1', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'default', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'test3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy_head', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded', 'State': 'available', 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 20:17:25 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '6318b155-7f52-4c08-b6bf-08aadc3d1530',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 154,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_placement_group_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"delete_place\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client = create_ec2_client()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"ec2 = boto3.client('ec2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"result=ec2.describe_placement_groups(GroupNames=[\\\"beefy2\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result['PlacementGroups'][0]['State']=='available'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'GroupName': 'gpu12', 'State': 'available', 'Strategy': 'cluster'}\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result['PlacementGroups'][0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'gpu3',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 28 Nov 2017 18:51:03 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'a92ecfb4-7897-4def-8edc-6bdccfe9ac61',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.describe_placement_groups(GroupNames=['gpu3'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Fri, 17 Nov 2017 05:26:01 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '9f5590d2-2b43-473b-bf03-0045eff22408',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.delete_placement_group(GroupName='test')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"try:\\n\",\n \" res = client.create_placement_group(GroupName='gpu3', Strategy='cluster')\\n\",\n \"except Exception as e:\\n\",\n \" aaa = e\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 160,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 20:27:50 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '4d501789-b047-47e8-9354-4513c345e722',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 160,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.delete_placement_group(GroupName=\\\"beefy\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'InvalidPlacementGroup.Duplicate'\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"aaa.response['Error']['Code']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\\"An error occurred (InvalidPlacementGroup.Duplicate) when calling the CreatePlacementGroup operation: The placement group 'gpu3' already exists.\\\"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"str(aaa)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def MaybeCreatePlacementGroup(name='tf_bm'):\\n\",\n \" client = boto3.client('ec2')\\n\",\n \" try:\\n\",\n \" client.describe_placement_groups(GroupNames=[name])\\n\",\n \" except boto3.exceptions.botocore.exceptions.ClientError as e:\\n\",\n \" res = client.create_placement_group(GroupName=name, Strategy='cluster')\\n\",\n \"\\n\",\n \" counter = 0\\n\",\n \" while True:\\n\",\n \" try:\\n\",\n \" res = client.describe_placement_groups(GroupNames=[name])\\n\",\n \" if res['PlacementGroups'][0]['State'] == 'available':\\n\",\n \" break\\n\",\n \" except:\\n\",\n \" pass\\n\",\n \" counter = counter + 1\\n\",\n \" if counter >= 10:\\n\",\n \" print('Failed to create placement group %s' % name)\\n\",\n \" time.sleep(10)\\n\",\n \"\\n\",\n \"\\n\",\n \"def DeletePlacementGroup(name='tf_bm'):\\n\",\n \" client = boto3.client('ec2')\\n\",\n \" try:\\n\",\n \" client.describe_placement_groups(GroupNames=[name])\\n\",\n \" except boto3.exceptions.botocore.exceptions.ClientError as e:\\n\",\n \" print(\\\"Placement group %s doesn't exit.\\\" % name)\\n\",\n \" return\\n\",\n \"\\n\",\n \" # Not sure whether delete_placement_group would throw or not.\\n\",\n \" res = client.delete_placement_group(GroupName=name)\\n\",\n \" if res['ResponseMetadata']['HTTPStatusCode'] != 200:\\n\",\n \" print('Failed to delete placement group %s' % name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Instance limits using limit checker\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = boto3.client('ec2')\\n\",\n \"response = ec2.describe_instances()\\n\",\n \"\\n\",\n \"instance_list = []\\n\",\n \"for reservation in response['Reservations']:\\n\",\n \" for instance in reservation['Instances']:\\n\",\n \" instance_list.append(instance)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"r4.xlarge running\\n\",\n \"c5.2xlarge terminated\\n\",\n \"c5.2xlarge terminated\\n\",\n \"c5.2xlarge terminated\\n\",\n \"c5.2xlarge terminated\\n\",\n \"p2.8xlarge running\\n\"\n ]\n }\n ],\n \"source\": [\n \"inst = 0\\n\",\n \"for i in instance_list:\\n\",\n \" print(i['InstanceType'], i['State']['Name'])\\n\",\n \" if i['InstanceType'].startswith('p2'):\\n\",\n \" inst = i\\n\",\n \" break\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"awslimitchecker 2.0.0 is AGPL-licensed free software; all users have a right to the full source code of this version. See \\n\"\n ]\n }\n ],\n \"source\": [\n \"from awslimitchecker.checker import AwsLimitChecker\\n\",\n \"c = AwsLimitChecker()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ERROR - unknown instance type 'c5.2xlarge'; not counting\\n\",\n \"ERROR - unknown instance type 'c5.2xlarge'; not counting\\n\",\n \"ERROR - unknown instance type 'c5.2xlarge'; not counting\\n\",\n \"ERROR - unknown instance type 'c5.2xlarge'; not counting\\n\"\n ]\n }\n ],\n \"source\": [\n \"c.find_usage()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"limits=c.get_limits()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'Running On-Demand c5.xlarge instances'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mll\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mlimits\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'EC2'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'Running On-Demand c5.xlarge instances'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mKeyError\\u001b[0m: 'Running On-Demand c5.xlarge instances'\"\n ]\n }\n ],\n \"source\": [\n \"ll=limits['EC2']['Running On-Demand c5.xlarge instances']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"usage = ll.get_current_usage()[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"usage.get_value()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"machine_limit = limits['EC2']['Running On-Demand r4.xlarge instances']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"machine_limit.get_limit_source()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"temp=machine_limit.get_current_usage()[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'AWS::EC2::Instance'\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"temp.aws_type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'6'\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"machine_limit.get_current_usage_str()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"20\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"machine_limit.get_limit()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Instance limits using boto3 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AccountAttributes': [{'AttributeName': 'max-instances',\\n\",\n \" 'AttributeValues': [{'AttributeValue': '20'}]}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 28 Nov 2017 21:11:48 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '16d4bdaa-fd5e-4376-953d-a13f93944fc8',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.describe_account_attributes(AttributeNames=[\\\"max-instances\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Instance state query\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" ec2 = boto3.client('ec2')\\n\",\n \" response = ec2.describe_instances()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" instance_list = []\\n\",\n \" for reservation in response['Reservations']:\\n\",\n \" for instance in reservation['Instances']:\\n\",\n \" instance_list.append(((instance['LaunchTime']), instance))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AmiLaunchIndex': 1,\\n\",\n \" 'Architecture': 'x86_64',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/xvda',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 2, 2, 3, 33, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-0736954def22c0445'}}],\\n\",\n \" 'ClientToken': '',\\n\",\n \" 'EbsOptimized': False,\\n\",\n \" 'EnaSupport': True,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'ImageId': 'ami-bf4193c7',\\n\",\n \" 'InstanceId': 'i-0af3d8d828b9c4cb9',\\n\",\n \" 'InstanceType': 'c5.18xlarge',\\n\",\n \" 'KeyName': 'yaroslav',\\n\",\n \" 'LaunchTime': datetime.datetime(2017, 12, 2, 2, 3, 32, tzinfo=tzutc()),\\n\",\n \" 'Monitoring': {'State': 'disabled'},\\n\",\n \" 'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-35-167-132-34.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '35.167.132.34'},\\n\",\n \" 'Attachment': {'AttachTime': datetime.datetime(2017, 12, 2, 2, 3, 32, tzinfo=tzutc()),\\n\",\n \" 'AttachmentId': 'eni-attach-a4470155',\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'Status': 'attached'},\\n\",\n \" 'Description': '',\\n\",\n \" 'Groups': [{'GroupId': 'sg-6f582012', 'GroupName': 'open'}],\\n\",\n \" 'Ipv6Addresses': [],\\n\",\n \" 'MacAddress': '06:73:ae:34:71:40',\\n\",\n \" 'NetworkInterfaceId': 'eni-9de313af',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PrivateDnsName': 'ip-172-31-43-11.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.43.11',\\n\",\n \" 'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-35-167-132-34.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '35.167.132.34'},\\n\",\n \" 'Primary': True,\\n\",\n \" 'PrivateDnsName': 'ip-172-31-43-11.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.43.11'}],\\n\",\n \" 'SourceDestCheck': True,\\n\",\n \" 'Status': 'in-use',\\n\",\n \" 'SubnetId': 'subnet-cdd15785',\\n\",\n \" 'VpcId': 'vpc-fe722098'}],\\n\",\n \" 'Placement': {'AvailabilityZone': 'us-west-2a',\\n\",\n \" 'GroupName': 'amazon3',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-31-43-11.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.43.11',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-35-167-132-34.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '35.167.132.34',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-6f582012', 'GroupName': 'open'}],\\n\",\n \" 'SourceDestCheck': True,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-cdd15785',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'amazon3'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-fe722098'}\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance_list[0][1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'running'\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance_list[0][1][\\\"State\\\"][\\\"Name\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Find out size of instance volume\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.create_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances=list(ec2.instances.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"inst=instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2018, 1, 15, 20, 16, 6, tzinfo=tzutc())\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inst.launch_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"vol=ec2.Volume('vol-0a98ee5f7f155b2b7')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client=u.create_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client.modify_volume?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Instance id: vol-01564523457cf1ffb, disk size: 500.0 GB, state: in-use\\n\"\n ]\n }\n ],\n \"source\": [\n \"import boto3\\n\",\n \"ec2 = boto3.resource('ec2', region_name='us-east-1')\\n\",\n \"instance_id='i-057cc0bd9fb49d07e'\\n\",\n \"instance = ec2.Instance(instance_id)\\n\",\n \"volumes = instance.volumes.all()\\n\",\n \"for v in volumes:\\n\",\n \" print(\\\"Instance id: %s, disk size: %.1f GB, state: %s\\\"%(v.id, v.size, v.state))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_name(instance_response):\\n\",\n \" names = [entry['Value'] for entry in instance_response.get('Tags',[]) if\\n\",\n \" entry['Key']=='Name']\\n\",\n \" if not names:\\n\",\n \" names = ['']\\n\",\n \" assert len(names)==1\\n\",\n \" return names[0]\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"ec2 = boto3.client('ec2')\\n\",\n \"response = ec2.describe_instances()\\n\",\n \"instances = defaultdict(lambda: [])\\n\",\n \"for reservation in response['Reservations']:\\n\",\n \" for instance_response in reservation['Instances']:\\n\",\n \" instances[get_name(instance_response)].append(instance_response)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'AmiLaunchIndex': 0,\\n\",\n \" 'Architecture': 'x86_64',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/sda1',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 4, 20, 33, 1, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-090c4a23e59922b4b'}}],\\n\",\n \" 'ClientToken': '',\\n\",\n \" 'EbsOptimized': False,\\n\",\n \" 'EnaSupport': True,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'ImageId': 'ami-f1e73689',\\n\",\n \" 'InstanceId': 'i-0565a2e6b11533428',\\n\",\n \" 'InstanceType': 'p3.16xlarge',\\n\",\n \" 'KeyName': 'yaroslav',\\n\",\n \" 'LaunchTime': datetime.datetime(2017, 12, 4, 20, 33, tzinfo=tzutc()),\\n\",\n \" 'Monitoring': {'State': 'disabled'},\\n\",\n \" 'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-52-26-171-202.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '52.26.171.202'},\\n\",\n \" 'Attachment': {'AttachTime': datetime.datetime(2017, 12, 4, 20, 33, tzinfo=tzutc()),\\n\",\n \" 'AttachmentId': 'eni-attach-d0085836',\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'Status': 'attached'},\\n\",\n \" 'Description': '',\\n\",\n \" 'Groups': [{'GroupId': 'sg-6f582012', 'GroupName': 'open'}],\\n\",\n \" 'Ipv6Addresses': [],\\n\",\n \" 'MacAddress': '0a:aa:5b:5d:5b:10',\\n\",\n \" 'NetworkInterfaceId': 'eni-9bdcaa9e',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PrivateDnsName': 'ip-172-31-4-50.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.4.50',\\n\",\n \" 'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-52-26-171-202.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '52.26.171.202'},\\n\",\n \" 'Primary': True,\\n\",\n \" 'PrivateDnsName': 'ip-172-31-4-50.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.4.50'}],\\n\",\n \" 'SourceDestCheck': True,\\n\",\n \" 'Status': 'in-use',\\n\",\n \" 'SubnetId': 'subnet-a93f12f2',\\n\",\n \" 'VpcId': 'vpc-fe722098'}],\\n\",\n \" 'Placement': {'AvailabilityZone': 'us-west-2c',\\n\",\n \" 'GroupName': '',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-31-4-50.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.31.4.50',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-52-26-171-202.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '52.26.171.202',\\n\",\n \" 'RootDeviceName': '/dev/sda1',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-6f582012', 'GroupName': 'open'}],\\n\",\n \" 'SourceDestCheck': True,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-a93f12f2',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'p3'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-fe722098'}]\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances['p3']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Find AMI\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"ec2 = boto3.resource('ec2', region_name='us-west-2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ubuntu-14.04-VC4.1.3-1-62ab724f-833e-4666-99d0-21e3ec7ad743-ami-d569eac2.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"for image in list(ec2.images.filter(Filters=[{'Name':'name', 'Values':['ubuntu-14.04*']}]).all()):\\n\",\n \" print(image.name)\\n\",\n \" break\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ubuntu-14.04-VC4.1.3-1-62ab724f-833e-4666-99d0-21e3ec7ad743-ami-d569eac2.3'\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"https://aws.amazon.com/marketplace/pp/B077GCH38C?qid=1512418106283&sr=0-2&ref_=srh_res_product_title\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ami-03cf0263'\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidParameterValue) when calling the DescribeImages operation: The filter 'Id' is invalid\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mimage\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mimages\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfilter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mFilters\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m\\\"Name\\\"\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\\"Id\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m\\\"Values\\\"\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"ami-405ade3a\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mall\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/collection.py\\u001b[0m in \\u001b[0;36m__iter__\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0mcount\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mpage\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpages\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mitem\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mpage\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0;32myield\\u001b[0m \\u001b[0mitem\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/collection.py\\u001b[0m in \\u001b[0;36mpages\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 159\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_parent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mservice_name\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 160\\u001b[0m self._py_operation_name, params)\\n\\u001b[0;32m--> 161\\u001b[0;31m \\u001b[0mpages\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_py_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 162\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 163\\u001b[0m \\u001b[0;31m# Now that we have a page iterator or single page of results\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidParameterValue) when calling the DescribeImages operation: The filter 'Id' is invalid\"\n ]\n }\n ],\n \"source\": [\n \"image = list(ec2.images.filter(Filters=[{\\\"Name\\\":\\\"Id\\\", \\\"Values\\\": [\\\"ami-405ade3a\\\"]}]).all())[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Debugging paramiko\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ssh_to_host ubuntu@ec2-52-34-58-144.us-west-2.compute.amazonaws.com\\n\"\n ]\n }\n ],\n \"source\": [\n \"import paramiko\\n\",\n \"username='ubuntu'\\n\",\n \"ssh_key=\\\"/Users/yaroslav/d/yaroslav.pem\\\"\\n\",\n \"hostname='ec2-52-34-58-144.us-west-2.compute.amazonaws.com'\\n\",\n \"\\n\",\n \"print(\\\"ssh_to_host %s@%s\\\"%(username, hostname))\\n\",\n \"k = paramiko.RSAKey.from_private_key_file(ssh_key)\\n\",\n \"ssh_client = paramiko.SSHClient()\\n\",\n \"ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n\",\n \"ssh_client.connect(hostname=hostname, username=username, pkey=k)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"for i in range(100):\\n\",\n \" ssh_client.exec_command(\\\"tmux send-keys -t tmux 'sleep 3; echo %d' Enter\\\"%(i,))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"( >>,\\n\",\n \" >>,\\n\",\n \" >>)\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ssh_client.exec_command(\\\"tmux send-keys -t tmux 'ls' Enter\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# New region setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"import os\\n\",\n \"def _create_ec2_client():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.client('ec2', region_name=REGION)\\n\",\n \"\\n\",\n \"def _create_efs_client():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.client('efs', region_name=REGION)\\n\",\n \"\\n\",\n \"def _create_efs_resource():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.resource('efs', region_name=REGION)\\n\",\n \"\\n\",\n \"def _create_ec2_resource():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.resource('ec2',region_name=REGION)\\n\",\n \"\\n\",\n \"def _is_good_response(response):\\n\",\n \" \\\"\\\"\\\"Helper method to check if boto3 call was a success.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" return response[\\\"ResponseMetadata\\\"]['HTTPStatusCode'] == 200\\n\",\n \"\\n\",\n \"def _get_name(tags):\\n\",\n \" \\\"\\\"\\\"Helper utility to extract name out of tags dictionary.\\n\",\n \" [{'Key': 'Name', 'Value': 'nexus'}] -> 'nexus'\\n\",\n \" \\n\",\n \" Assert fails if there's more than one name.\\n\",\n \" Returns '' if there's less than one name.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" names = [entry['Value'] for entry in tags if entry['Key']=='Name']\\n\",\n \" if not names:\\n\",\n \" return ''\\n\",\n \" if len(names)>1:\\n\",\n \" assert False, \\\"have more than one name: \\\"+str(names)\\n\",\n \" return names[0]\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating new security group\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 241,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"REGION='us-west-1'\\n\",\n \"ec2 = boto3.resource('ec2',region_name=REGION)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 243,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_groups=list(vpc.security_groups.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 245,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 245,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 422,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" DEFAULT_NAME = 'nexus'\\n\",\n \" security_group = u.get_security_group_dict()[DEFAULT_NAME]\\n\",\n \" keypair = u.get_keypair_dict()[DEFAULT_NAME]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 423,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 423,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keypair.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 420,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group = u.get_security_group_dict()['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 421,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 421,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.group_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 411,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"subnets = list(vpc.subnets.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 416,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.create_instances?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 415,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 23:44:30 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'c5de6a58-3c5f-48ba-a2a6-eed01188d239',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Vpcs': [{'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-984c4ff1',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': True,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-79c4f61d'}]}\"\n ]\n },\n \"execution_count\": 415,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_vpcs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 259,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 259,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_groups[0].group_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 260,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group = security_groups[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 261,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'Key': 'Name', 'Value': 'nexus'}]\"\n ]\n },\n \"execution_count\": 261,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"c.get_name(security_groups[0].)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## Creating network\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc = ec2.create_vpc(CidrBlock='192.168.0.0/16')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Tag(resource_id='vpc-2bb1584c', key='Name', value='nexus_vpc')]\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.create_tags(Tags=[{\\\"Key\\\": \\\"Name\\\", \\\"Value\\\": \\\"nexus_vpc\\\"}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 00:00:50 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '29be9d8e-8ae1-45f0-80d4-53d6a1aae72d',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Vpcs': [{'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-90cb8dfa',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-fcb80385',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus'}],\\n\",\n \" 'VpcId': 'vpc-1a054562'},\\n\",\n \" {'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-4805d222',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-fcb80385',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': True,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-2b72a053'},\\n\",\n \" {'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-b5ce88df',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-fcb80385',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus2'}],\\n\",\n \" 'VpcId': 'vpc-cbffbeb3'},\\n\",\n \" {'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-1e793f74',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-fcb80385',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-11bbfa69'}]}\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_vpcs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc.wait_until_available()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" ig = ec2.create_internet_gateway()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 00:01:21 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'c860ca96-78d0-420b-8ec0-a1279fc6bf5e',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \" ig.attach_to_vpc(VpcId=vpc.id)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 117,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-west-1'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 118,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"OrderedDict([('nexus_vpc', ec2.Vpc(id='vpc-14876473'))])\\n\"\n ]\n }\n ],\n \"source\": [\n \" from collections import OrderedDict\\n\",\n \" client = _create_ec2_client()\\n\",\n \" response = client.describe_vpcs()\\n\",\n \" assert _is_good_response(response)\\n\",\n \"\\n\",\n \" result = OrderedDict()\\n\",\n \" ec2 = _create_ec2_resource()\\n\",\n \" for vpc_response in response['Vpcs']:\\n\",\n \" key = _get_name(vpc_response.get('Tags', []))\\n\",\n \" if not key: # skip VPC's that don't have a name assigned\\n\",\n \" continue\\n\",\n \" \\n\",\n \" result[key] = ec2.Vpc(vpc_response['VpcId'])\\n\",\n \"\\n\",\n \" print(result)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 119,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client = _create_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 124,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vpc-2bb1584c'\"\n ]\n },\n \"execution_count\": 124,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 130,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 130,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.get_available_subresources()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-2bb1584c' has dependencies and cannot be deleted.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mvpc\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-2bb1584c' has dependencies and cannot be deleted.\"\n ]\n }\n ],\n \"source\": [\n \"vpc.delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 139,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'ec2.subnetsCollection' object does not support indexing\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msubnets\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mall\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: 'ec2.subnetsCollection' object does not support indexing\"\n ]\n }\n ],\n \"source\": [\n \"ec2.subnets.all()[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 140,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-2bb1584c' has dependencies and cannot be deleted.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mclient\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete_vpc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mVpcId\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvpc\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mid\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-2bb1584c' has dependencies and cannot be deleted.\"\n ]\n }\n ],\n \"source\": [\n \"response = client.delete_vpc(VpcId=vpc.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 152,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.RouteTable(id='rtb-f4ba3693'), ec2.RouteTable(id='rtb-a8b438cf')]\"\n ]\n },\n \"execution_count\": 152,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(vpc.route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 150,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ec2.RouteTable(id='rtb-f4ba3693')\\n\",\n \"ec2.RouteTable(id='rtb-a8b438cf')\\n\"\n ]\n }\n ],\n \"source\": [\n \"for r in vpc.route_tables.all():\\n\",\n \" print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 158,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"subnets = list(vpc.subnets.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 159,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 159,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 157,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0msubnet\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0msubnets\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"subnet = subnets[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 156,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 01:43:58 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'a322b866-3966-495f-9b6f-b05ceca514a5',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 156,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 72,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"kk.issubset(ig.attachments[0].keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def _get1(items, **kwargs):\\n\",\n \" \\\"\\\"\\\"Helper method to extract values, ie\\n\",\n \" response = [{'State': 'available', 'VpcId': 'vpc-2bb1584c'}]\\n\",\n \" _get1(response, State=-1, VpcId='vpc-2bb1584c') #=> 'available'\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # find the value of attribute to return\\n\",\n \" query_arg = None\\n\",\n \" for arg, value in kwargs.items():\\n\",\n \" if value == -1:\\n\",\n \" assert query_arg is None, \\\"Only single query arg (-1 valued) is allowed\\\"\\n\",\n \" query_arg = arg\\n\",\n \" result = []\\n\",\n \" \\n\",\n \" filterset = set(kwargs.keys())\\n\",\n \" for item in items:\\n\",\n \" match = True\\n\",\n \" assert filterset.issubset(item.keys()), \\\"Filter set contained %s which was not in record %s\\\" %(filterset.difference(item.keys()),\\n\",\n \" item)\\n\",\n \" for arg in item:\\n\",\n \" if arg == query_arg:\\n\",\n \" continue\\n\",\n \" if arg in kwargs:\\n\",\n \" if item[arg] != kwargs[arg]:\\n\",\n \" match = False\\n\",\n \" break\\n\",\n \" if match:\\n\",\n \" result.append(item[query_arg])\\n\",\n \" assert len(result) <= 1, \\\"%d values matched %s, only allow 1\\\" % (len(result), kwargs)\\n\",\n \" if result:\\n\",\n \" return result[0]\\n\",\n \" return None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'available'\"\n ]\n },\n \"execution_count\": 74,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"_get1(ig.attachments, State=-1, VpcId='vpc-2bb1584c')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ig.attachments?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for -: 'ec2.Vpc' and 'int'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mvpc\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;36m14876473\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: unsupported operand type(s) for -: 'ec2.Vpc' and 'int'\"\n ]\n }\n ],\n \"source\": [\n \"vpc-14876473\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client = boto3.client('ec2')\\n\",\n \"route_table = vpc.create_route_table()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc.create_route_table?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_table.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def _make_name(name):\\n\",\n \" return [{'Key': 'Name', 'Value': name}]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Tag(resource_id='rtb-a8b438cf', key='Name', value='nexus')]\"\n ]\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.create_tags(Tags=_make_name(\\\"nexus\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_table.create_tags?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'Key': 'Name', 'Value': 'nexus'}]\"\n ]\n },\n \"execution_count\": 88,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route = route_table.create_route(\\n\",\n \" DestinationCidrBlock='0.0.0.0/0',\\n\",\n \" GatewayId=ig.id\\n\",\n \")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'ec2.Route' object has no attribute 'load'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mload\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'ec2.Route' object has no attribute 'load'\"\n ]\n }\n ],\n \"source\": [\n \"route.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 94,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.get_available_subresources()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Route(route_table_id='rtb-a8b438cf', destination_cidr_block='192.168.0.0/16'),\\n\",\n \" ec2.Route(route_table_id='rtb-a8b438cf', destination_cidr_block='0.0.0.0/0')]\"\n ]\n },\n \"execution_count\": 95,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.routes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Failed\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(5):\\n\",\n \" if i == 6:\\n\",\n \" break\\n\",\n \"else:\\n\",\n \" print(\\\"Failed\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 160,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" subnet = vpc.create_subnet(CidrBlock='192.168.1.0/20')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 161,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'subnet-76901911'\"\n ]\n },\n \"execution_count\": 161,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 163,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_tables = list(vpc.route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 164,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.RouteTable(id='rtb-f4ba3693'), ec2.RouteTable(id='rtb-a8b438cf')]\"\n ]\n },\n \"execution_count\": 164,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_tables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 165,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_table = route_tables[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 167,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'rtb-f4ba3693'\"\n ]\n },\n \"execution_count\": 167,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 209,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_groups = list(vpc.security_groups.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 212,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0msecurity_groups\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"security_groups[1].delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 214,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.SecurityGroup(id='sg-4e8c6c37')\"\n ]\n },\n \"execution_count\": 214,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_groups[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"subnet.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 215,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (CannotDelete) when calling the DeleteSecurityGroup operation: the specified group: \\\"sg-4e8c6c37\\\" name: \\\"default\\\" cannot be deleted by a user\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0msecurity_groups\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (CannotDelete) when calling the DeleteSecurityGroup operation: the specified group: \\\"sg-4e8c6c37\\\" name: \\\"default\\\" cannot be deleted by a user\"\n ]\n }\n ],\n \"source\": [\n \"security_groups[0].delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 170,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Route(route_table_id='rtb-f4ba3693', destination_cidr_block='192.168.0.0/16')]\"\n ]\n },\n \"execution_count\": 170,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.routes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 172,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidParameterValue) when calling the DeleteRoute operation: cannot remove local route 192.168.0.0/16 in route table rtb-f4ba3693\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute_table\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mroutes\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidParameterValue) when calling the DeleteRoute operation: cannot remove local route 192.168.0.0/16 in route table rtb-f4ba3693\"\n ]\n }\n ],\n \"source\": [\n \"route_table.routes[0].delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 175,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gateways = list(vpc.internet_gateways.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 176,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gateway = gateways[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 180,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 01:51:15 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '230d20fa-3cf3-4344-afe6-159d0ac6f522',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 180,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gateway.detach_from_vpc(VpcId=vpc.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 186,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 186,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(vpc.internet_gateways.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 193,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_tables = list(vpc.route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 195,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_table = route_tables[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 199,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route = route_table.routes[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 200,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Route(route_table_id='rtb-f4ba3693', destination_cidr_block='192.168.0.0/16')\"\n ]\n },\n \"execution_count\": 200,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 201,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidParameterValue) when calling the DeleteRoute operation: cannot remove local route 192.168.0.0/16 in route table rtb-f4ba3693\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidParameterValue) when calling the DeleteRoute operation: cannot remove local route 192.168.0.0/16 in route table rtb-f4ba3693\"\n ]\n }\n ],\n \"source\": [\n \"route.delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 206,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.SecurityGroup(id='sg-4e8c6c37')]\"\n ]\n },\n \"execution_count\": 206,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(vpc.security_groups.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc.create_subnet?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"rr = route_table.routes[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.0.0.0/0'\"\n ]\n },\n \"execution_count\": 99,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rr.destination_cidr_block\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec.RouteTable('rtb-3b530d5d').routes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ResourceLoadException\",\n \"evalue\": \"ec2.Route has no load method\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mResourceLoadException\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstate\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mproperty_loader\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 341\\u001b[0m raise ResourceLoadException(\\n\\u001b[1;32m 342\\u001b[0m '{0} has no load method'.format(\\n\\u001b[0;32m--> 343\\u001b[0;31m self.__class__.__name__))\\n\\u001b[0m\\u001b[1;32m 344\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 345\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdata\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mResourceLoadException\\u001b[0m: ec2.Route has no load method\"\n ]\n }\n ],\n \"source\": [\n \"route.state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"response = client.describe_vpcs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"200\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response[\\\"ResponseMetadata\\\"]['HTTPStatusCode']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-984c4ff1',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': True,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-79c4f61d'},\\n\",\n \" {'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-35a2505d',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus_vpc'}],\\n\",\n \" 'VpcId': 'vpc-14876473'}]\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response['Vpcs']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-west-1'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_name(tags):\\n\",\n \" \\\"\\\"\\\"[{'Key': 'Name', 'Value': 'nexus'}] -> 'nexus'\\\"\\\"\\\"\\n\",\n \" names = [entry['Value'] for entry in tags if entry['Key']=='Name']\\n\",\n \" if not names:\\n\",\n \" names = ['']\\n\",\n \" assert len(names)==1, \\\"have more than one name: \\\"+str(names)\\n\",\n \" return names[0]\\n\",\n \"\\n\",\n \"def create_ec2_client():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.client('ec2', region_name=REGION)\\n\",\n \"\\n\",\n \"def create_ec2_resource():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.resource('ec2',region_name=REGION)\\n\",\n \"\\n\",\n \"def get_vpc_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionary of {name:[vpc1,vpc2, ],...} \\\"\\\"\\\"\\n\",\n \" from collections import defaultdict\\n\",\n \" result = defaultdict(list)\\n\",\n \" client = create_ec2_client()\\n\",\n \" response = client.describe_vpcs()\\n\",\n \" assert response[\\\"ResponseMetadata\\\"]['HTTPStatusCode'] == 200\\n\",\n \" for vpc_response in response['Vpcs']:\\n\",\n \" result[get_name(vpc_response.get('Tags', []))].append(vpc_response)\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vpc-14876473'\"\n ]\n },\n \"execution_count\": 85,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"defaultdict(list,\\n\",\n \" {'': [{'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-984c4ff1',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': True,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-79c4f61d'}],\\n\",\n \" 'nexus_vpc': [{'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-35a2505d',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus_vpc'}],\\n\",\n \" 'VpcId': 'vpc-14876473'}]})\"\n ]\n },\n \"execution_count\": 82,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_vpc_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 84,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'nexus' in get_vpc_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 05:20:58 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '1fe9496b-f3af-4c48-9841-35c9b0172bf6',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Vpcs': [{'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-4805d222',\\n\",\n \" 'CidrBlock': '172.31.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-fcb80385',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': True,\\n\",\n \" 'State': 'available',\\n\",\n \" 'VpcId': 'vpc-2b72a053'}]}\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client = create_ec2_client()\\n\",\n \"response = client.describe_vpcs()\\n\",\n \"response \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 05:06:27 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'aaf8c91c-8ef1-425b-87e9-e8c13a17ecf5',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Vpcs': [{'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockAssociationSet': [{'AssociationId': 'vpc-cidr-assoc-35a2505d',\\n\",\n \" 'CidrBlock': '192.168.0.0/16',\\n\",\n \" 'CidrBlockState': {'State': 'associated'}}],\\n\",\n \" 'DhcpOptionsId': 'dopt-4689da22',\\n\",\n \" 'InstanceTenancy': 'default',\\n\",\n \" 'IsDefault': False,\\n\",\n \" 'State': 'available',\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus_vpc'}],\\n\",\n \" 'VpcId': 'vpc-14876473'}]}\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_vpcs(VpcIds=[vpc.id])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 187,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_vpc_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionary of named VPCs {name: vpc_id}\\n\",\n \"\\n\",\n \" Assert fails if there's more than one VPC with same name.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" client = _create_ec2_client()\\n\",\n \" response = client.describe_vpcs()\\n\",\n \" assert _is_good_response(response)\\n\",\n \"\\n\",\n \" result = OrderedDict()\\n\",\n \" for vpc_response in response['Vpcs']:\\n\",\n \" key = _get_name(vpc_response.get('Tags', []))\\n\",\n \" if not key: # skip VPC's that don't have a name assigned\\n\",\n \" continue\\n\",\n \" \\n\",\n \" assert key not in result, (\\\"Duplicate VPC group \\\" + key)\\n\",\n \" result[key] = vpc_response['VpcId']\\n\",\n \" \\n\",\n \" return result\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 189,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('282e747a-pachydermcluster.kubernetes.com', 'vpc-f683678f'),\\n\",\n \" ('yong', 'vpc-d91a23bf'),\\n\",\n \" ('0726aa4c-pachydermcluster.kubernetes.com', 'vpc-14500972')])\"\n ]\n },\n \"execution_count\": 189,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def _get_name(tags):\\n\",\n \" \\\"\\\"\\\"Helper utility to extract name out of tags dictionary.\\n\",\n \" [{'Key': 'Name', 'Value': 'nexus'}] -> 'nexus'\\n\",\n \" \\n\",\n \" Assert fails if there's more than one name.\\n\",\n \" Returns '' if there's less than one name.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" names = [entry['Value'] for entry in tags if entry['Key']=='Name']\\n\",\n \" if not names:\\n\",\n \" names = ['']\\n\",\n \" assert len(names)==1, \\\"have more than one name: \\\"+str(names)\\n\",\n \" return names[0]\\n\",\n \"\\n\",\n \"get_vpc_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 191,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vvv=ec2.Vpc('vpc-d91a23bf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 192,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vpc-d91a23bf'\"\n ]\n },\n \"execution_count\": 192,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vvv.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.SecurityGroup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 186,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidPlacementGroup.Duplicate) when calling the CreatePlacementGroup operation: The placement group 'hi' already exists.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcreate_placement_group\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mGroupName\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'hi'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0mStrategy\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'cluster'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidPlacementGroup.Duplicate) when calling the CreatePlacementGroup operation: The placement group 'hi' already exists.\"\n ]\n }\n ],\n \"source\": [\n \"ec2.create_placement_group(GroupName='hi',Strategy='cluster')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"igw-f976c69d\\n\",\n \"rtb-eea42389\\n\"\n ]\n }\n ],\n \"source\": [\n \"ig = ec2.create_internet_gateway()\\n\",\n \"vpc.attach_internet_gateway(InternetGatewayId=ig.id)\\n\",\n \"print(ig.id)\\n\",\n \"\\n\",\n \"route_table = vpc.create_route_table()\\n\",\n \"route = route_table.create_route(\\n\",\n \" DestinationCidrBlock='0.0.0.0/0',\\n\",\n \" GatewayId=ig.id\\n\",\n \")\\n\",\n \"print(route_table.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 161,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gateway = ec2.create_internet_gateway()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 312,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gateway.attach_to_vpc?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 163,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vpc-14876473'\"\n ]\n },\n \"execution_count\": 163,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 165,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" subnet = vpc.create_subnet(CidrBlock='192.168.2.0/24')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 166,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'pending'\"\n ]\n },\n \"execution_count\": 166,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.state\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 195,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'KeyPairs': [{'KeyFingerprint': '28:6e:51:d6:81:3d:51:22:4b:20:4a:fb:cf:9b:1f:9f:1d:2a:a9:94',\\n\",\n \" 'KeyName': 'aws_pach_api_service'},\\n\",\n \" {'KeyFingerprint': '6b:07:eb:f9:ab:ff:4f:18:3c:65:6e:99:05:01:21:fc:71:be:be:13',\\n\",\n \" 'KeyName': 'aws_pach_api_service_stage'},\\n\",\n \" {'KeyFingerprint': 'f7:9a:33:d7:72:63:fe:27:30:9a:3b:49:e5:fa:dd:0b:9a:31:77:7a',\\n\",\n \" 'KeyName': 'brendan'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.0726aa4c-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.282e747a-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.6dbcbbe2-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.db5d0d76-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '42:79:9a:be:17:03:c9:de:dd:05:92:de:85:d7:29:07:cd:b7:a0:5b',\\n\",\n \" 'KeyName': 'nexus-oregon'},\\n\",\n \" {'KeyFingerprint': '4e:2b:72:59:5f:11:94:2d:5a:92:ba:10:2e:f0:10:da:22:03:d3:30',\\n\",\n \" 'KeyName': 'pach_service_stage'},\\n\",\n \" {'KeyFingerprint': '43:fe:a7:51:d7:ad:26:7c:ce:0e:94:53:31:36:9f:38:9f:63:17:1c',\\n\",\n \" 'KeyName': 'yaroslav'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:38:18 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '040f83ac-0c84-4f31-b6fa-9205581c6fae',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 195,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_key_pairs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 197,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"keypair=ec2.KeyPair('yaroslav')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 199,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'beefy3',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy_head', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'default', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'test3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon1', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded', 'State': 'available', 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:40:06 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'fb2119d5-8ed3-4077-90ef-9dc4785755b5',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 199,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_placement_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 205,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plgroup=ec2.PlacementGroup('beefy2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 209,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"grp=ec2.PlacementGroup('amazon')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 210,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidPlacementGroup.Unknown) when calling the DescribePlacementGroups operation: The Placement Group 'amazon' is unknown.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mgrp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mload\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 503\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 504\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 505\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 506\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdata\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 507\\u001b[0m \\u001b[0;31m# Create the docstring for the load/reload mehtods.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidPlacementGroup.Unknown) when calling the DescribePlacementGroups operation: The Placement Group 'amazon' is unknown.\"\n ]\n }\n ],\n \"source\": [\n \"grp.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 214,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.PlacementGroup(name='somegroup')\"\n ]\n },\n \"execution_count\": 214,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.create_placement_group(GroupName=\\\"somegroup\\\",Strategy=\\\"cluster\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 217,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"grp=ec2.PlacementGroup('somegroup')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 218,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"grp.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 222,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'somegroup'\"\n ]\n },\n \"execution_count\": 222,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"grp.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 216,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'beefy2',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy_head', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon1', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'test3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'default', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon3', 'State': 'available', 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:44:32 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '27bb8855-4926-4b52-9cdf-f8c2e2556861',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 216,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_placement_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 182,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Tag(resource_id='sg-c58e97a3', key='Name', value='default_vpc')]\"\n ]\n },\n \"execution_count\": 182,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.create_tags(Tags=[{\\\"Key\\\": \\\"Name\\\", \\\"Value\\\": \\\"default_vpc\\\"}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 194,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.SecurityGroup(id='sg-c58e97a3')\"\n ]\n },\n \"execution_count\": 194,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ec2.SecurityGroup('sg-c58e97a3')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 183,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group.create_tags?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 170,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_tables = list(vpc.route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 172,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Route(route_table_id='rtb-45a42322', destination_cidr_block='192.168.0.0/16')]\"\n ]\n },\n \"execution_count\": 172,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_tables[0].routes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"subnet-0d1f9c6a\\n\"\n ]\n }\n ],\n \"source\": [\n \"# create subnet\\n\",\n \"subnet = ec2.create_subnet(CidrBlock='192.168.1.0/24', VpcId=vpc.id)\\n\",\n \"print(subnet.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.RouteTableAssociation(id='rtbassoc-680ae60e')\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# associate the route table with the subnet\\n\",\n \"route_table.associate_with_subnet(SubnetId=subnet.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create sec group\\n\",\n \"sec_group = ec2.create_security_group(\\n\",\n \" GroupName='nexus', Description='nexus group', VpcId=vpc.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 02:56:09 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '9be7631e-7150-4029-98d3-5aac5e6d36b8',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# enable pinging\\n\",\n \"sec_group.authorize_ingress(\\n\",\n \" CidrIp='0.0.0.0/0',\\n\",\n \" IpProtocol='icmp',\\n\",\n \" FromPort=-1,\\n\",\n \" ToPort=-1\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"de\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 05:57:22 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '73a07462-2f68-4655-b0d6-a41eea1c4db1',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'SecurityGroups': [{'Description': 'slice_0 sec group',\\n\",\n \" 'GroupId': 'sg-39f9e55f',\\n\",\n \" 'GroupName': 'slice_0',\\n\",\n \" 'IpPermissions': [{'FromPort': 8888,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8888,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': -1,\\n\",\n \" 'IpProtocol': 'icmp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': -1,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-14876473'},\\n\",\n \" {'Description': 'nexus group',\\n\",\n \" 'GroupId': 'sg-4af9e52c',\\n\",\n \" 'GroupName': 'nexus',\\n\",\n \" 'IpPermissions': [{'FromPort': 8888,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8888,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': -1,\\n\",\n \" 'IpProtocol': 'icmp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': -1,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-14876473'},\\n\",\n \" {'Description': 'launch-wizard-1 created 2017-12-11T19:01:58.747-08:00',\\n\",\n \" 'GroupId': 'sg-61f6ea07',\\n\",\n \" 'GroupName': 'launch-wizard-1',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-79c4f61d'},\\n\",\n \" {'Description': 'default VPC security group',\\n\",\n \" 'GroupId': 'sg-73f8e415',\\n\",\n \" 'GroupName': 'default',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-73f8e415',\\n\",\n \" 'UserId': '316880547378'}]}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-14876473'},\\n\",\n \" {'Description': 'default VPC security group',\\n\",\n \" 'GroupId': 'sg-899ddbef',\\n\",\n \" 'GroupName': 'default',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-899ddbef',\\n\",\n \" 'UserId': '316880547378'}]}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-79c4f61d'}]}\"\n ]\n },\n \"execution_count\": 106,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_security_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 02:56:10 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '0ce3a5f7-7922-4f59-a1f6-0a61f5924b40',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# enable SSH\\n\",\n \"sec_group.authorize_ingress(IpProtocol=\\\"tcp\\\",CidrIp=\\\"0.0.0.0/0\\\",FromPort=22,ToPort=22)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# ipython notebook and tensorboard ports\\n\",\n \"response = sec_group.authorize_ingress(IpProtocol=\\\"tcp\\\",CidrIp=\\\"0.0.0.0/0\\\",FromPort=8888,ToPort=8888)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"200\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response['ResponseMetadata']['HTTPStatusCode']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-4af9e52c'\"\n ]\n },\n \"execution_count\": 103,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sec_group.group_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 177,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" security_group = ec2.create_security_group(\\n\",\n \" GroupName=\\\"hi\\\", Description=\\\"hi\\\",\\n\",\n \" VpcId=vpc.id)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 179,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" response = security_group.authorize_ingress(IpProtocol=\\\"tcp\\\",\\n\",\n \" CidrIp=\\\"0.0.0.0/0\\\",\\n\",\n \" FromPort=8888,ToPort=8888)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 180,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:20:45 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '14897fbc-151a-4c7f-b6a0-3f6d6edc0294',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 180,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 181,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group.authorize_ingress?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 224,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'somegroup'\"\n ]\n },\n \"execution_count\": 224,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"grp.group_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 229,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from pprint import pprint as pp\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 233,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['amazon',\\n\",\n \" 'sharded',\\n\",\n \" 'inet3',\\n\",\n \" 'amazon1',\\n\",\n \" 'amazon2',\\n\",\n \" 'sharded2',\\n\",\n \" 'default',\\n\",\n \" 'beefy_head',\\n\",\n \" 'beefy3',\\n\",\n \" 'amazon3',\\n\",\n \" 'test3',\\n\",\n \" 'inet',\\n\",\n \" 'beefy2']\"\n ]\n },\n \"execution_count\": 233,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[a['GroupName'] for a in client.describe_placement_groups()['PlacementGroups']]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 241,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'name' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mgroup\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mec2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcreate_placement_group\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mGroupName\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mStrategy\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'cluster'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'name' is not defined\"\n ]\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 253,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'default',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'test3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon1', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy_head', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'beefy3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sharded2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'inet', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'amazon', 'State': 'available', 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:56:15 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '25c1b8d6-9b87-4eb4-92cc-7321229f4e75',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 253,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_placement_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 251,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"group = ec2.create_placement_group(GroupName='default', Strategy='cluster')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 252,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'cluster'\"\n ]\n },\n \"execution_count\": 252,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.strategy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 245,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"group = ec2.PlacementGroup('test_group')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 246,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'cluster'\"\n ]\n },\n \"execution_count\": 246,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.strategy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 313,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.create_instances?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 424,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'sec_group' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m instances = ec2.create_instances(\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mImageId\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mami\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mInstanceType\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'c5.large'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mMaxCount\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mMinCount\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 6\\u001b[0;31m \\u001b[0mNetworkInterfaces\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0;34m'SubnetId'\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0msubnet\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mid\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'DeviceIndex'\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'AssociatePublicIpAddress'\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'Groups'\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0msec_group\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgroup_id\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 7\\u001b[0m Placement={'GroupName': grp.group_name})\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'sec_group' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"grp = ec2.create_placement_group(GroupName=\\\"somegroup\\\",Strategy=\\\"cluster\\\")\\n\",\n \"\\n\",\n \"ami=\\\"ami-45ead225\\\"\\n\",\n \"instances = ec2.create_instances(\\n\",\n \" ImageId=ami, InstanceType='c5.large', MaxCount=1, MinCount=1,\\n\",\n \" NetworkInterfaces=[{'SubnetId': subnet.id, 'DeviceIndex': 0, 'AssociatePublicIpAddress': True, 'Groups': [sec_group.group_id]}],\\n\",\n \" Placement={'GroupName': grp.group_name})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 173,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 13 Dec 2017 21:16:43 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '8be31302-d613-4b55-b0b5-33c984f2c84f',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'SecurityGroups': [{'Description': 'default VPC security group',\\n\",\n \" 'GroupId': 'sg-019aea7c',\\n\",\n \" 'GroupName': 'default',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-019aea7c',\\n\",\n \" 'UserId': '316880547378'}]}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-14500972'},\\n\",\n \" {'Description': 'CVC-baseline. 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'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'group to provide ssh access to studioml workers',\\n\",\n \" 'GroupId': 'sg-724b340f',\\n\",\n \" 'GroupName': 'c4d6e81b-b660-429b-a9ab-000146fee87d',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'launch-wizard-12 created 2017-11-22T16:23:41.264-05:00',\\n\",\n \" 'GroupId': 'sg-736c830f',\\n\",\n \" 'GroupName': 'pachyderm_REST_service',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 3000,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'HTTP requests'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 3000,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-14500972'},\\n\",\n \" {'Description': 'launch-wizard-2 created 2017-10-20T17:08:42.881-07:00',\\n\",\n \" 'GroupId': 'sg-73c1a80e',\\n\",\n \" 'GroupName': 'launch-wizard-2',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'Security group for masters',\\n\",\n \" 'GroupId': 'sg-78c66604',\\n\",\n \" 'GroupName': 'masters.282e747a-pachydermcluster.kubernetes.com',\\n\",\n \" 'IpPermissions': [{'FromPort': 1,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 4000,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 4003,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 8080,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8080,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-78c66604',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 1,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 30650,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 30650,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 443,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 443,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Tags': [{'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'Value': 'masters.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VpcId': 'vpc-f683678f'},\\n\",\n \" {'Description': 'launch-wizard-11 created 2017-11-17T13:48:16.930-08:00',\\n\",\n \" 'GroupId': 'sg-908596ed',\\n\",\n \" 'GroupName': 'launch-wizard-11',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'default VPC security group',\\n\",\n \" 'GroupId': 'sg-a0d4d4dd',\\n\",\n \" 'GroupName': 'default',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-a0d4d4dd',\\n\",\n \" 'UserId': '316880547378'}]}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-d91a23bf'},\\n\",\n \" {'Description': 'Security group for nodes',\\n\",\n \" 'GroupId': 'sg-af9dedd2',\\n\",\n \" 'GroupName': 'nodes.0726aa4c-pachydermcluster.kubernetes.com',\\n\",\n \" 'IpPermissions': [{'FromPort': 30080,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0',\\n\",\n \" 'Description': 'Pachyderm Dashboard'}],\\n\",\n \" 'Ipv6Ranges': [{'CidrIpv6': '::/0',\\n\",\n \" 'Description': 'Pachyderm Dashboard'}],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 30081,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-af9dedd2', 'UserId': '316880547378'},\\n\",\n \" {'GroupId': 'sg-eb9eee96', 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 30650,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'pachyderm service'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 30650,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Tags': [{'Key': 'Name',\\n\",\n \" 'Value': 'nodes.0726aa4c-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '0726aa4c-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VpcId': 'vpc-14500972'},\\n\",\n \" {'Description': 'group to provide ssh access to studioml workers',\\n\",\n \" 'GroupId': 'sg-ca2b6bb7',\\n\",\n \" 'GroupName': '78768c8a-287a-4be4-bbb0-6d531317f8d5',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'launch-wizard-1 created 2017-10-20T15:57:51.300-07:00',\\n\",\n \" 'GroupId': 'sg-cdb5dcb0',\\n\",\n \" 'GroupName': 'launch-wizard-1',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'launch-wizard-3 created 2017-10-24T13:43:54.899-07:00',\\n\",\n \" 'GroupId': 'sg-cee498b3',\\n\",\n \" 'GroupName': 'launch-wizard-3',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'group to provide ssh access to studioml workers',\\n\",\n \" 'GroupId': 'sg-dc601fa1',\\n\",\n \" 'GroupName': 'b10e3d58-b97f-4e9d-82f4-bd0cdad239f1',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'Security group for masters',\\n\",\n \" 'GroupId': 'sg-eb9eee96',\\n\",\n \" 'GroupName': 'masters.0726aa4c-pachydermcluster.kubernetes.com',\\n\",\n \" 'IpPermissions': [{'FromPort': 1,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 4000,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-af9dedd2',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 4003,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-af9dedd2',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 8080,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8080,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-eb9eee96',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 1,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': 'sg-af9dedd2',\\n\",\n \" 'UserId': '316880547378'}]},\\n\",\n \" {'FromPort': 30650,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 30650,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 443,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 443,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Tags': [{'Key': 'Name',\\n\",\n \" 'Value': 'masters.0726aa4c-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '0726aa4c-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VpcId': 'vpc-14500972'},\\n\",\n \" {'Description': 'group to provide ssh access to studioml workers',\\n\",\n \" 'GroupId': 'sg-ef4f3092',\\n\",\n \" 'GroupName': '079a259c-2d0e-45da-916c-1fb855ae2344',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'},\\n\",\n \" {'Description': 'launch-wizard-12 created 2017-12-08T16:19:44.702-05:00',\\n\",\n \" 'GroupId': 'sg-f750fa8b',\\n\",\n \" 'GroupName': 'launch-wizard-12',\\n\",\n \" 'IpPermissions': [{'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 3000,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0', 'Description': 'HTTP requests'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 3000,\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-f683678f'},\\n\",\n \" {'Description': 'launch-wizard-4 created 2017-10-24T13:48:12.400-07:00',\\n\",\n \" 'GroupId': 'sg-fde79b80',\\n\",\n \" 'GroupName': 'launch-wizard-4',\\n\",\n \" 'IpPermissions': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'IpPermissionsEgress': [{'IpProtocol': '-1',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'UserIdGroupPairs': []}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'VpcId': 'vpc-fe722098'}]}\"\n ]\n },\n \"execution_count\": 173,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_security_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 147,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Instance(id='i-07adfb26e0fd845a9')\"\n ]\n },\n \"execution_count\": 147,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 150,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ii.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 151,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'13.56.149.107'\"\n ]\n },\n \"execution_count\": 151,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ii.public_ip_address\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 146,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ii.wait_until_running()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 140,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Instance(id='i-07adfb26e0fd845a9')]\"\n ]\n },\n \"execution_count\": 140,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(instances)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"# http://boto3.readthedocs.io/en/latest/guide/ec2-example-key-pairs.html\\n\",\n \"KEY_PAIR_LOCATION=os.environ[\\\"HOME\\\"]+'/.nexus.pem'\\n\",\n \"KEY_PAIR_NAME='nexus'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"keypair = ec2.create_key_pair(KeyName='nexus')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"boto3.client('ec2', region_name=REGION)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1670\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"open(KEY_PAIR_LOCATION, 'w').write(keypair.key_material)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/Users/yaroslav/.nexus.pem'\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"KEY_PAIR_LOCATION\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'test',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 14:39:41 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '38ed4769-7f17-4a46-ba69-0c25a623959c',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 110,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_placement_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 116,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from collections import OrderedDict\\n\",\n \"def get_placement_group_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionary of {placement_group_name: state}\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" client = create_ec2_client()\\n\",\n \" response = client.describe_placement_groups()\\n\",\n \" assert _is_good_response(response)\\n\",\n \"\\n\",\n \" result = OrderedDict()\\n\",\n \" for placement_group_response in response['PlacementGroups']:\\n\",\n \" key = placement_group_response['GroupName']\\n\",\n \" assert key not in result, (\\\"Duplicate placement group \\\" +\\n\",\n \" placement_group_name)\\n\",\n \" result[key] = placement_group_response['State']\\n\",\n \" return result\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 117,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('amazon2', 'available'),\\n\",\n \" ('inet3', 'available'),\\n\",\n \" ('default', 'available'),\\n\",\n \" ('amazon3', 'available'),\\n\",\n \" ('beefy', 'available'),\\n\",\n \" ('inet', 'available'),\\n\",\n \" ('amazon1', 'available'),\\n\",\n \" ('beefy3', 'available'),\\n\",\n \" ('beefy2', 'available')])\"\n ]\n },\n \"execution_count\": 117,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_placement_group_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 14:25:45 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'd28f424e-d4da-4227-a25b-0d45155136b8',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 108,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.create_placement_group(GroupName=\\\"test\\\",Strategy=\\\"cluster\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def _maybe_create_placement_group(name):\\n\",\n \" client = boto3.client('ec2')\\n\",\n \" try:\\n\",\n \" client.describe_placement_groups(GroupNames=[name])\\n\",\n \" except boto3.exceptions.botocore.exceptions.ClientError as e:\\n\",\n \" print(\\\"Creating placement group: \\\"+name)\\n\",\n \" res = client.create_placement_group(GroupName=name, Strategy='cluster')\\n\",\n \"\\n\",\n \" counter = 0\\n\",\n \" while True:\\n\",\n \" try:\\n\",\n \" res = client.describe_placement_groups(GroupNames=[name])\\n\",\n \" if res['PlacementGroups'][0]['State'] == 'available':\\n\",\n \" print(\\\"Found placement group: \\\"+name)\\n\",\n \" break\\n\",\n \" except Exception as e:\\n\",\n \" print(e)\\n\",\n \" counter = counter + 1\\n\",\n \" if counter >= 10:\\n\",\n \" print('Failed to create placement group %s' % name)\\n\",\n \" time.sleep(TIMEOUT_SEC)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.system('chmod 400 %s'%(KEY_PAIR_LOCATION,))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# todo: assert result is 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def _is_good_response(response):\\n\",\n \" return response[\\\"ResponseMetadata\\\"]['HTTPStatusCode'] == 200\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_keypair_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionry of {keypairname: fingerprint}.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" client = create_ec2_client()\\n\",\n \" response=client.describe_key_pairs()\\n\",\n \" assert _is_good_response(response)\\n\",\n \" \\n\",\n \" from collections import defaultdict\\n\",\n \" result = {}\\n\",\n \" for keypair in response['KeyPairs']:\\n\",\n \" keypair_name = keypair.get('KeyName', '')\\n\",\n \" assert keypair_name not in result, \\\"Duplicate key \\\"+keypair_name\\n\",\n \" result[keypair_name] = keypair.get('KeyFingerprint','')\\n\",\n \" return result\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'aws_pach_api_service': '28:6e:51:d6:81:3d:51:22:4b:20:4a:fb:cf:9b:1f:9f:1d:2a:a9:94',\\n\",\n \" 'brendan': 'f7:9a:33:d7:72:63:fe:27:30:9a:3b:49:e5:fa:dd:0b:9a:31:77:7a',\\n\",\n \" 'kubernetes.0726aa4c-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'kubernetes.282e747a-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'kubernetes.6dbcbbe2-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'kubernetes.db5d0d76-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'nexus-oregon': '42:79:9a:be:17:03:c9:de:dd:05:92:de:85:d7:29:07:cd:b7:a0:5b',\\n\",\n \" 'pach_service_stage': '4e:2b:72:59:5f:11:94:2d:5a:92:ba:10:2e:f0:10:da:22:03:d3:30',\\n\",\n \" 'yaroslav': '43:fe:a7:51:d7:ad:26:7c:ce:0e:94:53:31:36:9f:38:9f:63:17:1c'}\"\n ]\n },\n \"execution_count\": 101,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_keypair_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'KeyPairs': [{'KeyFingerprint': 'e4:fc:cf:7f:78:f2:ce:0d:5e:c9:e3:12:85:72:68:b1:ad:47:38:55',\\n\",\n \" 'KeyName': 'nexus'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 12 Dec 2017 05:42:01 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'a765e248-3fe2-4d41-b854-2d5eba7a2114',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 96,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client=boto3.client('ec2', region_name='us-west-1')\\n\",\n \"response=client.describe_key_pairs()\\n\",\n \"response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nexus\\n\"\n ]\n }\n ],\n \"source\": [\n \"for r in response['KeyPairs']:\\n\",\n \" print(r['KeyName'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.SecurityGroup(id='sg-4af9e52c')\"\n ]\n },\n \"execution_count\": 102,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 265,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Subnet(id='subnet-bca924db')\"\n ]\n },\n \"execution_count\": 265,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 274,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 274,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.get_available_subresources()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 278,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"subnets=list(vpc.subnets.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 279,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ec2.Subnet(id='subnet-bca924db')\"\n ]\n },\n \"execution_count\": 279,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnets[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 268,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'Key': 'Name', 'Value': 'nexus_vpc'}]\"\n ]\n },\n \"execution_count\": 268,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create again with security key\\n\",\n \"ami=\\\"ami-45ead225\\\"\\n\",\n \"instances = ec2.create_instances(\\n\",\n \" ImageId=ami, InstanceType='t2.micro', MaxCount=1, MinCount=1,KeyName=KEY_PAIR_NAME,\\n\",\n \" NetworkInterfaces=[{'SubnetId': subnet.id, 'DeviceIndex': 0, 'AssociatePublicIpAddress': True, 'Groups': [sec_group.group_id]}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create again with security key\\n\",\n \"ami=\\\"ami-45ead225\\\"\\n\",\n \"instances = ec2.create_instances(\\n\",\n \" ImageId=ami, InstanceType='t2.micro', MaxCount=1, MinCount=1,KeyName=KEY_PAIR_NAME,\\n\",\n \" NetworkInterfaces=[{'SubnetId': subnet.id, 'DeviceIndex': 0, 'AssociatePublicIpAddress': True, 'Groups': [sec_group.group_id]}])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## List/delete vpc's\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 305,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def _create_ec2_client():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.client('ec2', region_name=REGION)\\n\",\n \"\\n\",\n \"\\n\",\n \"def _create_ec2_resource():\\n\",\n \" REGION = os.environ['AWS_DEFAULT_REGION']\\n\",\n \" return boto3.resource('ec2',region_name=REGION)\\n\",\n \"\\n\",\n \"def get_vpc_dict():\\n\",\n \" \\\"\\\"\\\"Returns dictionary of named VPCs {name: vpc}\\n\",\n \"\\n\",\n \" Assert fails if there's more than one VPC with same name.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" client = _create_ec2_client()\\n\",\n \" response = client.describe_vpcs()\\n\",\n \" assert _is_good_response(response)\\n\",\n \"\\n\",\n \" result = OrderedDict()\\n\",\n \" ec2 = _create_ec2_resource()\\n\",\n \" for vpc_response in response['Vpcs']:\\n\",\n \" key = _get_name(vpc_response.get('Tags', []))\\n\",\n \" if not key: # skip VPC's that don't have a name assigned\\n\",\n \" continue\\n\",\n \" \\n\",\n \" assert key not in result, (\\\"Duplicate VPC group \\\" + key)\\n\",\n \" result[key] = ec2.Vpc(vpc_response['VpcId'])\\n\",\n \"\\n\",\n \" return result\\n\",\n \"\\n\",\n \"def _get_name(tags):\\n\",\n \" \\\"\\\"\\\"Helper utility to extract name out of tags dictionary.\\n\",\n \" [{'Key': 'Name', 'Value': 'nexus'}] -> 'nexus'\\n\",\n \" \\n\",\n \" Assert fails if there's more than one name.\\n\",\n \" Returns '' if there's less than one name.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" names = [entry['Value'] for entry in tags if entry['Key']=='Name']\\n\",\n \" if not names:\\n\",\n \" names = ['']\\n\",\n \" assert len(names)==1, \\\"have more than one name: \\\"+str(names)\\n\",\n \" return names[0]\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 290,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'AvailabilityZones': [{'Messages': [],\\n\",\n \" 'RegionName': 'us-west-1',\\n\",\n \" 'State': 'available',\\n\",\n \" 'ZoneName': 'us-west-1a'},\\n\",\n \" {'Messages': [],\\n\",\n \" 'RegionName': 'us-west-1',\\n\",\n \" 'State': 'available',\\n\",\n \" 'ZoneName': 'us-west-1c'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 17:22:45 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '329b7ce0-1020-48f3-9751-516408b14097',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 290,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_availability_zones()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 310,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc=d['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 311,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-febcfd86' has dependencies and cannot be deleted.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mvpc\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (DependencyViolation) when calling the DeleteVpc operation: The vpc 'vpc-febcfd86' has dependencies and cannot be deleted.\"\n ]\n }\n ],\n \"source\": [\n \"vpc.delete() # not working, delete through console instead https://console.aws.amazon.com/vpc/home?region=us-east-1#vpcs:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Delete some vpc stuff\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 216,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-east-1'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 219,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.insert(0, os.environ[\\\"HOME\\\"]+'/git0/cluster')\\n\",\n \"import common_resources as c\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 233,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" existing_vpcs = c.get_vpc_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 234,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('nexus', ec2.Vpc(id='vpc-1a054562')),\\n\",\n \" ('nexus01', ec2.Vpc(id='vpc-c6c388be')),\\n\",\n \" ('nexus2', ec2.Vpc(id='vpc-cbffbeb3')),\\n\",\n \" ('nexus00', ec2.Vpc(id='vpc-58f3b820'))])\"\n ]\n },\n \"execution_count\": 234,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"existing_vpcs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 236,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc=existing_vpcs['nexus00']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 237,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc.load()\\n\",\n \"route_tables = list(vpc.route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 238,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (DependencyViolation) when calling the DeleteRouteTable operation: The routeTable 'rtb-8b57acf6' has dependencies and cannot be deleted.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute_tables\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (DependencyViolation) when calling the DeleteRouteTable operation: The routeTable 'rtb-8b57acf6' has dependencies and cannot be deleted.\"\n ]\n }\n ],\n \"source\": [\n \"route_tables[0].delete()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 240,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Route(route_table_id='rtb-8b57acf6', destination_cidr_block='192.168.0.0/16')]\"\n ]\n },\n \"execution_count\": 240,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_tables[0].routes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 239,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (DependencyViolation) when calling the DeleteRouteTable operation: The routeTable 'rtb-9043b8ed' has dependencies and cannot be deleted.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mroute_tables\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (DependencyViolation) when calling the DeleteRouteTable operation: The routeTable 'rtb-9043b8ed' has dependencies and cannot be deleted.\"\n ]\n }\n ],\n \"source\": [\n \"route_tables[1].delete()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Route tables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 267,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_tables = list(d['nexus'].route_tables.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 269,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"route_table = route_tables[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 271,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'rtb-aeaa5cd3'\"\n ]\n },\n \"execution_count\": 271,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 272,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 278,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ResourceMeta('ec2', identifiers=['id'])\"\n ]\n },\n \"execution_count\": 278,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"route_table.meta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Internet gateways\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 274,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"internet_gateways = list(vpc.internet_gateways.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 276,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"iga = internet_gateways[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 277,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"iga.create_tags\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Keypairs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'KeyPairs': [{'KeyFingerprint': '28:6e:51:d6:81:3d:51:22:4b:20:4a:fb:cf:9b:1f:9f:1d:2a:a9:94',\\n\",\n \" 'KeyName': 'aws_pach_api_service'},\\n\",\n \" {'KeyFingerprint': '6b:07:eb:f9:ab:ff:4f:18:3c:65:6e:99:05:01:21:fc:71:be:be:13',\\n\",\n \" 'KeyName': 'aws_pach_api_service_stage'},\\n\",\n \" {'KeyFingerprint': 'f7:9a:33:d7:72:63:fe:27:30:9a:3b:49:e5:fa:dd:0b:9a:31:77:7a',\\n\",\n \" 'KeyName': 'brendan'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.0726aa4c-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.282e747a-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.6dbcbbe2-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '1d:61:59:c6:eb:c3:ca:c4:a9:7a:7a:19:8d:1c:6d:4a',\\n\",\n \" 'KeyName': 'kubernetes.db5d0d76-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58'},\\n\",\n \" {'KeyFingerprint': '96:c7:f9:66:ad:29:2f:e6:56:06:89:c1:5e:d6:c9:53:07:67:9b:dd',\\n\",\n \" 'KeyName': 'nexus'},\\n\",\n \" {'KeyFingerprint': '42:79:9a:be:17:03:c9:de:dd:05:92:de:85:d7:29:07:cd:b7:a0:5b',\\n\",\n \" 'KeyName': 'nexus-oregon'},\\n\",\n \" {'KeyFingerprint': '4e:2b:72:59:5f:11:94:2d:5a:92:ba:10:2e:f0:10:da:22:03:d3:30',\\n\",\n \" 'KeyName': 'pach_service_stage'},\\n\",\n \" {'KeyFingerprint': '43:fe:a7:51:d7:ad:26:7c:ce:0e:94:53:31:36:9f:38:9f:63:17:1c',\\n\",\n \" 'KeyName': 'yaroslav'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 10 Jan 2018 05:13:06 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'dadac7c2-0f45-4eb9-8737-33da2175062a',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_key_pairs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"keypairs = u.get_keypair_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"keypair = keypairs['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keypair.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'2e:b3:5b:59:62:a4:b2:21:51:71:e3:88:3c:27:33:bb:3c:76:ea:63'\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keypair.key_fingerprint\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'mac.local'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import socket\\n\",\n \"#socket.gethostname()\\n\",\n \"socket.getfqdn()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'b0207aa6d182a61fb2a886607b046e11'\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import hashlib\\n\",\n \"hash_object = hashlib.md5(socket.gethostname().encode('ascii'))\\n\",\n \"hash_object.hexdigest()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'b0207aa6d182a61fb2a886607b046e11'\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import getpass\\n\",\n \"username = getpass.getuser()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'yaroslav'\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"username\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Enable DNS on VPC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 437,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc = u.get_vpc_dict()['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 440,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"response = vpc.modify_attribute(EnableDnsHostnames={\\\"Value\\\":True})\\n\",\n \"response = vpc.modify_attribute(EnableDnsSupport={\\\"Value\\\":True})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 441,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 20 Dec 2017 01:37:16 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'e52ad0de-54d2-4962-87d1-d59384c0bbf9',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 441,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.Vpc('vpc-aaaaaaaa').describe_attribute(Attribute='enableDnsHostnames')['EnableDnsHostnames']['Value']\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Efs troubleshooting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 442,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'us-east-1'\"\n ]\n },\n \"execution_count\": 442,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 443,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"vpc = u.get_vpc_dict()['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 446,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'EnableDnsSupport': {'Value': True},\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 20 Dec 2017 01:44:55 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '5e54000b-525f-42ce-a27f-ccfc34b1d4b3',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'VpcId': 'vpc-26e6b35e'}\"\n ]\n },\n \"execution_count\": 446,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.describe_attribute(Attribute='enableDnsSupport')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 447,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'EnableDnsHostnames': {'Value': True},\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 20 Dec 2017 01:45:12 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'c9fbb240-0ad9-4072-970d-ea2643176410',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'VpcId': 'vpc-26e6b35e'}\"\n ]\n },\n \"execution_count\": 447,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vpc.describe_attribute(Attribute='enableDnsHostnames')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 448,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"efs_id = u.get_efs_dict()['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 449,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.create_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 450,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances = list(ec2.instances.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 451,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance = instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 482,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"zone = instance.placement['AvailabilityZone']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 480,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('us-east-1c', 'fsmt-c5454a8c'),\\n\",\n \" ('us-east-1b', 'fsmt-c7454a8e'),\\n\",\n \" ('us-east-1f', 'fsmt-d8454a91'),\\n\",\n \" ('us-east-1a', 'fsmt-d9454a90'),\\n\",\n \" ('us-east-1e', 'fsmt-da454a93'),\\n\",\n \" ('us-east-1d', 'fsmt-db454a92')])\"\n ]\n },\n \"execution_count\": 480,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"importlib.reload(u)\\n\",\n \"u.get_mount_targets_dict(efs_id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 472,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'GroupId': 'sg-3d2a2148', 'GroupName': 'nexus'}]\"\n ]\n },\n \"execution_count\": 472,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.security_groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 484,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mount_target = u.get_mount_targets_dict(efs_id)[zone]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 474,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"efs_client = u.create_efs_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 486,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-length': '34',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Wed, 20 Dec 2017 02:04:55 GMT',\\n\",\n \" 'x-amzn-requestid': '2c486ab4-e52a-11e7-9043-3787982a1618'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '2c486ab4-e52a-11e7-9043-3787982a1618',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'SecurityGroups': ['sg-3d2a2148']}\"\n ]\n },\n \"execution_count\": 486,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.describe_mount_target_security_groups(MountTargetId=mount_target)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 488,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group = u.get_security_group_dict()['nexus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 489,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'FromPort': 8890,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8890,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 8888,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8888,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6006,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6006,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 22,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 22,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6379,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6379,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6008,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6008,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 6007,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 6007,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': -1,\\n\",\n \" 'IpProtocol': 'icmp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': -1,\\n\",\n \" 'UserIdGroupPairs': []},\\n\",\n \" {'FromPort': 8889,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [{'CidrIp': '0.0.0.0/0'}],\\n\",\n \" 'Ipv6Ranges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 8889,\\n\",\n \" 'UserIdGroupPairs': []}]\"\n ]\n },\n \"execution_count\": 489,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.ip_permissions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 490,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group.authorize_ingress()\\n\",\n \"\\n\",\n \"--source-group sg-1a2b3c4d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 494,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 494,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.group_name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 496,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group.authorize_ingress?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 497,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group.authorize_ingress?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 507,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (InvalidGroup.NotFound) when calling the AuthorizeSecurityGroupIngress operation: The security group 'sg-3d2a2148' does not exist in default VPC 'vpc-79c4f61d'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mGroupId\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0msecurity_group\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mid\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3\\u001b[0m \\u001b[0mIpProtocol\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'all'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mFromPort\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 4\\u001b[0;31m ToPort=0,SourceSecurityGroupName=security_group.group_id)\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (InvalidGroup.NotFound) when calling the AuthorizeSecurityGroupIngress operation: The security group 'sg-3d2a2148' does not exist in default VPC 'vpc-79c4f61d'\"\n ]\n }\n ],\n \"source\": [\n \"client.authorize_security_group_ingress(\\n\",\n \" GroupId=security_group.id,\\n\",\n \" IpProtocol='all', FromPort=0,\\n\",\n \"ToPort=0,SourceSecurityGroupName=security_group.group_id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 514,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"65535\"\n ]\n },\n \"execution_count\": 514,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"2**16-1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 517,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Wed, 20 Dec 2017 02:35:26 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '14836c3f-0ad1-4c14-9d93-332c642480e9',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 517,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_rule ={'FromPort': 0,\\n\",\n \" 'IpProtocol': 'tcp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': security_group.id}]} #id in VPC2\\n\",\n \"security_group.authorize_ingress(IpPermissions=[new_rule]) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 526,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Warning, got An error occurred (InvalidPermission.Duplicate) when calling the AuthorizeSecurityGroupIngress operation: the specified rule \\\"peer: sg-3d2a2148, UDP, from port: 0, to port: 65535, ALLOW\\\" already exists\\n\",\n \"Warning, got An error occurred (InvalidPermission.Duplicate) when calling the AuthorizeSecurityGroupIngress operation: the specified rule \\\"peer: sg-3d2a2148, UDP, from port: 0, to port: 65535, ALLOW\\\" already exists\\n\",\n \"Warning, got An error occurred (InvalidPermission.Duplicate) when calling the AuthorizeSecurityGroupIngress operation: the specified rule \\\"peer: sg-3d2a2148, UDP, from port: 0, to port: 65535, ALLOW\\\" already exists\\n\"\n ]\n }\n ],\n \"source\": [\n \" for protocol in ['tcp', 'udp', 'icmp']:\\n\",\n \" try:\\n\",\n \" rule ={'FromPort': 0,\\n\",\n \" 'IpProtocol': protocol,\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': security_group.id}]}\\n\",\n \" security_group.authorize_ingress(IpPermissions=[new_rule])\\n\",\n \" except Exception as e:\\n\",\n \" if e.response['Error']['Code']=='InvalidPermission.Duplicate':\\n\",\n \" print(\\\"Warning, got \\\"+str(e))\\n\",\n \" else:\\n\",\n \" assert False, \\\"Failed while authorizing ingress with \\\"+str(e)\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 527,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('nexus', 'fs-fa5b62b3')])\"\n ]\n },\n \"execution_count\": 527,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_efs_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 536,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" efs_id = u.get_efs_dict()[\\\"nexus\\\"]\\n\",\n \" region = 'us-east-1'\\n\",\n \" dns = \\\"{efs_id}.efs.{region}.amazonaws.com\\\".format(**locals())\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 537,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'fs-fa5b62b3.efs.us-east-1.amazonaws.com'\"\n ]\n },\n \"execution_count\": 537,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 534,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'5'\"\n ]\n },\n \"execution_count\": 534,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"{a}\\\".format(**locals())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 530,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'fs-fa5b62b3'\"\n ]\n },\n \"execution_count\": 530,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"locals()[\\\"efs_id\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 525,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 525,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.response['Error']['Code']=='InvalidPermission.Duplicate'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"new_rule ={'FromPort': 0,\\n\",\n \" 'IpProtocol': 'udp',\\n\",\n \" 'IpRanges': [],\\n\",\n \" 'PrefixListIds': [],\\n\",\n \" 'ToPort': 65535,\\n\",\n \" 'UserIdGroupPairs': [{'GroupId': security_group.id}]} #id in VPC2\\n\",\n \"security_group.authorize_ingress(IpPermissions=[new_rule]) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 511,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client.authorize_security_group_ingress?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 505,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sg-3d2a2148'\"\n ]\n },\n \"execution_count\": 505,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"aws ec2 authorize-security-group-ingress --group-id sg-3d2a2148 --protocol tcp --port 80 --source-group sg-3d2a2148\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 501,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client.authorize_security_group_ingress()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 498,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ClientError\",\n \"evalue\": \"An error occurred (VPCIdNotSpecified) when calling the AuthorizeSecurityGroupIngress operation: No default VPC for this user\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mClientError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 3\\u001b[0m \\u001b[0mIpProtocol\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'All'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mFromPort\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 5\\u001b[0;31m \\u001b[0mToPort\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 6\\u001b[0m )\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mdo_action\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 518\\u001b[0m \\u001b[0;31m# instance via ``self``.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 519\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mdo_action\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 520\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0maction\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 521\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 522\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mhasattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'load'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/action.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, parent, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 81\\u001b[0m operation_name, params)\\n\\u001b[1;32m 82\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 83\\u001b[0;31m \\u001b[0mresponse\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparent\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m**\\u001b[0m\\u001b[0mparams\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 84\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0mlogger\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdebug\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Response: %r'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mClientError\\u001b[0m: An error occurred (VPCIdNotSpecified) when calling the AuthorizeSecurityGroupIngress operation: No default VPC for this user\"\n ]\n }\n ],\n \"source\": [\n \" security_group.authorize_ingress(\\n\",\n \" SourceSecurityGroupName=security_group.id,\\n\",\n \" IpProtocol='All',\\n\",\n \" FromPort=-1,\\n\",\n \" ToPort=-1\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 500,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'vpc-26e6b35e'\"\n ]\n },\n \"execution_count\": 500,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"security_group.vpc_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" ec2 = boto3.resource('ec2')\\n\",\n \"\\n\",\n \" # TODO: add waiting so that instances that \\\"initializing\\\" are supported\\n\",\n \" instances = ec2.instances.filter(\\n\",\n \" Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for i in instances.all():\\n\",\n \" inst_name = u.get_name(i.tags)\\n\",\n \" key_name = i.key_name\\n\",\n \"\\n\",\n \" if inst_name == name:\\n\",\n \" if key_name != KEY_NAME:\\n\",\n \" print(\\\"name matches, but key name %s doesn't match %s, skipping\\\"%(key_name, KEY_NAME))\\n\",\n \" continue\\n\",\n \" result.append(i)\\n\",\n \"\\n\",\n \" ec2 = boto3.resource('ec2')\\n\",\n \" instances = ec2.instances.filter(\\n\",\n \" Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for i in instances:\\n\",\n \" names = []\\n\",\n \" if i.tags:\\n\",\n \" names = [tag['Value'] for tag in i.tags if tag['Key'] == 'Name']\\n\",\n \" key_name = i.key_name\\n\",\n \"\\n\",\n \" assert len(names) <= 1\\n\",\n \" if names:\\n\",\n \" inst_name = names[0]\\n\",\n \" else:\\n\",\n \" inst_name = ''\\n\",\n \" if inst_name == name:\\n\",\n \" if key_name != KEY_NAME:\\n\",\n \" print(\\\"name matches, but key name %s doesn't match %s, skipping\\\"%(key_name, KEY_NAME))\\n\",\n \" continue\\n\",\n \" result.append(i)\\n\",\n \" return result\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create EFS\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 291,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"os.environ['AWS_DEFAULT_REGION']='us-east-1'\\n\",\n \"efs_client=_create_efs_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 292,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'FileSystems': [],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-length': '50',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 17:23:01 GMT',\\n\",\n \" 'x-amzn-requestid': '439df84c-e4e1-11e7-bfec-e1d94534db9c'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '439df84c-e4e1-11e7-bfec-e1d94534db9c',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 292,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.describe_file_systems()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 293,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('nexus2', ec2.SecurityGroup(id='sg-50cfe125')),\\n\",\n \" ('nexus00', ec2.SecurityGroup(id='sg-891a13fc')),\\n\",\n \" ('nexus', ec2.SecurityGroup(id='sg-d0cae4a5'))])\"\n ]\n },\n \"execution_count\": 293,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"c.get_security_group_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 340,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"token = str(int(time.time()*1e6)) # epoch usec\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 346,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"An error occurred (FileSystemAlreadyExists) when calling the CreateFileSystem operation: None\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = None\\n\",\n \"try:\\n\",\n \" response = efs_client.create_file_system(CreationToken=token, PerformanceMode='generalPurpose')\\n\",\n \"except Exception as e:\\n\",\n \" print(e)\\n\",\n \" a = e\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 351,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'FileSystemAlreadyExists'\"\n ]\n },\n \"execution_count\": 351,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a.response['Error']['Code']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 366,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 366,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"importlib.reload(u)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 367,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"u.create_efs('nexus01')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 368,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('nexus01', 'fs-0f95ab46'), ('nexus00', 'fs-8397a9ca')])\"\n ]\n },\n \"execution_count\": 368,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_efs_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 352,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 352,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"response = efs_client.create_tags(FileSystemId=fs_id, Tags=[{\\\"Key\\\":\\\"name\\\", \\\"Value\\\":\\\"test_efs\\\"}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'fs-4bf940e2'\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fs_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 363,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fs_id = u.get1(efs_client.describe_file_systems()['FileSystems'], FileSystemId=-1, CreationToken=token)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 364,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:02:12 GMT',\\n\",\n \" 'x-amzn-requestid': 'bd865df2-e4e6-11e7-9a73-2b548ec68a6f'},\\n\",\n \" 'HTTPStatusCode': 204,\\n\",\n \" 'RequestId': 'bd865df2-e4e6-11e7-9a73-2b548ec68a6f',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 364,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.create_tags(FileSystemId=fs_id, Tags=u.make_name('nexus00'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 365,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('nexus00', 'fs-8397a9ca')])\"\n ]\n },\n \"execution_count\": 365,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_efs_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 307,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fs_id = c.get1(efs_client.describe_file_systems()['FileSystems'], FileSystemId=-1, CreationToken=token)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 310,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 17:29:19 GMT',\\n\",\n \" 'x-amzn-requestid': '25dd20d1-e4e2-11e7-851c-5bda6b65b79c'},\\n\",\n \" 'HTTPStatusCode': 204,\\n\",\n \" 'RequestId': '25dd20d1-e4e2-11e7-851c-5bda6b65b79c',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 310,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.create_tags(FileSystemId=fs_id, Tags=c.make_name('nexus00'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 313,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"fs-4498a60d\\n\",\n \"{'ResponseMetadata': {'RetryAttempts': 0, 'HTTPStatusCode': 200, 'RequestId': '46bef164-e4e2-11e7-978b-8d2563c5a66a', 'HTTPHeaders': {'x-amzn-requestid': '46bef164-e4e2-11e7-978b-8d2563c5a66a', 'content-type': 'application/json', 'date': 'Tue, 19 Dec 2017 17:30:15 GMT', 'content-length': '75'}}, 'Tags': [{'Value': 'nexus00', 'Key': 'Name'}]}\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = efs_client.describe_file_systems()\\n\",\n \"for efs_response in response['FileSystems']:\\n\",\n \" print(efs_response['FileSystemId'])\\n\",\n \" print(efs_client.describe_tags(FileSystemId=efs_response['FileSystemId']))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 326,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import util as u\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 334,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" \\\"\\\"\\\"Returns dictionary of {efs_name: efs_id}\\\"\\\"\\\"\\n\",\n \" # there's no EC2 resource for EFS objects, so return EFS_ID instead\\n\",\n \" # https://stackoverflow.com/questions/47870342/no-ec2-resource-for-efs-objects\\n\",\n \"\\n\",\n \" efs_client = u.create_efs_client()\\n\",\n \" response = efs_client.describe_file_systems()\\n\",\n \" assert u.is_good_response(response)\\n\",\n \" result = OrderedDict()\\n\",\n \" for efs_response in response['FileSystems']:\\n\",\n \" fs_id = efs_response['FileSystemId']\\n\",\n \" tag_response = efs_client.describe_tags(FileSystemId=fs_id)\\n\",\n \" assert u.is_good_response(tag_response)\\n\",\n \" key = u.get_name(tag_response['Tags'])\\n\",\n \" if not key: # skip EFS's without a name\\n\",\n \" continue\\n\",\n \" assert key not in result\\n\",\n \" result[key] = fs_id\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 370,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'fs-8397a9ca'\"\n ]\n },\n \"execution_count\": 370,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fs_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 371,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"efs_client.create_mount_target?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 373,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Subnet(id='subnet-f5e248a8')]\"\n ]\n },\n \"execution_count\": 373,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(vpc.subnets.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 374,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"subnet-f5e248a8\\n\"\n ]\n }\n ],\n \"source\": [\n \"for subnet in vpc.subnets.all():\\n\",\n \" print(subnet.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 388,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-length': '75',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:17:51 GMT',\\n\",\n \" 'x-amzn-requestid': 'ece8b9f3-e4e8-11e7-8125-435d90b564e8'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'ece8b9f3-e4e8-11e7-8125-435d90b564e8',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Tags': [{'Key': 'Name', 'Value': 'nexus00'}]}\"\n ]\n },\n \"execution_count\": 388,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.describe_tags(FileSystemId='fs-8397a9ca')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 386,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'FileSystems': [{'CreationTime': datetime.datetime(2017, 12, 19, 10, 3, 44, tzinfo=tzlocal()),\\n\",\n \" 'CreationToken': '1513706624330134',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'FileSystemId': 'fs-0f95ab46',\\n\",\n \" 'LifeCycleState': 'available',\\n\",\n \" 'Name': 'nexus01',\\n\",\n \" 'NumberOfMountTargets': 0,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PerformanceMode': 'generalPurpose',\\n\",\n \" 'SizeInBytes': {'Value': 6144}},\\n\",\n \" {'CreationTime': datetime.datetime(2017, 12, 19, 9, 52, 33, tzinfo=tzlocal()),\\n\",\n \" 'CreationToken': '1513705952536349',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'FileSystemId': 'fs-8397a9ca',\\n\",\n \" 'LifeCycleState': 'available',\\n\",\n \" 'Name': 'nexus00',\\n\",\n \" 'NumberOfMountTargets': 1,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PerformanceMode': 'generalPurpose',\\n\",\n \" 'SizeInBytes': {'Value': 6144}},\\n\",\n \" {'CreationTime': datetime.datetime(2017, 12, 19, 9, 52, 18, tzinfo=tzlocal()),\\n\",\n \" 'CreationToken': '1513704300869863',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'FileSystemId': 'fs-8897a9c1',\\n\",\n \" 'LifeCycleState': 'available',\\n\",\n \" 'NumberOfMountTargets': 0,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PerformanceMode': 'generalPurpose',\\n\",\n \" 'SizeInBytes': {'Value': 6144}}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-length': '914',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:14:06 GMT',\\n\",\n \" 'x-amzn-requestid': '666a7d7f-e4e8-11e7-b698-09471602bb2e'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '666a7d7f-e4e8-11e7-b698-09471602bb2e',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 386,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.describe_file_systems()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"efs_client.create_mount_target(fs_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 379,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"security_group=u.get_security_group_dict()['nexus']\\n\",\n \"subnet = list(vpc.subnets.all())[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 380,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'FileSystemId': 'fs-8397a9ca',\\n\",\n \" 'IpAddress': '192.168.1.217',\\n\",\n \" 'LifeCycleState': 'creating',\\n\",\n \" 'MountTargetId': 'fsmt-be9c90f7',\\n\",\n \" 'NetworkInterfaceId': 'eni-6083e1cd',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-length': '208',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:10:36 GMT',\\n\",\n \" 'x-amzn-requestid': 'e96b9703-e4e7-11e7-8125-435d90b564e8'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'e96b9703-e4e7-11e7-8125-435d90b564e8',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'SubnetId': 'subnet-f5e248a8'}\"\n ]\n },\n \"execution_count\": 380,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.create_mount_target(FileSystemId=fs_id, SubnetId=subnet.id, SecurityGroups=[security_group.id])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 393,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'MountTargets': [{'FileSystemId': 'fs-8397a9ca',\\n\",\n \" 'IpAddress': '192.168.1.217',\\n\",\n \" 'LifeCycleState': 'available',\\n\",\n \" 'MountTargetId': 'fsmt-be9c90f7',\\n\",\n \" 'NetworkInterfaceId': 'eni-6083e1cd',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'SubnetId': 'subnet-f5e248a8'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-length': '260',\\n\",\n \" 'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:23:30 GMT',\\n\",\n \" 'x-amzn-requestid': 'b765e2c5-e4e9-11e7-a0f8-71380bab6b2b'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': 'b765e2c5-e4e9-11e7-a0f8-71380bab6b2b',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 393,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.describe_mount_targets(FileSystemId=fs_id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 406,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'application/json',\\n\",\n \" 'date': 'Tue, 19 Dec 2017 18:34:11 GMT',\\n\",\n \" 'x-amzn-requestid': '34e2dd88-e4eb-11e7-9723-2f715784a176'},\\n\",\n \" 'HTTPStatusCode': 204,\\n\",\n \" 'RequestId': '34e2dd88-e4eb-11e7-9723-2f715784a176',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 406,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"efs_client.delete_file_system(FileSystemId='fs-8397a9ca')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 407,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'us-east-1c'\"\n ]\n },\n \"execution_count\": 407,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.availability_zone\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 405,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"MountTargetNotFound\",\n \"evalue\": \"An error occurred (MountTargetNotFound) when calling the DeleteMountTarget operation: mount target does not exist\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mMountTargetNotFound\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mefs_client\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdelete_mount_target\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mMountTargetId\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'fsmt-be9c90f7'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_api_call\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 310\\u001b[0m \\\"%s() only accepts keyword arguments.\\\" % py_operation_name)\\n\\u001b[1;32m 311\\u001b[0m \\u001b[0;31m# The \\\"self\\\" in this scope is referring to the BaseClient.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 312\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_make_api_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0moperation_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 314\\u001b[0m \\u001b[0m_api_call\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpy_operation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/botocore/client.py\\u001b[0m in \\u001b[0;36m_make_api_call\\u001b[0;34m(self, operation_name, api_params)\\u001b[0m\\n\\u001b[1;32m 603\\u001b[0m \\u001b[0merror_code\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Error\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Code\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 604\\u001b[0m \\u001b[0merror_class\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexceptions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfrom_code\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0merror_code\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 605\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0merror_class\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moperation_name\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 606\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 607\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mparsed_response\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mMountTargetNotFound\\u001b[0m: An error occurred (MountTargetNotFound) when calling the DeleteMountTarget operation: mount target does not exist\"\n ]\n }\n ],\n \"source\": [\n \"efs_client.delete_mount_target(MountTargetId='fsmt-be9c90f7')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 409,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"get_waiter() missing 1 required positional argument: 'waiter_name'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mefs_client\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_waiter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: get_waiter() missing 1 required positional argument: 'waiter_name'\"\n ]\n }\n ],\n \"source\": [\n \"efs_client.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 396,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.create_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 397,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"subnet = ec2.Subnet('subnet-f5e248a8')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 398,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'us-east-1c'\"\n ]\n },\n \"execution_count\": 398,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"subnet.availability_zone\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Subnet troubleshooting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"good_subnet_id='subnet-4862bf20'\\n\",\n \"bad_subnet_id='subnet-79920c56'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ec2_resource1=boto3.resource('ec2', 'us-east-1')\\n\",\n \"bad_subnet=ec2_resource1.Subnet('subnet-79920c56')\\n\",\n \"client1 = boto3.client('ec2', 'us-east-1')\\n\",\n \"\\n\",\n \"ec2_resource2=boto3.resource('ec2', 'us-east-2')\\n\",\n \"good_subnet=ec2_resource2.Subnet('subnet-4862bf20')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bad_subnet.map_public_ip_on_launch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"good_subnet.map_public_ip_on_launch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"can't set attribute\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mgood_subnet\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmap_public_ip_on_launch\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: can't set attribute\"\n ]\n }\n ],\n \"source\": [\n \"good_subnet.map_public_ip_on_launch=True\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"response = client1.modify_subnet_attribute(\\n\",\n \" MapPublicIpOnLaunch={\\n\",\n \" 'Value': True\\n\",\n \" },\\n\",\n \" SubnetId='subnet-79920c56'\\n\",\n \")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"boto3.resource('ec2', 'us-east-1')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Queryin instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os, sys, boto3\\n\",\n \"os.environ['AWS_DEFAULT_REGION']='us-west-2'\\n\",\n \"region = os.environ['AWS_DEFAULT_REGION']\\n\",\n \"client = boto3.client('ec2', region_name=region)\\n\",\n \"ec2 = u.create_ec2_resource()\\n\",\n \"response = client.describe_instances()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"instances = list(ec2.instances.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance=instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'34.216.9.68'\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.public_ip_address\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'r4.xlarge'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.instance_type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.create_instances?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'Reservations': [{'Groups': [],\\n\",\n \" 'Instances': [{'AmiLaunchIndex': 0,\\n\",\n \" 'Architecture': 'x86_64',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/xvda',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 19, 12, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-08604c56267074557'}},\\n\",\n \" {'DeviceName': '/dev/xvdbo',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 25, 16, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': False,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-031c9be6d29b8fac4'}}],\\n\",\n \" 'ClientToken': '21d58244-2ecc-0d59-b648-a3f5eb6950c9_subnet-117af859_1',\\n\",\n \" 'EbsOptimized': False,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'IamInstanceProfile': {'Arn': 'arn:aws:iam::316880547378:instance-profile/nodes.0726aa4c-pachydermcluster.kubernetes.com',\\n\",\n \" 'Id': 'AIPAJVWHPW5ZLSTJ2MT3Y'},\\n\",\n \" 'ImageId': 'ami-2fa0ba56',\\n\",\n \" 'InstanceId': 'i-056c288cf6677c393',\\n\",\n \" 'InstanceType': 'r4.xlarge',\\n\",\n \" 'KeyName': 'kubernetes.0726aa4c-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58',\\n\",\n \" 'LaunchTime': datetime.datetime(2017, 12, 26, 4, 19, 12, tzinfo=tzutc()),\\n\",\n \" 'Monitoring': {'State': 'enabled'},\\n\",\n \" 'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-216-9-68.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.216.9.68'},\\n\",\n \" 'Attachment': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 19, 12, tzinfo=tzutc()),\\n\",\n \" 'AttachmentId': 'eni-attach-bbaf024b',\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'Status': 'attached'},\\n\",\n \" 'Description': '',\\n\",\n \" 'Groups': [{'GroupId': 'sg-af9dedd2',\\n\",\n \" 'GroupName': 'nodes.0726aa4c-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'Ipv6Addresses': [],\\n\",\n \" 'MacAddress': '06:d3:b4:36:b7:9c',\\n\",\n \" 'NetworkInterfaceId': 'eni-a1ede693',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PrivateDnsName': 'ip-172-20-34-224.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.34.224',\\n\",\n \" 'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-216-9-68.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.216.9.68'},\\n\",\n \" 'Primary': True,\\n\",\n \" 'PrivateDnsName': 'ip-172-20-34-224.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.34.224'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'Status': 'in-use',\\n\",\n \" 'SubnetId': 'subnet-117af859',\\n\",\n \" 'VpcId': 'vpc-14500972'}],\\n\",\n \" 'Placement': {'AvailabilityZone': 'us-west-2a',\\n\",\n \" 'GroupName': '',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-20-34-224.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.34.224',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-34-216-9-68.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '34.216.9.68',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-af9dedd2',\\n\",\n \" 'GroupName': 'nodes.0726aa4c-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-117af859',\\n\",\n \" 'Tags': [{'Key': 'k8s.io/role/node', 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'Value': 'nodes.0726aa4c-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '0726aa4c-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'Value': 'nodes.0726aa4c-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-14500972'}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'RequesterId': '226008221399',\\n\",\n \" 'ReservationId': 'r-072746a0bbd36c0cb'},\\n\",\n \" {'Groups': [],\\n\",\n \" 'Instances': [{'AmiLaunchIndex': 0,\\n\",\n \" 'Architecture': 'x86_64',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/xvda',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 19, 14, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-0ce1c1c25a016e374'}}],\\n\",\n \" 'ClientToken': 'a5858244-2ee4-7132-15ac-5dab66c51cd1_subnet-40dcfc26_1',\\n\",\n \" 'EbsOptimized': False,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'IamInstanceProfile': {'Arn': 'arn:aws:iam::316880547378:instance-profile/nodes.282e747a-pachydermcluster.kubernetes.com',\\n\",\n \" 'Id': 'AIPAIAJQXKIXJTIS7DJ36'},\\n\",\n \" 'ImageId': 'ami-3fa07b47',\\n\",\n \" 'InstanceId': 'i-02df584528e76280e',\\n\",\n \" 'InstanceType': 'r4.xlarge',\\n\",\n \" 'KeyName': 'kubernetes.282e747a-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58',\\n\",\n \" 'LaunchTime': datetime.datetime(2017, 12, 26, 4, 19, 13, tzinfo=tzutc()),\\n\",\n \" 'Monitoring': {'State': 'enabled'},\\n\",\n \" 'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-213-240-110.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.213.240.110'},\\n\",\n \" 'Attachment': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 19, 13, tzinfo=tzutc()),\\n\",\n \" 'AttachmentId': 'eni-attach-bf7aded8',\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'Status': 'attached'},\\n\",\n \" 'Description': '',\\n\",\n \" 'Groups': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'GroupName': 'nodes.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'Ipv6Addresses': [],\\n\",\n \" 'MacAddress': '02:c9:e8:90:63:50',\\n\",\n \" 'NetworkInterfaceId': 'eni-7e05045d',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PrivateDnsName': 'ip-172-20-36-46.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.36.46',\\n\",\n \" 'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-213-240-110.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.213.240.110'},\\n\",\n \" 'Primary': True,\\n\",\n \" 'PrivateDnsName': 'ip-172-20-36-46.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.36.46'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'Status': 'in-use',\\n\",\n \" 'SubnetId': 'subnet-40dcfc26',\\n\",\n \" 'VpcId': 'vpc-f683678f'}],\\n\",\n \" 'Placement': {'AvailabilityZone': 'us-west-2b',\\n\",\n \" 'GroupName': '',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-20-36-46.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.36.46',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-34-213-240-110.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '34.213.240.110',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'GroupName': 'nodes.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-40dcfc26',\\n\",\n \" 'Tags': [{'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'Value': 'nodes.282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/role/node', 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'Value': 'nodes.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-f683678f'}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'RequesterId': '226008221399',\\n\",\n \" 'ReservationId': 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'',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-20-61-239.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.61.239',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-54-148-55-168.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '54.148.55.168',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-56c7672a',\\n\",\n \" 'GroupName': 'nodes.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-40dcfc26',\\n\",\n \" 'Tags': [{'Key': 'k8s.io/role/node', 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'Value': 'nodes.282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'Value': 'nodes.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-f683678f'}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'RequesterId': '226008221399',\\n\",\n \" 'ReservationId': 'r-002b24a4f9ea0d6ac'},\\n\",\n \" {'Groups': [],\\n\",\n \" 'Instances': [{'AmiLaunchIndex': 0,\\n\",\n \" 'Architecture': 'x86_64',\\n\",\n \" 'BlockDeviceMappings': [{'DeviceName': '/dev/xvda',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 18, 47, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-0d2b1a12eee59fde7'}},\\n\",\n \" {'DeviceName': '/dev/xvdu',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 21, 9, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': False,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-0f5f2efeef56aabc5'}},\\n\",\n \" {'DeviceName': '/dev/xvdv',\\n\",\n \" 'Ebs': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 21, 9, tzinfo=tzutc()),\\n\",\n \" 'DeleteOnTermination': False,\\n\",\n \" 'Status': 'attached',\\n\",\n \" 'VolumeId': 'vol-0511a82cb21d7c038'}}],\\n\",\n \" 'ClientToken': '91c58244-2d40-61b7-0393-daa08f957908_subnet-40dcfc26_1',\\n\",\n \" 'EbsOptimized': False,\\n\",\n \" 'Hypervisor': 'xen',\\n\",\n \" 'IamInstanceProfile': {'Arn': 'arn:aws:iam::316880547378:instance-profile/masters.282e747a-pachydermcluster.kubernetes.com',\\n\",\n \" 'Id': 'AIPAIVSSNE6EH2S57SNZG'},\\n\",\n \" 'ImageId': 'ami-3fa07b47',\\n\",\n \" 'InstanceId': 'i-0b186c01e65d805c0',\\n\",\n \" 'InstanceType': 'r4.xlarge',\\n\",\n \" 'KeyName': 'kubernetes.282e747a-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58',\\n\",\n \" 'LaunchTime': datetime.datetime(2017, 12, 26, 4, 18, 46, tzinfo=tzutc()),\\n\",\n \" 'Monitoring': {'State': 'enabled'},\\n\",\n \" 'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-214-219-29.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.214.219.29'},\\n\",\n \" 'Attachment': {'AttachTime': datetime.datetime(2017, 12, 26, 4, 18, 46, tzinfo=tzutc()),\\n\",\n \" 'AttachmentId': 'eni-attach-cc7adeab',\\n\",\n \" 'DeleteOnTermination': True,\\n\",\n \" 'DeviceIndex': 0,\\n\",\n \" 'Status': 'attached'},\\n\",\n \" 'Description': '',\\n\",\n \" 'Groups': [{'GroupId': 'sg-78c66604',\\n\",\n \" 'GroupName': 'masters.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'Ipv6Addresses': [],\\n\",\n \" 'MacAddress': '02:f8:fb:30:3e:56',\\n\",\n \" 'NetworkInterfaceId': 'eni-ca0100e9',\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'PrivateDnsName': 'ip-172-20-60-51.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.60.51',\\n\",\n \" 'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',\\n\",\n \" 'PublicDnsName': 'ec2-34-214-219-29.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIp': '34.214.219.29'},\\n\",\n \" 'Primary': True,\\n\",\n \" 'PrivateDnsName': 'ip-172-20-60-51.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.60.51'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'Status': 'in-use',\\n\",\n \" 'SubnetId': 'subnet-40dcfc26',\\n\",\n \" 'VpcId': 'vpc-f683678f'}],\\n\",\n \" 'Placement': {'AvailabilityZone': 'us-west-2b',\\n\",\n \" 'GroupName': '',\\n\",\n \" 'Tenancy': 'default'},\\n\",\n \" 'PrivateDnsName': 'ip-172-20-60-51.us-west-2.compute.internal',\\n\",\n \" 'PrivateIpAddress': '172.20.60.51',\\n\",\n \" 'ProductCodes': [],\\n\",\n \" 'PublicDnsName': 'ec2-34-214-219-29.us-west-2.compute.amazonaws.com',\\n\",\n \" 'PublicIpAddress': '34.214.219.29',\\n\",\n \" 'RootDeviceName': '/dev/xvda',\\n\",\n \" 'RootDeviceType': 'ebs',\\n\",\n \" 'SecurityGroups': [{'GroupId': 'sg-78c66604',\\n\",\n \" 'GroupName': 'masters.282e747a-pachydermcluster.kubernetes.com'}],\\n\",\n \" 'SourceDestCheck': False,\\n\",\n \" 'State': {'Code': 16, 'Name': 'running'},\\n\",\n \" 'StateTransitionReason': '',\\n\",\n \" 'SubnetId': 'subnet-40dcfc26',\\n\",\n \" 'Tags': [{'Key': 'Name',\\n\",\n \" 'Value': 'master-us-west-2b.masters.282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'Value': '282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'Value': 'master-us-west-2b.masters.282e747a-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/role/master', 'Value': '1'}],\\n\",\n \" 'VirtualizationType': 'hvm',\\n\",\n \" 'VpcId': 'vpc-f683678f'}],\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'RequesterId': '226008221399',\\n\",\n \" 'ReservationId': 'r-00610366bd2fcbfc8'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 09 Jan 2018 22:06:19 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '089bcb03-9efe-49fd-99ce-7c48f93499fb',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_instances()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sys.path.insert(0, os.environ['HOME']+'/git0/cluster')\\n\",\n \"import util as u\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sanc00'\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_name(instance.tags)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2017, 12, 22, 0, 50, 10, tzinfo=tzutc())\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.launch_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2017, 12, 22, 0, 50, 10, tzinfo=tzutc())\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.launch_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'Key': 'Name', 'Value': 'sanc00'}]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.tags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 155,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ami=\\\"ami-45ead225\\\"\\n\",\n \"instances = ec2.create_instances(\\n\",\n \" ImageId=ami, InstanceType='t2.micro', MaxCount=1, MinCount=1,\\n\",\n \" NetworkInterfaces=[{'SubnetId': subnet.id, 'DeviceIndex': 0, 'AssociatePublicIpAddress': True, 'Groups': [sec_group.group_id]}])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 538,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'i-00b80c5e0d4981f6b'\"\n ]\n },\n \"execution_count\": 538,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances[0].id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 159,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance = instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 540,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance.load()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 542,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'NoneType' object has no attribute 'get'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0minstance\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0miam_instance_profile\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/envs/sep22/lib/python3.5/site-packages/boto3/resources/factory.py\\u001b[0m in \\u001b[0;36mproperty_loader\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 343\\u001b[0m self.__class__.__name__))\\n\\u001b[1;32m 344\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 345\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmeta\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdata\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 346\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 347\\u001b[0m \\u001b[0mproperty_loader\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msnake_cased\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'NoneType' object has no attribute 'get'\"\n ]\n }\n ],\n \"source\": [\n \"instance.iam_instance_profile\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Uninitialize many instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"i-05a227278056dea5e\\n\",\n \"i-0d32740b5d1a6b0b8\\n\",\n \"i-0f2fc22c05c1e523d\\n\",\n \"i-02c967a42d0542711\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os,boto3\\n\",\n \"os.environ['AWS_DEFAULT_REGION']='us-east-1'\\n\",\n \"region = os.environ['AWS_DEFAULT_REGION']\\n\",\n \"client = boto3.client('ec2', region_name=region)\\n\",\n \"ec2 = boto3.resource('ec2', region_name=region)\\n\",\n \"\\n\",\n \"for instance in ec2.instances.all():\\n\",\n \" if instance.state['Name'] == 'running':\\n\",\n \" print(instance.id)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance=list(ec2.instances.all())[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'54.224.204.197'\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.public_ip_address\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'nexus'\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.key_name\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Job level debugging\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.insert(0, os.environ['HOME']+'/git0/cluster')\\n\",\n \"import aws\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Found existing job cifar-worker\\n\"\n ]\n }\n ],\n \"source\": [\n \"worker_job = aws.server_job('cifar-worker', num_tasks=1,availability_zone='us-east-1c')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2017-12-27 04:50:31.728987 cifar-worker:0: (192.168.35.110) Running initialize\\n\",\n \"ssh_to_host ubuntu@54.83.131.12\\n\",\n \"2017-12-27 04:50:33.672796 cifar-worker:0: (192.168.35.110) Checking for initialized file\\n\",\n \"2017-12-27 04:50:34.239068 cifar-worker:0: (192.168.35.110) downloading /tmp/is_initialized to /tmp/tasklogs/0-1514346633855078.is_initialized\\n\",\n \"2017-12-27 04:50:35.107455 cifar-worker:0: (192.168.35.110) reusing previous initialized state\\n\",\n \"2017-12-27 04:50:35.274303 cifar-worker:0: (192.168.35.110) run_sync: tmux kill-session -t tmux\\n\",\n \"2017-12-27 04:50:35.941431 cifar-worker:0: (192.168.35.110) run_sync returned: \\n\",\n \"2017-12-27 04:50:36.111033 cifar-worker:0: (192.168.35.110) run_sync: tmux new-session -s tmux -n 0 -d\\n\",\n \"2017-12-27 04:50:36.651738 cifar-worker:0: (192.168.35.110) run_sync returned: \\n\",\n \"2017-12-27 04:50:37.012613 cifar-worker:0: (192.168.35.110) Checking for initialized file\\n\",\n \"2017-12-27 04:50:37.528438 cifar-worker:0: (192.168.35.110) downloading /tmp/is_initialized to /tmp/tasklogs/0-1514346637184593.is_initialized\\n\"\n ]\n }\n ],\n \"source\": [\n \"worker_job.wait_until_ready()\\n\",\n \"worker_task = worker_job.tasks[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2017-12-27 04:50:46.878479 cifar-worker:0: (192.168.35.110) tmux> ls\\n\",\n \"2017-12-27 04:50:48.154443 cifar-worker:0: (192.168.35.110) run_sync: mkdir -p /tmp/tmux\\n\",\n \"2017-12-27 04:50:48.933499 cifar-worker:0: (192.168.35.110) run_sync returned: \\n\",\n \"2017-12-27 04:50:49.086868 cifar-worker:0: (192.168.35.110) run_sync: tmux send-keys -t tmux:0 'ls; echo $? > /tmp/tmux/2.1514346649086852.out' Enter\\n\",\n \"2017-12-27 04:50:49.597799 cifar-worker:0: (192.168.35.110) run_sync returned: \\n\",\n \"2017-12-27 04:50:50.730751 cifar-worker:0: (192.168.35.110) downloading /tmp/tmux/2.1514346649086852.out to /tmp/tmux/1514346650231037\\n\"\n ]\n }\n ],\n \"source\": [\n \"worker_task.run('ls')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2017-12-27 04:51:44.540383 cifar-worker:0: (192.168.35.110) Remote file cifar10_main.py exists, skipping\\n\"\n ]\n }\n ],\n \"source\": [\n \"open('/tmp/hi.txt','w').write(\\\"hello\\\")\\n\",\n \"worker_task.upload('/tmp/hi.txt', 'cifar10_main.py')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Time conversions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from operator import itemgetter\\n\",\n \"\\n\",\n \"ec2 = u.create_ec2_resource()\\n\",\n \"instances = [(u.seconds_from_datetime(i.launch_time), i) for i in ec2.instances.all()]\\n\",\n \"sorted_instances = sorted(instances, key=itemgetter(0))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"inst=sorted_instances[-1][1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.gpubox.deleteme'\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_name(inst.tags)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2018, 1, 27, 0, 58, 46, tzinfo=tzutc())\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inst.launch_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1517043526.0\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dt=inst.launch_time\\n\",\n \"time.mktime(dt.utctimetuple())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'datetime.date' object has no attribute 'tzinfo'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mpytz\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mutc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpytz\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mUTC\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mutc\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlocalize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdate\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfromtimestamp\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1517043526.0\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/yaroslav/anaconda/lib/python3.5/site-packages/pytz/__init__.py\\u001b[0m in \\u001b[0;36mlocalize\\u001b[0;34m(self, dt, is_dst)\\u001b[0m\\n\\u001b[1;32m 224\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mlocalize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mis_dst\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 225\\u001b[0m \\u001b[0;34m'''Convert naive time to local time'''\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 226\\u001b[0;31m \\u001b[0;32mif\\u001b[0m \\u001b[0mdt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtzinfo\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 227\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Not naive datetime (tzinfo is already set)'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 228\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mdt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mreplace\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtzinfo\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'datetime.date' object has no attribute 'tzinfo'\"\n ]\n }\n ],\n \"source\": [\n \" import pytz\\n\",\n \" utc = pytz.UTC\\n\",\n \" utc.localize(date.fromtimestamp(1517043526.0))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"inst.reboot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# AMI retrieval\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pytorch.imagenet.source.v2 ami-78dfe807\\n\",\n \"pytorch.imagenet ami-a9bb9bd6\\n\"\n ]\n }\n ],\n \"source\": [\n \"filter = {'Name': 'name', 'Values' : ['pytorch*']}\\n\",\n \"for i in ec2.images.filter(Filters = [filter], Owners=['self']):\\n\",\n \" print(i.name, i.id)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ami-a9bb9bd6'\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def lookup_ami(wildcard):\\n\",\n \" filter = {'Name': 'name', 'Values' : [wildcard]}\\n\",\n \" images = list(ec2.images.filter(Filters = [filter], Owners=['self']))\\n\",\n \" assert len(images)<=1, \\\"Multiple images match \\\"+str(wildcard)\\n\",\n \" assert len(images)>=0, \\\"No images match \\\"+str(wildcard)\\n\",\n \" return i\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"i\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Clean-up placement groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client = u.create_ec2_client()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = u.create_ec2_resource()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Deleting tf\\n\",\n \" Succeeded\\n\",\n \"Deleting run01\\n\",\n \" Succeeded\\n\",\n \"Deleting c5\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench9\\n\",\n \" Succeeded\\n\",\n \"Deleting gloop2p\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench44\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro16\\n\",\n \" Succeeded\\n\",\n \"Deleting placement\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro_test\\n\",\n \" Succeeded\\n\",\n \"Deleting tfbench\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench6\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench7\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro-friday-16\\n\",\n \" Succeeded\\n\",\n \"Deleting simple\\n\",\n \" Succeeded\\n\",\n \"Deleting tfbench44\\n\",\n \" Succeeded\\n\",\n \"Deleting singlerun\\n\",\n \" Failed\\n\",\n \"Deleting raybench20\\n\",\n \" Succeeded\\n\",\n \"Deleting many02\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench2\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench17\\n\",\n \" Succeeded\\n\",\n \"Deleting run03\\n\",\n \" Succeeded\\n\",\n \"Deleting eight\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench3\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro-friday-8\\n\",\n \" Succeeded\\n\",\n \"Deleting c5amazon00\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench13\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench4\\n\",\n \" Succeeded\\n\",\n \"Deleting async\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench12\\n\",\n \" Succeeded\\n\",\n \"Deleting nccl\\n\",\n \" Succeeded\\n\",\n \"Deleting pytorch_cluster_f\\n\",\n \" Succeeded\\n\",\n \"Deleting p2pgpu\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench3\\n\",\n \" Succeeded\\n\",\n \"Deleting run02\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench19\\n\",\n \" Succeeded\\n\",\n \"Deleting many01\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench16\\n\",\n \" Succeeded\\n\",\n \"Deleting nexus00\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench14\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench5\\n\",\n \" Succeeded\\n\",\n \"Deleting run\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench18\\n\",\n \" Succeeded\\n\",\n \"Deleting somegroup\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench5\\n\",\n \" Succeeded\\n\",\n \"Deleting sixteen\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro_16\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench11\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench\\n\",\n \" Succeeded\\n\",\n \"Deleting raybench15\\n\",\n \" Succeeded\\n\",\n \"Deleting allreduce\\n\",\n \" Succeeded\\n\",\n \"Deleting tfbench4\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench10\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench4\\n\",\n \" Succeeded\\n\",\n \"Deleting gpu\\n\",\n \" Succeeded\\n\",\n \"Deleting psbench8\\n\",\n \" Succeeded\\n\",\n \"Deleting run2\\n\",\n \" Succeeded\\n\",\n \"Deleting yaro8\\n\",\n \" Failed\\n\",\n \"Deleting c5amazon01\\n\",\n \" Succeeded\\n\"\n ]\n }\n ],\n \"source\": [\n \"for group in ec2.placement_groups.all():\\n\",\n \" try:\\n\",\n \" print(\\\"Deleting \\\"+group.name)\\n\",\n \" group.delete()\\n\",\n \" except:\\n\",\n \" print(\\\" Failed\\\")\\n\",\n \" else:\\n\",\n \" print(\\\" Succeeded\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'c5amazon00'\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"group.name\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'PlacementGroups': [{'GroupName': 'raybench3',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'sixteen', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'singlerun', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'c5amazon00', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'tfbench', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'p2pgpu', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'tfbench4', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench7', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'async', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'somegroup', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench11', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench16', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench19', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench12', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench10', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro-friday-8', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'run2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench15', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench5', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench4', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'eight', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'pytorch_cluster_f',\\n\",\n \" 'State': 'available',\\n\",\n \" 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'tfbench44', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench6', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench44', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro-friday-16', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'gpu', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'run03', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'many02', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'run01', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro8', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench17', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench3', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'nccl', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench4', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'c5amazon01', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench20', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench8', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'simple', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'many01', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench5', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro_16', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro16', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench9', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench14', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench18', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'run', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'nexus00', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'c5', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'placement', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'psbench13', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'tf', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'yaro_test', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'raybench2', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'run02', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'allreduce', 'State': 'available', 'Strategy': 'cluster'},\\n\",\n \" {'GroupName': 'gloop2p', 'State': 'available', 'Strategy': 'cluster'}],\\n\",\n \" 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Sat, 14 Jul 2018 15:27:38 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '114c5728-9464-4fee-b444-ba723c196f48',\\n\",\n \" 'RetryAttempts': 0}}\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_placement_groups()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" client = u.create_ec2_client()\\n\",\n \" try:\\n\",\n \" # TODO: check that the error is actually about placement group\\n\",\n \" client.describe_placement_groups(GroupNames=[name])\\n\",\n \" except boto3.exceptions.botocore.exceptions.ClientError as e:\\n\",\n \" # \\n\",\n \" print(\\\"Creating placement group: \\\"+name)\\n\",\n \" res = client.create_placement_group(GroupName=name, Strategy='cluster')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Volume info\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"volumes = list(ec2.volumes.all())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"vol = volumes[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'i-06257b1a1409aba20'\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vol.attachments[0][\\\"InstanceId\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"inst=ec2.Instance('i-06257b1a1409aba20')\\n\",\n \"u.get_name(inst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"''\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"u.get_name(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Sun, 22 Jul 2018 16:51:55 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '45e3f987-5c1d-4c1c-8d14-0cc667eeccf3',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Snapshots': [{'Description': 'Copied for DestinationAmi ami-78dfe807 from SourceAmi ami-6583d71d for SourceSnapshot snap-0221e898577adee42. Task created on 1,531,301,312,003.',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-02c1f2467776319db',\\n\",\n \" 'StartTime': datetime.datetime(2018, 7, 11, 9, 28, 46, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-ffffffff',\\n\",\n \" 'VolumeSize': 500},\\n\",\n \" {'Description': 'Copied for DestinationAmi ami-a9bb9bd6 from SourceAmi ami-53c8822b for SourceSnapshot snap-0f26a36b43deea971. Task created on 1,530,640,163,368.',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-0e7edc7112e923950',\\n\",\n \" 'StartTime': datetime.datetime(2018, 7, 3, 17, 49, 28, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-ffffffff',\\n\",\n \" 'VolumeSize': 1000},\\n\",\n \" {'Description': 'Created by CreateImage(i-0ddb9b1d8504b342f) for ami-5b524f21 from vol-00cbc9a74e2ebff28',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-00cecc84f1adf0af0',\\n\",\n \" 'StartTime': datetime.datetime(2018, 2, 13, 23, 41, 34, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-00cbc9a74e2ebff28',\\n\",\n \" 'VolumeSize': 500},\\n\",\n \" {'Description': 'Created by CreateImage(i-016e2869519aa7eef) for ami-f9d6dc83 from vol-009d3f7a768ce25c3',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-07f0a9369c4974e2f',\\n\",\n \" 'StartTime': datetime.datetime(2018, 2, 8, 23, 36, 3, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-009d3f7a768ce25c3',\\n\",\n \" 'VolumeSize': 500},\\n\",\n \" {'Description': 'Created by CreateImage(i-041f134bc867a0556) for ami-67c4871d from vol-04d57187dc3f33301',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-07d6b1a1dcbf8a688',\\n\",\n \" 'StartTime': datetime.datetime(2017, 12, 27, 3, 14, 29, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-04d57187dc3f33301',\\n\",\n \" 'VolumeSize': 50},\\n\",\n \" {'Description': 'Created by CreateImage(i-0884617da04b5a90e) for ami-1fc08065 from vol-0318f9cf517b9bdce',\\n\",\n \" 'Encrypted': False,\\n\",\n \" 'OwnerId': '316880547378',\\n\",\n \" 'Progress': '100%',\\n\",\n \" 'SnapshotId': 'snap-0d28af9890f62c69b',\\n\",\n \" 'StartTime': datetime.datetime(2017, 12, 26, 8, 2, tzinfo=tzutc()),\\n\",\n \" 'State': 'completed',\\n\",\n \" 'VolumeId': 'vol-0318f9cf517b9bdce',\\n\",\n \" 'VolumeSize': 50}]}\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client = u.create_ec2_client()\\n\",\n \"client.describe_snapshots(OwnerIds=['self'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Spot instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"args = {'ImageId': 'ami-18693660',\\n\",\n \" 'InstanceType': 'p2.xlarge',\\n\",\n \" 'KeyName': 'nexus-yaroslav'}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"spot_requests = client.request_spot_instances(SpotPrice=\\\"0.01\\\",\\n\",\n \" LaunchSpecification=args,\\n\",\n \" InstanceCount=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ValidUntil=datetime(2015, 1, 1),\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client.request_spot_instances?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2018, 7, 27, 7, 24, 39, 738095)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"datetime.datetime.now()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import pytz\\n\",\n \"import datetime as dt\\n\",\n \"# datetime is not timezone aware, use pytz to fix\\n\",\n \"now = dt.datetime.utcnow().replace(tzinfo=pytz.utc)\\n\",\n \"expiration = now + dt.timedelta(minutes=15)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"spot_requests2 = client.request_spot_instances(SpotPrice=\\\"0.01\\\",\\n\",\n \" LaunchSpecification=args,\\n\",\n \" InstanceCount=1,\\n\",\n \" ValidUntil=expiration)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2018, 7, 27, 14, 28, 50, 915418, tzinfo=)\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2018, 7, 27, 7, 27, 32, 601104)\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"datetime.datetime.now()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Scratch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" name = 'gpubox00'\\n\",\n \" KEY_NAME='nexus'\\n\",\n \" ec2 = boto3.resource('ec2')\\n\",\n \" instances = ec2.instances.filter(\\n\",\n \" Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\\n\",\n \" instances = list(instances)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import sys, inspect\\n\",\n \"def hello():\\n\",\n \" print(inspect.stack()[1][3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def helloparent():\\n\",\n \" hello()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\\n\",\n \" 'date': 'Tue, 17 Jul 2018 15:27:15 GMT',\\n\",\n \" 'server': 'AmazonEC2',\\n\",\n \" 'transfer-encoding': 'chunked',\\n\",\n \" 'vary': 'Accept-Encoding'},\\n\",\n \" 'HTTPStatusCode': 200,\\n\",\n \" 'RequestId': '460077d1-68e6-4c2c-96d1-2a1c7b91d4f7',\\n\",\n \" 'RetryAttempts': 0},\\n\",\n \" 'Tags': [{'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'subnet-2f22b800',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'subnet-2f22b800',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': 'us-east-1a.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/df86c4e8-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'subnet-2f22b800',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'kubernetes.io/role/elb',\\n\",\n \" 'ResourceId': 'subnet-2f22b800',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'subnet-f39755dd',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'subnet-f39755dd',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': 'us-east-1a.6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/6a4d8d28-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'subnet-f39755dd',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'kubernetes.io/role/elb',\\n\",\n \" 'ResourceId': 'subnet-f39755dd',\\n\",\n \" 'ResourceType': 'subnet',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'rtb-17fa1168',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-17fa1168',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'rtb-2fa1c652',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-2fa1c652',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-89e169f4',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'nexus2'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-a8b10cd5',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'deleteme'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-d89204a5',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'gpubox'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-e701859a',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'naxus'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'rtb-ff3bf682',\\n\",\n \" 'ResourceType': 'route-table',\\n\",\n \" 'Value': 'nexus'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com-dynamic-pvc-4d54a48f-607b-11e8-ba9c-12f672ac1996'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/6a4d8d28-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pv/name',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'pvc-4d54a48f-607b-11e8-ba9c-12f672ac1996'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/name',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'etcd-storage-etcd-0'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/namespace',\\n\",\n \" 'ResourceId': 'vol-02c09e98c01898a7f',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'default'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-02eafaf70f4a5cfa8',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-02eafaf70f4a5cfa8',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a.etcd-main.6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/etcd/main',\\n\",\n \" 'ResourceId': 'vol-02eafaf70f4a5cfa8',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a/a'},\\n\",\n \" {'Key': 'k8s.io/role/master',\\n\",\n \" 'ResourceId': 'vol-02eafaf70f4a5cfa8',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com-dynamic-pvc-be84b9b6-fa31-11e7-ab45-1263c630187a'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/df86c4e8-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pv/name',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'pvc-be84b9b6-fa31-11e7-ab45-1263c630187a'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/name',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'etcd-storage-etcd-0'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/namespace',\\n\",\n \" 'ResourceId': 'vol-048e29f604d2900a7',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'default'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-05763dbf313f5a28e',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-05763dbf313f5a28e',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a.etcd-events.6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/etcd/events',\\n\",\n \" 'ResourceId': 'vol-05763dbf313f5a28e',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a/a'},\\n\",\n \" {'Key': 'k8s.io/role/master',\\n\",\n \" 'ResourceId': 'vol-05763dbf313f5a28e',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-06fb03280cf2598fb',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-06fb03280cf2598fb',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a.etcd-events.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/etcd/events',\\n\",\n \" 'ResourceId': 'vol-06fb03280cf2598fb',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a/a'},\\n\",\n \" {'Key': 'k8s.io/role/master',\\n\",\n \" 'ResourceId': 'vol-06fb03280cf2598fb',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com-dynamic-pvc-7dd40002-1d90-11e8-8f83-129abe8e441c'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/df86c4e8-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pv/name',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'pvc-7dd40002-1d90-11e8-8f83-129abe8e441c'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/name',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'grafana-storage-pvc'},\\n\",\n \" {'Key': 'kubernetes.io/created-for/pvc/namespace',\\n\",\n \" 'ResourceId': 'vol-08f8b6fef5df993b0',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'kube-system'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'vol-0f0e841d0cc657002',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'vol-0f0e841d0cc657002',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a.etcd-main.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/etcd/main',\\n\",\n \" 'ResourceId': 'vol-0f0e841d0cc657002',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': 'a/a'},\\n\",\n \" {'Key': 'k8s.io/role/master',\\n\",\n \" 'ResourceId': 'vol-0f0e841d0cc657002',\\n\",\n \" 'ResourceType': 'volume',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'dopt-3d06ec45',\\n\",\n \" 'ResourceType': 'dhcp-options',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'dopt-3d06ec45',\\n\",\n \" 'ResourceType': 'dhcp-options',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'dopt-e39a4198',\\n\",\n \" 'ResourceType': 'dhcp-options',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'dopt-e39a4198',\\n\",\n \" 'ResourceType': 'dhcp-options',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-1515af61',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'deleteme'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'sg-2ff68867',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': '6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-2ff68867',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'masters.6a4d8d28-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-47402933',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'gpubox'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'sg-65034311',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-65034311',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'masters.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'sg-8b9f12fc',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-8b9f12fc',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'load_balancer'},\\n\",\n \" {'Key': 'kubernetes.io/cluster/df86c4e8-pachydermcluster.kubernetes.com',\\n\",\n \" 'ResourceId': 'sg-8b9f12fc',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'owned'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-98f973ec',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'naxus'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'sg-a81176dc',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 'nexus2'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'sg-c583fd8d',\\n\",\n \" 'ResourceType': 'security-group',\\n\",\n \" 'Value': 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'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0e0453b14573a1345',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0e0453b14573a1345',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0e0453b14573a1345',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0e0453b14573a1345',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0e3d07cc3746a8a83',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 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\" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0e40a3e828fe67323',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0e40a3e828fe67323',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0e40a3e828fe67323',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0e40a3e828fe67323',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0e89afea593fe4520',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '0.gpubox.aws-eval'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0e9d3ae6cac412c2b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0e9d3ae6cac412c2b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0e9d3ae6cac412c2b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0e9d3ae6cac412c2b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0e9d3ae6cac412c2b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0ebcdf4dd7b2056a5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0ebcdf4dd7b2056a5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0ebcdf4dd7b2056a5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0ebcdf4dd7b2056a5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0ebcdf4dd7b2056a5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0ec403275902ed3cf',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0ec403275902ed3cf',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0ec403275902ed3cf',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0ec403275902ed3cf',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0ec403275902ed3cf',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0ed66aeb37bec05db',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0ed66aeb37bec05db',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0ed66aeb37bec05db',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0ed66aeb37bec05db',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0ed66aeb37bec05db',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0ee78c269b25bafb9',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0ee78c269b25bafb9',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'master-us-east-1a.masters.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0ee78c269b25bafb9',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'master-us-east-1a.masters.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0ee78c269b25bafb9',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'master-us-east-1a'},\\n\",\n \" {'Key': 'k8s.io/role/master',\\n\",\n \" 'ResourceId': 'i-0ee78c269b25bafb9',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0ef85000b1d4159d2',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0ef85000b1d4159d2',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0ef85000b1d4159d2',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0ef85000b1d4159d2',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0ef85000b1d4159d2',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0efa16b5fc8c3f82b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0efa16b5fc8c3f82b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0efa16b5fc8c3f82b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0efa16b5fc8c3f82b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0efa16b5fc8c3f82b',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f22d1b34bae312a0',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f22d1b34bae312a0',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f22d1b34bae312a0',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f22d1b34bae312a0',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f22d1b34bae312a0',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f2875a0c81840e7f',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '0.tb.yuxin_numpy'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f297747caf5305d4',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f297747caf5305d4',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f297747caf5305d4',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f297747caf5305d4',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f297747caf5305d4',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f5445032b0b09942',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '0.gpubox.nccl01'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f5ee58d080019f27',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f5ee58d080019f27',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f5ee58d080019f27',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f5ee58d080019f27',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f5ee58d080019f27',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f5ff88e175eb4b0d',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f5ff88e175eb4b0d',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f5ff88e175eb4b0d',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f5ff88e175eb4b0d',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f5ff88e175eb4b0d',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f70b9f1d2723b338',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f70b9f1d2723b338',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f70b9f1d2723b338',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f70b9f1d2723b338',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f70b9f1d2723b338',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f731d8cee3f32f56',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f731d8cee3f32f56',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f731d8cee3f32f56',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f731d8cee3f32f56',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f731d8cee3f32f56',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'},\\n\",\n \" {'Key': 'KubernetesCluster',\\n\",\n \" 'ResourceId': 'i-0f836a9c6b1b5b4e5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'Name',\\n\",\n \" 'ResourceId': 'i-0f836a9c6b1b5b4e5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'aws:autoscaling:groupName',\\n\",\n \" 'ResourceId': 'i-0f836a9c6b1b5b4e5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes.df86c4e8-pachydermcluster.kubernetes.com'},\\n\",\n \" {'Key': 'k8s.io/cluster-autoscaler/node-template/label/kops.k8s.io/instancegroup',\\n\",\n \" 'ResourceId': 'i-0f836a9c6b1b5b4e5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': 'nodes'},\\n\",\n \" {'Key': 'k8s.io/role/node',\\n\",\n \" 'ResourceId': 'i-0f836a9c6b1b5b4e5',\\n\",\n \" 'ResourceType': 'instance',\\n\",\n \" 'Value': '1'}]}\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"client.describe_tags()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fragment='0'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from operator import itemgetter\\n\",\n \"from tzlocal import get_localzone # $ pip install tzlocal\\n\",\n \"\\n\",\n \"\\n\",\n \"def toseconds(dt):\\n\",\n \" # to invert:\\n\",\n \" # import pytz\\n\",\n \" # utc = pytz.UTC\\n\",\n \" # utc.localize(datetime.fromtimestamp(seconds))\\n\",\n \" return time.mktime(dt.utctimetuple())\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import importlib\\n\",\n \"importlib.reload(u)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using username 'ubuntu'\\n\",\n \"Using region us-east-1\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:22:55-07:00 with key nexus-yaroslav\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:04:22-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:04:22-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:04:22-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:03:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:53-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:02:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-17 08:01:54-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 14:11:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:15:11-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-07-06 13:09:30-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-16 12:58:15-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-15 11:48:39-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-15 11:48:39-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-03 19:04:14-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-01 07:33:13-07:00 with key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-06-01 07:33:13-07:00 with key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-31 15:26:38-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-25 17:19:33-07:00 with key kubernetes.6a4d8d28-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key pachyderm-jump-box, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-22 08:34:54-07:00 with key pachyderm-jump-box\\n\",\n \"Got key xview-api-prod, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-20 19:48:55-07:00 with key xview-api-prod\\n\",\n \"Got key xview-api-prod, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-20 19:48:19-07:00 with key xview-api-prod\\n\",\n \"Got key xview-api-prod, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-20 17:24:18-07:00 with key xview-api-prod\\n\",\n \"Got key xview-api-prod, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-20 17:23:15-07:00 with key xview-api-prod\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 23:55:22-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 23:53:23-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 22:21:10-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 22:21:10-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key aws_east_api_prod, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 08:56:58-07:00 with key aws_east_api_prod\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:14:06-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:14:06-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:14:06-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:14:06-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:14:06-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:36-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:13:07-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-05-03 00:12:37-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-04-05 20:27:05-07:00 with key nexus-yaroslav\\n\",\n \"Got key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58, expected nexus-yaroslav, skipping\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-03-21 18:27:21-07:00 with key kubernetes.df86c4e8-pachydermcluster.kubernetes.com-79:6b:55:2e:89:4d:be:42:29:3b:ba:cd:22:c8:ee:58\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-03-17 16:03:12-07:00 with key nexus-yaroslav\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-03-12 17:56:52-07:00 with key nexus-yaroslav\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-02-13 15:57:44-08:00 with key nexus-yaroslav\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-01-31 17:01:03-08:00 with key nexus-yaroslav\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-01-31 15:09:05-08:00 with key nexus-yaroslav\\n\",\n \"Found to 0.tb.tfbench in us-east-1 launched at 2018-01-24 20:56:09-08:00 with key nexus-yaroslav\\n\"\n ]\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"client.delete_tags?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 434,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ec2.Instance(id='i-07a0cd1b69980ebc8')\\n\",\n \"ec2.Instance(id='i-0f99e16b39aacbca1')\\n\"\n ]\n }\n ],\n \"source\": [\n \"a\\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for i in instances.all():\\n\",\n \" print(i)\\n\",\n \" name = u.get_name(i.tags)\\n\",\n \" key_name = i.key_name\\n\",\n \"\\n\",\n \" assert len(names) <= 1\\n\",\n \" if names:\\n\",\n \" inst_name = names[0]\\n\",\n \" else:\\n\",\n \" inst_name = ''\\n\",\n \" if inst_name == name:\\n\",\n \" if key_name != KEY_NAME:\\n\",\n \" print(\\\"name matches, but key name %s doesn't match %s, skipping\\\"%(key_name, KEY_NAME))\\n\",\n \" continue\\n\",\n \" result.append(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 436,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2.create_vpc?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'2017-12-04 17:31:27.013135'\"\n ]\n },\n \"execution_count\": 80,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"current_seconds=time.time();\\n\",\n \"remainder_micros=int(1e6*(current_seconds-int(current_seconds)))\\n\",\n \"time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(current_seconds))\\n\",\n \"full_time_str = \\\"%s.%06d\\\"%(time_str, remainder_micros)\\n\",\n \"full_time_str\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'2017-12-04 17:31:14.381412'\"\n ]\n },\n \"execution_count\": 75,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['plasma_manager91853411',\\n\",\n \" 'plasma_store10537880',\\n\",\n \" 'plasma_store18236878',\\n\",\n \" 'plasma_store88828496',\\n\",\n \" 'scheduler62714006',\\n\",\n \" 'plasma_store41416591',\\n\",\n \" 'plasma_manager69489913',\\n\",\n \" 'scheduler4710266',\\n\",\n \" 'scheduler8096252',\\n\",\n \" 'plasma_store25595318',\\n\",\n \" 'scheduler85908030',\\n\",\n \" 'scheduler37020026',\\n\",\n \" 'plasma_manager34279968',\\n\",\n \" 'plasma_manager16268969',\\n\",\n \" 'plasma_store61975526',\\n\",\n \" 'plasma_store59559392',\\n\",\n \" 'plasma_manager48870941',\\n\",\n \" 'plasma_manager4501529',\\n\",\n \" 'scheduler91987771',\\n\",\n \" 'plasma_store34948235',\\n\",\n \" 'com.apple.launchd.phjQmyOsHP',\\n\",\n \" 'scheduler85745644',\\n\",\n \" 'plasma_manager76483440',\\n\",\n \" 'plasma_manager98848736',\\n\",\n \" 'plasma_manager63116115',\\n\",\n \" 'at-hRSIKu',\\n\",\n \" 'scheduler35081158',\\n\",\n \" 'plasma_store13954523',\\n\",\n \" 'plasma_manager91698017',\\n\",\n \" 'plasma_manager95221948',\\n\",\n \" 'scheduler267754',\\n\",\n \" 'plasma_manager413673',\\n\",\n \" 'scheduler29933459',\\n\",\n \" 'scheduler69376320',\\n\",\n \" 'plasma_manager72378285',\\n\",\n \" 'scheduler46128327',\\n\",\n \" 'plasma_manager620644',\\n\",\n \" 'scheduler94136826',\\n\",\n \" 'raylogs',\\n\",\n \" 'scheduler10661819',\\n\",\n \" 'plasma_store4931042',\\n\",\n \" 'adobegc.log',\\n\",\n \" 'scheduler17834366',\\n\",\n \" 'boost_interprocess',\\n\",\n \" 'plasma_store28239973',\\n\",\n \" 'plasma_store49175967',\\n\",\n \" 'scheduler84647737',\\n\",\n \" 'scheduler2952570',\\n\",\n \" 'plasma_store81600479',\\n\",\n \" 'scheduler49818626',\\n\",\n \" 'plasma_manager79244000',\\n\",\n \" 'tasklogs',\\n\",\n \" 'tmux',\\n\",\n \" 'scheduler14706058',\\n\",\n \" 'plasma_manager38459732',\\n\",\n \" 'plasma_manager17318940',\\n\",\n \" 'plasma_manager90318312',\\n\",\n \" 'scheduler49593698',\\n\",\n \" 'plasma_manager38935970',\\n\",\n \" 'plasma_store32560338',\\n\",\n \" 'scheduler31529091',\\n\",\n \" 'plasma_store33949921',\\n\",\n \" 'scheduler79823530',\\n\",\n \" 'plasma_manager34074245',\\n\",\n \" 'plasma_store55028483',\\n\",\n \" 'plasma_store81236797',\\n\",\n \" 'plasma_store14921337',\\n\",\n \" 'plasma_store76575142',\\n\",\n \" 'plasma_manager8466500',\\n\",\n \" 'plasma_manager59591766',\\n\",\n \" 'plasma_store59747806',\\n\",\n \" 'plasma_store21929172',\\n\",\n \" 'plasma_store20840957',\\n\",\n \" 'plasma_manager18349304',\\n\",\n \" 'com.apple.launchd.YOICqQ8WsA',\\n\",\n \" 'tmux-501',\\n\",\n \" 'scheduler65783973',\\n\",\n \" 'plasma_store23994906',\\n\",\n \" 'plasma_manager73815591',\\n\",\n \" 'scheduler35171712',\\n\",\n \" 'plasma_store11482292']\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.listdir('.')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/private/tmp'\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"os.chdir('/tmp')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"KEY_NAME='yaroslav'\\n\",\n \"\\n\",\n \"def lookup_aws_instances(name):\\n\",\n \" \\\"\\\"\\\"Returns all AWS instances for given job.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ec2 = boto3.resource('ec2')\\n\",\n \" instances = ec2.instances.filter(\\n\",\n \" Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for i in instances:\\n\",\n \" names = []\\n\",\n \" if i.tags:\\n\",\n \" names = [tag['Value'] for tag in i.tags if tag['Key'] == 'Name']\\n\",\n \" key_name = i.key_name\\n\",\n \"\\n\",\n \" assert len(names) <= 1\\n\",\n \" if names:\\n\",\n \" inst_name = names[0]\\n\",\n \" else:\\n\",\n \" inst_name = ''\\n\",\n \" if inst_name == name:\\n\",\n \" if key_name != KEY_NAME:\\n\",\n \" print(\\\"name matches, but key name %s doesn't match %s, skipping\\\"%(key_name, KEY_NAME))\\n\",\n \" continue\\n\",\n \" result.append(i)\\n\",\n \" return result\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instances=lookup_aws_instances(\\\"hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"instance = instances[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'54.68.66.51'\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instance.public_ip_address\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'async_sgd_benchmark.py'\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.path.basename(\\\"https://gist.githubusercontent.com/robertnishihara/4e246d6942cd692a0838414ff01975c1/raw/486a6a3f8c5cfc3d198e73cea50507a829e0cc9d/async_sgd_benchmark.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['__class__',\\n\",\n \" '__delattr__',\\n\",\n \" '__dict__',\\n\",\n \" '__dir__',\\n\",\n \" '__doc__',\\n\",\n \" '__eq__',\\n\",\n \" '__format__',\\n\",\n \" '__ge__',\\n\",\n \" '__getattribute__',\\n\",\n \" '__gt__',\\n\",\n \" '__hash__',\\n\",\n \" '__init__',\\n\",\n \" '__le__',\\n\",\n \" '__lt__',\\n\",\n \" '__module__',\\n\",\n \" '__ne__',\\n\",\n \" '__new__',\\n\",\n \" '__reduce__',\\n\",\n \" '__reduce_ex__',\\n\",\n \" '__repr__',\\n\",\n \" '__setattr__',\\n\",\n \" '__sizeof__',\\n\",\n \" '__str__',\\n\",\n \" '__subclasshook__',\\n\",\n \" '__weakref__',\\n\",\n \" '_id',\\n\",\n \" 'ami_launch_index',\\n\",\n \" 'architecture',\\n\",\n \" 'attach_classic_link_vpc',\\n\",\n \" 'attach_volume',\\n\",\n \" 'block_device_mappings',\\n\",\n \" 'classic_address',\\n\",\n \" 'client_token',\\n\",\n \" 'console_output',\\n\",\n \" 'create_image',\\n\",\n \" 'create_tags',\\n\",\n \" 'delete_tags',\\n\",\n \" 'describe_attribute',\\n\",\n \" 'detach_classic_link_vpc',\\n\",\n \" 'detach_volume',\\n\",\n \" 'ebs_optimized',\\n\",\n \" 'elastic_gpu_associations',\\n\",\n \" 'ena_support',\\n\",\n \" 'get_available_subresources',\\n\",\n \" 'hypervisor',\\n\",\n \" 'iam_instance_profile',\\n\",\n \" 'id',\\n\",\n \" 'image',\\n\",\n \" 'image_id',\\n\",\n \" 'instance_id',\\n\",\n \" 'instance_lifecycle',\\n\",\n \" 'instance_type',\\n\",\n \" 'kernel_id',\\n\",\n \" 'key_name',\\n\",\n \" 'key_pair',\\n\",\n \" 'launch_time',\\n\",\n \" 'load',\\n\",\n \" 'meta',\\n\",\n \" 'modify_attribute',\\n\",\n \" 'monitor',\\n\",\n \" 'monitoring',\\n\",\n \" 'network_interfaces',\\n\",\n \" 'network_interfaces_attribute',\\n\",\n \" 'password_data',\\n\",\n \" 'placement',\\n\",\n \" 'placement_group',\\n\",\n \" 'platform',\\n\",\n \" 'private_dns_name',\\n\",\n \" 'private_ip_address',\\n\",\n \" 'product_codes',\\n\",\n \" 'public_dns_name',\\n\",\n \" 'public_ip_address',\\n\",\n \" 'ramdisk_id',\\n\",\n \" 'reboot',\\n\",\n \" 'reload',\\n\",\n \" 'report_status',\\n\",\n \" 'reset_attribute',\\n\",\n \" 'reset_kernel',\\n\",\n \" 'reset_ramdisk',\\n\",\n \" 'reset_source_dest_check',\\n\",\n \" 'root_device_name',\\n\",\n \" 'root_device_type',\\n\",\n \" 'security_groups',\\n\",\n \" 'source_dest_check',\\n\",\n \" 'spot_instance_request_id',\\n\",\n \" 'sriov_net_support',\\n\",\n \" 'start',\\n\",\n \" 'state',\\n\",\n \" 'state_reason',\\n\",\n \" 'state_transition_reason',\\n\",\n \" 'stop',\\n\",\n \" 'subnet',\\n\",\n \" 'subnet_id',\\n\",\n \" 'tags',\\n\",\n \" 'terminate',\\n\",\n \" 'unmonitor',\\n\",\n \" 'virtualization_type',\\n\",\n \" 'volumes',\\n\",\n \" 'vpc',\\n\",\n \" 'vpc_addresses',\\n\",\n \" 'vpc_id',\\n\",\n \" 'wait_until_exists',\\n\",\n \" 'wait_until_running',\\n\",\n \" 'wait_until_stopped',\\n\",\n \" 'wait_until_terminated']\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dir(instance)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = boto3.resource('ec2')\\n\",\n \"aws_instances = ec2.create_instances?\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"aws_instances = ec2.create_instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"aws_instances = ec2.create_instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ec2 = boto3.client('ec2')\\n\",\n \"response = ec2.describe_instances()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"ec2 = boto3.resource('ec2')\\n\",\n \"AMI = 'ami-9ddb0fe5'\\n\",\n \"KEY_NAME = 'yaroslav' # AWS key-name to use\\n\",\n \"SECURITY_GROUP = 'open' # security group for all instances\\n\",\n \"placement_group='ebs-test'\\n\",\n \"\\n\",\n \"ec2 = boto3.resource('ec2')\\n\",\n \"instances = ec2.create_instances(\\n\",\n \"ImageId=AMI,\\n\",\n \"InstanceType='c5.large',\\n\",\n \"MinCount=1,\\n\",\n \"MaxCount=1,\\n\",\n \"SecurityGroups=[SECURITY_GROUP],\\n\",\n \"KeyName=KEY_NAME)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \" ec2 = boto3.resource('ec2')\\n\",\n \" instances2 = ec2.create_instances(\\n\",\n \" ImageId=AMI,\\n\",\n \" InstanceType='c5.2xlarge',\\n\",\n \" MinCount=4,\\n\",\n \" MaxCount=4,\\n\",\n \" SecurityGroups=[SECURITY_GROUP],\\n\",\n \" KeyName=KEY_NAME)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ec2.Instance(id='i-037156ba385ad4fe5'),\\n\",\n \" ec2.Instance(id='i-01c4a7e0f5ca56b32'),\\n\",\n \" ec2.Instance(id='i-0805ec38fd3cf71b9'),\\n\",\n \" ec2.Instance(id='i-09322ea431460f284'),\\n\",\n \" ec2.Instance(id='i-00356bd9050161866'),\\n\",\n \" ec2.Instance(id='i-0cdde35372adcf3d3'),\\n\",\n \" ec2.Instance(id='i-0aae50b99f628f851'),\\n\",\n \" ec2.Instance(id='i-0a2c0879c1d4118f6')]\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"instances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('i-04043b4d7436be162', 'testjob'),\\n\",\n \" ('i-04101fcf8a211e862', 'testjob'),\\n\",\n \" ('i-0b1b3c658b51e3fe6',\\n\",\n \" 'master-us-west-2a.masters.90c444a2-pachydermcluster.kubernetes.com'),\\n\",\n \" ('i-0aaa1f818181e50d0',\\n\",\n \" 'master-us-west-2a.masters.0726aa4c-pachydermcluster.kubernetes.com'),\\n\",\n \" ('i-07f83369033a741a3', 'pachyderm-webservice'),\\n\",\n \" ('i-08259f03e6c8061c3', 'testjob'),\\n\",\n \" ('i-008877bc5b2a6ef43', 'testjob'),\\n\",\n \" ('i-03baf8ed44431961c', 'nodes.0726aa4c-pachydermcluster.kubernetes.com'),\\n\",\n \" ('i-0498d6623c44de7b7', 'nodes.0726aa4c-pachydermcluster.kubernetes.com'),\\n\",\n \" ('i-02831a5e1defa3caa', 'nodes.90c444a2-pachydermcluster.kubernetes.com'),\\n\",\n \" ('i-01c7202a3c06f35d3', 'nodes.90c444a2-pachydermcluster.kubernetes.com')]\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[(i.id, tag['Value']) for i in instances for tag in i.tags if tag['Key'] == 'Name']\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {},\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "benchmark_huggingface_predict.py", "content": "# Simple benchmark to time prediction using huggingface API\n# 150ms prediction on first word (18 word context)\n# 50ms prediction on each following word\n\nimport sys\nimport argparse\nimport logging\nimport math\nimport os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport tqdm\nfrom tensorboardX import SummaryWriter\nfrom torch.utils.data import DataLoader, Dataset\nfrom tqdm import trange\n\nimport pytorch_pretrained_bert\nfrom data_loader import get_data_loader\nfrom model_sampler import print_samples\nfrom pytorch_pretrained_bert import GPT2LMHeadModel, GPT2Tokenizer, OpenAIAdam\nfrom torch.utils.data import DataLoader, Dataset, Subset\nmodel_name = 'gpt2'\nenc = GPT2Tokenizer.from_pretrained(model_name)\nmodel = GPT2LMHeadModel.from_pretrained(model_name)\n\n\nmodel_name = 'gpt2'\nenc = GPT2Tokenizer.from_pretrained(model_name)\nmodel = GPT2LMHeadModel.from_pretrained(model_name)\ndevice='cpu'\nbeam_width = 130\nstopwords = []\n\ndef to_list(tensor):\n return list(tensor.cpu().numpy())\n\ndef predict(line, max_predictions):\n \"\"\"Give continuation of the line with at most max_predictions BPE tokens. Returns line extended with predictions of\n the model.\"\"\"\n\n line_encoded = enc.encode(line)\n line_encoded = torch.tensor(line_encoded)\n line_encoded = line_encoded.unsqueeze_(0) # batch of size 1\n line_encoded_list = list(line_encoded[0].numpy())\n line_encoded = line_encoded.to(device)\n state = None\n\n for i in range(max_predictions):\n with timeit('forward'):\n logits, state = model(line_encoded, past=state)\n \n # predicted = argmax(logits[0,-1,:])\n\n # [[idx1, idx2, ...]]\n with timeit('topk'):\n _, line_encoded_candidates = torch.topk(logits[:,-1,:], k=beam_width, dim=-1)\n\n # determine which candidates are stopwords by decoding them and\n # comparing against NLTK stopword list\n \n line_encoded_candidates = to_list(line_encoded_candidates[0])\n is_stopword = []\n for s in line_encoded_candidates:\n is_stopword.append(enc.decode([s.item()]).strip() in stopwords)\n\n \n # find first prediction which is not a stopword\n predicted = None\n for (idx, candidate) in enumerate(line_encoded_candidates):\n if is_stopword[idx]:\n # print('skipping stopword ', idx)\n continue\n else:\n predicted = candidate\n break\n assert predicted is not None\n line_encoded = torch.tensor([[predicted]]).to(device)\n line_encoded_list.append(predicted)\n\n return enc.decode(line_encoded_list)\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n\n def __init__(self, tag=\"\"):\n self.tag = tag\n\n def __enter__(self):\n self.start = time.perf_counter()\n return self\n\n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000 * (self.end - self.start)\n newtag = 'times/' + self.tag\n print(newtag, interval_ms)\n\nif __name__=='__main__':\n line = \"both its sun-speckled shade and the cool grass beneath were a welcome respite after the stifling kitchen \"\n for i in range(10):\n with timeit('predict'):\n print(predict(line, max_predictions=15))\n" }, { "path": "bin/tfversion", "content": "#!/usr/bin/env python\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\nimport tensorflow as tf\nversion=tf.__version__\nprint(\"version: %s\"%(version,))\ncommit = tf.__git_version__\nprint(\"__git_version__: %s\"%(commit,))\n# commit looks like this\n# 'v1.0.0-65-g4763edf-dirty'\ncommit = commit.replace(\"'\",\"\")\nif commit.endswith('-dirty'):\n dirty = True\n commit = commit[:-len('-dirty')]\ncommit=commit.rsplit('-g', 1)[1]\nurl = 'https://github.com/tensorflow/tensorflow/commit/'+commit\nprint(\"Commit %s\" %(url,))" }, { "path": "clipping-profile.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Memory usage of clipping ops\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Install memory profiler\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import urllib.request\\n\",\n \"response = urllib.request.urlopen(\\\"https://raw.githubusercontent.com/yaroslavvb/memory_util/master/memory_util.py\\\")\\n\",\n \"open(\\\"memory_util.py\\\", \\\"wb\\\").write(response.read())\\n\",\n \"\\n\",\n \"import memory_util\\n\",\n \"memory_util.vlog(1)\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sanity check\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 7 a(1555-gpu_bfc) 1000192 1000192 gpu_bfc\\n\",\n \" 9 b(1556-gpu_bfc) 1000192 2000384 gpu_bfc\\n\",\n \" 11 c(1557-gpu_bfc) 1000192 3000576 gpu_bfc\\n\",\n \" 13 a(1555-gpu_bfc) -1000192 2000384 gpu_bfc\\n\",\n \" 14 b(1556-gpu_bfc) -1000192 1000192 gpu_bfc\\n\",\n \" 15 c(1557-gpu_bfc) -1000192 0 gpu_bfc\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tensorflow.python.ops import gen_random_ops\\n\",\n \"tf.reset_default_graph()\\n\",\n \"sess = tf.Session()\\n\",\n \"dtype = tf.float32\\n\",\n \"a = gen_random_ops._random_uniform((250*1000,), dtype, name=\\\"a\\\")\\n\",\n \"b = gen_random_ops._random_uniform((250*1000,), dtype, name=\\\"b\\\")\\n\",\n \"c = tf.add(a, b, name=\\\"c\\\")\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(c.op)\\n\",\n \"memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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knqZphIkroZJpKkboaJJKmbYSJJ6maYSJK6GSaSpG6GiSSpm2EiSepm\\nmEiSuhkmkqRuhokkqZthIknqZphIkroZJpKkboaJJKmbYSJJ6maYSJK6GSaSpG6GiSSpm2EiSepm\\nmEiSuhkmkqRuXWGS5IdJHkuyP8l4q70qyb4kT7b381s9ST6VZCLJo0neMNLP1tb+ySRbR+pvbP1P\\ntGNzqjEkScM4HVcm/7GqNlXVWPt8C3BfVW0A7mufAa4ENrTXNuAOmAoG4FbgUuAS4NaRcLgDeO/I\\ncZtnGUOSNIDFuM21BdjRtncAV4/U764pDwDnJVkNXAHsq6pjVfUssA/Y3PatrKoHqqqAu0/qa7ox\\nJEkD6A2TAv4pySNJtrXahVU12bZ/AlzYttcAT48ce6jVTlU/NE39VGNIkgZwTufxf1ZVh5P8G2Bf\\nkn8Z3VlVlaQ6xzilU43RAm4bwLp16xZzGpK0rHVdmVTV4fZ+BPgaU888ftpuUdHej7Tmh4GLRg5f\\n22qnqq+dps4pxjh5fndW1VhVja1atWqhpylJmsWCwyTJy5P80Ylt4HLgcWA3cGJF1lZgV9veDVzf\\nVnVdBjzXblXtBS5Pcn578H45sLftO57ksraK6/qT+ppuDEnSAHpuc10IfK2t1j0H+GJV/WOSh4F7\\nk9wA/Ah4Z2u/B7gKmAB+CbwHoKqOJfko8HBr95GqOta23wd8DngZ8PX2Avj4DGNIkgaw4DCpqqeA\\nP52m/gzwtmnqBdw0Q1/bge3T1MeB1811DEnSMPwFvCSpm2EiSepmmEiSuhkmkqRuhokkqZthIknq\\nZphIkroZJpKkboaJJKmbYSJJ6maYSJK6GSaSpG6GiSSpm2EiSepmmEiSuhkmkqRuhokkqZthIknq\\nZphIkroZJpKkboaJJKmbYSJJ6maYSJK6GSaSpG5ndJgk2Zzk+0kmktwy9Hwkabk6Y8MkyQrg08CV\\nwEbg2iQbh52VJC1PZ2yYAJcAE1X1VFX9BtgJbBl4TpK0LJ3JYbIGeHrk86FWkyQtsXOGnsBiSrIN\\n2Aawbt26BfWx8d+uPJ1TkqQltVT/D0tVLclAp1uSNwEfrqor2ucPAVTVf5uu/djYWI2Pjy/hDCXp\\nzJfkkaoam63dmXyb62FgQ5KLk5wLXAPsHnhOkrQsnbG3uarq+SQ3A3uBFcD2qjow8LQkaVk6Y8ME\\noKr2AHuGnockLXdn8m0uSdLvCcNEktTNMJEkdTNMJEndDBNJUrcz9keL85XkKPCjBR5+AfCvp3E6\\nZwLPeXnwnJeHnnP+d1W1arZGyyZMeiQZn8svQM8mnvPy4DkvD0txzt7mkiR1M0wkSd0Mk7m5c+gJ\\nDMBzXh485+Vh0c/ZZyaSpG5emUiSuhkms0iyOcn3k0wkuWXo+Sy2JNuTHEny+NBzWSpJLkpyf5KD\\nSQ4kef/Qc1psSf4wyUNJvtfO+W+HntNSSLIiyXeT/MPQc1kKSX6Y5LEk+5Ms6h908jbXKSRZAfwf\\n4M+Z+rPADwPXVtXBQSe2iJL8B+AXwN1V9bqh57MUkqwGVlfVd5L8EfAIcPVZ/u85wMur6hdJXgJ8\\nC3h/VT0w8NQWVZL/AowBK6vqL4aez2JL8kNgrKoW/Xc1Xpmc2iXARFU9VVW/AXYCWwae06Kqqm8C\\nx4aex1Kqqsmq+k7b/jnwBLBm2Fktrpryi/bxJe11Vn+zTLIW+E/A/xx6Lmcjw+TU1gBPj3w+xFn+\\nP5nlLsl64PXAg8POZPG1Wz77gSPAvqo628/5vwP/Ffjt0BNZQgX8U5JHkmxbzIEME6lJ8grgK8AH\\nqur40PNZbFX1QlVtAtYClyQ5a29rJvkL4EhVPTL0XJbYn1XVG4ArgZvabexFYZic2mHgopHPa1tN\\nZ5n23OArwBeq6qtDz2cpVdXPgPuBzUPPZRG9GfjP7RnCTuCtSf7XsFNafFV1uL0fAb7G1K37RWGY\\nnNrDwIYkFyc5F7gG2D3wnHSatYfRdwFPVNUnh57PUkiyKsl5bftlTC0y+ZdhZ7V4qupDVbW2qtYz\\n9d/xN6rqrwae1qJK8vK2oIQkLwcuBxZtlaZhcgpV9TxwM7CXqYey91bVgWFntbiSfAn4NvDvkxxK\\ncsPQc1oCbwbezdS31f3tddXQk1pkq4H7kzzK1JemfVW1LJbLLiMXAt9K8j3gIeB/V9U/LtZgLg2W\\nJHXzykSS1M0wkSR1M0wkSd0ME0lSN8NEktTNMJEkdTNMJEndDBNJUrf/BxSnAsS8qqDsAAAAAElF\\nTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"memory_util.plot_memory_timeline(stderr, ignore_less_than_bytes=1000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## More complex example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAW4AAAEDCAYAAAAVyO4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nTSn5CrgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"n = 250*1000*1000\\n\",\n \"dtype = tf.float32\\n\",\n \"lower = gen_random_ops._random_uniform((n,), dtype, name=\\\"l\\\")\\n\",\n \"upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \"x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \"sess = tf.Session()\\n\",\n \"y = tf.minimum(upper, tf.maximum(lower, x, name=\\\"cl\\\"), name=\\\"cu\\\")\\n\",\n \"loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \"grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \"memory_util.plot_memory_timeline(stderr, ignore_less_than_bytes=1000**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Profile memory of a single maximum\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEDCAYAAADOc0QpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nZLhLUoMMd0lq0P8HEWjGXWNQa2cAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"n = 250*1000*1000\\n\",\n \"dtype = tf.float32\\n\",\n \"upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \"x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \"sess = tf.Session()\\n\",\n \"y = tf.minimum(upper, x, name=\\\"cu\\\")\\n\",\n \"loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \"grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \"memory_util.plot_memory_timeline(stderr, ignore_less_than_bytes=1000**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Profile memory of a chain of max\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def memory_req_minimum(chain_length):\\n\",\n \" \\\"\\\"\\\"Returns memory req to compute chain of min ops\\\"\\\"\\\"\\n\",\n \" tf.reset_default_graph()\\n\",\n \" n = 250*1000*1000\\n\",\n \" dtype = tf.float32\\n\",\n \" upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \" x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \" sess = tf.Session()\\n\",\n \" y = x\\n\",\n \" for i in range(chain_length):\\n\",\n \" y = tf.minimum(upper, y, name=\\\"cu\\\")\\n\",\n \" loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \" grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \" with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \" return memory_util.peak_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Chain length mem req\\n\",\n \" 0 1000001024\\n\",\n \" 1 4000001024\\n\",\n \" 2 4250001408\\n\",\n \" 3 4750001408\\n\",\n \" 4 5750001408\\n\",\n \" 5 6000001536\\n\",\n \" 6 7000000120\\n\",\n \" 7 8000000136\\n\",\n \" 8 9000000152\\n\",\n \" 9 10000000168\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"%12s %12s\\\" %(\\\"Chain length\\\", \\\"mem req\\\"))\\n\",\n \"for i in range(10):\\n\",\n \" print(\\\"%12d %12d\\\"%(i, memory_req_minimum(i)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Profile memory of chain on min/max ops\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def memory_req_minmax(chain_length):\\n\",\n \" \\\"\\\"\\\"Returns memory req to compute chain of min ops\\\"\\\"\\\"\\n\",\n \" tf.reset_default_graph()\\n\",\n \" n = 250*1000*1000\\n\",\n \" dtype = tf.float32\\n\",\n \" upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \" \\n\",\n \" x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \" sess = tf.Session()\\n\",\n \" y = x\\n\",\n \" for i in range(chain_length):\\n\",\n \" y = tf.minimum(upper, tf.maximum(upper, y, name=\\\"cl\\\"), name=\\\"cu\\\")\\n\",\n \" loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \" grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \" with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \" return memory_util.peak_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Chain length mem req\\n\",\n \" 0 1000001024\\n\",\n \" 1 4250001408\\n\",\n \" 2 5750001408\\n\",\n \" 3 7000000120\\n\",\n \" 4 9000000152\\n\",\n \" 5 11000000184\\n\",\n \" 6 13000000212\\n\",\n \" 7 15000000248\\n\",\n \" 8 17000000280\\n\",\n \" 9 19000000312\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"%12s %12s\\\" %(\\\"Chain length\\\", \\\"mem req\\\"))\\n\",\n \"for i in range(10):\\n\",\n \" print(\\\"%12d %12d\\\"%(i, memory_req_minmax(i)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Clipping using map_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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zjv2HTWPDCtVuyYmXNZvXVfA1uItEwsracM+COwwjn3q6hFrwDhFlDT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\\nzP4/GCQWbNt5m78AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"n = 100#250*1000*1000\\n\",\n \"dtype = tf.float32\\n\",\n \"lower = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \"upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \"x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \"sess = tf.Session(config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0))))\\n\",\n \"\\n\",\n \"y = x\\n\",\n \"#chain_length = 2\\n\",\n \"for i in range(chain_length):\\n\",\n \" y = tf.map_fn(lambda args: tf.clip_by_value(args[0], args[1], args[2]), (y, lower, upper), dtype=(dtype))\\n\",\n \" loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \" grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \"memory_util.plot_memory_timeline(stderr, ignore_less_than_bytes=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def memory_req_mapfn(chain_length):\\n\",\n \" \\\"\\\"\\\"Returns memory req to compute chain of min ops\\\"\\\"\\\"\\n\",\n \" tf.reset_default_graph()\\n\",\n \" n = 250\\n\",\n \" dtype = tf.float32\\n\",\n \" upper = gen_random_ops._random_uniform((n,), dtype, name=\\\"u\\\")\\n\",\n \" lower = gen_random_ops._random_uniform((n,), dtype, name=\\\"l\\\")\\n\",\n \" \\n\",\n \" x = gen_random_ops._random_uniform((n,), dtype, name=\\\"x\\\")\\n\",\n \" sess = tf.Session()\\n\",\n \" y = x\\n\",\n \" for i in range(chain_length):\\n\",\n \" y = tf.map_fn(lambda args: tf.clip_by_value(args[0], args[1], args[2]), (y, lower, upper), dtype=(dtype))\\n\",\n \" loss = tf.reduce_sum(y, name=\\\"loss\\\")\\n\",\n \" grad = tf.gradients(loss, [x])[0]\\n\",\n \"\\n\",\n \" with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \" return memory_util.peak_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Chain length mem req\\n\",\n \" 0 2048\\n\",\n \" 1 391856\\n\",\n \" 2 653435\\n\",\n \" 3 914501\\n\",\n \" 4 1175823\\n\",\n \" 5 1437145\\n\",\n \" 6 1697698\\n\",\n \" 7 1960045\\n\",\n \" 8 2221111\\n\",\n \" 9 2482433\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"%12s %12s\\\" %(\\\"Chain length\\\", \\\"mem req\\\"))\\n\",\n \"for i in range(10):\\n\",\n \" print(\\\"%12d %12d\\\"%(i, memory_req_mapfn(i)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/yaroslav/git2/notebooks\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pwd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"12px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "cluster/.gitignore", "content": "/.DS_Store\n" }, { "path": "cluster/README.md", "content": "# cluster\ntrain on AWS\n" }, { "path": "cluster/async_adder.py", "content": "#!/usr/bin/env python\nimport base64\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport threading\nimport time\nimport pickle\n\nfrom tensorflow.python.summary import summary as summary_lib\nfrom tensorflow.python import pywrap_tensorflow\nfrom tensorflow.python.util import compat\nfrom tensorflow.core.util import event_pb2\nfrom tensorflow.core.framework import summary_pb2\nfrom tensorflow.python.training import training_util # TOOD: not needed?\n\nfrom tensorflow.python.framework import device as pydev\n\nfrom myutil import timeit\n\n# TODO: when ps server restarts, it doesn't reinitialize the variables\n# TODO: document TF_CONFIG\n\nRETRY_DELAY_SEC = 5\n\n# TODO: replace with \"sharded\"\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 1000, \"number of times to repeat experiment\")\nflags.DEFINE_integer(\"iters_per_step\", 10, \"number of additions per step\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"whether to have verbose logging\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to collect CPU profile\")\n\n# internal flags, set by client\nFLAGS = flags.FLAGS\n\n\n# TODO: remove logdir prefix, it should be global settings that doesn't change\n# todo: name not needed?\n#flags.DEFINE_string('logdir', '', 'where to event logs')\nflags.DEFINE_string('name', 'default',\n 'tag used to keep track of machines in this experiment')\n\n\n# TODO: switch back to regular (not traced) runs\ntimeline_counter = 0\nrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE, output_partition_graphs=True)\ndef traced_run(fetches):\n \"\"\"Runs fetches, dumps timeline files in current directory.\"\"\"\n\n global timeline_counter\n run_metadata = tf.RunMetadata()\n\n config = load_config()\n log_fn = \"%s-%s-%s\"%(config.task_type, config.task_id, timeline_counter)\n sess = tf.get_default_session()\n \n root = os.getcwd()+\"/data\"\n os.system('mkdir -p '+root)\n \n from tensorflow.python.client import timeline\n\n results = sess.run(fetches,\n options=run_options,\n run_metadata=run_metadata);\n tl = timeline.Timeline(step_stats=run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format(show_memory=True,\n show_dataflow=False)\n open(root+\"/timeline_%s.json\"%(log_fn,), \"w\").write(ctf)\n open(root+\"/stepstats_%s.pbtxt\"%(log_fn,), \"w\").write(str(\n run_metadata.step_stats))\n timeline_counter+=1\n return results\n\n\ndef sessrun(fetches):\n sess = tf.get_default_session()\n return sess.run(fetches)\n return traced_run(fetches)\n\n\ndef get_ps_device(task=0, op_device_str=''):\n device_str = '/job:ps'\n device = pydev.DeviceSpec.from_string(device_str)\n device.task = task\n op_device = pydev.DeviceSpec.from_string(op_device_str)\n device.merge_from(op_device)\n return device.to_string()\n\n# todo: private methods\ndef get_worker_device(task, op_device_str=''):\n device_str = '/job:worker'\n device = pydev.DeviceSpec.from_string(device_str)\n device.task = task\n op_device = pydev.DeviceSpec.from_string(op_device_str)\n device.merge_from(op_device)\n return device.to_string()\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n \n config.operation_timeout_in_ms = 10*1000 # abort after 10 seconds\n return config\n\ndef write_event(tag, value, step):\n event = event_pb2.Event(\n wall_time=time.time(),\n step=step,\n summary=summary_pb2.Summary(\n value=[summary_pb2.Summary.Value(\n tag=tag, simple_value=value)]))\n\n # todo: not flush so often?\n writer.WriteEvent(event)\n writer.Flush()\n\n return event\n\ndef make_params():\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n dtype=tf.int32\n ps_device = get_ps_device(0)\n with tf.device(ps_device):\n params = tf.get_variable(\"params\", [params_size], dtype,\n initializer=tf.ones_initializer())\n return params\n \ndef run_worker():\n \"\"\"Main worker loop.\"\"\"\n\n # todo: rename \"config\" into distributed_config\n config = load_config()\n cluster_spec = config.cluster_spec\n # import pdb; pdb.set_trace()\n\n ps_tasks = len(cluster_spec['ps'])\n assert ps_tasks >= 0\n\n # returns device like /job:worker/task:0\n worker_device = ''\n assert config.task_type == 'worker'\n \n if config.task_id == 1:\n time.sleep(60) # slow-down second worker\n \n worker_device = get_worker_device(config.task_id)\n\n ps_device = get_ps_device(0)\n\n # todo: replace with int64\n # todo: replace with varscope.getvariable like in alextp suggestion\n with timeit(\"worker graph create\"):\n params = make_params()\n with tf.device(worker_device):\n val = tf.ones((), dtype=params.dtype)\n grads = tf.fill([params.shape[0]], val)\n # todo: add two-way communication\n\n with tf.device(ps_device):\n update = params.assign_add(grads)\n params0 = params[0]\n\n #uninitialized_op = tf.report_uninitialized_variables()\n initialized_op = tf.is_variable_initialized(params)\n \n # todo: check how estimator does it\n # TODO: retries for errors during server creation?\n # it can fail if assigned port is unavailable\n with timeit(\"worker server start\"):\n server = tf.train.Server(cluster_spec, config=session_config(),\n job_name=config.task_type,\n task_index=config.task_id)\n\n # follow logic in prepare_session\n # https://github.com/tensorflow/tensorflow/blob/22586bdf900640217deac6dc826054bc6e785518/tensorflow/python/training/session_manager.py#L71\n\n def create_session():\n # uninited_list = ['somevariable']\n is_initialized = False\n while not is_initialized:\n try:\n with timeit(\"session creation\"):\n sess = tf.InteractiveSession(server.target, config=session_config())\n with timeit(\"sessrun\"):\n # uninited_list = sessrun(uninitialized_op)\n is_initialized = sessrun(initialized_op)\n except Exception as e:\n print(\"Initialization failed with %s, retrying\" %(e,))\n print((\"Model not initialized, \"\n \"retrying in %.1f seconds\" %(RETRY_DELAY_SEC,)))\n time.sleep(RETRY_DELAY_SEC)\n return sess\n \n # are there failures in creating session\n with timeit('create session'):\n sess = tf.InteractiveSession(server.target, config=session_config())\n \n # only run initialization on worker task 0\n if config.task_id == 0:\n sess_run_succeeded = False\n while not sess_run_succeeded:\n try:\n with timeit('intialize vars'):\n sessrun(params.initializer)\n sess_run_succeeded = True\n except Exception as e:\n print(\"Initialization failed with %s, retrying \"\n \"in %.1f sec\" %(e, RETRY_DELAY_SEC))\n # this can fail if workers too too long to come up and\n # sessrun failed with DeadlineExceeded\n time.sleep(RETRY_DELAY_SEC)\n \n\n for step in range(FLAGS.iters):\n start_time = time.time()\n for i in range(FLAGS.iters_per_step):\n sess_run_succeeded = False\n while not sess_run_succeeded:\n try:\n sessrun(update)\n sess_run_succeeded = True\n # Exception when ps restarts, need to recreate session\n except Exception as e: \n print((\"sess run failed with %s, \"\n \"retrying in %.1f seconds\" %(e, RETRY_DELAY_SEC,)))\n time.sleep(RETRY_DELAY_SEC)\n sess = create_session()\n\n elapsed_time = time.time() - start_time\n rate = float(FLAGS.iters_per_step)*FLAGS.data_mb/elapsed_time\n event = write_event('rate', rate, step)\n print('%.2f MB/s'%(rate,))\n\n\n# replacement of estimators.run_config.ClusterConfig that works with sparse\n# cluster config\n\nclass MyClusterConfig:\n def __init__(self):\n self.task_id = -1\n self.task_type = \"asdf\"\n self.cluster_spec = {\"asdf\":\"asdf\"}\n\n def __str__(self):\n return self.__dict__.__str__()\n\ndef load_config():\n \"\"\"Returns ClusterConfig object. Config contains task spec and cluster spec in dictionary-like form as below\n # {\"task\": {\"index\": 0, \"type\": \"worker\"}, \"cluster\": {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n \"\"\"\n # old way that doesn't work for sparse format\n # if 'TF_CONFIG' not in os.environ:\n # # try loading encoded version\n # if 'TF_CONFIG_BASE16' in os.environ:\n # tf_config_str = base64.b16decode(os.environ['TF_CONFIG_BASE16'])\n # tf_config_str = tf_config_str.decode('ascii')\n # os.environ['TF_CONFIG'] = tf_config_str\n # del os.environ['TF_CONFIG_BASE16']\n # else:\n # assert False, \"Must specify TF_CONFIG or TF_CONFIG_BASE16\"\n \n# from tensorflow.contrib.learn.python.learn.estimators.run_config import ClusterConfig\n \n config = MyClusterConfig()\n config_dict = pickle.loads(base64.b16decode(os.environ[\"TF_PICKLE_BASE16\"]))\n config.task_type = config_dict[\"task\"][\"type\"]\n config.task_id = config_dict[\"task\"][\"index\"]\n config.cluster_spec = config_dict[\"cluster\"]\n return config\n\ndef run_ps():\n config = load_config()\n \n assert config.task_type == 'ps'\n params = make_params()\n \n with timeit('create server'):\n print(\"Starting server with target %s\"%(config.cluster_spec[config.task_type][config.task_id]))\n server = tf.train.Server(config.cluster_spec, config=session_config(),\n job_name=config.task_type,\n task_index=config.task_id)\n\n # doing init run from ps master fails with\n # sess run failed with No worker known as /job:worker/replica:0/task:1\n # [[Node: Fill_S3 = _Recv[client_terminated=false, recv_device=\"/job:ps/replica:0/task:0/device:CPU:0\", send_device=\"/job:worker/replica:0/task:1/device:CPU:0\", send_device_incarnation=7403937842608207616, tensor_name=\"edge_3_Fill\", tensor_type=DT_INT32, _device=\"/job:ps/replica:0/task:0/device:CPU:0\"]()]], retrying in 5.0 seconds\n\n # todo: replace with dequeue for graceful shutdown\n # todo: done_queue from sharded_ps_benchmark\n # done_queue = create_done_queue(0)\n time.sleep(365*24*3600)\n\ndef _get_master():\n \"\"\"Returns the appropriate string for local grpc TensorFlow master.\n For compat with server.target, return bytes instead of string.\n\n The address is derived from server spec, so it may not match the value\n returned by server.target stared locally (server.target can be localhost:129)\n \"\"\"\n\n def _get_master_str():\n config = load_config()\n task_type = config.task_type\n task_id = config.task_id\n cluster_spec = config.cluster_spec\n\n if not cluster_spec:\n return ''\n\n # If there is only one node in the cluster, do things locally.\n jobs = cluster_spec.jobs\n if len(jobs) == 1 and len(cluster_spec.job_tasks(jobs[0])) == 1:\n return ''\n\n # Lookup the master in cluster_spec using task_type and task_id,\n # if possible.\n if task_type:\n if task_type not in jobs:\n raise ValueError(\n '%s is not a valid task_type in the cluster_spec:\\n'\n '%s\\n\\n'\n 'Note that these values may be coming from the TF_CONFIG environment '\n 'variable.' % (task_type, cluster_spec))\n addresses = cluster_spec.job_tasks(task_type)\n if task_id >= len(addresses) or task_id < 0:\n raise ValueError(\n '%d is not a valid task_id for task_type %s in the '\n 'cluster_spec:\\n'\n '%s\\n\\n'\n 'Note that these value may be coming from the TF_CONFIG environment '\n 'variable.' % (task_id, task_type, cluster_spec))\n return 'grpc://' + addresses[task_id]\n\n # For backwards compatibility, we return empty string if task_type was\n # not set (task_type did not previously exist).\n return ''\n\n return _get_master_str().encode('ascii')\n\ndef main():\n global writer\n config = load_config()\n\n # todo: factor out common logic\n logdir = os.environ[\"LOGDIR\"]\n writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(logdir+'/events'))\n\n if config.task_type == 'worker':\n run_worker()\n elif config.task_type == 'ps':\n run_ps()\n else:\n assert False, \"Unknown task type \"+str(config.task_type)\n \n writer.Close()\n\n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/aws.py", "content": "import threading\nimport base64\nimport struct\nfrom collections import OrderedDict\nfrom pprint import pprint as pp\nimport argparse\nimport boto3\nimport os\nimport sys\nimport time\n#import util as myutil\nimport yaml\n\nfrom myutil import timeit\n\nmodule_path=os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, module_path+'/tf-tools/benchmark/runner')\n\n# TODO: decide on name to show in TMUX window\n# TODO: should we do jobname=run5-worker, or have each job also have\n# associated \"run\" name \"run5\" in addition to jobname \"worker\"?\n# TODO: check for instance type when reusing, and kill if necessary\n\n# TODO: support \"kill\"\n\n# Todo: better clean-up (at least kill python processes to free ports)\n\n# TODO: bash errors get swallowed\n# ubuntu@ip-172-31-33-25:~$ source ~/.bashrc || export PATH=~/anaconda3/bin:$PATH || source activate py2 || cd ~/git0/benchmarks/scripts/tf_cnn_benchmarks || killall python< || export TF_PICKLE_BASE16=80037D7100285807000000636C7573746572710163636F6C6C656374696F6E730A64656661756C74646963740A7102636275696C74696E730A646963740A7103857104527105285802000000707371067D71074B0058110000003137322E33312E33332E32353A333030307108735806000000776F726B657271095D710A58110000003137322E33312E34302E34303A33303030710B617558040000007461736B710C7D710D285805000000696E646578710E4B00580400000074797065710F680675752E || export LOGDIR=/efs/logs/default || CUDA_VISIBLE_DEVICES='' python tf_cnn_benchmarks.py --local_parameter_device=gpu --worker_hosts=172.31.40.40:3000 --ps_hosts=172.31.33.25:3000 --job_name=ps --task_index=0\n# -bash: syntax error near unexpected token `||'\n\n# TODO: option to reuse stopped instances\n# TODO: ability to get instance id for a given task\n\n# User specific config. These settings are reused for all jobs.\nAMI = 'ami-9ddb0fe5'\nKEY_NAME = 'yaroslav' # AWS key-name to use, only modify instances with this\n# key\nSSH_KEY_PATH = os.environ['HOME']+'/d/yaroslav.pem' # location of .pem file locally\nSECURITY_GROUP = 'open' # security group for all instances\nDEFAULT_PORT = 3000\n\nLOCAL_TASKLOGDIR_PREFIX='/temp/tasklogs'\nLOCAL_LOGDIR_PREFIX='/temp/logs'\n\ndef _ExecuteCommandInThread(ssh_client,\n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False):\n \"\"\"Returns a thread that executes the given cmd. Non-Blocking call.\n\n \n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n cmd: cmd to run in the ssh_client\n stdout_file: local file to write standard output of the cmd to\n stderr_file: local file to write standard error of the cmd to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error, e.g. non-'0' exit code.\n\n returns a thread that executes the given cmd\n\n \"\"\"\n t = threading.Thread(\n target=_ExecuteCommandAndStreamOutput,\n args=(ssh_client, cmd, stdout_file, stderr_file, line_extractor,\n print_error))\n t.start()\n return t\n\n\ndef _StreamOutputToFile(fd, file, line_extractor, cmd=None):\n \"\"\"Stream output to local file print select content to console\n\n Streams output to a local file and if a line_extractor is passed\n uses it to determine which data is printed to the local console.\n\n \"\"\"\n def func(fd, file, line_extractor):\n with open(file, 'ab+') as f:\n if cmd:\n line = cmd + '\\n'\n f.write(line.encode('utf-8'))\n try:\n for line in iter(lambda: fd.readline(2048), ''):\n f.write(line.encode('utf-8', errors='ignore'))\n f.flush()\n if line_extractor:\n line_extractor(line)\n except UnicodeDecodeError as err:\n print('UnicodeDecodeError parsing stdout/stderr, bug in paramiko:{}'\n .format(err))\n t = threading.Thread(target=func, args=(fd, file, line_extractor))\n t.start()\n return t\n\ndef _ExecuteCommandAndStreamOutput(ssh_client,\n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False,\n ok_exit_status=[0]):\n \"\"\"Executes cmd in ssh_client. Blocking call.\n\n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n cmd: cmd to run in the ssh_client\n stdout_file: local file to write standard output of the cmd to\n stderr_file: local file to write standard error of the cmd to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error\n ok_exit_status: List of status codes that are not errors, defaults to '0'\n\n \"\"\"\n _, stdout, stderr = ssh_client.exec_command(cmd, get_pty=True)\n if stdout_file:\n t1 = _StreamOutputToFile(stdout, stdout_file, line_extractor, cmd=cmd)\n if stderr_file:\n t2 = _StreamOutputToFile(stderr, stderr_file, line_extractor)\n if stdout_file:\n t1.join()\n if stderr_file:\n t2.join()\n exit_status = stdout.channel.recv_exit_status()\n if exit_status in ok_exit_status:\n return True\n else:\n if print_error:\n print('Command execution failed! Check log. Exit Status({}):{}'.format(exit_status, cmd))\n return False\n\n\ndef lookup_aws_instances(name):\n \"\"\"Returns all AWS instances for given job.\"\"\"\n ec2 = boto3.resource('ec2')\n instances = ec2.instances.filter(\n Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\n\n result = []\n for i in instances:\n names = []\n if i.tags:\n names = [tag['Value'] for tag in i.tags if tag['Key'] == 'Name']\n key_name = i.key_name\n\n assert len(names) <= 1\n if names:\n inst_name = names[0]\n else:\n inst_name = ''\n if inst_name == name:\n if key_name != KEY_NAME:\n print(\"name matches, but key name %s doesn't match %s, skipping\"%(key_name, KEY_NAME))\n continue\n result.append(i)\n return result\n\ndef tf_job(name, num_tasks, instance_type=None, placement_group=''):\n \"\"\"Creates TensorFlow job on AWS cluster. If job with same name already\n exist on AWS cluster, then reuse those instances instead of creating new.\"\"\"\n\n if instance_type is None:\n instance_type = 'c5.large'\n # assume if given job exists, it's been configured properly\n # this is a performance optimization to avoid AWS startup delay\n instances = lookup_aws_instances(name)\n if instances:\n # TODO: change >= to ==?\n # TODO: otherwise you have problem where job has more tasks than requested\n assert len(instances) >= num_tasks, (\"Found job with same name, but number\"\n \" of tasks %d doesn't match requested %d, kill job manually.\"%(len(instances), num_tasks))\n print(\"Found existing job \"+name)\n else:\n print(\"Launching new job \"+name)\n\n ec2 = boto3.resource('ec2')\n instances = ec2.create_instances(\n ImageId=AMI,\n InstanceType=instance_type,\n MinCount=num_tasks,\n MaxCount=num_tasks,\n SecurityGroups=[SECURITY_GROUP],\n KeyName=KEY_NAME)\n \n for instance in instances:\n tag = ec2.create_tags(\n Resources=[instance.id], Tags=[{\n 'Key': 'Name',\n 'Value': name\n }])\n\n assert len(instances) == num_tasks\n print('{} Instances created'.format(len(instances)))\n \n job = Job(name, instances=instances)\n \n # todo: setup EFS logdir\n # todo: setup remote tasklogdir?\n\n return job\n\ndef terminate_job(name):\n instances = lookup_aws_instances(name)\n for i in instances:\n print(\"Killing '%s' '%s' '%s'\" %(name, i.id, i.instance_type))\n i.terminate()\n\n for i in instances:\n i.load()\n while True:\n if i.state['Name'] == 'terminated':\n break\n print(\"Waiting for %s to die, instance state is %s\"%(i.id, i.state))\n time.sleep(5)\n i.load()\n\nimport paramiko\ndef _ssh_to_host(hostname,\n ssh_key=None,\n username='ubuntu',\n retry=10):\n\n \"\"\"Create ssh connection to host\n\n Creates and returns and ssh connection to the host passed in. \n\n Args:\n hostname: host name or ip address of the system to connect to.\n retry: number of time to retry.\n ssh_key: full path to the ssk hey to use to connect.\n username: username to connect with.\n\n returns SSH client connected to host.\n\n \"\"\"\n\n k = paramiko.RSAKey.from_private_key_file(ssh_key)\n \n ssh_client = paramiko.SSHClient()\n ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n counter = retry\n while counter > 0:\n try:\n ssh_client.connect(hostname=hostname, username=username, pkey=k)\n break\n except Exception as e:\n counter = counter - 1\n print('Exception connecting to host via ssh (could be a timeout):'.format(e))\n if counter == 0:\n print('Got impatient with retrying ssh to host. Time to give up.')\n return None\n\n return ssh_client\n\n\nclass Job:\n def __init__(self, name, instances):\n self.name = name\n self.tasks = []\n # todo: make task_ids asignment deterministic\n for task_id, instance in enumerate(instances):\n self.tasks.append(Task(instance, self, task_id))\n\n def wait_until_ready(self):\n \"\"\"Waits until all tasks in the job are available and initialized.\"\"\"\n for task in self.tasks:\n task.wait_until_ready()\n # todo: initialization should start async in constructor instead of here\n\ndef _encode_float(value):\n ba = bytearray(struct.pack('d', value)) \n return base64.b16encode(ba).decode('ascii')\n\ndef _decode_float(b16):\n return struct.unpack('d', base64.b16decode(b16))[0]\n\nclass Task:\n def __init__(self, instance, job, task_id):\n self.instance = instance\n self.job = job\n self.id = task_id\n self.initialized = False\n self.local_tasklogdir = '%s/%s/%s' %(LOCAL_TASKLOGDIR_PREFIX, self.job.name,\n self.id)\n self.last_stdout = None # path of last stdout file location\n self.last_stderr = None # path of last stderr file location\n\n\n def wait_until_ready(self):\n while not self.initialized:\n self.initialize()\n if self.initialized:\n break\n time.sleep(5)\n self.connect_instructions = ''\n \n\n def initialize(self):\n # todo: do we need to wait until public_ip is available?\n assert self.public_ip\n # todo: this sometimes fails because public_ip is not ready\n # add query/wait\n self.ssh_client = _ssh_to_host(self.public_ip, SSH_KEY_PATH)\n if self.ssh_client is None:\n print(\"SSH into %s:%s failed\" %(self.job.name, self.id,))\n return\n \n # this blocks until instance is up\n self.initialized = True\n \n def run_sync(self, cmd):\n \"\"\"Runs given cmd in the task, returns stdout/stderr as strings.\n Because it blocks until cmd is done, use it for short cmds.\"\"\"\n # TODO: run doesn't preserve tty\n # find paramiko recipe to use tty and use that\n stdin, stdout, stderr = self.ssh_client.exec_command(cmd)\n stdout_str = stdout.read().decode('ascii')\n stderr_str = stderr.read().decode('ascii')\n return stdout_str, stderr_str\n\n def _setup_tasklogdir(self):\n if not os.path.exists(self.local_tasklogdir):\n os.system('mkdir -p '+self.local_tasklogdir)\n \n def run(self, cmd, mirror_output=False):\n \"\"\"Runs given command in the task, streams stdout/stderr to local files.\"\"\"\n\n assert self.initialized, (\"Trying to run command on task that's not \"\n \"initialized\")\n \n self._setup_tasklogdir()\n # todo: switch from encoded floats to integer micros\n print(\"---\", cmd)\n timestamp = _encode_float(time.time())\n stdout_fn = \"%s/%s.stdout\"%(self.local_tasklogdir, timestamp)\n stderr_fn = \"%s/%s.stderr\"%(self.local_tasklogdir, timestamp)\n self.last_stdout = stdout_fn\n self.last_stderr = stderr_fn\n\n if mirror_output:\n def line_extractor(line):\n print(line)\n else:\n line_extractor = None\n \n _ExecuteCommandInThread(ssh_client=self.ssh_client,\n cmd=cmd,\n stdout_file=stdout_fn,\n stderr_file=stderr_fn,\n line_extractor=line_extractor)\n\n def upload(self, local_file, remote_file=None):\n \"\"\"Uploads file to remote instance. If location not specified, dumps it\n in default directory with same name.\"\"\"\n self.wait_until_ready()\n\n # TODO: self.ssh_client is sometimes None\n sftp = self.ssh_client.open_sftp()\n if remote_file is None:\n remote_file = os.path.basename(local_file)\n sftp.put(local_file, remote_file)\n\n def _upload_directory(self, local_directory, remote_directory):\n pass\n \n @property\n def public_ip(self):\n self.instance.load()\n return self.instance.public_ip_address\n\n @property\n def port(self):\n return DEFAULT_PORT\n\n @property\n def ip(self): # private ip\n self.instance.load()\n return self.instance.private_ip_address\n" }, { "path": "cluster/benchmark_grpc_recv.py", "content": "#!/usr/bin/env python\n#\n# Dependencies:\n# portpicker (pip install portpicker)\n# tcmalloc4 (sudo apt-get install google-perftools)\n#\n# TODO: add baseline numbers\n# Generating profile:\n#\n# rm /tmp/profile*\n# python benchmark_grpc_recv.py --data_mb=512 --profile\n# export p=/tmp/profile.out.0_27680\n# google-pprof `which python` $p --svg > /tmp/profile.0.svg\n# export p=/tmp/profile.out.1_27683\n# google-pprof `which python` $p --svg > /tmp/profile.1.svg\n\n\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport threading\nimport time\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 1000, \"number of times to repeat experiment\")\nflags.DEFINE_integer(\"iters_per_step\", 100, \"number of additions per step\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"whether to have verbose logging\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to collect CPU profile\")\n\n# internal flags, set by client\nflags.DEFINE_string(\"task_index\", \"\", \"# of current task\")\nflags.DEFINE_string(\"port0\", \"12222\", \"port of worker1, used as master\")\nflags.DEFINE_string(\"port1\", \"12223\", \"port of worker2\")\nFLAGS = flags.FLAGS\n\n\nflags.DEFINE_string('localdir_prefix', '/temp/logs',\n 'where to mirror worker logs locally')\nflags.DEFINE_string('logdir_prefix', '/efs/logs',\n 'where to dump EFS logs')\nflags.DEFINE_string('name', 'default',\n 'tag used to keep track of machines in this experiment')\n\n\n# setup local cluster from flags\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n graph_options = tf.GraphOptions(optimizer_options=optimizer_options)\n config = tf.ConfigProto(graph_options=graph_options,\n intra_op_parallelism_threads=10,\n inter_op_parallelism_threads=10)\n\n\nhost = \"127.0.0.1\"\ndef clusterspec():\n cluster = {\"worker\": [host+\":\"+FLAGS.port0, host+\":\"+FLAGS.port1]}\n return tf.train.ClusterSpec(cluster).as_cluster_def()\n \n \ndef create_graph(device0, device1):\n \"\"\"Create graph that keeps var1 on device0, var2 on device1 and adds them\"\"\"\n \n tf.reset_default_graph()\n dtype=tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n\n with tf.device(device0):\n var1 = tf.get_variable(\"var1\", [params_size], dtype,\n initializer=tf.ones_initializer())\n with tf.device(device1):\n var2 = tf.get_variable(\"var2\", [params_size], dtype,\n initializer=tf.ones_initializer())\n add_op = var1.assign_add(var2)\n\n init_op = tf.global_variables_initializer()\n return init_op, add_op\n\ndef create_done_queue(i):\n \"\"\"Queue used to signal death for i'th worker.\"\"\"\n \n with tf.device(\"/job:worker/task:%s\" % (i)):\n return tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue\"+\n str(i))\n\nfrom tensorflow.python.summary import summary as summary_lib\nfrom tensorflow.python import pywrap_tensorflow\nfrom tensorflow.python.util import compat\nfrom tensorflow.core.util import event_pb2\nfrom tensorflow.core.framework import summary_pb2\nfrom tensorflow.python.training import training_util # TOOD: not needed?\n\n\ndef make_event(tag, value, step):\n event = event_pb2.Event(\n wall_time=time.time(),\n step=step,\n summary=summary_pb2.Summary(\n value=[summary_pb2.Summary.Value(\n tag=tag, simple_value=value)]))\n return event\n\ndef run_benchmark(sess, init_op, add_op):\n \"\"\"Returns MB/s rate of addition.\"\"\"\n\n\n logdir=FLAGS.logdir_prefix+'/'+FLAGS.name\n os.system('mkdir -p '+logdir)\n \n # TODO: make events follow same format as eager writer\n writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(logdir+'/events'))\n filename = compat.as_text(writer.FileName())\n training_util.get_or_create_global_step()\n\n sess.run(init_op)\n\n for step in range(FLAGS.iters):\n start_time = time.time()\n for i in range(FLAGS.iters_per_step):\n sess.run(add_op.op)\n\n elapsed_time = time.time() - start_time\n rate = float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n event = make_event('rate', rate, step)\n writer.WriteEvent(event)\n writer.Flush()\n writer.Close()\n # add event\n\n\ndef run_benchmark_local():\n ops = create_graph(None, None)\n sess = tf.Session(config=session_config())\n return run_benchmark(sess, *ops)\n\n\ndef run_benchmark_distributed():\n ops = create_graph(\"/job:worker/task:0\", \"/job:worker/task:1\")\n queues = [create_done_queue(0), create_done_queue(1)]\n\n # launch distributed service\n\n\n port0, port1 = [portpicker.pick_unused_port() for _ in range(2)]\n flags = \" \".join(sys.argv) # pass parent flags to children\n \n def run_worker(w):\n my_env = os.environ.copy()\n if not FLAGS.verbose:\n my_env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n my_env[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n if FLAGS.profile:\n my_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\n my_env[\"CPUPROFILE\"]=\"/tmp/profile.out.%s\"%(w)\n cmd = \"python %s --task=%d --port0=%s --port1=%s\"%(flags, w, port0, port1)\n subprocess.Popen(cmd, shell=True, stderr=subprocess.STDOUT,\n env=my_env)\n \n run_worker(0)\n run_worker(1)\n\n sess = tf.Session(\"grpc://%s:%s\"%(host, port0), config=session_config())\n rate = run_benchmark(sess, *ops)\n\n # bring down workers\n if FLAGS.verbose:\n print(\"Killing workers.\")\n sess.run(queues[1].enqueue(1))\n # todo: sleep to avoid killing master too early?\n sess.run(queues[0].enqueue(1)) # bring down master last\n \n return rate\n\nif __name__=='__main__':\n if not FLAGS.task_index:\n\n rate1 = run_benchmark_local()\n rate2 = run_benchmark_distributed()\n\n if FLAGS.verbose:\n print(\"Adding data in %d MB chunks\" %(FLAGS.data_mb))\n print(\"Local rate: %.2f MB/s\" %(rate1,))\n print(\"Distributed rate: %.2f MB/s\" %(rate2,))\n\n else: # Launch TensorFlow server\n server = tf.train.Server(clusterspec(), config=session_config(),\n job_name=\"worker\",\n task_index=int(FLAGS.task_index))\n queue = create_done_queue(FLAGS.task_index)\n sess = tf.Session(server.target, config=session_config())\n sess.run(queue.dequeue())\n time.sleep(1) # give chance for master session.run call to return\n if FLAGS.verbose:\n print(\"Worker %s quitting.\" %(FLAGS.task_index))\n" }, { "path": "cluster/benchmarks/.gitignore", "content": "*.pyc\n" }, { "path": "cluster/benchmarks/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"{}\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright {yyyy} {name of copyright owner}\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "cluster/benchmarks/README.md", "content": "# Instructions for adding distributed benchmarks to continuous run:\n\n1. You can add your benchmark file under\n [tensorflow/benchmarks/scripts](https://github.com/tensorflow/benchmarks/tree/master/scripts) directory. The benchmark should accept `task_index`, `job_name`, `ps_hosts` and `worker_hosts` flags. You can copy-paste the following flag definitions:\n\n ```python\n tf.app.flags.DEFINE_integer(\"task_index\", None, \"Task index, should be >= 0.\")\n tf.app.flags.DEFINE_string(\"job_name\", None, \"job name: worker or ps\")\n tf.app.flags.DEFINE_string(\"ps_hosts\", None, \"Comma-separated list of hostname:port pairs\")\n tf.app.flags.DEFINE_string(\"worker_hosts\", None, \"Comma-separated list of hostname:port pairs\")\n ```\n2. Report benchmark values by calling `store_data_in_json` from your benchmark\n code. This function is defined in\n [benchmark\\_util.py](https://github.com/tensorflow/benchmarks/blob/master/scripts/util/benchmark_util.py).\n3. Create a Dockerfile that sets up dependencies and runs your benchmark. For\n example, see [Dockerfile.tf\\_cnn\\_benchmarks](https://github.com/tensorflow/benchmarks/blob/master/scripts/Dockerfile.tf_cnn_benchmarks).\n4. Add the benchmark to\n [benchmark\\_configs.yml](https://github.com/tensorflow/benchmarks/blob/master/scripts/benchmark_configs.yml)\n * Set `benchmark_name` to a descriptive name for your benchmark and make sure\n it is unique.\n * Set `worker_count` and `ps_count`.\n * Set `docker_file` to the Dockerfile path starting with `benchmarks/`\n directory.\n * Optionally, you can pass flags to your benchmark by adding `args` list.\n5. Send PR with the changes to annarev.\n\nCurrently running benchmarks:\nhttps://benchmarks-dot-tensorflow-testing.appspot.com/\n\nFor any questions, please contact annarev@google.com.\n" }, { "path": "cluster/benchmarks/bower_components/d3/.bower.json", "content": "{\n \"name\": \"d3\",\n \"version\": \"3.5.5\",\n \"main\": \"d3.js\",\n \"scripts\": [\n \"d3.js\"\n ],\n \"ignore\": [\n \".DS_Store\",\n \".git\",\n \".gitignore\",\n \".npmignore\",\n \".spmignore\",\n \".travis.yml\",\n \"Makefile\",\n \"bin\",\n \"component.json\",\n \"composer.json\",\n \"index.js\",\n \"lib\",\n \"node_modules\",\n \"package.json\",\n \"src\",\n \"test\"\n ],\n \"homepage\": \"https://github.com/mbostock-bower/d3-bower\",\n \"_release\": \"3.5.5\",\n \"_resolution\": {\n \"type\": \"version\",\n \"tag\": \"v3.5.5\",\n \"commit\": \"264ea13e4ed8583b37a91f7640aa22fdee6b2f26\"\n },\n \"_source\": \"https://github.com/mbostock-bower/d3-bower.git\",\n \"_target\": \"3.5.5\",\n \"_originalSource\": \"d3\"\n}" }, { "path": "cluster/benchmarks/bower_components/d3/.gitattributes", "content": "bower.json -diff merge=ours\ncomponent.json -diff merge=ours\nd3.js -diff merge=ours\nd3.min.js -diff merge=ours\npackage.js -diff merge=ours\n" }, { "path": "cluster/benchmarks/bower_components/d3/CONTRIBUTING.md", "content": "# Contributing\n\n**Important:** these GitHub issues are for *bug reports and feature requests only*. Please use [StackOverflow](http://stackoverflow.com/questions/tagged/d3.js) or the [d3-js Google group](https://groups.google.com/d/forum/d3-js) for general help.\n\nIf you’re looking for ways to contribute, please [peruse open issues](https://github.com/mbostock/d3/issues?milestone=&page=1&state=open). The icebox is a good place to find ideas that are not currently in development. If you already have an idea, please check past issues to see whether your idea or a similar one was previously discussed.\n\nBefore submitting a pull request, consider implementing a live example first, say using [bl.ocks.org](http://bl.ocks.org). Real-world use cases go a long way to demonstrating the usefulness of a proposed feature. The more complex a feature’s implementation, the more usefulness it should provide. Share your demo using the #d3js tag on Twitter or by sending it to the [d3-js Google group](https://groups.google.com/d/forum/d3-js).\n\nIf your proposed feature does not involve changing core functionality, consider submitting it instead as a [D3 plugin](https://github.com/d3/d3-plugins). New core features should be for general use, whereas plugins are suitable for more specialized use cases. When in doubt, it’s easier to start with a plugin before “graduating” to core.\n\nTo contribute new documentation or add examples to the gallery, just [edit the Wiki](https://github.com/mbostock/d3/wiki)!\n\n## How to Submit a Pull Request\n\n1. Click the “Fork” button to create your personal fork of the D3 repository.\n\n2. After cloning your fork of the D3 repository in the terminal, run `npm install` to install D3’s dependencies.\n\n3. Create a new branch for your new feature. For example: `git checkout -b my-awesome-feature`. A dedicated branch for your pull request means you can develop multiple features at the same time, and ensures that your pull request is stable even if you later decide to develop an unrelated feature.\n\n4. The `d3.js` and `d3.min.js` files are built from source files in the `src` directory. _Do not edit `d3.js` directly._ Instead, edit the source files, and then run `make` to build the generated files.\n\n5. Use `make test` to run tests and verify your changes. If you are adding a new feature, you should add new tests! If you are changing existing functionality, make sure the existing tests run, or update them as appropriate.\n\n6. Sign D3’s [Individual Contributor License Agreement](https://docs.google.com/forms/d/1CzjdBKtDuA8WeuFJinadx956xLQ4Xriv7-oDvXnZMaI/viewform). Unless you are submitting a trivial patch (such as fixing a typo), this form is needed to verify that you are able to contribute.\n\n7. Submit your pull request, and good luck!\n" }, { "path": "cluster/benchmarks/bower_components/d3/LICENSE", "content": "Copyright (c) 2010-2015, Michael Bostock\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* The name Michael Bostock may not be used to endorse or promote products\n derived from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL MICHAEL BOSTOCK BE LIABLE FOR ANY DIRECT,\nINDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\nBUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\nDATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\nOF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\nNEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\nEVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n" }, { "path": "cluster/benchmarks/bower_components/d3/README.md", "content": "# Data-Driven Documents\n\n\n\n**D3.js** is a JavaScript library for manipulating documents based on data. **D3** helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.\n\nWant to learn more? [See the wiki.](https://github.com/mbostock/d3/wiki)\n\nFor examples, [see the gallery](https://github.com/mbostock/d3/wiki/Gallery) and [mbostock’s bl.ocks](http://bl.ocks.org/mbostock).\n" }, { "path": "cluster/benchmarks/bower_components/d3/bower.json", "content": "{\n \"name\": \"d3\",\n \"version\": \"3.5.5\",\n \"main\": \"d3.js\",\n \"scripts\": [\n \"d3.js\"\n ],\n \"ignore\": [\n \".DS_Store\",\n \".git\",\n \".gitignore\",\n \".npmignore\",\n \".spmignore\",\n \".travis.yml\",\n \"Makefile\",\n \"bin\",\n \"component.json\",\n \"composer.json\",\n \"index.js\",\n \"lib\",\n \"node_modules\",\n \"package.json\",\n \"src\",\n \"test\"\n ]\n}\n" }, { "path": "cluster/benchmarks/bower_components/d3/d3.js", "content": "!function() {\n var d3 = {\n version: \"3.5.5\"\n };\n var d3_arraySlice = [].slice, d3_array = function(list) {\n return d3_arraySlice.call(list);\n };\n var d3_document = this.document;\n function d3_documentElement(node) {\n return node && (node.ownerDocument || node.document || node).documentElement;\n }\n function d3_window(node) {\n return node && (node.ownerDocument && node.ownerDocument.defaultView || node.document && node || node.defaultView);\n }\n if (d3_document) {\n try {\n d3_array(d3_document.documentElement.childNodes)[0].nodeType;\n } catch (e) {\n d3_array = function(list) {\n var i = list.length, array = new Array(i);\n while (i--) array[i] = list[i];\n return array;\n };\n }\n }\n if (!Date.now) Date.now = function() {\n return +new Date();\n };\n if (d3_document) {\n try {\n d3_document.createElement(\"DIV\").style.setProperty(\"opacity\", 0, \"\");\n } catch (error) {\n var d3_element_prototype = this.Element.prototype, d3_element_setAttribute = d3_element_prototype.setAttribute, d3_element_setAttributeNS = d3_element_prototype.setAttributeNS, d3_style_prototype = this.CSSStyleDeclaration.prototype, d3_style_setProperty = d3_style_prototype.setProperty;\n d3_element_prototype.setAttribute = function(name, value) {\n d3_element_setAttribute.call(this, name, value + \"\");\n };\n d3_element_prototype.setAttributeNS = function(space, local, value) {\n d3_element_setAttributeNS.call(this, space, local, value + \"\");\n };\n d3_style_prototype.setProperty = function(name, value, priority) {\n d3_style_setProperty.call(this, name, value + \"\", priority);\n };\n }\n }\n d3.ascending = d3_ascending;\n function d3_ascending(a, b) {\n return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN;\n }\n d3.descending = function(a, b) {\n return b < a ? -1 : b > a ? 1 : b >= a ? 0 : NaN;\n };\n d3.min = function(array, f) {\n var i = -1, n = array.length, a, b;\n if (arguments.length === 1) {\n while (++i < n) if ((b = array[i]) != null && b >= b) {\n a = b;\n break;\n }\n while (++i < n) if ((b = array[i]) != null && a > b) a = b;\n } else {\n while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n a = b;\n break;\n }\n while (++i < n) if ((b = f.call(array, array[i], i)) != null && a > b) a = b;\n }\n return a;\n };\n d3.max = function(array, f) {\n var i = -1, n = array.length, a, b;\n if (arguments.length === 1) {\n while (++i < n) if ((b = array[i]) != null && b >= b) {\n a = b;\n break;\n }\n while (++i < n) if ((b = array[i]) != null && b > a) a = b;\n } else {\n while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n a = b;\n break;\n }\n while (++i < n) if ((b = f.call(array, array[i], i)) != null && b > a) a = b;\n }\n return a;\n };\n d3.extent = function(array, f) {\n var i = -1, n = array.length, a, b, c;\n if (arguments.length === 1) {\n while (++i < n) if ((b = array[i]) != null && b >= b) {\n a = c = b;\n break;\n }\n while (++i < n) if ((b = array[i]) != null) {\n if (a > b) a = b;\n if (c < b) c = b;\n }\n } else {\n while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) {\n a = c = b;\n break;\n }\n while (++i < n) if ((b = f.call(array, array[i], i)) != null) {\n if (a > b) a = b;\n if (c < b) c = b;\n }\n }\n return [ a, c ];\n };\n function d3_number(x) {\n return x === null ? NaN : +x;\n }\n function d3_numeric(x) {\n return !isNaN(x);\n }\n d3.sum = function(array, f) {\n var s = 0, n = array.length, a, i = -1;\n if (arguments.length === 1) {\n while (++i < n) if (d3_numeric(a = +array[i])) s += a;\n } else {\n while (++i < n) if (d3_numeric(a = +f.call(array, array[i], i))) s += a;\n }\n return s;\n };\n d3.mean = function(array, f) {\n var s = 0, n = array.length, a, i = -1, j = n;\n if (arguments.length === 1) {\n while (++i < n) if (d3_numeric(a = d3_number(array[i]))) s += a; else --j;\n } else {\n while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) s += a; else --j;\n }\n if (j) return s / j;\n };\n d3.quantile = function(values, p) {\n var H = (values.length - 1) * p + 1, h = Math.floor(H), v = +values[h - 1], e = H - h;\n return e ? v + e * (values[h] - v) : v;\n };\n d3.median = function(array, f) {\n var numbers = [], n = array.length, a, i = -1;\n if (arguments.length === 1) {\n while (++i < n) if (d3_numeric(a = d3_number(array[i]))) numbers.push(a);\n } else {\n while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) numbers.push(a);\n }\n if (numbers.length) return d3.quantile(numbers.sort(d3_ascending), .5);\n };\n d3.variance = function(array, f) {\n var n = array.length, m = 0, a, d, s = 0, i = -1, j = 0;\n if (arguments.length === 1) {\n while (++i < n) {\n if (d3_numeric(a = d3_number(array[i]))) {\n d = a - m;\n m += d / ++j;\n s += d * (a - m);\n }\n }\n } else {\n while (++i < n) {\n if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) {\n d = a - m;\n m += d / ++j;\n s += d * (a - m);\n }\n }\n }\n if (j > 1) return s / (j - 1);\n };\n d3.deviation = function() {\n var v = d3.variance.apply(this, arguments);\n return v ? Math.sqrt(v) : v;\n };\n function d3_bisector(compare) {\n return {\n left: function(a, x, lo, hi) {\n if (arguments.length < 3) lo = 0;\n if (arguments.length < 4) hi = a.length;\n while (lo < hi) {\n var mid = lo + hi >>> 1;\n if (compare(a[mid], x) < 0) lo = mid + 1; else hi = mid;\n }\n return lo;\n },\n right: function(a, x, lo, hi) {\n if (arguments.length < 3) lo = 0;\n if (arguments.length < 4) hi = a.length;\n while (lo < hi) {\n var mid = lo + hi >>> 1;\n if (compare(a[mid], x) > 0) hi = mid; else lo = mid + 1;\n }\n return lo;\n }\n };\n }\n var d3_bisect = d3_bisector(d3_ascending);\n d3.bisectLeft = d3_bisect.left;\n d3.bisect = d3.bisectRight = d3_bisect.right;\n d3.bisector = function(f) {\n return d3_bisector(f.length === 1 ? function(d, x) {\n return d3_ascending(f(d), x);\n } : f);\n };\n d3.shuffle = function(array, i0, i1) {\n if ((m = arguments.length) < 3) {\n i1 = array.length;\n if (m < 2) i0 = 0;\n }\n var m = i1 - i0, t, i;\n while (m) {\n i = Math.random() * m-- | 0;\n t = array[m + i0], array[m + i0] = array[i + i0], array[i + i0] = t;\n }\n return array;\n };\n d3.permute = function(array, indexes) {\n var i = indexes.length, permutes = new Array(i);\n while (i--) permutes[i] = array[indexes[i]];\n return permutes;\n };\n d3.pairs = function(array) {\n var i = 0, n = array.length - 1, p0, p1 = array[0], pairs = new Array(n < 0 ? 0 : n);\n while (i < n) pairs[i] = [ p0 = p1, p1 = array[++i] ];\n return pairs;\n };\n d3.zip = function() {\n if (!(n = arguments.length)) return [];\n for (var i = -1, m = d3.min(arguments, d3_zipLength), zips = new Array(m); ++i < m; ) {\n for (var j = -1, n, zip = zips[i] = new Array(n); ++j < n; ) {\n zip[j] = arguments[j][i];\n }\n }\n return zips;\n };\n function d3_zipLength(d) {\n return d.length;\n }\n d3.transpose = function(matrix) {\n return d3.zip.apply(d3, matrix);\n };\n d3.keys = function(map) {\n var keys = [];\n for (var key in map) keys.push(key);\n return keys;\n };\n d3.values = function(map) {\n var values = [];\n for (var key in map) values.push(map[key]);\n return values;\n };\n d3.entries = function(map) {\n var entries = [];\n for (var key in map) entries.push({\n key: key,\n value: map[key]\n });\n return entries;\n };\n d3.merge = function(arrays) {\n var n = arrays.length, m, i = -1, j = 0, merged, array;\n while (++i < n) j += arrays[i].length;\n merged = new Array(j);\n while (--n >= 0) {\n array = arrays[n];\n m = array.length;\n while (--m >= 0) {\n merged[--j] = array[m];\n }\n }\n return merged;\n };\n var abs = Math.abs;\n d3.range = function(start, stop, step) {\n if (arguments.length < 3) {\n step = 1;\n if (arguments.length < 2) {\n stop = start;\n start = 0;\n }\n }\n if ((stop - start) / step === Infinity) throw new Error(\"infinite range\");\n var range = [], k = d3_range_integerScale(abs(step)), i = -1, j;\n start *= k, stop *= k, step *= k;\n if (step < 0) while ((j = start + step * ++i) > stop) range.push(j / k); else while ((j = start + step * ++i) < stop) range.push(j / k);\n return range;\n };\n function d3_range_integerScale(x) {\n var k = 1;\n while (x * k % 1) k *= 10;\n return k;\n }\n function d3_class(ctor, properties) {\n for (var key in properties) {\n Object.defineProperty(ctor.prototype, key, {\n value: properties[key],\n enumerable: false\n });\n }\n }\n d3.map = function(object, f) {\n var map = new d3_Map();\n if (object instanceof d3_Map) {\n object.forEach(function(key, value) {\n map.set(key, value);\n });\n } else if (Array.isArray(object)) {\n var i = -1, n = object.length, o;\n if (arguments.length === 1) while (++i < n) map.set(i, object[i]); else while (++i < n) map.set(f.call(object, o = object[i], i), o);\n } else {\n for (var key in object) map.set(key, object[key]);\n }\n return map;\n };\n function d3_Map() {\n this._ = Object.create(null);\n }\n var d3_map_proto = \"__proto__\", d3_map_zero = \"\\x00\";\n d3_class(d3_Map, {\n has: d3_map_has,\n get: function(key) {\n return this._[d3_map_escape(key)];\n },\n set: function(key, value) {\n return this._[d3_map_escape(key)] = value;\n },\n remove: d3_map_remove,\n keys: d3_map_keys,\n values: function() {\n var values = [];\n for (var key in this._) values.push(this._[key]);\n return values;\n },\n entries: function() {\n var entries = [];\n for (var key in this._) entries.push({\n key: d3_map_unescape(key),\n value: this._[key]\n });\n return entries;\n },\n size: d3_map_size,\n empty: d3_map_empty,\n forEach: function(f) {\n for (var key in this._) f.call(this, d3_map_unescape(key), this._[key]);\n }\n });\n function d3_map_escape(key) {\n return (key += \"\") === d3_map_proto || key[0] === d3_map_zero ? d3_map_zero + key : key;\n }\n function d3_map_unescape(key) {\n return (key += \"\")[0] === d3_map_zero ? key.slice(1) : key;\n }\n function d3_map_has(key) {\n return d3_map_escape(key) in this._;\n }\n function d3_map_remove(key) {\n return (key = d3_map_escape(key)) in this._ && delete this._[key];\n }\n function d3_map_keys() {\n var keys = [];\n for (var key in this._) keys.push(d3_map_unescape(key));\n return keys;\n }\n function d3_map_size() {\n var size = 0;\n for (var key in this._) ++size;\n return size;\n }\n function d3_map_empty() {\n for (var key in this._) return false;\n return true;\n }\n d3.nest = function() {\n var nest = {}, keys = [], sortKeys = [], sortValues, rollup;\n function map(mapType, array, depth) {\n if (depth >= keys.length) return rollup ? rollup.call(nest, array) : sortValues ? array.sort(sortValues) : array;\n var i = -1, n = array.length, key = keys[depth++], keyValue, object, setter, valuesByKey = new d3_Map(), values;\n while (++i < n) {\n if (values = valuesByKey.get(keyValue = key(object = array[i]))) {\n values.push(object);\n } else {\n valuesByKey.set(keyValue, [ object ]);\n }\n }\n if (mapType) {\n object = mapType();\n setter = function(keyValue, values) {\n object.set(keyValue, map(mapType, values, depth));\n };\n } else {\n object = {};\n setter = function(keyValue, values) {\n object[keyValue] = map(mapType, values, depth);\n };\n }\n valuesByKey.forEach(setter);\n return object;\n }\n function entries(map, depth) {\n if (depth >= keys.length) return map;\n var array = [], sortKey = sortKeys[depth++];\n map.forEach(function(key, keyMap) {\n array.push({\n key: key,\n values: entries(keyMap, depth)\n });\n });\n return sortKey ? array.sort(function(a, b) {\n return sortKey(a.key, b.key);\n }) : array;\n }\n nest.map = function(array, mapType) {\n return map(mapType, array, 0);\n };\n nest.entries = function(array) {\n return entries(map(d3.map, array, 0), 0);\n };\n nest.key = function(d) {\n keys.push(d);\n return nest;\n };\n nest.sortKeys = function(order) {\n sortKeys[keys.length - 1] = order;\n return nest;\n };\n nest.sortValues = function(order) {\n sortValues = order;\n return nest;\n };\n nest.rollup = function(f) {\n rollup = f;\n return nest;\n };\n return nest;\n };\n d3.set = function(array) {\n var set = new d3_Set();\n if (array) for (var i = 0, n = array.length; i < n; ++i) set.add(array[i]);\n return set;\n };\n function d3_Set() {\n this._ = Object.create(null);\n }\n d3_class(d3_Set, {\n has: d3_map_has,\n add: function(key) {\n this._[d3_map_escape(key += \"\")] = true;\n return key;\n },\n remove: d3_map_remove,\n values: d3_map_keys,\n size: d3_map_size,\n empty: d3_map_empty,\n forEach: function(f) {\n for (var key in this._) f.call(this, d3_map_unescape(key));\n }\n });\n d3.behavior = {};\n function d3_identity(d) {\n return d;\n }\n d3.rebind = function(target, source) {\n var i = 1, n = arguments.length, method;\n while (++i < n) target[method = arguments[i]] = d3_rebind(target, source, source[method]);\n return target;\n };\n function d3_rebind(target, source, method) {\n return function() {\n var value = method.apply(source, arguments);\n return value === source ? target : value;\n };\n }\n function d3_vendorSymbol(object, name) {\n if (name in object) return name;\n name = name.charAt(0).toUpperCase() + name.slice(1);\n for (var i = 0, n = d3_vendorPrefixes.length; i < n; ++i) {\n var prefixName = d3_vendorPrefixes[i] + name;\n if (prefixName in object) return prefixName;\n }\n }\n var d3_vendorPrefixes = [ \"webkit\", \"ms\", \"moz\", \"Moz\", \"o\", \"O\" ];\n function d3_noop() {}\n d3.dispatch = function() {\n var dispatch = new d3_dispatch(), i = -1, n = arguments.length;\n while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch);\n return dispatch;\n };\n function d3_dispatch() {}\n d3_dispatch.prototype.on = function(type, listener) {\n var i = type.indexOf(\".\"), name = \"\";\n if (i >= 0) {\n name = type.slice(i + 1);\n type = type.slice(0, i);\n }\n if (type) return arguments.length < 2 ? this[type].on(name) : this[type].on(name, listener);\n if (arguments.length === 2) {\n if (listener == null) for (type in this) {\n if (this.hasOwnProperty(type)) this[type].on(name, null);\n }\n return this;\n }\n };\n function d3_dispatch_event(dispatch) {\n var listeners = [], listenerByName = new d3_Map();\n function event() {\n var z = listeners, i = -1, n = z.length, l;\n while (++i < n) if (l = z[i].on) l.apply(this, arguments);\n return dispatch;\n }\n event.on = function(name, listener) {\n var l = listenerByName.get(name), i;\n if (arguments.length < 2) return l && l.on;\n if (l) {\n l.on = null;\n listeners = listeners.slice(0, i = listeners.indexOf(l)).concat(listeners.slice(i + 1));\n listenerByName.remove(name);\n }\n if (listener) listeners.push(listenerByName.set(name, {\n on: listener\n }));\n return dispatch;\n };\n return event;\n }\n d3.event = null;\n function d3_eventPreventDefault() {\n d3.event.preventDefault();\n }\n function d3_eventSource() {\n var e = d3.event, s;\n while (s = e.sourceEvent) e = s;\n return e;\n }\n function d3_eventDispatch(target) {\n var dispatch = new d3_dispatch(), i = 0, n = arguments.length;\n while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch);\n dispatch.of = function(thiz, argumentz) {\n return function(e1) {\n try {\n var e0 = e1.sourceEvent = d3.event;\n e1.target = target;\n d3.event = e1;\n dispatch[e1.type].apply(thiz, argumentz);\n } finally {\n d3.event = e0;\n }\n };\n };\n return dispatch;\n }\n d3.requote = function(s) {\n return s.replace(d3_requote_re, \"\\\\$&\");\n };\n var d3_requote_re = /[\\\\\\^\\$\\*\\+\\?\\|\\[\\]\\(\\)\\.\\{\\}]/g;\n var d3_subclass = {}.__proto__ ? function(object, prototype) {\n object.__proto__ = prototype;\n } : function(object, prototype) {\n for (var property in prototype) object[property] = prototype[property];\n };\n function d3_selection(groups) {\n d3_subclass(groups, d3_selectionPrototype);\n return groups;\n }\n var d3_select = function(s, n) {\n return n.querySelector(s);\n }, d3_selectAll = function(s, n) {\n return n.querySelectorAll(s);\n }, d3_selectMatches = function(n, s) {\n var d3_selectMatcher = n.matches || n[d3_vendorSymbol(n, \"matchesSelector\")];\n d3_selectMatches = function(n, s) {\n return d3_selectMatcher.call(n, s);\n };\n return d3_selectMatches(n, s);\n };\n if (typeof Sizzle === \"function\") {\n d3_select = function(s, n) {\n return Sizzle(s, n)[0] || null;\n };\n d3_selectAll = Sizzle;\n d3_selectMatches = Sizzle.matchesSelector;\n }\n d3.selection = function() {\n return d3.select(d3_document.documentElement);\n };\n var d3_selectionPrototype = d3.selection.prototype = [];\n d3_selectionPrototype.select = function(selector) {\n var subgroups = [], subgroup, subnode, group, node;\n selector = d3_selection_selector(selector);\n for (var j = -1, m = this.length; ++j < m; ) {\n subgroups.push(subgroup = []);\n subgroup.parentNode = (group = this[j]).parentNode;\n for (var i = -1, n = group.length; ++i < n; ) {\n if (node = group[i]) {\n subgroup.push(subnode = selector.call(node, node.__data__, i, j));\n if (subnode && \"__data__\" in node) subnode.__data__ = node.__data__;\n } else {\n subgroup.push(null);\n }\n }\n }\n return d3_selection(subgroups);\n };\n function d3_selection_selector(selector) {\n return typeof selector === \"function\" ? selector : function() {\n return d3_select(selector, this);\n };\n }\n d3_selectionPrototype.selectAll = function(selector) {\n var subgroups = [], subgroup, node;\n selector = d3_selection_selectorAll(selector);\n for (var j = -1, m = this.length; ++j < m; ) {\n for (var group = this[j], i = -1, n = group.length; ++i < n; ) {\n if (node = group[i]) {\n subgroups.push(subgroup = d3_array(selector.call(node, node.__data__, i, j)));\n subgroup.parentNode = node;\n }\n }\n }\n return d3_selection(subgroups);\n };\n function d3_selection_selectorAll(selector) {\n return typeof selector === \"function\" ? selector : function() {\n return d3_selectAll(selector, this);\n };\n }\n var d3_nsPrefix = {\n svg: \"http://www.w3.org/2000/svg\",\n xhtml: \"http://www.w3.org/1999/xhtml\",\n xlink: \"http://www.w3.org/1999/xlink\",\n xml: \"http://www.w3.org/XML/1998/namespace\",\n xmlns: \"http://www.w3.org/2000/xmlns/\"\n };\n d3.ns = {\n prefix: d3_nsPrefix,\n qualify: function(name) {\n var i = name.indexOf(\":\"), prefix = name;\n if (i >= 0) {\n prefix = name.slice(0, i);\n name = name.slice(i + 1);\n }\n return d3_nsPrefix.hasOwnProperty(prefix) ? {\n space: d3_nsPrefix[prefix],\n local: name\n } : name;\n }\n };\n d3_selectionPrototype.attr = function(name, value) {\n if (arguments.length < 2) {\n if (typeof name === \"string\") {\n var node = this.node();\n name = d3.ns.qualify(name);\n return name.local ? node.getAttributeNS(name.space, name.local) : node.getAttribute(name);\n }\n for (value in name) this.each(d3_selection_attr(value, name[value]));\n return this;\n }\n return this.each(d3_selection_attr(name, value));\n };\n function d3_selection_attr(name, value) {\n name = d3.ns.qualify(name);\n function attrNull() {\n this.removeAttribute(name);\n }\n function attrNullNS() {\n this.removeAttributeNS(name.space, name.local);\n }\n function attrConstant() {\n this.setAttribute(name, value);\n }\n function attrConstantNS() {\n this.setAttributeNS(name.space, name.local, value);\n }\n function attrFunction() {\n var x = value.apply(this, arguments);\n if (x == null) this.removeAttribute(name); else this.setAttribute(name, x);\n }\n function attrFunctionNS() {\n var x = value.apply(this, arguments);\n if (x == null) this.removeAttributeNS(name.space, name.local); else this.setAttributeNS(name.space, name.local, x);\n }\n return value == null ? name.local ? attrNullNS : attrNull : typeof value === \"function\" ? name.local ? attrFunctionNS : attrFunction : name.local ? attrConstantNS : attrConstant;\n }\n function d3_collapse(s) {\n return s.trim().replace(/\\s+/g, \" \");\n }\n d3_selectionPrototype.classed = function(name, value) {\n if (arguments.length < 2) {\n if (typeof name === \"string\") {\n var node = this.node(), n = (name = d3_selection_classes(name)).length, i = -1;\n if (value = node.classList) {\n while (++i < n) if (!value.contains(name[i])) return false;\n } else {\n value = node.getAttribute(\"class\");\n while (++i < n) if (!d3_selection_classedRe(name[i]).test(value)) return false;\n }\n return true;\n }\n for (value in name) this.each(d3_selection_classed(value, name[value]));\n return this;\n }\n return this.each(d3_selection_classed(name, value));\n };\n function d3_selection_classedRe(name) {\n return new RegExp(\"(?:^|\\\\s+)\" + d3.requote(name) + \"(?:\\\\s+|$)\", \"g\");\n }\n function d3_selection_classes(name) {\n return (name + \"\").trim().split(/^|\\s+/);\n }\n function d3_selection_classed(name, value) {\n name = d3_selection_classes(name).map(d3_selection_classedName);\n var n = name.length;\n function classedConstant() {\n var i = -1;\n while (++i < n) name[i](this, value);\n }\n function classedFunction() {\n var i = -1, x = value.apply(this, arguments);\n while (++i < n) name[i](this, x);\n }\n return typeof value === \"function\" ? classedFunction : classedConstant;\n }\n function d3_selection_classedName(name) {\n var re = d3_selection_classedRe(name);\n return function(node, value) {\n if (c = node.classList) return value ? c.add(name) : c.remove(name);\n var c = node.getAttribute(\"class\") || \"\";\n if (value) {\n re.lastIndex = 0;\n if (!re.test(c)) node.setAttribute(\"class\", d3_collapse(c + \" \" + name));\n } else {\n node.setAttribute(\"class\", d3_collapse(c.replace(re, \" \")));\n }\n };\n }\n d3_selectionPrototype.style = function(name, value, priority) {\n var n = arguments.length;\n if (n < 3) {\n if (typeof name !== \"string\") {\n if (n < 2) value = \"\";\n for (priority in name) this.each(d3_selection_style(priority, name[priority], value));\n return this;\n }\n if (n < 2) {\n var node = this.node();\n return d3_window(node).getComputedStyle(node, null).getPropertyValue(name);\n }\n priority = \"\";\n }\n return this.each(d3_selection_style(name, value, priority));\n };\n function d3_selection_style(name, value, priority) {\n function styleNull() {\n this.style.removeProperty(name);\n }\n function styleConstant() {\n this.style.setProperty(name, value, priority);\n }\n function styleFunction() {\n var x = value.apply(this, arguments);\n if (x == null) this.style.removeProperty(name); else this.style.setProperty(name, x, priority);\n }\n return value == null ? styleNull : typeof value === \"function\" ? styleFunction : styleConstant;\n }\n d3_selectionPrototype.property = function(name, value) {\n if (arguments.length < 2) {\n if (typeof name === \"string\") return this.node()[name];\n for (value in name) this.each(d3_selection_property(value, name[value]));\n return this;\n }\n return this.each(d3_selection_property(name, value));\n };\n function d3_selection_property(name, value) {\n function propertyNull() {\n delete this[name];\n }\n function propertyConstant() {\n this[name] = value;\n }\n function propertyFunction() {\n var x = value.apply(this, arguments);\n if (x == null) delete this[name]; else this[name] = x;\n }\n return value == null ? propertyNull : typeof value === \"function\" ? propertyFunction : propertyConstant;\n }\n d3_selectionPrototype.text = function(value) {\n return arguments.length ? this.each(typeof value === \"function\" ? function() {\n var v = value.apply(this, arguments);\n this.textContent = v == null ? \"\" : v;\n } : value == null ? function() {\n this.textContent = \"\";\n } : function() {\n this.textContent = value;\n }) : this.node().textContent;\n };\n d3_selectionPrototype.html = function(value) {\n return arguments.length ? this.each(typeof value === \"function\" ? function() {\n var v = value.apply(this, arguments);\n this.innerHTML = v == null ? \"\" : v;\n } : value == null ? function() {\n this.innerHTML = \"\";\n } : function() {\n this.innerHTML = value;\n }) : this.node().innerHTML;\n };\n d3_selectionPrototype.append = function(name) {\n name = d3_selection_creator(name);\n return this.select(function() {\n return this.appendChild(name.apply(this, arguments));\n });\n };\n function d3_selection_creator(name) {\n function create() {\n var document = this.ownerDocument, namespace = this.namespaceURI;\n return namespace ? document.createElementNS(namespace, name) : document.createElement(name);\n }\n function createNS() {\n return this.ownerDocument.createElementNS(name.space, name.local);\n }\n return typeof name === \"function\" ? name : (name = d3.ns.qualify(name)).local ? createNS : create;\n }\n d3_selectionPrototype.insert = function(name, before) {\n name = d3_selection_creator(name);\n before = d3_selection_selector(before);\n return this.select(function() {\n return this.insertBefore(name.apply(this, arguments), before.apply(this, arguments) || null);\n });\n };\n d3_selectionPrototype.remove = function() {\n return this.each(d3_selectionRemove);\n };\n function d3_selectionRemove() {\n var parent = this.parentNode;\n if (parent) parent.removeChild(this);\n }\n d3_selectionPrototype.data = function(value, key) {\n var i = -1, n = this.length, group, node;\n if (!arguments.length) {\n value = new Array(n = (group = this[0]).length);\n while (++i < n) {\n if (node = group[i]) {\n value[i] = node.__data__;\n }\n }\n return value;\n }\n function bind(group, groupData) {\n var i, n = group.length, m = groupData.length, n0 = Math.min(n, m), updateNodes = new Array(m), enterNodes = new Array(m), exitNodes = new Array(n), node, nodeData;\n if (key) {\n var nodeByKeyValue = new d3_Map(), keyValues = new Array(n), keyValue;\n for (i = -1; ++i < n; ) {\n if (nodeByKeyValue.has(keyValue = key.call(node = group[i], node.__data__, i))) {\n exitNodes[i] = node;\n } else {\n nodeByKeyValue.set(keyValue, node);\n }\n keyValues[i] = keyValue;\n }\n for (i = -1; ++i < m; ) {\n if (!(node = nodeByKeyValue.get(keyValue = key.call(groupData, nodeData = groupData[i], i)))) {\n enterNodes[i] = d3_selection_dataNode(nodeData);\n } else if (node !== true) {\n updateNodes[i] = node;\n node.__data__ = nodeData;\n }\n nodeByKeyValue.set(keyValue, true);\n }\n for (i = -1; ++i < n; ) {\n if (nodeByKeyValue.get(keyValues[i]) !== true) {\n exitNodes[i] = group[i];\n }\n }\n } else {\n for (i = -1; ++i < n0; ) {\n node = group[i];\n nodeData = groupData[i];\n if (node) {\n node.__data__ = nodeData;\n updateNodes[i] = node;\n } else {\n enterNodes[i] = d3_selection_dataNode(nodeData);\n }\n }\n for (;i < m; ++i) {\n enterNodes[i] = d3_selection_dataNode(groupData[i]);\n }\n for (;i < n; ++i) {\n exitNodes[i] = group[i];\n }\n }\n enterNodes.update = updateNodes;\n enterNodes.parentNode = updateNodes.parentNode = exitNodes.parentNode = group.parentNode;\n enter.push(enterNodes);\n update.push(updateNodes);\n exit.push(exitNodes);\n }\n var enter = d3_selection_enter([]), update = d3_selection([]), exit = d3_selection([]);\n if (typeof value === \"function\") {\n while (++i < n) {\n bind(group = this[i], value.call(group, group.parentNode.__data__, i));\n }\n } else {\n while (++i < n) {\n bind(group = this[i], value);\n }\n }\n update.enter = function() {\n return enter;\n };\n update.exit = function() {\n return exit;\n };\n return update;\n };\n function d3_selection_dataNode(data) {\n return {\n __data__: data\n };\n }\n d3_selectionPrototype.datum = function(value) {\n return arguments.length ? this.property(\"__data__\", value) : this.property(\"__data__\");\n };\n d3_selectionPrototype.filter = function(filter) {\n var subgroups = [], subgroup, group, node;\n if (typeof filter !== \"function\") filter = d3_selection_filter(filter);\n for (var j = 0, m = this.length; j < m; j++) {\n subgroups.push(subgroup = []);\n subgroup.parentNode = (group = this[j]).parentNode;\n for (var i = 0, n = group.length; i < n; i++) {\n if ((node = group[i]) && filter.call(node, node.__data__, i, j)) {\n subgroup.push(node);\n }\n }\n }\n return d3_selection(subgroups);\n };\n function d3_selection_filter(selector) {\n return function() {\n return d3_selectMatches(this, selector);\n };\n }\n d3_selectionPrototype.order = function() {\n for (var j = -1, m = this.length; ++j < m; ) {\n for (var group = this[j], i = group.length - 1, next = group[i], node; --i >= 0; ) {\n if (node = group[i]) {\n if (next && next !== node.nextSibling) next.parentNode.insertBefore(node, next);\n next = node;\n }\n }\n }\n return this;\n };\n d3_selectionPrototype.sort = function(comparator) {\n comparator = d3_selection_sortComparator.apply(this, arguments);\n for (var j = -1, m = this.length; ++j < m; ) this[j].sort(comparator);\n return this.order();\n };\n function d3_selection_sortComparator(comparator) {\n if (!arguments.length) comparator = d3_ascending;\n return function(a, b) {\n return a && b ? comparator(a.__data__, b.__data__) : !a - !b;\n };\n }\n d3_selectionPrototype.each = function(callback) {\n return d3_selection_each(this, function(node, i, j) {\n callback.call(node, node.__data__, i, j);\n });\n };\n function d3_selection_each(groups, callback) {\n for (var j = 0, m = groups.length; j < m; j++) {\n for (var group = groups[j], i = 0, n = group.length, node; i < n; i++) {\n if (node = group[i]) callback(node, i, j);\n }\n }\n return groups;\n }\n d3_selectionPrototype.call = function(callback) {\n var args = d3_array(arguments);\n callback.apply(args[0] = this, args);\n return this;\n };\n d3_selectionPrototype.empty = function() {\n return !this.node();\n };\n d3_selectionPrototype.node = function() {\n for (var j = 0, m = this.length; j < m; j++) {\n for (var group = this[j], i = 0, n = group.length; i < n; i++) {\n var node = group[i];\n if (node) return node;\n }\n }\n return null;\n };\n d3_selectionPrototype.size = function() {\n var n = 0;\n d3_selection_each(this, function() {\n ++n;\n });\n return n;\n };\n function d3_selection_enter(selection) {\n d3_subclass(selection, d3_selection_enterPrototype);\n return selection;\n }\n var d3_selection_enterPrototype = [];\n d3.selection.enter = d3_selection_enter;\n d3.selection.enter.prototype = d3_selection_enterPrototype;\n d3_selection_enterPrototype.append = d3_selectionPrototype.append;\n d3_selection_enterPrototype.empty = d3_selectionPrototype.empty;\n d3_selection_enterPrototype.node = d3_selectionPrototype.node;\n d3_selection_enterPrototype.call = d3_selectionPrototype.call;\n d3_selection_enterPrototype.size = d3_selectionPrototype.size;\n d3_selection_enterPrototype.select = function(selector) {\n var subgroups = [], subgroup, subnode, upgroup, group, node;\n for (var j = -1, m = this.length; ++j < m; ) {\n upgroup = (group = this[j]).update;\n subgroups.push(subgroup = []);\n subgroup.parentNode = group.parentNode;\n for (var i = -1, n = group.length; ++i < n; ) {\n if (node = group[i]) {\n subgroup.push(upgroup[i] = subnode = selector.call(group.parentNode, node.__data__, i, j));\n subnode.__data__ = node.__data__;\n } else {\n subgroup.push(null);\n }\n }\n }\n return d3_selection(subgroups);\n };\n d3_selection_enterPrototype.insert = function(name, before) {\n if (arguments.length < 2) before = d3_selection_enterInsertBefore(this);\n return d3_selectionPrototype.insert.call(this, name, before);\n };\n function d3_selection_enterInsertBefore(enter) {\n var i0, j0;\n return function(d, i, j) {\n var group = enter[j].update, n = group.length, node;\n if (j != j0) j0 = j, i0 = 0;\n if (i >= i0) i0 = i + 1;\n while (!(node = group[i0]) && ++i0 < n) ;\n return node;\n };\n }\n d3.select = function(node) {\n var group;\n if (typeof node === \"string\") {\n group = [ d3_select(node, d3_document) ];\n group.parentNode = d3_document.documentElement;\n } else {\n group = [ node ];\n group.parentNode = d3_documentElement(node);\n }\n return d3_selection([ group ]);\n };\n d3.selectAll = function(nodes) {\n var group;\n if (typeof nodes === \"string\") {\n group = d3_array(d3_selectAll(nodes, d3_document));\n group.parentNode = d3_document.documentElement;\n } else {\n group = nodes;\n group.parentNode = null;\n }\n return d3_selection([ group ]);\n };\n d3_selectionPrototype.on = function(type, listener, capture) {\n var n = arguments.length;\n if (n < 3) {\n if (typeof type !== \"string\") {\n if (n < 2) listener = false;\n for (capture in type) this.each(d3_selection_on(capture, type[capture], listener));\n return this;\n }\n if (n < 2) return (n = this.node()[\"__on\" + type]) && n._;\n capture = false;\n }\n return this.each(d3_selection_on(type, listener, capture));\n };\n function d3_selection_on(type, listener, capture) {\n var name = \"__on\" + type, i = type.indexOf(\".\"), wrap = d3_selection_onListener;\n if (i > 0) type = type.slice(0, i);\n var filter = d3_selection_onFilters.get(type);\n if (filter) type = filter, wrap = d3_selection_onFilter;\n function onRemove() {\n var l = this[name];\n if (l) {\n this.removeEventListener(type, l, l.$);\n delete this[name];\n }\n }\n function onAdd() {\n var l = wrap(listener, d3_array(arguments));\n onRemove.call(this);\n this.addEventListener(type, this[name] = l, l.$ = capture);\n l._ = listener;\n }\n function removeAll() {\n var re = new RegExp(\"^__on([^.]+)\" + d3.requote(type) + \"$\"), match;\n for (var name in this) {\n if (match = name.match(re)) {\n var l = this[name];\n this.removeEventListener(match[1], l, l.$);\n delete this[name];\n }\n }\n }\n return i ? listener ? onAdd : onRemove : listener ? d3_noop : removeAll;\n }\n var d3_selection_onFilters = d3.map({\n mouseenter: \"mouseover\",\n mouseleave: \"mouseout\"\n });\n if (d3_document) {\n d3_selection_onFilters.forEach(function(k) {\n if (\"on\" + k in d3_document) d3_selection_onFilters.remove(k);\n });\n }\n function d3_selection_onListener(listener, argumentz) {\n return function(e) {\n var o = d3.event;\n d3.event = e;\n argumentz[0] = this.__data__;\n try {\n listener.apply(this, argumentz);\n } finally {\n d3.event = o;\n }\n };\n }\n function d3_selection_onFilter(listener, argumentz) {\n var l = d3_selection_onListener(listener, argumentz);\n return function(e) {\n var target = this, related = e.relatedTarget;\n if (!related || related !== target && !(related.compareDocumentPosition(target) & 8)) {\n l.call(target, e);\n }\n };\n }\n var d3_event_dragSelect, d3_event_dragId = 0;\n function d3_event_dragSuppress(node) {\n var name = \".dragsuppress-\" + ++d3_event_dragId, click = \"click\" + name, w = d3.select(d3_window(node)).on(\"touchmove\" + name, d3_eventPreventDefault).on(\"dragstart\" + name, d3_eventPreventDefault).on(\"selectstart\" + name, d3_eventPreventDefault);\n if (d3_event_dragSelect == null) {\n d3_event_dragSelect = \"onselectstart\" in node ? false : d3_vendorSymbol(node.style, \"userSelect\");\n }\n if (d3_event_dragSelect) {\n var style = d3_documentElement(node).style, select = style[d3_event_dragSelect];\n style[d3_event_dragSelect] = \"none\";\n }\n return function(suppressClick) {\n w.on(name, null);\n if (d3_event_dragSelect) style[d3_event_dragSelect] = select;\n if (suppressClick) {\n var off = function() {\n w.on(click, null);\n };\n w.on(click, function() {\n d3_eventPreventDefault();\n off();\n }, true);\n setTimeout(off, 0);\n }\n };\n }\n d3.mouse = function(container) {\n return d3_mousePoint(container, d3_eventSource());\n };\n var d3_mouse_bug44083 = this.navigator && /WebKit/.test(this.navigator.userAgent) ? -1 : 0;\n function d3_mousePoint(container, e) {\n if (e.changedTouches) e = e.changedTouches[0];\n var svg = container.ownerSVGElement || container;\n if (svg.createSVGPoint) {\n var point = svg.createSVGPoint();\n if (d3_mouse_bug44083 < 0) {\n var window = d3_window(container);\n if (window.scrollX || window.scrollY) {\n svg = d3.select(\"body\").append(\"svg\").style({\n position: \"absolute\",\n top: 0,\n left: 0,\n margin: 0,\n padding: 0,\n border: \"none\"\n }, \"important\");\n var ctm = svg[0][0].getScreenCTM();\n d3_mouse_bug44083 = !(ctm.f || ctm.e);\n svg.remove();\n }\n }\n if (d3_mouse_bug44083) point.x = e.pageX, point.y = e.pageY; else point.x = e.clientX, \n point.y = e.clientY;\n point = point.matrixTransform(container.getScreenCTM().inverse());\n return [ point.x, point.y ];\n }\n var rect = container.getBoundingClientRect();\n return [ e.clientX - rect.left - container.clientLeft, e.clientY - rect.top - container.clientTop ];\n }\n d3.touch = function(container, touches, identifier) {\n if (arguments.length < 3) identifier = touches, touches = d3_eventSource().changedTouches;\n if (touches) for (var i = 0, n = touches.length, touch; i < n; ++i) {\n if ((touch = touches[i]).identifier === identifier) {\n return d3_mousePoint(container, touch);\n }\n }\n };\n d3.behavior.drag = function() {\n var event = d3_eventDispatch(drag, \"drag\", \"dragstart\", \"dragend\"), origin = null, mousedown = dragstart(d3_noop, d3.mouse, d3_window, \"mousemove\", \"mouseup\"), touchstart = dragstart(d3_behavior_dragTouchId, d3.touch, d3_identity, \"touchmove\", \"touchend\");\n function drag() {\n this.on(\"mousedown.drag\", mousedown).on(\"touchstart.drag\", touchstart);\n }\n function dragstart(id, position, subject, move, end) {\n return function() {\n var that = this, target = d3.event.target, parent = that.parentNode, dispatch = event.of(that, arguments), dragged = 0, dragId = id(), dragName = \".drag\" + (dragId == null ? \"\" : \"-\" + dragId), dragOffset, dragSubject = d3.select(subject(target)).on(move + dragName, moved).on(end + dragName, ended), dragRestore = d3_event_dragSuppress(target), position0 = position(parent, dragId);\n if (origin) {\n dragOffset = origin.apply(that, arguments);\n dragOffset = [ dragOffset.x - position0[0], dragOffset.y - position0[1] ];\n } else {\n dragOffset = [ 0, 0 ];\n }\n dispatch({\n type: \"dragstart\"\n });\n function moved() {\n var position1 = position(parent, dragId), dx, dy;\n if (!position1) return;\n dx = position1[0] - position0[0];\n dy = position1[1] - position0[1];\n dragged |= dx | dy;\n position0 = position1;\n dispatch({\n type: \"drag\",\n x: position1[0] + dragOffset[0],\n y: position1[1] + dragOffset[1],\n dx: dx,\n dy: dy\n });\n }\n function ended() {\n if (!position(parent, dragId)) return;\n dragSubject.on(move + dragName, null).on(end + dragName, null);\n dragRestore(dragged && d3.event.target === target);\n dispatch({\n type: \"dragend\"\n });\n }\n };\n }\n drag.origin = function(x) {\n if (!arguments.length) return origin;\n origin = x;\n return drag;\n };\n return d3.rebind(drag, event, \"on\");\n };\n function d3_behavior_dragTouchId() {\n return d3.event.changedTouches[0].identifier;\n }\n d3.touches = function(container, touches) {\n if (arguments.length < 2) touches = d3_eventSource().touches;\n return touches ? d3_array(touches).map(function(touch) {\n var point = d3_mousePoint(container, touch);\n point.identifier = touch.identifier;\n return point;\n }) : [];\n };\n var ε = 1e-6, ε2 = ε * ε, π = Math.PI, τ = 2 * π, τε = τ - ε, halfπ = π / 2, d3_radians = π / 180, d3_degrees = 180 / π;\n function d3_sgn(x) {\n return x > 0 ? 1 : x < 0 ? -1 : 0;\n }\n function d3_cross2d(a, b, c) {\n return (b[0] - a[0]) * (c[1] - a[1]) - (b[1] - a[1]) * (c[0] - a[0]);\n }\n function d3_acos(x) {\n return x > 1 ? 0 : x < -1 ? π : Math.acos(x);\n }\n function d3_asin(x) {\n return x > 1 ? halfπ : x < -1 ? -halfπ : Math.asin(x);\n }\n function d3_sinh(x) {\n return ((x = Math.exp(x)) - 1 / x) / 2;\n }\n function d3_cosh(x) {\n return ((x = Math.exp(x)) + 1 / x) / 2;\n }\n function d3_tanh(x) {\n return ((x = Math.exp(2 * x)) - 1) / (x + 1);\n }\n function d3_haversin(x) {\n return (x = Math.sin(x / 2)) * x;\n }\n var ρ = Math.SQRT2, ρ2 = 2, ρ4 = 4;\n d3.interpolateZoom = function(p0, p1) {\n var ux0 = p0[0], uy0 = p0[1], w0 = p0[2], ux1 = p1[0], uy1 = p1[1], w1 = p1[2];\n var dx = ux1 - ux0, dy = uy1 - uy0, d2 = dx * dx + dy * dy, d1 = Math.sqrt(d2), b0 = (w1 * w1 - w0 * w0 + ρ4 * d2) / (2 * w0 * ρ2 * d1), b1 = (w1 * w1 - w0 * w0 - ρ4 * d2) / (2 * w1 * ρ2 * d1), r0 = Math.log(Math.sqrt(b0 * b0 + 1) - b0), r1 = Math.log(Math.sqrt(b1 * b1 + 1) - b1), dr = r1 - r0, S = (dr || Math.log(w1 / w0)) / ρ;\n function interpolate(t) {\n var s = t * S;\n if (dr) {\n var coshr0 = d3_cosh(r0), u = w0 / (ρ2 * d1) * (coshr0 * d3_tanh(ρ * s + r0) - d3_sinh(r0));\n return [ ux0 + u * dx, uy0 + u * dy, w0 * coshr0 / d3_cosh(ρ * s + r0) ];\n }\n return [ ux0 + t * dx, uy0 + t * dy, w0 * Math.exp(ρ * s) ];\n }\n interpolate.duration = S * 1e3;\n return interpolate;\n };\n d3.behavior.zoom = function() {\n var view = {\n x: 0,\n y: 0,\n k: 1\n }, translate0, center0, center, size = [ 960, 500 ], scaleExtent = d3_behavior_zoomInfinity, duration = 250, zooming = 0, mousedown = \"mousedown.zoom\", mousemove = \"mousemove.zoom\", mouseup = \"mouseup.zoom\", mousewheelTimer, touchstart = \"touchstart.zoom\", touchtime, event = d3_eventDispatch(zoom, \"zoomstart\", \"zoom\", \"zoomend\"), x0, x1, y0, y1;\n if (!d3_behavior_zoomWheel) {\n d3_behavior_zoomWheel = \"onwheel\" in d3_document ? (d3_behavior_zoomDelta = function() {\n return -d3.event.deltaY * (d3.event.deltaMode ? 120 : 1);\n }, \"wheel\") : \"onmousewheel\" in d3_document ? (d3_behavior_zoomDelta = function() {\n return d3.event.wheelDelta;\n }, \"mousewheel\") : (d3_behavior_zoomDelta = function() {\n return -d3.event.detail;\n }, \"MozMousePixelScroll\");\n }\n function zoom(g) {\n g.on(mousedown, mousedowned).on(d3_behavior_zoomWheel + \".zoom\", mousewheeled).on(\"dblclick.zoom\", dblclicked).on(touchstart, touchstarted);\n }\n zoom.event = function(g) {\n g.each(function() {\n var dispatch = event.of(this, arguments), view1 = view;\n if (d3_transitionInheritId) {\n d3.select(this).transition().each(\"start.zoom\", function() {\n view = this.__chart__ || {\n x: 0,\n y: 0,\n k: 1\n };\n zoomstarted(dispatch);\n }).tween(\"zoom:zoom\", function() {\n var dx = size[0], dy = size[1], cx = center0 ? center0[0] : dx / 2, cy = center0 ? center0[1] : dy / 2, i = d3.interpolateZoom([ (cx - view.x) / view.k, (cy - view.y) / view.k, dx / view.k ], [ (cx - view1.x) / view1.k, (cy - view1.y) / view1.k, dx / view1.k ]);\n return function(t) {\n var l = i(t), k = dx / l[2];\n this.__chart__ = view = {\n x: cx - l[0] * k,\n y: cy - l[1] * k,\n k: k\n };\n zoomed(dispatch);\n };\n }).each(\"interrupt.zoom\", function() {\n zoomended(dispatch);\n }).each(\"end.zoom\", function() {\n zoomended(dispatch);\n });\n } else {\n this.__chart__ = view;\n zoomstarted(dispatch);\n zoomed(dispatch);\n zoomended(dispatch);\n }\n });\n };\n zoom.translate = function(_) {\n if (!arguments.length) return [ view.x, view.y ];\n view = {\n x: +_[0],\n y: +_[1],\n k: view.k\n };\n rescale();\n return zoom;\n };\n zoom.scale = function(_) {\n if (!arguments.length) return view.k;\n view = {\n x: view.x,\n y: view.y,\n k: +_\n };\n rescale();\n return zoom;\n };\n zoom.scaleExtent = function(_) {\n if (!arguments.length) return scaleExtent;\n scaleExtent = _ == null ? d3_behavior_zoomInfinity : [ +_[0], +_[1] ];\n return zoom;\n };\n zoom.center = function(_) {\n if (!arguments.length) return center;\n center = _ && [ +_[0], +_[1] ];\n return zoom;\n };\n zoom.size = function(_) {\n if (!arguments.length) return size;\n size = _ && [ +_[0], +_[1] ];\n return zoom;\n };\n zoom.duration = function(_) {\n if (!arguments.length) return duration;\n duration = +_;\n return zoom;\n };\n zoom.x = function(z) {\n if (!arguments.length) return x1;\n x1 = z;\n x0 = z.copy();\n view = {\n x: 0,\n y: 0,\n k: 1\n };\n return zoom;\n };\n zoom.y = function(z) {\n if (!arguments.length) return y1;\n y1 = z;\n y0 = z.copy();\n view = {\n x: 0,\n y: 0,\n k: 1\n };\n return zoom;\n };\n function location(p) {\n return [ (p[0] - view.x) / view.k, (p[1] - view.y) / view.k ];\n }\n function point(l) {\n return [ l[0] * view.k + view.x, l[1] * view.k + view.y ];\n }\n function scaleTo(s) {\n view.k = Math.max(scaleExtent[0], Math.min(scaleExtent[1], s));\n }\n function translateTo(p, l) {\n l = point(l);\n view.x += p[0] - l[0];\n view.y += p[1] - l[1];\n }\n function zoomTo(that, p, l, k) {\n that.__chart__ = {\n x: view.x,\n y: view.y,\n k: view.k\n };\n scaleTo(Math.pow(2, k));\n translateTo(center0 = p, l);\n that = d3.select(that);\n if (duration > 0) that = that.transition().duration(duration);\n that.call(zoom.event);\n }\n function rescale() {\n if (x1) x1.domain(x0.range().map(function(x) {\n return (x - view.x) / view.k;\n }).map(x0.invert));\n if (y1) y1.domain(y0.range().map(function(y) {\n return (y - view.y) / view.k;\n }).map(y0.invert));\n }\n function zoomstarted(dispatch) {\n if (!zooming++) dispatch({\n type: \"zoomstart\"\n });\n }\n function zoomed(dispatch) {\n rescale();\n dispatch({\n type: \"zoom\",\n scale: view.k,\n translate: [ view.x, view.y ]\n });\n }\n function zoomended(dispatch) {\n if (!--zooming) dispatch({\n type: \"zoomend\"\n });\n center0 = null;\n }\n function mousedowned() {\n var that = this, target = d3.event.target, dispatch = event.of(that, arguments), dragged = 0, subject = d3.select(d3_window(that)).on(mousemove, moved).on(mouseup, ended), location0 = location(d3.mouse(that)), dragRestore = d3_event_dragSuppress(that);\n d3_selection_interrupt.call(that);\n zoomstarted(dispatch);\n function moved() {\n dragged = 1;\n translateTo(d3.mouse(that), location0);\n zoomed(dispatch);\n }\n function ended() {\n subject.on(mousemove, null).on(mouseup, null);\n dragRestore(dragged && d3.event.target === target);\n zoomended(dispatch);\n }\n }\n function touchstarted() {\n var that = this, dispatch = event.of(that, arguments), locations0 = {}, distance0 = 0, scale0, zoomName = \".zoom-\" + d3.event.changedTouches[0].identifier, touchmove = \"touchmove\" + zoomName, touchend = \"touchend\" + zoomName, targets = [], subject = d3.select(that), dragRestore = d3_event_dragSuppress(that);\n started();\n zoomstarted(dispatch);\n subject.on(mousedown, null).on(touchstart, started);\n function relocate() {\n var touches = d3.touches(that);\n scale0 = view.k;\n touches.forEach(function(t) {\n if (t.identifier in locations0) locations0[t.identifier] = location(t);\n });\n return touches;\n }\n function started() {\n var target = d3.event.target;\n d3.select(target).on(touchmove, moved).on(touchend, ended);\n targets.push(target);\n var changed = d3.event.changedTouches;\n for (var i = 0, n = changed.length; i < n; ++i) {\n locations0[changed[i].identifier] = null;\n }\n var touches = relocate(), now = Date.now();\n if (touches.length === 1) {\n if (now - touchtime < 500) {\n var p = touches[0];\n zoomTo(that, p, locations0[p.identifier], Math.floor(Math.log(view.k) / Math.LN2) + 1);\n d3_eventPreventDefault();\n }\n touchtime = now;\n } else if (touches.length > 1) {\n var p = touches[0], q = touches[1], dx = p[0] - q[0], dy = p[1] - q[1];\n distance0 = dx * dx + dy * dy;\n }\n }\n function moved() {\n var touches = d3.touches(that), p0, l0, p1, l1;\n d3_selection_interrupt.call(that);\n for (var i = 0, n = touches.length; i < n; ++i, l1 = null) {\n p1 = touches[i];\n if (l1 = locations0[p1.identifier]) {\n if (l0) break;\n p0 = p1, l0 = l1;\n }\n }\n if (l1) {\n var distance1 = (distance1 = p1[0] - p0[0]) * distance1 + (distance1 = p1[1] - p0[1]) * distance1, scale1 = distance0 && Math.sqrt(distance1 / distance0);\n p0 = [ (p0[0] + p1[0]) / 2, (p0[1] + p1[1]) / 2 ];\n l0 = [ (l0[0] + l1[0]) / 2, (l0[1] + l1[1]) / 2 ];\n scaleTo(scale1 * scale0);\n }\n touchtime = null;\n translateTo(p0, l0);\n zoomed(dispatch);\n }\n function ended() {\n if (d3.event.touches.length) {\n var changed = d3.event.changedTouches;\n for (var i = 0, n = changed.length; i < n; ++i) {\n delete locations0[changed[i].identifier];\n }\n for (var identifier in locations0) {\n return void relocate();\n }\n }\n d3.selectAll(targets).on(zoomName, null);\n subject.on(mousedown, mousedowned).on(touchstart, touchstarted);\n dragRestore();\n zoomended(dispatch);\n }\n }\n function mousewheeled() {\n var dispatch = event.of(this, arguments);\n if (mousewheelTimer) clearTimeout(mousewheelTimer); else translate0 = location(center0 = center || d3.mouse(this)), \n d3_selection_interrupt.call(this), zoomstarted(dispatch);\n mousewheelTimer = setTimeout(function() {\n mousewheelTimer = null;\n zoomended(dispatch);\n }, 50);\n d3_eventPreventDefault();\n scaleTo(Math.pow(2, d3_behavior_zoomDelta() * .002) * view.k);\n translateTo(center0, translate0);\n zoomed(dispatch);\n }\n function dblclicked() {\n var p = d3.mouse(this), k = Math.log(view.k) / Math.LN2;\n zoomTo(this, p, location(p), d3.event.shiftKey ? Math.ceil(k) - 1 : Math.floor(k) + 1);\n }\n return d3.rebind(zoom, event, \"on\");\n };\n var d3_behavior_zoomInfinity = [ 0, Infinity ], d3_behavior_zoomDelta, d3_behavior_zoomWheel;\n d3.color = d3_color;\n function d3_color() {}\n d3_color.prototype.toString = function() {\n return this.rgb() + \"\";\n };\n d3.hsl = d3_hsl;\n function d3_hsl(h, s, l) {\n return this instanceof d3_hsl ? void (this.h = +h, this.s = +s, this.l = +l) : arguments.length < 2 ? h instanceof d3_hsl ? new d3_hsl(h.h, h.s, h.l) : d3_rgb_parse(\"\" + h, d3_rgb_hsl, d3_hsl) : new d3_hsl(h, s, l);\n }\n var d3_hslPrototype = d3_hsl.prototype = new d3_color();\n d3_hslPrototype.brighter = function(k) {\n k = Math.pow(.7, arguments.length ? k : 1);\n return new d3_hsl(this.h, this.s, this.l / k);\n };\n d3_hslPrototype.darker = function(k) {\n k = Math.pow(.7, arguments.length ? k : 1);\n return new d3_hsl(this.h, this.s, k * this.l);\n };\n d3_hslPrototype.rgb = function() {\n return d3_hsl_rgb(this.h, this.s, this.l);\n };\n function d3_hsl_rgb(h, s, l) {\n var m1, m2;\n h = isNaN(h) ? 0 : (h %= 360) < 0 ? h + 360 : h;\n s = isNaN(s) ? 0 : s < 0 ? 0 : s > 1 ? 1 : s;\n l = l < 0 ? 0 : l > 1 ? 1 : l;\n m2 = l <= .5 ? l * (1 + s) : l + s - l * s;\n m1 = 2 * l - m2;\n function v(h) {\n if (h > 360) h -= 360; else if (h < 0) h += 360;\n if (h < 60) return m1 + (m2 - m1) * h / 60;\n if (h < 180) return m2;\n if (h < 240) return m1 + (m2 - m1) * (240 - h) / 60;\n return m1;\n }\n function vv(h) {\n return Math.round(v(h) * 255);\n }\n return new d3_rgb(vv(h + 120), vv(h), vv(h - 120));\n }\n d3.hcl = d3_hcl;\n function d3_hcl(h, c, l) {\n return this instanceof d3_hcl ? void (this.h = +h, this.c = +c, this.l = +l) : arguments.length < 2 ? h instanceof d3_hcl ? new d3_hcl(h.h, h.c, h.l) : h instanceof d3_lab ? d3_lab_hcl(h.l, h.a, h.b) : d3_lab_hcl((h = d3_rgb_lab((h = d3.rgb(h)).r, h.g, h.b)).l, h.a, h.b) : new d3_hcl(h, c, l);\n }\n var d3_hclPrototype = d3_hcl.prototype = new d3_color();\n d3_hclPrototype.brighter = function(k) {\n return new d3_hcl(this.h, this.c, Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)));\n };\n d3_hclPrototype.darker = function(k) {\n return new d3_hcl(this.h, this.c, Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)));\n };\n d3_hclPrototype.rgb = function() {\n return d3_hcl_lab(this.h, this.c, this.l).rgb();\n };\n function d3_hcl_lab(h, c, l) {\n if (isNaN(h)) h = 0;\n if (isNaN(c)) c = 0;\n return new d3_lab(l, Math.cos(h *= d3_radians) * c, Math.sin(h) * c);\n }\n d3.lab = d3_lab;\n function d3_lab(l, a, b) {\n return this instanceof d3_lab ? void (this.l = +l, this.a = +a, this.b = +b) : arguments.length < 2 ? l instanceof d3_lab ? new d3_lab(l.l, l.a, l.b) : l instanceof d3_hcl ? d3_hcl_lab(l.h, l.c, l.l) : d3_rgb_lab((l = d3_rgb(l)).r, l.g, l.b) : new d3_lab(l, a, b);\n }\n var d3_lab_K = 18;\n var d3_lab_X = .95047, d3_lab_Y = 1, d3_lab_Z = 1.08883;\n var d3_labPrototype = d3_lab.prototype = new d3_color();\n d3_labPrototype.brighter = function(k) {\n return new d3_lab(Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)), this.a, this.b);\n };\n d3_labPrototype.darker = function(k) {\n return new d3_lab(Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)), this.a, this.b);\n };\n d3_labPrototype.rgb = function() {\n return d3_lab_rgb(this.l, this.a, this.b);\n };\n function d3_lab_rgb(l, a, b) {\n var y = (l + 16) / 116, x = y + a / 500, z = y - b / 200;\n x = d3_lab_xyz(x) * d3_lab_X;\n y = d3_lab_xyz(y) * d3_lab_Y;\n z = d3_lab_xyz(z) * d3_lab_Z;\n return new d3_rgb(d3_xyz_rgb(3.2404542 * x - 1.5371385 * y - .4985314 * z), d3_xyz_rgb(-.969266 * x + 1.8760108 * y + .041556 * z), d3_xyz_rgb(.0556434 * x - .2040259 * y + 1.0572252 * z));\n }\n function d3_lab_hcl(l, a, b) {\n return l > 0 ? new d3_hcl(Math.atan2(b, a) * d3_degrees, Math.sqrt(a * a + b * b), l) : new d3_hcl(NaN, NaN, l);\n }\n function d3_lab_xyz(x) {\n return x > .206893034 ? x * x * x : (x - 4 / 29) / 7.787037;\n }\n function d3_xyz_lab(x) {\n return x > .008856 ? Math.pow(x, 1 / 3) : 7.787037 * x + 4 / 29;\n }\n function d3_xyz_rgb(r) {\n return Math.round(255 * (r <= .00304 ? 12.92 * r : 1.055 * Math.pow(r, 1 / 2.4) - .055));\n }\n d3.rgb = d3_rgb;\n function d3_rgb(r, g, b) {\n return this instanceof d3_rgb ? void (this.r = ~~r, this.g = ~~g, this.b = ~~b) : arguments.length < 2 ? r instanceof d3_rgb ? new d3_rgb(r.r, r.g, r.b) : d3_rgb_parse(\"\" + r, d3_rgb, d3_hsl_rgb) : new d3_rgb(r, g, b);\n }\n function d3_rgbNumber(value) {\n return new d3_rgb(value >> 16, value >> 8 & 255, value & 255);\n }\n function d3_rgbString(value) {\n return d3_rgbNumber(value) + \"\";\n }\n var d3_rgbPrototype = d3_rgb.prototype = new d3_color();\n d3_rgbPrototype.brighter = function(k) {\n k = Math.pow(.7, arguments.length ? k : 1);\n var r = this.r, g = this.g, b = this.b, i = 30;\n if (!r && !g && !b) return new d3_rgb(i, i, i);\n if (r && r < i) r = i;\n if (g && g < i) g = i;\n if (b && b < i) b = i;\n return new d3_rgb(Math.min(255, r / k), Math.min(255, g / k), Math.min(255, b / k));\n };\n d3_rgbPrototype.darker = function(k) {\n k = Math.pow(.7, arguments.length ? k : 1);\n return new d3_rgb(k * this.r, k * this.g, k * this.b);\n };\n d3_rgbPrototype.hsl = function() {\n return d3_rgb_hsl(this.r, this.g, this.b);\n };\n d3_rgbPrototype.toString = function() {\n return \"#\" + d3_rgb_hex(this.r) + d3_rgb_hex(this.g) + d3_rgb_hex(this.b);\n };\n function d3_rgb_hex(v) {\n return v < 16 ? \"0\" + Math.max(0, v).toString(16) : Math.min(255, v).toString(16);\n }\n function d3_rgb_parse(format, rgb, hsl) {\n var r = 0, g = 0, b = 0, m1, m2, color;\n m1 = /([a-z]+)\\((.*)\\)/i.exec(format);\n if (m1) {\n m2 = m1[2].split(\",\");\n switch (m1[1]) {\n case \"hsl\":\n {\n return hsl(parseFloat(m2[0]), parseFloat(m2[1]) / 100, parseFloat(m2[2]) / 100);\n }\n\n case \"rgb\":\n {\n return rgb(d3_rgb_parseNumber(m2[0]), d3_rgb_parseNumber(m2[1]), d3_rgb_parseNumber(m2[2]));\n }\n }\n }\n if (color = d3_rgb_names.get(format.toLowerCase())) {\n return rgb(color.r, color.g, color.b);\n }\n if (format != null && format.charAt(0) === \"#\" && !isNaN(color = parseInt(format.slice(1), 16))) {\n if (format.length === 4) {\n r = (color & 3840) >> 4;\n r = r >> 4 | r;\n g = color & 240;\n g = g >> 4 | g;\n b = color & 15;\n b = b << 4 | b;\n } else if (format.length === 7) {\n r = (color & 16711680) >> 16;\n g = (color & 65280) >> 8;\n b = color & 255;\n }\n }\n return rgb(r, g, b);\n }\n function d3_rgb_hsl(r, g, b) {\n var min = Math.min(r /= 255, g /= 255, b /= 255), max = Math.max(r, g, b), d = max - min, h, s, l = (max + min) / 2;\n if (d) {\n s = l < .5 ? d / (max + min) : d / (2 - max - min);\n if (r == max) h = (g - b) / d + (g < b ? 6 : 0); else if (g == max) h = (b - r) / d + 2; else h = (r - g) / d + 4;\n h *= 60;\n } else {\n h = NaN;\n s = l > 0 && l < 1 ? 0 : h;\n }\n return new d3_hsl(h, s, l);\n }\n function d3_rgb_lab(r, g, b) {\n r = d3_rgb_xyz(r);\n g = d3_rgb_xyz(g);\n b = d3_rgb_xyz(b);\n var x = d3_xyz_lab((.4124564 * r + .3575761 * g + .1804375 * b) / d3_lab_X), y = d3_xyz_lab((.2126729 * r + .7151522 * g + .072175 * b) / d3_lab_Y), z = d3_xyz_lab((.0193339 * r + .119192 * g + .9503041 * b) / d3_lab_Z);\n return d3_lab(116 * y - 16, 500 * (x - y), 200 * (y - z));\n }\n function d3_rgb_xyz(r) {\n return (r /= 255) <= .04045 ? r / 12.92 : Math.pow((r + .055) / 1.055, 2.4);\n }\n function d3_rgb_parseNumber(c) {\n var f = parseFloat(c);\n return c.charAt(c.length - 1) === \"%\" ? Math.round(f * 2.55) : f;\n }\n var d3_rgb_names = d3.map({\n aliceblue: 15792383,\n antiquewhite: 16444375,\n aqua: 65535,\n aquamarine: 8388564,\n azure: 15794175,\n beige: 16119260,\n bisque: 16770244,\n black: 0,\n blanchedalmond: 16772045,\n blue: 255,\n blueviolet: 9055202,\n brown: 10824234,\n burlywood: 14596231,\n cadetblue: 6266528,\n chartreuse: 8388352,\n chocolate: 13789470,\n coral: 16744272,\n cornflowerblue: 6591981,\n cornsilk: 16775388,\n crimson: 14423100,\n cyan: 65535,\n darkblue: 139,\n darkcyan: 35723,\n darkgoldenrod: 12092939,\n darkgray: 11119017,\n darkgreen: 25600,\n darkgrey: 11119017,\n darkkhaki: 12433259,\n darkmagenta: 9109643,\n darkolivegreen: 5597999,\n darkorange: 16747520,\n darkorchid: 10040012,\n darkred: 9109504,\n darksalmon: 15308410,\n darkseagreen: 9419919,\n darkslateblue: 4734347,\n darkslategray: 3100495,\n darkslategrey: 3100495,\n darkturquoise: 52945,\n darkviolet: 9699539,\n deeppink: 16716947,\n deepskyblue: 49151,\n dimgray: 6908265,\n dimgrey: 6908265,\n dodgerblue: 2003199,\n firebrick: 11674146,\n floralwhite: 16775920,\n forestgreen: 2263842,\n fuchsia: 16711935,\n gainsboro: 14474460,\n ghostwhite: 16316671,\n gold: 16766720,\n goldenrod: 14329120,\n gray: 8421504,\n green: 32768,\n greenyellow: 11403055,\n grey: 8421504,\n honeydew: 15794160,\n hotpink: 16738740,\n indianred: 13458524,\n indigo: 4915330,\n ivory: 16777200,\n khaki: 15787660,\n lavender: 15132410,\n lavenderblush: 16773365,\n lawngreen: 8190976,\n lemonchiffon: 16775885,\n lightblue: 11393254,\n lightcoral: 15761536,\n lightcyan: 14745599,\n lightgoldenrodyellow: 16448210,\n lightgray: 13882323,\n lightgreen: 9498256,\n lightgrey: 13882323,\n lightpink: 16758465,\n lightsalmon: 16752762,\n lightseagreen: 2142890,\n lightskyblue: 8900346,\n lightslategray: 7833753,\n lightslategrey: 7833753,\n lightsteelblue: 11584734,\n lightyellow: 16777184,\n lime: 65280,\n limegreen: 3329330,\n linen: 16445670,\n magenta: 16711935,\n maroon: 8388608,\n mediumaquamarine: 6737322,\n mediumblue: 205,\n mediumorchid: 12211667,\n mediumpurple: 9662683,\n mediumseagreen: 3978097,\n mediumslateblue: 8087790,\n mediumspringgreen: 64154,\n mediumturquoise: 4772300,\n mediumvioletred: 13047173,\n midnightblue: 1644912,\n mintcream: 16121850,\n mistyrose: 16770273,\n moccasin: 16770229,\n navajowhite: 16768685,\n navy: 128,\n oldlace: 16643558,\n olive: 8421376,\n olivedrab: 7048739,\n orange: 16753920,\n orangered: 16729344,\n orchid: 14315734,\n palegoldenrod: 15657130,\n palegreen: 10025880,\n paleturquoise: 11529966,\n palevioletred: 14381203,\n papayawhip: 16773077,\n peachpuff: 16767673,\n peru: 13468991,\n pink: 16761035,\n plum: 14524637,\n powderblue: 11591910,\n purple: 8388736,\n rebeccapurple: 6697881,\n red: 16711680,\n rosybrown: 12357519,\n royalblue: 4286945,\n saddlebrown: 9127187,\n salmon: 16416882,\n sandybrown: 16032864,\n seagreen: 3050327,\n seashell: 16774638,\n sienna: 10506797,\n silver: 12632256,\n skyblue: 8900331,\n slateblue: 6970061,\n slategray: 7372944,\n slategrey: 7372944,\n snow: 16775930,\n springgreen: 65407,\n steelblue: 4620980,\n tan: 13808780,\n teal: 32896,\n thistle: 14204888,\n tomato: 16737095,\n turquoise: 4251856,\n violet: 15631086,\n wheat: 16113331,\n white: 16777215,\n whitesmoke: 16119285,\n yellow: 16776960,\n yellowgreen: 10145074\n });\n d3_rgb_names.forEach(function(key, value) {\n d3_rgb_names.set(key, d3_rgbNumber(value));\n });\n function d3_functor(v) {\n return typeof v === \"function\" ? v : function() {\n return v;\n };\n }\n d3.functor = d3_functor;\n d3.xhr = d3_xhrType(d3_identity);\n function d3_xhrType(response) {\n return function(url, mimeType, callback) {\n if (arguments.length === 2 && typeof mimeType === \"function\") callback = mimeType, \n mimeType = null;\n return d3_xhr(url, mimeType, response, callback);\n };\n }\n function d3_xhr(url, mimeType, response, callback) {\n var xhr = {}, dispatch = d3.dispatch(\"beforesend\", \"progress\", \"load\", \"error\"), headers = {}, request = new XMLHttpRequest(), responseType = null;\n if (this.XDomainRequest && !(\"withCredentials\" in request) && /^(http(s)?:)?\\/\\//.test(url)) request = new XDomainRequest();\n \"onload\" in request ? request.onload = request.onerror = respond : request.onreadystatechange = function() {\n request.readyState > 3 && respond();\n };\n function respond() {\n var status = request.status, result;\n if (!status && d3_xhrHasResponse(request) || status >= 200 && status < 300 || status === 304) {\n try {\n result = response.call(xhr, request);\n } catch (e) {\n dispatch.error.call(xhr, e);\n return;\n }\n dispatch.load.call(xhr, result);\n } else {\n dispatch.error.call(xhr, request);\n }\n }\n request.onprogress = function(event) {\n var o = d3.event;\n d3.event = event;\n try {\n dispatch.progress.call(xhr, request);\n } finally {\n d3.event = o;\n }\n };\n xhr.header = function(name, value) {\n name = (name + \"\").toLowerCase();\n if (arguments.length < 2) return headers[name];\n if (value == null) delete headers[name]; else headers[name] = value + \"\";\n return xhr;\n };\n xhr.mimeType = function(value) {\n if (!arguments.length) return mimeType;\n mimeType = value == null ? null : value + \"\";\n return xhr;\n };\n xhr.responseType = function(value) {\n if (!arguments.length) return responseType;\n responseType = value;\n return xhr;\n };\n xhr.response = function(value) {\n response = value;\n return xhr;\n };\n [ \"get\", \"post\" ].forEach(function(method) {\n xhr[method] = function() {\n return xhr.send.apply(xhr, [ method ].concat(d3_array(arguments)));\n };\n });\n xhr.send = function(method, data, callback) {\n if (arguments.length === 2 && typeof data === \"function\") callback = data, data = null;\n request.open(method, url, true);\n if (mimeType != null && !(\"accept\" in headers)) headers[\"accept\"] = mimeType + \",*/*\";\n if (request.setRequestHeader) for (var name in headers) request.setRequestHeader(name, headers[name]);\n if (mimeType != null && request.overrideMimeType) request.overrideMimeType(mimeType);\n if (responseType != null) request.responseType = responseType;\n if (callback != null) xhr.on(\"error\", callback).on(\"load\", function(request) {\n callback(null, request);\n });\n dispatch.beforesend.call(xhr, request);\n request.send(data == null ? null : data);\n return xhr;\n };\n xhr.abort = function() {\n request.abort();\n return xhr;\n };\n d3.rebind(xhr, dispatch, \"on\");\n return callback == null ? xhr : xhr.get(d3_xhr_fixCallback(callback));\n }\n function d3_xhr_fixCallback(callback) {\n return callback.length === 1 ? function(error, request) {\n callback(error == null ? request : null);\n } : callback;\n }\n function d3_xhrHasResponse(request) {\n var type = request.responseType;\n return type && type !== \"text\" ? request.response : request.responseText;\n }\n d3.dsv = function(delimiter, mimeType) {\n var reFormat = new RegExp('[\"' + delimiter + \"\\n]\"), delimiterCode = delimiter.charCodeAt(0);\n function dsv(url, row, callback) {\n if (arguments.length < 3) callback = row, row = null;\n var xhr = d3_xhr(url, mimeType, row == null ? response : typedResponse(row), callback);\n xhr.row = function(_) {\n return arguments.length ? xhr.response((row = _) == null ? response : typedResponse(_)) : row;\n };\n return xhr;\n }\n function response(request) {\n return dsv.parse(request.responseText);\n }\n function typedResponse(f) {\n return function(request) {\n return dsv.parse(request.responseText, f);\n };\n }\n dsv.parse = function(text, f) {\n var o;\n return dsv.parseRows(text, function(row, i) {\n if (o) return o(row, i - 1);\n var a = new Function(\"d\", \"return {\" + row.map(function(name, i) {\n return JSON.stringify(name) + \": d[\" + i + \"]\";\n }).join(\",\") + \"}\");\n o = f ? function(row, i) {\n return f(a(row), i);\n } : a;\n });\n };\n dsv.parseRows = function(text, f) {\n var EOL = {}, EOF = {}, rows = [], N = text.length, I = 0, n = 0, t, eol;\n function token() {\n if (I >= N) return EOF;\n if (eol) return eol = false, EOL;\n var j = I;\n if (text.charCodeAt(j) === 34) {\n var i = j;\n while (i++ < N) {\n if (text.charCodeAt(i) === 34) {\n if (text.charCodeAt(i + 1) !== 34) break;\n ++i;\n }\n }\n I = i + 2;\n var c = text.charCodeAt(i + 1);\n if (c === 13) {\n eol = true;\n if (text.charCodeAt(i + 2) === 10) ++I;\n } else if (c === 10) {\n eol = true;\n }\n return text.slice(j + 1, i).replace(/\"\"/g, '\"');\n }\n while (I < N) {\n var c = text.charCodeAt(I++), k = 1;\n if (c === 10) eol = true; else if (c === 13) {\n eol = true;\n if (text.charCodeAt(I) === 10) ++I, ++k;\n } else if (c !== delimiterCode) continue;\n return text.slice(j, I - k);\n }\n return text.slice(j);\n }\n while ((t = token()) !== EOF) {\n var a = [];\n while (t !== EOL && t !== EOF) {\n a.push(t);\n t = token();\n }\n if (f && (a = f(a, n++)) == null) continue;\n rows.push(a);\n }\n return rows;\n };\n dsv.format = function(rows) {\n if (Array.isArray(rows[0])) return dsv.formatRows(rows);\n var fieldSet = new d3_Set(), fields = [];\n rows.forEach(function(row) {\n for (var field in row) {\n if (!fieldSet.has(field)) {\n fields.push(fieldSet.add(field));\n }\n }\n });\n return [ fields.map(formatValue).join(delimiter) ].concat(rows.map(function(row) {\n return fields.map(function(field) {\n return formatValue(row[field]);\n }).join(delimiter);\n })).join(\"\\n\");\n };\n dsv.formatRows = function(rows) {\n return rows.map(formatRow).join(\"\\n\");\n };\n function formatRow(row) {\n return row.map(formatValue).join(delimiter);\n }\n function formatValue(text) {\n return reFormat.test(text) ? '\"' + text.replace(/\\\"/g, '\"\"') + '\"' : text;\n }\n return dsv;\n };\n d3.csv = d3.dsv(\",\", \"text/csv\");\n d3.tsv = d3.dsv(\"\t\", \"text/tab-separated-values\");\n var d3_timer_queueHead, d3_timer_queueTail, d3_timer_interval, d3_timer_timeout, d3_timer_active, d3_timer_frame = this[d3_vendorSymbol(this, \"requestAnimationFrame\")] || function(callback) {\n setTimeout(callback, 17);\n };\n d3.timer = function(callback, delay, then) {\n var n = arguments.length;\n if (n < 2) delay = 0;\n if (n < 3) then = Date.now();\n var time = then + delay, timer = {\n c: callback,\n t: time,\n f: false,\n n: null\n };\n if (d3_timer_queueTail) d3_timer_queueTail.n = timer; else d3_timer_queueHead = timer;\n d3_timer_queueTail = timer;\n if (!d3_timer_interval) {\n d3_timer_timeout = clearTimeout(d3_timer_timeout);\n d3_timer_interval = 1;\n d3_timer_frame(d3_timer_step);\n }\n };\n function d3_timer_step() {\n var now = d3_timer_mark(), delay = d3_timer_sweep() - now;\n if (delay > 24) {\n if (isFinite(delay)) {\n clearTimeout(d3_timer_timeout);\n d3_timer_timeout = setTimeout(d3_timer_step, delay);\n }\n d3_timer_interval = 0;\n } else {\n d3_timer_interval = 1;\n d3_timer_frame(d3_timer_step);\n }\n }\n d3.timer.flush = function() {\n d3_timer_mark();\n d3_timer_sweep();\n };\n function d3_timer_mark() {\n var now = Date.now();\n d3_timer_active = d3_timer_queueHead;\n while (d3_timer_active) {\n if (now >= d3_timer_active.t) d3_timer_active.f = d3_timer_active.c(now - d3_timer_active.t);\n d3_timer_active = d3_timer_active.n;\n }\n return now;\n }\n function d3_timer_sweep() {\n var t0, t1 = d3_timer_queueHead, time = Infinity;\n while (t1) {\n if (t1.f) {\n t1 = t0 ? t0.n = t1.n : d3_timer_queueHead = t1.n;\n } else {\n if (t1.t < time) time = t1.t;\n t1 = (t0 = t1).n;\n }\n }\n d3_timer_queueTail = t0;\n return time;\n }\n function d3_format_precision(x, p) {\n return p - (x ? Math.ceil(Math.log(x) / Math.LN10) : 1);\n }\n d3.round = function(x, n) {\n return n ? Math.round(x * (n = Math.pow(10, n))) / n : Math.round(x);\n };\n var d3_formatPrefixes = [ \"y\", \"z\", \"a\", \"f\", \"p\", \"n\", \"µ\", \"m\", \"\", \"k\", \"M\", \"G\", \"T\", \"P\", \"E\", \"Z\", \"Y\" ].map(d3_formatPrefix);\n d3.formatPrefix = function(value, precision) {\n var i = 0;\n if (value) {\n if (value < 0) value *= -1;\n if (precision) value = d3.round(value, d3_format_precision(value, precision));\n i = 1 + Math.floor(1e-12 + Math.log(value) / Math.LN10);\n i = Math.max(-24, Math.min(24, Math.floor((i - 1) / 3) * 3));\n }\n return d3_formatPrefixes[8 + i / 3];\n };\n function d3_formatPrefix(d, i) {\n var k = Math.pow(10, abs(8 - i) * 3);\n return {\n scale: i > 8 ? function(d) {\n return d / k;\n } : function(d) {\n return d * k;\n },\n symbol: d\n };\n }\n function d3_locale_numberFormat(locale) {\n var locale_decimal = locale.decimal, locale_thousands = locale.thousands, locale_grouping = locale.grouping, locale_currency = locale.currency, formatGroup = locale_grouping && locale_thousands ? function(value, width) {\n var i = value.length, t = [], j = 0, g = locale_grouping[0], length = 0;\n while (i > 0 && g > 0) {\n if (length + g + 1 > width) g = Math.max(1, width - length);\n t.push(value.substring(i -= g, i + g));\n if ((length += g + 1) > width) break;\n g = locale_grouping[j = (j + 1) % locale_grouping.length];\n }\n return t.reverse().join(locale_thousands);\n } : d3_identity;\n return function(specifier) {\n var match = d3_format_re.exec(specifier), fill = match[1] || \" \", align = match[2] || \">\", sign = match[3] || \"-\", symbol = match[4] || \"\", zfill = match[5], width = +match[6], comma = match[7], precision = match[8], type = match[9], scale = 1, prefix = \"\", suffix = \"\", integer = false, exponent = true;\n if (precision) precision = +precision.substring(1);\n if (zfill || fill === \"0\" && align === \"=\") {\n zfill = fill = \"0\";\n align = \"=\";\n }\n switch (type) {\n case \"n\":\n comma = true;\n type = \"g\";\n break;\n\n case \"%\":\n scale = 100;\n suffix = \"%\";\n type = \"f\";\n break;\n\n case \"p\":\n scale = 100;\n suffix = \"%\";\n type = \"r\";\n break;\n\n case \"b\":\n case \"o\":\n case \"x\":\n case \"X\":\n if (symbol === \"#\") prefix = \"0\" + type.toLowerCase();\n\n case \"c\":\n exponent = false;\n\n case \"d\":\n integer = true;\n precision = 0;\n break;\n\n case \"s\":\n scale = -1;\n type = \"r\";\n break;\n }\n if (symbol === \"$\") prefix = locale_currency[0], suffix = locale_currency[1];\n if (type == \"r\" && !precision) type = \"g\";\n if (precision != null) {\n if (type == \"g\") precision = Math.max(1, Math.min(21, precision)); else if (type == \"e\" || type == \"f\") precision = Math.max(0, Math.min(20, precision));\n }\n type = d3_format_types.get(type) || d3_format_typeDefault;\n var zcomma = zfill && comma;\n return function(value) {\n var fullSuffix = suffix;\n if (integer && value % 1) return \"\";\n var negative = value < 0 || value === 0 && 1 / value < 0 ? (value = -value, \"-\") : sign === \"-\" ? \"\" : sign;\n if (scale < 0) {\n var unit = d3.formatPrefix(value, precision);\n value = unit.scale(value);\n fullSuffix = unit.symbol + suffix;\n } else {\n value *= scale;\n }\n value = type(value, precision);\n var i = value.lastIndexOf(\".\"), before, after;\n if (i < 0) {\n var j = exponent ? value.lastIndexOf(\"e\") : -1;\n if (j < 0) before = value, after = \"\"; else before = value.substring(0, j), after = value.substring(j);\n } else {\n before = value.substring(0, i);\n after = locale_decimal + value.substring(i + 1);\n }\n if (!zfill && comma) before = formatGroup(before, Infinity);\n var length = prefix.length + before.length + after.length + (zcomma ? 0 : negative.length), padding = length < width ? new Array(length = width - length + 1).join(fill) : \"\";\n if (zcomma) before = formatGroup(padding + before, padding.length ? width - after.length : Infinity);\n negative += prefix;\n value = before + after;\n return (align === \"<\" ? negative + value + padding : align === \">\" ? padding + negative + value : align === \"^\" ? padding.substring(0, length >>= 1) + negative + value + padding.substring(length) : negative + (zcomma ? value : padding + value)) + fullSuffix;\n };\n };\n }\n var d3_format_re = /(?:([^{])?([<>=^]))?([+\\- ])?([$#])?(0)?(\\d+)?(,)?(\\.-?\\d+)?([a-z%])?/i;\n var d3_format_types = d3.map({\n b: function(x) {\n return x.toString(2);\n },\n c: function(x) {\n return String.fromCharCode(x);\n },\n o: function(x) {\n return x.toString(8);\n },\n x: function(x) {\n return x.toString(16);\n },\n X: function(x) {\n return x.toString(16).toUpperCase();\n },\n g: function(x, p) {\n return x.toPrecision(p);\n },\n e: function(x, p) {\n return x.toExponential(p);\n },\n f: function(x, p) {\n return x.toFixed(p);\n },\n r: function(x, p) {\n return (x = d3.round(x, d3_format_precision(x, p))).toFixed(Math.max(0, Math.min(20, d3_format_precision(x * (1 + 1e-15), p))));\n }\n });\n function d3_format_typeDefault(x) {\n return x + \"\";\n }\n var d3_time = d3.time = {}, d3_date = Date;\n function d3_date_utc() {\n this._ = new Date(arguments.length > 1 ? Date.UTC.apply(this, arguments) : arguments[0]);\n }\n d3_date_utc.prototype = {\n getDate: function() {\n return this._.getUTCDate();\n },\n getDay: function() {\n return this._.getUTCDay();\n },\n getFullYear: function() {\n return this._.getUTCFullYear();\n },\n getHours: function() {\n return this._.getUTCHours();\n },\n getMilliseconds: function() {\n return this._.getUTCMilliseconds();\n },\n getMinutes: function() {\n return this._.getUTCMinutes();\n },\n getMonth: function() {\n return this._.getUTCMonth();\n },\n getSeconds: function() {\n return this._.getUTCSeconds();\n },\n getTime: function() {\n return this._.getTime();\n },\n getTimezoneOffset: function() {\n return 0;\n },\n valueOf: function() {\n return this._.valueOf();\n },\n setDate: function() {\n d3_time_prototype.setUTCDate.apply(this._, arguments);\n },\n setDay: function() {\n d3_time_prototype.setUTCDay.apply(this._, arguments);\n },\n setFullYear: function() {\n d3_time_prototype.setUTCFullYear.apply(this._, arguments);\n },\n setHours: function() {\n d3_time_prototype.setUTCHours.apply(this._, arguments);\n },\n setMilliseconds: function() {\n d3_time_prototype.setUTCMilliseconds.apply(this._, arguments);\n },\n setMinutes: function() {\n d3_time_prototype.setUTCMinutes.apply(this._, arguments);\n },\n setMonth: function() {\n d3_time_prototype.setUTCMonth.apply(this._, arguments);\n },\n setSeconds: function() {\n d3_time_prototype.setUTCSeconds.apply(this._, arguments);\n },\n setTime: function() {\n d3_time_prototype.setTime.apply(this._, arguments);\n }\n };\n var d3_time_prototype = Date.prototype;\n function d3_time_interval(local, step, number) {\n function round(date) {\n var d0 = local(date), d1 = offset(d0, 1);\n return date - d0 < d1 - date ? d0 : d1;\n }\n function ceil(date) {\n step(date = local(new d3_date(date - 1)), 1);\n return date;\n }\n function offset(date, k) {\n step(date = new d3_date(+date), k);\n return date;\n }\n function range(t0, t1, dt) {\n var time = ceil(t0), times = [];\n if (dt > 1) {\n while (time < t1) {\n if (!(number(time) % dt)) times.push(new Date(+time));\n step(time, 1);\n }\n } else {\n while (time < t1) times.push(new Date(+time)), step(time, 1);\n }\n return times;\n }\n function range_utc(t0, t1, dt) {\n try {\n d3_date = d3_date_utc;\n var utc = new d3_date_utc();\n utc._ = t0;\n return range(utc, t1, dt);\n } finally {\n d3_date = Date;\n }\n }\n local.floor = local;\n local.round = round;\n local.ceil = ceil;\n local.offset = offset;\n local.range = range;\n var utc = local.utc = d3_time_interval_utc(local);\n utc.floor = utc;\n utc.round = d3_time_interval_utc(round);\n utc.ceil = d3_time_interval_utc(ceil);\n utc.offset = d3_time_interval_utc(offset);\n utc.range = range_utc;\n return local;\n }\n function d3_time_interval_utc(method) {\n return function(date, k) {\n try {\n d3_date = d3_date_utc;\n var utc = new d3_date_utc();\n utc._ = date;\n return method(utc, k)._;\n } finally {\n d3_date = Date;\n }\n };\n }\n d3_time.year = d3_time_interval(function(date) {\n date = d3_time.day(date);\n date.setMonth(0, 1);\n return date;\n }, function(date, offset) {\n date.setFullYear(date.getFullYear() + offset);\n }, function(date) {\n return date.getFullYear();\n });\n d3_time.years = d3_time.year.range;\n d3_time.years.utc = d3_time.year.utc.range;\n d3_time.day = d3_time_interval(function(date) {\n var day = new d3_date(2e3, 0);\n day.setFullYear(date.getFullYear(), date.getMonth(), date.getDate());\n return day;\n }, function(date, offset) {\n date.setDate(date.getDate() + offset);\n }, function(date) {\n return date.getDate() - 1;\n });\n d3_time.days = d3_time.day.range;\n d3_time.days.utc = d3_time.day.utc.range;\n d3_time.dayOfYear = function(date) {\n var year = d3_time.year(date);\n return Math.floor((date - year - (date.getTimezoneOffset() - year.getTimezoneOffset()) * 6e4) / 864e5);\n };\n [ \"sunday\", \"monday\", \"tuesday\", \"wednesday\", \"thursday\", \"friday\", \"saturday\" ].forEach(function(day, i) {\n i = 7 - i;\n var interval = d3_time[day] = d3_time_interval(function(date) {\n (date = d3_time.day(date)).setDate(date.getDate() - (date.getDay() + i) % 7);\n return date;\n }, function(date, offset) {\n date.setDate(date.getDate() + Math.floor(offset) * 7);\n }, function(date) {\n var day = d3_time.year(date).getDay();\n return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7) - (day !== i);\n });\n d3_time[day + \"s\"] = interval.range;\n d3_time[day + \"s\"].utc = interval.utc.range;\n d3_time[day + \"OfYear\"] = function(date) {\n var day = d3_time.year(date).getDay();\n return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7);\n };\n });\n d3_time.week = d3_time.sunday;\n d3_time.weeks = d3_time.sunday.range;\n d3_time.weeks.utc = d3_time.sunday.utc.range;\n d3_time.weekOfYear = d3_time.sundayOfYear;\n function d3_locale_timeFormat(locale) {\n var locale_dateTime = locale.dateTime, locale_date = locale.date, locale_time = locale.time, locale_periods = locale.periods, locale_days = locale.days, locale_shortDays = locale.shortDays, locale_months = locale.months, locale_shortMonths = locale.shortMonths;\n function d3_time_format(template) {\n var n = template.length;\n function format(date) {\n var string = [], i = -1, j = 0, c, p, f;\n while (++i < n) {\n if (template.charCodeAt(i) === 37) {\n string.push(template.slice(j, i));\n if ((p = d3_time_formatPads[c = template.charAt(++i)]) != null) c = template.charAt(++i);\n if (f = d3_time_formats[c]) c = f(date, p == null ? c === \"e\" ? \" \" : \"0\" : p);\n string.push(c);\n j = i + 1;\n }\n }\n string.push(template.slice(j, i));\n return string.join(\"\");\n }\n format.parse = function(string) {\n var d = {\n y: 1900,\n m: 0,\n d: 1,\n H: 0,\n M: 0,\n S: 0,\n L: 0,\n Z: null\n }, i = d3_time_parse(d, template, string, 0);\n if (i != string.length) return null;\n if (\"p\" in d) d.H = d.H % 12 + d.p * 12;\n var localZ = d.Z != null && d3_date !== d3_date_utc, date = new (localZ ? d3_date_utc : d3_date)();\n if (\"j\" in d) date.setFullYear(d.y, 0, d.j); else if (\"w\" in d && (\"W\" in d || \"U\" in d)) {\n date.setFullYear(d.y, 0, 1);\n date.setFullYear(d.y, 0, \"W\" in d ? (d.w + 6) % 7 + d.W * 7 - (date.getDay() + 5) % 7 : d.w + d.U * 7 - (date.getDay() + 6) % 7);\n } else date.setFullYear(d.y, d.m, d.d);\n date.setHours(d.H + (d.Z / 100 | 0), d.M + d.Z % 100, d.S, d.L);\n return localZ ? date._ : date;\n };\n format.toString = function() {\n return template;\n };\n return format;\n }\n function d3_time_parse(date, template, string, j) {\n var c, p, t, i = 0, n = template.length, m = string.length;\n while (i < n) {\n if (j >= m) return -1;\n c = template.charCodeAt(i++);\n if (c === 37) {\n t = template.charAt(i++);\n p = d3_time_parsers[t in d3_time_formatPads ? template.charAt(i++) : t];\n if (!p || (j = p(date, string, j)) < 0) return -1;\n } else if (c != string.charCodeAt(j++)) {\n return -1;\n }\n }\n return j;\n }\n d3_time_format.utc = function(template) {\n var local = d3_time_format(template);\n function format(date) {\n try {\n d3_date = d3_date_utc;\n var utc = new d3_date();\n utc._ = date;\n return local(utc);\n } finally {\n d3_date = Date;\n }\n }\n format.parse = function(string) {\n try {\n d3_date = d3_date_utc;\n var date = local.parse(string);\n return date && date._;\n } finally {\n d3_date = Date;\n }\n };\n format.toString = local.toString;\n return format;\n };\n d3_time_format.multi = d3_time_format.utc.multi = d3_time_formatMulti;\n var d3_time_periodLookup = d3.map(), d3_time_dayRe = d3_time_formatRe(locale_days), d3_time_dayLookup = d3_time_formatLookup(locale_days), d3_time_dayAbbrevRe = d3_time_formatRe(locale_shortDays), d3_time_dayAbbrevLookup = d3_time_formatLookup(locale_shortDays), d3_time_monthRe = d3_time_formatRe(locale_months), d3_time_monthLookup = d3_time_formatLookup(locale_months), d3_time_monthAbbrevRe = d3_time_formatRe(locale_shortMonths), d3_time_monthAbbrevLookup = d3_time_formatLookup(locale_shortMonths);\n locale_periods.forEach(function(p, i) {\n d3_time_periodLookup.set(p.toLowerCase(), i);\n });\n var d3_time_formats = {\n a: function(d) {\n return locale_shortDays[d.getDay()];\n },\n A: function(d) {\n return locale_days[d.getDay()];\n },\n b: function(d) {\n return locale_shortMonths[d.getMonth()];\n },\n B: function(d) {\n return locale_months[d.getMonth()];\n },\n c: d3_time_format(locale_dateTime),\n d: function(d, p) {\n return d3_time_formatPad(d.getDate(), p, 2);\n },\n e: function(d, p) {\n return d3_time_formatPad(d.getDate(), p, 2);\n },\n H: function(d, p) {\n return d3_time_formatPad(d.getHours(), p, 2);\n },\n I: function(d, p) {\n return d3_time_formatPad(d.getHours() % 12 || 12, p, 2);\n },\n j: function(d, p) {\n return d3_time_formatPad(1 + d3_time.dayOfYear(d), p, 3);\n },\n L: function(d, p) {\n return d3_time_formatPad(d.getMilliseconds(), p, 3);\n },\n m: function(d, p) {\n return d3_time_formatPad(d.getMonth() + 1, p, 2);\n },\n M: function(d, p) {\n return d3_time_formatPad(d.getMinutes(), p, 2);\n },\n p: function(d) {\n return locale_periods[+(d.getHours() >= 12)];\n },\n S: function(d, p) {\n return d3_time_formatPad(d.getSeconds(), p, 2);\n },\n U: function(d, p) {\n return d3_time_formatPad(d3_time.sundayOfYear(d), p, 2);\n },\n w: function(d) {\n return d.getDay();\n },\n W: function(d, p) {\n return d3_time_formatPad(d3_time.mondayOfYear(d), p, 2);\n },\n x: d3_time_format(locale_date),\n X: d3_time_format(locale_time),\n y: function(d, p) {\n return d3_time_formatPad(d.getFullYear() % 100, p, 2);\n },\n Y: function(d, p) {\n return d3_time_formatPad(d.getFullYear() % 1e4, p, 4);\n },\n Z: d3_time_zone,\n \"%\": function() {\n return \"%\";\n }\n };\n var d3_time_parsers = {\n a: d3_time_parseWeekdayAbbrev,\n A: d3_time_parseWeekday,\n b: d3_time_parseMonthAbbrev,\n B: d3_time_parseMonth,\n c: d3_time_parseLocaleFull,\n d: d3_time_parseDay,\n e: d3_time_parseDay,\n H: d3_time_parseHour24,\n I: d3_time_parseHour24,\n j: d3_time_parseDayOfYear,\n L: d3_time_parseMilliseconds,\n m: d3_time_parseMonthNumber,\n M: d3_time_parseMinutes,\n p: d3_time_parseAmPm,\n S: d3_time_parseSeconds,\n U: d3_time_parseWeekNumberSunday,\n w: d3_time_parseWeekdayNumber,\n W: d3_time_parseWeekNumberMonday,\n x: d3_time_parseLocaleDate,\n X: d3_time_parseLocaleTime,\n y: d3_time_parseYear,\n Y: d3_time_parseFullYear,\n Z: d3_time_parseZone,\n \"%\": d3_time_parseLiteralPercent\n };\n function d3_time_parseWeekdayAbbrev(date, string, i) {\n d3_time_dayAbbrevRe.lastIndex = 0;\n var n = d3_time_dayAbbrevRe.exec(string.slice(i));\n return n ? (date.w = d3_time_dayAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1;\n }\n function d3_time_parseWeekday(date, string, i) {\n d3_time_dayRe.lastIndex = 0;\n var n = d3_time_dayRe.exec(string.slice(i));\n return n ? (date.w = d3_time_dayLookup.get(n[0].toLowerCase()), i + n[0].length) : -1;\n }\n function d3_time_parseMonthAbbrev(date, string, i) {\n d3_time_monthAbbrevRe.lastIndex = 0;\n var n = d3_time_monthAbbrevRe.exec(string.slice(i));\n return n ? (date.m = d3_time_monthAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1;\n }\n function d3_time_parseMonth(date, string, i) {\n d3_time_monthRe.lastIndex = 0;\n var n = d3_time_monthRe.exec(string.slice(i));\n return n ? (date.m = d3_time_monthLookup.get(n[0].toLowerCase()), i + n[0].length) : -1;\n }\n function d3_time_parseLocaleFull(date, string, i) {\n return d3_time_parse(date, d3_time_formats.c.toString(), string, i);\n }\n function d3_time_parseLocaleDate(date, string, i) {\n return d3_time_parse(date, d3_time_formats.x.toString(), string, i);\n }\n function d3_time_parseLocaleTime(date, string, i) {\n return d3_time_parse(date, d3_time_formats.X.toString(), string, i);\n }\n function d3_time_parseAmPm(date, string, i) {\n var n = d3_time_periodLookup.get(string.slice(i, i += 2).toLowerCase());\n return n == null ? -1 : (date.p = n, i);\n }\n return d3_time_format;\n }\n var d3_time_formatPads = {\n \"-\": \"\",\n _: \" \",\n \"0\": \"0\"\n }, d3_time_numberRe = /^\\s*\\d+/, d3_time_percentRe = /^%/;\n function d3_time_formatPad(value, fill, width) {\n var sign = value < 0 ? \"-\" : \"\", string = (sign ? -value : value) + \"\", length = string.length;\n return sign + (length < width ? new Array(width - length + 1).join(fill) + string : string);\n }\n function d3_time_formatRe(names) {\n return new RegExp(\"^(?:\" + names.map(d3.requote).join(\"|\") + \")\", \"i\");\n }\n function d3_time_formatLookup(names) {\n var map = new d3_Map(), i = -1, n = names.length;\n while (++i < n) map.set(names[i].toLowerCase(), i);\n return map;\n }\n function d3_time_parseWeekdayNumber(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 1));\n return n ? (date.w = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseWeekNumberSunday(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i));\n return n ? (date.U = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseWeekNumberMonday(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i));\n return n ? (date.W = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseFullYear(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 4));\n return n ? (date.y = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseYear(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.y = d3_time_expandYear(+n[0]), i + n[0].length) : -1;\n }\n function d3_time_parseZone(date, string, i) {\n return /^[+-]\\d{4}$/.test(string = string.slice(i, i + 5)) ? (date.Z = -string, \n i + 5) : -1;\n }\n function d3_time_expandYear(d) {\n return d + (d > 68 ? 1900 : 2e3);\n }\n function d3_time_parseMonthNumber(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.m = n[0] - 1, i + n[0].length) : -1;\n }\n function d3_time_parseDay(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.d = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseDayOfYear(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 3));\n return n ? (date.j = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseHour24(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.H = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseMinutes(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.M = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseSeconds(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 2));\n return n ? (date.S = +n[0], i + n[0].length) : -1;\n }\n function d3_time_parseMilliseconds(date, string, i) {\n d3_time_numberRe.lastIndex = 0;\n var n = d3_time_numberRe.exec(string.slice(i, i + 3));\n return n ? (date.L = +n[0], i + n[0].length) : -1;\n }\n function d3_time_zone(d) {\n var z = d.getTimezoneOffset(), zs = z > 0 ? \"-\" : \"+\", zh = abs(z) / 60 | 0, zm = abs(z) % 60;\n return zs + d3_time_formatPad(zh, \"0\", 2) + d3_time_formatPad(zm, \"0\", 2);\n }\n function d3_time_parseLiteralPercent(date, string, i) {\n d3_time_percentRe.lastIndex = 0;\n var n = d3_time_percentRe.exec(string.slice(i, i + 1));\n return n ? i + n[0].length : -1;\n }\n function d3_time_formatMulti(formats) {\n var n = formats.length, i = -1;\n while (++i < n) formats[i][0] = this(formats[i][0]);\n return function(date) {\n var i = 0, f = formats[i];\n while (!f[1](date)) f = formats[++i];\n return f[0](date);\n };\n }\n d3.locale = function(locale) {\n return {\n numberFormat: d3_locale_numberFormat(locale),\n timeFormat: d3_locale_timeFormat(locale)\n };\n };\n var d3_locale_enUS = d3.locale({\n decimal: \".\",\n thousands: \",\",\n grouping: [ 3 ],\n currency: [ \"$\", \"\" ],\n dateTime: \"%a %b %e %X %Y\",\n date: \"%m/%d/%Y\",\n time: \"%H:%M:%S\",\n periods: [ \"AM\", \"PM\" ],\n days: [ \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" ],\n shortDays: [ \"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\" ],\n months: [ \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\" ],\n shortMonths: [ \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\" ]\n });\n d3.format = d3_locale_enUS.numberFormat;\n d3.geo = {};\n function d3_adder() {}\n d3_adder.prototype = {\n s: 0,\n t: 0,\n add: function(y) {\n d3_adderSum(y, this.t, d3_adderTemp);\n d3_adderSum(d3_adderTemp.s, this.s, this);\n if (this.s) this.t += d3_adderTemp.t; else this.s = d3_adderTemp.t;\n },\n reset: function() {\n this.s = this.t = 0;\n },\n valueOf: function() {\n return this.s;\n }\n };\n var d3_adderTemp = new d3_adder();\n function d3_adderSum(a, b, o) {\n var x = o.s = a + b, bv = x - a, av = x - bv;\n o.t = a - av + (b - bv);\n }\n d3.geo.stream = function(object, listener) {\n if (object && d3_geo_streamObjectType.hasOwnProperty(object.type)) {\n d3_geo_streamObjectType[object.type](object, listener);\n } else {\n d3_geo_streamGeometry(object, listener);\n }\n };\n function d3_geo_streamGeometry(geometry, listener) {\n if (geometry && d3_geo_streamGeometryType.hasOwnProperty(geometry.type)) {\n d3_geo_streamGeometryType[geometry.type](geometry, listener);\n }\n }\n var d3_geo_streamObjectType = {\n Feature: function(feature, listener) {\n d3_geo_streamGeometry(feature.geometry, listener);\n },\n FeatureCollection: function(object, listener) {\n var features = object.features, i = -1, n = features.length;\n while (++i < n) d3_geo_streamGeometry(features[i].geometry, listener);\n }\n };\n var d3_geo_streamGeometryType = {\n Sphere: function(object, listener) {\n listener.sphere();\n },\n Point: function(object, listener) {\n object = object.coordinates;\n listener.point(object[0], object[1], object[2]);\n },\n MultiPoint: function(object, listener) {\n var coordinates = object.coordinates, i = -1, n = coordinates.length;\n while (++i < n) object = coordinates[i], listener.point(object[0], object[1], object[2]);\n },\n LineString: function(object, listener) {\n d3_geo_streamLine(object.coordinates, listener, 0);\n },\n MultiLineString: function(object, listener) {\n var coordinates = object.coordinates, i = -1, n = coordinates.length;\n while (++i < n) d3_geo_streamLine(coordinates[i], listener, 0);\n },\n Polygon: function(object, listener) {\n d3_geo_streamPolygon(object.coordinates, listener);\n },\n MultiPolygon: function(object, listener) {\n var coordinates = object.coordinates, i = -1, n = coordinates.length;\n while (++i < n) d3_geo_streamPolygon(coordinates[i], listener);\n },\n GeometryCollection: function(object, listener) {\n var geometries = object.geometries, i = -1, n = geometries.length;\n while (++i < n) d3_geo_streamGeometry(geometries[i], listener);\n }\n };\n function d3_geo_streamLine(coordinates, listener, closed) {\n var i = -1, n = coordinates.length - closed, coordinate;\n listener.lineStart();\n while (++i < n) coordinate = coordinates[i], listener.point(coordinate[0], coordinate[1], coordinate[2]);\n listener.lineEnd();\n }\n function d3_geo_streamPolygon(coordinates, listener) {\n var i = -1, n = coordinates.length;\n listener.polygonStart();\n while (++i < n) d3_geo_streamLine(coordinates[i], listener, 1);\n listener.polygonEnd();\n }\n d3.geo.area = function(object) {\n d3_geo_areaSum = 0;\n d3.geo.stream(object, d3_geo_area);\n return d3_geo_areaSum;\n };\n var d3_geo_areaSum, d3_geo_areaRingSum = new d3_adder();\n var d3_geo_area = {\n sphere: function() {\n d3_geo_areaSum += 4 * π;\n },\n point: d3_noop,\n lineStart: d3_noop,\n lineEnd: d3_noop,\n polygonStart: function() {\n d3_geo_areaRingSum.reset();\n d3_geo_area.lineStart = d3_geo_areaRingStart;\n },\n polygonEnd: function() {\n var area = 2 * d3_geo_areaRingSum;\n d3_geo_areaSum += area < 0 ? 4 * π + area : area;\n d3_geo_area.lineStart = d3_geo_area.lineEnd = d3_geo_area.point = d3_noop;\n }\n };\n function d3_geo_areaRingStart() {\n var λ00, φ00, λ0, cosφ0, sinφ0;\n d3_geo_area.point = function(λ, φ) {\n d3_geo_area.point = nextPoint;\n λ0 = (λ00 = λ) * d3_radians, cosφ0 = Math.cos(φ = (φ00 = φ) * d3_radians / 2 + π / 4), \n sinφ0 = Math.sin(φ);\n };\n function nextPoint(λ, φ) {\n λ *= d3_radians;\n φ = φ * d3_radians / 2 + π / 4;\n var dλ = λ - λ0, sdλ = dλ >= 0 ? 1 : -1, adλ = sdλ * dλ, cosφ = Math.cos(φ), sinφ = Math.sin(φ), k = sinφ0 * sinφ, u = cosφ0 * cosφ + k * Math.cos(adλ), v = k * sdλ * Math.sin(adλ);\n d3_geo_areaRingSum.add(Math.atan2(v, u));\n λ0 = λ, cosφ0 = cosφ, sinφ0 = sinφ;\n }\n d3_geo_area.lineEnd = function() {\n nextPoint(λ00, φ00);\n };\n }\n function d3_geo_cartesian(spherical) {\n var λ = spherical[0], φ = spherical[1], cosφ = Math.cos(φ);\n return [ cosφ * Math.cos(λ), cosφ * Math.sin(λ), Math.sin(φ) ];\n }\n function d3_geo_cartesianDot(a, b) {\n return a[0] * b[0] + a[1] * b[1] + a[2] * b[2];\n }\n function d3_geo_cartesianCross(a, b) {\n return [ a[1] * b[2] - a[2] * b[1], a[2] * b[0] - a[0] * b[2], a[0] * b[1] - a[1] * b[0] ];\n }\n function d3_geo_cartesianAdd(a, b) {\n a[0] += b[0];\n a[1] += b[1];\n a[2] += b[2];\n }\n function d3_geo_cartesianScale(vector, k) {\n return [ vector[0] * k, vector[1] * k, vector[2] * k ];\n }\n function d3_geo_cartesianNormalize(d) {\n var l = Math.sqrt(d[0] * d[0] + d[1] * d[1] + d[2] * d[2]);\n d[0] /= l;\n d[1] /= l;\n d[2] /= l;\n }\n function d3_geo_spherical(cartesian) {\n return [ Math.atan2(cartesian[1], cartesian[0]), d3_asin(cartesian[2]) ];\n }\n function d3_geo_sphericalEqual(a, b) {\n return abs(a[0] - b[0]) < ε && abs(a[1] - b[1]) < ε;\n }\n d3.geo.bounds = function() {\n var λ0, φ0, λ1, φ1, λ_, λ__, φ__, p0, dλSum, ranges, range;\n var bound = {\n point: point,\n lineStart: lineStart,\n lineEnd: lineEnd,\n polygonStart: function() {\n bound.point = ringPoint;\n bound.lineStart = ringStart;\n bound.lineEnd = ringEnd;\n dλSum = 0;\n d3_geo_area.polygonStart();\n },\n polygonEnd: function() {\n d3_geo_area.polygonEnd();\n bound.point = point;\n bound.lineStart = lineStart;\n bound.lineEnd = lineEnd;\n if (d3_geo_areaRingSum < 0) λ0 = -(λ1 = 180), φ0 = -(φ1 = 90); else if (dλSum > ε) φ1 = 90; else if (dλSum < -ε) φ0 = -90;\n range[0] = λ0, range[1] = λ1;\n }\n };\n function point(λ, φ) {\n ranges.push(range = [ λ0 = λ, λ1 = λ ]);\n if (φ < φ0) φ0 = φ;\n if (φ > φ1) φ1 = φ;\n }\n function linePoint(λ, φ) {\n var p = d3_geo_cartesian([ λ * d3_radians, φ * d3_radians ]);\n if (p0) {\n var normal = d3_geo_cartesianCross(p0, p), equatorial = [ normal[1], -normal[0], 0 ], inflection = d3_geo_cartesianCross(equatorial, normal);\n d3_geo_cartesianNormalize(inflection);\n inflection = d3_geo_spherical(inflection);\n var dλ = λ - λ_, s = dλ > 0 ? 1 : -1, λi = inflection[0] * d3_degrees * s, antimeridian = abs(dλ) > 180;\n if (antimeridian ^ (s * λ_ < λi && λi < s * λ)) {\n var φi = inflection[1] * d3_degrees;\n if (φi > φ1) φ1 = φi;\n } else if (λi = (λi + 360) % 360 - 180, antimeridian ^ (s * λ_ < λi && λi < s * λ)) {\n var φi = -inflection[1] * d3_degrees;\n if (φi < φ0) φ0 = φi;\n } else {\n if (φ < φ0) φ0 = φ;\n if (φ > φ1) φ1 = φ;\n }\n if (antimeridian) {\n if (λ < λ_) {\n if (angle(λ0, λ) > angle(λ0, λ1)) λ1 = λ;\n } else {\n if (angle(λ, λ1) > angle(λ0, λ1)) λ0 = λ;\n }\n } else {\n if (λ1 >= λ0) {\n if (λ < λ0) λ0 = λ;\n if (λ > λ1) λ1 = λ;\n } else {\n if (λ > λ_) {\n if (angle(λ0, λ) > angle(λ0, λ1)) λ1 = λ;\n } else {\n if (angle(λ, λ1) > angle(λ0, λ1)) λ0 = λ;\n }\n }\n }\n } else {\n point(λ, φ);\n }\n p0 = p, λ_ = λ;\n }\n function lineStart() {\n bound.point = linePoint;\n }\n function lineEnd() {\n range[0] = λ0, range[1] = λ1;\n bound.point = point;\n p0 = null;\n }\n function ringPoint(λ, φ) {\n if (p0) {\n var dλ = λ - λ_;\n dλSum += abs(dλ) > 180 ? dλ + (dλ > 0 ? 360 : -360) : dλ;\n } else λ__ = λ, φ__ = φ;\n d3_geo_area.point(λ, φ);\n linePoint(λ, φ);\n }\n function ringStart() {\n d3_geo_area.lineStart();\n }\n function ringEnd() {\n ringPoint(λ__, φ__);\n d3_geo_area.lineEnd();\n if (abs(dλSum) > ε) λ0 = -(λ1 = 180);\n range[0] = λ0, range[1] = λ1;\n p0 = null;\n }\n function angle(λ0, λ1) {\n return (λ1 -= λ0) < 0 ? λ1 + 360 : λ1;\n }\n function compareRanges(a, b) {\n return a[0] - b[0];\n }\n function withinRange(x, range) {\n return range[0] <= range[1] ? range[0] <= x && x <= range[1] : x < range[0] || range[1] < x;\n }\n return function(feature) {\n φ1 = λ1 = -(λ0 = φ0 = Infinity);\n ranges = [];\n d3.geo.stream(feature, bound);\n var n = ranges.length;\n if (n) {\n ranges.sort(compareRanges);\n for (var i = 1, a = ranges[0], b, merged = [ a ]; i < n; ++i) {\n b = ranges[i];\n if (withinRange(b[0], a) || withinRange(b[1], a)) {\n if (angle(a[0], b[1]) > angle(a[0], a[1])) a[1] = b[1];\n if (angle(b[0], a[1]) > angle(a[0], a[1])) a[0] = b[0];\n } else {\n merged.push(a = b);\n }\n }\n var best = -Infinity, dλ;\n for (var n = merged.length - 1, i = 0, a = merged[n], b; i <= n; a = b, ++i) {\n b = merged[i];\n if ((dλ = angle(a[1], b[0])) > best) best = dλ, λ0 = b[0], λ1 = a[1];\n }\n }\n ranges = range = null;\n return λ0 === Infinity || φ0 === Infinity ? [ [ NaN, NaN ], [ NaN, NaN ] ] : [ [ λ0, φ0 ], [ λ1, φ1 ] ];\n };\n }();\n d3.geo.centroid = function(object) {\n d3_geo_centroidW0 = d3_geo_centroidW1 = d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0;\n d3.geo.stream(object, d3_geo_centroid);\n var x = d3_geo_centroidX2, y = d3_geo_centroidY2, z = d3_geo_centroidZ2, m = x * x + y * y + z * z;\n if (m < ε2) {\n x = d3_geo_centroidX1, y = d3_geo_centroidY1, z = d3_geo_centroidZ1;\n if (d3_geo_centroidW1 < ε) x = d3_geo_centroidX0, y = d3_geo_centroidY0, z = d3_geo_centroidZ0;\n m = x * x + y * y + z * z;\n if (m < ε2) return [ NaN, NaN ];\n }\n return [ Math.atan2(y, x) * d3_degrees, d3_asin(z / Math.sqrt(m)) * d3_degrees ];\n };\n var d3_geo_centroidW0, d3_geo_centroidW1, d3_geo_centroidX0, d3_geo_centroidY0, d3_geo_centroidZ0, d3_geo_centroidX1, d3_geo_centroidY1, d3_geo_centroidZ1, d3_geo_centroidX2, d3_geo_centroidY2, d3_geo_centroidZ2;\n var d3_geo_centroid = {\n sphere: d3_noop,\n point: d3_geo_centroidPoint,\n lineStart: d3_geo_centroidLineStart,\n lineEnd: d3_geo_centroidLineEnd,\n polygonStart: function() {\n d3_geo_centroid.lineStart = d3_geo_centroidRingStart;\n },\n polygonEnd: function() {\n d3_geo_centroid.lineStart = d3_geo_centroidLineStart;\n }\n };\n function d3_geo_centroidPoint(λ, φ) {\n λ *= d3_radians;\n var cosφ = Math.cos(φ *= d3_radians);\n d3_geo_centroidPointXYZ(cosφ * Math.cos(λ), cosφ * Math.sin(λ), Math.sin(φ));\n }\n function d3_geo_centroidPointXYZ(x, y, z) {\n ++d3_geo_centroidW0;\n d3_geo_centroidX0 += (x - d3_geo_centroidX0) / d3_geo_centroidW0;\n d3_geo_centroidY0 += (y - d3_geo_centroidY0) / d3_geo_centroidW0;\n d3_geo_centroidZ0 += (z - d3_geo_centroidZ0) / d3_geo_centroidW0;\n }\n function d3_geo_centroidLineStart() {\n var x0, y0, z0;\n d3_geo_centroid.point = function(λ, φ) {\n λ *= d3_radians;\n var cosφ = Math.cos(φ *= d3_radians);\n x0 = cosφ * Math.cos(λ);\n y0 = cosφ * Math.sin(λ);\n z0 = Math.sin(φ);\n d3_geo_centroid.point = nextPoint;\n d3_geo_centroidPointXYZ(x0, y0, z0);\n };\n function nextPoint(λ, φ) {\n λ *= d3_radians;\n var cosφ = Math.cos(φ *= d3_radians), x = cosφ * Math.cos(λ), y = cosφ * Math.sin(λ), z = Math.sin(φ), w = Math.atan2(Math.sqrt((w = y0 * z - z0 * y) * w + (w = z0 * x - x0 * z) * w + (w = x0 * y - y0 * x) * w), x0 * x + y0 * y + z0 * z);\n d3_geo_centroidW1 += w;\n d3_geo_centroidX1 += w * (x0 + (x0 = x));\n d3_geo_centroidY1 += w * (y0 + (y0 = y));\n d3_geo_centroidZ1 += w * (z0 + (z0 = z));\n d3_geo_centroidPointXYZ(x0, y0, z0);\n }\n }\n function d3_geo_centroidLineEnd() {\n d3_geo_centroid.point = d3_geo_centroidPoint;\n }\n function d3_geo_centroidRingStart() {\n var λ00, φ00, x0, y0, z0;\n d3_geo_centroid.point = function(λ, φ) {\n λ00 = λ, φ00 = φ;\n d3_geo_centroid.point = nextPoint;\n λ *= d3_radians;\n var cosφ = Math.cos(φ *= d3_radians);\n x0 = cosφ * Math.cos(λ);\n y0 = cosφ * Math.sin(λ);\n z0 = Math.sin(φ);\n d3_geo_centroidPointXYZ(x0, y0, z0);\n };\n d3_geo_centroid.lineEnd = function() {\n nextPoint(λ00, φ00);\n d3_geo_centroid.lineEnd = d3_geo_centroidLineEnd;\n d3_geo_centroid.point = d3_geo_centroidPoint;\n };\n function nextPoint(λ, φ) {\n λ *= d3_radians;\n var cosφ = Math.cos(φ *= d3_radians), x = cosφ * Math.cos(λ), y = cosφ * Math.sin(λ), z = Math.sin(φ), cx = y0 * z - z0 * y, cy = z0 * x - x0 * z, cz = x0 * y - y0 * x, m = Math.sqrt(cx * cx + cy * cy + cz * cz), u = x0 * x + y0 * y + z0 * z, v = m && -d3_acos(u) / m, w = Math.atan2(m, u);\n d3_geo_centroidX2 += v * cx;\n d3_geo_centroidY2 += v * cy;\n d3_geo_centroidZ2 += v * cz;\n d3_geo_centroidW1 += w;\n d3_geo_centroidX1 += w * (x0 + (x0 = x));\n d3_geo_centroidY1 += w * (y0 + (y0 = y));\n d3_geo_centroidZ1 += w * (z0 + (z0 = z));\n d3_geo_centroidPointXYZ(x0, y0, z0);\n }\n }\n function d3_geo_compose(a, b) {\n function compose(x, y) {\n return x = a(x, y), b(x[0], x[1]);\n }\n if (a.invert && b.invert) compose.invert = function(x, y) {\n return x = b.invert(x, y), x && a.invert(x[0], x[1]);\n };\n return compose;\n }\n function d3_true() {\n return true;\n }\n function d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener) {\n var subject = [], clip = [];\n segments.forEach(function(segment) {\n if ((n = segment.length - 1) <= 0) return;\n var n, p0 = segment[0], p1 = segment[n];\n if (d3_geo_sphericalEqual(p0, p1)) {\n listener.lineStart();\n for (var i = 0; i < n; ++i) listener.point((p0 = segment[i])[0], p0[1]);\n listener.lineEnd();\n return;\n }\n var a = new d3_geo_clipPolygonIntersection(p0, segment, null, true), b = new d3_geo_clipPolygonIntersection(p0, null, a, false);\n a.o = b;\n subject.push(a);\n clip.push(b);\n a = new d3_geo_clipPolygonIntersection(p1, segment, null, false);\n b = new d3_geo_clipPolygonIntersection(p1, null, a, true);\n a.o = b;\n subject.push(a);\n clip.push(b);\n });\n clip.sort(compare);\n d3_geo_clipPolygonLinkCircular(subject);\n d3_geo_clipPolygonLinkCircular(clip);\n if (!subject.length) return;\n for (var i = 0, entry = clipStartInside, n = clip.length; i < n; ++i) {\n clip[i].e = entry = !entry;\n }\n var start = subject[0], points, point;\n while (1) {\n var current = start, isSubject = true;\n while (current.v) if ((current = current.n) === start) return;\n points = current.z;\n listener.lineStart();\n do {\n current.v = current.o.v = true;\n if (current.e) {\n if (isSubject) {\n for (var i = 0, n = points.length; i < n; ++i) listener.point((point = points[i])[0], point[1]);\n } else {\n interpolate(current.x, current.n.x, 1, listener);\n }\n current = current.n;\n } else {\n if (isSubject) {\n points = current.p.z;\n for (var i = points.length - 1; i >= 0; --i) listener.point((point = points[i])[0], point[1]);\n } else {\n interpolate(current.x, current.p.x, -1, listener);\n }\n current = current.p;\n }\n current = current.o;\n points = current.z;\n isSubject = !isSubject;\n } while (!current.v);\n listener.lineEnd();\n }\n }\n function d3_geo_clipPolygonLinkCircular(array) {\n if (!(n = array.length)) return;\n var n, i = 0, a = array[0], b;\n while (++i < n) {\n a.n = b = array[i];\n b.p = a;\n a = b;\n }\n a.n = b = array[0];\n b.p = a;\n }\n function d3_geo_clipPolygonIntersection(point, points, other, entry) {\n this.x = point;\n this.z = points;\n this.o = other;\n this.e = entry;\n this.v = false;\n this.n = this.p = null;\n }\n function d3_geo_clip(pointVisible, clipLine, interpolate, clipStart) {\n return function(rotate, listener) {\n var line = clipLine(listener), rotatedClipStart = rotate.invert(clipStart[0], clipStart[1]);\n var clip = {\n point: point,\n lineStart: lineStart,\n lineEnd: lineEnd,\n polygonStart: function() {\n clip.point = pointRing;\n clip.lineStart = ringStart;\n clip.lineEnd = ringEnd;\n segments = [];\n polygon = [];\n },\n polygonEnd: function() {\n clip.point = point;\n clip.lineStart = lineStart;\n clip.lineEnd = lineEnd;\n segments = d3.merge(segments);\n var clipStartInside = d3_geo_pointInPolygon(rotatedClipStart, polygon);\n if (segments.length) {\n if (!polygonStarted) listener.polygonStart(), polygonStarted = true;\n d3_geo_clipPolygon(segments, d3_geo_clipSort, clipStartInside, interpolate, listener);\n } else if (clipStartInside) {\n if (!polygonStarted) listener.polygonStart(), polygonStarted = true;\n listener.lineStart();\n interpolate(null, null, 1, listener);\n listener.lineEnd();\n }\n if (polygonStarted) listener.polygonEnd(), polygonStarted = false;\n segments = polygon = null;\n },\n sphere: function() {\n listener.polygonStart();\n listener.lineStart();\n interpolate(null, null, 1, listener);\n listener.lineEnd();\n listener.polygonEnd();\n }\n };\n function point(λ, φ) {\n var point = rotate(λ, φ);\n if (pointVisible(λ = point[0], φ = point[1])) listener.point(λ, φ);\n }\n function pointLine(λ, φ) {\n var point = rotate(λ, φ);\n line.point(point[0], point[1]);\n }\n function lineStart() {\n clip.point = pointLine;\n line.lineStart();\n }\n function lineEnd() {\n clip.point = point;\n line.lineEnd();\n }\n var segments;\n var buffer = d3_geo_clipBufferListener(), ringListener = clipLine(buffer), polygonStarted = false, polygon, ring;\n function pointRing(λ, φ) {\n ring.push([ λ, φ ]);\n var point = rotate(λ, φ);\n ringListener.point(point[0], point[1]);\n }\n function ringStart() {\n ringListener.lineStart();\n ring = [];\n }\n function ringEnd() {\n pointRing(ring[0][0], ring[0][1]);\n ringListener.lineEnd();\n var clean = ringListener.clean(), ringSegments = buffer.buffer(), segment, n = ringSegments.length;\n ring.pop();\n polygon.push(ring);\n ring = null;\n if (!n) return;\n if (clean & 1) {\n segment = ringSegments[0];\n var n = segment.length - 1, i = -1, point;\n if (n > 0) {\n if (!polygonStarted) listener.polygonStart(), polygonStarted = true;\n listener.lineStart();\n while (++i < n) listener.point((point = segment[i])[0], point[1]);\n listener.lineEnd();\n }\n return;\n }\n if (n > 1 && clean & 2) ringSegments.push(ringSegments.pop().concat(ringSegments.shift()));\n segments.push(ringSegments.filter(d3_geo_clipSegmentLength1));\n }\n return clip;\n };\n }\n function d3_geo_clipSegmentLength1(segment) {\n return segment.length > 1;\n }\n function d3_geo_clipBufferListener() {\n var lines = [], line;\n return {\n lineStart: function() {\n lines.push(line = []);\n },\n point: function(λ, φ) {\n line.push([ λ, φ ]);\n },\n lineEnd: d3_noop,\n buffer: function() {\n var buffer = lines;\n lines = [];\n line = null;\n return buffer;\n },\n rejoin: function() {\n if (lines.length > 1) lines.push(lines.pop().concat(lines.shift()));\n }\n };\n }\n function d3_geo_clipSort(a, b) {\n return ((a = a.x)[0] < 0 ? a[1] - halfπ - ε : halfπ - a[1]) - ((b = b.x)[0] < 0 ? b[1] - halfπ - ε : halfπ - b[1]);\n }\n var d3_geo_clipAntimeridian = d3_geo_clip(d3_true, d3_geo_clipAntimeridianLine, d3_geo_clipAntimeridianInterpolate, [ -π, -π / 2 ]);\n function d3_geo_clipAntimeridianLine(listener) {\n var λ0 = NaN, φ0 = NaN, sλ0 = NaN, clean;\n return {\n lineStart: function() {\n listener.lineStart();\n clean = 1;\n },\n point: function(λ1, φ1) {\n var sλ1 = λ1 > 0 ? π : -π, dλ = abs(λ1 - λ0);\n if (abs(dλ - π) < ε) {\n listener.point(λ0, φ0 = (φ0 + φ1) / 2 > 0 ? halfπ : -halfπ);\n listener.point(sλ0, φ0);\n listener.lineEnd();\n listener.lineStart();\n listener.point(sλ1, φ0);\n listener.point(λ1, φ0);\n clean = 0;\n } else if (sλ0 !== sλ1 && dλ >= π) {\n if (abs(λ0 - sλ0) < ε) λ0 -= sλ0 * ε;\n if (abs(λ1 - sλ1) < ε) λ1 -= sλ1 * ε;\n φ0 = d3_geo_clipAntimeridianIntersect(λ0, φ0, λ1, φ1);\n listener.point(sλ0, φ0);\n listener.lineEnd();\n listener.lineStart();\n listener.point(sλ1, φ0);\n clean = 0;\n }\n listener.point(λ0 = λ1, φ0 = φ1);\n sλ0 = sλ1;\n },\n lineEnd: function() {\n listener.lineEnd();\n λ0 = φ0 = NaN;\n },\n clean: function() {\n return 2 - clean;\n }\n };\n }\n function d3_geo_clipAntimeridianIntersect(λ0, φ0, λ1, φ1) {\n var cosφ0, cosφ1, sinλ0_λ1 = Math.sin(λ0 - λ1);\n return abs(sinλ0_λ1) > ε ? Math.atan((Math.sin(φ0) * (cosφ1 = Math.cos(φ1)) * Math.sin(λ1) - Math.sin(φ1) * (cosφ0 = Math.cos(φ0)) * Math.sin(λ0)) / (cosφ0 * cosφ1 * sinλ0_λ1)) : (φ0 + φ1) / 2;\n }\n function d3_geo_clipAntimeridianInterpolate(from, to, direction, listener) {\n var φ;\n if (from == null) {\n φ = direction * halfπ;\n listener.point(-π, φ);\n listener.point(0, φ);\n listener.point(π, φ);\n listener.point(π, 0);\n listener.point(π, -φ);\n listener.point(0, -φ);\n listener.point(-π, -φ);\n listener.point(-π, 0);\n listener.point(-π, φ);\n } else if (abs(from[0] - to[0]) > ε) {\n var s = from[0] < to[0] ? π : -π;\n φ = direction * s / 2;\n listener.point(-s, φ);\n listener.point(0, φ);\n listener.point(s, φ);\n } else {\n listener.point(to[0], to[1]);\n }\n }\n function d3_geo_pointInPolygon(point, polygon) {\n var meridian = point[0], parallel = point[1], meridianNormal = [ Math.sin(meridian), -Math.cos(meridian), 0 ], polarAngle = 0, winding = 0;\n d3_geo_areaRingSum.reset();\n for (var i = 0, n = polygon.length; i < n; ++i) {\n var ring = polygon[i], m = ring.length;\n if (!m) continue;\n var point0 = ring[0], λ0 = point0[0], φ0 = point0[1] / 2 + π / 4, sinφ0 = Math.sin(φ0), cosφ0 = Math.cos(φ0), j = 1;\n while (true) {\n if (j === m) j = 0;\n point = ring[j];\n var λ = point[0], φ = point[1] / 2 + π / 4, sinφ = Math.sin(φ), cosφ = Math.cos(φ), dλ = λ - λ0, sdλ = dλ >= 0 ? 1 : -1, adλ = sdλ * dλ, antimeridian = adλ > π, k = sinφ0 * sinφ;\n d3_geo_areaRingSum.add(Math.atan2(k * sdλ * Math.sin(adλ), cosφ0 * cosφ + k * Math.cos(adλ)));\n polarAngle += antimeridian ? dλ + sdλ * τ : dλ;\n if (antimeridian ^ λ0 >= meridian ^ λ >= meridian) {\n var arc = d3_geo_cartesianCross(d3_geo_cartesian(point0), d3_geo_cartesian(point));\n d3_geo_cartesianNormalize(arc);\n var intersection = d3_geo_cartesianCross(meridianNormal, arc);\n d3_geo_cartesianNormalize(intersection);\n var φarc = (antimeridian ^ dλ >= 0 ? -1 : 1) * d3_asin(intersection[2]);\n if (parallel > φarc || parallel === φarc && (arc[0] || arc[1])) {\n winding += antimeridian ^ dλ >= 0 ? 1 : -1;\n }\n }\n if (!j++) break;\n λ0 = λ, sinφ0 = sinφ, cosφ0 = cosφ, point0 = point;\n }\n }\n return (polarAngle < -ε || polarAngle < ε && d3_geo_areaRingSum < 0) ^ winding & 1;\n }\n function d3_geo_clipCircle(radius) {\n var cr = Math.cos(radius), smallRadius = cr > 0, notHemisphere = abs(cr) > ε, interpolate = d3_geo_circleInterpolate(radius, 6 * d3_radians);\n return d3_geo_clip(visible, clipLine, interpolate, smallRadius ? [ 0, -radius ] : [ -π, radius - π ]);\n function visible(λ, φ) {\n return Math.cos(λ) * Math.cos(φ) > cr;\n }\n function clipLine(listener) {\n var point0, c0, v0, v00, clean;\n return {\n lineStart: function() {\n v00 = v0 = false;\n clean = 1;\n },\n point: function(λ, φ) {\n var point1 = [ λ, φ ], point2, v = visible(λ, φ), c = smallRadius ? v ? 0 : code(λ, φ) : v ? code(λ + (λ < 0 ? π : -π), φ) : 0;\n if (!point0 && (v00 = v0 = v)) listener.lineStart();\n if (v !== v0) {\n point2 = intersect(point0, point1);\n if (d3_geo_sphericalEqual(point0, point2) || d3_geo_sphericalEqual(point1, point2)) {\n point1[0] += ε;\n point1[1] += ε;\n v = visible(point1[0], point1[1]);\n }\n }\n if (v !== v0) {\n clean = 0;\n if (v) {\n listener.lineStart();\n point2 = intersect(point1, point0);\n listener.point(point2[0], point2[1]);\n } else {\n point2 = intersect(point0, point1);\n listener.point(point2[0], point2[1]);\n listener.lineEnd();\n }\n point0 = point2;\n } else if (notHemisphere && point0 && smallRadius ^ v) {\n var t;\n if (!(c & c0) && (t = intersect(point1, point0, true))) {\n clean = 0;\n if (smallRadius) {\n listener.lineStart();\n listener.point(t[0][0], t[0][1]);\n listener.point(t[1][0], t[1][1]);\n listener.lineEnd();\n } else {\n listener.point(t[1][0], t[1][1]);\n listener.lineEnd();\n listener.lineStart();\n listener.point(t[0][0], t[0][1]);\n }\n }\n }\n if (v && (!point0 || !d3_geo_sphericalEqual(point0, point1))) {\n listener.point(point1[0], point1[1]);\n }\n point0 = point1, v0 = v, c0 = c;\n },\n lineEnd: function() {\n if (v0) listener.lineEnd();\n point0 = null;\n },\n clean: function() {\n return clean | (v00 && v0) << 1;\n }\n };\n }\n function intersect(a, b, two) {\n var pa = d3_geo_cartesian(a), pb = d3_geo_cartesian(b);\n var n1 = [ 1, 0, 0 ], n2 = d3_geo_cartesianCross(pa, pb), n2n2 = d3_geo_cartesianDot(n2, n2), n1n2 = n2[0], determinant = n2n2 - n1n2 * n1n2;\n if (!determinant) return !two && a;\n var c1 = cr * n2n2 / determinant, c2 = -cr * n1n2 / determinant, n1xn2 = d3_geo_cartesianCross(n1, n2), A = d3_geo_cartesianScale(n1, c1), B = d3_geo_cartesianScale(n2, c2);\n d3_geo_cartesianAdd(A, B);\n var u = n1xn2, w = d3_geo_cartesianDot(A, u), uu = d3_geo_cartesianDot(u, u), t2 = w * w - uu * (d3_geo_cartesianDot(A, A) - 1);\n if (t2 < 0) return;\n var t = Math.sqrt(t2), q = d3_geo_cartesianScale(u, (-w - t) / uu);\n d3_geo_cartesianAdd(q, A);\n q = d3_geo_spherical(q);\n if (!two) return q;\n var λ0 = a[0], λ1 = b[0], φ0 = a[1], φ1 = b[1], z;\n if (λ1 < λ0) z = λ0, λ0 = λ1, λ1 = z;\n var δλ = λ1 - λ0, polar = abs(δλ - π) < ε, meridian = polar || δλ < ε;\n if (!polar && φ1 < φ0) z = φ0, φ0 = φ1, φ1 = z;\n if (meridian ? polar ? φ0 + φ1 > 0 ^ q[1] < (abs(q[0] - λ0) < ε ? φ0 : φ1) : φ0 <= q[1] && q[1] <= φ1 : δλ > π ^ (λ0 <= q[0] && q[0] <= λ1)) {\n var q1 = d3_geo_cartesianScale(u, (-w + t) / uu);\n d3_geo_cartesianAdd(q1, A);\n return [ q, d3_geo_spherical(q1) ];\n }\n }\n function code(λ, φ) {\n var r = smallRadius ? radius : π - radius, code = 0;\n if (λ < -r) code |= 1; else if (λ > r) code |= 2;\n if (φ < -r) code |= 4; else if (φ > r) code |= 8;\n return code;\n }\n }\n function d3_geom_clipLine(x0, y0, x1, y1) {\n return function(line) {\n var a = line.a, b = line.b, ax = a.x, ay = a.y, bx = b.x, by = b.y, t0 = 0, t1 = 1, dx = bx - ax, dy = by - ay, r;\n r = x0 - ax;\n if (!dx && r > 0) return;\n r /= dx;\n if (dx < 0) {\n if (r < t0) return;\n if (r < t1) t1 = r;\n } else if (dx > 0) {\n if (r > t1) return;\n if (r > t0) t0 = r;\n }\n r = x1 - ax;\n if (!dx && r < 0) return;\n r /= dx;\n if (dx < 0) {\n if (r > t1) return;\n if (r > t0) t0 = r;\n } else if (dx > 0) {\n if (r < t0) return;\n if (r < t1) t1 = r;\n }\n r = y0 - ay;\n if (!dy && r > 0) return;\n r /= dy;\n if (dy < 0) {\n if (r < t0) return;\n if (r < t1) t1 = r;\n } else if (dy > 0) {\n if (r > t1) return;\n if (r > t0) t0 = r;\n }\n r = y1 - ay;\n if (!dy && r < 0) return;\n r /= dy;\n if (dy < 0) {\n if (r > t1) return;\n if (r > t0) t0 = r;\n } else if (dy > 0) {\n if (r < t0) return;\n if (r < t1) t1 = r;\n }\n if (t0 > 0) line.a = {\n x: ax + t0 * dx,\n y: ay + t0 * dy\n };\n if (t1 < 1) line.b = {\n x: ax + t1 * dx,\n y: ay + t1 * dy\n };\n return line;\n };\n }\n var d3_geo_clipExtentMAX = 1e9;\n d3.geo.clipExtent = function() {\n var x0, y0, x1, y1, stream, clip, clipExtent = {\n stream: function(output) {\n if (stream) stream.valid = false;\n stream = clip(output);\n stream.valid = true;\n return stream;\n },\n extent: function(_) {\n if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ];\n clip = d3_geo_clipExtent(x0 = +_[0][0], y0 = +_[0][1], x1 = +_[1][0], y1 = +_[1][1]);\n if (stream) stream.valid = false, stream = null;\n return clipExtent;\n }\n };\n return clipExtent.extent([ [ 0, 0 ], [ 960, 500 ] ]);\n };\n function d3_geo_clipExtent(x0, y0, x1, y1) {\n return function(listener) {\n var listener_ = listener, bufferListener = d3_geo_clipBufferListener(), clipLine = d3_geom_clipLine(x0, y0, x1, y1), segments, polygon, ring;\n var clip = {\n point: point,\n lineStart: lineStart,\n lineEnd: lineEnd,\n polygonStart: function() {\n listener = bufferListener;\n segments = [];\n polygon = [];\n clean = true;\n },\n polygonEnd: function() {\n listener = listener_;\n segments = d3.merge(segments);\n var clipStartInside = insidePolygon([ x0, y1 ]), inside = clean && clipStartInside, visible = segments.length;\n if (inside || visible) {\n listener.polygonStart();\n if (inside) {\n listener.lineStart();\n interpolate(null, null, 1, listener);\n listener.lineEnd();\n }\n if (visible) {\n d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener);\n }\n listener.polygonEnd();\n }\n segments = polygon = ring = null;\n }\n };\n function insidePolygon(p) {\n var wn = 0, n = polygon.length, y = p[1];\n for (var i = 0; i < n; ++i) {\n for (var j = 1, v = polygon[i], m = v.length, a = v[0], b; j < m; ++j) {\n b = v[j];\n if (a[1] <= y) {\n if (b[1] > y && d3_cross2d(a, b, p) > 0) ++wn;\n } else {\n if (b[1] <= y && d3_cross2d(a, b, p) < 0) --wn;\n }\n a = b;\n }\n }\n return wn !== 0;\n }\n function interpolate(from, to, direction, listener) {\n var a = 0, a1 = 0;\n if (from == null || (a = corner(from, direction)) !== (a1 = corner(to, direction)) || comparePoints(from, to) < 0 ^ direction > 0) {\n do {\n listener.point(a === 0 || a === 3 ? x0 : x1, a > 1 ? y1 : y0);\n } while ((a = (a + direction + 4) % 4) !== a1);\n } else {\n listener.point(to[0], to[1]);\n }\n }\n function pointVisible(x, y) {\n return x0 <= x && x <= x1 && y0 <= y && y <= y1;\n }\n function point(x, y) {\n if (pointVisible(x, y)) listener.point(x, y);\n }\n var x__, y__, v__, x_, y_, v_, first, clean;\n function lineStart() {\n clip.point = linePoint;\n if (polygon) polygon.push(ring = []);\n first = true;\n v_ = false;\n x_ = y_ = NaN;\n }\n function lineEnd() {\n if (segments) {\n linePoint(x__, y__);\n if (v__ && v_) bufferListener.rejoin();\n segments.push(bufferListener.buffer());\n }\n clip.point = point;\n if (v_) listener.lineEnd();\n }\n function linePoint(x, y) {\n x = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, x));\n y = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, y));\n var v = pointVisible(x, y);\n if (polygon) ring.push([ x, y ]);\n if (first) {\n x__ = x, y__ = y, v__ = v;\n first = false;\n if (v) {\n listener.lineStart();\n listener.point(x, y);\n }\n } else {\n if (v && v_) listener.point(x, y); else {\n var l = {\n a: {\n x: x_,\n y: y_\n },\n b: {\n x: x,\n y: y\n }\n };\n if (clipLine(l)) {\n if (!v_) {\n listener.lineStart();\n listener.point(l.a.x, l.a.y);\n }\n listener.point(l.b.x, l.b.y);\n if (!v) listener.lineEnd();\n clean = false;\n } else if (v) {\n listener.lineStart();\n listener.point(x, y);\n clean = false;\n }\n }\n }\n x_ = x, y_ = y, v_ = v;\n }\n return clip;\n };\n function corner(p, direction) {\n return abs(p[0] - x0) < ε ? direction > 0 ? 0 : 3 : abs(p[0] - x1) < ε ? direction > 0 ? 2 : 1 : abs(p[1] - y0) < ε ? direction > 0 ? 1 : 0 : direction > 0 ? 3 : 2;\n }\n function compare(a, b) {\n return comparePoints(a.x, b.x);\n }\n function comparePoints(a, b) {\n var ca = corner(a, 1), cb = corner(b, 1);\n return ca !== cb ? ca - cb : ca === 0 ? b[1] - a[1] : ca === 1 ? a[0] - b[0] : ca === 2 ? a[1] - b[1] : b[0] - a[0];\n }\n }\n function d3_geo_conic(projectAt) {\n var φ0 = 0, φ1 = π / 3, m = d3_geo_projectionMutator(projectAt), p = m(φ0, φ1);\n p.parallels = function(_) {\n if (!arguments.length) return [ φ0 / π * 180, φ1 / π * 180 ];\n return m(φ0 = _[0] * π / 180, φ1 = _[1] * π / 180);\n };\n return p;\n }\n function d3_geo_conicEqualArea(φ0, φ1) {\n var sinφ0 = Math.sin(φ0), n = (sinφ0 + Math.sin(φ1)) / 2, C = 1 + sinφ0 * (2 * n - sinφ0), ρ0 = Math.sqrt(C) / n;\n function forward(λ, φ) {\n var ρ = Math.sqrt(C - 2 * n * Math.sin(φ)) / n;\n return [ ρ * Math.sin(λ *= n), ρ0 - ρ * Math.cos(λ) ];\n }\n forward.invert = function(x, y) {\n var ρ0_y = ρ0 - y;\n return [ Math.atan2(x, ρ0_y) / n, d3_asin((C - (x * x + ρ0_y * ρ0_y) * n * n) / (2 * n)) ];\n };\n return forward;\n }\n (d3.geo.conicEqualArea = function() {\n return d3_geo_conic(d3_geo_conicEqualArea);\n }).raw = d3_geo_conicEqualArea;\n d3.geo.albers = function() {\n return d3.geo.conicEqualArea().rotate([ 96, 0 ]).center([ -.6, 38.7 ]).parallels([ 29.5, 45.5 ]).scale(1070);\n };\n d3.geo.albersUsa = function() {\n var lower48 = d3.geo.albers();\n var alaska = d3.geo.conicEqualArea().rotate([ 154, 0 ]).center([ -2, 58.5 ]).parallels([ 55, 65 ]);\n var hawaii = d3.geo.conicEqualArea().rotate([ 157, 0 ]).center([ -3, 19.9 ]).parallels([ 8, 18 ]);\n var point, pointStream = {\n point: function(x, y) {\n point = [ x, y ];\n }\n }, lower48Point, alaskaPoint, hawaiiPoint;\n function albersUsa(coordinates) {\n var x = coordinates[0], y = coordinates[1];\n point = null;\n (lower48Point(x, y), point) || (alaskaPoint(x, y), point) || hawaiiPoint(x, y);\n return point;\n }\n albersUsa.invert = function(coordinates) {\n var k = lower48.scale(), t = lower48.translate(), x = (coordinates[0] - t[0]) / k, y = (coordinates[1] - t[1]) / k;\n return (y >= .12 && y < .234 && x >= -.425 && x < -.214 ? alaska : y >= .166 && y < .234 && x >= -.214 && x < -.115 ? hawaii : lower48).invert(coordinates);\n };\n albersUsa.stream = function(stream) {\n var lower48Stream = lower48.stream(stream), alaskaStream = alaska.stream(stream), hawaiiStream = hawaii.stream(stream);\n return {\n point: function(x, y) {\n lower48Stream.point(x, y);\n alaskaStream.point(x, y);\n hawaiiStream.point(x, y);\n },\n sphere: function() {\n lower48Stream.sphere();\n alaskaStream.sphere();\n hawaiiStream.sphere();\n },\n lineStart: function() {\n lower48Stream.lineStart();\n alaskaStream.lineStart();\n hawaiiStream.lineStart();\n },\n lineEnd: function() {\n lower48Stream.lineEnd();\n alaskaStream.lineEnd();\n hawaiiStream.lineEnd();\n },\n polygonStart: function() {\n lower48Stream.polygonStart();\n alaskaStream.polygonStart();\n hawaiiStream.polygonStart();\n },\n polygonEnd: function() {\n lower48Stream.polygonEnd();\n alaskaStream.polygonEnd();\n hawaiiStream.polygonEnd();\n }\n };\n };\n albersUsa.precision = function(_) {\n if (!arguments.length) return lower48.precision();\n lower48.precision(_);\n alaska.precision(_);\n hawaii.precision(_);\n return albersUsa;\n };\n albersUsa.scale = function(_) {\n if (!arguments.length) return lower48.scale();\n lower48.scale(_);\n alaska.scale(_ * .35);\n hawaii.scale(_);\n return albersUsa.translate(lower48.translate());\n };\n albersUsa.translate = function(_) {\n if (!arguments.length) return lower48.translate();\n var k = lower48.scale(), x = +_[0], y = +_[1];\n lower48Point = lower48.translate(_).clipExtent([ [ x - .455 * k, y - .238 * k ], [ x + .455 * k, y + .238 * k ] ]).stream(pointStream).point;\n alaskaPoint = alaska.translate([ x - .307 * k, y + .201 * k ]).clipExtent([ [ x - .425 * k + ε, y + .12 * k + ε ], [ x - .214 * k - ε, y + .234 * k - ε ] ]).stream(pointStream).point;\n hawaiiPoint = hawaii.translate([ x - .205 * k, y + .212 * k ]).clipExtent([ [ x - .214 * k + ε, y + .166 * k + ε ], [ x - .115 * k - ε, y + .234 * k - ε ] ]).stream(pointStream).point;\n return albersUsa;\n };\n return albersUsa.scale(1070);\n };\n var d3_geo_pathAreaSum, d3_geo_pathAreaPolygon, d3_geo_pathArea = {\n point: d3_noop,\n lineStart: d3_noop,\n lineEnd: d3_noop,\n polygonStart: function() {\n d3_geo_pathAreaPolygon = 0;\n d3_geo_pathArea.lineStart = d3_geo_pathAreaRingStart;\n },\n polygonEnd: function() {\n d3_geo_pathArea.lineStart = d3_geo_pathArea.lineEnd = d3_geo_pathArea.point = d3_noop;\n d3_geo_pathAreaSum += abs(d3_geo_pathAreaPolygon / 2);\n }\n };\n function d3_geo_pathAreaRingStart() {\n var x00, y00, x0, y0;\n d3_geo_pathArea.point = function(x, y) {\n d3_geo_pathArea.point = nextPoint;\n x00 = x0 = x, y00 = y0 = y;\n };\n function nextPoint(x, y) {\n d3_geo_pathAreaPolygon += y0 * x - x0 * y;\n x0 = x, y0 = y;\n }\n d3_geo_pathArea.lineEnd = function() {\n nextPoint(x00, y00);\n };\n }\n var d3_geo_pathBoundsX0, d3_geo_pathBoundsY0, d3_geo_pathBoundsX1, d3_geo_pathBoundsY1;\n var d3_geo_pathBounds = {\n point: d3_geo_pathBoundsPoint,\n lineStart: d3_noop,\n lineEnd: d3_noop,\n polygonStart: d3_noop,\n polygonEnd: d3_noop\n };\n function d3_geo_pathBoundsPoint(x, y) {\n if (x < d3_geo_pathBoundsX0) d3_geo_pathBoundsX0 = x;\n if (x > d3_geo_pathBoundsX1) d3_geo_pathBoundsX1 = x;\n if (y < d3_geo_pathBoundsY0) d3_geo_pathBoundsY0 = y;\n if (y > d3_geo_pathBoundsY1) d3_geo_pathBoundsY1 = y;\n }\n function d3_geo_pathBuffer() {\n var pointCircle = d3_geo_pathBufferCircle(4.5), buffer = [];\n var stream = {\n point: point,\n lineStart: function() {\n stream.point = pointLineStart;\n },\n lineEnd: lineEnd,\n polygonStart: function() {\n stream.lineEnd = lineEndPolygon;\n },\n polygonEnd: function() {\n stream.lineEnd = lineEnd;\n stream.point = point;\n },\n pointRadius: function(_) {\n pointCircle = d3_geo_pathBufferCircle(_);\n return stream;\n },\n result: function() {\n if (buffer.length) {\n var result = buffer.join(\"\");\n buffer = [];\n return result;\n }\n }\n };\n function point(x, y) {\n buffer.push(\"M\", x, \",\", y, pointCircle);\n }\n function pointLineStart(x, y) {\n buffer.push(\"M\", x, \",\", y);\n stream.point = pointLine;\n }\n function pointLine(x, y) {\n buffer.push(\"L\", x, \",\", y);\n }\n function lineEnd() {\n stream.point = point;\n }\n function lineEndPolygon() {\n buffer.push(\"Z\");\n }\n return stream;\n }\n function d3_geo_pathBufferCircle(radius) {\n return \"m0,\" + radius + \"a\" + radius + \",\" + radius + \" 0 1,1 0,\" + -2 * radius + \"a\" + radius + \",\" + radius + \" 0 1,1 0,\" + 2 * radius + \"z\";\n }\n var d3_geo_pathCentroid = {\n point: d3_geo_pathCentroidPoint,\n lineStart: d3_geo_pathCentroidLineStart,\n lineEnd: d3_geo_pathCentroidLineEnd,\n polygonStart: function() {\n d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidRingStart;\n },\n polygonEnd: function() {\n d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint;\n d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidLineStart;\n d3_geo_pathCentroid.lineEnd = d3_geo_pathCentroidLineEnd;\n }\n };\n function d3_geo_pathCentroidPoint(x, y) {\n d3_geo_centroidX0 += x;\n d3_geo_centroidY0 += y;\n ++d3_geo_centroidZ0;\n }\n function d3_geo_pathCentroidLineStart() {\n var x0, y0;\n d3_geo_pathCentroid.point = function(x, y) {\n d3_geo_pathCentroid.point = nextPoint;\n d3_geo_pathCentroidPoint(x0 = x, y0 = y);\n };\n function nextPoint(x, y) {\n var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy);\n d3_geo_centroidX1 += z * (x0 + x) / 2;\n d3_geo_centroidY1 += z * (y0 + y) / 2;\n d3_geo_centroidZ1 += z;\n d3_geo_pathCentroidPoint(x0 = x, y0 = y);\n }\n }\n function d3_geo_pathCentroidLineEnd() {\n d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint;\n }\n function d3_geo_pathCentroidRingStart() {\n var x00, y00, x0, y0;\n d3_geo_pathCentroid.point = function(x, y) {\n d3_geo_pathCentroid.point = nextPoint;\n d3_geo_pathCentroidPoint(x00 = x0 = x, y00 = y0 = y);\n };\n function nextPoint(x, y) {\n var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy);\n d3_geo_centroidX1 += z * (x0 + x) / 2;\n d3_geo_centroidY1 += z * (y0 + y) / 2;\n d3_geo_centroidZ1 += z;\n z = y0 * x - x0 * y;\n d3_geo_centroidX2 += z * (x0 + x);\n d3_geo_centroidY2 += z * (y0 + y);\n d3_geo_centroidZ2 += z * 3;\n d3_geo_pathCentroidPoint(x0 = x, y0 = y);\n }\n d3_geo_pathCentroid.lineEnd = function() {\n nextPoint(x00, y00);\n };\n }\n function d3_geo_pathContext(context) {\n var pointRadius = 4.5;\n var stream = {\n point: point,\n lineStart: function() {\n stream.point = pointLineStart;\n },\n lineEnd: lineEnd,\n polygonStart: function() {\n stream.lineEnd = lineEndPolygon;\n },\n polygonEnd: function() {\n stream.lineEnd = lineEnd;\n stream.point = point;\n },\n pointRadius: function(_) {\n pointRadius = _;\n return stream;\n },\n result: d3_noop\n };\n function point(x, y) {\n context.moveTo(x + pointRadius, y);\n context.arc(x, y, pointRadius, 0, τ);\n }\n function pointLineStart(x, y) {\n context.moveTo(x, y);\n stream.point = pointLine;\n }\n function pointLine(x, y) {\n context.lineTo(x, y);\n }\n function lineEnd() {\n stream.point = point;\n }\n function lineEndPolygon() {\n context.closePath();\n }\n return stream;\n }\n function d3_geo_resample(project) {\n var δ2 = .5, cosMinDistance = Math.cos(30 * d3_radians), maxDepth = 16;\n function resample(stream) {\n return (maxDepth ? resampleRecursive : resampleNone)(stream);\n }\n function resampleNone(stream) {\n return d3_geo_transformPoint(stream, function(x, y) {\n x = project(x, y);\n stream.point(x[0], x[1]);\n });\n }\n function resampleRecursive(stream) {\n var λ00, φ00, x00, y00, a00, b00, c00, λ0, x0, y0, a0, b0, c0;\n var resample = {\n point: point,\n lineStart: lineStart,\n lineEnd: lineEnd,\n polygonStart: function() {\n stream.polygonStart();\n resample.lineStart = ringStart;\n },\n polygonEnd: function() {\n stream.polygonEnd();\n resample.lineStart = lineStart;\n }\n };\n function point(x, y) {\n x = project(x, y);\n stream.point(x[0], x[1]);\n }\n function lineStart() {\n x0 = NaN;\n resample.point = linePoint;\n stream.lineStart();\n }\n function linePoint(λ, φ) {\n var c = d3_geo_cartesian([ λ, φ ]), p = project(λ, φ);\n resampleLineTo(x0, y0, λ0, a0, b0, c0, x0 = p[0], y0 = p[1], λ0 = λ, a0 = c[0], b0 = c[1], c0 = c[2], maxDepth, stream);\n stream.point(x0, y0);\n }\n function lineEnd() {\n resample.point = point;\n stream.lineEnd();\n }\n function ringStart() {\n lineStart();\n resample.point = ringPoint;\n resample.lineEnd = ringEnd;\n }\n function ringPoint(λ, φ) {\n linePoint(λ00 = λ, φ00 = φ), x00 = x0, y00 = y0, a00 = a0, b00 = b0, c00 = c0;\n resample.point = linePoint;\n }\n function ringEnd() {\n resampleLineTo(x0, y0, λ0, a0, b0, c0, x00, y00, λ00, a00, b00, c00, maxDepth, stream);\n resample.lineEnd = lineEnd;\n lineEnd();\n }\n return resample;\n }\n function resampleLineTo(x0, y0, λ0, a0, b0, c0, x1, y1, λ1, a1, b1, c1, depth, stream) {\n var dx = x1 - x0, dy = y1 - y0, d2 = dx * dx + dy * dy;\n if (d2 > 4 * δ2 && depth--) {\n var a = a0 + a1, b = b0 + b1, c = c0 + c1, m = Math.sqrt(a * a + b * b + c * c), φ2 = Math.asin(c /= m), λ2 = abs(abs(c) - 1) < ε || abs(λ0 - λ1) < ε ? (λ0 + λ1) / 2 : Math.atan2(b, a), p = project(λ2, φ2), x2 = p[0], y2 = p[1], dx2 = x2 - x0, dy2 = y2 - y0, dz = dy * dx2 - dx * dy2;\n if (dz * dz / d2 > δ2 || abs((dx * dx2 + dy * dy2) / d2 - .5) > .3 || a0 * a1 + b0 * b1 + c0 * c1 < cosMinDistance) {\n resampleLineTo(x0, y0, λ0, a0, b0, c0, x2, y2, λ2, a /= m, b /= m, c, depth, stream);\n stream.point(x2, y2);\n resampleLineTo(x2, y2, λ2, a, b, c, x1, y1, λ1, a1, b1, c1, depth, stream);\n }\n }\n }\n resample.precision = function(_) {\n if (!arguments.length) return Math.sqrt(δ2);\n maxDepth = (δ2 = _ * _) > 0 && 16;\n return resample;\n };\n return resample;\n }\n d3.geo.path = function() {\n var pointRadius = 4.5, projection, context, projectStream, contextStream, cacheStream;\n function path(object) {\n if (object) {\n if (typeof pointRadius === \"function\") contextStream.pointRadius(+pointRadius.apply(this, arguments));\n if (!cacheStream || !cacheStream.valid) cacheStream = projectStream(contextStream);\n d3.geo.stream(object, cacheStream);\n }\n return contextStream.result();\n }\n path.area = function(object) {\n d3_geo_pathAreaSum = 0;\n d3.geo.stream(object, projectStream(d3_geo_pathArea));\n return d3_geo_pathAreaSum;\n };\n path.centroid = function(object) {\n d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0;\n d3.geo.stream(object, projectStream(d3_geo_pathCentroid));\n return d3_geo_centroidZ2 ? [ d3_geo_centroidX2 / d3_geo_centroidZ2, d3_geo_centroidY2 / d3_geo_centroidZ2 ] : d3_geo_centroidZ1 ? [ d3_geo_centroidX1 / d3_geo_centroidZ1, d3_geo_centroidY1 / d3_geo_centroidZ1 ] : d3_geo_centroidZ0 ? [ d3_geo_centroidX0 / d3_geo_centroidZ0, d3_geo_centroidY0 / d3_geo_centroidZ0 ] : [ NaN, NaN ];\n };\n path.bounds = function(object) {\n d3_geo_pathBoundsX1 = d3_geo_pathBoundsY1 = -(d3_geo_pathBoundsX0 = d3_geo_pathBoundsY0 = Infinity);\n d3.geo.stream(object, projectStream(d3_geo_pathBounds));\n return [ [ d3_geo_pathBoundsX0, d3_geo_pathBoundsY0 ], [ d3_geo_pathBoundsX1, d3_geo_pathBoundsY1 ] ];\n };\n path.projection = function(_) {\n if (!arguments.length) return projection;\n projectStream = (projection = _) ? _.stream || d3_geo_pathProjectStream(_) : d3_identity;\n return reset();\n };\n path.context = function(_) {\n if (!arguments.length) return context;\n contextStream = (context = _) == null ? new d3_geo_pathBuffer() : new d3_geo_pathContext(_);\n if (typeof pointRadius !== \"function\") contextStream.pointRadius(pointRadius);\n return reset();\n };\n path.pointRadius = function(_) {\n if (!arguments.length) return pointRadius;\n pointRadius = typeof _ === \"function\" ? _ : (contextStream.pointRadius(+_), +_);\n return path;\n };\n function reset() {\n cacheStream = null;\n return path;\n }\n return path.projection(d3.geo.albersUsa()).context(null);\n };\n function d3_geo_pathProjectStream(project) {\n var resample = d3_geo_resample(function(x, y) {\n return project([ x * d3_degrees, y * d3_degrees ]);\n });\n return function(stream) {\n return d3_geo_projectionRadians(resample(stream));\n };\n }\n d3.geo.transform = function(methods) {\n return {\n stream: function(stream) {\n var transform = new d3_geo_transform(stream);\n for (var k in methods) transform[k] = methods[k];\n return transform;\n }\n };\n };\n function d3_geo_transform(stream) {\n this.stream = stream;\n }\n d3_geo_transform.prototype = {\n point: function(x, y) {\n this.stream.point(x, y);\n },\n sphere: function() {\n this.stream.sphere();\n },\n lineStart: function() {\n this.stream.lineStart();\n },\n lineEnd: function() {\n this.stream.lineEnd();\n },\n polygonStart: function() {\n this.stream.polygonStart();\n },\n polygonEnd: function() {\n this.stream.polygonEnd();\n }\n };\n function d3_geo_transformPoint(stream, point) {\n return {\n point: point,\n sphere: function() {\n stream.sphere();\n },\n lineStart: function() {\n stream.lineStart();\n },\n lineEnd: function() {\n stream.lineEnd();\n },\n polygonStart: function() {\n stream.polygonStart();\n },\n polygonEnd: function() {\n stream.polygonEnd();\n }\n };\n }\n d3.geo.projection = d3_geo_projection;\n d3.geo.projectionMutator = d3_geo_projectionMutator;\n function d3_geo_projection(project) {\n return d3_geo_projectionMutator(function() {\n return project;\n })();\n }\n function d3_geo_projectionMutator(projectAt) {\n var project, rotate, projectRotate, projectResample = d3_geo_resample(function(x, y) {\n x = project(x, y);\n return [ x[0] * k + δx, δy - x[1] * k ];\n }), k = 150, x = 480, y = 250, λ = 0, φ = 0, δλ = 0, δφ = 0, δγ = 0, δx, δy, preclip = d3_geo_clipAntimeridian, postclip = d3_identity, clipAngle = null, clipExtent = null, stream;\n function projection(point) {\n point = projectRotate(point[0] * d3_radians, point[1] * d3_radians);\n return [ point[0] * k + δx, δy - point[1] * k ];\n }\n function invert(point) {\n point = projectRotate.invert((point[0] - δx) / k, (δy - point[1]) / k);\n return point && [ point[0] * d3_degrees, point[1] * d3_degrees ];\n }\n projection.stream = function(output) {\n if (stream) stream.valid = false;\n stream = d3_geo_projectionRadians(preclip(rotate, projectResample(postclip(output))));\n stream.valid = true;\n return stream;\n };\n projection.clipAngle = function(_) {\n if (!arguments.length) return clipAngle;\n preclip = _ == null ? (clipAngle = _, d3_geo_clipAntimeridian) : d3_geo_clipCircle((clipAngle = +_) * d3_radians);\n return invalidate();\n };\n projection.clipExtent = function(_) {\n if (!arguments.length) return clipExtent;\n clipExtent = _;\n postclip = _ ? d3_geo_clipExtent(_[0][0], _[0][1], _[1][0], _[1][1]) : d3_identity;\n return invalidate();\n };\n projection.scale = function(_) {\n if (!arguments.length) return k;\n k = +_;\n return reset();\n };\n projection.translate = function(_) {\n if (!arguments.length) return [ x, y ];\n x = +_[0];\n y = +_[1];\n return reset();\n };\n projection.center = function(_) {\n if (!arguments.length) return [ λ * d3_degrees, φ * d3_degrees ];\n λ = _[0] % 360 * d3_radians;\n φ = _[1] % 360 * d3_radians;\n return reset();\n };\n projection.rotate = function(_) {\n if (!arguments.length) return [ δλ * d3_degrees, δφ * d3_degrees, δγ * d3_degrees ];\n δλ = _[0] % 360 * d3_radians;\n δφ = _[1] % 360 * d3_radians;\n δγ = _.length > 2 ? _[2] % 360 * d3_radians : 0;\n return reset();\n };\n d3.rebind(projection, projectResample, \"precision\");\n function reset() {\n projectRotate = d3_geo_compose(rotate = d3_geo_rotation(δλ, δφ, δγ), project);\n var center = project(λ, φ);\n δx = x - center[0] * k;\n δy = y + center[1] * k;\n return invalidate();\n }\n function invalidate() {\n if (stream) stream.valid = false, stream = null;\n return projection;\n }\n return function() {\n project = projectAt.apply(this, arguments);\n projection.invert = project.invert && invert;\n return reset();\n };\n }\n function d3_geo_projectionRadians(stream) {\n return d3_geo_transformPoint(stream, function(x, y) {\n stream.point(x * d3_radians, y * d3_radians);\n });\n }\n function d3_geo_equirectangular(λ, φ) {\n return [ λ, φ ];\n }\n (d3.geo.equirectangular = function() {\n return d3_geo_projection(d3_geo_equirectangular);\n }).raw = d3_geo_equirectangular.invert = d3_geo_equirectangular;\n d3.geo.rotation = function(rotate) {\n rotate = d3_geo_rotation(rotate[0] % 360 * d3_radians, rotate[1] * d3_radians, rotate.length > 2 ? rotate[2] * d3_radians : 0);\n function forward(coordinates) {\n coordinates = rotate(coordinates[0] * d3_radians, coordinates[1] * d3_radians);\n return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates;\n }\n forward.invert = function(coordinates) {\n coordinates = rotate.invert(coordinates[0] * d3_radians, coordinates[1] * d3_radians);\n return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates;\n };\n return forward;\n };\n function d3_geo_identityRotation(λ, φ) {\n return [ λ > π ? λ - τ : λ < -π ? λ + τ : λ, φ ];\n }\n d3_geo_identityRotation.invert = d3_geo_equirectangular;\n function d3_geo_rotation(δλ, δφ, δγ) {\n return δλ ? δφ || δγ ? d3_geo_compose(d3_geo_rotationλ(δλ), d3_geo_rotationφγ(δφ, δγ)) : d3_geo_rotationλ(δλ) : δφ || δγ ? d3_geo_rotationφγ(δφ, δγ) : d3_geo_identityRotation;\n }\n function d3_geo_forwardRotationλ(δλ) {\n return function(λ, φ) {\n return λ += δλ, [ λ > π ? λ - τ : λ < -π ? λ + τ : λ, φ ];\n };\n }\n function d3_geo_rotationλ(δλ) {\n var rotation = d3_geo_forwardRotationλ(δλ);\n rotation.invert = d3_geo_forwardRotationλ(-δλ);\n return rotation;\n }\n function d3_geo_rotationφγ(δφ, δγ) {\n var cosδφ = Math.cos(δφ), sinδφ = Math.sin(δφ), cosδγ = Math.cos(δγ), sinδγ = Math.sin(δγ);\n function rotation(λ, φ) {\n var cosφ = Math.cos(φ), x = Math.cos(λ) * cosφ, y = Math.sin(λ) * cosφ, z = Math.sin(φ), k = z * cosδφ + x * sinδφ;\n return [ Math.atan2(y * cosδγ - k * sinδγ, x * cosδφ - z * sinδφ), d3_asin(k * cosδγ + y * sinδγ) ];\n }\n rotation.invert = function(λ, φ) {\n var cosφ = Math.cos(φ), x = Math.cos(λ) * cosφ, y = Math.sin(λ) * cosφ, z = Math.sin(φ), k = z * cosδγ - y * sinδγ;\n return [ Math.atan2(y * cosδγ + z * sinδγ, x * cosδφ + k * sinδφ), d3_asin(k * cosδφ - x * sinδφ) ];\n };\n return rotation;\n }\n d3.geo.circle = function() {\n var origin = [ 0, 0 ], angle, precision = 6, interpolate;\n function circle() {\n var center = typeof origin === \"function\" ? origin.apply(this, arguments) : origin, rotate = d3_geo_rotation(-center[0] * d3_radians, -center[1] * d3_radians, 0).invert, ring = [];\n interpolate(null, null, 1, {\n point: function(x, y) {\n ring.push(x = rotate(x, y));\n x[0] *= d3_degrees, x[1] *= d3_degrees;\n }\n });\n return {\n type: \"Polygon\",\n coordinates: [ ring ]\n };\n }\n circle.origin = function(x) {\n if (!arguments.length) return origin;\n origin = x;\n return circle;\n };\n circle.angle = function(x) {\n if (!arguments.length) return angle;\n interpolate = d3_geo_circleInterpolate((angle = +x) * d3_radians, precision * d3_radians);\n return circle;\n };\n circle.precision = function(_) {\n if (!arguments.length) return precision;\n interpolate = d3_geo_circleInterpolate(angle * d3_radians, (precision = +_) * d3_radians);\n return circle;\n };\n return circle.angle(90);\n };\n function d3_geo_circleInterpolate(radius, precision) {\n var cr = Math.cos(radius), sr = Math.sin(radius);\n return function(from, to, direction, listener) {\n var step = direction * precision;\n if (from != null) {\n from = d3_geo_circleAngle(cr, from);\n to = d3_geo_circleAngle(cr, to);\n if (direction > 0 ? from < to : from > to) from += direction * τ;\n } else {\n from = radius + direction * τ;\n to = radius - .5 * step;\n }\n for (var point, t = from; direction > 0 ? t > to : t < to; t -= step) {\n listener.point((point = d3_geo_spherical([ cr, -sr * Math.cos(t), -sr * Math.sin(t) ]))[0], point[1]);\n }\n };\n }\n function d3_geo_circleAngle(cr, point) {\n var a = d3_geo_cartesian(point);\n a[0] -= cr;\n d3_geo_cartesianNormalize(a);\n var angle = d3_acos(-a[1]);\n return ((-a[2] < 0 ? -angle : angle) + 2 * Math.PI - ε) % (2 * Math.PI);\n }\n d3.geo.distance = function(a, b) {\n var Δλ = (b[0] - a[0]) * d3_radians, φ0 = a[1] * d3_radians, φ1 = b[1] * d3_radians, sinΔλ = Math.sin(Δλ), cosΔλ = Math.cos(Δλ), sinφ0 = Math.sin(φ0), cosφ0 = Math.cos(φ0), sinφ1 = Math.sin(φ1), cosφ1 = Math.cos(φ1), t;\n return Math.atan2(Math.sqrt((t = cosφ1 * sinΔλ) * t + (t = cosφ0 * sinφ1 - sinφ0 * cosφ1 * cosΔλ) * t), sinφ0 * sinφ1 + cosφ0 * cosφ1 * cosΔλ);\n };\n d3.geo.graticule = function() {\n var x1, x0, X1, X0, y1, y0, Y1, Y0, dx = 10, dy = dx, DX = 90, DY = 360, x, y, X, Y, precision = 2.5;\n function graticule() {\n return {\n type: \"MultiLineString\",\n coordinates: lines()\n };\n }\n function lines() {\n return d3.range(Math.ceil(X0 / DX) * DX, X1, DX).map(X).concat(d3.range(Math.ceil(Y0 / DY) * DY, Y1, DY).map(Y)).concat(d3.range(Math.ceil(x0 / dx) * dx, x1, dx).filter(function(x) {\n return abs(x % DX) > ε;\n }).map(x)).concat(d3.range(Math.ceil(y0 / dy) * dy, y1, dy).filter(function(y) {\n return abs(y % DY) > ε;\n }).map(y));\n }\n graticule.lines = function() {\n return lines().map(function(coordinates) {\n return {\n type: \"LineString\",\n coordinates: coordinates\n };\n });\n };\n graticule.outline = function() {\n return {\n type: \"Polygon\",\n coordinates: [ X(X0).concat(Y(Y1).slice(1), X(X1).reverse().slice(1), Y(Y0).reverse().slice(1)) ]\n };\n };\n graticule.extent = function(_) {\n if (!arguments.length) return graticule.minorExtent();\n return graticule.majorExtent(_).minorExtent(_);\n };\n graticule.majorExtent = function(_) {\n if (!arguments.length) return [ [ X0, Y0 ], [ X1, Y1 ] ];\n X0 = +_[0][0], X1 = +_[1][0];\n Y0 = +_[0][1], Y1 = +_[1][1];\n if (X0 > X1) _ = X0, X0 = X1, X1 = _;\n if (Y0 > Y1) _ = Y0, Y0 = Y1, Y1 = _;\n return graticule.precision(precision);\n };\n graticule.minorExtent = function(_) {\n if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ];\n x0 = +_[0][0], x1 = +_[1][0];\n y0 = +_[0][1], y1 = +_[1][1];\n if (x0 > x1) _ = x0, x0 = x1, x1 = _;\n if (y0 > y1) _ = y0, y0 = y1, y1 = _;\n return graticule.precision(precision);\n };\n graticule.step = function(_) {\n if (!arguments.length) return graticule.minorStep();\n return graticule.majorStep(_).minorStep(_);\n };\n graticule.majorStep = function(_) {\n if (!arguments.length) return [ DX, DY ];\n DX = +_[0], DY = +_[1];\n return graticule;\n };\n graticule.minorStep = function(_) {\n if (!arguments.length) return [ dx, dy ];\n dx = +_[0], dy = +_[1];\n return graticule;\n };\n graticule.precision = function(_) {\n if (!arguments.length) return precision;\n precision = +_;\n x = d3_geo_graticuleX(y0, y1, 90);\n y = d3_geo_graticuleY(x0, x1, precision);\n X = d3_geo_graticuleX(Y0, Y1, 90);\n Y = d3_geo_graticuleY(X0, X1, precision);\n return graticule;\n };\n return graticule.majorExtent([ [ -180, -90 + ε ], [ 180, 90 - ε ] ]).minorExtent([ [ -180, -80 - ε ], [ 180, 80 + ε ] ]);\n };\n function d3_geo_graticuleX(y0, y1, dy) {\n var y = d3.range(y0, y1 - ε, dy).concat(y1);\n return function(x) {\n return y.map(function(y) {\n return [ x, y ];\n });\n };\n }\n function d3_geo_graticuleY(x0, x1, dx) {\n var x = d3.range(x0, x1 - ε, dx).concat(x1);\n return function(y) {\n return x.map(function(x) {\n return [ x, y ];\n });\n };\n }\n function d3_source(d) {\n return d.source;\n }\n function d3_target(d) {\n return d.target;\n }\n d3.geo.greatArc = function() {\n var source = d3_source, source_, target = d3_target, target_;\n function greatArc() {\n return {\n type: \"LineString\",\n coordinates: [ source_ || source.apply(this, arguments), target_ || target.apply(this, arguments) ]\n };\n }\n greatArc.distance = function() {\n return d3.geo.distance(source_ || source.apply(this, arguments), target_ || target.apply(this, arguments));\n };\n greatArc.source = function(_) {\n if (!arguments.length) return source;\n source = _, source_ = typeof _ === \"function\" ? null : _;\n return greatArc;\n };\n greatArc.target = function(_) {\n if (!arguments.length) return target;\n target = _, target_ = typeof _ === \"function\" ? null : _;\n return greatArc;\n };\n greatArc.precision = function() {\n return arguments.length ? greatArc : 0;\n };\n return greatArc;\n };\n d3.geo.interpolate = function(source, target) {\n return d3_geo_interpolate(source[0] * d3_radians, source[1] * d3_radians, target[0] * d3_radians, target[1] * d3_radians);\n };\n function d3_geo_interpolate(x0, y0, x1, y1) {\n var cy0 = Math.cos(y0), sy0 = Math.sin(y0), cy1 = Math.cos(y1), sy1 = Math.sin(y1), kx0 = cy0 * Math.cos(x0), ky0 = cy0 * Math.sin(x0), kx1 = cy1 * Math.cos(x1), ky1 = cy1 * Math.sin(x1), d = 2 * Math.asin(Math.sqrt(d3_haversin(y1 - y0) + cy0 * cy1 * d3_haversin(x1 - x0))), k = 1 / Math.sin(d);\n var interpolate = d ? function(t) {\n var B = Math.sin(t *= d) * k, A = Math.sin(d - t) * k, x = A * kx0 + B * kx1, y = A * ky0 + B * ky1, z = A * sy0 + B * sy1;\n return [ Math.atan2(y, x) * d3_degrees, Math.atan2(z, Math.sqrt(x * x + y * y)) * d3_degrees ];\n } : function() {\n return [ x0 * d3_degrees, y0 * d3_degrees ];\n };\n interpolate.distance = d;\n return interpolate;\n }\n d3.geo.length = function(object) {\n d3_geo_lengthSum = 0;\n d3.geo.stream(object, d3_geo_length);\n return d3_geo_lengthSum;\n };\n var d3_geo_lengthSum;\n var d3_geo_length = {\n sphere: d3_noop,\n point: d3_noop,\n lineStart: d3_geo_lengthLineStart,\n lineEnd: d3_noop,\n polygonStart: d3_noop,\n polygonEnd: d3_noop\n };\n function d3_geo_lengthLineStart() {\n var λ0, sinφ0, cosφ0;\n d3_geo_length.point = function(λ, φ) {\n λ0 = λ * d3_radians, sinφ0 = Math.sin(φ *= d3_radians), cosφ0 = Math.cos(φ);\n d3_geo_length.point = nextPoint;\n };\n d3_geo_length.lineEnd = function() {\n d3_geo_length.point = d3_geo_length.lineEnd = d3_noop;\n };\n function nextPoint(λ, φ) {\n var sinφ = Math.sin(φ *= d3_radians), cosφ = Math.cos(φ), t = abs((λ *= d3_radians) - λ0), cosΔλ = Math.cos(t);\n d3_geo_lengthSum += Math.atan2(Math.sqrt((t = cosφ * Math.sin(t)) * t + (t = cosφ0 * sinφ - sinφ0 * cosφ * cosΔλ) * t), sinφ0 * sinφ + cosφ0 * cosφ * cosΔλ);\n λ0 = λ, sinφ0 = sinφ, cosφ0 = cosφ;\n }\n }\n function d3_geo_azimuthal(scale, angle) {\n function azimuthal(λ, φ) {\n var cosλ = Math.cos(λ), cosφ = Math.cos(φ), k = scale(cosλ * cosφ);\n return [ k * cosφ * Math.sin(λ), k * Math.sin(φ) ];\n }\n azimuthal.invert = function(x, y) {\n var ρ = Math.sqrt(x * x + y * y), c = angle(ρ), sinc = Math.sin(c), cosc = Math.cos(c);\n return [ Math.atan2(x * sinc, ρ * cosc), Math.asin(ρ && y * sinc / ρ) ];\n };\n return azimuthal;\n }\n var d3_geo_azimuthalEqualArea = d3_geo_azimuthal(function(cosλcosφ) {\n return Math.sqrt(2 / (1 + cosλcosφ));\n }, function(ρ) {\n return 2 * Math.asin(ρ / 2);\n });\n (d3.geo.azimuthalEqualArea = function() {\n return d3_geo_projection(d3_geo_azimuthalEqualArea);\n }).raw = d3_geo_azimuthalEqualArea;\n var d3_geo_azimuthalEquidistant = d3_geo_azimuthal(function(cosλcosφ) {\n var c = Math.acos(cosλcosφ);\n return c && c / Math.sin(c);\n }, d3_identity);\n (d3.geo.azimuthalEquidistant = function() {\n return d3_geo_projection(d3_geo_azimuthalEquidistant);\n }).raw = d3_geo_azimuthalEquidistant;\n function d3_geo_conicConformal(φ0, φ1) {\n var cosφ0 = Math.cos(φ0), t = function(φ) {\n return Math.tan(π / 4 + φ / 2);\n }, n = φ0 === φ1 ? Math.sin(φ0) : Math.log(cosφ0 / Math.cos(φ1)) / Math.log(t(φ1) / t(φ0)), F = cosφ0 * Math.pow(t(φ0), n) / n;\n if (!n) return d3_geo_mercator;\n function forward(λ, φ) {\n if (F > 0) {\n if (φ < -halfπ + ε) φ = -halfπ + ε;\n } else {\n if (φ > halfπ - ε) φ = halfπ - ε;\n }\n var ρ = F / Math.pow(t(φ), n);\n return [ ρ * Math.sin(n * λ), F - ρ * Math.cos(n * λ) ];\n }\n forward.invert = function(x, y) {\n var ρ0_y = F - y, ρ = d3_sgn(n) * Math.sqrt(x * x + ρ0_y * ρ0_y);\n return [ Math.atan2(x, ρ0_y) / n, 2 * Math.atan(Math.pow(F / ρ, 1 / n)) - halfπ ];\n };\n return forward;\n }\n (d3.geo.conicConformal = function() {\n return d3_geo_conic(d3_geo_conicConformal);\n }).raw = d3_geo_conicConformal;\n function d3_geo_conicEquidistant(φ0, φ1) {\n var cosφ0 = Math.cos(φ0), n = φ0 === φ1 ? Math.sin(φ0) : (cosφ0 - Math.cos(φ1)) / (φ1 - φ0), G = cosφ0 / n + φ0;\n if (abs(n) < ε) return d3_geo_equirectangular;\n function forward(λ, φ) {\n var ρ = G - φ;\n return [ ρ * Math.sin(n * λ), G - ρ * Math.cos(n * λ) ];\n }\n forward.invert = function(x, y) {\n var ρ0_y = G - y;\n return [ Math.atan2(x, ρ0_y) / n, G - d3_sgn(n) * Math.sqrt(x * x + ρ0_y * ρ0_y) ];\n };\n return forward;\n }\n (d3.geo.conicEquidistant = function() {\n return d3_geo_conic(d3_geo_conicEquidistant);\n }).raw = d3_geo_conicEquidistant;\n var d3_geo_gnomonic = d3_geo_azimuthal(function(cosλcosφ) {\n return 1 / cosλcosφ;\n }, Math.atan);\n (d3.geo.gnomonic = function() {\n return d3_geo_projection(d3_geo_gnomonic);\n }).raw = d3_geo_gnomonic;\n function d3_geo_mercator(λ, φ) {\n return [ λ, Math.log(Math.tan(π / 4 + φ / 2)) ];\n }\n d3_geo_mercator.invert = function(x, y) {\n return [ x, 2 * Math.atan(Math.exp(y)) - halfπ ];\n };\n function d3_geo_mercatorProjection(project) {\n var m = d3_geo_projection(project), scale = m.scale, translate = m.translate, clipExtent = m.clipExtent, clipAuto;\n m.scale = function() {\n var v = scale.apply(m, arguments);\n return v === m ? clipAuto ? m.clipExtent(null) : m : v;\n };\n m.translate = function() {\n var v = translate.apply(m, arguments);\n return v === m ? clipAuto ? m.clipExtent(null) : m : v;\n };\n m.clipExtent = function(_) {\n var v = clipExtent.apply(m, arguments);\n if (v === m) {\n if (clipAuto = _ == null) {\n var k = π * scale(), t = translate();\n clipExtent([ [ t[0] - k, t[1] - k ], [ t[0] + k, t[1] + k ] ]);\n }\n } else if (clipAuto) {\n v = null;\n }\n return v;\n };\n return m.clipExtent(null);\n }\n (d3.geo.mercator = function() {\n return d3_geo_mercatorProjection(d3_geo_mercator);\n }).raw = d3_geo_mercator;\n var d3_geo_orthographic = d3_geo_azimuthal(function() {\n return 1;\n }, Math.asin);\n (d3.geo.orthographic = function() {\n return d3_geo_projection(d3_geo_orthographic);\n }).raw = d3_geo_orthographic;\n var d3_geo_stereographic = d3_geo_azimuthal(function(cosλcosφ) {\n return 1 / (1 + cosλcosφ);\n }, function(ρ) {\n return 2 * Math.atan(ρ);\n });\n (d3.geo.stereographic = function() {\n return d3_geo_projection(d3_geo_stereographic);\n }).raw = d3_geo_stereographic;\n function d3_geo_transverseMercator(λ, φ) {\n return [ Math.log(Math.tan(π / 4 + φ / 2)), -λ ];\n }\n d3_geo_transverseMercator.invert = function(x, y) {\n return [ -y, 2 * Math.atan(Math.exp(x)) - halfπ ];\n };\n (d3.geo.transverseMercator = function() {\n var projection = d3_geo_mercatorProjection(d3_geo_transverseMercator), center = projection.center, rotate = projection.rotate;\n projection.center = function(_) {\n return _ ? center([ -_[1], _[0] ]) : (_ = center(), [ _[1], -_[0] ]);\n };\n projection.rotate = function(_) {\n return _ ? rotate([ _[0], _[1], _.length > 2 ? _[2] + 90 : 90 ]) : (_ = rotate(), \n [ _[0], _[1], _[2] - 90 ]);\n };\n return rotate([ 0, 0, 90 ]);\n }).raw = d3_geo_transverseMercator;\n d3.geom = {};\n function d3_geom_pointX(d) {\n return d[0];\n }\n function d3_geom_pointY(d) {\n return d[1];\n }\n d3.geom.hull = function(vertices) {\n var x = d3_geom_pointX, y = d3_geom_pointY;\n if (arguments.length) return hull(vertices);\n function hull(data) {\n if (data.length < 3) return [];\n var fx = d3_functor(x), fy = d3_functor(y), i, n = data.length, points = [], flippedPoints = [];\n for (i = 0; i < n; i++) {\n points.push([ +fx.call(this, data[i], i), +fy.call(this, data[i], i), i ]);\n }\n points.sort(d3_geom_hullOrder);\n for (i = 0; i < n; i++) flippedPoints.push([ points[i][0], -points[i][1] ]);\n var upper = d3_geom_hullUpper(points), lower = d3_geom_hullUpper(flippedPoints);\n var skipLeft = lower[0] === upper[0], skipRight = lower[lower.length - 1] === upper[upper.length - 1], polygon = [];\n for (i = upper.length - 1; i >= 0; --i) polygon.push(data[points[upper[i]][2]]);\n for (i = +skipLeft; i < lower.length - skipRight; ++i) polygon.push(data[points[lower[i]][2]]);\n return polygon;\n }\n hull.x = function(_) {\n return arguments.length ? (x = _, hull) : x;\n };\n hull.y = function(_) {\n return arguments.length ? (y = _, hull) : y;\n };\n return hull;\n };\n function d3_geom_hullUpper(points) {\n var n = points.length, hull = [ 0, 1 ], hs = 2;\n for (var i = 2; i < n; i++) {\n while (hs > 1 && d3_cross2d(points[hull[hs - 2]], points[hull[hs - 1]], points[i]) <= 0) --hs;\n hull[hs++] = i;\n }\n return hull.slice(0, hs);\n }\n function d3_geom_hullOrder(a, b) {\n return a[0] - b[0] || a[1] - b[1];\n }\n d3.geom.polygon = function(coordinates) {\n d3_subclass(coordinates, d3_geom_polygonPrototype);\n return coordinates;\n };\n var d3_geom_polygonPrototype = d3.geom.polygon.prototype = [];\n d3_geom_polygonPrototype.area = function() {\n var i = -1, n = this.length, a, b = this[n - 1], area = 0;\n while (++i < n) {\n a = b;\n b = this[i];\n area += a[1] * b[0] - a[0] * b[1];\n }\n return area * .5;\n };\n d3_geom_polygonPrototype.centroid = function(k) {\n var i = -1, n = this.length, x = 0, y = 0, a, b = this[n - 1], c;\n if (!arguments.length) k = -1 / (6 * this.area());\n while (++i < n) {\n a = b;\n b = this[i];\n c = a[0] * b[1] - b[0] * a[1];\n x += (a[0] + b[0]) * c;\n y += (a[1] + b[1]) * c;\n }\n return [ x * k, y * k ];\n };\n d3_geom_polygonPrototype.clip = function(subject) {\n var input, closed = d3_geom_polygonClosed(subject), i = -1, n = this.length - d3_geom_polygonClosed(this), j, m, a = this[n - 1], b, c, d;\n while (++i < n) {\n input = subject.slice();\n subject.length = 0;\n b = this[i];\n c = input[(m = input.length - closed) - 1];\n j = -1;\n while (++j < m) {\n d = input[j];\n if (d3_geom_polygonInside(d, a, b)) {\n if (!d3_geom_polygonInside(c, a, b)) {\n subject.push(d3_geom_polygonIntersect(c, d, a, b));\n }\n subject.push(d);\n } else if (d3_geom_polygonInside(c, a, b)) {\n subject.push(d3_geom_polygonIntersect(c, d, a, b));\n }\n c = d;\n }\n if (closed) subject.push(subject[0]);\n a = b;\n }\n return subject;\n };\n function d3_geom_polygonInside(p, a, b) {\n return (b[0] - a[0]) * (p[1] - a[1]) < (b[1] - a[1]) * (p[0] - a[0]);\n }\n function d3_geom_polygonIntersect(c, d, a, b) {\n var x1 = c[0], x3 = a[0], x21 = d[0] - x1, x43 = b[0] - x3, y1 = c[1], y3 = a[1], y21 = d[1] - y1, y43 = b[1] - y3, ua = (x43 * (y1 - y3) - y43 * (x1 - x3)) / (y43 * x21 - x43 * y21);\n return [ x1 + ua * x21, y1 + ua * y21 ];\n }\n function d3_geom_polygonClosed(coordinates) {\n var a = coordinates[0], b = coordinates[coordinates.length - 1];\n return !(a[0] - b[0] || a[1] - b[1]);\n }\n var d3_geom_voronoiEdges, d3_geom_voronoiCells, d3_geom_voronoiBeaches, d3_geom_voronoiBeachPool = [], d3_geom_voronoiFirstCircle, d3_geom_voronoiCircles, d3_geom_voronoiCirclePool = [];\n function d3_geom_voronoiBeach() {\n d3_geom_voronoiRedBlackNode(this);\n this.edge = this.site = this.circle = null;\n }\n function d3_geom_voronoiCreateBeach(site) {\n var beach = d3_geom_voronoiBeachPool.pop() || new d3_geom_voronoiBeach();\n beach.site = site;\n return beach;\n }\n function d3_geom_voronoiDetachBeach(beach) {\n d3_geom_voronoiDetachCircle(beach);\n d3_geom_voronoiBeaches.remove(beach);\n d3_geom_voronoiBeachPool.push(beach);\n d3_geom_voronoiRedBlackNode(beach);\n }\n function d3_geom_voronoiRemoveBeach(beach) {\n var circle = beach.circle, x = circle.x, y = circle.cy, vertex = {\n x: x,\n y: y\n }, previous = beach.P, next = beach.N, disappearing = [ beach ];\n d3_geom_voronoiDetachBeach(beach);\n var lArc = previous;\n while (lArc.circle && abs(x - lArc.circle.x) < ε && abs(y - lArc.circle.cy) < ε) {\n previous = lArc.P;\n disappearing.unshift(lArc);\n d3_geom_voronoiDetachBeach(lArc);\n lArc = previous;\n }\n disappearing.unshift(lArc);\n d3_geom_voronoiDetachCircle(lArc);\n var rArc = next;\n while (rArc.circle && abs(x - rArc.circle.x) < ε && abs(y - rArc.circle.cy) < ε) {\n next = rArc.N;\n disappearing.push(rArc);\n d3_geom_voronoiDetachBeach(rArc);\n rArc = next;\n }\n disappearing.push(rArc);\n d3_geom_voronoiDetachCircle(rArc);\n var nArcs = disappearing.length, iArc;\n for (iArc = 1; iArc < nArcs; ++iArc) {\n rArc = disappearing[iArc];\n lArc = disappearing[iArc - 1];\n d3_geom_voronoiSetEdgeEnd(rArc.edge, lArc.site, rArc.site, vertex);\n }\n lArc = disappearing[0];\n rArc = disappearing[nArcs - 1];\n rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, rArc.site, null, vertex);\n d3_geom_voronoiAttachCircle(lArc);\n d3_geom_voronoiAttachCircle(rArc);\n }\n function d3_geom_voronoiAddBeach(site) {\n var x = site.x, directrix = site.y, lArc, rArc, dxl, dxr, node = d3_geom_voronoiBeaches._;\n while (node) {\n dxl = d3_geom_voronoiLeftBreakPoint(node, directrix) - x;\n if (dxl > ε) node = node.L; else {\n dxr = x - d3_geom_voronoiRightBreakPoint(node, directrix);\n if (dxr > ε) {\n if (!node.R) {\n lArc = node;\n break;\n }\n node = node.R;\n } else {\n if (dxl > -ε) {\n lArc = node.P;\n rArc = node;\n } else if (dxr > -ε) {\n lArc = node;\n rArc = node.N;\n } else {\n lArc = rArc = node;\n }\n break;\n }\n }\n }\n var newArc = d3_geom_voronoiCreateBeach(site);\n d3_geom_voronoiBeaches.insert(lArc, newArc);\n if (!lArc && !rArc) return;\n if (lArc === rArc) {\n d3_geom_voronoiDetachCircle(lArc);\n rArc = d3_geom_voronoiCreateBeach(lArc.site);\n d3_geom_voronoiBeaches.insert(newArc, rArc);\n newArc.edge = rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site);\n d3_geom_voronoiAttachCircle(lArc);\n d3_geom_voronoiAttachCircle(rArc);\n return;\n }\n if (!rArc) {\n newArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site);\n return;\n }\n d3_geom_voronoiDetachCircle(lArc);\n d3_geom_voronoiDetachCircle(rArc);\n var lSite = lArc.site, ax = lSite.x, ay = lSite.y, bx = site.x - ax, by = site.y - ay, rSite = rArc.site, cx = rSite.x - ax, cy = rSite.y - ay, d = 2 * (bx * cy - by * cx), hb = bx * bx + by * by, hc = cx * cx + cy * cy, vertex = {\n x: (cy * hb - by * hc) / d + ax,\n y: (bx * hc - cx * hb) / d + ay\n };\n d3_geom_voronoiSetEdgeEnd(rArc.edge, lSite, rSite, vertex);\n newArc.edge = d3_geom_voronoiCreateEdge(lSite, site, null, vertex);\n rArc.edge = d3_geom_voronoiCreateEdge(site, rSite, null, vertex);\n d3_geom_voronoiAttachCircle(lArc);\n d3_geom_voronoiAttachCircle(rArc);\n }\n function d3_geom_voronoiLeftBreakPoint(arc, directrix) {\n var site = arc.site, rfocx = site.x, rfocy = site.y, pby2 = rfocy - directrix;\n if (!pby2) return rfocx;\n var lArc = arc.P;\n if (!lArc) return -Infinity;\n site = lArc.site;\n var lfocx = site.x, lfocy = site.y, plby2 = lfocy - directrix;\n if (!plby2) return lfocx;\n var hl = lfocx - rfocx, aby2 = 1 / pby2 - 1 / plby2, b = hl / plby2;\n if (aby2) return (-b + Math.sqrt(b * b - 2 * aby2 * (hl * hl / (-2 * plby2) - lfocy + plby2 / 2 + rfocy - pby2 / 2))) / aby2 + rfocx;\n return (rfocx + lfocx) / 2;\n }\n function d3_geom_voronoiRightBreakPoint(arc, directrix) {\n var rArc = arc.N;\n if (rArc) return d3_geom_voronoiLeftBreakPoint(rArc, directrix);\n var site = arc.site;\n return site.y === directrix ? site.x : Infinity;\n }\n function d3_geom_voronoiCell(site) {\n this.site = site;\n this.edges = [];\n }\n d3_geom_voronoiCell.prototype.prepare = function() {\n var halfEdges = this.edges, iHalfEdge = halfEdges.length, edge;\n while (iHalfEdge--) {\n edge = halfEdges[iHalfEdge].edge;\n if (!edge.b || !edge.a) halfEdges.splice(iHalfEdge, 1);\n }\n halfEdges.sort(d3_geom_voronoiHalfEdgeOrder);\n return halfEdges.length;\n };\n function d3_geom_voronoiCloseCells(extent) {\n var x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], x2, y2, x3, y3, cells = d3_geom_voronoiCells, iCell = cells.length, cell, iHalfEdge, halfEdges, nHalfEdges, start, end;\n while (iCell--) {\n cell = cells[iCell];\n if (!cell || !cell.prepare()) continue;\n halfEdges = cell.edges;\n nHalfEdges = halfEdges.length;\n iHalfEdge = 0;\n while (iHalfEdge < nHalfEdges) {\n end = halfEdges[iHalfEdge].end(), x3 = end.x, y3 = end.y;\n start = halfEdges[++iHalfEdge % nHalfEdges].start(), x2 = start.x, y2 = start.y;\n if (abs(x3 - x2) > ε || abs(y3 - y2) > ε) {\n halfEdges.splice(iHalfEdge, 0, new d3_geom_voronoiHalfEdge(d3_geom_voronoiCreateBorderEdge(cell.site, end, abs(x3 - x0) < ε && y1 - y3 > ε ? {\n x: x0,\n y: abs(x2 - x0) < ε ? y2 : y1\n } : abs(y3 - y1) < ε && x1 - x3 > ε ? {\n x: abs(y2 - y1) < ε ? x2 : x1,\n y: y1\n } : abs(x3 - x1) < ε && y3 - y0 > ε ? {\n x: x1,\n y: abs(x2 - x1) < ε ? y2 : y0\n } : abs(y3 - y0) < ε && x3 - x0 > ε ? {\n x: abs(y2 - y0) < ε ? x2 : x0,\n y: y0\n } : null), cell.site, null));\n ++nHalfEdges;\n }\n }\n }\n }\n function d3_geom_voronoiHalfEdgeOrder(a, b) {\n return b.angle - a.angle;\n }\n function d3_geom_voronoiCircle() {\n d3_geom_voronoiRedBlackNode(this);\n this.x = this.y = this.arc = this.site = this.cy = null;\n }\n function d3_geom_voronoiAttachCircle(arc) {\n var lArc = arc.P, rArc = arc.N;\n if (!lArc || !rArc) return;\n var lSite = lArc.site, cSite = arc.site, rSite = rArc.site;\n if (lSite === rSite) return;\n var bx = cSite.x, by = cSite.y, ax = lSite.x - bx, ay = lSite.y - by, cx = rSite.x - bx, cy = rSite.y - by;\n var d = 2 * (ax * cy - ay * cx);\n if (d >= -ε2) return;\n var ha = ax * ax + ay * ay, hc = cx * cx + cy * cy, x = (cy * ha - ay * hc) / d, y = (ax * hc - cx * ha) / d, cy = y + by;\n var circle = d3_geom_voronoiCirclePool.pop() || new d3_geom_voronoiCircle();\n circle.arc = arc;\n circle.site = cSite;\n circle.x = x + bx;\n circle.y = cy + Math.sqrt(x * x + y * y);\n circle.cy = cy;\n arc.circle = circle;\n var before = null, node = d3_geom_voronoiCircles._;\n while (node) {\n if (circle.y < node.y || circle.y === node.y && circle.x <= node.x) {\n if (node.L) node = node.L; else {\n before = node.P;\n break;\n }\n } else {\n if (node.R) node = node.R; else {\n before = node;\n break;\n }\n }\n }\n d3_geom_voronoiCircles.insert(before, circle);\n if (!before) d3_geom_voronoiFirstCircle = circle;\n }\n function d3_geom_voronoiDetachCircle(arc) {\n var circle = arc.circle;\n if (circle) {\n if (!circle.P) d3_geom_voronoiFirstCircle = circle.N;\n d3_geom_voronoiCircles.remove(circle);\n d3_geom_voronoiCirclePool.push(circle);\n d3_geom_voronoiRedBlackNode(circle);\n arc.circle = null;\n }\n }\n function d3_geom_voronoiClipEdges(extent) {\n var edges = d3_geom_voronoiEdges, clip = d3_geom_clipLine(extent[0][0], extent[0][1], extent[1][0], extent[1][1]), i = edges.length, e;\n while (i--) {\n e = edges[i];\n if (!d3_geom_voronoiConnectEdge(e, extent) || !clip(e) || abs(e.a.x - e.b.x) < ε && abs(e.a.y - e.b.y) < ε) {\n e.a = e.b = null;\n edges.splice(i, 1);\n }\n }\n }\n function d3_geom_voronoiConnectEdge(edge, extent) {\n var vb = edge.b;\n if (vb) return true;\n var va = edge.a, x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], lSite = edge.l, rSite = edge.r, lx = lSite.x, ly = lSite.y, rx = rSite.x, ry = rSite.y, fx = (lx + rx) / 2, fy = (ly + ry) / 2, fm, fb;\n if (ry === ly) {\n if (fx < x0 || fx >= x1) return;\n if (lx > rx) {\n if (!va) va = {\n x: fx,\n y: y0\n }; else if (va.y >= y1) return;\n vb = {\n x: fx,\n y: y1\n };\n } else {\n if (!va) va = {\n x: fx,\n y: y1\n }; else if (va.y < y0) return;\n vb = {\n x: fx,\n y: y0\n };\n }\n } else {\n fm = (lx - rx) / (ry - ly);\n fb = fy - fm * fx;\n if (fm < -1 || fm > 1) {\n if (lx > rx) {\n if (!va) va = {\n x: (y0 - fb) / fm,\n y: y0\n }; else if (va.y >= y1) return;\n vb = {\n x: (y1 - fb) / fm,\n y: y1\n };\n } else {\n if (!va) va = {\n x: (y1 - fb) / fm,\n y: y1\n }; else if (va.y < y0) return;\n vb = {\n x: (y0 - fb) / fm,\n y: y0\n };\n }\n } else {\n if (ly < ry) {\n if (!va) va = {\n x: x0,\n y: fm * x0 + fb\n }; else if (va.x >= x1) return;\n vb = {\n x: x1,\n y: fm * x1 + fb\n };\n } else {\n if (!va) va = {\n x: x1,\n y: fm * x1 + fb\n }; else if (va.x < x0) return;\n vb = {\n x: x0,\n y: fm * x0 + fb\n };\n }\n }\n }\n edge.a = va;\n edge.b = vb;\n return true;\n }\n function d3_geom_voronoiEdge(lSite, rSite) {\n this.l = lSite;\n this.r = rSite;\n this.a = this.b = null;\n }\n function d3_geom_voronoiCreateEdge(lSite, rSite, va, vb) {\n var edge = new d3_geom_voronoiEdge(lSite, rSite);\n d3_geom_voronoiEdges.push(edge);\n if (va) d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, va);\n if (vb) d3_geom_voronoiSetEdgeEnd(edge, rSite, lSite, vb);\n d3_geom_voronoiCells[lSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, lSite, rSite));\n d3_geom_voronoiCells[rSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, rSite, lSite));\n return edge;\n }\n function d3_geom_voronoiCreateBorderEdge(lSite, va, vb) {\n var edge = new d3_geom_voronoiEdge(lSite, null);\n edge.a = va;\n edge.b = vb;\n d3_geom_voronoiEdges.push(edge);\n return edge;\n }\n function d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, vertex) {\n if (!edge.a && !edge.b) {\n edge.a = vertex;\n edge.l = lSite;\n edge.r = rSite;\n } else if (edge.l === rSite) {\n edge.b = vertex;\n } else {\n edge.a = vertex;\n }\n }\n function d3_geom_voronoiHalfEdge(edge, lSite, rSite) {\n var va = edge.a, vb = edge.b;\n this.edge = edge;\n this.site = lSite;\n this.angle = rSite ? Math.atan2(rSite.y - lSite.y, rSite.x - lSite.x) : edge.l === lSite ? Math.atan2(vb.x - va.x, va.y - vb.y) : Math.atan2(va.x - vb.x, vb.y - va.y);\n }\n d3_geom_voronoiHalfEdge.prototype = {\n start: function() {\n return this.edge.l === this.site ? this.edge.a : this.edge.b;\n },\n end: function() {\n return this.edge.l === this.site ? this.edge.b : this.edge.a;\n }\n };\n function d3_geom_voronoiRedBlackTree() {\n this._ = null;\n }\n function d3_geom_voronoiRedBlackNode(node) {\n node.U = node.C = node.L = node.R = node.P = node.N = null;\n }\n d3_geom_voronoiRedBlackTree.prototype = {\n insert: function(after, node) {\n var parent, grandpa, uncle;\n if (after) {\n node.P = after;\n node.N = after.N;\n if (after.N) after.N.P = node;\n after.N = node;\n if (after.R) {\n after = after.R;\n while (after.L) after = after.L;\n after.L = node;\n } else {\n after.R = node;\n }\n parent = after;\n } else if (this._) {\n after = d3_geom_voronoiRedBlackFirst(this._);\n node.P = null;\n node.N = after;\n after.P = after.L = node;\n parent = after;\n } else {\n node.P = node.N = null;\n this._ = node;\n parent = null;\n }\n node.L = node.R = null;\n node.U = parent;\n node.C = true;\n after = node;\n while (parent && parent.C) {\n grandpa = parent.U;\n if (parent === grandpa.L) {\n uncle = grandpa.R;\n if (uncle && uncle.C) {\n parent.C = uncle.C = false;\n grandpa.C = true;\n after = grandpa;\n } else {\n if (after === parent.R) {\n d3_geom_voronoiRedBlackRotateLeft(this, parent);\n after = parent;\n parent = after.U;\n }\n parent.C = false;\n grandpa.C = true;\n d3_geom_voronoiRedBlackRotateRight(this, grandpa);\n }\n } else {\n uncle = grandpa.L;\n if (uncle && uncle.C) {\n parent.C = uncle.C = false;\n grandpa.C = true;\n after = grandpa;\n } else {\n if (after === parent.L) {\n d3_geom_voronoiRedBlackRotateRight(this, parent);\n after = parent;\n parent = after.U;\n }\n parent.C = false;\n grandpa.C = true;\n d3_geom_voronoiRedBlackRotateLeft(this, grandpa);\n }\n }\n parent = after.U;\n }\n this._.C = false;\n },\n remove: function(node) {\n if (node.N) node.N.P = node.P;\n if (node.P) node.P.N = node.N;\n node.N = node.P = null;\n var parent = node.U, sibling, left = node.L, right = node.R, next, red;\n if (!left) next = right; else if (!right) next = left; else next = d3_geom_voronoiRedBlackFirst(right);\n if (parent) {\n if (parent.L === node) parent.L = next; else parent.R = next;\n } else {\n this._ = next;\n }\n if (left && right) {\n red = next.C;\n next.C = node.C;\n next.L = left;\n left.U = next;\n if (next !== right) {\n parent = next.U;\n next.U = node.U;\n node = next.R;\n parent.L = node;\n next.R = right;\n right.U = next;\n } else {\n next.U = parent;\n parent = next;\n node = next.R;\n }\n } else {\n red = node.C;\n node = next;\n }\n if (node) node.U = parent;\n if (red) return;\n if (node && node.C) {\n node.C = false;\n return;\n }\n do {\n if (node === this._) break;\n if (node === parent.L) {\n sibling = parent.R;\n if (sibling.C) {\n sibling.C = false;\n parent.C = true;\n d3_geom_voronoiRedBlackRotateLeft(this, parent);\n sibling = parent.R;\n }\n if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) {\n if (!sibling.R || !sibling.R.C) {\n sibling.L.C = false;\n sibling.C = true;\n d3_geom_voronoiRedBlackRotateRight(this, sibling);\n sibling = parent.R;\n }\n sibling.C = parent.C;\n parent.C = sibling.R.C = false;\n d3_geom_voronoiRedBlackRotateLeft(this, parent);\n node = this._;\n break;\n }\n } else {\n sibling = parent.L;\n if (sibling.C) {\n sibling.C = false;\n parent.C = true;\n d3_geom_voronoiRedBlackRotateRight(this, parent);\n sibling = parent.L;\n }\n if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) {\n if (!sibling.L || !sibling.L.C) {\n sibling.R.C = false;\n sibling.C = true;\n d3_geom_voronoiRedBlackRotateLeft(this, sibling);\n sibling = parent.L;\n }\n sibling.C = parent.C;\n parent.C = sibling.L.C = false;\n d3_geom_voronoiRedBlackRotateRight(this, parent);\n node = this._;\n break;\n }\n }\n sibling.C = true;\n node = parent;\n parent = parent.U;\n } while (!node.C);\n if (node) node.C = false;\n }\n };\n function d3_geom_voronoiRedBlackRotateLeft(tree, node) {\n var p = node, q = node.R, parent = p.U;\n if (parent) {\n if (parent.L === p) parent.L = q; else parent.R = q;\n } else {\n tree._ = q;\n }\n q.U = parent;\n p.U = q;\n p.R = q.L;\n if (p.R) p.R.U = p;\n q.L = p;\n }\n function d3_geom_voronoiRedBlackRotateRight(tree, node) {\n var p = node, q = node.L, parent = p.U;\n if (parent) {\n if (parent.L === p) parent.L = q; else parent.R = q;\n } else {\n tree._ = q;\n }\n q.U = parent;\n p.U = q;\n p.L = q.R;\n if (p.L) p.L.U = p;\n q.R = p;\n }\n function d3_geom_voronoiRedBlackFirst(node) {\n while (node.L) node = node.L;\n return node;\n }\n function d3_geom_voronoi(sites, bbox) {\n var site = sites.sort(d3_geom_voronoiVertexOrder).pop(), x0, y0, circle;\n d3_geom_voronoiEdges = [];\n d3_geom_voronoiCells = new Array(sites.length);\n d3_geom_voronoiBeaches = new d3_geom_voronoiRedBlackTree();\n d3_geom_voronoiCircles = new d3_geom_voronoiRedBlackTree();\n while (true) {\n circle = d3_geom_voronoiFirstCircle;\n if (site && (!circle || site.y < circle.y || site.y === circle.y && site.x < circle.x)) {\n if (site.x !== x0 || site.y !== y0) {\n d3_geom_voronoiCells[site.i] = new d3_geom_voronoiCell(site);\n d3_geom_voronoiAddBeach(site);\n x0 = site.x, y0 = site.y;\n }\n site = sites.pop();\n } else if (circle) {\n d3_geom_voronoiRemoveBeach(circle.arc);\n } else {\n break;\n }\n }\n if (bbox) d3_geom_voronoiClipEdges(bbox), d3_geom_voronoiCloseCells(bbox);\n var diagram = {\n cells: d3_geom_voronoiCells,\n edges: d3_geom_voronoiEdges\n };\n d3_geom_voronoiBeaches = d3_geom_voronoiCircles = d3_geom_voronoiEdges = d3_geom_voronoiCells = null;\n return diagram;\n }\n function d3_geom_voronoiVertexOrder(a, b) {\n return b.y - a.y || b.x - a.x;\n }\n d3.geom.voronoi = function(points) {\n var x = d3_geom_pointX, y = d3_geom_pointY, fx = x, fy = y, clipExtent = d3_geom_voronoiClipExtent;\n if (points) return voronoi(points);\n function voronoi(data) {\n var polygons = new Array(data.length), x0 = clipExtent[0][0], y0 = clipExtent[0][1], x1 = clipExtent[1][0], y1 = clipExtent[1][1];\n d3_geom_voronoi(sites(data), clipExtent).cells.forEach(function(cell, i) {\n var edges = cell.edges, site = cell.site, polygon = polygons[i] = edges.length ? edges.map(function(e) {\n var s = e.start();\n return [ s.x, s.y ];\n }) : site.x >= x0 && site.x <= x1 && site.y >= y0 && site.y <= y1 ? [ [ x0, y1 ], [ x1, y1 ], [ x1, y0 ], [ x0, y0 ] ] : [];\n polygon.point = data[i];\n });\n return polygons;\n }\n function sites(data) {\n return data.map(function(d, i) {\n return {\n x: Math.round(fx(d, i) / ε) * ε,\n y: Math.round(fy(d, i) / ε) * ε,\n i: i\n };\n });\n }\n voronoi.links = function(data) {\n return d3_geom_voronoi(sites(data)).edges.filter(function(edge) {\n return edge.l && edge.r;\n }).map(function(edge) {\n return {\n source: data[edge.l.i],\n target: data[edge.r.i]\n };\n });\n };\n voronoi.triangles = function(data) {\n var triangles = [];\n d3_geom_voronoi(sites(data)).cells.forEach(function(cell, i) {\n var site = cell.site, edges = cell.edges.sort(d3_geom_voronoiHalfEdgeOrder), j = -1, m = edges.length, e0, s0, e1 = edges[m - 1].edge, s1 = e1.l === site ? e1.r : e1.l;\n while (++j < m) {\n e0 = e1;\n s0 = s1;\n e1 = edges[j].edge;\n s1 = e1.l === site ? e1.r : e1.l;\n if (i < s0.i && i < s1.i && d3_geom_voronoiTriangleArea(site, s0, s1) < 0) {\n triangles.push([ data[i], data[s0.i], data[s1.i] ]);\n }\n }\n });\n return triangles;\n };\n voronoi.x = function(_) {\n return arguments.length ? (fx = d3_functor(x = _), voronoi) : x;\n };\n voronoi.y = function(_) {\n return arguments.length ? (fy = d3_functor(y = _), voronoi) : y;\n };\n voronoi.clipExtent = function(_) {\n if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent;\n clipExtent = _ == null ? d3_geom_voronoiClipExtent : _;\n return voronoi;\n };\n voronoi.size = function(_) {\n if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent && clipExtent[1];\n return voronoi.clipExtent(_ && [ [ 0, 0 ], _ ]);\n };\n return voronoi;\n };\n var d3_geom_voronoiClipExtent = [ [ -1e6, -1e6 ], [ 1e6, 1e6 ] ];\n function d3_geom_voronoiTriangleArea(a, b, c) {\n return (a.x - c.x) * (b.y - a.y) - (a.x - b.x) * (c.y - a.y);\n }\n d3.geom.delaunay = function(vertices) {\n return d3.geom.voronoi().triangles(vertices);\n };\n d3.geom.quadtree = function(points, x1, y1, x2, y2) {\n var x = d3_geom_pointX, y = d3_geom_pointY, compat;\n if (compat = arguments.length) {\n x = d3_geom_quadtreeCompatX;\n y = d3_geom_quadtreeCompatY;\n if (compat === 3) {\n y2 = y1;\n x2 = x1;\n y1 = x1 = 0;\n }\n return quadtree(points);\n }\n function quadtree(data) {\n var d, fx = d3_functor(x), fy = d3_functor(y), xs, ys, i, n, x1_, y1_, x2_, y2_;\n if (x1 != null) {\n x1_ = x1, y1_ = y1, x2_ = x2, y2_ = y2;\n } else {\n x2_ = y2_ = -(x1_ = y1_ = Infinity);\n xs = [], ys = [];\n n = data.length;\n if (compat) for (i = 0; i < n; ++i) {\n d = data[i];\n if (d.x < x1_) x1_ = d.x;\n if (d.y < y1_) y1_ = d.y;\n if (d.x > x2_) x2_ = d.x;\n if (d.y > y2_) y2_ = d.y;\n xs.push(d.x);\n ys.push(d.y);\n } else for (i = 0; i < n; ++i) {\n var x_ = +fx(d = data[i], i), y_ = +fy(d, i);\n if (x_ < x1_) x1_ = x_;\n if (y_ < y1_) y1_ = y_;\n if (x_ > x2_) x2_ = x_;\n if (y_ > y2_) y2_ = y_;\n xs.push(x_);\n ys.push(y_);\n }\n }\n var dx = x2_ - x1_, dy = y2_ - y1_;\n if (dx > dy) y2_ = y1_ + dx; else x2_ = x1_ + dy;\n function insert(n, d, x, y, x1, y1, x2, y2) {\n if (isNaN(x) || isNaN(y)) return;\n if (n.leaf) {\n var nx = n.x, ny = n.y;\n if (nx != null) {\n if (abs(nx - x) + abs(ny - y) < .01) {\n insertChild(n, d, x, y, x1, y1, x2, y2);\n } else {\n var nPoint = n.point;\n n.x = n.y = n.point = null;\n insertChild(n, nPoint, nx, ny, x1, y1, x2, y2);\n insertChild(n, d, x, y, x1, y1, x2, y2);\n }\n } else {\n n.x = x, n.y = y, n.point = d;\n }\n } else {\n insertChild(n, d, x, y, x1, y1, x2, y2);\n }\n }\n function insertChild(n, d, x, y, x1, y1, x2, y2) {\n var xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym, i = below << 1 | right;\n n.leaf = false;\n n = n.nodes[i] || (n.nodes[i] = d3_geom_quadtreeNode());\n if (right) x1 = xm; else x2 = xm;\n if (below) y1 = ym; else y2 = ym;\n insert(n, d, x, y, x1, y1, x2, y2);\n }\n var root = d3_geom_quadtreeNode();\n root.add = function(d) {\n insert(root, d, +fx(d, ++i), +fy(d, i), x1_, y1_, x2_, y2_);\n };\n root.visit = function(f) {\n d3_geom_quadtreeVisit(f, root, x1_, y1_, x2_, y2_);\n };\n root.find = function(point) {\n return d3_geom_quadtreeFind(root, point[0], point[1], x1_, y1_, x2_, y2_);\n };\n i = -1;\n if (x1 == null) {\n while (++i < n) {\n insert(root, data[i], xs[i], ys[i], x1_, y1_, x2_, y2_);\n }\n --i;\n } else data.forEach(root.add);\n xs = ys = data = d = null;\n return root;\n }\n quadtree.x = function(_) {\n return arguments.length ? (x = _, quadtree) : x;\n };\n quadtree.y = function(_) {\n return arguments.length ? (y = _, quadtree) : y;\n };\n quadtree.extent = function(_) {\n if (!arguments.length) return x1 == null ? null : [ [ x1, y1 ], [ x2, y2 ] ];\n if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = +_[0][0], y1 = +_[0][1], x2 = +_[1][0], \n y2 = +_[1][1];\n return quadtree;\n };\n quadtree.size = function(_) {\n if (!arguments.length) return x1 == null ? null : [ x2 - x1, y2 - y1 ];\n if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = y1 = 0, x2 = +_[0], y2 = +_[1];\n return quadtree;\n };\n return quadtree;\n };\n function d3_geom_quadtreeCompatX(d) {\n return d.x;\n }\n function d3_geom_quadtreeCompatY(d) {\n return d.y;\n }\n function d3_geom_quadtreeNode() {\n return {\n leaf: true,\n nodes: [],\n point: null,\n x: null,\n y: null\n };\n }\n function d3_geom_quadtreeVisit(f, node, x1, y1, x2, y2) {\n if (!f(node, x1, y1, x2, y2)) {\n var sx = (x1 + x2) * .5, sy = (y1 + y2) * .5, children = node.nodes;\n if (children[0]) d3_geom_quadtreeVisit(f, children[0], x1, y1, sx, sy);\n if (children[1]) d3_geom_quadtreeVisit(f, children[1], sx, y1, x2, sy);\n if (children[2]) d3_geom_quadtreeVisit(f, children[2], x1, sy, sx, y2);\n if (children[3]) d3_geom_quadtreeVisit(f, children[3], sx, sy, x2, y2);\n }\n }\n function d3_geom_quadtreeFind(root, x, y, x0, y0, x3, y3) {\n var minDistance2 = Infinity, closestPoint;\n (function find(node, x1, y1, x2, y2) {\n if (x1 > x3 || y1 > y3 || x2 < x0 || y2 < y0) return;\n if (point = node.point) {\n var point, dx = x - node.x, dy = y - node.y, distance2 = dx * dx + dy * dy;\n if (distance2 < minDistance2) {\n var distance = Math.sqrt(minDistance2 = distance2);\n x0 = x - distance, y0 = y - distance;\n x3 = x + distance, y3 = y + distance;\n closestPoint = point;\n }\n }\n var children = node.nodes, xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym;\n for (var i = below << 1 | right, j = i + 4; i < j; ++i) {\n if (node = children[i & 3]) switch (i & 3) {\n case 0:\n find(node, x1, y1, xm, ym);\n break;\n\n case 1:\n find(node, xm, y1, x2, ym);\n break;\n\n case 2:\n find(node, x1, ym, xm, y2);\n break;\n\n case 3:\n find(node, xm, ym, x2, y2);\n break;\n }\n }\n })(root, x0, y0, x3, y3);\n return closestPoint;\n }\n d3.interpolateRgb = d3_interpolateRgb;\n function d3_interpolateRgb(a, b) {\n a = d3.rgb(a);\n b = d3.rgb(b);\n var ar = a.r, ag = a.g, ab = a.b, br = b.r - ar, bg = b.g - ag, bb = b.b - ab;\n return function(t) {\n return \"#\" + d3_rgb_hex(Math.round(ar + br * t)) + d3_rgb_hex(Math.round(ag + bg * t)) + d3_rgb_hex(Math.round(ab + bb * t));\n };\n }\n d3.interpolateObject = d3_interpolateObject;\n function d3_interpolateObject(a, b) {\n var i = {}, c = {}, k;\n for (k in a) {\n if (k in b) {\n i[k] = d3_interpolate(a[k], b[k]);\n } else {\n c[k] = a[k];\n }\n }\n for (k in b) {\n if (!(k in a)) {\n c[k] = b[k];\n }\n }\n return function(t) {\n for (k in i) c[k] = i[k](t);\n return c;\n };\n }\n d3.interpolateNumber = d3_interpolateNumber;\n function d3_interpolateNumber(a, b) {\n a = +a, b = +b;\n return function(t) {\n return a * (1 - t) + b * t;\n };\n }\n d3.interpolateString = d3_interpolateString;\n function d3_interpolateString(a, b) {\n var bi = d3_interpolate_numberA.lastIndex = d3_interpolate_numberB.lastIndex = 0, am, bm, bs, i = -1, s = [], q = [];\n a = a + \"\", b = b + \"\";\n while ((am = d3_interpolate_numberA.exec(a)) && (bm = d3_interpolate_numberB.exec(b))) {\n if ((bs = bm.index) > bi) {\n bs = b.slice(bi, bs);\n if (s[i]) s[i] += bs; else s[++i] = bs;\n }\n if ((am = am[0]) === (bm = bm[0])) {\n if (s[i]) s[i] += bm; else s[++i] = bm;\n } else {\n s[++i] = null;\n q.push({\n i: i,\n x: d3_interpolateNumber(am, bm)\n });\n }\n bi = d3_interpolate_numberB.lastIndex;\n }\n if (bi < b.length) {\n bs = b.slice(bi);\n if (s[i]) s[i] += bs; else s[++i] = bs;\n }\n return s.length < 2 ? q[0] ? (b = q[0].x, function(t) {\n return b(t) + \"\";\n }) : function() {\n return b;\n } : (b = q.length, function(t) {\n for (var i = 0, o; i < b; ++i) s[(o = q[i]).i] = o.x(t);\n return s.join(\"\");\n });\n }\n var d3_interpolate_numberA = /[-+]?(?:\\d+\\.?\\d*|\\.?\\d+)(?:[eE][-+]?\\d+)?/g, d3_interpolate_numberB = new RegExp(d3_interpolate_numberA.source, \"g\");\n d3.interpolate = d3_interpolate;\n function d3_interpolate(a, b) {\n var i = d3.interpolators.length, f;\n while (--i >= 0 && !(f = d3.interpolators[i](a, b))) ;\n return f;\n }\n d3.interpolators = [ function(a, b) {\n var t = typeof b;\n return (t === \"string\" ? d3_rgb_names.has(b) || /^(#|rgb\\(|hsl\\()/.test(b) ? d3_interpolateRgb : d3_interpolateString : b instanceof d3_color ? d3_interpolateRgb : Array.isArray(b) ? d3_interpolateArray : t === \"object\" && isNaN(b) ? d3_interpolateObject : d3_interpolateNumber)(a, b);\n } ];\n d3.interpolateArray = d3_interpolateArray;\n function d3_interpolateArray(a, b) {\n var x = [], c = [], na = a.length, nb = b.length, n0 = Math.min(a.length, b.length), i;\n for (i = 0; i < n0; ++i) x.push(d3_interpolate(a[i], b[i]));\n for (;i < na; ++i) c[i] = a[i];\n for (;i < nb; ++i) c[i] = b[i];\n return function(t) {\n for (i = 0; i < n0; ++i) c[i] = x[i](t);\n return c;\n };\n }\n var d3_ease_default = function() {\n return d3_identity;\n };\n var d3_ease = d3.map({\n linear: d3_ease_default,\n poly: d3_ease_poly,\n quad: function() {\n return d3_ease_quad;\n },\n cubic: function() {\n return d3_ease_cubic;\n },\n sin: function() {\n return d3_ease_sin;\n },\n exp: function() {\n return d3_ease_exp;\n },\n circle: function() {\n return d3_ease_circle;\n },\n elastic: d3_ease_elastic,\n back: d3_ease_back,\n bounce: function() {\n return d3_ease_bounce;\n }\n });\n var d3_ease_mode = d3.map({\n \"in\": d3_identity,\n out: d3_ease_reverse,\n \"in-out\": d3_ease_reflect,\n \"out-in\": function(f) {\n return d3_ease_reflect(d3_ease_reverse(f));\n }\n });\n d3.ease = function(name) {\n var i = name.indexOf(\"-\"), t = i >= 0 ? name.slice(0, i) : name, m = i >= 0 ? name.slice(i + 1) : \"in\";\n t = d3_ease.get(t) || d3_ease_default;\n m = d3_ease_mode.get(m) || d3_identity;\n return d3_ease_clamp(m(t.apply(null, d3_arraySlice.call(arguments, 1))));\n };\n function d3_ease_clamp(f) {\n return function(t) {\n return t <= 0 ? 0 : t >= 1 ? 1 : f(t);\n };\n }\n function d3_ease_reverse(f) {\n return function(t) {\n return 1 - f(1 - t);\n };\n }\n function d3_ease_reflect(f) {\n return function(t) {\n return .5 * (t < .5 ? f(2 * t) : 2 - f(2 - 2 * t));\n };\n }\n function d3_ease_quad(t) {\n return t * t;\n }\n function d3_ease_cubic(t) {\n return t * t * t;\n }\n function d3_ease_cubicInOut(t) {\n if (t <= 0) return 0;\n if (t >= 1) return 1;\n var t2 = t * t, t3 = t2 * t;\n return 4 * (t < .5 ? t3 : 3 * (t - t2) + t3 - .75);\n }\n function d3_ease_poly(e) {\n return function(t) {\n return Math.pow(t, e);\n };\n }\n function d3_ease_sin(t) {\n return 1 - Math.cos(t * halfπ);\n }\n function d3_ease_exp(t) {\n return Math.pow(2, 10 * (t - 1));\n }\n function d3_ease_circle(t) {\n return 1 - Math.sqrt(1 - t * t);\n }\n function d3_ease_elastic(a, p) {\n var s;\n if (arguments.length < 2) p = .45;\n if (arguments.length) s = p / τ * Math.asin(1 / a); else a = 1, s = p / 4;\n return function(t) {\n return 1 + a * Math.pow(2, -10 * t) * Math.sin((t - s) * τ / p);\n };\n }\n function d3_ease_back(s) {\n if (!s) s = 1.70158;\n return function(t) {\n return t * t * ((s + 1) * t - s);\n };\n }\n function d3_ease_bounce(t) {\n return t < 1 / 2.75 ? 7.5625 * t * t : t < 2 / 2.75 ? 7.5625 * (t -= 1.5 / 2.75) * t + .75 : t < 2.5 / 2.75 ? 7.5625 * (t -= 2.25 / 2.75) * t + .9375 : 7.5625 * (t -= 2.625 / 2.75) * t + .984375;\n }\n d3.interpolateHcl = d3_interpolateHcl;\n function d3_interpolateHcl(a, b) {\n a = d3.hcl(a);\n b = d3.hcl(b);\n var ah = a.h, ac = a.c, al = a.l, bh = b.h - ah, bc = b.c - ac, bl = b.l - al;\n if (isNaN(bc)) bc = 0, ac = isNaN(ac) ? b.c : ac;\n if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360;\n return function(t) {\n return d3_hcl_lab(ah + bh * t, ac + bc * t, al + bl * t) + \"\";\n };\n }\n d3.interpolateHsl = d3_interpolateHsl;\n function d3_interpolateHsl(a, b) {\n a = d3.hsl(a);\n b = d3.hsl(b);\n var ah = a.h, as = a.s, al = a.l, bh = b.h - ah, bs = b.s - as, bl = b.l - al;\n if (isNaN(bs)) bs = 0, as = isNaN(as) ? b.s : as;\n if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360;\n return function(t) {\n return d3_hsl_rgb(ah + bh * t, as + bs * t, al + bl * t) + \"\";\n };\n }\n d3.interpolateLab = d3_interpolateLab;\n function d3_interpolateLab(a, b) {\n a = d3.lab(a);\n b = d3.lab(b);\n var al = a.l, aa = a.a, ab = a.b, bl = b.l - al, ba = b.a - aa, bb = b.b - ab;\n return function(t) {\n return d3_lab_rgb(al + bl * t, aa + ba * t, ab + bb * t) + \"\";\n };\n }\n d3.interpolateRound = d3_interpolateRound;\n function d3_interpolateRound(a, b) {\n b -= a;\n return function(t) {\n return Math.round(a + b * t);\n };\n }\n d3.transform = function(string) {\n var g = d3_document.createElementNS(d3.ns.prefix.svg, \"g\");\n return (d3.transform = function(string) {\n if (string != null) {\n g.setAttribute(\"transform\", string);\n var t = g.transform.baseVal.consolidate();\n }\n return new d3_transform(t ? t.matrix : d3_transformIdentity);\n })(string);\n };\n function d3_transform(m) {\n var r0 = [ m.a, m.b ], r1 = [ m.c, m.d ], kx = d3_transformNormalize(r0), kz = d3_transformDot(r0, r1), ky = d3_transformNormalize(d3_transformCombine(r1, r0, -kz)) || 0;\n if (r0[0] * r1[1] < r1[0] * r0[1]) {\n r0[0] *= -1;\n r0[1] *= -1;\n kx *= -1;\n kz *= -1;\n }\n this.rotate = (kx ? Math.atan2(r0[1], r0[0]) : Math.atan2(-r1[0], r1[1])) * d3_degrees;\n this.translate = [ m.e, m.f ];\n this.scale = [ kx, ky ];\n this.skew = ky ? Math.atan2(kz, ky) * d3_degrees : 0;\n }\n d3_transform.prototype.toString = function() {\n return \"translate(\" + this.translate + \")rotate(\" + this.rotate + \")skewX(\" + this.skew + \")scale(\" + this.scale + \")\";\n };\n function d3_transformDot(a, b) {\n return a[0] * b[0] + a[1] * b[1];\n }\n function d3_transformNormalize(a) {\n var k = Math.sqrt(d3_transformDot(a, a));\n if (k) {\n a[0] /= k;\n a[1] /= k;\n }\n return k;\n }\n function d3_transformCombine(a, b, k) {\n a[0] += k * b[0];\n a[1] += k * b[1];\n return a;\n }\n var d3_transformIdentity = {\n a: 1,\n b: 0,\n c: 0,\n d: 1,\n e: 0,\n f: 0\n };\n d3.interpolateTransform = d3_interpolateTransform;\n function d3_interpolateTransform(a, b) {\n var s = [], q = [], n, A = d3.transform(a), B = d3.transform(b), ta = A.translate, tb = B.translate, ra = A.rotate, rb = B.rotate, wa = A.skew, wb = B.skew, ka = A.scale, kb = B.scale;\n if (ta[0] != tb[0] || ta[1] != tb[1]) {\n s.push(\"translate(\", null, \",\", null, \")\");\n q.push({\n i: 1,\n x: d3_interpolateNumber(ta[0], tb[0])\n }, {\n i: 3,\n x: d3_interpolateNumber(ta[1], tb[1])\n });\n } else if (tb[0] || tb[1]) {\n s.push(\"translate(\" + tb + \")\");\n } else {\n s.push(\"\");\n }\n if (ra != rb) {\n if (ra - rb > 180) rb += 360; else if (rb - ra > 180) ra += 360;\n q.push({\n i: s.push(s.pop() + \"rotate(\", null, \")\") - 2,\n x: d3_interpolateNumber(ra, rb)\n });\n } else if (rb) {\n s.push(s.pop() + \"rotate(\" + rb + \")\");\n }\n if (wa != wb) {\n q.push({\n i: s.push(s.pop() + \"skewX(\", null, \")\") - 2,\n x: d3_interpolateNumber(wa, wb)\n });\n } else if (wb) {\n s.push(s.pop() + \"skewX(\" + wb + \")\");\n }\n if (ka[0] != kb[0] || ka[1] != kb[1]) {\n n = s.push(s.pop() + \"scale(\", null, \",\", null, \")\");\n q.push({\n i: n - 4,\n x: d3_interpolateNumber(ka[0], kb[0])\n }, {\n i: n - 2,\n x: d3_interpolateNumber(ka[1], kb[1])\n });\n } else if (kb[0] != 1 || kb[1] != 1) {\n s.push(s.pop() + \"scale(\" + kb + \")\");\n }\n n = q.length;\n return function(t) {\n var i = -1, o;\n while (++i < n) s[(o = q[i]).i] = o.x(t);\n return s.join(\"\");\n };\n }\n function d3_uninterpolateNumber(a, b) {\n b = (b -= a = +a) || 1 / b;\n return function(x) {\n return (x - a) / b;\n };\n }\n function d3_uninterpolateClamp(a, b) {\n b = (b -= a = +a) || 1 / b;\n return function(x) {\n return Math.max(0, Math.min(1, (x - a) / b));\n };\n }\n d3.layout = {};\n d3.layout.bundle = function() {\n return function(links) {\n var paths = [], i = -1, n = links.length;\n while (++i < n) paths.push(d3_layout_bundlePath(links[i]));\n return paths;\n };\n };\n function d3_layout_bundlePath(link) {\n var start = link.source, end = link.target, lca = d3_layout_bundleLeastCommonAncestor(start, end), points = [ start ];\n while (start !== lca) {\n start = start.parent;\n points.push(start);\n }\n var k = points.length;\n while (end !== lca) {\n points.splice(k, 0, end);\n end = end.parent;\n }\n return points;\n }\n function d3_layout_bundleAncestors(node) {\n var ancestors = [], parent = node.parent;\n while (parent != null) {\n ancestors.push(node);\n node = parent;\n parent = parent.parent;\n }\n ancestors.push(node);\n return ancestors;\n }\n function d3_layout_bundleLeastCommonAncestor(a, b) {\n if (a === b) return a;\n var aNodes = d3_layout_bundleAncestors(a), bNodes = d3_layout_bundleAncestors(b), aNode = aNodes.pop(), bNode = bNodes.pop(), sharedNode = null;\n while (aNode === bNode) {\n sharedNode = aNode;\n aNode = aNodes.pop();\n bNode = bNodes.pop();\n }\n return sharedNode;\n }\n d3.layout.chord = function() {\n var chord = {}, chords, groups, matrix, n, padding = 0, sortGroups, sortSubgroups, sortChords;\n function relayout() {\n var subgroups = {}, groupSums = [], groupIndex = d3.range(n), subgroupIndex = [], k, x, x0, i, j;\n chords = [];\n groups = [];\n k = 0, i = -1;\n while (++i < n) {\n x = 0, j = -1;\n while (++j < n) {\n x += matrix[i][j];\n }\n groupSums.push(x);\n subgroupIndex.push(d3.range(n));\n k += x;\n }\n if (sortGroups) {\n groupIndex.sort(function(a, b) {\n return sortGroups(groupSums[a], groupSums[b]);\n });\n }\n if (sortSubgroups) {\n subgroupIndex.forEach(function(d, i) {\n d.sort(function(a, b) {\n return sortSubgroups(matrix[i][a], matrix[i][b]);\n });\n });\n }\n k = (τ - padding * n) / k;\n x = 0, i = -1;\n while (++i < n) {\n x0 = x, j = -1;\n while (++j < n) {\n var di = groupIndex[i], dj = subgroupIndex[di][j], v = matrix[di][dj], a0 = x, a1 = x += v * k;\n subgroups[di + \"-\" + dj] = {\n index: di,\n subindex: dj,\n startAngle: a0,\n endAngle: a1,\n value: v\n };\n }\n groups[di] = {\n index: di,\n startAngle: x0,\n endAngle: x,\n value: (x - x0) / k\n };\n x += padding;\n }\n i = -1;\n while (++i < n) {\n j = i - 1;\n while (++j < n) {\n var source = subgroups[i + \"-\" + j], target = subgroups[j + \"-\" + i];\n if (source.value || target.value) {\n chords.push(source.value < target.value ? {\n source: target,\n target: source\n } : {\n source: source,\n target: target\n });\n }\n }\n }\n if (sortChords) resort();\n }\n function resort() {\n chords.sort(function(a, b) {\n return sortChords((a.source.value + a.target.value) / 2, (b.source.value + b.target.value) / 2);\n });\n }\n chord.matrix = function(x) {\n if (!arguments.length) return matrix;\n n = (matrix = x) && matrix.length;\n chords = groups = null;\n return chord;\n };\n chord.padding = function(x) {\n if (!arguments.length) return padding;\n padding = x;\n chords = groups = null;\n return chord;\n };\n chord.sortGroups = function(x) {\n if (!arguments.length) return sortGroups;\n sortGroups = x;\n chords = groups = null;\n return chord;\n };\n chord.sortSubgroups = function(x) {\n if (!arguments.length) return sortSubgroups;\n sortSubgroups = x;\n chords = null;\n return chord;\n };\n chord.sortChords = function(x) {\n if (!arguments.length) return sortChords;\n sortChords = x;\n if (chords) resort();\n return chord;\n };\n chord.chords = function() {\n if (!chords) relayout();\n return chords;\n };\n chord.groups = function() {\n if (!groups) relayout();\n return groups;\n };\n return chord;\n };\n d3.layout.force = function() {\n var force = {}, event = d3.dispatch(\"start\", \"tick\", \"end\"), size = [ 1, 1 ], drag, alpha, friction = .9, linkDistance = d3_layout_forceLinkDistance, linkStrength = d3_layout_forceLinkStrength, charge = -30, chargeDistance2 = d3_layout_forceChargeDistance2, gravity = .1, theta2 = .64, nodes = [], links = [], distances, strengths, charges;\n function repulse(node) {\n return function(quad, x1, _, x2) {\n if (quad.point !== node) {\n var dx = quad.cx - node.x, dy = quad.cy - node.y, dw = x2 - x1, dn = dx * dx + dy * dy;\n if (dw * dw / theta2 < dn) {\n if (dn < chargeDistance2) {\n var k = quad.charge / dn;\n node.px -= dx * k;\n node.py -= dy * k;\n }\n return true;\n }\n if (quad.point && dn && dn < chargeDistance2) {\n var k = quad.pointCharge / dn;\n node.px -= dx * k;\n node.py -= dy * k;\n }\n }\n return !quad.charge;\n };\n }\n force.tick = function() {\n if ((alpha *= .99) < .005) {\n event.end({\n type: \"end\",\n alpha: alpha = 0\n });\n return true;\n }\n var n = nodes.length, m = links.length, q, i, o, s, t, l, k, x, y;\n for (i = 0; i < m; ++i) {\n o = links[i];\n s = o.source;\n t = o.target;\n x = t.x - s.x;\n y = t.y - s.y;\n if (l = x * x + y * y) {\n l = alpha * strengths[i] * ((l = Math.sqrt(l)) - distances[i]) / l;\n x *= l;\n y *= l;\n t.x -= x * (k = s.weight / (t.weight + s.weight));\n t.y -= y * k;\n s.x += x * (k = 1 - k);\n s.y += y * k;\n }\n }\n if (k = alpha * gravity) {\n x = size[0] / 2;\n y = size[1] / 2;\n i = -1;\n if (k) while (++i < n) {\n o = nodes[i];\n o.x += (x - o.x) * k;\n o.y += (y - o.y) * k;\n }\n }\n if (charge) {\n d3_layout_forceAccumulate(q = d3.geom.quadtree(nodes), alpha, charges);\n i = -1;\n while (++i < n) {\n if (!(o = nodes[i]).fixed) {\n q.visit(repulse(o));\n }\n }\n }\n i = -1;\n while (++i < n) {\n o = nodes[i];\n if (o.fixed) {\n o.x = o.px;\n o.y = o.py;\n } else {\n o.x -= (o.px - (o.px = o.x)) * friction;\n o.y -= (o.py - (o.py = o.y)) * friction;\n }\n }\n event.tick({\n type: \"tick\",\n alpha: alpha\n });\n };\n force.nodes = function(x) {\n if (!arguments.length) return nodes;\n nodes = x;\n return force;\n };\n force.links = function(x) {\n if (!arguments.length) return links;\n links = x;\n return force;\n };\n force.size = function(x) {\n if (!arguments.length) return size;\n size = x;\n return force;\n };\n force.linkDistance = function(x) {\n if (!arguments.length) return linkDistance;\n linkDistance = typeof x === \"function\" ? x : +x;\n return force;\n };\n force.distance = force.linkDistance;\n force.linkStrength = function(x) {\n if (!arguments.length) return linkStrength;\n linkStrength = typeof x === \"function\" ? x : +x;\n return force;\n };\n force.friction = function(x) {\n if (!arguments.length) return friction;\n friction = +x;\n return force;\n };\n force.charge = function(x) {\n if (!arguments.length) return charge;\n charge = typeof x === \"function\" ? x : +x;\n return force;\n };\n force.chargeDistance = function(x) {\n if (!arguments.length) return Math.sqrt(chargeDistance2);\n chargeDistance2 = x * x;\n return force;\n };\n force.gravity = function(x) {\n if (!arguments.length) return gravity;\n gravity = +x;\n return force;\n };\n force.theta = function(x) {\n if (!arguments.length) return Math.sqrt(theta2);\n theta2 = x * x;\n return force;\n };\n force.alpha = function(x) {\n if (!arguments.length) return alpha;\n x = +x;\n if (alpha) {\n if (x > 0) alpha = x; else alpha = 0;\n } else if (x > 0) {\n event.start({\n type: \"start\",\n alpha: alpha = x\n });\n d3.timer(force.tick);\n }\n return force;\n };\n force.start = function() {\n var i, n = nodes.length, m = links.length, w = size[0], h = size[1], neighbors, o;\n for (i = 0; i < n; ++i) {\n (o = nodes[i]).index = i;\n o.weight = 0;\n }\n for (i = 0; i < m; ++i) {\n o = links[i];\n if (typeof o.source == \"number\") o.source = nodes[o.source];\n if (typeof o.target == \"number\") o.target = nodes[o.target];\n ++o.source.weight;\n ++o.target.weight;\n }\n for (i = 0; i < n; ++i) {\n o = nodes[i];\n if (isNaN(o.x)) o.x = position(\"x\", w);\n if (isNaN(o.y)) o.y = position(\"y\", h);\n if (isNaN(o.px)) o.px = o.x;\n if (isNaN(o.py)) o.py = o.y;\n }\n distances = [];\n if (typeof linkDistance === \"function\") for (i = 0; i < m; ++i) distances[i] = +linkDistance.call(this, links[i], i); else for (i = 0; i < m; ++i) distances[i] = linkDistance;\n strengths = [];\n if (typeof linkStrength === \"function\") for (i = 0; i < m; ++i) strengths[i] = +linkStrength.call(this, links[i], i); else for (i = 0; i < m; ++i) strengths[i] = linkStrength;\n charges = [];\n if (typeof charge === \"function\") for (i = 0; i < n; ++i) charges[i] = +charge.call(this, nodes[i], i); else for (i = 0; i < n; ++i) charges[i] = charge;\n function position(dimension, size) {\n if (!neighbors) {\n neighbors = new Array(n);\n for (j = 0; j < n; ++j) {\n neighbors[j] = [];\n }\n for (j = 0; j < m; ++j) {\n var o = links[j];\n neighbors[o.source.index].push(o.target);\n neighbors[o.target.index].push(o.source);\n }\n }\n var candidates = neighbors[i], j = -1, l = candidates.length, x;\n while (++j < l) if (!isNaN(x = candidates[j][dimension])) return x;\n return Math.random() * size;\n }\n return force.resume();\n };\n force.resume = function() {\n return force.alpha(.1);\n };\n force.stop = function() {\n return force.alpha(0);\n };\n force.drag = function() {\n if (!drag) drag = d3.behavior.drag().origin(d3_identity).on(\"dragstart.force\", d3_layout_forceDragstart).on(\"drag.force\", dragmove).on(\"dragend.force\", d3_layout_forceDragend);\n if (!arguments.length) return drag;\n this.on(\"mouseover.force\", d3_layout_forceMouseover).on(\"mouseout.force\", d3_layout_forceMouseout).call(drag);\n };\n function dragmove(d) {\n d.px = d3.event.x, d.py = d3.event.y;\n force.resume();\n }\n return d3.rebind(force, event, \"on\");\n };\n function d3_layout_forceDragstart(d) {\n d.fixed |= 2;\n }\n function d3_layout_forceDragend(d) {\n d.fixed &= ~6;\n }\n function d3_layout_forceMouseover(d) {\n d.fixed |= 4;\n d.px = d.x, d.py = d.y;\n }\n function d3_layout_forceMouseout(d) {\n d.fixed &= ~4;\n }\n function d3_layout_forceAccumulate(quad, alpha, charges) {\n var cx = 0, cy = 0;\n quad.charge = 0;\n if (!quad.leaf) {\n var nodes = quad.nodes, n = nodes.length, i = -1, c;\n while (++i < n) {\n c = nodes[i];\n if (c == null) continue;\n d3_layout_forceAccumulate(c, alpha, charges);\n quad.charge += c.charge;\n cx += c.charge * c.cx;\n cy += c.charge * c.cy;\n }\n }\n if (quad.point) {\n if (!quad.leaf) {\n quad.point.x += Math.random() - .5;\n quad.point.y += Math.random() - .5;\n }\n var k = alpha * charges[quad.point.index];\n quad.charge += quad.pointCharge = k;\n cx += k * quad.point.x;\n cy += k * quad.point.y;\n }\n quad.cx = cx / quad.charge;\n quad.cy = cy / quad.charge;\n }\n var d3_layout_forceLinkDistance = 20, d3_layout_forceLinkStrength = 1, d3_layout_forceChargeDistance2 = Infinity;\n d3.layout.hierarchy = function() {\n var sort = d3_layout_hierarchySort, children = d3_layout_hierarchyChildren, value = d3_layout_hierarchyValue;\n function hierarchy(root) {\n var stack = [ root ], nodes = [], node;\n root.depth = 0;\n while ((node = stack.pop()) != null) {\n nodes.push(node);\n if ((childs = children.call(hierarchy, node, node.depth)) && (n = childs.length)) {\n var n, childs, child;\n while (--n >= 0) {\n stack.push(child = childs[n]);\n child.parent = node;\n child.depth = node.depth + 1;\n }\n if (value) node.value = 0;\n node.children = childs;\n } else {\n if (value) node.value = +value.call(hierarchy, node, node.depth) || 0;\n delete node.children;\n }\n }\n d3_layout_hierarchyVisitAfter(root, function(node) {\n var childs, parent;\n if (sort && (childs = node.children)) childs.sort(sort);\n if (value && (parent = node.parent)) parent.value += node.value;\n });\n return nodes;\n }\n hierarchy.sort = function(x) {\n if (!arguments.length) return sort;\n sort = x;\n return hierarchy;\n };\n hierarchy.children = function(x) {\n if (!arguments.length) return children;\n children = x;\n return hierarchy;\n };\n hierarchy.value = function(x) {\n if (!arguments.length) return value;\n value = x;\n return hierarchy;\n };\n hierarchy.revalue = function(root) {\n if (value) {\n d3_layout_hierarchyVisitBefore(root, function(node) {\n if (node.children) node.value = 0;\n });\n d3_layout_hierarchyVisitAfter(root, function(node) {\n var parent;\n if (!node.children) node.value = +value.call(hierarchy, node, node.depth) || 0;\n if (parent = node.parent) parent.value += node.value;\n });\n }\n return root;\n };\n return hierarchy;\n };\n function d3_layout_hierarchyRebind(object, hierarchy) {\n d3.rebind(object, hierarchy, \"sort\", \"children\", \"value\");\n object.nodes = object;\n object.links = d3_layout_hierarchyLinks;\n return object;\n }\n function d3_layout_hierarchyVisitBefore(node, callback) {\n var nodes = [ node ];\n while ((node = nodes.pop()) != null) {\n callback(node);\n if ((children = node.children) && (n = children.length)) {\n var n, children;\n while (--n >= 0) nodes.push(children[n]);\n }\n }\n }\n function d3_layout_hierarchyVisitAfter(node, callback) {\n var nodes = [ node ], nodes2 = [];\n while ((node = nodes.pop()) != null) {\n nodes2.push(node);\n if ((children = node.children) && (n = children.length)) {\n var i = -1, n, children;\n while (++i < n) nodes.push(children[i]);\n }\n }\n while ((node = nodes2.pop()) != null) {\n callback(node);\n }\n }\n function d3_layout_hierarchyChildren(d) {\n return d.children;\n }\n function d3_layout_hierarchyValue(d) {\n return d.value;\n }\n function d3_layout_hierarchySort(a, b) {\n return b.value - a.value;\n }\n function d3_layout_hierarchyLinks(nodes) {\n return d3.merge(nodes.map(function(parent) {\n return (parent.children || []).map(function(child) {\n return {\n source: parent,\n target: child\n };\n });\n }));\n }\n d3.layout.partition = function() {\n var hierarchy = d3.layout.hierarchy(), size = [ 1, 1 ];\n function position(node, x, dx, dy) {\n var children = node.children;\n node.x = x;\n node.y = node.depth * dy;\n node.dx = dx;\n node.dy = dy;\n if (children && (n = children.length)) {\n var i = -1, n, c, d;\n dx = node.value ? dx / node.value : 0;\n while (++i < n) {\n position(c = children[i], x, d = c.value * dx, dy);\n x += d;\n }\n }\n }\n function depth(node) {\n var children = node.children, d = 0;\n if (children && (n = children.length)) {\n var i = -1, n;\n while (++i < n) d = Math.max(d, depth(children[i]));\n }\n return 1 + d;\n }\n function partition(d, i) {\n var nodes = hierarchy.call(this, d, i);\n position(nodes[0], 0, size[0], size[1] / depth(nodes[0]));\n return nodes;\n }\n partition.size = function(x) {\n if (!arguments.length) return size;\n size = x;\n return partition;\n };\n return d3_layout_hierarchyRebind(partition, hierarchy);\n };\n d3.layout.pie = function() {\n var value = Number, sort = d3_layout_pieSortByValue, startAngle = 0, endAngle = τ, padAngle = 0;\n function pie(data) {\n var n = data.length, values = data.map(function(d, i) {\n return +value.call(pie, d, i);\n }), a = +(typeof startAngle === \"function\" ? startAngle.apply(this, arguments) : startAngle), da = (typeof endAngle === \"function\" ? endAngle.apply(this, arguments) : endAngle) - a, p = Math.min(Math.abs(da) / n, +(typeof padAngle === \"function\" ? padAngle.apply(this, arguments) : padAngle)), pa = p * (da < 0 ? -1 : 1), k = (da - n * pa) / d3.sum(values), index = d3.range(n), arcs = [], v;\n if (sort != null) index.sort(sort === d3_layout_pieSortByValue ? function(i, j) {\n return values[j] - values[i];\n } : function(i, j) {\n return sort(data[i], data[j]);\n });\n index.forEach(function(i) {\n arcs[i] = {\n data: data[i],\n value: v = values[i],\n startAngle: a,\n endAngle: a += v * k + pa,\n padAngle: p\n };\n });\n return arcs;\n }\n pie.value = function(_) {\n if (!arguments.length) return value;\n value = _;\n return pie;\n };\n pie.sort = function(_) {\n if (!arguments.length) return sort;\n sort = _;\n return pie;\n };\n pie.startAngle = function(_) {\n if (!arguments.length) return startAngle;\n startAngle = _;\n return pie;\n };\n pie.endAngle = function(_) {\n if (!arguments.length) return endAngle;\n endAngle = _;\n return pie;\n };\n pie.padAngle = function(_) {\n if (!arguments.length) return padAngle;\n padAngle = _;\n return pie;\n };\n return pie;\n };\n var d3_layout_pieSortByValue = {};\n d3.layout.stack = function() {\n var values = d3_identity, order = d3_layout_stackOrderDefault, offset = d3_layout_stackOffsetZero, out = d3_layout_stackOut, x = d3_layout_stackX, y = d3_layout_stackY;\n function stack(data, index) {\n if (!(n = data.length)) return data;\n var series = data.map(function(d, i) {\n return values.call(stack, d, i);\n });\n var points = series.map(function(d) {\n return d.map(function(v, i) {\n return [ x.call(stack, v, i), y.call(stack, v, i) ];\n });\n });\n var orders = order.call(stack, points, index);\n series = d3.permute(series, orders);\n points = d3.permute(points, orders);\n var offsets = offset.call(stack, points, index);\n var m = series[0].length, n, i, j, o;\n for (j = 0; j < m; ++j) {\n out.call(stack, series[0][j], o = offsets[j], points[0][j][1]);\n for (i = 1; i < n; ++i) {\n out.call(stack, series[i][j], o += points[i - 1][j][1], points[i][j][1]);\n }\n }\n return data;\n }\n stack.values = function(x) {\n if (!arguments.length) return values;\n values = x;\n return stack;\n };\n stack.order = function(x) {\n if (!arguments.length) return order;\n order = typeof x === \"function\" ? x : d3_layout_stackOrders.get(x) || d3_layout_stackOrderDefault;\n return stack;\n };\n stack.offset = function(x) {\n if (!arguments.length) return offset;\n offset = typeof x === \"function\" ? x : d3_layout_stackOffsets.get(x) || d3_layout_stackOffsetZero;\n return stack;\n };\n stack.x = function(z) {\n if (!arguments.length) return x;\n x = z;\n return stack;\n };\n stack.y = function(z) {\n if (!arguments.length) return y;\n y = z;\n return stack;\n };\n stack.out = function(z) {\n if (!arguments.length) return out;\n out = z;\n return stack;\n };\n return stack;\n };\n function d3_layout_stackX(d) {\n return d.x;\n }\n function d3_layout_stackY(d) {\n return d.y;\n }\n function d3_layout_stackOut(d, y0, y) {\n d.y0 = y0;\n d.y = y;\n }\n var d3_layout_stackOrders = d3.map({\n \"inside-out\": function(data) {\n var n = data.length, i, j, max = data.map(d3_layout_stackMaxIndex), sums = data.map(d3_layout_stackReduceSum), index = d3.range(n).sort(function(a, b) {\n return max[a] - max[b];\n }), top = 0, bottom = 0, tops = [], bottoms = [];\n for (i = 0; i < n; ++i) {\n j = index[i];\n if (top < bottom) {\n top += sums[j];\n tops.push(j);\n } else {\n bottom += sums[j];\n bottoms.push(j);\n }\n }\n return bottoms.reverse().concat(tops);\n },\n reverse: function(data) {\n return d3.range(data.length).reverse();\n },\n \"default\": d3_layout_stackOrderDefault\n });\n var d3_layout_stackOffsets = d3.map({\n silhouette: function(data) {\n var n = data.length, m = data[0].length, sums = [], max = 0, i, j, o, y0 = [];\n for (j = 0; j < m; ++j) {\n for (i = 0, o = 0; i < n; i++) o += data[i][j][1];\n if (o > max) max = o;\n sums.push(o);\n }\n for (j = 0; j < m; ++j) {\n y0[j] = (max - sums[j]) / 2;\n }\n return y0;\n },\n wiggle: function(data) {\n var n = data.length, x = data[0], m = x.length, i, j, k, s1, s2, s3, dx, o, o0, y0 = [];\n y0[0] = o = o0 = 0;\n for (j = 1; j < m; ++j) {\n for (i = 0, s1 = 0; i < n; ++i) s1 += data[i][j][1];\n for (i = 0, s2 = 0, dx = x[j][0] - x[j - 1][0]; i < n; ++i) {\n for (k = 0, s3 = (data[i][j][1] - data[i][j - 1][1]) / (2 * dx); k < i; ++k) {\n s3 += (data[k][j][1] - data[k][j - 1][1]) / dx;\n }\n s2 += s3 * data[i][j][1];\n }\n y0[j] = o -= s1 ? s2 / s1 * dx : 0;\n if (o < o0) o0 = o;\n }\n for (j = 0; j < m; ++j) y0[j] -= o0;\n return y0;\n },\n expand: function(data) {\n var n = data.length, m = data[0].length, k = 1 / n, i, j, o, y0 = [];\n for (j = 0; j < m; ++j) {\n for (i = 0, o = 0; i < n; i++) o += data[i][j][1];\n if (o) for (i = 0; i < n; i++) data[i][j][1] /= o; else for (i = 0; i < n; i++) data[i][j][1] = k;\n }\n for (j = 0; j < m; ++j) y0[j] = 0;\n return y0;\n },\n zero: d3_layout_stackOffsetZero\n });\n function d3_layout_stackOrderDefault(data) {\n return d3.range(data.length);\n }\n function d3_layout_stackOffsetZero(data) {\n var j = -1, m = data[0].length, y0 = [];\n while (++j < m) y0[j] = 0;\n return y0;\n }\n function d3_layout_stackMaxIndex(array) {\n var i = 1, j = 0, v = array[0][1], k, n = array.length;\n for (;i < n; ++i) {\n if ((k = array[i][1]) > v) {\n j = i;\n v = k;\n }\n }\n return j;\n }\n function d3_layout_stackReduceSum(d) {\n return d.reduce(d3_layout_stackSum, 0);\n }\n function d3_layout_stackSum(p, d) {\n return p + d[1];\n }\n d3.layout.histogram = function() {\n var frequency = true, valuer = Number, ranger = d3_layout_histogramRange, binner = d3_layout_histogramBinSturges;\n function histogram(data, i) {\n var bins = [], values = data.map(valuer, this), range = ranger.call(this, values, i), thresholds = binner.call(this, range, values, i), bin, i = -1, n = values.length, m = thresholds.length - 1, k = frequency ? 1 : 1 / n, x;\n while (++i < m) {\n bin = bins[i] = [];\n bin.dx = thresholds[i + 1] - (bin.x = thresholds[i]);\n bin.y = 0;\n }\n if (m > 0) {\n i = -1;\n while (++i < n) {\n x = values[i];\n if (x >= range[0] && x <= range[1]) {\n bin = bins[d3.bisect(thresholds, x, 1, m) - 1];\n bin.y += k;\n bin.push(data[i]);\n }\n }\n }\n return bins;\n }\n histogram.value = function(x) {\n if (!arguments.length) return valuer;\n valuer = x;\n return histogram;\n };\n histogram.range = function(x) {\n if (!arguments.length) return ranger;\n ranger = d3_functor(x);\n return histogram;\n };\n histogram.bins = function(x) {\n if (!arguments.length) return binner;\n binner = typeof x === \"number\" ? function(range) {\n return d3_layout_histogramBinFixed(range, x);\n } : d3_functor(x);\n return histogram;\n };\n histogram.frequency = function(x) {\n if (!arguments.length) return frequency;\n frequency = !!x;\n return histogram;\n };\n return histogram;\n };\n function d3_layout_histogramBinSturges(range, values) {\n return d3_layout_histogramBinFixed(range, Math.ceil(Math.log(values.length) / Math.LN2 + 1));\n }\n function d3_layout_histogramBinFixed(range, n) {\n var x = -1, b = +range[0], m = (range[1] - b) / n, f = [];\n while (++x <= n) f[x] = m * x + b;\n return f;\n }\n function d3_layout_histogramRange(values) {\n return [ d3.min(values), d3.max(values) ];\n }\n d3.layout.pack = function() {\n var hierarchy = d3.layout.hierarchy().sort(d3_layout_packSort), padding = 0, size = [ 1, 1 ], radius;\n function pack(d, i) {\n var nodes = hierarchy.call(this, d, i), root = nodes[0], w = size[0], h = size[1], r = radius == null ? Math.sqrt : typeof radius === \"function\" ? radius : function() {\n return radius;\n };\n root.x = root.y = 0;\n d3_layout_hierarchyVisitAfter(root, function(d) {\n d.r = +r(d.value);\n });\n d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings);\n if (padding) {\n var dr = padding * (radius ? 1 : Math.max(2 * root.r / w, 2 * root.r / h)) / 2;\n d3_layout_hierarchyVisitAfter(root, function(d) {\n d.r += dr;\n });\n d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings);\n d3_layout_hierarchyVisitAfter(root, function(d) {\n d.r -= dr;\n });\n }\n d3_layout_packTransform(root, w / 2, h / 2, radius ? 1 : 1 / Math.max(2 * root.r / w, 2 * root.r / h));\n return nodes;\n }\n pack.size = function(_) {\n if (!arguments.length) return size;\n size = _;\n return pack;\n };\n pack.radius = function(_) {\n if (!arguments.length) return radius;\n radius = _ == null || typeof _ === \"function\" ? _ : +_;\n return pack;\n };\n pack.padding = function(_) {\n if (!arguments.length) return padding;\n padding = +_;\n return pack;\n };\n return d3_layout_hierarchyRebind(pack, hierarchy);\n };\n function d3_layout_packSort(a, b) {\n return a.value - b.value;\n }\n function d3_layout_packInsert(a, b) {\n var c = a._pack_next;\n a._pack_next = b;\n b._pack_prev = a;\n b._pack_next = c;\n c._pack_prev = b;\n }\n function d3_layout_packSplice(a, b) {\n a._pack_next = b;\n b._pack_prev = a;\n }\n function d3_layout_packIntersects(a, b) {\n var dx = b.x - a.x, dy = b.y - a.y, dr = a.r + b.r;\n return .999 * dr * dr > dx * dx + dy * dy;\n }\n function d3_layout_packSiblings(node) {\n if (!(nodes = node.children) || !(n = nodes.length)) return;\n var nodes, xMin = Infinity, xMax = -Infinity, yMin = Infinity, yMax = -Infinity, a, b, c, i, j, k, n;\n function bound(node) {\n xMin = Math.min(node.x - node.r, xMin);\n xMax = Math.max(node.x + node.r, xMax);\n yMin = Math.min(node.y - node.r, yMin);\n yMax = Math.max(node.y + node.r, yMax);\n }\n nodes.forEach(d3_layout_packLink);\n a = nodes[0];\n a.x = -a.r;\n a.y = 0;\n bound(a);\n if (n > 1) {\n b = nodes[1];\n b.x = b.r;\n b.y = 0;\n bound(b);\n if (n > 2) {\n c = nodes[2];\n d3_layout_packPlace(a, b, c);\n bound(c);\n d3_layout_packInsert(a, c);\n a._pack_prev = c;\n d3_layout_packInsert(c, b);\n b = a._pack_next;\n for (i = 3; i < n; i++) {\n d3_layout_packPlace(a, b, c = nodes[i]);\n var isect = 0, s1 = 1, s2 = 1;\n for (j = b._pack_next; j !== b; j = j._pack_next, s1++) {\n if (d3_layout_packIntersects(j, c)) {\n isect = 1;\n break;\n }\n }\n if (isect == 1) {\n for (k = a._pack_prev; k !== j._pack_prev; k = k._pack_prev, s2++) {\n if (d3_layout_packIntersects(k, c)) {\n break;\n }\n }\n }\n if (isect) {\n if (s1 < s2 || s1 == s2 && b.r < a.r) d3_layout_packSplice(a, b = j); else d3_layout_packSplice(a = k, b);\n i--;\n } else {\n d3_layout_packInsert(a, c);\n b = c;\n bound(c);\n }\n }\n }\n }\n var cx = (xMin + xMax) / 2, cy = (yMin + yMax) / 2, cr = 0;\n for (i = 0; i < n; i++) {\n c = nodes[i];\n c.x -= cx;\n c.y -= cy;\n cr = Math.max(cr, c.r + Math.sqrt(c.x * c.x + c.y * c.y));\n }\n node.r = cr;\n nodes.forEach(d3_layout_packUnlink);\n }\n function d3_layout_packLink(node) {\n node._pack_next = node._pack_prev = node;\n }\n function d3_layout_packUnlink(node) {\n delete node._pack_next;\n delete node._pack_prev;\n }\n function d3_layout_packTransform(node, x, y, k) {\n var children = node.children;\n node.x = x += k * node.x;\n node.y = y += k * node.y;\n node.r *= k;\n if (children) {\n var i = -1, n = children.length;\n while (++i < n) d3_layout_packTransform(children[i], x, y, k);\n }\n }\n function d3_layout_packPlace(a, b, c) {\n var db = a.r + c.r, dx = b.x - a.x, dy = b.y - a.y;\n if (db && (dx || dy)) {\n var da = b.r + c.r, dc = dx * dx + dy * dy;\n da *= da;\n db *= db;\n var x = .5 + (db - da) / (2 * dc), y = Math.sqrt(Math.max(0, 2 * da * (db + dc) - (db -= dc) * db - da * da)) / (2 * dc);\n c.x = a.x + x * dx + y * dy;\n c.y = a.y + x * dy - y * dx;\n } else {\n c.x = a.x + db;\n c.y = a.y;\n }\n }\n d3.layout.tree = function() {\n var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = null;\n function tree(d, i) {\n var nodes = hierarchy.call(this, d, i), root0 = nodes[0], root1 = wrapTree(root0);\n d3_layout_hierarchyVisitAfter(root1, firstWalk), root1.parent.m = -root1.z;\n d3_layout_hierarchyVisitBefore(root1, secondWalk);\n if (nodeSize) d3_layout_hierarchyVisitBefore(root0, sizeNode); else {\n var left = root0, right = root0, bottom = root0;\n d3_layout_hierarchyVisitBefore(root0, function(node) {\n if (node.x < left.x) left = node;\n if (node.x > right.x) right = node;\n if (node.depth > bottom.depth) bottom = node;\n });\n var tx = separation(left, right) / 2 - left.x, kx = size[0] / (right.x + separation(right, left) / 2 + tx), ky = size[1] / (bottom.depth || 1);\n d3_layout_hierarchyVisitBefore(root0, function(node) {\n node.x = (node.x + tx) * kx;\n node.y = node.depth * ky;\n });\n }\n return nodes;\n }\n function wrapTree(root0) {\n var root1 = {\n A: null,\n children: [ root0 ]\n }, queue = [ root1 ], node1;\n while ((node1 = queue.pop()) != null) {\n for (var children = node1.children, child, i = 0, n = children.length; i < n; ++i) {\n queue.push((children[i] = child = {\n _: children[i],\n parent: node1,\n children: (child = children[i].children) && child.slice() || [],\n A: null,\n a: null,\n z: 0,\n m: 0,\n c: 0,\n s: 0,\n t: null,\n i: i\n }).a = child);\n }\n }\n return root1.children[0];\n }\n function firstWalk(v) {\n var children = v.children, siblings = v.parent.children, w = v.i ? siblings[v.i - 1] : null;\n if (children.length) {\n d3_layout_treeShift(v);\n var midpoint = (children[0].z + children[children.length - 1].z) / 2;\n if (w) {\n v.z = w.z + separation(v._, w._);\n v.m = v.z - midpoint;\n } else {\n v.z = midpoint;\n }\n } else if (w) {\n v.z = w.z + separation(v._, w._);\n }\n v.parent.A = apportion(v, w, v.parent.A || siblings[0]);\n }\n function secondWalk(v) {\n v._.x = v.z + v.parent.m;\n v.m += v.parent.m;\n }\n function apportion(v, w, ancestor) {\n if (w) {\n var vip = v, vop = v, vim = w, vom = vip.parent.children[0], sip = vip.m, sop = vop.m, sim = vim.m, som = vom.m, shift;\n while (vim = d3_layout_treeRight(vim), vip = d3_layout_treeLeft(vip), vim && vip) {\n vom = d3_layout_treeLeft(vom);\n vop = d3_layout_treeRight(vop);\n vop.a = v;\n shift = vim.z + sim - vip.z - sip + separation(vim._, vip._);\n if (shift > 0) {\n d3_layout_treeMove(d3_layout_treeAncestor(vim, v, ancestor), v, shift);\n sip += shift;\n sop += shift;\n }\n sim += vim.m;\n sip += vip.m;\n som += vom.m;\n sop += vop.m;\n }\n if (vim && !d3_layout_treeRight(vop)) {\n vop.t = vim;\n vop.m += sim - sop;\n }\n if (vip && !d3_layout_treeLeft(vom)) {\n vom.t = vip;\n vom.m += sip - som;\n ancestor = v;\n }\n }\n return ancestor;\n }\n function sizeNode(node) {\n node.x *= size[0];\n node.y = node.depth * size[1];\n }\n tree.separation = function(x) {\n if (!arguments.length) return separation;\n separation = x;\n return tree;\n };\n tree.size = function(x) {\n if (!arguments.length) return nodeSize ? null : size;\n nodeSize = (size = x) == null ? sizeNode : null;\n return tree;\n };\n tree.nodeSize = function(x) {\n if (!arguments.length) return nodeSize ? size : null;\n nodeSize = (size = x) == null ? null : sizeNode;\n return tree;\n };\n return d3_layout_hierarchyRebind(tree, hierarchy);\n };\n function d3_layout_treeSeparation(a, b) {\n return a.parent == b.parent ? 1 : 2;\n }\n function d3_layout_treeLeft(v) {\n var children = v.children;\n return children.length ? children[0] : v.t;\n }\n function d3_layout_treeRight(v) {\n var children = v.children, n;\n return (n = children.length) ? children[n - 1] : v.t;\n }\n function d3_layout_treeMove(wm, wp, shift) {\n var change = shift / (wp.i - wm.i);\n wp.c -= change;\n wp.s += shift;\n wm.c += change;\n wp.z += shift;\n wp.m += shift;\n }\n function d3_layout_treeShift(v) {\n var shift = 0, change = 0, children = v.children, i = children.length, w;\n while (--i >= 0) {\n w = children[i];\n w.z += shift;\n w.m += shift;\n shift += w.s + (change += w.c);\n }\n }\n function d3_layout_treeAncestor(vim, v, ancestor) {\n return vim.a.parent === v.parent ? vim.a : ancestor;\n }\n d3.layout.cluster = function() {\n var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = false;\n function cluster(d, i) {\n var nodes = hierarchy.call(this, d, i), root = nodes[0], previousNode, x = 0;\n d3_layout_hierarchyVisitAfter(root, function(node) {\n var children = node.children;\n if (children && children.length) {\n node.x = d3_layout_clusterX(children);\n node.y = d3_layout_clusterY(children);\n } else {\n node.x = previousNode ? x += separation(node, previousNode) : 0;\n node.y = 0;\n previousNode = node;\n }\n });\n var left = d3_layout_clusterLeft(root), right = d3_layout_clusterRight(root), x0 = left.x - separation(left, right) / 2, x1 = right.x + separation(right, left) / 2;\n d3_layout_hierarchyVisitAfter(root, nodeSize ? function(node) {\n node.x = (node.x - root.x) * size[0];\n node.y = (root.y - node.y) * size[1];\n } : function(node) {\n node.x = (node.x - x0) / (x1 - x0) * size[0];\n node.y = (1 - (root.y ? node.y / root.y : 1)) * size[1];\n });\n return nodes;\n }\n cluster.separation = function(x) {\n if (!arguments.length) return separation;\n separation = x;\n return cluster;\n };\n cluster.size = function(x) {\n if (!arguments.length) return nodeSize ? null : size;\n nodeSize = (size = x) == null;\n return cluster;\n };\n cluster.nodeSize = function(x) {\n if (!arguments.length) return nodeSize ? size : null;\n nodeSize = (size = x) != null;\n return cluster;\n };\n return d3_layout_hierarchyRebind(cluster, hierarchy);\n };\n function d3_layout_clusterY(children) {\n return 1 + d3.max(children, function(child) {\n return child.y;\n });\n }\n function d3_layout_clusterX(children) {\n return children.reduce(function(x, child) {\n return x + child.x;\n }, 0) / children.length;\n }\n function d3_layout_clusterLeft(node) {\n var children = node.children;\n return children && children.length ? d3_layout_clusterLeft(children[0]) : node;\n }\n function d3_layout_clusterRight(node) {\n var children = node.children, n;\n return children && (n = children.length) ? d3_layout_clusterRight(children[n - 1]) : node;\n }\n d3.layout.treemap = function() {\n var hierarchy = d3.layout.hierarchy(), round = Math.round, size = [ 1, 1 ], padding = null, pad = d3_layout_treemapPadNull, sticky = false, stickies, mode = \"squarify\", ratio = .5 * (1 + Math.sqrt(5));\n function scale(children, k) {\n var i = -1, n = children.length, child, area;\n while (++i < n) {\n area = (child = children[i]).value * (k < 0 ? 0 : k);\n child.area = isNaN(area) || area <= 0 ? 0 : area;\n }\n }\n function squarify(node) {\n var children = node.children;\n if (children && children.length) {\n var rect = pad(node), row = [], remaining = children.slice(), child, best = Infinity, score, u = mode === \"slice\" ? rect.dx : mode === \"dice\" ? rect.dy : mode === \"slice-dice\" ? node.depth & 1 ? rect.dy : rect.dx : Math.min(rect.dx, rect.dy), n;\n scale(remaining, rect.dx * rect.dy / node.value);\n row.area = 0;\n while ((n = remaining.length) > 0) {\n row.push(child = remaining[n - 1]);\n row.area += child.area;\n if (mode !== \"squarify\" || (score = worst(row, u)) <= best) {\n remaining.pop();\n best = score;\n } else {\n row.area -= row.pop().area;\n position(row, u, rect, false);\n u = Math.min(rect.dx, rect.dy);\n row.length = row.area = 0;\n best = Infinity;\n }\n }\n if (row.length) {\n position(row, u, rect, true);\n row.length = row.area = 0;\n }\n children.forEach(squarify);\n }\n }\n function stickify(node) {\n var children = node.children;\n if (children && children.length) {\n var rect = pad(node), remaining = children.slice(), child, row = [];\n scale(remaining, rect.dx * rect.dy / node.value);\n row.area = 0;\n while (child = remaining.pop()) {\n row.push(child);\n row.area += child.area;\n if (child.z != null) {\n position(row, child.z ? rect.dx : rect.dy, rect, !remaining.length);\n row.length = row.area = 0;\n }\n }\n children.forEach(stickify);\n }\n }\n function worst(row, u) {\n var s = row.area, r, rmax = 0, rmin = Infinity, i = -1, n = row.length;\n while (++i < n) {\n if (!(r = row[i].area)) continue;\n if (r < rmin) rmin = r;\n if (r > rmax) rmax = r;\n }\n s *= s;\n u *= u;\n return s ? Math.max(u * rmax * ratio / s, s / (u * rmin * ratio)) : Infinity;\n }\n function position(row, u, rect, flush) {\n var i = -1, n = row.length, x = rect.x, y = rect.y, v = u ? round(row.area / u) : 0, o;\n if (u == rect.dx) {\n if (flush || v > rect.dy) v = rect.dy;\n while (++i < n) {\n o = row[i];\n o.x = x;\n o.y = y;\n o.dy = v;\n x += o.dx = Math.min(rect.x + rect.dx - x, v ? round(o.area / v) : 0);\n }\n o.z = true;\n o.dx += rect.x + rect.dx - x;\n rect.y += v;\n rect.dy -= v;\n } else {\n if (flush || v > rect.dx) v = rect.dx;\n while (++i < n) {\n o = row[i];\n o.x = x;\n o.y = y;\n o.dx = v;\n y += o.dy = Math.min(rect.y + rect.dy - y, v ? round(o.area / v) : 0);\n }\n o.z = false;\n o.dy += rect.y + rect.dy - y;\n rect.x += v;\n rect.dx -= v;\n }\n }\n function treemap(d) {\n var nodes = stickies || hierarchy(d), root = nodes[0];\n root.x = 0;\n root.y = 0;\n root.dx = size[0];\n root.dy = size[1];\n if (stickies) hierarchy.revalue(root);\n scale([ root ], root.dx * root.dy / root.value);\n (stickies ? stickify : squarify)(root);\n if (sticky) stickies = nodes;\n return nodes;\n }\n treemap.size = function(x) {\n if (!arguments.length) return size;\n size = x;\n return treemap;\n };\n treemap.padding = function(x) {\n if (!arguments.length) return padding;\n function padFunction(node) {\n var p = x.call(treemap, node, node.depth);\n return p == null ? d3_layout_treemapPadNull(node) : d3_layout_treemapPad(node, typeof p === \"number\" ? [ p, p, p, p ] : p);\n }\n function padConstant(node) {\n return d3_layout_treemapPad(node, x);\n }\n var type;\n pad = (padding = x) == null ? d3_layout_treemapPadNull : (type = typeof x) === \"function\" ? padFunction : type === \"number\" ? (x = [ x, x, x, x ], \n padConstant) : padConstant;\n return treemap;\n };\n treemap.round = function(x) {\n if (!arguments.length) return round != Number;\n round = x ? Math.round : Number;\n return treemap;\n };\n treemap.sticky = function(x) {\n if (!arguments.length) return sticky;\n sticky = x;\n stickies = null;\n return treemap;\n };\n treemap.ratio = function(x) {\n if (!arguments.length) return ratio;\n ratio = x;\n return treemap;\n };\n treemap.mode = function(x) {\n if (!arguments.length) return mode;\n mode = x + \"\";\n return treemap;\n };\n return d3_layout_hierarchyRebind(treemap, hierarchy);\n };\n function d3_layout_treemapPadNull(node) {\n return {\n x: node.x,\n y: node.y,\n dx: node.dx,\n dy: node.dy\n };\n }\n function d3_layout_treemapPad(node, padding) {\n var x = node.x + padding[3], y = node.y + padding[0], dx = node.dx - padding[1] - padding[3], dy = node.dy - padding[0] - padding[2];\n if (dx < 0) {\n x += dx / 2;\n dx = 0;\n }\n if (dy < 0) {\n y += dy / 2;\n dy = 0;\n }\n return {\n x: x,\n y: y,\n dx: dx,\n dy: dy\n };\n }\n d3.random = {\n normal: function(µ, σ) {\n var n = arguments.length;\n if (n < 2) σ = 1;\n if (n < 1) µ = 0;\n return function() {\n var x, y, r;\n do {\n x = Math.random() * 2 - 1;\n y = Math.random() * 2 - 1;\n r = x * x + y * y;\n } while (!r || r > 1);\n return µ + σ * x * Math.sqrt(-2 * Math.log(r) / r);\n };\n },\n logNormal: function() {\n var random = d3.random.normal.apply(d3, arguments);\n return function() {\n return Math.exp(random());\n };\n },\n bates: function(m) {\n var random = d3.random.irwinHall(m);\n return function() {\n return random() / m;\n };\n },\n irwinHall: function(m) {\n return function() {\n for (var s = 0, j = 0; j < m; j++) s += Math.random();\n return s;\n };\n }\n };\n d3.scale = {};\n function d3_scaleExtent(domain) {\n var start = domain[0], stop = domain[domain.length - 1];\n return start < stop ? [ start, stop ] : [ stop, start ];\n }\n function d3_scaleRange(scale) {\n return scale.rangeExtent ? scale.rangeExtent() : d3_scaleExtent(scale.range());\n }\n function d3_scale_bilinear(domain, range, uninterpolate, interpolate) {\n var u = uninterpolate(domain[0], domain[1]), i = interpolate(range[0], range[1]);\n return function(x) {\n return i(u(x));\n };\n }\n function d3_scale_nice(domain, nice) {\n var i0 = 0, i1 = domain.length - 1, x0 = domain[i0], x1 = domain[i1], dx;\n if (x1 < x0) {\n dx = i0, i0 = i1, i1 = dx;\n dx = x0, x0 = x1, x1 = dx;\n }\n domain[i0] = nice.floor(x0);\n domain[i1] = nice.ceil(x1);\n return domain;\n }\n function d3_scale_niceStep(step) {\n return step ? {\n floor: function(x) {\n return Math.floor(x / step) * step;\n },\n ceil: function(x) {\n return Math.ceil(x / step) * step;\n }\n } : d3_scale_niceIdentity;\n }\n var d3_scale_niceIdentity = {\n floor: d3_identity,\n ceil: d3_identity\n };\n function d3_scale_polylinear(domain, range, uninterpolate, interpolate) {\n var u = [], i = [], j = 0, k = Math.min(domain.length, range.length) - 1;\n if (domain[k] < domain[0]) {\n domain = domain.slice().reverse();\n range = range.slice().reverse();\n }\n while (++j <= k) {\n u.push(uninterpolate(domain[j - 1], domain[j]));\n i.push(interpolate(range[j - 1], range[j]));\n }\n return function(x) {\n var j = d3.bisect(domain, x, 1, k) - 1;\n return i[j](u[j](x));\n };\n }\n d3.scale.linear = function() {\n return d3_scale_linear([ 0, 1 ], [ 0, 1 ], d3_interpolate, false);\n };\n function d3_scale_linear(domain, range, interpolate, clamp) {\n var output, input;\n function rescale() {\n var linear = Math.min(domain.length, range.length) > 2 ? d3_scale_polylinear : d3_scale_bilinear, uninterpolate = clamp ? d3_uninterpolateClamp : d3_uninterpolateNumber;\n output = linear(domain, range, uninterpolate, interpolate);\n input = linear(range, domain, uninterpolate, d3_interpolate);\n return scale;\n }\n function scale(x) {\n return output(x);\n }\n scale.invert = function(y) {\n return input(y);\n };\n scale.domain = function(x) {\n if (!arguments.length) return domain;\n domain = x.map(Number);\n return rescale();\n };\n scale.range = function(x) {\n if (!arguments.length) return range;\n range = x;\n return rescale();\n };\n scale.rangeRound = function(x) {\n return scale.range(x).interpolate(d3_interpolateRound);\n };\n scale.clamp = function(x) {\n if (!arguments.length) return clamp;\n clamp = x;\n return rescale();\n };\n scale.interpolate = function(x) {\n if (!arguments.length) return interpolate;\n interpolate = x;\n return rescale();\n };\n scale.ticks = function(m) {\n return d3_scale_linearTicks(domain, m);\n };\n scale.tickFormat = function(m, format) {\n return d3_scale_linearTickFormat(domain, m, format);\n };\n scale.nice = function(m) {\n d3_scale_linearNice(domain, m);\n return rescale();\n };\n scale.copy = function() {\n return d3_scale_linear(domain, range, interpolate, clamp);\n };\n return rescale();\n }\n function d3_scale_linearRebind(scale, linear) {\n return d3.rebind(scale, linear, \"range\", \"rangeRound\", \"interpolate\", \"clamp\");\n }\n function d3_scale_linearNice(domain, m) {\n return d3_scale_nice(domain, d3_scale_niceStep(d3_scale_linearTickRange(domain, m)[2]));\n }\n function d3_scale_linearTickRange(domain, m) {\n if (m == null) m = 10;\n var extent = d3_scaleExtent(domain), span = extent[1] - extent[0], step = Math.pow(10, Math.floor(Math.log(span / m) / Math.LN10)), err = m / span * step;\n if (err <= .15) step *= 10; else if (err <= .35) step *= 5; else if (err <= .75) step *= 2;\n extent[0] = Math.ceil(extent[0] / step) * step;\n extent[1] = Math.floor(extent[1] / step) * step + step * .5;\n extent[2] = step;\n return extent;\n }\n function d3_scale_linearTicks(domain, m) {\n return d3.range.apply(d3, d3_scale_linearTickRange(domain, m));\n }\n function d3_scale_linearTickFormat(domain, m, format) {\n var range = d3_scale_linearTickRange(domain, m);\n if (format) {\n var match = d3_format_re.exec(format);\n match.shift();\n if (match[8] === \"s\") {\n var prefix = d3.formatPrefix(Math.max(abs(range[0]), abs(range[1])));\n if (!match[7]) match[7] = \".\" + d3_scale_linearPrecision(prefix.scale(range[2]));\n match[8] = \"f\";\n format = d3.format(match.join(\"\"));\n return function(d) {\n return format(prefix.scale(d)) + prefix.symbol;\n };\n }\n if (!match[7]) match[7] = \".\" + d3_scale_linearFormatPrecision(match[8], range);\n format = match.join(\"\");\n } else {\n format = \",.\" + d3_scale_linearPrecision(range[2]) + \"f\";\n }\n return d3.format(format);\n }\n var d3_scale_linearFormatSignificant = {\n s: 1,\n g: 1,\n p: 1,\n r: 1,\n e: 1\n };\n function d3_scale_linearPrecision(value) {\n return -Math.floor(Math.log(value) / Math.LN10 + .01);\n }\n function d3_scale_linearFormatPrecision(type, range) {\n var p = d3_scale_linearPrecision(range[2]);\n return type in d3_scale_linearFormatSignificant ? Math.abs(p - d3_scale_linearPrecision(Math.max(abs(range[0]), abs(range[1])))) + +(type !== \"e\") : p - (type === \"%\") * 2;\n }\n d3.scale.log = function() {\n return d3_scale_log(d3.scale.linear().domain([ 0, 1 ]), 10, true, [ 1, 10 ]);\n };\n function d3_scale_log(linear, base, positive, domain) {\n function log(x) {\n return (positive ? Math.log(x < 0 ? 0 : x) : -Math.log(x > 0 ? 0 : -x)) / Math.log(base);\n }\n function pow(x) {\n return positive ? Math.pow(base, x) : -Math.pow(base, -x);\n }\n function scale(x) {\n return linear(log(x));\n }\n scale.invert = function(x) {\n return pow(linear.invert(x));\n };\n scale.domain = function(x) {\n if (!arguments.length) return domain;\n positive = x[0] >= 0;\n linear.domain((domain = x.map(Number)).map(log));\n return scale;\n };\n scale.base = function(_) {\n if (!arguments.length) return base;\n base = +_;\n linear.domain(domain.map(log));\n return scale;\n };\n scale.nice = function() {\n var niced = d3_scale_nice(domain.map(log), positive ? Math : d3_scale_logNiceNegative);\n linear.domain(niced);\n domain = niced.map(pow);\n return scale;\n };\n scale.ticks = function() {\n var extent = d3_scaleExtent(domain), ticks = [], u = extent[0], v = extent[1], i = Math.floor(log(u)), j = Math.ceil(log(v)), n = base % 1 ? 2 : base;\n if (isFinite(j - i)) {\n if (positive) {\n for (;i < j; i++) for (var k = 1; k < n; k++) ticks.push(pow(i) * k);\n ticks.push(pow(i));\n } else {\n ticks.push(pow(i));\n for (;i++ < j; ) for (var k = n - 1; k > 0; k--) ticks.push(pow(i) * k);\n }\n for (i = 0; ticks[i] < u; i++) {}\n for (j = ticks.length; ticks[j - 1] > v; j--) {}\n ticks = ticks.slice(i, j);\n }\n return ticks;\n };\n scale.tickFormat = function(n, format) {\n if (!arguments.length) return d3_scale_logFormat;\n if (arguments.length < 2) format = d3_scale_logFormat; else if (typeof format !== \"function\") format = d3.format(format);\n var k = Math.max(.1, n / scale.ticks().length), f = positive ? (e = 1e-12, Math.ceil) : (e = -1e-12, \n Math.floor), e;\n return function(d) {\n return d / pow(f(log(d) + e)) <= k ? format(d) : \"\";\n };\n };\n scale.copy = function() {\n return d3_scale_log(linear.copy(), base, positive, domain);\n };\n return d3_scale_linearRebind(scale, linear);\n }\n var d3_scale_logFormat = d3.format(\".0e\"), d3_scale_logNiceNegative = {\n floor: function(x) {\n return -Math.ceil(-x);\n },\n ceil: function(x) {\n return -Math.floor(-x);\n }\n };\n d3.scale.pow = function() {\n return d3_scale_pow(d3.scale.linear(), 1, [ 0, 1 ]);\n };\n function d3_scale_pow(linear, exponent, domain) {\n var powp = d3_scale_powPow(exponent), powb = d3_scale_powPow(1 / exponent);\n function scale(x) {\n return linear(powp(x));\n }\n scale.invert = function(x) {\n return powb(linear.invert(x));\n };\n scale.domain = function(x) {\n if (!arguments.length) return domain;\n linear.domain((domain = x.map(Number)).map(powp));\n return scale;\n };\n scale.ticks = function(m) {\n return d3_scale_linearTicks(domain, m);\n };\n scale.tickFormat = function(m, format) {\n return d3_scale_linearTickFormat(domain, m, format);\n };\n scale.nice = function(m) {\n return scale.domain(d3_scale_linearNice(domain, m));\n };\n scale.exponent = function(x) {\n if (!arguments.length) return exponent;\n powp = d3_scale_powPow(exponent = x);\n powb = d3_scale_powPow(1 / exponent);\n linear.domain(domain.map(powp));\n return scale;\n };\n scale.copy = function() {\n return d3_scale_pow(linear.copy(), exponent, domain);\n };\n return d3_scale_linearRebind(scale, linear);\n }\n function d3_scale_powPow(e) {\n return function(x) {\n return x < 0 ? -Math.pow(-x, e) : Math.pow(x, e);\n };\n }\n d3.scale.sqrt = function() {\n return d3.scale.pow().exponent(.5);\n };\n d3.scale.ordinal = function() {\n return d3_scale_ordinal([], {\n t: \"range\",\n a: [ [] ]\n });\n };\n function d3_scale_ordinal(domain, ranger) {\n var index, range, rangeBand;\n function scale(x) {\n return range[((index.get(x) || (ranger.t === \"range\" ? index.set(x, domain.push(x)) : NaN)) - 1) % range.length];\n }\n function steps(start, step) {\n return d3.range(domain.length).map(function(i) {\n return start + step * i;\n });\n }\n scale.domain = function(x) {\n if (!arguments.length) return domain;\n domain = [];\n index = new d3_Map();\n var i = -1, n = x.length, xi;\n while (++i < n) if (!index.has(xi = x[i])) index.set(xi, domain.push(xi));\n return scale[ranger.t].apply(scale, ranger.a);\n };\n scale.range = function(x) {\n if (!arguments.length) return range;\n range = x;\n rangeBand = 0;\n ranger = {\n t: \"range\",\n a: arguments\n };\n return scale;\n };\n scale.rangePoints = function(x, padding) {\n if (arguments.length < 2) padding = 0;\n var start = x[0], stop = x[1], step = domain.length < 2 ? (start = (start + stop) / 2, \n 0) : (stop - start) / (domain.length - 1 + padding);\n range = steps(start + step * padding / 2, step);\n rangeBand = 0;\n ranger = {\n t: \"rangePoints\",\n a: arguments\n };\n return scale;\n };\n scale.rangeRoundPoints = function(x, padding) {\n if (arguments.length < 2) padding = 0;\n var start = x[0], stop = x[1], step = domain.length < 2 ? (start = stop = Math.round((start + stop) / 2), \n 0) : (stop - start) / (domain.length - 1 + padding) | 0;\n range = steps(start + Math.round(step * padding / 2 + (stop - start - (domain.length - 1 + padding) * step) / 2), step);\n rangeBand = 0;\n ranger = {\n t: \"rangeRoundPoints\",\n a: arguments\n };\n return scale;\n };\n scale.rangeBands = function(x, padding, outerPadding) {\n if (arguments.length < 2) padding = 0;\n if (arguments.length < 3) outerPadding = padding;\n var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = (stop - start) / (domain.length - padding + 2 * outerPadding);\n range = steps(start + step * outerPadding, step);\n if (reverse) range.reverse();\n rangeBand = step * (1 - padding);\n ranger = {\n t: \"rangeBands\",\n a: arguments\n };\n return scale;\n };\n scale.rangeRoundBands = function(x, padding, outerPadding) {\n if (arguments.length < 2) padding = 0;\n if (arguments.length < 3) outerPadding = padding;\n var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = Math.floor((stop - start) / (domain.length - padding + 2 * outerPadding));\n range = steps(start + Math.round((stop - start - (domain.length - padding) * step) / 2), step);\n if (reverse) range.reverse();\n rangeBand = Math.round(step * (1 - padding));\n ranger = {\n t: \"rangeRoundBands\",\n a: arguments\n };\n return scale;\n };\n scale.rangeBand = function() {\n return rangeBand;\n };\n scale.rangeExtent = function() {\n return d3_scaleExtent(ranger.a[0]);\n };\n scale.copy = function() {\n return d3_scale_ordinal(domain, ranger);\n };\n return scale.domain(domain);\n }\n d3.scale.category10 = function() {\n return d3.scale.ordinal().range(d3_category10);\n };\n d3.scale.category20 = function() {\n return d3.scale.ordinal().range(d3_category20);\n };\n d3.scale.category20b = function() {\n return d3.scale.ordinal().range(d3_category20b);\n };\n d3.scale.category20c = function() {\n return d3.scale.ordinal().range(d3_category20c);\n };\n var d3_category10 = [ 2062260, 16744206, 2924588, 14034728, 9725885, 9197131, 14907330, 8355711, 12369186, 1556175 ].map(d3_rgbString);\n var d3_category20 = [ 2062260, 11454440, 16744206, 16759672, 2924588, 10018698, 14034728, 16750742, 9725885, 12955861, 9197131, 12885140, 14907330, 16234194, 8355711, 13092807, 12369186, 14408589, 1556175, 10410725 ].map(d3_rgbString);\n var d3_category20b = [ 3750777, 5395619, 7040719, 10264286, 6519097, 9216594, 11915115, 13556636, 9202993, 12426809, 15186514, 15190932, 8666169, 11356490, 14049643, 15177372, 8077683, 10834324, 13528509, 14589654 ].map(d3_rgbString);\n var d3_category20c = [ 3244733, 7057110, 10406625, 13032431, 15095053, 16616764, 16625259, 16634018, 3253076, 7652470, 10607003, 13101504, 7695281, 10394312, 12369372, 14342891, 6513507, 9868950, 12434877, 14277081 ].map(d3_rgbString);\n d3.scale.quantile = function() {\n return d3_scale_quantile([], []);\n };\n function d3_scale_quantile(domain, range) {\n var thresholds;\n function rescale() {\n var k = 0, q = range.length;\n thresholds = [];\n while (++k < q) thresholds[k - 1] = d3.quantile(domain, k / q);\n return scale;\n }\n function scale(x) {\n if (!isNaN(x = +x)) return range[d3.bisect(thresholds, x)];\n }\n scale.domain = function(x) {\n if (!arguments.length) return domain;\n domain = x.map(d3_number).filter(d3_numeric).sort(d3_ascending);\n return rescale();\n };\n scale.range = function(x) {\n if (!arguments.length) return range;\n range = x;\n return rescale();\n };\n scale.quantiles = function() {\n return thresholds;\n };\n scale.invertExtent = function(y) {\n y = range.indexOf(y);\n return y < 0 ? [ NaN, NaN ] : [ y > 0 ? thresholds[y - 1] : domain[0], y < thresholds.length ? thresholds[y] : domain[domain.length - 1] ];\n };\n scale.copy = function() {\n return d3_scale_quantile(domain, range);\n };\n return rescale();\n }\n d3.scale.quantize = function() {\n return d3_scale_quantize(0, 1, [ 0, 1 ]);\n };\n function d3_scale_quantize(x0, x1, range) {\n var kx, i;\n function scale(x) {\n return range[Math.max(0, Math.min(i, Math.floor(kx * (x - x0))))];\n }\n function rescale() {\n kx = range.length / (x1 - x0);\n i = range.length - 1;\n return scale;\n }\n scale.domain = function(x) {\n if (!arguments.length) return [ x0, x1 ];\n x0 = +x[0];\n x1 = +x[x.length - 1];\n return rescale();\n };\n scale.range = function(x) {\n if (!arguments.length) return range;\n range = x;\n return rescale();\n };\n scale.invertExtent = function(y) {\n y = range.indexOf(y);\n y = y < 0 ? NaN : y / kx + x0;\n return [ y, y + 1 / kx ];\n };\n scale.copy = function() {\n return d3_scale_quantize(x0, x1, range);\n };\n return rescale();\n }\n d3.scale.threshold = function() {\n return d3_scale_threshold([ .5 ], [ 0, 1 ]);\n };\n function d3_scale_threshold(domain, range) {\n function scale(x) {\n if (x <= x) return range[d3.bisect(domain, x)];\n }\n scale.domain = function(_) {\n if (!arguments.length) return domain;\n domain = _;\n return scale;\n };\n scale.range = function(_) {\n if (!arguments.length) return range;\n range = _;\n return scale;\n };\n scale.invertExtent = function(y) {\n y = range.indexOf(y);\n return [ domain[y - 1], domain[y] ];\n };\n scale.copy = function() {\n return d3_scale_threshold(domain, range);\n };\n return scale;\n }\n d3.scale.identity = function() {\n return d3_scale_identity([ 0, 1 ]);\n };\n function d3_scale_identity(domain) {\n function identity(x) {\n return +x;\n }\n identity.invert = identity;\n identity.domain = identity.range = function(x) {\n if (!arguments.length) return domain;\n domain = x.map(identity);\n return identity;\n };\n identity.ticks = function(m) {\n return d3_scale_linearTicks(domain, m);\n };\n identity.tickFormat = function(m, format) {\n return d3_scale_linearTickFormat(domain, m, format);\n };\n identity.copy = function() {\n return d3_scale_identity(domain);\n };\n return identity;\n }\n d3.svg = {};\n function d3_zero() {\n return 0;\n }\n d3.svg.arc = function() {\n var innerRadius = d3_svg_arcInnerRadius, outerRadius = d3_svg_arcOuterRadius, cornerRadius = d3_zero, padRadius = d3_svg_arcAuto, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle, padAngle = d3_svg_arcPadAngle;\n function arc() {\n var r0 = Math.max(0, +innerRadius.apply(this, arguments)), r1 = Math.max(0, +outerRadius.apply(this, arguments)), a0 = startAngle.apply(this, arguments) - halfπ, a1 = endAngle.apply(this, arguments) - halfπ, da = Math.abs(a1 - a0), cw = a0 > a1 ? 0 : 1;\n if (r1 < r0) rc = r1, r1 = r0, r0 = rc;\n if (da >= τε) return circleSegment(r1, cw) + (r0 ? circleSegment(r0, 1 - cw) : \"\") + \"Z\";\n var rc, cr, rp, ap, p0 = 0, p1 = 0, x0, y0, x1, y1, x2, y2, x3, y3, path = [];\n if (ap = (+padAngle.apply(this, arguments) || 0) / 2) {\n rp = padRadius === d3_svg_arcAuto ? Math.sqrt(r0 * r0 + r1 * r1) : +padRadius.apply(this, arguments);\n if (!cw) p1 *= -1;\n if (r1) p1 = d3_asin(rp / r1 * Math.sin(ap));\n if (r0) p0 = d3_asin(rp / r0 * Math.sin(ap));\n }\n if (r1) {\n x0 = r1 * Math.cos(a0 + p1);\n y0 = r1 * Math.sin(a0 + p1);\n x1 = r1 * Math.cos(a1 - p1);\n y1 = r1 * Math.sin(a1 - p1);\n var l1 = Math.abs(a1 - a0 - 2 * p1) <= π ? 0 : 1;\n if (p1 && d3_svg_arcSweep(x0, y0, x1, y1) === cw ^ l1) {\n var h1 = (a0 + a1) / 2;\n x0 = r1 * Math.cos(h1);\n y0 = r1 * Math.sin(h1);\n x1 = y1 = null;\n }\n } else {\n x0 = y0 = 0;\n }\n if (r0) {\n x2 = r0 * Math.cos(a1 - p0);\n y2 = r0 * Math.sin(a1 - p0);\n x3 = r0 * Math.cos(a0 + p0);\n y3 = r0 * Math.sin(a0 + p0);\n var l0 = Math.abs(a0 - a1 + 2 * p0) <= π ? 0 : 1;\n if (p0 && d3_svg_arcSweep(x2, y2, x3, y3) === 1 - cw ^ l0) {\n var h0 = (a0 + a1) / 2;\n x2 = r0 * Math.cos(h0);\n y2 = r0 * Math.sin(h0);\n x3 = y3 = null;\n }\n } else {\n x2 = y2 = 0;\n }\n if ((rc = Math.min(Math.abs(r1 - r0) / 2, +cornerRadius.apply(this, arguments))) > .001) {\n cr = r0 < r1 ^ cw ? 0 : 1;\n var oc = x3 == null ? [ x2, y2 ] : x1 == null ? [ x0, y0 ] : d3_geom_polygonIntersect([ x0, y0 ], [ x3, y3 ], [ x1, y1 ], [ x2, y2 ]), ax = x0 - oc[0], ay = y0 - oc[1], bx = x1 - oc[0], by = y1 - oc[1], kc = 1 / Math.sin(Math.acos((ax * bx + ay * by) / (Math.sqrt(ax * ax + ay * ay) * Math.sqrt(bx * bx + by * by))) / 2), lc = Math.sqrt(oc[0] * oc[0] + oc[1] * oc[1]);\n if (x1 != null) {\n var rc1 = Math.min(rc, (r1 - lc) / (kc + 1)), t30 = d3_svg_arcCornerTangents(x3 == null ? [ x2, y2 ] : [ x3, y3 ], [ x0, y0 ], r1, rc1, cw), t12 = d3_svg_arcCornerTangents([ x1, y1 ], [ x2, y2 ], r1, rc1, cw);\n if (rc === rc1) {\n path.push(\"M\", t30[0], \"A\", rc1, \",\", rc1, \" 0 0,\", cr, \" \", t30[1], \"A\", r1, \",\", r1, \" 0 \", 1 - cw ^ d3_svg_arcSweep(t30[1][0], t30[1][1], t12[1][0], t12[1][1]), \",\", cw, \" \", t12[1], \"A\", rc1, \",\", rc1, \" 0 0,\", cr, \" \", t12[0]);\n } else {\n path.push(\"M\", t30[0], \"A\", rc1, \",\", rc1, \" 0 1,\", cr, \" \", t12[0]);\n }\n } else {\n path.push(\"M\", x0, \",\", y0);\n }\n if (x3 != null) {\n var rc0 = Math.min(rc, (r0 - lc) / (kc - 1)), t03 = d3_svg_arcCornerTangents([ x0, y0 ], [ x3, y3 ], r0, -rc0, cw), t21 = d3_svg_arcCornerTangents([ x2, y2 ], x1 == null ? [ x0, y0 ] : [ x1, y1 ], r0, -rc0, cw);\n if (rc === rc0) {\n path.push(\"L\", t21[0], \"A\", rc0, \",\", rc0, \" 0 0,\", cr, \" \", t21[1], \"A\", r0, \",\", r0, \" 0 \", cw ^ d3_svg_arcSweep(t21[1][0], t21[1][1], t03[1][0], t03[1][1]), \",\", 1 - cw, \" \", t03[1], \"A\", rc0, \",\", rc0, \" 0 0,\", cr, \" \", t03[0]);\n } else {\n path.push(\"L\", t21[0], \"A\", rc0, \",\", rc0, \" 0 0,\", cr, \" \", t03[0]);\n }\n } else {\n path.push(\"L\", x2, \",\", y2);\n }\n } else {\n path.push(\"M\", x0, \",\", y0);\n if (x1 != null) path.push(\"A\", r1, \",\", r1, \" 0 \", l1, \",\", cw, \" \", x1, \",\", y1);\n path.push(\"L\", x2, \",\", y2);\n if (x3 != null) path.push(\"A\", r0, \",\", r0, \" 0 \", l0, \",\", 1 - cw, \" \", x3, \",\", y3);\n }\n path.push(\"Z\");\n return path.join(\"\");\n }\n function circleSegment(r1, cw) {\n return \"M0,\" + r1 + \"A\" + r1 + \",\" + r1 + \" 0 1,\" + cw + \" 0,\" + -r1 + \"A\" + r1 + \",\" + r1 + \" 0 1,\" + cw + \" 0,\" + r1;\n }\n arc.innerRadius = function(v) {\n if (!arguments.length) return innerRadius;\n innerRadius = d3_functor(v);\n return arc;\n };\n arc.outerRadius = function(v) {\n if (!arguments.length) return outerRadius;\n outerRadius = d3_functor(v);\n return arc;\n };\n arc.cornerRadius = function(v) {\n if (!arguments.length) return cornerRadius;\n cornerRadius = d3_functor(v);\n return arc;\n };\n arc.padRadius = function(v) {\n if (!arguments.length) return padRadius;\n padRadius = v == d3_svg_arcAuto ? d3_svg_arcAuto : d3_functor(v);\n return arc;\n };\n arc.startAngle = function(v) {\n if (!arguments.length) return startAngle;\n startAngle = d3_functor(v);\n return arc;\n };\n arc.endAngle = function(v) {\n if (!arguments.length) return endAngle;\n endAngle = d3_functor(v);\n return arc;\n };\n arc.padAngle = function(v) {\n if (!arguments.length) return padAngle;\n padAngle = d3_functor(v);\n return arc;\n };\n arc.centroid = function() {\n var r = (+innerRadius.apply(this, arguments) + +outerRadius.apply(this, arguments)) / 2, a = (+startAngle.apply(this, arguments) + +endAngle.apply(this, arguments)) / 2 - halfπ;\n return [ Math.cos(a) * r, Math.sin(a) * r ];\n };\n return arc;\n };\n var d3_svg_arcAuto = \"auto\";\n function d3_svg_arcInnerRadius(d) {\n return d.innerRadius;\n }\n function d3_svg_arcOuterRadius(d) {\n return d.outerRadius;\n }\n function d3_svg_arcStartAngle(d) {\n return d.startAngle;\n }\n function d3_svg_arcEndAngle(d) {\n return d.endAngle;\n }\n function d3_svg_arcPadAngle(d) {\n return d && d.padAngle;\n }\n function d3_svg_arcSweep(x0, y0, x1, y1) {\n return (x0 - x1) * y0 - (y0 - y1) * x0 > 0 ? 0 : 1;\n }\n function d3_svg_arcCornerTangents(p0, p1, r1, rc, cw) {\n var x01 = p0[0] - p1[0], y01 = p0[1] - p1[1], lo = (cw ? rc : -rc) / Math.sqrt(x01 * x01 + y01 * y01), ox = lo * y01, oy = -lo * x01, x1 = p0[0] + ox, y1 = p0[1] + oy, x2 = p1[0] + ox, y2 = p1[1] + oy, x3 = (x1 + x2) / 2, y3 = (y1 + y2) / 2, dx = x2 - x1, dy = y2 - y1, d2 = dx * dx + dy * dy, r = r1 - rc, D = x1 * y2 - x2 * y1, d = (dy < 0 ? -1 : 1) * Math.sqrt(r * r * d2 - D * D), cx0 = (D * dy - dx * d) / d2, cy0 = (-D * dx - dy * d) / d2, cx1 = (D * dy + dx * d) / d2, cy1 = (-D * dx + dy * d) / d2, dx0 = cx0 - x3, dy0 = cy0 - y3, dx1 = cx1 - x3, dy1 = cy1 - y3;\n if (dx0 * dx0 + dy0 * dy0 > dx1 * dx1 + dy1 * dy1) cx0 = cx1, cy0 = cy1;\n return [ [ cx0 - ox, cy0 - oy ], [ cx0 * r1 / r, cy0 * r1 / r ] ];\n }\n function d3_svg_line(projection) {\n var x = d3_geom_pointX, y = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, tension = .7;\n function line(data) {\n var segments = [], points = [], i = -1, n = data.length, d, fx = d3_functor(x), fy = d3_functor(y);\n function segment() {\n segments.push(\"M\", interpolate(projection(points), tension));\n }\n while (++i < n) {\n if (defined.call(this, d = data[i], i)) {\n points.push([ +fx.call(this, d, i), +fy.call(this, d, i) ]);\n } else if (points.length) {\n segment();\n points = [];\n }\n }\n if (points.length) segment();\n return segments.length ? segments.join(\"\") : null;\n }\n line.x = function(_) {\n if (!arguments.length) return x;\n x = _;\n return line;\n };\n line.y = function(_) {\n if (!arguments.length) return y;\n y = _;\n return line;\n };\n line.defined = function(_) {\n if (!arguments.length) return defined;\n defined = _;\n return line;\n };\n line.interpolate = function(_) {\n if (!arguments.length) return interpolateKey;\n if (typeof _ === \"function\") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key;\n return line;\n };\n line.tension = function(_) {\n if (!arguments.length) return tension;\n tension = _;\n return line;\n };\n return line;\n }\n d3.svg.line = function() {\n return d3_svg_line(d3_identity);\n };\n var d3_svg_lineInterpolators = d3.map({\n linear: d3_svg_lineLinear,\n \"linear-closed\": d3_svg_lineLinearClosed,\n step: d3_svg_lineStep,\n \"step-before\": d3_svg_lineStepBefore,\n \"step-after\": d3_svg_lineStepAfter,\n basis: d3_svg_lineBasis,\n \"basis-open\": d3_svg_lineBasisOpen,\n \"basis-closed\": d3_svg_lineBasisClosed,\n bundle: d3_svg_lineBundle,\n cardinal: d3_svg_lineCardinal,\n \"cardinal-open\": d3_svg_lineCardinalOpen,\n \"cardinal-closed\": d3_svg_lineCardinalClosed,\n monotone: d3_svg_lineMonotone\n });\n d3_svg_lineInterpolators.forEach(function(key, value) {\n value.key = key;\n value.closed = /-closed$/.test(key);\n });\n function d3_svg_lineLinear(points) {\n return points.join(\"L\");\n }\n function d3_svg_lineLinearClosed(points) {\n return d3_svg_lineLinear(points) + \"Z\";\n }\n function d3_svg_lineStep(points) {\n var i = 0, n = points.length, p = points[0], path = [ p[0], \",\", p[1] ];\n while (++i < n) path.push(\"H\", (p[0] + (p = points[i])[0]) / 2, \"V\", p[1]);\n if (n > 1) path.push(\"H\", p[0]);\n return path.join(\"\");\n }\n function d3_svg_lineStepBefore(points) {\n var i = 0, n = points.length, p = points[0], path = [ p[0], \",\", p[1] ];\n while (++i < n) path.push(\"V\", (p = points[i])[1], \"H\", p[0]);\n return path.join(\"\");\n }\n function d3_svg_lineStepAfter(points) {\n var i = 0, n = points.length, p = points[0], path = [ p[0], \",\", p[1] ];\n while (++i < n) path.push(\"H\", (p = points[i])[0], \"V\", p[1]);\n return path.join(\"\");\n }\n function d3_svg_lineCardinalOpen(points, tension) {\n return points.length < 4 ? d3_svg_lineLinear(points) : points[1] + d3_svg_lineHermite(points.slice(1, -1), d3_svg_lineCardinalTangents(points, tension));\n }\n function d3_svg_lineCardinalClosed(points, tension) {\n return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite((points.push(points[0]), \n points), d3_svg_lineCardinalTangents([ points[points.length - 2] ].concat(points, [ points[1] ]), tension));\n }\n function d3_svg_lineCardinal(points, tension) {\n return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineCardinalTangents(points, tension));\n }\n function d3_svg_lineHermite(points, tangents) {\n if (tangents.length < 1 || points.length != tangents.length && points.length != tangents.length + 2) {\n return d3_svg_lineLinear(points);\n }\n var quad = points.length != tangents.length, path = \"\", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1;\n if (quad) {\n path += \"Q\" + (p[0] - t0[0] * 2 / 3) + \",\" + (p[1] - t0[1] * 2 / 3) + \",\" + p[0] + \",\" + p[1];\n p0 = points[1];\n pi = 2;\n }\n if (tangents.length > 1) {\n t = tangents[1];\n p = points[pi];\n pi++;\n path += \"C\" + (p0[0] + t0[0]) + \",\" + (p0[1] + t0[1]) + \",\" + (p[0] - t[0]) + \",\" + (p[1] - t[1]) + \",\" + p[0] + \",\" + p[1];\n for (var i = 2; i < tangents.length; i++, pi++) {\n p = points[pi];\n t = tangents[i];\n path += \"S\" + (p[0] - t[0]) + \",\" + (p[1] - t[1]) + \",\" + p[0] + \",\" + p[1];\n }\n }\n if (quad) {\n var lp = points[pi];\n path += \"Q\" + (p[0] + t[0] * 2 / 3) + \",\" + (p[1] + t[1] * 2 / 3) + \",\" + lp[0] + \",\" + lp[1];\n }\n return path;\n }\n function d3_svg_lineCardinalTangents(points, tension) {\n var tangents = [], a = (1 - tension) / 2, p0, p1 = points[0], p2 = points[1], i = 1, n = points.length;\n while (++i < n) {\n p0 = p1;\n p1 = p2;\n p2 = points[i];\n tangents.push([ a * (p2[0] - p0[0]), a * (p2[1] - p0[1]) ]);\n }\n return tangents;\n }\n function d3_svg_lineBasis(points) {\n if (points.length < 3) return d3_svg_lineLinear(points);\n var i = 1, n = points.length, pi = points[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, \",\", y0, \"L\", d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ];\n points.push(points[n - 1]);\n while (++i <= n) {\n pi = points[i];\n px.shift();\n px.push(pi[0]);\n py.shift();\n py.push(pi[1]);\n d3_svg_lineBasisBezier(path, px, py);\n }\n points.pop();\n path.push(\"L\", pi);\n return path.join(\"\");\n }\n function d3_svg_lineBasisOpen(points) {\n if (points.length < 4) return d3_svg_lineLinear(points);\n var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ];\n while (++i < 3) {\n pi = points[i];\n px.push(pi[0]);\n py.push(pi[1]);\n }\n path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + \",\" + d3_svg_lineDot4(d3_svg_lineBasisBezier3, py));\n --i;\n while (++i < n) {\n pi = points[i];\n px.shift();\n px.push(pi[0]);\n py.shift();\n py.push(pi[1]);\n d3_svg_lineBasisBezier(path, px, py);\n }\n return path.join(\"\");\n }\n function d3_svg_lineBasisClosed(points) {\n var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = [];\n while (++i < 4) {\n pi = points[i % n];\n px.push(pi[0]);\n py.push(pi[1]);\n }\n path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ];\n --i;\n while (++i < m) {\n pi = points[i % n];\n px.shift();\n px.push(pi[0]);\n py.shift();\n py.push(pi[1]);\n d3_svg_lineBasisBezier(path, px, py);\n }\n return path.join(\"\");\n }\n function d3_svg_lineBundle(points, tension) {\n var n = points.length - 1;\n if (n) {\n var x0 = points[0][0], y0 = points[0][1], dx = points[n][0] - x0, dy = points[n][1] - y0, i = -1, p, t;\n while (++i <= n) {\n p = points[i];\n t = i / n;\n p[0] = tension * p[0] + (1 - tension) * (x0 + t * dx);\n p[1] = tension * p[1] + (1 - tension) * (y0 + t * dy);\n }\n }\n return d3_svg_lineBasis(points);\n }\n function d3_svg_lineDot4(a, b) {\n return a[0] * b[0] + a[1] * b[1] + a[2] * b[2] + a[3] * b[3];\n }\n var d3_svg_lineBasisBezier1 = [ 0, 2 / 3, 1 / 3, 0 ], d3_svg_lineBasisBezier2 = [ 0, 1 / 3, 2 / 3, 0 ], d3_svg_lineBasisBezier3 = [ 0, 1 / 6, 2 / 3, 1 / 6 ];\n function d3_svg_lineBasisBezier(path, x, y) {\n path.push(\"C\", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier1, y), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier2, x), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier2, y), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier3, x), \",\", d3_svg_lineDot4(d3_svg_lineBasisBezier3, y));\n }\n function d3_svg_lineSlope(p0, p1) {\n return (p1[1] - p0[1]) / (p1[0] - p0[0]);\n }\n function d3_svg_lineFiniteDifferences(points) {\n var i = 0, j = points.length - 1, m = [], p0 = points[0], p1 = points[1], d = m[0] = d3_svg_lineSlope(p0, p1);\n while (++i < j) {\n m[i] = (d + (d = d3_svg_lineSlope(p0 = p1, p1 = points[i + 1]))) / 2;\n }\n m[i] = d;\n return m;\n }\n function d3_svg_lineMonotoneTangents(points) {\n var tangents = [], d, a, b, s, m = d3_svg_lineFiniteDifferences(points), i = -1, j = points.length - 1;\n while (++i < j) {\n d = d3_svg_lineSlope(points[i], points[i + 1]);\n if (abs(d) < ε) {\n m[i] = m[i + 1] = 0;\n } else {\n a = m[i] / d;\n b = m[i + 1] / d;\n s = a * a + b * b;\n if (s > 9) {\n s = d * 3 / Math.sqrt(s);\n m[i] = s * a;\n m[i + 1] = s * b;\n }\n }\n }\n i = -1;\n while (++i <= j) {\n s = (points[Math.min(j, i + 1)][0] - points[Math.max(0, i - 1)][0]) / (6 * (1 + m[i] * m[i]));\n tangents.push([ s || 0, m[i] * s || 0 ]);\n }\n return tangents;\n }\n function d3_svg_lineMonotone(points) {\n return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineMonotoneTangents(points));\n }\n d3.svg.line.radial = function() {\n var line = d3_svg_line(d3_svg_lineRadial);\n line.radius = line.x, delete line.x;\n line.angle = line.y, delete line.y;\n return line;\n };\n function d3_svg_lineRadial(points) {\n var point, i = -1, n = points.length, r, a;\n while (++i < n) {\n point = points[i];\n r = point[0];\n a = point[1] - halfπ;\n point[0] = r * Math.cos(a);\n point[1] = r * Math.sin(a);\n }\n return points;\n }\n function d3_svg_area(projection) {\n var x0 = d3_geom_pointX, x1 = d3_geom_pointX, y0 = 0, y1 = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, interpolateReverse = interpolate, L = \"L\", tension = .7;\n function area(data) {\n var segments = [], points0 = [], points1 = [], i = -1, n = data.length, d, fx0 = d3_functor(x0), fy0 = d3_functor(y0), fx1 = x0 === x1 ? function() {\n return x;\n } : d3_functor(x1), fy1 = y0 === y1 ? function() {\n return y;\n } : d3_functor(y1), x, y;\n function segment() {\n segments.push(\"M\", interpolate(projection(points1), tension), L, interpolateReverse(projection(points0.reverse()), tension), \"Z\");\n }\n while (++i < n) {\n if (defined.call(this, d = data[i], i)) {\n points0.push([ x = +fx0.call(this, d, i), y = +fy0.call(this, d, i) ]);\n points1.push([ +fx1.call(this, d, i), +fy1.call(this, d, i) ]);\n } else if (points0.length) {\n segment();\n points0 = [];\n points1 = [];\n }\n }\n if (points0.length) segment();\n return segments.length ? segments.join(\"\") : null;\n }\n area.x = function(_) {\n if (!arguments.length) return x1;\n x0 = x1 = _;\n return area;\n };\n area.x0 = function(_) {\n if (!arguments.length) return x0;\n x0 = _;\n return area;\n };\n area.x1 = function(_) {\n if (!arguments.length) return x1;\n x1 = _;\n return area;\n };\n area.y = function(_) {\n if (!arguments.length) return y1;\n y0 = y1 = _;\n return area;\n };\n area.y0 = function(_) {\n if (!arguments.length) return y0;\n y0 = _;\n return area;\n };\n area.y1 = function(_) {\n if (!arguments.length) return y1;\n y1 = _;\n return area;\n };\n area.defined = function(_) {\n if (!arguments.length) return defined;\n defined = _;\n return area;\n };\n area.interpolate = function(_) {\n if (!arguments.length) return interpolateKey;\n if (typeof _ === \"function\") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key;\n interpolateReverse = interpolate.reverse || interpolate;\n L = interpolate.closed ? \"M\" : \"L\";\n return area;\n };\n area.tension = function(_) {\n if (!arguments.length) return tension;\n tension = _;\n return area;\n };\n return area;\n }\n d3_svg_lineStepBefore.reverse = d3_svg_lineStepAfter;\n d3_svg_lineStepAfter.reverse = d3_svg_lineStepBefore;\n d3.svg.area = function() {\n return d3_svg_area(d3_identity);\n };\n d3.svg.area.radial = function() {\n var area = d3_svg_area(d3_svg_lineRadial);\n area.radius = area.x, delete area.x;\n area.innerRadius = area.x0, delete area.x0;\n area.outerRadius = area.x1, delete area.x1;\n area.angle = area.y, delete area.y;\n area.startAngle = area.y0, delete area.y0;\n area.endAngle = area.y1, delete area.y1;\n return area;\n };\n d3.svg.chord = function() {\n var source = d3_source, target = d3_target, radius = d3_svg_chordRadius, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle;\n function chord(d, i) {\n var s = subgroup(this, source, d, i), t = subgroup(this, target, d, i);\n return \"M\" + s.p0 + arc(s.r, s.p1, s.a1 - s.a0) + (equals(s, t) ? curve(s.r, s.p1, s.r, s.p0) : curve(s.r, s.p1, t.r, t.p0) + arc(t.r, t.p1, t.a1 - t.a0) + curve(t.r, t.p1, s.r, s.p0)) + \"Z\";\n }\n function subgroup(self, f, d, i) {\n var subgroup = f.call(self, d, i), r = radius.call(self, subgroup, i), a0 = startAngle.call(self, subgroup, i) - halfπ, a1 = endAngle.call(self, subgroup, i) - halfπ;\n return {\n r: r,\n a0: a0,\n a1: a1,\n p0: [ r * Math.cos(a0), r * Math.sin(a0) ],\n p1: [ r * Math.cos(a1), r * Math.sin(a1) ]\n };\n }\n function equals(a, b) {\n return a.a0 == b.a0 && a.a1 == b.a1;\n }\n function arc(r, p, a) {\n return \"A\" + r + \",\" + r + \" 0 \" + +(a > π) + \",1 \" + p;\n }\n function curve(r0, p0, r1, p1) {\n return \"Q 0,0 \" + p1;\n }\n chord.radius = function(v) {\n if (!arguments.length) return radius;\n radius = d3_functor(v);\n return chord;\n };\n chord.source = function(v) {\n if (!arguments.length) return source;\n source = d3_functor(v);\n return chord;\n };\n chord.target = function(v) {\n if (!arguments.length) return target;\n target = d3_functor(v);\n return chord;\n };\n chord.startAngle = function(v) {\n if (!arguments.length) return startAngle;\n startAngle = d3_functor(v);\n return chord;\n };\n chord.endAngle = function(v) {\n if (!arguments.length) return endAngle;\n endAngle = d3_functor(v);\n return chord;\n };\n return chord;\n };\n function d3_svg_chordRadius(d) {\n return d.radius;\n }\n d3.svg.diagonal = function() {\n var source = d3_source, target = d3_target, projection = d3_svg_diagonalProjection;\n function diagonal(d, i) {\n var p0 = source.call(this, d, i), p3 = target.call(this, d, i), m = (p0.y + p3.y) / 2, p = [ p0, {\n x: p0.x,\n y: m\n }, {\n x: p3.x,\n y: m\n }, p3 ];\n p = p.map(projection);\n return \"M\" + p[0] + \"C\" + p[1] + \" \" + p[2] + \" \" + p[3];\n }\n diagonal.source = function(x) {\n if (!arguments.length) return source;\n source = d3_functor(x);\n return diagonal;\n };\n diagonal.target = function(x) {\n if (!arguments.length) return target;\n target = d3_functor(x);\n return diagonal;\n };\n diagonal.projection = function(x) {\n if (!arguments.length) return projection;\n projection = x;\n return diagonal;\n };\n return diagonal;\n };\n function d3_svg_diagonalProjection(d) {\n return [ d.x, d.y ];\n }\n d3.svg.diagonal.radial = function() {\n var diagonal = d3.svg.diagonal(), projection = d3_svg_diagonalProjection, projection_ = diagonal.projection;\n diagonal.projection = function(x) {\n return arguments.length ? projection_(d3_svg_diagonalRadialProjection(projection = x)) : projection;\n };\n return diagonal;\n };\n function d3_svg_diagonalRadialProjection(projection) {\n return function() {\n var d = projection.apply(this, arguments), r = d[0], a = d[1] - halfπ;\n return [ r * Math.cos(a), r * Math.sin(a) ];\n };\n }\n d3.svg.symbol = function() {\n var type = d3_svg_symbolType, size = d3_svg_symbolSize;\n function symbol(d, i) {\n return (d3_svg_symbols.get(type.call(this, d, i)) || d3_svg_symbolCircle)(size.call(this, d, i));\n }\n symbol.type = function(x) {\n if (!arguments.length) return type;\n type = d3_functor(x);\n return symbol;\n };\n symbol.size = function(x) {\n if (!arguments.length) return size;\n size = d3_functor(x);\n return symbol;\n };\n return symbol;\n };\n function d3_svg_symbolSize() {\n return 64;\n }\n function d3_svg_symbolType() {\n return \"circle\";\n }\n function d3_svg_symbolCircle(size) {\n var r = Math.sqrt(size / π);\n return \"M0,\" + r + \"A\" + r + \",\" + r + \" 0 1,1 0,\" + -r + \"A\" + r + \",\" + r + \" 0 1,1 0,\" + r + \"Z\";\n }\n var d3_svg_symbols = d3.map({\n circle: d3_svg_symbolCircle,\n cross: function(size) {\n var r = Math.sqrt(size / 5) / 2;\n return \"M\" + -3 * r + \",\" + -r + \"H\" + -r + \"V\" + -3 * r + \"H\" + r + \"V\" + -r + \"H\" + 3 * r + \"V\" + r + \"H\" + r + \"V\" + 3 * r + \"H\" + -r + \"V\" + r + \"H\" + -3 * r + \"Z\";\n },\n diamond: function(size) {\n var ry = Math.sqrt(size / (2 * d3_svg_symbolTan30)), rx = ry * d3_svg_symbolTan30;\n return \"M0,\" + -ry + \"L\" + rx + \",0\" + \" 0,\" + ry + \" \" + -rx + \",0\" + \"Z\";\n },\n square: function(size) {\n var r = Math.sqrt(size) / 2;\n return \"M\" + -r + \",\" + -r + \"L\" + r + \",\" + -r + \" \" + r + \",\" + r + \" \" + -r + \",\" + r + \"Z\";\n },\n \"triangle-down\": function(size) {\n var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2;\n return \"M0,\" + ry + \"L\" + rx + \",\" + -ry + \" \" + -rx + \",\" + -ry + \"Z\";\n },\n \"triangle-up\": function(size) {\n var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2;\n return \"M0,\" + -ry + \"L\" + rx + \",\" + ry + \" \" + -rx + \",\" + ry + \"Z\";\n }\n });\n d3.svg.symbolTypes = d3_svg_symbols.keys();\n var d3_svg_symbolSqrt3 = Math.sqrt(3), d3_svg_symbolTan30 = Math.tan(30 * d3_radians);\n d3_selectionPrototype.transition = function(name) {\n var id = d3_transitionInheritId || ++d3_transitionId, ns = d3_transitionNamespace(name), subgroups = [], subgroup, node, transition = d3_transitionInherit || {\n time: Date.now(),\n ease: d3_ease_cubicInOut,\n delay: 0,\n duration: 250\n };\n for (var j = -1, m = this.length; ++j < m; ) {\n subgroups.push(subgroup = []);\n for (var group = this[j], i = -1, n = group.length; ++i < n; ) {\n if (node = group[i]) d3_transitionNode(node, i, ns, id, transition);\n subgroup.push(node);\n }\n }\n return d3_transition(subgroups, ns, id);\n };\n d3_selectionPrototype.interrupt = function(name) {\n return this.each(name == null ? d3_selection_interrupt : d3_selection_interruptNS(d3_transitionNamespace(name)));\n };\n var d3_selection_interrupt = d3_selection_interruptNS(d3_transitionNamespace());\n function d3_selection_interruptNS(ns) {\n return function() {\n var lock, active;\n if ((lock = this[ns]) && (active = lock[lock.active])) {\n if (--lock.count) delete lock[lock.active]; else delete this[ns];\n lock.active += .5;\n active.event && active.event.interrupt.call(this, this.__data__, active.index);\n }\n };\n }\n function d3_transition(groups, ns, id) {\n d3_subclass(groups, d3_transitionPrototype);\n groups.namespace = ns;\n groups.id = id;\n return groups;\n }\n var d3_transitionPrototype = [], d3_transitionId = 0, d3_transitionInheritId, d3_transitionInherit;\n d3_transitionPrototype.call = d3_selectionPrototype.call;\n d3_transitionPrototype.empty = d3_selectionPrototype.empty;\n d3_transitionPrototype.node = d3_selectionPrototype.node;\n d3_transitionPrototype.size = d3_selectionPrototype.size;\n d3.transition = function(selection, name) {\n return selection && selection.transition ? d3_transitionInheritId ? selection.transition(name) : selection : d3.selection().transition(selection);\n };\n d3.transition.prototype = d3_transitionPrototype;\n d3_transitionPrototype.select = function(selector) {\n var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnode, node;\n selector = d3_selection_selector(selector);\n for (var j = -1, m = this.length; ++j < m; ) {\n subgroups.push(subgroup = []);\n for (var group = this[j], i = -1, n = group.length; ++i < n; ) {\n if ((node = group[i]) && (subnode = selector.call(node, node.__data__, i, j))) {\n if (\"__data__\" in node) subnode.__data__ = node.__data__;\n d3_transitionNode(subnode, i, ns, id, node[ns][id]);\n subgroup.push(subnode);\n } else {\n subgroup.push(null);\n }\n }\n }\n return d3_transition(subgroups, ns, id);\n };\n d3_transitionPrototype.selectAll = function(selector) {\n var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnodes, node, subnode, transition;\n selector = d3_selection_selectorAll(selector);\n for (var j = -1, m = this.length; ++j < m; ) {\n for (var group = this[j], i = -1, n = group.length; ++i < n; ) {\n if (node = group[i]) {\n transition = node[ns][id];\n subnodes = selector.call(node, node.__data__, i, j);\n subgroups.push(subgroup = []);\n for (var k = -1, o = subnodes.length; ++k < o; ) {\n if (subnode = subnodes[k]) d3_transitionNode(subnode, k, ns, id, transition);\n subgroup.push(subnode);\n }\n }\n }\n }\n return d3_transition(subgroups, ns, id);\n };\n d3_transitionPrototype.filter = function(filter) {\n var subgroups = [], subgroup, group, node;\n if (typeof filter !== \"function\") filter = d3_selection_filter(filter);\n for (var j = 0, m = this.length; j < m; j++) {\n subgroups.push(subgroup = []);\n for (var group = this[j], i = 0, n = group.length; i < n; i++) {\n if ((node = group[i]) && filter.call(node, node.__data__, i, j)) {\n subgroup.push(node);\n }\n }\n }\n return d3_transition(subgroups, this.namespace, this.id);\n };\n d3_transitionPrototype.tween = function(name, tween) {\n var id = this.id, ns = this.namespace;\n if (arguments.length < 2) return this.node()[ns][id].tween.get(name);\n return d3_selection_each(this, tween == null ? function(node) {\n node[ns][id].tween.remove(name);\n } : function(node) {\n node[ns][id].tween.set(name, tween);\n });\n };\n function d3_transition_tween(groups, name, value, tween) {\n var id = groups.id, ns = groups.namespace;\n return d3_selection_each(groups, typeof value === \"function\" ? function(node, i, j) {\n node[ns][id].tween.set(name, tween(value.call(node, node.__data__, i, j)));\n } : (value = tween(value), function(node) {\n node[ns][id].tween.set(name, value);\n }));\n }\n d3_transitionPrototype.attr = function(nameNS, value) {\n if (arguments.length < 2) {\n for (value in nameNS) this.attr(value, nameNS[value]);\n return this;\n }\n var interpolate = nameNS == \"transform\" ? d3_interpolateTransform : d3_interpolate, name = d3.ns.qualify(nameNS);\n function attrNull() {\n this.removeAttribute(name);\n }\n function attrNullNS() {\n this.removeAttributeNS(name.space, name.local);\n }\n function attrTween(b) {\n return b == null ? attrNull : (b += \"\", function() {\n var a = this.getAttribute(name), i;\n return a !== b && (i = interpolate(a, b), function(t) {\n this.setAttribute(name, i(t));\n });\n });\n }\n function attrTweenNS(b) {\n return b == null ? attrNullNS : (b += \"\", function() {\n var a = this.getAttributeNS(name.space, name.local), i;\n return a !== b && (i = interpolate(a, b), function(t) {\n this.setAttributeNS(name.space, name.local, i(t));\n });\n });\n }\n return d3_transition_tween(this, \"attr.\" + nameNS, value, name.local ? attrTweenNS : attrTween);\n };\n d3_transitionPrototype.attrTween = function(nameNS, tween) {\n var name = d3.ns.qualify(nameNS);\n function attrTween(d, i) {\n var f = tween.call(this, d, i, this.getAttribute(name));\n return f && function(t) {\n this.setAttribute(name, f(t));\n };\n }\n function attrTweenNS(d, i) {\n var f = tween.call(this, d, i, this.getAttributeNS(name.space, name.local));\n return f && function(t) {\n this.setAttributeNS(name.space, name.local, f(t));\n };\n }\n return this.tween(\"attr.\" + nameNS, name.local ? attrTweenNS : attrTween);\n };\n d3_transitionPrototype.style = function(name, value, priority) {\n var n = arguments.length;\n if (n < 3) {\n if (typeof name !== \"string\") {\n if (n < 2) value = \"\";\n for (priority in name) this.style(priority, name[priority], value);\n return this;\n }\n priority = \"\";\n }\n function styleNull() {\n this.style.removeProperty(name);\n }\n function styleString(b) {\n return b == null ? styleNull : (b += \"\", function() {\n var a = d3_window(this).getComputedStyle(this, null).getPropertyValue(name), i;\n return a !== b && (i = d3_interpolate(a, b), function(t) {\n this.style.setProperty(name, i(t), priority);\n });\n });\n }\n return d3_transition_tween(this, \"style.\" + name, value, styleString);\n };\n d3_transitionPrototype.styleTween = function(name, tween, priority) {\n if (arguments.length < 3) priority = \"\";\n function styleTween(d, i) {\n var f = tween.call(this, d, i, d3_window(this).getComputedStyle(this, null).getPropertyValue(name));\n return f && function(t) {\n this.style.setProperty(name, f(t), priority);\n };\n }\n return this.tween(\"style.\" + name, styleTween);\n };\n d3_transitionPrototype.text = function(value) {\n return d3_transition_tween(this, \"text\", value, d3_transition_text);\n };\n function d3_transition_text(b) {\n if (b == null) b = \"\";\n return function() {\n this.textContent = b;\n };\n }\n d3_transitionPrototype.remove = function() {\n var ns = this.namespace;\n return this.each(\"end.transition\", function() {\n var p;\n if (this[ns].count < 2 && (p = this.parentNode)) p.removeChild(this);\n });\n };\n d3_transitionPrototype.ease = function(value) {\n var id = this.id, ns = this.namespace;\n if (arguments.length < 1) return this.node()[ns][id].ease;\n if (typeof value !== \"function\") value = d3.ease.apply(d3, arguments);\n return d3_selection_each(this, function(node) {\n node[ns][id].ease = value;\n });\n };\n d3_transitionPrototype.delay = function(value) {\n var id = this.id, ns = this.namespace;\n if (arguments.length < 1) return this.node()[ns][id].delay;\n return d3_selection_each(this, typeof value === \"function\" ? function(node, i, j) {\n node[ns][id].delay = +value.call(node, node.__data__, i, j);\n } : (value = +value, function(node) {\n node[ns][id].delay = value;\n }));\n };\n d3_transitionPrototype.duration = function(value) {\n var id = this.id, ns = this.namespace;\n if (arguments.length < 1) return this.node()[ns][id].duration;\n return d3_selection_each(this, typeof value === \"function\" ? function(node, i, j) {\n node[ns][id].duration = Math.max(1, value.call(node, node.__data__, i, j));\n } : (value = Math.max(1, value), function(node) {\n node[ns][id].duration = value;\n }));\n };\n d3_transitionPrototype.each = function(type, listener) {\n var id = this.id, ns = this.namespace;\n if (arguments.length < 2) {\n var inherit = d3_transitionInherit, inheritId = d3_transitionInheritId;\n try {\n d3_transitionInheritId = id;\n d3_selection_each(this, function(node, i, j) {\n d3_transitionInherit = node[ns][id];\n type.call(node, node.__data__, i, j);\n });\n } finally {\n d3_transitionInherit = inherit;\n d3_transitionInheritId = inheritId;\n }\n } else {\n d3_selection_each(this, function(node) {\n var transition = node[ns][id];\n (transition.event || (transition.event = d3.dispatch(\"start\", \"end\", \"interrupt\"))).on(type, listener);\n });\n }\n return this;\n };\n d3_transitionPrototype.transition = function() {\n var id0 = this.id, id1 = ++d3_transitionId, ns = this.namespace, subgroups = [], subgroup, group, node, transition;\n for (var j = 0, m = this.length; j < m; j++) {\n subgroups.push(subgroup = []);\n for (var group = this[j], i = 0, n = group.length; i < n; i++) {\n if (node = group[i]) {\n transition = node[ns][id0];\n d3_transitionNode(node, i, ns, id1, {\n time: transition.time,\n ease: transition.ease,\n delay: transition.delay + transition.duration,\n duration: transition.duration\n });\n }\n subgroup.push(node);\n }\n }\n return d3_transition(subgroups, ns, id1);\n };\n function d3_transitionNamespace(name) {\n return name == null ? \"__transition__\" : \"__transition_\" + name + \"__\";\n }\n function d3_transitionNode(node, i, ns, id, inherit) {\n var lock = node[ns] || (node[ns] = {\n active: 0,\n count: 0\n }), transition = lock[id];\n if (!transition) {\n var time = inherit.time;\n transition = lock[id] = {\n tween: new d3_Map(),\n time: time,\n delay: inherit.delay,\n duration: inherit.duration,\n ease: inherit.ease,\n index: i\n };\n inherit = null;\n ++lock.count;\n d3.timer(function(elapsed) {\n var delay = transition.delay, duration, ease, timer = d3_timer_active, tweened = [];\n timer.t = delay + time;\n if (delay <= elapsed) return start(elapsed - delay);\n timer.c = start;\n function start(elapsed) {\n if (lock.active > id) return stop();\n var active = lock[lock.active];\n if (active) {\n --lock.count;\n delete lock[lock.active];\n active.event && active.event.interrupt.call(node, node.__data__, active.index);\n }\n lock.active = id;\n transition.event && transition.event.start.call(node, node.__data__, i);\n transition.tween.forEach(function(key, value) {\n if (value = value.call(node, node.__data__, i)) {\n tweened.push(value);\n }\n });\n ease = transition.ease;\n duration = transition.duration;\n d3.timer(function() {\n timer.c = tick(elapsed || 1) ? d3_true : tick;\n return 1;\n }, 0, time);\n }\n function tick(elapsed) {\n if (lock.active !== id) return 1;\n var t = elapsed / duration, e = ease(t), n = tweened.length;\n while (n > 0) {\n tweened[--n].call(node, e);\n }\n if (t >= 1) {\n transition.event && transition.event.end.call(node, node.__data__, i);\n return stop();\n }\n }\n function stop() {\n if (--lock.count) delete lock[id]; else delete node[ns];\n return 1;\n }\n }, 0, time);\n }\n }\n d3.svg.axis = function() {\n var scale = d3.scale.linear(), orient = d3_svg_axisDefaultOrient, innerTickSize = 6, outerTickSize = 6, tickPadding = 3, tickArguments_ = [ 10 ], tickValues = null, tickFormat_;\n function axis(g) {\n g.each(function() {\n var g = d3.select(this);\n var scale0 = this.__chart__ || scale, scale1 = this.__chart__ = scale.copy();\n var ticks = tickValues == null ? scale1.ticks ? scale1.ticks.apply(scale1, tickArguments_) : scale1.domain() : tickValues, tickFormat = tickFormat_ == null ? scale1.tickFormat ? scale1.tickFormat.apply(scale1, tickArguments_) : d3_identity : tickFormat_, tick = g.selectAll(\".tick\").data(ticks, scale1), tickEnter = tick.enter().insert(\"g\", \".domain\").attr(\"class\", \"tick\").style(\"opacity\", ε), tickExit = d3.transition(tick.exit()).style(\"opacity\", ε).remove(), tickUpdate = d3.transition(tick.order()).style(\"opacity\", 1), tickSpacing = Math.max(innerTickSize, 0) + tickPadding, tickTransform;\n var range = d3_scaleRange(scale1), path = g.selectAll(\".domain\").data([ 0 ]), pathUpdate = (path.enter().append(\"path\").attr(\"class\", \"domain\"), \n d3.transition(path));\n tickEnter.append(\"line\");\n tickEnter.append(\"text\");\n var lineEnter = tickEnter.select(\"line\"), lineUpdate = tickUpdate.select(\"line\"), text = tick.select(\"text\").text(tickFormat), textEnter = tickEnter.select(\"text\"), textUpdate = tickUpdate.select(\"text\"), sign = orient === \"top\" || orient === \"left\" ? -1 : 1, x1, x2, y1, y2;\n if (orient === \"bottom\" || orient === \"top\") {\n tickTransform = d3_svg_axisX, x1 = \"x\", y1 = \"y\", x2 = \"x2\", y2 = \"y2\";\n text.attr(\"dy\", sign < 0 ? \"0em\" : \".71em\").style(\"text-anchor\", \"middle\");\n pathUpdate.attr(\"d\", \"M\" + range[0] + \",\" + sign * outerTickSize + \"V0H\" + range[1] + \"V\" + sign * outerTickSize);\n } else {\n tickTransform = d3_svg_axisY, x1 = \"y\", y1 = \"x\", x2 = \"y2\", y2 = \"x2\";\n text.attr(\"dy\", \".32em\").style(\"text-anchor\", sign < 0 ? \"end\" : \"start\");\n pathUpdate.attr(\"d\", \"M\" + sign * outerTickSize + \",\" + range[0] + \"H0V\" + range[1] + \"H\" + sign * outerTickSize);\n }\n lineEnter.attr(y2, sign * innerTickSize);\n textEnter.attr(y1, sign * tickSpacing);\n lineUpdate.attr(x2, 0).attr(y2, sign * innerTickSize);\n textUpdate.attr(x1, 0).attr(y1, sign * tickSpacing);\n if (scale1.rangeBand) {\n var x = scale1, dx = x.rangeBand() / 2;\n scale0 = scale1 = function(d) {\n return x(d) + dx;\n };\n } else if (scale0.rangeBand) {\n scale0 = scale1;\n } else {\n tickExit.call(tickTransform, scale1, scale0);\n }\n tickEnter.call(tickTransform, scale0, scale1);\n tickUpdate.call(tickTransform, scale1, scale1);\n });\n }\n axis.scale = function(x) {\n if (!arguments.length) return scale;\n scale = x;\n return axis;\n };\n axis.orient = function(x) {\n if (!arguments.length) return orient;\n orient = x in d3_svg_axisOrients ? x + \"\" : d3_svg_axisDefaultOrient;\n return axis;\n };\n axis.ticks = function() {\n if (!arguments.length) return tickArguments_;\n tickArguments_ = arguments;\n return axis;\n };\n axis.tickValues = function(x) {\n if (!arguments.length) return tickValues;\n tickValues = x;\n return axis;\n };\n axis.tickFormat = function(x) {\n if (!arguments.length) return tickFormat_;\n tickFormat_ = x;\n return axis;\n };\n axis.tickSize = function(x) {\n var n = arguments.length;\n if (!n) return innerTickSize;\n innerTickSize = +x;\n outerTickSize = +arguments[n - 1];\n return axis;\n };\n axis.innerTickSize = function(x) {\n if (!arguments.length) return innerTickSize;\n innerTickSize = +x;\n return axis;\n };\n axis.outerTickSize = function(x) {\n if (!arguments.length) return outerTickSize;\n outerTickSize = +x;\n return axis;\n };\n axis.tickPadding = function(x) {\n if (!arguments.length) return tickPadding;\n tickPadding = +x;\n return axis;\n };\n axis.tickSubdivide = function() {\n return arguments.length && axis;\n };\n return axis;\n };\n var d3_svg_axisDefaultOrient = \"bottom\", d3_svg_axisOrients = {\n top: 1,\n right: 1,\n bottom: 1,\n left: 1\n };\n function d3_svg_axisX(selection, x0, x1) {\n selection.attr(\"transform\", function(d) {\n var v0 = x0(d);\n return \"translate(\" + (isFinite(v0) ? v0 : x1(d)) + \",0)\";\n });\n }\n function d3_svg_axisY(selection, y0, y1) {\n selection.attr(\"transform\", function(d) {\n var v0 = y0(d);\n return \"translate(0,\" + (isFinite(v0) ? v0 : y1(d)) + \")\";\n });\n }\n d3.svg.brush = function() {\n var event = d3_eventDispatch(brush, \"brushstart\", \"brush\", \"brushend\"), x = null, y = null, xExtent = [ 0, 0 ], yExtent = [ 0, 0 ], xExtentDomain, yExtentDomain, xClamp = true, yClamp = true, resizes = d3_svg_brushResizes[0];\n function brush(g) {\n g.each(function() {\n var g = d3.select(this).style(\"pointer-events\", \"all\").style(\"-webkit-tap-highlight-color\", \"rgba(0,0,0,0)\").on(\"mousedown.brush\", brushstart).on(\"touchstart.brush\", brushstart);\n var background = g.selectAll(\".background\").data([ 0 ]);\n background.enter().append(\"rect\").attr(\"class\", \"background\").style(\"visibility\", \"hidden\").style(\"cursor\", \"crosshair\");\n g.selectAll(\".extent\").data([ 0 ]).enter().append(\"rect\").attr(\"class\", \"extent\").style(\"cursor\", \"move\");\n var resize = g.selectAll(\".resize\").data(resizes, d3_identity);\n resize.exit().remove();\n resize.enter().append(\"g\").attr(\"class\", function(d) {\n return \"resize \" + d;\n }).style(\"cursor\", function(d) {\n return d3_svg_brushCursor[d];\n }).append(\"rect\").attr(\"x\", function(d) {\n return /[ew]$/.test(d) ? -3 : null;\n }).attr(\"y\", function(d) {\n return /^[ns]/.test(d) ? -3 : null;\n }).attr(\"width\", 6).attr(\"height\", 6).style(\"visibility\", \"hidden\");\n resize.style(\"display\", brush.empty() ? \"none\" : null);\n var gUpdate = d3.transition(g), backgroundUpdate = d3.transition(background), range;\n if (x) {\n range = d3_scaleRange(x);\n backgroundUpdate.attr(\"x\", range[0]).attr(\"width\", range[1] - range[0]);\n redrawX(gUpdate);\n }\n if (y) {\n range = d3_scaleRange(y);\n backgroundUpdate.attr(\"y\", range[0]).attr(\"height\", range[1] - range[0]);\n redrawY(gUpdate);\n }\n redraw(gUpdate);\n });\n }\n brush.event = function(g) {\n g.each(function() {\n var event_ = event.of(this, arguments), extent1 = {\n x: xExtent,\n y: yExtent,\n i: xExtentDomain,\n j: yExtentDomain\n }, extent0 = this.__chart__ || extent1;\n this.__chart__ = extent1;\n if (d3_transitionInheritId) {\n d3.select(this).transition().each(\"start.brush\", function() {\n xExtentDomain = extent0.i;\n yExtentDomain = extent0.j;\n xExtent = extent0.x;\n yExtent = extent0.y;\n event_({\n type: \"brushstart\"\n });\n }).tween(\"brush:brush\", function() {\n var xi = d3_interpolateArray(xExtent, extent1.x), yi = d3_interpolateArray(yExtent, extent1.y);\n xExtentDomain = yExtentDomain = null;\n return function(t) {\n xExtent = extent1.x = xi(t);\n yExtent = extent1.y = yi(t);\n event_({\n type: \"brush\",\n mode: \"resize\"\n });\n };\n }).each(\"end.brush\", function() {\n xExtentDomain = extent1.i;\n yExtentDomain = extent1.j;\n event_({\n type: \"brush\",\n mode: \"resize\"\n });\n event_({\n type: \"brushend\"\n });\n });\n } else {\n event_({\n type: \"brushstart\"\n });\n event_({\n type: \"brush\",\n mode: \"resize\"\n });\n event_({\n type: \"brushend\"\n });\n }\n });\n };\n function redraw(g) {\n g.selectAll(\".resize\").attr(\"transform\", function(d) {\n return \"translate(\" + xExtent[+/e$/.test(d)] + \",\" + yExtent[+/^s/.test(d)] + \")\";\n });\n }\n function redrawX(g) {\n g.select(\".extent\").attr(\"x\", xExtent[0]);\n g.selectAll(\".extent,.n>rect,.s>rect\").attr(\"width\", xExtent[1] - xExtent[0]);\n }\n function redrawY(g) {\n g.select(\".extent\").attr(\"y\", yExtent[0]);\n g.selectAll(\".extent,.e>rect,.w>rect\").attr(\"height\", yExtent[1] - yExtent[0]);\n }\n function brushstart() {\n var target = this, eventTarget = d3.select(d3.event.target), event_ = event.of(target, arguments), g = d3.select(target), resizing = eventTarget.datum(), resizingX = !/^(n|s)$/.test(resizing) && x, resizingY = !/^(e|w)$/.test(resizing) && y, dragging = eventTarget.classed(\"extent\"), dragRestore = d3_event_dragSuppress(target), center, origin = d3.mouse(target), offset;\n var w = d3.select(d3_window(target)).on(\"keydown.brush\", keydown).on(\"keyup.brush\", keyup);\n if (d3.event.changedTouches) {\n w.on(\"touchmove.brush\", brushmove).on(\"touchend.brush\", brushend);\n } else {\n w.on(\"mousemove.brush\", brushmove).on(\"mouseup.brush\", brushend);\n }\n g.interrupt().selectAll(\"*\").interrupt();\n if (dragging) {\n origin[0] = xExtent[0] - origin[0];\n origin[1] = yExtent[0] - origin[1];\n } else if (resizing) {\n var ex = +/w$/.test(resizing), ey = +/^n/.test(resizing);\n offset = [ xExtent[1 - ex] - origin[0], yExtent[1 - ey] - origin[1] ];\n origin[0] = xExtent[ex];\n origin[1] = yExtent[ey];\n } else if (d3.event.altKey) center = origin.slice();\n g.style(\"pointer-events\", \"none\").selectAll(\".resize\").style(\"display\", null);\n d3.select(\"body\").style(\"cursor\", eventTarget.style(\"cursor\"));\n event_({\n type: \"brushstart\"\n });\n brushmove();\n function keydown() {\n if (d3.event.keyCode == 32) {\n if (!dragging) {\n center = null;\n origin[0] -= xExtent[1];\n origin[1] -= yExtent[1];\n dragging = 2;\n }\n d3_eventPreventDefault();\n }\n }\n function keyup() {\n if (d3.event.keyCode == 32 && dragging == 2) {\n origin[0] += xExtent[1];\n origin[1] += yExtent[1];\n dragging = 0;\n d3_eventPreventDefault();\n }\n }\n function brushmove() {\n var point = d3.mouse(target), moved = false;\n if (offset) {\n point[0] += offset[0];\n point[1] += offset[1];\n }\n if (!dragging) {\n if (d3.event.altKey) {\n if (!center) center = [ (xExtent[0] + xExtent[1]) / 2, (yExtent[0] + yExtent[1]) / 2 ];\n origin[0] = xExtent[+(point[0] < center[0])];\n origin[1] = yExtent[+(point[1] < center[1])];\n } else center = null;\n }\n if (resizingX && move1(point, x, 0)) {\n redrawX(g);\n moved = true;\n }\n if (resizingY && move1(point, y, 1)) {\n redrawY(g);\n moved = true;\n }\n if (moved) {\n redraw(g);\n event_({\n type: \"brush\",\n mode: dragging ? \"move\" : \"resize\"\n });\n }\n }\n function move1(point, scale, i) {\n var range = d3_scaleRange(scale), r0 = range[0], r1 = range[1], position = origin[i], extent = i ? yExtent : xExtent, size = extent[1] - extent[0], min, max;\n if (dragging) {\n r0 -= position;\n r1 -= size + position;\n }\n min = (i ? yClamp : xClamp) ? Math.max(r0, Math.min(r1, point[i])) : point[i];\n if (dragging) {\n max = (min += position) + size;\n } else {\n if (center) position = Math.max(r0, Math.min(r1, 2 * center[i] - min));\n if (position < min) {\n max = min;\n min = position;\n } else {\n max = position;\n }\n }\n if (extent[0] != min || extent[1] != max) {\n if (i) yExtentDomain = null; else xExtentDomain = null;\n extent[0] = min;\n extent[1] = max;\n return true;\n }\n }\n function brushend() {\n brushmove();\n g.style(\"pointer-events\", \"all\").selectAll(\".resize\").style(\"display\", brush.empty() ? \"none\" : null);\n d3.select(\"body\").style(\"cursor\", null);\n w.on(\"mousemove.brush\", null).on(\"mouseup.brush\", null).on(\"touchmove.brush\", null).on(\"touchend.brush\", null).on(\"keydown.brush\", null).on(\"keyup.brush\", null);\n dragRestore();\n event_({\n type: \"brushend\"\n });\n }\n }\n brush.x = function(z) {\n if (!arguments.length) return x;\n x = z;\n resizes = d3_svg_brushResizes[!x << 1 | !y];\n return brush;\n };\n brush.y = function(z) {\n if (!arguments.length) return y;\n y = z;\n resizes = d3_svg_brushResizes[!x << 1 | !y];\n return brush;\n };\n brush.clamp = function(z) {\n if (!arguments.length) return x && y ? [ xClamp, yClamp ] : x ? xClamp : y ? yClamp : null;\n if (x && y) xClamp = !!z[0], yClamp = !!z[1]; else if (x) xClamp = !!z; else if (y) yClamp = !!z;\n return brush;\n };\n brush.extent = function(z) {\n var x0, x1, y0, y1, t;\n if (!arguments.length) {\n if (x) {\n if (xExtentDomain) {\n x0 = xExtentDomain[0], x1 = xExtentDomain[1];\n } else {\n x0 = xExtent[0], x1 = xExtent[1];\n if (x.invert) x0 = x.invert(x0), x1 = x.invert(x1);\n if (x1 < x0) t = x0, x0 = x1, x1 = t;\n }\n }\n if (y) {\n if (yExtentDomain) {\n y0 = yExtentDomain[0], y1 = yExtentDomain[1];\n } else {\n y0 = yExtent[0], y1 = yExtent[1];\n if (y.invert) y0 = y.invert(y0), y1 = y.invert(y1);\n if (y1 < y0) t = y0, y0 = y1, y1 = t;\n }\n }\n return x && y ? [ [ x0, y0 ], [ x1, y1 ] ] : x ? [ x0, x1 ] : y && [ y0, y1 ];\n }\n if (x) {\n x0 = z[0], x1 = z[1];\n if (y) x0 = x0[0], x1 = x1[0];\n xExtentDomain = [ x0, x1 ];\n if (x.invert) x0 = x(x0), x1 = x(x1);\n if (x1 < x0) t = x0, x0 = x1, x1 = t;\n if (x0 != xExtent[0] || x1 != xExtent[1]) xExtent = [ x0, x1 ];\n }\n if (y) {\n y0 = z[0], y1 = z[1];\n if (x) y0 = y0[1], y1 = y1[1];\n yExtentDomain = [ y0, y1 ];\n if (y.invert) y0 = y(y0), y1 = y(y1);\n if (y1 < y0) t = y0, y0 = y1, y1 = t;\n if (y0 != yExtent[0] || y1 != yExtent[1]) yExtent = [ y0, y1 ];\n }\n return brush;\n };\n brush.clear = function() {\n if (!brush.empty()) {\n xExtent = [ 0, 0 ], yExtent = [ 0, 0 ];\n xExtentDomain = yExtentDomain = null;\n }\n return brush;\n };\n brush.empty = function() {\n return !!x && xExtent[0] == xExtent[1] || !!y && yExtent[0] == yExtent[1];\n };\n return d3.rebind(brush, event, \"on\");\n };\n var d3_svg_brushCursor = {\n n: \"ns-resize\",\n e: \"ew-resize\",\n s: \"ns-resize\",\n w: \"ew-resize\",\n nw: \"nwse-resize\",\n ne: \"nesw-resize\",\n se: \"nwse-resize\",\n sw: \"nesw-resize\"\n };\n var d3_svg_brushResizes = [ [ \"n\", \"e\", \"s\", \"w\", \"nw\", \"ne\", \"se\", \"sw\" ], [ \"e\", \"w\" ], [ \"n\", \"s\" ], [] ];\n var d3_time_format = d3_time.format = d3_locale_enUS.timeFormat;\n var d3_time_formatUtc = d3_time_format.utc;\n var d3_time_formatIso = d3_time_formatUtc(\"%Y-%m-%dT%H:%M:%S.%LZ\");\n d3_time_format.iso = Date.prototype.toISOString && +new Date(\"2000-01-01T00:00:00.000Z\") ? d3_time_formatIsoNative : d3_time_formatIso;\n function d3_time_formatIsoNative(date) {\n return date.toISOString();\n }\n d3_time_formatIsoNative.parse = function(string) {\n var date = new Date(string);\n return isNaN(date) ? null : date;\n };\n d3_time_formatIsoNative.toString = d3_time_formatIso.toString;\n d3_time.second = d3_time_interval(function(date) {\n return new d3_date(Math.floor(date / 1e3) * 1e3);\n }, function(date, offset) {\n date.setTime(date.getTime() + Math.floor(offset) * 1e3);\n }, function(date) {\n return date.getSeconds();\n });\n d3_time.seconds = d3_time.second.range;\n d3_time.seconds.utc = d3_time.second.utc.range;\n d3_time.minute = d3_time_interval(function(date) {\n return new d3_date(Math.floor(date / 6e4) * 6e4);\n }, function(date, offset) {\n date.setTime(date.getTime() + Math.floor(offset) * 6e4);\n }, function(date) {\n return date.getMinutes();\n });\n d3_time.minutes = d3_time.minute.range;\n d3_time.minutes.utc = d3_time.minute.utc.range;\n d3_time.hour = d3_time_interval(function(date) {\n var timezone = date.getTimezoneOffset() / 60;\n return new d3_date((Math.floor(date / 36e5 - timezone) + timezone) * 36e5);\n }, function(date, offset) {\n date.setTime(date.getTime() + Math.floor(offset) * 36e5);\n }, function(date) {\n return date.getHours();\n });\n d3_time.hours = d3_time.hour.range;\n d3_time.hours.utc = d3_time.hour.utc.range;\n d3_time.month = d3_time_interval(function(date) {\n date = d3_time.day(date);\n date.setDate(1);\n return date;\n }, function(date, offset) {\n date.setMonth(date.getMonth() + offset);\n }, function(date) {\n return date.getMonth();\n });\n d3_time.months = d3_time.month.range;\n d3_time.months.utc = d3_time.month.utc.range;\n function d3_time_scale(linear, methods, format) {\n function scale(x) {\n return linear(x);\n }\n scale.invert = function(x) {\n return d3_time_scaleDate(linear.invert(x));\n };\n scale.domain = function(x) {\n if (!arguments.length) return linear.domain().map(d3_time_scaleDate);\n linear.domain(x);\n return scale;\n };\n function tickMethod(extent, count) {\n var span = extent[1] - extent[0], target = span / count, i = d3.bisect(d3_time_scaleSteps, target);\n return i == d3_time_scaleSteps.length ? [ methods.year, d3_scale_linearTickRange(extent.map(function(d) {\n return d / 31536e6;\n }), count)[2] ] : !i ? [ d3_time_scaleMilliseconds, d3_scale_linearTickRange(extent, count)[2] ] : methods[target / d3_time_scaleSteps[i - 1] < d3_time_scaleSteps[i] / target ? i - 1 : i];\n }\n scale.nice = function(interval, skip) {\n var domain = scale.domain(), extent = d3_scaleExtent(domain), method = interval == null ? tickMethod(extent, 10) : typeof interval === \"number\" && tickMethod(extent, interval);\n if (method) interval = method[0], skip = method[1];\n function skipped(date) {\n return !isNaN(date) && !interval.range(date, d3_time_scaleDate(+date + 1), skip).length;\n }\n return scale.domain(d3_scale_nice(domain, skip > 1 ? {\n floor: function(date) {\n while (skipped(date = interval.floor(date))) date = d3_time_scaleDate(date - 1);\n return date;\n },\n ceil: function(date) {\n while (skipped(date = interval.ceil(date))) date = d3_time_scaleDate(+date + 1);\n return date;\n }\n } : interval));\n };\n scale.ticks = function(interval, skip) {\n var extent = d3_scaleExtent(scale.domain()), method = interval == null ? tickMethod(extent, 10) : typeof interval === \"number\" ? tickMethod(extent, interval) : !interval.range && [ {\n range: interval\n }, skip ];\n if (method) interval = method[0], skip = method[1];\n return interval.range(extent[0], d3_time_scaleDate(+extent[1] + 1), skip < 1 ? 1 : skip);\n };\n scale.tickFormat = function() {\n return format;\n };\n scale.copy = function() {\n return d3_time_scale(linear.copy(), methods, format);\n };\n return d3_scale_linearRebind(scale, linear);\n }\n function d3_time_scaleDate(t) {\n return new Date(t);\n }\n var d3_time_scaleSteps = [ 1e3, 5e3, 15e3, 3e4, 6e4, 3e5, 9e5, 18e5, 36e5, 108e5, 216e5, 432e5, 864e5, 1728e5, 6048e5, 2592e6, 7776e6, 31536e6 ];\n var d3_time_scaleLocalMethods = [ [ d3_time.second, 1 ], [ d3_time.second, 5 ], [ d3_time.second, 15 ], [ d3_time.second, 30 ], [ d3_time.minute, 1 ], [ d3_time.minute, 5 ], [ d3_time.minute, 15 ], [ d3_time.minute, 30 ], [ d3_time.hour, 1 ], [ d3_time.hour, 3 ], [ d3_time.hour, 6 ], [ d3_time.hour, 12 ], [ d3_time.day, 1 ], [ d3_time.day, 2 ], [ d3_time.week, 1 ], [ d3_time.month, 1 ], [ d3_time.month, 3 ], [ d3_time.year, 1 ] ];\n var d3_time_scaleLocalFormat = d3_time_format.multi([ [ \".%L\", function(d) {\n return d.getMilliseconds();\n } ], [ \":%S\", function(d) {\n return d.getSeconds();\n } ], [ \"%I:%M\", function(d) {\n return d.getMinutes();\n } ], [ \"%I %p\", function(d) {\n return d.getHours();\n } ], [ \"%a %d\", function(d) {\n return d.getDay() && d.getDate() != 1;\n } ], [ \"%b %d\", function(d) {\n return d.getDate() != 1;\n } ], [ \"%B\", function(d) {\n return d.getMonth();\n } ], [ \"%Y\", d3_true ] ]);\n var d3_time_scaleMilliseconds = {\n range: function(start, stop, step) {\n return d3.range(Math.ceil(start / step) * step, +stop, step).map(d3_time_scaleDate);\n },\n floor: d3_identity,\n ceil: d3_identity\n };\n d3_time_scaleLocalMethods.year = d3_time.year;\n d3_time.scale = function() {\n return d3_time_scale(d3.scale.linear(), d3_time_scaleLocalMethods, d3_time_scaleLocalFormat);\n };\n var d3_time_scaleUtcMethods = d3_time_scaleLocalMethods.map(function(m) {\n return [ m[0].utc, m[1] ];\n });\n var d3_time_scaleUtcFormat = d3_time_formatUtc.multi([ [ \".%L\", function(d) {\n return d.getUTCMilliseconds();\n } ], [ \":%S\", function(d) {\n return d.getUTCSeconds();\n } ], [ \"%I:%M\", function(d) {\n return d.getUTCMinutes();\n } ], [ \"%I %p\", function(d) {\n return d.getUTCHours();\n } ], [ \"%a %d\", function(d) {\n return d.getUTCDay() && d.getUTCDate() != 1;\n } ], [ \"%b %d\", function(d) {\n return d.getUTCDate() != 1;\n } ], [ \"%B\", function(d) {\n return d.getUTCMonth();\n } ], [ \"%Y\", d3_true ] ]);\n d3_time_scaleUtcMethods.year = d3_time.year.utc;\n d3_time.scale.utc = function() {\n return d3_time_scale(d3.scale.linear(), d3_time_scaleUtcMethods, d3_time_scaleUtcFormat);\n };\n d3.text = d3_xhrType(function(request) {\n return request.responseText;\n });\n d3.json = function(url, callback) {\n return d3_xhr(url, \"application/json\", d3_json, callback);\n };\n function d3_json(request) {\n return JSON.parse(request.responseText);\n }\n d3.html = function(url, callback) {\n return d3_xhr(url, \"text/html\", d3_html, callback);\n };\n function d3_html(request) {\n var range = d3_document.createRange();\n range.selectNode(d3_document.body);\n return range.createContextualFragment(request.responseText);\n }\n d3.xml = d3_xhrType(function(request) {\n return request.responseXML;\n });\n if (typeof define === \"function\" && define.amd) define(d3); else if (typeof module === \"object\" && module.exports) module.exports = d3;\n this.d3 = d3;\n}();" }, { "path": "cluster/benchmarks/bower_components/d3/package.js", "content": "// Package metadata for Meteor.js.\n\nPackage.describe({\n name: \"d3js:d3\", // http://atmospherejs.com/d3js/d3\n summary: \"D3 (official): A JavaScript visualization library for HTML and SVG.\",\n version: \"3.5.5\",\n git: \"https://github.com/mbostock/d3.git\"\n});\n\nPackage.onUse(function(api) {\n api.versionsFrom([\"METEOR@1.0\"]);\n api.addFiles(\"d3.js\", \"client\");\n});\n" }, { "path": "cluster/benchmarks/bower_components/plottable/.bower.json", "content": "{\n \"name\": \"plottable\",\n \"description\": \"A modular charting library built on D3\",\n \"version\": \"2.2.0\",\n \"main\": [\n \"plottable.js\",\n \"plottable.css\"\n ],\n \"typescript\": {\n \"definition\": \"plottable.d.ts\"\n },\n \"license\": \"MIT\",\n \"ignore\": [\n \"**/*\",\n \"!bower.json\",\n \"!plottable.js\",\n \"!plottable.css\",\n \"!plottable.min.js\",\n \"!plottable.d.ts\"\n ],\n \"keywords\": [\n \"plottable\",\n \"plottablejs\",\n \"plottable.js\",\n \"d3\",\n \"data viz\",\n \"chart\",\n \"charts\",\n \"reusable charts\",\n \"visualization\",\n \"scatterplot\",\n \"bar chart\",\n \"plot\",\n \"plots\"\n ],\n \"dependencies\": {\n \"d3\": \"3.5.5\"\n },\n \"homepage\": \"http://plottablejs.org\",\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git://github.com/palantir/plottable.git\"\n },\n \"devDependencies\": {\n \"chai\": \"2.0.0\",\n \"mocha\": \"2.2.5\",\n \"jQuery\": \"2.1.0\",\n \"jquery.simulate\": \"1.2.0\",\n \"requirejs\": \"2.1.18\",\n \"sinon\": \"1.16.1\"\n },\n \"_release\": \"2.2.0\",\n \"_resolution\": {\n \"type\": \"version\",\n \"tag\": \"v2.2.0\",\n \"commit\": \"e36001d8b6640cd23599905255d61b4ab58a648d\"\n },\n \"_source\": \"https://github.com/palantir/plottable.git\",\n \"_target\": \"^2.2.0\",\n \"_originalSource\": \"plottable\",\n \"_direct\": true\n}" }, { "path": "cluster/benchmarks/bower_components/plottable/bower.json", "content": "{\n \"name\": \"plottable\",\n \"description\": \"A modular charting library built on D3\",\n \"version\": \"2.2.0\",\n \"main\": [\n \"plottable.js\",\n \"plottable.css\"\n ],\n \"typescript\": {\n \"definition\": \"plottable.d.ts\"\n },\n \"license\": \"MIT\",\n \"ignore\": [\n \"**/*\",\n \"!bower.json\",\n \"!plottable.js\",\n \"!plottable.css\",\n \"!plottable.min.js\",\n \"!plottable.d.ts\"\n ],\n \"keywords\": [\n \"plottable\",\n \"plottablejs\",\n \"plottable.js\",\n \"d3\",\n \"data viz\",\n \"chart\",\n \"charts\",\n \"reusable charts\",\n \"visualization\",\n \"scatterplot\",\n \"bar chart\",\n \"plot\",\n \"plots\"\n ],\n \"dependencies\": {\n \"d3\": \"3.5.5\"\n },\n \"homepage\": \"http://plottablejs.org\",\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git://github.com/palantir/plottable.git\"\n },\n \"devDependencies\": {\n \"chai\": \"2.0.0\",\n \"mocha\": \"2.2.5\",\n \"jQuery\": \"2.1.0\",\n \"jquery.simulate\": \"1.2.0\",\n \"requirejs\": \"2.1.18\",\n \"sinon\": \"1.16.1\"\n }\n}\n" }, { "path": "cluster/benchmarks/bower_components/plottable/plottable.css", "content": "\n.plottable-colors-0 {\n background-color: #5279c7; /* INDIGO */\n}\n\n.plottable-colors-1 {\n background-color: #fd373e; /* CORAL_RED */\n}\n\n.plottable-colors-2 {\n background-color: #63c261; /* FERN */\n}\n\n.plottable-colors-3 {\n background-color: #fad419; /* BRIGHT_SUN */\n}\n\n.plottable-colors-4 {\n background-color: #2c2b6f; /* JACARTA */\n}\n\n.plottable-colors-5 {\n background-color: #ff7939; /* BURNING_ORANGE */\n}\n\n.plottable-colors-6 {\n background-color: #db2e65; /* CERISE_RED */\n}\n\n.plottable-colors-7 {\n background-color: #99ce50; /* CONIFER */\n}\n\n.plottable-colors-8 {\n background-color: #962565; /* ROYAL_HEATH */\n}\n\n.plottable-colors-9 {\n background-color: #06cccc; /* ROBINS_EGG_BLUE */\n}\n\nsvg.plottable {\n display : block; /* SVGs must be block elements for width/height calculations to work in Firefox. */\n pointer-events: visibleFill;\n}\n\n.plottable .background-fill {\n fill: none;\n pointer-events: none;\n}\n\n.plottable .bounding-box {\n /* Invisible pink bounding-box to allow for collision testing */\n fill: pink;\n visibility: hidden;\n}\n\n.plottable .label text {\n font-family: \"Helvetica Neue\", sans-serif;\n fill: #32313F;\n}\n\n.plottable .bar-label-text-area text {\n font-family: \"Helvetica Neue\", sans-serif;\n font-size: 14px;\n}\n\n.plottable .label-area text {\n fill: #32313F;\n font-family: \"Helvetica Neue\", sans-serif;\n font-size: 14px;\n}\n\n.plottable .light-label text {\n fill: white;\n}\n\n.plottable .dark-label text {\n fill: #32313F;\n}\n\n.plottable .off-bar-label text {\n fill: #32313F;\n}\n\n.plottable .stacked-bar-plot .off-bar-label {\n /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */\n visibility: hidden !important;\n}\n\n.plottable .axis-label text {\n font-size: 10px;\n font-weight: bold;\n letter-spacing: 1px;\n line-height: normal;\n text-transform: uppercase;\n}\n\n.plottable .title-label text {\n font-size: 20px;\n font-weight: bold;\n}\n\n.plottable .axis line.baseline {\n stroke: #CCC;\n stroke-width: 1px;\n}\n\n.plottable .axis line.tick-mark {\n stroke: #CCC;\n stroke-width: 1px;\n}\n\n.plottable .axis text {\n fill: #32313F;\n font-family: \"Helvetica Neue\", sans-serif;\n font-size: 12px;\n font-weight: 200;\n line-height: normal;\n}\n\n.plottable .axis .annotation-circle {\n fill: white;\n stroke-width: 1px;\n stroke: #CCC;\n}\n\n.plottable .axis .annotation-line {\n stroke: #CCC;\n stroke-width: 1px;\n}\n\n.plottable .axis .annotation-rect {\n stroke: #CCC;\n stroke-width: 1px;\n fill: white;\n}\n\n.plottable .bar-plot .baseline {\n stroke: #999;\n}\n\n.plottable .gridlines line {\n stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */\n opacity: 0.25;\n stroke-width: 1px;\n}\n\n.plottable .selection-box-layer .selection-area {\n fill: black;\n fill-opacity: 0.03;\n stroke: #CCC;\n}\n/* DragBoxLayer */\n.plottable .drag-box-layer.x-resizable .drag-edge-lr {\n cursor: ew-resize;\n}\n.plottable .drag-box-layer.y-resizable .drag-edge-tb {\n cursor: ns-resize;\n}\n\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {\n cursor: nwse-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {\n cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {\n cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {\n cursor: nwse-resize;\n}\n\n.plottable .drag-box-layer.movable .selection-area {\n cursor: move; /* IE fallback */\n cursor: -moz-grab;\n cursor: -webkit-grab;\n cursor: grab;\n}\n\n.plottable .drag-box-layer.movable .selection-area:active {\n cursor: -moz-grabbing;\n cursor: -webkit-grabbing;\n cursor: grabbing;\n}\n/* /DragBoxLayer */\n\n.plottable .guide-line-layer line.guide-line {\n stroke: #CCC;\n stroke-width: 1px;\n}\n\n.plottable .drag-line-layer.enabled.vertical line.drag-edge {\n cursor: ew-resize;\n}\n\n.plottable .drag-line-layer.enabled.horizontal line.drag-edge {\n cursor: ns-resize;\n}\n\n.plottable .legend text {\n fill: #32313F;\n font-family: \"Helvetica Neue\", sans-serif;\n font-size: 12px;\n font-weight: bold;\n line-height: normal;\n}\n\n.plottable .interpolated-color-legend rect.swatch-bounding-box {\n fill: none;\n stroke: #CCC;\n stroke-width: 1px;\n pointer-events: none;\n}\n\n.plottable .waterfall-plot line.connector {\n stroke: #CCC;\n stroke-width: 1px;\n}\n\n.plottable .pie-plot .arc.outline {\n stroke-linejoin: round;\n}\n" }, { "path": "cluster/benchmarks/bower_components/plottable/plottable.d.ts", "content": "declare namespace Plottable.Utils.Math {\n /**\n * Checks if x is between a and b.\n *\n * @param {number} x The value to test if in range\n * @param {number} a The beginning of the (inclusive) range\n * @param {number} b The ending of the (inclusive) range\n * @return {boolean} Whether x is in [a, b]\n */\n function inRange(x: number, a: number, b: number): boolean;\n /**\n * Clamps x to the range [min, max].\n *\n * @param {number} x The value to be clamped.\n * @param {number} min The minimum value.\n * @param {number} max The maximum value.\n * @return {number} A clamped value in the range [min, max].\n */\n function clamp(x: number, min: number, max: number): number;\n /**\n * Applies the accessor, if provided, to each element of `array` and returns the maximum value.\n * If no maximum value can be computed, returns defaultValue.\n */\n function max(array: C[], defaultValue: C): C;\n function max(array: T[], accessor: (t?: T, i?: number) => C, defaultValue: C): C;\n /**\n * Applies the accessor, if provided, to each element of `array` and returns the minimum value.\n * If no minimum value can be computed, returns defaultValue.\n */\n function min(array: C[], defaultValue: C): C;\n function min(array: T[], accessor: (t?: T, i?: number) => C, defaultValue: C): C;\n /**\n * Returns true **only** if x is NaN\n */\n function isNaN(n: any): boolean;\n /**\n * Returns true if the argument is a number, which is not NaN\n * Numbers represented as strings do not pass this function\n */\n function isValidNumber(n: any): boolean;\n /**\n * Generates an array of consecutive, strictly increasing numbers\n * in the range [start, stop) separeted by step\n */\n function range(start: number, stop: number, step?: number): number[];\n /**\n * Returns the square of the distance between two points\n *\n * @param {Point} p1\n * @param {Point} p2\n * @return {number} dist(p1, p2)^2\n */\n function distanceSquared(p1: Point, p2: Point): number;\n function degreesToRadians(degree: number): number;\n}\ndeclare namespace Plottable.Utils {\n /**\n * Shim for ES6 map.\n * https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map\n */\n class Map {\n private _keyValuePairs;\n private _es6Map;\n constructor();\n set(key: K, value: V): this;\n get(key: K): V;\n has(key: K): boolean;\n forEach(callbackFn: (value: V, key: K, map: Map) => void, thisArg?: any): void;\n delete(key: K): boolean;\n }\n}\ndeclare namespace Plottable.Utils {\n /**\n * Shim for ES6 set.\n * https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Set\n */\n class Set {\n size: number;\n private _values;\n private _es6Set;\n constructor();\n add(value: T): this;\n delete(value: T): boolean;\n has(value: T): boolean;\n forEach(callback: (value: T, value2: T, set: Set) => void, thisArg?: any): void;\n }\n}\ndeclare namespace Plottable.Utils.DOM {\n /**\n * Gets the bounding box of an element.\n * @param {d3.Selection} element\n * @returns {SVGRed} The bounding box.\n */\n function elementBBox(element: d3.Selection): SVGRect;\n /**\n * Screen refresh rate which is assumed to be 60fps\n */\n var SCREEN_REFRESH_RATE_MILLISECONDS: number;\n /**\n * Polyfill for `window.requestAnimationFrame`.\n * If the function exists, then we use the function directly.\n * Otherwise, we set a timeout on `SCREEN_REFRESH_RATE_MILLISECONDS` and then perform the function.\n *\n * @param {() => void} callback The callback to call in the next animation frame\n */\n function requestAnimationFramePolyfill(callback: () => void): void;\n /**\n * Calculates the width of the element.\n * The width includes the padding and the border on the element's left and right sides.\n *\n * @param {Element} element The element to query\n * @returns {number} The width of the element.\n */\n function elementWidth(element: Element): number;\n /**\n * Calculates the height of the element.\n * The height includes the padding the and the border on the element's top and bottom sides.\n *\n * @param {Element} element The element to query\n * @returns {number} The height of the element\n */\n function elementHeight(element: Element): number;\n /**\n * Retrieves the number array representing the translation for the selection\n *\n * @param {d3.Selection} selection The selection to query\n * @returns {[number, number]} The number array representing the translation\n */\n function translate(selection: d3.Selection): [number, number];\n /**\n * Translates the given selection by the input x / y pixel amounts.\n *\n * @param {d3.Selection} selection The selection to translate\n * @param {number} x The amount to translate in the x direction\n * @param {number} y The amount to translate in the y direction\n * @returns {d3.Selection} The input selection\n */\n function translate(selection: d3.Selection, x: number, y: number): d3.Selection;\n /**\n * Checks if the first ClientRect overlaps the second.\n *\n * @param {ClientRect} clientRectA The first ClientRect\n * @param {ClientRect} clientRectB The second ClientRect\n * @returns {boolean} If the ClientRects overlap each other.\n */\n function clientRectsOverlap(clientRectA: ClientRect, clientRectB: ClientRect): boolean;\n /**\n * Returns true if and only if innerClientRect is inside outerClientRect.\n *\n * @param {ClientRect} innerClientRect The first ClientRect\n * @param {ClientRect} outerClientRect The second ClientRect\n * @returns {boolean} If and only if the innerClientRect is inside outerClientRect.\n */\n function clientRectInside(innerClientRect: ClientRect, outerClientRect: ClientRect): boolean;\n /**\n * Retrieves the bounding svg of the input element\n *\n * @param {SVGElement} element The element to query\n * @returns {SVGElement} The bounding svg\n */\n function boundingSVG(element: SVGElement): SVGElement;\n /**\n * Generates a ClipPath ID that is unique for this instance of Plottable\n */\n function generateUniqueClipPathId(): string;\n /**\n * Returns true if the supplied coordinates or Ranges intersect or are contained by bbox.\n *\n * @param {number | Range} xValOrRange The x coordinate or Range to test\n * @param {number | Range} yValOrRange The y coordinate or Range to test\n * @param {SVGRect} bbox The bbox\n * @param {number} tolerance Amount by which to expand bbox, in each dimension, before\n * testing intersection\n *\n * @returns {boolean} True if the supplied coordinates or Ranges intersect or are\n * contained by bbox, false otherwise.\n */\n function intersectsBBox(xValOrRange: number | Range, yValOrRange: number | Range, bbox: SVGRect, tolerance?: number): boolean;\n}\ndeclare namespace Plottable.Utils.Color {\n /**\n * Return contrast ratio between two colors\n * Based on implementation from chroma.js by Gregor Aisch (gka) (licensed under BSD)\n * chroma.js may be found here: https://github.com/gka/chroma.js\n * License may be found here: https://github.com/gka/chroma.js/blob/master/LICENSE\n * see http://www.w3.org/TR/2008/REC-WCAG20-20081211/#contrast-ratiodef\n */\n function contrast(a: string, b: string): number;\n /**\n * Returns a brighter copy of this color. Each channel is multiplied by 0.7 ^ -factor.\n * Channel values are capped at the maximum value of 255, and the minimum value of 30.\n */\n function lightenColor(color: string, factor: number): string;\n /**\n * Gets the Hex Code of the color resulting by applying the className CSS class to the\n * colorTester selection. Returns null if the tester is transparent.\n *\n * @param {d3.Selection} colorTester The d3 selection to apply the CSS class to\n * @param {string} className The name of the class to be applied\n * @return {string} The hex code of the computed color\n */\n function colorTest(colorTester: d3.Selection, className: string): string;\n}\ndeclare namespace Plottable.Utils.Array {\n /**\n * Takes two arrays of numbers and adds them together\n *\n * @param {number[]} aList The first array of numbers\n * @param {number[]} bList The second array of numbers\n * @return {number[]} An array of numbers where x[i] = aList[i] + bList[i]\n */\n function add(aList: number[], bList: number[]): number[];\n /**\n * Take an array of values, and return the unique values.\n * Will work iff ∀ a, b, a.toString() == b.toString() => a == b; will break on Object inputs\n *\n * @param {T[]} values The values to find uniqueness for\n * @return {T[]} The unique values\n */\n function uniq(arr: T[]): T[];\n /**\n * @param {T[][]} a The 2D array that will have its elements joined together.\n * @return {T[]} Every array in a, concatenated together in the order they appear.\n */\n function flatten(a: T[][]): T[];\n /**\n * Creates an array of length `count`, filled with value or (if value is a function), value()\n *\n * @param {T | ((index?: number) => T)} value The value to fill the array with or a value generator (called with index as arg)\n * @param {number} count The length of the array to generate\n * @return {any[]}\n */\n function createFilledArray(value: T | ((index?: number) => T), count: number): T[];\n}\ndeclare namespace Plottable.Utils {\n /**\n * A set of callbacks which can be all invoked at once.\n * Each callback exists at most once in the set (based on reference equality).\n * All callbacks should have the same signature.\n */\n class CallbackSet extends Set {\n callCallbacks(...args: any[]): this;\n }\n}\ndeclare namespace Plottable.Utils.Stacking {\n type StackedDatum = {\n value: number;\n offset: number;\n };\n type StackingResult = Utils.Map>;\n /**\n * Computes the StackingResult (value and offset) for each data point in each Dataset.\n *\n * @param {Dataset[]} datasets The Datasets to be stacked on top of each other in the order of stacking\n * @param {Accessor} keyAccessor Accessor for the key of the data\n * @param {Accessor} valueAccessor Accessor for the value of the data\n * @return {StackingResult} value and offset for each datapoint in each Dataset\n */\n function stack(datasets: Dataset[], keyAccessor: Accessor, valueAccessor: Accessor): StackingResult;\n /**\n * Computes the total extent over all data points in all Datasets, taking stacking into consideration.\n *\n * @param {StackingResult} stackingResult The value and offset information for each datapoint in each dataset\n * @oaram {Accessor} keyAccessor Accessor for the key of the data existent in the stackingResult\n * @param {Accessor} filter A filter for data to be considered when computing the total extent\n * @return {[number, number]} The total extent\n */\n function stackedExtent(stackingResult: StackingResult, keyAccessor: Accessor, filter: Accessor): number[];\n /**\n * Normalizes a key used for stacking\n *\n * @param {any} key The key to be normalized\n * @return {string} The stringified key\n */\n function normalizeKey(key: any): string;\n}\ndeclare namespace Plottable.Utils.Window {\n /**\n * Print a warning message to the console, if it is available.\n *\n * @param {string} The warnings to print\n */\n function warn(warning: string): void;\n /**\n * Is like setTimeout, but activates synchronously if time=0\n * We special case 0 because of an observed issue where calling setTimeout causes visible flickering.\n * We believe this is because when requestAnimationFrame calls into the paint function, as soon as that function finishes\n * evaluating, the results are painted to the screen. As a result, if we want something to occur immediately but call setTimeout\n * with time=0, then it is pushed to the call stack and rendered in the next frame, so the component that was rendered via\n * setTimeout appears out-of-sync with the rest of the plot.\n */\n function setTimeout(f: Function, time: number, ...args: any[]): number;\n /**\n * Sends a deprecation warning to the console. The warning includes the name of the deprecated method,\n * version number of the deprecation, and an optional message.\n *\n * To be used in the first line of a deprecated method.\n *\n * @param {string} callingMethod The name of the method being deprecated\n * @param {string} version The version when the tagged method became obsolete\n * @param {string?} message Optional message to be shown with the warning\n */\n function deprecated(callingMethod: string, version: string, message?: string): void;\n}\ndeclare namespace Plottable.Utils {\n class ClientToSVGTranslator {\n private static _TRANSLATOR_KEY;\n private _svg;\n private _measureRect;\n /**\n * Returns the ClientToSVGTranslator for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n */\n static getTranslator(elem: SVGElement): ClientToSVGTranslator;\n constructor(svg: SVGElement);\n /**\n * Computes the position relative to the in svg-coordinate-space.\n */\n computePosition(clientX: number, clientY: number): Point;\n /**\n * Checks whether event happened inside element.\n */\n insideSVG(e: Event): boolean;\n }\n}\ndeclare namespace Plottable.Configs {\n /**\n * Specifies if Plottable should show warnings.\n */\n var SHOW_WARNINGS: boolean;\n /**\n * Specifies if Plottable should add elements to text.\n */\n var ADD_TITLE_ELEMENTS: boolean;\n}\ndeclare namespace Plottable {\n var version: string;\n}\ndeclare namespace Plottable {\n type DatasetCallback = (dataset: Dataset) => void;\n class Dataset {\n private _data;\n private _metadata;\n private _callbacks;\n /**\n * A Dataset contains an array of data and some metadata.\n * Changes to the data or metadata will cause anything subscribed to the Dataset to update.\n *\n * @constructor\n * @param {any[]} [data=[]] The data for this Dataset.\n * @param {any} [metadata={}] An object containing additional information.\n */\n constructor(data?: any[], metadata?: any);\n /**\n * Adds a callback to be called when the Dataset updates.\n *\n * @param {DatasetCallback} callback.\n * @returns {Dataset} The calling Dataset.\n */\n onUpdate(callback: DatasetCallback): this;\n /**\n * Removes a callback that would be called when the Dataset updates.\n *\n * @param {DatasetCallback} callback\n * @returns {Dataset} The calling Dataset.\n */\n offUpdate(callback: DatasetCallback): this;\n /**\n * Gets the data.\n *\n * @returns {any[]}\n */\n data(): any[];\n /**\n * Sets the data.\n *\n * @param {any[]} data\n * @returns {Dataset} The calling Dataset.\n */\n data(data: any[]): this;\n /**\n * Gets the metadata.\n *\n * @returns {any}\n */\n metadata(): any;\n /**\n * Sets the metadata.\n *\n * @param {any} metadata\n * @returns {Dataset} The calling Dataset.\n */\n metadata(metadata: any): this;\n }\n}\ndeclare namespace Plottable.RenderPolicies {\n /**\n * A policy for rendering Components.\n */\n interface RenderPolicy {\n render(): any;\n }\n /**\n * Renders Components immediately after they are enqueued.\n * Useful for debugging, horrible for performance.\n */\n class Immediate implements RenderPolicy {\n render(): void;\n }\n /**\n * The default way to render, which only tries to render every frame\n * (usually, 1/60th of a second).\n */\n class AnimationFrame implements RenderPolicy {\n render(): void;\n }\n /**\n * Renders with `setTimeout()`.\n * Generally an inferior way to render compared to `requestAnimationFrame`,\n * but useful for browsers that don't suppoort `requestAnimationFrame`.\n */\n class Timeout implements RenderPolicy {\n private _timeoutMsec;\n render(): void;\n }\n}\n/**\n * The RenderController is responsible for enqueueing and synchronizing\n * layout and render calls for Components.\n *\n * Layout and render calls occur inside an animation callback\n * (window.requestAnimationFrame if available).\n *\n * RenderController.flush() immediately lays out and renders all Components currently enqueued.\n *\n * To always have immediate rendering (useful for debugging), call\n * ```typescript\n * Plottable.RenderController.setRenderPolicy(\n * new Plottable.RenderPolicies.Immediate()\n * );\n * ```\n */\ndeclare namespace Plottable.RenderController {\n namespace Policy {\n var IMMEDIATE: string;\n var ANIMATION_FRAME: string;\n var TIMEOUT: string;\n }\n function renderPolicy(): RenderPolicies.RenderPolicy;\n function renderPolicy(renderPolicy: string): void;\n /**\n * Enqueues the Component for rendering.\n *\n * @param {Component} component\n */\n function registerToRender(component: Component): void;\n /**\n * Enqueues the Component for layout and rendering.\n *\n * @param {Component} component\n */\n function registerToComputeLayout(component: Component): void;\n /**\n * Renders all Components waiting to be rendered immediately\n * instead of waiting until the next frame.\n *\n * Useful to call when debugging.\n */\n function flush(): void;\n}\ndeclare namespace Plottable {\n /**\n * Accesses a specific datum property.\n */\n interface Accessor {\n (datum: any, index: number, dataset: Dataset): T;\n }\n /**\n * Retrieves a scaled datum property.\n * Essentially passes the result of an Accessor through a Scale.\n */\n type Projector = (datum: any, index: number, dataset: Dataset) => any;\n /**\n * A mapping from attributes (\"x\", \"fill\", etc.) to the functions that get\n * that information out of the data.\n */\n type AttributeToProjector = {\n [attr: string]: Projector;\n };\n /**\n * A function that generates attribute values from the datum and index.\n * Essentially a Projector with a particular Dataset rolled in.\n */\n type AppliedProjector = (datum: any, index: number) => any;\n /**\n * A mapping from attributes to the AppliedProjectors used to generate them.\n */\n type AttributeToAppliedProjector = {\n [attr: string]: AppliedProjector;\n };\n /**\n * Space request used during layout negotiation.\n *\n * @member {number} minWidth The minimum acceptable width given the offered space.\n * @member {number} minHeight the minimum acceptable height given the offered space.\n */\n type SpaceRequest = {\n minWidth: number;\n minHeight: number;\n };\n /**\n * Min and max values for a particular property.\n */\n type Range = {\n min: number;\n max: number;\n };\n /**\n * A location in pixel-space.\n */\n type Point = {\n x: number;\n y: number;\n };\n /**\n * The corners of a box.\n */\n type Bounds = {\n topLeft: Point;\n bottomRight: Point;\n };\n /**\n * An object representing a data-backed visual entity inside a Component.\n */\n interface Entity {\n datum: any;\n position: Point;\n selection: d3.Selection;\n component: C;\n }\n}\ndeclare namespace Plottable {\n type Formatter = (d: any) => string;\n}\ndeclare namespace Plottable.Formatters {\n /**\n * Creates a formatter for currency values.\n *\n * @param {number} [precision] The number of decimal places to show (default 2).\n * @param {string} [symbol] The currency symbol to use (default \"$\").\n * @param {boolean} [prefix] Whether to prepend or append the currency symbol (default true).\n *\n * @returns {Formatter} A formatter for currency values.\n */\n function currency(precision?: number, symbol?: string, prefix?: boolean): (d: any) => string;\n /**\n * Creates a formatter that displays exactly [precision] decimal places.\n *\n * @param {number} [precision] The number of decimal places to show (default 3).\n *\n * @returns {Formatter} A formatter that displays exactly [precision] decimal places.\n */\n function fixed(precision?: number): (d: any) => string;\n /**\n * Creates a formatter that formats numbers to show no more than\n * [maxNumberOfDecimalPlaces] decimal places. All other values are stringified.\n *\n * @param {number} [maxNumberOfDecimalPlaces] The number of decimal places to show (default 3).\n *\n * @returns {Formatter} A formatter for general values.\n */\n function general(maxNumberOfDecimalPlaces?: number): (d: any) => string;\n /**\n * Creates a formatter that stringifies its input.\n *\n * @returns {Formatter} A formatter that stringifies its input.\n */\n function identity(): (d: any) => string;\n /**\n * Creates a formatter for percentage values.\n * Multiplies the input by 100 and appends \"%\".\n *\n * @param {number} [precision] The number of decimal places to show (default 0).\n *\n * @returns {Formatter} A formatter for percentage values.\n */\n function percentage(precision?: number): (d: any) => string;\n /**\n * Creates a formatter for values that displays [numberOfSignificantFigures] significant figures\n * and puts SI notation.\n *\n * @param {number} [numberOfSignificantFigures] The number of significant figures to show (default 3).\n *\n * @returns {Formatter} A formatter for SI values.\n */\n function siSuffix(numberOfSignificantFigures?: number): (d: any) => string;\n /**\n * Creates a formatter for values that displays abbreviated values\n * and uses standard short scale suffixes\n * - K - thousands - 10 ^ 3\n * - M - millions - 10 ^ 6\n * - B - billions - 10 ^ 9\n * - T - trillions - 10 ^ 12\n * - Q - quadrillions - 10 ^ 15\n *\n * Numbers with a magnitude outside of (10 ^ (-precision), 10 ^ 15) are shown using\n * scientific notation to avoid creating extremely long decimal strings.\n *\n * @param {number} [precision] the number of decimal places to show (default 3)\n * @returns {Formatter} A formatter with short scale formatting\n */\n function shortScale(precision?: number): (num: number) => string;\n /**\n * Creates a multi time formatter that displays dates.\n *\n * @returns {Formatter} A formatter for time/date values.\n */\n function multiTime(): (d: any) => string;\n /**\n * Creates a time formatter that displays time/date using given specifier.\n *\n * List of directives can be found on: https://github.com/mbostock/d3/wiki/Time-Formatting#format\n *\n * @param {string} [specifier] The specifier for the formatter.\n *\n * @returns {Formatter} A formatter for time/date values.\n */\n function time(specifier: string): Formatter;\n}\ndeclare namespace Plottable {\n /**\n * A SymbolFactory is a function that takes in a symbolSize which is the edge length of the render area\n * and returns a string representing the 'd' attribute of the resultant 'path' element\n */\n type SymbolFactory = (symbolSize: number) => string;\n}\ndeclare namespace Plottable.SymbolFactories {\n function circle(): SymbolFactory;\n function square(): SymbolFactory;\n function cross(): SymbolFactory;\n function diamond(): SymbolFactory;\n function triangleUp(): SymbolFactory;\n function triangleDown(): SymbolFactory;\n}\ndeclare namespace Plottable.Scales {\n /**\n * A function that supplies domain values to be included into a Scale.\n *\n * @param {Scale} scale\n * @returns {D[]} An array of values that should be included in the Scale.\n */\n interface IncludedValuesProvider {\n (scale: Scale): D[];\n }\n /**\n * A function that supplies padding exception values for the Scale.\n * If one end of the domain is set to an excepted value as a result of autoDomain()-ing,\n * that end of the domain will not be padded.\n *\n * @param {QuantitativeScale} scale\n * @returns {D[]} An array of values that should not be padded.\n */\n interface PaddingExceptionsProvider {\n (scale: QuantitativeScale): D[];\n }\n}\ndeclare namespace Plottable {\n interface ScaleCallback> {\n (scale: S): any;\n }\n class Scale {\n private _callbacks;\n private _autoDomainAutomatically;\n private _domainModificationInProgress;\n private _includedValuesProviders;\n /**\n * A Scale is a function (in the mathematical sense) that maps values from a domain to a range.\n *\n * @constructor\n */\n constructor();\n /**\n * Given an array of potential domain values, computes the extent of those values.\n *\n * @param {D[]} values\n * @returns {D[]} The extent of the input values.\n */\n extentOfValues(values: D[]): D[];\n protected _getAllIncludedValues(): D[];\n protected _getExtent(): D[];\n /**\n * Adds a callback to be called when the Scale updates.\n *\n * @param {ScaleCallback} callback.\n * @returns {Scale} The calling Scale.\n */\n onUpdate(callback: ScaleCallback): this;\n /**\n * Removes a callback that would be called when the Scale updates.\n *\n * @param {ScaleCallback} callback.\n * @returns {Scale} The calling Scale.\n */\n offUpdate(callback: ScaleCallback): this;\n protected _dispatchUpdate(): void;\n /**\n * Sets the Scale's domain so that it spans the Extents of all its ExtentsProviders.\n *\n * @returns {Scale} The calling Scale.\n */\n autoDomain(): this;\n protected _autoDomainIfAutomaticMode(): void;\n /**\n * Computes the range value corresponding to a given domain value.\n *\n * @param {D} value\n * @returns {R} The range value corresponding to the supplied domain value.\n */\n scale(value: D): R;\n /**\n * Gets the domain.\n *\n * @returns {D[]} The current domain.\n */\n domain(): D[];\n /**\n * Sets the domain.\n *\n * @param {D[]} values\n * @returns {Scale} The calling Scale.\n */\n domain(values: D[]): this;\n protected _getDomain(): D[];\n protected _setDomain(values: D[]): void;\n protected _backingScaleDomain(): D[];\n protected _backingScaleDomain(values: D[]): this;\n /**\n * Gets the range.\n *\n * @returns {R[]} The current range.\n */\n range(): R[];\n /**\n * Sets the range.\n *\n * @param {R[]} values\n * @returns {Scale} The calling Scale.\n */\n range(values: R[]): this;\n protected _getRange(): R[];\n protected _setRange(values: R[]): void;\n /**\n * Adds an IncludedValuesProvider to the Scale.\n *\n * @param {Scales.IncludedValuesProvider} provider\n * @returns {Scale} The calling Scale.\n */\n addIncludedValuesProvider(provider: Scales.IncludedValuesProvider): this;\n /**\n * Removes the IncludedValuesProvider from the Scale.\n *\n * @param {Scales.IncludedValuesProvider} provider\n * @returns {Scale} The calling Scale.\n */\n removeIncludedValuesProvider(provider: Scales.IncludedValuesProvider): this;\n }\n}\ndeclare namespace Plottable {\n class QuantitativeScale extends Scale {\n protected static _DEFAULT_NUM_TICKS: number;\n private _tickGenerator;\n private _padProportion;\n private _paddingExceptionsProviders;\n private _domainMin;\n private _domainMax;\n private _snappingDomainEnabled;\n /**\n * A QuantitativeScale is a Scale that maps number-like values to numbers.\n * It is invertible and continuous.\n *\n * @constructor\n */\n constructor();\n autoDomain(): this;\n protected _autoDomainIfAutomaticMode(): void;\n protected _getExtent(): D[];\n /**\n * Adds a padding exception provider.\n * If one end of the domain is set to an excepted value as a result of autoDomain()-ing,\n * that end of the domain will not be padded.\n *\n * @param {Scales.PaddingExceptionProvider} provider The provider function.\n * @returns {QuantitativeScale} The calling QuantitativeScale.\n */\n addPaddingExceptionsProvider(provider: Scales.PaddingExceptionsProvider): this;\n /**\n * Removes the padding exception provider.\n *\n * @param {Scales.PaddingExceptionProvider} provider The provider function.\n * @returns {QuantitativeScale} The calling QuantitativeScale.\n */\n removePaddingExceptionsProvider(provider: Scales.PaddingExceptionsProvider): this;\n /**\n * Gets the padding proportion.\n */\n padProportion(): number;\n /**\n * Sets the padding porportion.\n * When autoDomain()-ing, the computed domain will be expanded by this proportion,\n * then rounded to human-readable values.\n *\n * @param {number} padProportion The padding proportion. Passing 0 disables padding.\n * @returns {QuantitativeScale} The calling QuantitativeScale.\n */\n padProportion(padProportion: number): this;\n private _padDomain(domain);\n /**\n * Gets whether or not the scale snaps its domain to nice values.\n */\n snappingDomainEnabled(): boolean;\n /**\n * Sets whether or not the scale snaps its domain to nice values.\n */\n snappingDomainEnabled(snappingDomainEnabled: boolean): this;\n protected _expandSingleValueDomain(singleValueDomain: D[]): D[];\n /**\n * Computes the domain value corresponding to a supplied range value.\n *\n * @param {number} value: A value from the Scale's range.\n * @returns {D} The domain value corresponding to the supplied range value.\n */\n invert(value: number): D;\n domain(): D[];\n domain(values: D[]): this;\n /**\n * Gets the lower end of the domain.\n *\n * @return {D}\n */\n domainMin(): D;\n /**\n * Sets the lower end of the domain.\n *\n * @return {QuantitativeScale} The calling QuantitativeScale.\n */\n domainMin(domainMin: D): this;\n /**\n * Gets the upper end of the domain.\n *\n * @return {D}\n */\n domainMax(): D;\n /**\n * Sets the upper end of the domain.\n *\n * @return {QuantitativeScale} The calling QuantitativeScale.\n */\n domainMax(domainMax: D): this;\n extentOfValues(values: D[]): D[];\n protected _setDomain(values: D[]): void;\n /**\n * Gets the array of tick values generated by the default algorithm.\n */\n defaultTicks(): D[];\n /**\n * Gets an array of tick values spanning the domain.\n *\n * @returns {D[]}\n */\n ticks(): D[];\n /**\n * Given a domain, expands its domain onto \"nice\" values, e.g. whole\n * numbers.\n */\n protected _niceDomain(domain: D[], count?: number): D[];\n protected _defaultExtent(): D[];\n /**\n * Gets the TickGenerator.\n */\n tickGenerator(): Scales.TickGenerators.TickGenerator;\n /**\n * Sets the TickGenerator\n *\n * @param {TickGenerator} generator\n * @return {QuantitativeScale} The calling QuantitativeScale.\n */\n tickGenerator(generator: Scales.TickGenerators.TickGenerator): this;\n }\n}\ndeclare namespace Plottable.Scales {\n class Linear extends QuantitativeScale {\n private _d3Scale;\n /**\n * @constructor\n */\n constructor();\n protected _defaultExtent(): number[];\n protected _expandSingleValueDomain(singleValueDomain: number[]): number[];\n scale(value: number): number;\n protected _getDomain(): number[];\n protected _backingScaleDomain(): number[];\n protected _backingScaleDomain(values: number[]): this;\n protected _getRange(): number[];\n protected _setRange(values: number[]): void;\n invert(value: number): number;\n defaultTicks(): number[];\n protected _niceDomain(domain: number[], count?: number): number[];\n }\n}\ndeclare namespace Plottable.Scales {\n class ModifiedLog extends QuantitativeScale {\n private _base;\n private _d3Scale;\n private _pivot;\n private _untransformedDomain;\n /**\n * A ModifiedLog Scale acts as a regular log scale for large numbers.\n * As it approaches 0, it gradually becomes linear.\n * Consequently, a ModifiedLog Scale can process 0 and negative numbers.\n *\n * For x >= base, scale(x) = log(x).\n *\n * For 0 < x < base, scale(x) will become more and more\n * linear as it approaches 0.\n *\n * At x == 0, scale(x) == 0.\n *\n * For negative values, scale(-x) = -scale(x).\n *\n * The range and domain for the scale should also be set, using the\n * range() and domain() accessors, respectively.\n *\n * For `range`, provide a two-element array giving the minimum and\n * maximum of values produced when scaling.\n *\n * For `domain` provide a two-element array giving the minimum and\n * maximum of the values that will be scaled.\n *\n * @constructor\n * @param {number} [base=10]\n * The base of the log. Must be > 1.\n *\n */\n constructor(base?: number);\n /**\n * Returns an adjusted log10 value for graphing purposes. The first\n * adjustment is that negative values are changed to positive during\n * the calculations, and then the answer is negated at the end. The\n * second is that, for values less than 10, an increasingly large\n * (0 to 1) scaling factor is added such that at 0 the value is\n * adjusted to 1, resulting in a returned result of 0.\n */\n private _adjustedLog(x);\n private _invertedAdjustedLog(x);\n scale(x: number): number;\n invert(x: number): number;\n protected _getDomain(): number[];\n protected _setDomain(values: number[]): void;\n protected _backingScaleDomain(): number[];\n protected _backingScaleDomain(values: number[]): this;\n ticks(): number[];\n /**\n * Return an appropriate number of ticks from lower to upper.\n *\n * This will first try to fit as many powers of this.base as it can from\n * lower to upper.\n *\n * If it still has ticks after that, it will generate ticks in \"clusters\",\n * e.g. [20, 30, ... 90, 100] would be a cluster, [200, 300, ... 900, 1000]\n * would be another cluster.\n *\n * This function will generate clusters as large as it can while not\n * drastically exceeding its number of ticks.\n */\n private _logTicks(lower, upper);\n /**\n * How many ticks does the range [lower, upper] deserve?\n *\n * e.g. if your domain was [10, 1000] and I asked _howManyTicks(10, 100),\n * I would get 1/2 of the ticks. The range 10, 100 takes up 1/2 of the\n * distance when plotted.\n */\n private _howManyTicks(lower, upper);\n protected _niceDomain(domain: number[], count?: number): number[];\n protected _defaultExtent(): number[];\n protected _expandSingleValueDomain(singleValueDomain: number[]): number[];\n protected _getRange(): number[];\n protected _setRange(values: number[]): void;\n defaultTicks(): number[];\n }\n}\ndeclare namespace Plottable.Scales {\n class Category extends Scale {\n private _d3Scale;\n private _range;\n private _innerPadding;\n private _outerPadding;\n /**\n * A Category Scale maps strings to numbers.\n *\n * @constructor\n */\n constructor();\n extentOfValues(values: string[]): string[];\n protected _getExtent(): string[];\n domain(): string[];\n domain(values: string[]): this;\n range(): [number, number];\n range(values: [number, number]): this;\n private static _convertToPlottableInnerPadding(d3InnerPadding);\n private static _convertToPlottableOuterPadding(d3OuterPadding, d3InnerPadding);\n private _setBands();\n /**\n * Returns the width of the range band.\n *\n * @returns {number} The range band width\n */\n rangeBand(): number;\n /**\n * Returns the step width of the scale.\n *\n * The step width is the pixel distance between adjacent values in the domain.\n *\n * @returns {number}\n */\n stepWidth(): number;\n /**\n * Gets the inner padding.\n *\n * The inner padding is defined as the padding in between bands on the scale,\n * expressed as a multiple of the rangeBand().\n *\n * @returns {number}\n */\n innerPadding(): number;\n /**\n * Sets the inner padding.\n *\n * The inner padding is defined as the padding in between bands on the scale,\n * expressed as a multiple of the rangeBand().\n *\n * @returns {Category} The calling Category Scale.\n */\n innerPadding(innerPadding: number): this;\n /**\n * Gets the outer padding.\n *\n * The outer padding is the padding in between the outer bands and the edges of the range,\n * expressed as a multiple of the rangeBand().\n *\n * @returns {number}\n */\n outerPadding(): number;\n /**\n * Sets the outer padding.\n *\n * The outer padding is the padding in between the outer bands and the edges of the range,\n * expressed as a multiple of the rangeBand().\n *\n * @returns {Category} The calling Category Scale.\n */\n outerPadding(outerPadding: number): this;\n scale(value: string): number;\n protected _getDomain(): string[];\n protected _backingScaleDomain(): string[];\n protected _backingScaleDomain(values: string[]): this;\n protected _getRange(): number[];\n protected _setRange(values: number[]): void;\n }\n}\ndeclare namespace Plottable.Scales {\n class Color extends Scale {\n private static _LOOP_LIGHTEN_FACTOR;\n private static _MAXIMUM_COLORS_FROM_CSS;\n private static _plottableColorCache;\n private _d3Scale;\n /**\n * A Color Scale maps string values to color hex values expressed as a string.\n *\n * @constructor\n * @param {string} [scaleType] One of \"Category10\"/\"Category20\"/\"Category20b\"/\"Category20c\".\n * (see https://github.com/mbostock/d3/wiki/Ordinal-Scales#categorical-colors)\n * If not supplied, reads the colors defined using CSS -- see plottable.css.\n */\n constructor(scaleType?: string);\n extentOfValues(values: string[]): string[];\n protected _getExtent(): string[];\n static invalidateColorCache(): void;\n private static _getPlottableColors();\n /**\n * Returns the color-string corresponding to a given string.\n * If there are not enough colors in the range(), a lightened version of an existing color will be used.\n *\n * @param {string} value\n * @returns {string}\n */\n scale(value: string): string;\n protected _getDomain(): string[];\n protected _backingScaleDomain(): string[];\n protected _backingScaleDomain(values: string[]): this;\n protected _getRange(): string[];\n protected _setRange(values: string[]): void;\n }\n}\ndeclare namespace Plottable.Scales {\n class Time extends QuantitativeScale {\n private _d3Scale;\n /**\n * A Time Scale maps Date objects to numbers.\n *\n * @constructor\n */\n constructor();\n /**\n * Returns an array of ticks values separated by the specified interval.\n *\n * @param {string} interval A string specifying the interval unit.\n * @param {number?} [step] The number of multiples of the interval between consecutive ticks.\n * @return {Date[]}\n */\n tickInterval(interval: string, step?: number): Date[];\n protected _setDomain(values: Date[]): void;\n protected _defaultExtent(): Date[];\n protected _expandSingleValueDomain(singleValueDomain: Date[]): Date[];\n scale(value: Date): number;\n protected _getDomain(): Date[];\n protected _backingScaleDomain(): Date[];\n protected _backingScaleDomain(values: Date[]): this;\n protected _getRange(): number[];\n protected _setRange(values: number[]): void;\n invert(value: number): Date;\n defaultTicks(): Date[];\n protected _niceDomain(domain: Date[]): Date[];\n /**\n * Transforms the Plottable TimeInterval string into a d3 time interval equivalent.\n * If the provided TimeInterval is incorrect, the default is d3.time.year\n */\n static timeIntervalToD3Time(timeInterval: string): d3.time.Interval;\n }\n}\ndeclare namespace Plottable.Scales {\n class InterpolatedColor extends Scale {\n static REDS: string[];\n static BLUES: string[];\n static POSNEG: string[];\n private _colorRange;\n private _colorScale;\n private _d3Scale;\n /**\n * An InterpolatedColor Scale maps numbers to color hex values, expressed as strings.\n *\n * @param {string} [scaleType=\"linear\"] One of \"linear\"/\"log\"/\"sqrt\"/\"pow\".\n */\n constructor(scaleType?: string);\n extentOfValues(values: number[]): number[];\n /**\n * Generates the converted QuantitativeScale.\n */\n private _d3InterpolatedScale();\n /**\n * Generates the d3 interpolator for colors.\n */\n private _interpolateColors();\n private _resetScale();\n autoDomain(): this;\n scale(value: number): string;\n protected _getDomain(): number[];\n protected _backingScaleDomain(): number[];\n protected _backingScaleDomain(values: number[]): this;\n protected _getRange(): string[];\n protected _setRange(range: string[]): void;\n }\n}\ndeclare namespace Plottable.Scales.TickGenerators {\n /**\n * Generates an array of tick values for the specified scale.\n *\n * @param {QuantitativeScale} scale\n * @returns {D[]}\n */\n interface TickGenerator {\n (scale: Plottable.QuantitativeScale): D[];\n }\n /**\n * Creates a TickGenerator using the specified interval.\n *\n * Generates ticks at multiples of the interval while also including the domain boundaries.\n *\n * @param {number} interval\n * @returns {TickGenerator}\n */\n function intervalTickGenerator(interval: number): TickGenerator;\n /**\n * Creates a TickGenerator returns only integer tick values.\n *\n * @returns {TickGenerator}\n */\n function integerTickGenerator(): TickGenerator;\n}\ndeclare namespace Plottable {\n namespace Drawers {\n /**\n * A step for the drawer to draw.\n *\n * Specifies how AttributeToProjector needs to be animated.\n */\n type DrawStep = {\n attrToProjector: AttributeToProjector;\n animator: Animator;\n };\n /**\n * A DrawStep that carries an AttributeToAppliedProjector map.\n */\n type AppliedDrawStep = {\n attrToAppliedProjector: AttributeToAppliedProjector;\n animator: Animator;\n };\n }\n class Drawer {\n private _renderArea;\n protected _svgElementName: string;\n protected _className: string;\n private _dataset;\n private _cachedSelectionValid;\n private _cachedSelection;\n /**\n * A Drawer draws svg elements based on the input Dataset.\n *\n * @constructor\n * @param {Dataset} dataset The dataset associated with this Drawer\n */\n constructor(dataset: Dataset);\n /**\n * Retrieves the renderArea selection for the Drawer.\n */\n renderArea(): d3.Selection;\n /**\n * Sets the renderArea selection for the Drawer.\n *\n * @param {d3.Selection} Selection containing the to render to.\n * @returns {Drawer} The calling Drawer.\n */\n renderArea(area: d3.Selection): this;\n /**\n * Removes the Drawer and its renderArea\n */\n remove(): void;\n /**\n * Binds data to selection\n *\n * @param{any[]} data The data to be drawn\n */\n private _bindSelectionData(data);\n protected _applyDefaultAttributes(selection: d3.Selection): void;\n /**\n * Draws data using one step\n *\n * @param{AppliedDrawStep} step The step, how data should be drawn.\n */\n private _drawStep(step);\n private _appliedProjectors(attrToProjector);\n /**\n * Calculates the total time it takes to use the input drawSteps to draw the input data\n *\n * @param {any[]} data The data that would have been drawn\n * @param {Drawers.DrawStep[]} drawSteps The DrawSteps to use\n * @returns {number} The total time it takes to draw\n */\n totalDrawTime(data: any[], drawSteps: Drawers.DrawStep[]): number;\n /**\n * Draws the data into the renderArea using the spefic steps and metadata\n *\n * @param{any[]} data The data to be drawn\n * @param{DrawStep[]} drawSteps The list of steps, which needs to be drawn\n */\n draw(data: any[], drawSteps: Drawers.DrawStep[]): this;\n selection(): d3.Selection;\n /**\n * Returns the CSS selector for this Drawer's visual elements.\n */\n selector(): string;\n /**\n * Returns the D3 selection corresponding to the datum with the specified index.\n */\n selectionForIndex(index: number): d3.Selection;\n }\n}\ndeclare namespace Plottable.Drawers {\n class Line extends Drawer {\n constructor(dataset: Dataset);\n protected _applyDefaultAttributes(selection: d3.Selection): void;\n selectionForIndex(index: number): d3.Selection;\n }\n}\ndeclare namespace Plottable.Drawers {\n class Area extends Drawer {\n constructor(dataset: Dataset);\n protected _applyDefaultAttributes(selection: d3.Selection): void;\n selectionForIndex(index: number): d3.Selection;\n }\n}\ndeclare namespace Plottable.Drawers {\n class Rectangle extends Drawer {\n constructor(dataset: Dataset);\n }\n}\ndeclare namespace Plottable.Drawers {\n class Arc extends Drawer {\n constructor(dataset: Dataset);\n protected _applyDefaultAttributes(selection: d3.Selection): void;\n }\n}\ndeclare namespace Plottable.Drawers {\n class ArcOutline extends Drawer {\n constructor(dataset: Dataset);\n protected _applyDefaultAttributes(selection: d3.Selection): void;\n }\n}\ndeclare namespace Plottable.Drawers {\n class Symbol extends Drawer {\n constructor(dataset: Dataset);\n }\n}\ndeclare namespace Plottable.Drawers {\n class Segment extends Drawer {\n constructor(dataset: Dataset);\n }\n}\ndeclare namespace Plottable {\n type ComponentCallback = (component: Component) => void;\n namespace Components {\n class Alignment {\n static TOP: string;\n static BOTTOM: string;\n static LEFT: string;\n static RIGHT: string;\n static CENTER: string;\n }\n }\n class Component {\n private _element;\n private _content;\n protected _boundingBox: d3.Selection;\n private _backgroundContainer;\n private _foregroundContainer;\n protected _clipPathEnabled: boolean;\n private _origin;\n private _parent;\n private _xAlignment;\n private static _xAlignToProportion;\n private _yAlignment;\n private static _yAlignToProportion;\n protected _isSetup: boolean;\n protected _isAnchored: boolean;\n private _boxes;\n private _boxContainer;\n private _rootSVG;\n private _isTopLevelComponent;\n private _width;\n private _height;\n private _cssClasses;\n private _destroyed;\n private _clipPathID;\n private _onAnchorCallbacks;\n private _onDetachCallbacks;\n private static _SAFARI_EVENT_BACKING_CLASS;\n constructor();\n /**\n * Attaches the Component as a child of a given d3 Selection.\n *\n * @param {d3.Selection} selection.\n * @returns {Component} The calling Component.\n */\n anchor(selection: d3.Selection): this;\n /**\n * Adds a callback to be called on anchoring the Component to the DOM.\n * If the Component is already anchored, the callback is called immediately.\n *\n * @param {ComponentCallback} callback\n * @return {Component}\n */\n onAnchor(callback: ComponentCallback): this;\n /**\n * Removes a callback that would be called on anchoring the Component to the DOM.\n * The callback is identified by reference equality.\n *\n * @param {ComponentCallback} callback\n * @return {Component}\n */\n offAnchor(callback: ComponentCallback): this;\n /**\n * Creates additional elements as necessary for the Component to function.\n * Called during anchor() if the Component's element has not been created yet.\n * Override in subclasses to provide additional functionality.\n */\n protected _setup(): void;\n /**\n * Given available space in pixels, returns the minimum width and height this Component will need.\n *\n * @param {number} availableWidth\n * @param {number} availableHeight\n * @returns {SpaceRequest}\n */\n requestedSpace(availableWidth: number, availableHeight: number): SpaceRequest;\n /**\n * Computes and sets the size, position, and alignment of the Component from the specified values.\n * If no parameters are supplied and the Component is a root node,\n * they are inferred from the size of the Component's element.\n *\n * @param {Point} [origin] Origin of the space offered to the Component.\n * @param {number} [availableWidth] Available width in pixels.\n * @param {number} [availableHeight] Available height in pixels.\n * @returns {Component} The calling Component.\n */\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n protected _sizeFromOffer(availableWidth: number, availableHeight: number): {\n width: number;\n height: number;\n };\n /**\n * Queues the Component for rendering.\n *\n * @returns {Component} The calling Component.\n */\n render(): this;\n private _scheduleComputeLayout();\n /**\n * Renders the Component without waiting for the next frame.\n */\n renderImmediately(): this;\n /**\n * Causes the Component to re-layout and render.\n *\n * This function should be called when a CSS change has occured that could\n * influence the layout of the Component, such as changing the font size.\n *\n * @returns {Component} The calling Component.\n */\n redraw(): this;\n /**\n * Renders the Component to a given .\n *\n * @param {String|d3.Selection} element A selector-string for the , or a d3 selection containing an .\n * @returns {Component} The calling Component.\n */\n renderTo(element: String | Element | d3.Selection): this;\n /**\n * Gets the x alignment of the Component.\n */\n xAlignment(): string;\n /**\n * Sets the x alignment of the Component.\n *\n * @param {string} xAlignment The x alignment of the Component (\"left\"/\"center\"/\"right\").\n * @returns {Component} The calling Component.\n */\n xAlignment(xAlignment: string): this;\n /**\n * Gets the y alignment of the Component.\n */\n yAlignment(): string;\n /**\n * Sets the y alignment of the Component.\n *\n * @param {string} yAlignment The y alignment of the Component (\"top\"/\"center\"/\"bottom\").\n * @returns {Component} The calling Component.\n */\n yAlignment(yAlignment: string): this;\n private _addBox(className?, parentElement?);\n private _generateClipPath();\n private _updateClipPath();\n /**\n * Checks if the Component has a given CSS class.\n *\n * @param {string} cssClass The CSS class to check for.\n */\n hasClass(cssClass: string): boolean;\n /**\n * Adds a given CSS class to the Component.\n *\n * @param {string} cssClass The CSS class to add.\n * @returns {Component} The calling Component.\n */\n addClass(cssClass: string): this;\n /**\n * Removes a given CSS class from the Component.\n *\n * @param {string} cssClass The CSS class to remove.\n * @returns {Component} The calling Component.\n */\n removeClass(cssClass: string): this;\n /**\n * Checks if the Component has a fixed width or if it grows to fill available space.\n * Returns false by default on the base Component class.\n */\n fixedWidth(): boolean;\n /**\n * Checks if the Component has a fixed height or if it grows to fill available space.\n * Returns false by default on the base Component class.\n */\n fixedHeight(): boolean;\n /**\n * Detaches a Component from the DOM. The Component can be reused.\n *\n * This should only be used if you plan on reusing the calling Component. Otherwise, use destroy().\n *\n * @returns The calling Component.\n */\n detach(): this;\n /**\n * Adds a callback to be called when the Component is detach()-ed.\n *\n * @param {ComponentCallback} callback\n * @return {Component} The calling Component.\n */\n onDetach(callback: ComponentCallback): this;\n /**\n * Removes a callback to be called when the Component is detach()-ed.\n * The callback is identified by reference equality.\n *\n * @param {ComponentCallback} callback\n * @return {Component} The calling Component.\n */\n offDetach(callback: ComponentCallback): this;\n /**\n * Gets the parent ComponentContainer for this Component.\n */\n parent(): ComponentContainer;\n /**\n * Sets the parent ComponentContainer for this Component.\n * An error will be thrown if the parent does not contain this Component.\n * Adding a Component to a ComponentContainer should be done\n * using the appropriate method on the ComponentContainer.\n */\n parent(parent: ComponentContainer): this;\n /**\n * Removes a Component from the DOM and disconnects all listeners.\n */\n destroy(): void;\n /**\n * Gets the width of the Component in pixels.\n */\n width(): number;\n /**\n * Gets the height of the Component in pixels.\n */\n height(): number;\n /**\n * Gets the origin of the Component relative to its parent.\n *\n * @return {Point}\n */\n origin(): Point;\n /**\n * Gets the origin of the Component relative to the root .\n *\n * @return {Point}\n */\n originToSVG(): Point;\n /**\n * Gets the Selection containing the in front of the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection}\n */\n foreground(): d3.Selection;\n /**\n * Gets a Selection containing a that holds the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection} content selection for the Component\n */\n content(): d3.Selection;\n /**\n * Gets the Selection containing the behind the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection} background selection for the Component\n */\n background(): d3.Selection;\n }\n}\ndeclare namespace Plottable {\n class ComponentContainer extends Component {\n private _detachCallback;\n constructor();\n anchor(selection: d3.Selection): this;\n render(): this;\n /**\n * Checks whether the specified Component is in the ComponentContainer.\n */\n has(component: Component): boolean;\n protected _adoptAndAnchor(component: Component): void;\n /**\n * Removes the specified Component from the ComponentContainer.\n */\n remove(component: Component): this;\n /**\n * Carry out the actual removal of a Component.\n * Implementation dependent on the type of container.\n *\n * @return {boolean} true if the Component was successfully removed, false otherwise.\n */\n protected _remove(component: Component): boolean;\n /**\n * Invokes a callback on each Component in the ComponentContainer.\n */\n protected _forEach(callback: (component: Component) => void): void;\n /**\n * Destroys the ComponentContainer and all Components within it.\n */\n destroy(): void;\n }\n}\ndeclare namespace Plottable.Components {\n class Group extends ComponentContainer {\n private _components;\n /**\n * Constructs a Group.\n *\n * A Group contains Components that will be rendered on top of each other.\n * Components added later will be rendered above Components already in the Group.\n *\n * @constructor\n * @param {Component[]} [components=[]] Components to be added to the Group.\n */\n constructor(components?: Component[]);\n protected _forEach(callback: (component: Component) => any): void;\n /**\n * Checks whether the specified Component is in the Group.\n */\n has(component: Component): boolean;\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n protected _sizeFromOffer(availableWidth: number, availableHeight: number): {\n width: number;\n height: number;\n };\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n /**\n * @return {Component[]} The Components in this Group.\n */\n components(): Component[];\n /**\n * Adds a Component to this Group.\n * The added Component will be rendered above Components already in the Group.\n */\n append(component: Component): this;\n protected _remove(component: Component): boolean;\n }\n}\ndeclare namespace Plottable.Components {\n class PlotGroup extends Group {\n entityNearest(point: Point): Plots.PlotEntity;\n /**\n * Adds a Plot to this Plot Group.\n * The added Plot will be rendered above Plots already in the Group.\n */\n append(plot: Plot): this;\n }\n}\ndeclare namespace Plottable {\n class Axis extends Component {\n /**\n * The css class applied to each end tick mark (the line on the end tick).\n */\n static END_TICK_MARK_CLASS: string;\n /**\n * The css class applied to each tick mark (the line on the tick).\n */\n static TICK_MARK_CLASS: string;\n /**\n * The css class applied to each tick label (the text associated with the tick).\n */\n static TICK_LABEL_CLASS: string;\n /**\n * The css class applied to each annotation line, which extends from the axis to the rect.\n */\n static ANNOTATION_LINE_CLASS: string;\n /**\n * The css class applied to each annotation rect, which surrounds the annotation label.\n */\n static ANNOTATION_RECT_CLASS: string;\n /**\n * The css class applied to each annotation circle, which denotes which tick is being annotated.\n */\n static ANNOTATION_CIRCLE_CLASS: string;\n /**\n * The css class applied to each annotation label, which shows the formatted annotation text.\n */\n static ANNOTATION_LABEL_CLASS: string;\n private static _ANNOTATION_LABEL_PADDING;\n protected _tickMarkContainer: d3.Selection;\n protected _tickLabelContainer: d3.Selection;\n protected _baseline: d3.Selection;\n protected _scale: Scale;\n private _formatter;\n private _orientation;\n private _endTickLength;\n private _innerTickLength;\n private _tickLabelPadding;\n private _margin;\n private _showEndTickLabels;\n private _rescaleCallback;\n private _annotatedTicks;\n private _annotationFormatter;\n private _annotationsEnabled;\n private _annotationTierCount;\n private _annotationContainer;\n private _annotationMeasurer;\n private _annotationWriter;\n /**\n * Constructs an Axis.\n * An Axis is a visual representation of a Scale.\n *\n * @constructor\n * @param {Scale} scale\n * @param {string} orientation One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n constructor(scale: Scale, orientation: string);\n destroy(): void;\n protected _isHorizontal(): boolean;\n protected _computeWidth(): number;\n protected _computeHeight(): number;\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n fixedHeight(): boolean;\n fixedWidth(): boolean;\n protected _rescale(): void;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n protected _setup(): void;\n protected _getTickValues(): D[];\n renderImmediately(): this;\n /**\n * Gets the annotated ticks.\n */\n annotatedTicks(): D[];\n /**\n * Sets the annotated ticks.\n *\n * @returns {Axis} The calling Axis.\n */\n annotatedTicks(annotatedTicks: D[]): this;\n /**\n * Gets the Formatter for the annotations.\n */\n annotationFormatter(): Formatter;\n /**\n * Sets the Formatter for the annotations.\n *\n * @returns {Axis} The calling Axis.\n */\n annotationFormatter(annotationFormatter: Formatter): this;\n /**\n * Gets if annotations are enabled.\n */\n annotationsEnabled(): boolean;\n /**\n * Sets if annotations are enabled.\n *\n * @returns {Axis} The calling Axis.\n */\n annotationsEnabled(annotationsEnabled: boolean): this;\n /**\n * Gets the count of annotation tiers to render.\n */\n annotationTierCount(): number;\n /**\n * Sets the count of annotation tiers to render.\n *\n * @returns {Axis} The calling Axis.\n */\n annotationTierCount(annotationTierCount: number): this;\n protected _drawAnnotations(): void;\n private _annotatedTicksToRender();\n /**\n * Retrieves the size of the core pieces.\n *\n * The core pieces include the labels, the end tick marks, the inner tick marks, and the tick label padding.\n */\n protected _coreSize(): number;\n protected _annotationTierHeight(): number;\n private _annotationToTier(measurements);\n protected _removeAnnotations(): void;\n protected _generateBaselineAttrHash(): {\n [key: string]: number;\n };\n protected _generateTickMarkAttrHash(isEndTickMark?: boolean): {\n [key: string]: number | ((d: any) => number);\n };\n protected _setDefaultAlignment(): void;\n /**\n * Gets the Formatter on the Axis. Tick values are passed through the\n * Formatter before being displayed.\n */\n formatter(): Formatter;\n /**\n * Sets the Formatter on the Axis. Tick values are passed through the\n * Formatter before being displayed.\n *\n * @param {Formatter} formatter\n * @returns {Axis} The calling Axis.\n */\n formatter(formatter: Formatter): this;\n /**\n * Gets the tick mark length in pixels.\n */\n innerTickLength(): number;\n /**\n * Sets the tick mark length in pixels.\n *\n * @param {number} length\n * @returns {Axis} The calling Axis.\n */\n innerTickLength(length: number): this;\n /**\n * Gets the end tick mark length in pixels.\n */\n endTickLength(): number;\n /**\n * Sets the end tick mark length in pixels.\n *\n * @param {number} length\n * @returns {Axis} The calling Axis.\n */\n endTickLength(length: number): this;\n protected _maxLabelTickLength(): number;\n /**\n * Gets the padding between each tick mark and its associated label in pixels.\n */\n tickLabelPadding(): number;\n /**\n * Sets the padding between each tick mark and its associated label in pixels.\n *\n * @param {number} padding\n * @returns {Axis} The calling Axis.\n */\n tickLabelPadding(padding: number): this;\n /**\n * Gets the margin in pixels.\n * The margin is the amount of space between the tick labels and the outer edge of the Axis.\n * The margin also determines the space that annotations will reside in if annotations are enabled.\n */\n margin(): number;\n /**\n * Sets the margin in pixels.\n * The margin is the amount of space between the tick labels and the outer edge of the Axis.\n * The margin also determines the space that annotations will reside in if annotations are enabled.\n *\n * @param {number} size\n * @returns {Axis} The calling Axis.\n */\n margin(size: number): this;\n /**\n * Gets the orientation of the Axis.\n */\n orientation(): string;\n /**\n * Sets the orientation of the Axis.\n *\n * @param {number} orientation One of \"top\"/\"bottom\"/\"left\"/\"right\".\n * @returns {Axis} The calling Axis.\n */\n orientation(orientation: string): this;\n /**\n * Gets whether the Axis shows the end tick labels.\n */\n showEndTickLabels(): boolean;\n /**\n * Sets whether the Axis shows the end tick labels.\n *\n * @param {boolean} show\n * @returns {Axis} The calling Axis.\n */\n showEndTickLabels(show: boolean): this;\n }\n}\ndeclare namespace Plottable {\n namespace TimeInterval {\n var second: string;\n var minute: string;\n var hour: string;\n var day: string;\n var week: string;\n var month: string;\n var year: string;\n }\n}\ndeclare namespace Plottable.Axes {\n /**\n * Defines a configuration for a Time Axis tier.\n * For details on how ticks are generated see: https://github.com/mbostock/d3/wiki/Time-Scales#ticks\n * interval - A time unit associated with this configuration (seconds, minutes, hours, etc).\n * step - number of intervals between each tick.\n * formatter - formatter used to format tick labels.\n */\n type TimeAxisTierConfiguration = {\n interval: string;\n step: number;\n formatter: Formatter;\n };\n /**\n * An array of linked TimeAxisTierConfigurations.\n * Each configuration will be shown on a different tier.\n * Currently, up to two tiers are supported.\n */\n type TimeAxisConfiguration = TimeAxisTierConfiguration[];\n class Time extends Axis {\n /**\n * The CSS class applied to each Time Axis tier\n */\n static TIME_AXIS_TIER_CLASS: string;\n private static _DEFAULT_TIME_AXIS_CONFIGURATIONS;\n private _tierLabelContainers;\n private _tierMarkContainers;\n private _tierBaselines;\n private _tierHeights;\n private _possibleTimeAxisConfigurations;\n private _numTiers;\n private _measurer;\n private _mostPreciseConfigIndex;\n private _tierLabelPositions;\n private static _LONG_DATE;\n /**\n * Constructs a Time Axis.\n *\n * A Time Axis is a visual representation of a Time Scale.\n *\n * @constructor\n * @param {Scales.Time} scale\n * @param {string} orientation One of \"top\"/\"bottom\".\n */\n constructor(scale: Scales.Time, orientation: string);\n /**\n * Gets the label positions for each tier.\n */\n tierLabelPositions(): string[];\n /**\n * Sets the label positions for each tier.\n *\n * @param {string[]} newPositions The positions for each tier. \"bottom\" and \"center\" are the only supported values.\n * @returns {Axes.Time} The calling Time Axis.\n */\n tierLabelPositions(newPositions: string[]): this;\n /**\n * Gets the possible TimeAxisConfigurations.\n */\n axisConfigurations(): TimeAxisConfiguration[];\n /**\n * Sets the possible TimeAxisConfigurations.\n * The Time Axis will choose the most precise configuration that will display in the available space.\n *\n * @param {TimeAxisConfiguration[]} configurations\n * @returns {Axes.Time} The calling Time Axis.\n */\n axisConfigurations(configurations: TimeAxisConfiguration[]): this;\n /**\n * Gets the index of the most precise TimeAxisConfiguration that will fit in the current width.\n */\n private _getMostPreciseConfigurationIndex();\n orientation(): string;\n orientation(orientation: string): this;\n protected _computeHeight(): number;\n private _getIntervalLength(config);\n private _maxWidthForInterval(config);\n /**\n * Check if tier configuration fits in the current width.\n */\n private _checkTimeAxisTierConfigurationWidth(config);\n protected _sizeFromOffer(availableWidth: number, availableHeight: number): {\n width: number;\n height: number;\n };\n protected _setup(): void;\n private _setupDomElements();\n private _getTickIntervalValues(config);\n protected _getTickValues(): any[];\n private _cleanTiers();\n private _getTickValuesForConfiguration(config);\n private _renderTierLabels(container, config, index);\n private _renderTickMarks(tickValues, index);\n private _renderLabellessTickMarks(tickValues);\n private _generateLabellessTicks();\n renderImmediately(): this;\n private _hideOverflowingTiers();\n private _hideOverlappingAndCutOffLabels(index);\n }\n}\ndeclare namespace Plottable.Axes {\n class Numeric extends Axis {\n private _tickLabelPositioning;\n private _usesTextWidthApproximation;\n private _measurer;\n private _wrapper;\n /**\n * Constructs a Numeric Axis.\n *\n * A Numeric Axis is a visual representation of a QuantitativeScale.\n *\n * @constructor\n * @param {QuantitativeScale} scale\n * @param {string} orientation One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n constructor(scale: QuantitativeScale, orientation: string);\n protected _setup(): void;\n protected _computeWidth(): number;\n private _computeExactTextWidth();\n private _computeApproximateTextWidth();\n protected _computeHeight(): number;\n protected _getTickValues(): number[];\n protected _rescale(): void;\n renderImmediately(): this;\n private _showAllTickMarks();\n /**\n * Hides the Tick Marks which have no corresponding Tick Labels\n */\n private _hideTickMarksWithoutLabel();\n /**\n * Gets the tick label position relative to the tick marks.\n *\n * @returns {string} The current tick label position.\n */\n tickLabelPosition(): string;\n /**\n * Sets the tick label position relative to the tick marks.\n *\n * @param {string} position \"top\"/\"center\"/\"bottom\" for a vertical Numeric Axis,\n * \"left\"/\"center\"/\"right\" for a horizontal Numeric Axis.\n * @returns {Numeric} The calling Numeric Axis.\n */\n tickLabelPosition(position: string): this;\n /**\n * Gets the approximate text width setting.\n *\n * @returns {boolean} The current text width approximation setting.\n */\n usesTextWidthApproximation(): boolean;\n /**\n * Sets the approximate text width setting. Approximating text width\n * measurements can drastically speed up plot rendering, but the plot may\n * have extra white space that would be eliminated by exact measurements.\n * Additionally, very abnormal fonts may not approximate reasonably.\n *\n * @param {boolean} The new text width approximation setting.\n * @returns {Axes.Numeric} The calling Axes.Numeric.\n */\n usesTextWidthApproximation(enable: boolean): this;\n private _hideEndTickLabels();\n private _hideOverflowingTickLabels();\n private _hideOverlappingTickLabels();\n /**\n * The method is responsible for evenly spacing the labels on the axis.\n * @return test to see if taking every `interval` recrangle from `rects`\n * will result in labels not overlapping\n *\n * For top, bottom, left, right positioning of the thicks, we want the padding\n * between the labels to be 3x, such that the label will be `padding` distance\n * from the tick and 2 * `padding` distance (or more) from the next tick\n *\n */\n private _hasOverlapWithInterval(interval, rects);\n }\n}\ndeclare namespace Plottable.Axes {\n class Category extends Axis {\n private _tickLabelAngle;\n private _measurer;\n private _wrapper;\n private _writer;\n /**\n * Constructs a Category Axis.\n *\n * A Category Axis is a visual representation of a Category Scale.\n *\n * @constructor\n * @param {Scales.Category} scale\n * @param {string} [orientation=\"bottom\"] One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n constructor(scale: Scales.Category, orientation: string);\n protected _setup(): void;\n protected _rescale(): this;\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n protected _coreSize(): number;\n protected _getTickValues(): string[];\n /**\n * Gets the tick label angle in degrees.\n */\n tickLabelAngle(): number;\n /**\n * Sets the tick label angle in degrees.\n * Right now only -90/0/90 are supported. 0 is horizontal.\n *\n * @param {number} angle\n * @returns {Category} The calling Category Axis.\n */\n tickLabelAngle(angle: number): this;\n /**\n * Measures the size of the ticks while also writing them to the DOM.\n * @param {d3.Selection} ticks The tick elements to be written to.\n */\n private _drawTicks(axisWidth, axisHeight, scale, ticks);\n /**\n * Measures the size of the ticks without making any (permanent) DOM\n * changes.\n *\n * @param {string[]} ticks The strings that will be printed on the ticks.\n */\n private _measureTicks(axisWidth, axisHeight, scale, ticks);\n renderImmediately(): this;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n }\n}\ndeclare namespace Plottable.Components {\n class Label extends Component {\n private _textContainer;\n private _text;\n private _angle;\n private _measurer;\n private _wrapper;\n private _writer;\n private _padding;\n /**\n * A Label is a Component that displays a single line of text.\n *\n * @constructor\n * @param {string} [displayText=\"\"] The text of the Label.\n * @param {number} [angle=0] The angle of the Label in degrees (-90/0/90). 0 is horizontal.\n */\n constructor(displayText?: string, angle?: number);\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n protected _setup(): void;\n /**\n * Gets the Label's text.\n */\n text(): string;\n /**\n * Sets the Label's text.\n *\n * @param {string} displayText\n * @returns {Label} The calling Label.\n */\n text(displayText: string): this;\n /**\n * Gets the angle of the Label in degrees.\n */\n angle(): number;\n /**\n * Sets the angle of the Label in degrees.\n *\n * @param {number} angle One of -90/0/90. 0 is horizontal.\n * @returns {Label} The calling Label.\n */\n angle(angle: number): this;\n /**\n * Gets the amount of padding around the Label in pixels.\n */\n padding(): number;\n /**\n * Sets the amount of padding around the Label in pixels.\n *\n * @param {number} padAmount\n * @returns {Label} The calling Label.\n */\n padding(padAmount: number): this;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n renderImmediately(): this;\n }\n class TitleLabel extends Label {\n static TITLE_LABEL_CLASS: string;\n /**\n * @constructor\n * @param {string} [text]\n * @param {number} [angle] One of -90/0/90. 0 is horizontal.\n */\n constructor(text?: string, angle?: number);\n }\n class AxisLabel extends Label {\n static AXIS_LABEL_CLASS: string;\n /**\n * @constructor\n * @param {string} [text]\n * @param {number} [angle] One of -90/0/90. 0 is horizontal.\n */\n constructor(text?: string, angle?: number);\n }\n}\ndeclare namespace Plottable.Components {\n class Legend extends Component {\n /**\n * The css class applied to each legend row\n */\n static LEGEND_ROW_CLASS: string;\n /**\n * The css class applied to each legend entry\n */\n static LEGEND_ENTRY_CLASS: string;\n /**\n * The css class applied to each legend symbol\n */\n static LEGEND_SYMBOL_CLASS: string;\n private _padding;\n private _colorScale;\n private _formatter;\n private _maxEntriesPerRow;\n private _comparator;\n private _measurer;\n private _wrapper;\n private _writer;\n private _symbolFactoryAccessor;\n private _symbolOpacityAccessor;\n private _redrawCallback;\n /**\n * The Legend consists of a series of entries, each with a color and label taken from the Color Scale.\n *\n * @constructor\n * @param {Scale.Color} scale\n */\n constructor(colorScale: Scales.Color);\n protected _setup(): void;\n /**\n * Gets the Formatter for the entry texts.\n */\n formatter(): Formatter;\n /**\n * Sets the Formatter for the entry texts.\n *\n * @param {Formatter} formatter\n * @returns {Legend} The calling Legend.\n */\n formatter(formatter: Formatter): this;\n /**\n * Gets the maximum number of entries per row.\n *\n * @returns {number}\n */\n maxEntriesPerRow(): number;\n /**\n * Sets the maximum number of entries perrow.\n *\n * @param {number} maxEntriesPerRow\n * @returns {Legend} The calling Legend.\n */\n maxEntriesPerRow(maxEntriesPerRow: number): this;\n /**\n * Gets the current comparator for the Legend's entries.\n *\n * @returns {(a: string, b: string) => number}\n */\n comparator(): (a: string, b: string) => number;\n /**\n * Sets a new comparator for the Legend's entries.\n * The comparator is used to set the display order of the entries.\n *\n * @param {(a: string, b: string) => number} comparator\n * @returns {Legend} The calling Legend.\n */\n comparator(comparator: (a: string, b: string) => number): this;\n /**\n * Gets the Color Scale.\n *\n * @returns {Scales.Color}\n */\n colorScale(): Scales.Color;\n /**\n * Sets the Color Scale.\n *\n * @param {Scales.Color} scale\n * @returns {Legend} The calling Legend.\n */\n colorScale(colorScale: Scales.Color): this;\n destroy(): void;\n private _calculateLayoutInfo(availableWidth, availableHeight);\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n private _packRows(availableWidth, entries, entryLengths);\n /**\n * Gets the Entities (representing Legend entries) at a particular point.\n * Returns an empty array if no Entities are present at that location.\n *\n * @param {Point} p\n * @returns {Entity[]}\n */\n entitiesAt(p: Point): Entity[];\n renderImmediately(): this;\n /**\n * Gets the function determining the symbols of the Legend.\n *\n * @returns {(datum: any, index: number) => symbolFactory}\n */\n symbol(): (datum: any, index: number) => SymbolFactory;\n /**\n * Sets the function determining the symbols of the Legend.\n *\n * @param {(datum: any, index: number) => SymbolFactory} symbol\n * @returns {Legend} The calling Legend\n */\n symbol(symbol: (datum: any, index: number) => SymbolFactory): this;\n /**\n * Gets the opacity of the symbols of the Legend.\n *\n * @returns {(datum: any, index: number) => number}\n */\n symbolOpacity(): (datum: any, index: number) => number;\n /**\n * Sets the opacity of the symbols of the Legend.\n *\n * @param {number | ((datum: any, index: number) => number)} symbolOpacity\n * @returns {Legend} The calling Legend\n */\n symbolOpacity(symbolOpacity: number | ((datum: any, index: number) => number)): this;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n }\n}\ndeclare namespace Plottable.Components {\n class InterpolatedColorLegend extends Component {\n private static _DEFAULT_NUM_SWATCHES;\n private _measurer;\n private _wrapper;\n private _writer;\n private _scale;\n private _orientation;\n private _textPadding;\n private _formatter;\n private _expands;\n private _swatchContainer;\n private _swatchBoundingBox;\n private _lowerLabel;\n private _upperLabel;\n private _redrawCallback;\n /**\n * The css class applied to the legend labels.\n */\n static LEGEND_LABEL_CLASS: string;\n /**\n * Creates an InterpolatedColorLegend.\n *\n * The InterpolatedColorLegend consists of a sequence of swatches that show the\n * associated InterpolatedColor Scale sampled at various points.\n * Two labels show the maximum and minimum values of the InterpolatedColor Scale.\n *\n * @constructor\n * @param {Scales.InterpolatedColor} interpolatedColorScale\n */\n constructor(interpolatedColorScale: Scales.InterpolatedColor);\n destroy(): void;\n /**\n * Gets the Formatter for the labels.\n */\n formatter(): Formatter;\n /**\n * Sets the Formatter for the labels.\n *\n * @param {Formatter} formatter\n * @returns {InterpolatedColorLegend} The calling InterpolatedColorLegend.\n */\n formatter(formatter: Formatter): this;\n /**\n * Gets whether the InterpolatedColorLegend expands to occupy all offered space in the long direction\n */\n expands(): boolean;\n /**\n * Sets whether the InterpolatedColorLegend expands to occupy all offered space in the long direction\n *\n * @param {expands} boolean\n * @returns {InterpolatedColorLegend} The calling InterpolatedColorLegend.\n */\n expands(expands: boolean): this;\n private static _ensureOrientation(orientation);\n /**\n * Gets the orientation.\n */\n orientation(): string;\n /**\n * Sets the orientation.\n *\n * @param {string} orientation One of \"horizontal\"/\"left\"/\"right\".\n * @returns {InterpolatedColorLegend} The calling InterpolatedColorLegend.\n */\n orientation(orientation: string): this;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n private _generateTicks(numSwatches?);\n protected _setup(): void;\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n private _isVertical();\n renderImmediately(): this;\n }\n}\ndeclare namespace Plottable.Components {\n class Gridlines extends Component {\n private _xScale;\n private _yScale;\n private _xLinesContainer;\n private _yLinesContainer;\n private _renderCallback;\n /**\n * @constructor\n * @param {QuantitativeScale} xScale The scale to base the x gridlines on. Pass null if no gridlines are desired.\n * @param {QuantitativeScale} yScale The scale to base the y gridlines on. Pass null if no gridlines are desired.\n */\n constructor(xScale: QuantitativeScale, yScale: QuantitativeScale);\n destroy(): this;\n protected _setup(): void;\n renderImmediately(): this;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n private _redrawXLines();\n private _redrawYLines();\n }\n}\ndeclare namespace Plottable.Components {\n class Table extends ComponentContainer {\n private _rowPadding;\n private _columnPadding;\n private _rows;\n private _rowWeights;\n private _columnWeights;\n private _nRows;\n private _nCols;\n private _calculatedLayout;\n /**\n * A Table combines Components in the form of a grid. A\n * common case is combining a y-axis, x-axis, and the plotted data via\n * ```typescript\n * new Table([[yAxis, plot],\n * [null, xAxis]]);\n * ```\n *\n * @constructor\n * @param {Component[][]} [rows=[]] A 2-D array of Components to be added to the Table.\n * null can be used if a cell is empty.\n */\n constructor(rows?: Component[][]);\n protected _forEach(callback: (component: Component) => any): void;\n /**\n * Checks whether the specified Component is in the Table.\n */\n has(component: Component): boolean;\n /**\n * Returns the Component at the specified row and column index.\n *\n * @param {number} rowIndex\n * @param {number} columnIndex\n * @returns {Component} The Component at the specified position, or null if no Component is there.\n */\n componentAt(rowIndex: number, columnIndex: number): Component;\n /**\n * Adds a Component in the specified row and column position.\n *\n * For example, instead of calling `new Table([[a, b], [null, c]])`, you\n * could call\n * var table = new Plottable.Components.Table();\n * table.add(a, 0, 0);\n * table.add(b, 0, 1);\n * table.add(c, 1, 1);\n *\n * @param {Component} component The Component to be added.\n * @param {number} row\n * @param {number} col\n * @returns {Table} The calling Table.\n */\n add(component: Component, row: number, col: number): this;\n protected _remove(component: Component): boolean;\n private _iterateLayout(availableWidth, availableHeight, isFinalOffer?);\n private _determineGuarantees(offeredWidths, offeredHeights, isFinalOffer?);\n requestedSpace(offeredWidth: number, offeredHeight: number): SpaceRequest;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n /**\n * Gets the padding above and below each row in pixels.\n */\n rowPadding(): number;\n /**\n * Sets the padding above and below each row in pixels.\n *\n * @param {number} rowPadding\n * @returns {Table} The calling Table.\n */\n rowPadding(rowPadding: number): this;\n /**\n * Gets the padding to the left and right of each column in pixels.\n */\n columnPadding(): number;\n /**\n * Sets the padding to the left and right of each column in pixels.\n *\n * @param {number} columnPadding\n * @returns {Table} The calling Table.\n */\n columnPadding(columnPadding: number): this;\n /**\n * Gets the weight of the specified row.\n *\n * @param {number} index\n */\n rowWeight(index: number): number;\n /**\n * Sets the weight of the specified row.\n * Space is allocated to rows based on their weight. Rows with higher weights receive proportionally more space.\n *\n * A common case would be to have one row take up 2/3rds of the space,\n * and the other row take up 1/3rd.\n *\n * Example:\n *\n * ```JavaScript\n * plot = new Plottable.Component.Table([\n * [row1],\n * [row2]\n * ]);\n *\n * // assign twice as much space to the first row\n * plot\n * .rowWeight(0, 2)\n * .rowWeight(1, 1)\n * ```\n *\n * @param {number} index\n * @param {number} weight\n * @returns {Table} The calling Table.\n */\n rowWeight(index: number, weight: number): this;\n /**\n * Gets the weight of the specified column.\n *\n * @param {number} index\n */\n columnWeight(index: number): number;\n /**\n * Sets the weight of the specified column.\n * Space is allocated to columns based on their weight. Columns with higher weights receive proportionally more space.\n *\n * Please see `rowWeight` docs for an example.\n *\n * @param {number} index\n * @param {number} weight\n * @returns {Table} The calling Table.\n */\n columnWeight(index: number, weight: number): this;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n private _padTableToSize(nRows, nCols);\n private static _calcComponentWeights(setWeights, componentGroups, fixityAccessor);\n private static _calcProportionalSpace(weights, freeSpace);\n private static _fixedSpace(componentGroup, fixityAccessor);\n }\n}\ndeclare namespace Plottable.Components {\n enum PropertyMode {\n VALUE = 0,\n PIXEL = 1,\n }\n class SelectionBoxLayer extends Component {\n protected _box: d3.Selection;\n private _boxArea;\n private _boxVisible;\n private _boxBounds;\n private _xExtent;\n private _yExtent;\n private _xScale;\n private _yScale;\n private _adjustBoundsCallback;\n protected _xBoundsMode: PropertyMode;\n protected _yBoundsMode: PropertyMode;\n constructor();\n protected _setup(): void;\n protected _sizeFromOffer(availableWidth: number, availableHeight: number): {\n width: number;\n height: number;\n };\n /**\n * Gets the Bounds of the box.\n */\n bounds(): Bounds;\n /**\n * Sets the Bounds of the box.\n *\n * @param {Bounds} newBounds\n * @return {SelectionBoxLayer} The calling SelectionBoxLayer.\n */\n bounds(newBounds: Bounds): this;\n protected _setBounds(newBounds: Bounds): void;\n private _getBounds();\n renderImmediately(): this;\n /**\n * Gets whether the box is being shown.\n */\n boxVisible(): boolean;\n /**\n * Shows or hides the selection box.\n *\n * @param {boolean} show Whether or not to show the box.\n * @return {SelectionBoxLayer} The calling SelectionBoxLayer.\n */\n boxVisible(show: boolean): this;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n /**\n * Gets the x scale for this SelectionBoxLayer.\n */\n xScale(): QuantitativeScale;\n /**\n * Sets the x scale for this SelectionBoxLayer.\n *\n * @returns {SelectionBoxLayer} The calling SelectionBoxLayer.\n */\n xScale(xScale: QuantitativeScale): this;\n /**\n * Gets the y scale for this SelectionBoxLayer.\n */\n yScale(): QuantitativeScale;\n /**\n * Sets the y scale for this SelectionBoxLayer.\n *\n * @returns {SelectionBoxLayer} The calling SelectionBoxLayer.\n */\n yScale(yScale: QuantitativeScale): this;\n /**\n * Gets the data values backing the left and right edges of the box.\n *\n * Returns an undefined array if the edges are not backed by a scale.\n */\n xExtent(): (number | {\n valueOf(): number;\n })[];\n /**\n * Sets the data values backing the left and right edges of the box.\n */\n xExtent(xExtent: (number | {\n valueOf(): number;\n })[]): this;\n private _getXExtent();\n protected _setXExtent(xExtent: (number | {\n valueOf(): number;\n })[]): void;\n /**\n * Gets the data values backing the top and bottom edges of the box.\n *\n * Returns an undefined array if the edges are not backed by a scale.\n */\n yExtent(): (number | {\n valueOf(): number;\n })[];\n /**\n * Sets the data values backing the top and bottom edges of the box.\n */\n yExtent(yExtent: (number | {\n valueOf(): number;\n })[]): this;\n private _getYExtent();\n protected _setYExtent(yExtent: (number | {\n valueOf(): number;\n })[]): void;\n destroy(): void;\n }\n}\ndeclare namespace Plottable.Components {\n class GuideLineLayer extends Component {\n static ORIENTATION_VERTICAL: string;\n static ORIENTATION_HORIZONTAL: string;\n private _orientation;\n private _value;\n private _scale;\n private _pixelPosition;\n private _scaleUpdateCallback;\n private _guideLine;\n private _mode;\n constructor(orientation: string);\n protected _setup(): void;\n protected _sizeFromOffer(availableWidth: number, availableHeight: number): {\n width: number;\n height: number;\n };\n protected _isVertical(): boolean;\n fixedWidth(): boolean;\n fixedHeight(): boolean;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n renderImmediately(): this;\n private _syncPixelPositionAndValue();\n protected _setPixelPositionWithoutChangingMode(pixelPosition: number): void;\n /**\n * Gets the QuantitativeScale on the GuideLineLayer.\n *\n * @return {QuantitativeScale}\n */\n scale(): QuantitativeScale;\n /**\n * Sets the QuantitativeScale on the GuideLineLayer.\n * If value() was the last property set, pixelPosition() will be updated according to the new scale.\n * If pixelPosition() was the last property set, value() will be updated according to the new scale.\n *\n * @param {QuantitativeScale} scale\n * @return {GuideLineLayer} The calling GuideLineLayer.\n */\n scale(scale: QuantitativeScale): this;\n /**\n * Gets the value of the guide line in data-space.\n *\n * @return {D}\n */\n value(): D;\n /**\n * Sets the value of the guide line in data-space.\n * If the GuideLineLayer has a scale, pixelPosition() will be updated now and whenever the scale updates.\n *\n * @param {D} value\n * @return {GuideLineLayer} The calling GuideLineLayer.\n */\n value(value: D): this;\n /**\n * Gets the position of the guide line in pixel-space.\n *\n * @return {number}\n */\n pixelPosition(): number;\n /**\n * Sets the position of the guide line in pixel-space.\n * If the GuideLineLayer has a scale, the value() will be updated now and whenever the scale updates.\n *\n * @param {number} pixelPosition\n * @return {GuideLineLayer} The calling GuideLineLayer.\n */\n pixelPosition(pixelPosition: number): this;\n destroy(): void;\n }\n}\ndeclare namespace Plottable.Plots {\n interface PlotEntity extends Entity {\n dataset: Dataset;\n index: number;\n component: Plot;\n }\n interface AccessorScaleBinding {\n accessor: Accessor;\n scale?: Scale;\n }\n namespace Animator {\n var MAIN: string;\n var RESET: string;\n }\n}\ndeclare namespace Plottable {\n class Plot extends Component {\n protected static _ANIMATION_MAX_DURATION: number;\n private _dataChanged;\n private _datasetToDrawer;\n protected _renderArea: d3.Selection;\n private _attrBindings;\n private _attrExtents;\n private _includedValuesProvider;\n private _animate;\n private _animators;\n protected _renderCallback: ScaleCallback>;\n private _onDatasetUpdateCallback;\n protected _propertyExtents: d3.Map;\n protected _propertyBindings: d3.Map>;\n /**\n * A Plot draws some visualization of the inputted Datasets.\n *\n * @constructor\n */\n constructor();\n anchor(selection: d3.Selection): this;\n protected _setup(): void;\n destroy(): void;\n protected _createNodesForDataset(dataset: Dataset): Drawer;\n protected _createDrawer(dataset: Dataset): Drawer;\n protected _getAnimator(key: string): Animator;\n protected _onDatasetUpdate(): void;\n /**\n * Gets the AccessorScaleBinding for a particular attribute.\n *\n * @param {string} attr\n */\n attr(attr: string): Plots.AccessorScaleBinding;\n /**\n * Sets a particular attribute to a constant value or the result of an Accessor.\n *\n * @param {string} attr\n * @param {number|string|Accessor|Accessor} attrValue\n * @returns {Plot} The calling Plot.\n */\n attr(attr: string, attrValue: number | string | Accessor | Accessor): this;\n /**\n * Sets a particular attribute to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the attribute values when autoDomain()-ing.\n *\n * @param {string} attr\n * @param {A|Accessor} attrValue\n * @param {Scale} scale The Scale used to scale the attrValue.\n * @returns {Plot} The calling Plot.\n */\n attr(attr: string, attrValue: A | Accessor, scale: Scale): this;\n protected _bindProperty(property: string, value: any, scale: Scale): void;\n private _bindAttr(attr, value, scale);\n protected _generateAttrToProjector(): AttributeToProjector;\n renderImmediately(): this;\n /**\n * Returns whether the plot will be animated.\n */\n animated(): boolean;\n /**\n * Enables or disables animation.\n */\n animated(willAnimate: boolean): this;\n detach(): this;\n /**\n * @returns {Scale[]} A unique array of all scales currently used by the Plot.\n */\n private _scales();\n /**\n * Updates the extents associated with each attribute, then autodomains all scales the Plot uses.\n */\n protected _updateExtents(): void;\n private _updateExtentsForAttr(attr);\n protected _updateExtentsForProperty(property: string): void;\n protected _filterForProperty(property: string): Accessor;\n private _updateExtentsForKey(key, bindings, extents, filter);\n private _computeExtent(dataset, accScaleBinding, filter);\n /**\n * Override in subclass to add special extents, such as included values\n */\n protected _extentsForProperty(property: string): any[];\n private _includedValuesForScale(scale);\n /**\n * Get the Animator associated with the specified Animator key.\n *\n * @return {Animator}\n */\n animator(animatorKey: string): Animator;\n /**\n * Set the Animator associated with the specified Animator key.\n *\n * @param {string} animatorKey\n * @param {Animator} animator\n * @returns {Plot} The calling Plot.\n */\n animator(animatorKey: string, animator: Animator): this;\n /**\n * Adds a Dataset to the Plot.\n *\n * @param {Dataset} dataset\n * @returns {Plot} The calling Plot.\n */\n addDataset(dataset: Dataset): this;\n protected _addDataset(dataset: Dataset): this;\n /**\n * Removes a Dataset from the Plot.\n *\n * @param {Dataset} dataset\n * @returns {Plot} The calling Plot.\n */\n removeDataset(dataset: Dataset): this;\n protected _removeDataset(dataset: Dataset): this;\n protected _removeDatasetNodes(dataset: Dataset): void;\n datasets(): Dataset[];\n datasets(datasets: Dataset[]): this;\n protected _getDrawersInOrder(): Drawer[];\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _additionalPaint(time: number): void;\n protected _getDataToDraw(): Utils.Map;\n private _paint();\n /**\n * Retrieves Selections of this Plot for the specified Datasets.\n *\n * @param {Dataset[]} [datasets] The Datasets to retrieve the Selections for.\n * If not provided, Selections will be retrieved for all Datasets on the Plot.\n * @returns {d3.Selection}\n */\n selections(datasets?: Dataset[]): d3.Selection;\n /**\n * Gets the Entities associated with the specified Datasets.\n *\n * @param {dataset[]} datasets The Datasets to retrieve the Entities for.\n * If not provided, returns defaults to all Datasets on the Plot.\n * @return {Plots.PlotEntity[]}\n */\n entities(datasets?: Dataset[]): Plots.PlotEntity[];\n private _lightweightEntities(datasets?);\n private _lightweightPlotEntityToPlotEntity(entity);\n /**\n * Returns the PlotEntity nearest to the query point by the Euclidian norm, or undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {Plots.PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n entityNearest(queryPoint: Point): Plots.PlotEntity;\n protected _entityVisibleOnPlot(pixelPoint: Point, datum: any, index: number, dataset: Dataset): boolean;\n protected _uninstallScaleForKey(scale: Scale, key: string): void;\n protected _installScaleForKey(scale: Scale, key: string): void;\n protected _propertyProjectors(): AttributeToProjector;\n protected static _scaledAccessor(binding: Plots.AccessorScaleBinding): Accessor;\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): Point;\n protected _animateOnNextRender(): boolean;\n }\n}\ndeclare namespace Plottable.Plots {\n class Pie extends Plot {\n private static _INNER_RADIUS_KEY;\n private static _OUTER_RADIUS_KEY;\n private static _SECTOR_VALUE_KEY;\n private _startAngles;\n private _endAngles;\n private _labelFormatter;\n private _labelsEnabled;\n private _strokeDrawers;\n /**\n * @constructor\n */\n constructor();\n protected _setup(): void;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n addDataset(dataset: Dataset): this;\n protected _addDataset(dataset: Dataset): this;\n removeDataset(dataset: Dataset): this;\n protected _removeDatasetNodes(dataset: Dataset): void;\n protected _removeDataset(dataset: Dataset): this;\n selections(datasets?: Dataset[]): d3.Selection;\n protected _onDatasetUpdate(): void;\n protected _createDrawer(dataset: Dataset): Drawers.Arc;\n entities(datasets?: Dataset[]): PlotEntity[];\n /**\n * Gets the AccessorScaleBinding for the sector value.\n */\n sectorValue(): AccessorScaleBinding;\n /**\n * Sets the sector value to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} sectorValue\n * @returns {Pie} The calling Pie Plot.\n */\n sectorValue(sectorValue: number | Accessor): this;\n /**\n * Sets the sector value to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {S|Accessor} sectorValue\n * @param {Scale} scale\n * @returns {Pie} The calling Pie Plot.\n */\n sectorValue(sectorValue: S | Accessor, scale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for the inner radius.\n */\n innerRadius(): AccessorScaleBinding;\n /**\n * Sets the inner radius to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} innerRadius\n * @returns {Pie} The calling Pie Plot.\n */\n innerRadius(innerRadius: number | Accessor): any;\n /**\n * Sets the inner radius to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {R|Accessor} innerRadius\n * @param {Scale} scale\n * @returns {Pie} The calling Pie Plot.\n */\n innerRadius(innerRadius: R | Accessor, scale: Scale): any;\n /**\n * Gets the AccessorScaleBinding for the outer radius.\n */\n outerRadius(): AccessorScaleBinding;\n /**\n * Sets the outer radius to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} outerRadius\n * @returns {Pie} The calling Pie Plot.\n */\n outerRadius(outerRadius: number | Accessor): this;\n /**\n * Sets the outer radius to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {R|Accessor} outerRadius\n * @param {Scale} scale\n * @returns {Pie} The calling Pie Plot.\n */\n outerRadius(outerRadius: R | Accessor, scale: Scale): this;\n /**\n * Get whether slice labels are enabled.\n *\n * @returns {boolean} Whether slices should display labels or not.\n */\n labelsEnabled(): boolean;\n /**\n * Sets whether labels are enabled.\n *\n * @param {boolean} labelsEnabled\n * @returns {Pie} The calling Pie Plot.\n */\n labelsEnabled(enabled: boolean): this;\n /**\n * Gets the Formatter for the labels.\n */\n labelFormatter(): Formatter;\n /**\n * Sets the Formatter for the labels.\n *\n * @param {Formatter} formatter\n * @returns {Pie} The calling Pie Plot.\n */\n labelFormatter(formatter: Formatter): this;\n entitiesAt(queryPoint: Point): PlotEntity[];\n protected _propertyProjectors(): AttributeToProjector;\n private _updatePieAngles();\n protected _getDataToDraw(): Utils.Map;\n protected static _isValidData(value: any): boolean;\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): {\n x: number;\n y: number;\n };\n protected _additionalPaint(time: number): void;\n private _generateStrokeDrawSteps();\n private _sliceIndexForPoint(p);\n private _drawLabels();\n }\n}\ndeclare namespace Plottable {\n class XYPlot extends Plot {\n protected static _X_KEY: string;\n protected static _Y_KEY: string;\n private _autoAdjustXScaleDomain;\n private _autoAdjustYScaleDomain;\n private _adjustYDomainOnChangeFromXCallback;\n private _adjustXDomainOnChangeFromYCallback;\n private _deferredRendering;\n private _cachedDomainX;\n private _cachedDomainY;\n /**\n * An XYPlot is a Plot that displays data along two primary directions, X and Y.\n *\n * @constructor\n * @param {Scale} xScale The x scale to use.\n * @param {Scale} yScale The y scale to use.\n */\n constructor();\n /**\n * Returns the whether or not the rendering is deferred for performance boost.\n * @return {boolean} The deferred rendering option\n */\n deferredRendering(): boolean;\n /**\n * Sets / unsets the deferred rendering option\n * Activating this option improves the performance of plot interaction (pan / zoom) by\n * performing lazy renders, only after the interaction has stopped. Because re-rendering\n * is no longer performed during the interaction, the zooming might experience a small\n * resolution degradation, before the lazy re-render is performed.\n *\n * This option is intended for cases where performance is an issue.\n */\n deferredRendering(deferredRendering: boolean): this;\n /**\n * Gets the AccessorScaleBinding for X.\n */\n x(): Plots.AccessorScaleBinding;\n /**\n * Sets X to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} x\n * @returns {XYPlot} The calling XYPlot.\n */\n x(x: number | Accessor): this;\n /**\n * Sets X to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {X|Accessor} x\n * @param {Scale} xScale\n * @returns {XYPlot} The calling XYPlot.\n */\n x(x: X | Accessor, xScale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for Y.\n */\n y(): Plots.AccessorScaleBinding;\n /**\n * Sets Y to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} y\n * @returns {XYPlot} The calling XYPlot.\n */\n y(y: number | Accessor): this;\n /**\n * Sets Y to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {Y|Accessor} y\n * @param {Scale} yScale\n * @returns {XYPlot} The calling XYPlot.\n */\n y(y: Y | Accessor, yScale: Scale): this;\n protected _filterForProperty(property: string): (datum: any, index: number, dataset: Dataset) => boolean;\n private _makeFilterByProperty(property);\n protected _uninstallScaleForKey(scale: Scale, key: string): void;\n protected _installScaleForKey(scale: Scale, key: string): void;\n destroy(): this;\n /**\n * Gets the automatic domain adjustment mode for visible points.\n */\n autorangeMode(): string;\n /**\n * Sets the automatic domain adjustment mode for visible points to operate against the X Scale, Y Scale, or neither.\n * If \"x\" or \"y\" is specified the adjustment is immediately performed.\n *\n * @param {string} autorangeMode One of \"x\"/\"y\"/\"none\".\n * \"x\" will adjust the x Scale in relation to changes in the y domain.\n * \"y\" will adjust the y Scale in relation to changes in the x domain.\n * \"none\" means neither Scale will change automatically.\n * @returns {XYPlot} The calling XYPlot.\n */\n autorangeMode(autorangeMode: string): this;\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n private _updateXExtentsAndAutodomain();\n private _updateYExtentsAndAutodomain();\n /**\n * Adjusts the domains of both X and Y scales to show all data.\n * This call does not override the autorange() behavior.\n *\n * @returns {XYPlot} The calling XYPlot.\n */\n showAllData(): this;\n private _adjustYDomainOnChangeFromX();\n private _adjustXDomainOnChangeFromY();\n protected _projectorsReady(): boolean;\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): Point;\n protected _getDataToDraw(): Utils.Map;\n }\n}\ndeclare namespace Plottable.Plots {\n class Rectangle extends XYPlot {\n private static _X2_KEY;\n private static _Y2_KEY;\n private _labelsEnabled;\n private _label;\n /**\n * A Rectangle Plot displays rectangles based on the data.\n * The left and right edges of each rectangle can be set with x() and x2().\n * If only x() is set the Rectangle Plot will attempt to compute the correct left and right edge positions.\n * The top and bottom edges of each rectangle can be set with y() and y2().\n * If only y() is set the Rectangle Plot will attempt to compute the correct top and bottom edge positions.\n *\n * @constructor\n * @param {Scale.Scale} xScale\n * @param {Scale.Scale} yScale\n */\n constructor();\n protected _createDrawer(dataset: Dataset): Drawers.Rectangle;\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _updateExtentsForProperty(property: string): void;\n protected _filterForProperty(property: string): (datum: any, index: number, dataset: Dataset) => boolean;\n /**\n * Gets the AccessorScaleBinding for X.\n */\n x(): AccessorScaleBinding;\n /**\n * Sets X to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} x\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n x(x: number | Accessor): this;\n /**\n * Sets X to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {X|Accessor} x\n * @param {Scale} xScale\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n x(x: X | Accessor, xScale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for X2.\n */\n x2(): AccessorScaleBinding;\n /**\n * Sets X2 to a constant number or the result of an Accessor.\n * If a Scale has been set for X, it will also be used to scale X2.\n *\n * @param {number|Accessor|X|Accessor} x2\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n x2(x2: number | Accessor | X | Accessor): this;\n /**\n * Gets the AccessorScaleBinding for Y.\n */\n y(): AccessorScaleBinding;\n /**\n * Sets Y to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} y\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n y(y: number | Accessor): this;\n /**\n * Sets Y to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {Y|Accessor} y\n * @param {Scale} yScale\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n y(y: Y | Accessor, yScale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for Y2.\n */\n y2(): AccessorScaleBinding;\n /**\n * Sets Y2 to a constant number or the result of an Accessor.\n * If a Scale has been set for Y, it will also be used to scale Y2.\n *\n * @param {number|Accessor|Y|Accessor} y2\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n y2(y2: number | Accessor | Y | Accessor): this;\n /**\n * Gets the PlotEntities at a particular Point.\n *\n * @param {Point} point The point to query.\n * @returns {PlotEntity[]} The PlotEntities at the particular point\n */\n entitiesAt(point: Point): PlotEntity[];\n /**\n * Gets the Entities that intersect the Bounds.\n *\n * @param {Bounds} bounds\n * @returns {PlotEntity[]}\n */\n entitiesIn(bounds: Bounds): PlotEntity[];\n /**\n * Gets the Entities that intersect the area defined by the ranges.\n *\n * @param {Range} xRange\n * @param {Range} yRange\n * @returns {PlotEntity[]}\n */\n entitiesIn(xRange: Range, yRange: Range): PlotEntity[];\n private _entityBBox(datum, index, dataset, attrToProjector);\n private _entitiesIntersecting(xValOrRange, yValOrRange);\n /**\n * Gets the accessor for labels.\n *\n * @returns {Accessor}\n */\n label(): Accessor;\n /**\n * Sets the text of labels to the result of an Accessor.\n *\n * @param {Accessor} label\n * @returns {Plots.Rectangle} The calling Rectangle Plot.\n */\n label(label: Accessor): this;\n /**\n * Gets whether labels are enabled.\n *\n * @returns {boolean}\n */\n labelsEnabled(): boolean;\n /**\n * Sets whether labels are enabled.\n * Labels too big to be contained in the rectangle, cut off by edges, or blocked by other rectangles will not be shown.\n *\n * @param {boolean} labelsEnabled\n * @returns {Rectangle} The calling Rectangle Plot.\n */\n labelsEnabled(enabled: boolean): this;\n protected _propertyProjectors(): AttributeToProjector;\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): {\n x: any;\n y: any;\n };\n private _rectangleWidth(scale);\n protected _getDataToDraw(): Utils.Map;\n protected _additionalPaint(time: number): void;\n private _drawLabels();\n private _drawLabel(dataToDraw, dataset, datasetIndex);\n private _overlayLabel(labelXRange, labelYRange, datumIndex, datasetIndex, dataToDraw);\n }\n}\ndeclare namespace Plottable.Plots {\n class Scatter extends XYPlot {\n private static _SIZE_KEY;\n private static _SYMBOL_KEY;\n /**\n * A Scatter Plot draws a symbol at each data point.\n *\n * @constructor\n */\n constructor();\n protected _createDrawer(dataset: Dataset): Drawers.Symbol;\n /**\n * Gets the AccessorScaleBinding for the size property of the plot.\n * The size property corresponds to the area of the symbol.\n */\n size(): AccessorScaleBinding;\n /**\n * Sets the size property to a constant number or the result of an Accessor.\n *\n * @param {number|Accessor} size\n * @returns {Plots.Scatter} The calling Scatter Plot.\n */\n size(size: number | Accessor): this;\n /**\n * Sets the size property to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {S|Accessor} sectorValue\n * @param {Scale} scale\n * @returns {Plots.Scatter} The calling Scatter Plot.\n */\n size(size: S | Accessor, scale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for the symbol property of the plot.\n * The symbol property corresponds to how the symbol will be drawn.\n */\n symbol(): AccessorScaleBinding;\n /**\n * Sets the symbol property to an Accessor.\n *\n * @param {Accessor} symbol\n * @returns {Plots.Scatter} The calling Scatter Plot.\n */\n symbol(symbol: Accessor): this;\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _entityVisibleOnPlot(pixelPoint: Point, datum: any, index: number, dataset: Dataset): boolean;\n protected _propertyProjectors(): AttributeToProjector;\n /**\n * Gets the Entities that intersect the Bounds.\n *\n * @param {Bounds} bounds\n * @returns {PlotEntity[]}\n */\n entitiesIn(bounds: Bounds): PlotEntity[];\n /**\n * Gets the Entities that intersect the area defined by the ranges.\n *\n * @param {Range} xRange\n * @param {Range} yRange\n * @returns {PlotEntity[]}\n */\n entitiesIn(xRange: Range, yRange: Range): PlotEntity[];\n /**\n * Gets the Entities at a particular Point.\n *\n * @param {Point} p\n * @returns {PlotEntity[]}\n */\n entitiesAt(p: Point): PlotEntity[];\n }\n}\ndeclare namespace Plottable.Plots {\n class Bar extends XYPlot {\n static ORIENTATION_VERTICAL: string;\n static ORIENTATION_HORIZONTAL: string;\n private static _BAR_WIDTH_RATIO;\n private static _SINGLE_BAR_DIMENSION_RATIO;\n private static _BAR_AREA_CLASS;\n private static _LABEL_AREA_CLASS;\n private static _LABEL_VERTICAL_PADDING;\n private static _LABEL_HORIZONTAL_PADDING;\n private _baseline;\n private _baselineValue;\n protected _isVertical: boolean;\n private _labelFormatter;\n private _labelsEnabled;\n private _hideBarsIfAnyAreTooWide;\n private _labelConfig;\n private _baselineValueProvider;\n private _barPixelWidth;\n private _updateBarPixelWidthCallback;\n /**\n * A Bar Plot draws bars growing out from a baseline to some value\n *\n * @constructor\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n constructor(orientation?: string);\n x(): Plots.AccessorScaleBinding;\n x(x: number | Accessor): this;\n x(x: X | Accessor, xScale: Scale): this;\n y(): Plots.AccessorScaleBinding;\n y(y: number | Accessor): this;\n y(y: Y | Accessor, yScale: Scale): this;\n /**\n * Gets the orientation of the plot\n *\n * @return \"vertical\" | \"horizontal\"\n */\n orientation(): string;\n render(): this;\n protected _createDrawer(dataset: Dataset): Drawers.Rectangle;\n protected _setup(): void;\n /**\n * Gets the baseline value.\n * The baseline is the line that the bars are drawn from.\n *\n * @returns {X|Y}\n */\n baselineValue(): X | Y;\n /**\n * Sets the baseline value.\n * The baseline is the line that the bars are drawn from.\n *\n * @param {X|Y} value\n * @returns {Bar} The calling Bar Plot.\n */\n baselineValue(value: X | Y): this;\n addDataset(dataset: Dataset): this;\n protected _addDataset(dataset: Dataset): this;\n removeDataset(dataset: Dataset): this;\n protected _removeDataset(dataset: Dataset): this;\n datasets(): Dataset[];\n datasets(datasets: Dataset[]): this;\n /**\n * Get whether bar labels are enabled.\n *\n * @returns {boolean} Whether bars should display labels or not.\n */\n labelsEnabled(): boolean;\n /**\n * Sets whether labels are enabled.\n *\n * @param {boolean} labelsEnabled\n * @returns {Bar} The calling Bar Plot.\n */\n labelsEnabled(enabled: boolean): this;\n /**\n * Gets the Formatter for the labels.\n */\n labelFormatter(): Formatter;\n /**\n * Sets the Formatter for the labels.\n *\n * @param {Formatter} formatter\n * @returns {Bar} The calling Bar Plot.\n */\n labelFormatter(formatter: Formatter): this;\n protected _createNodesForDataset(dataset: Dataset): Drawer;\n protected _removeDatasetNodes(dataset: Dataset): void;\n /**\n * Returns the PlotEntity nearest to the query point according to the following algorithm:\n * - If the query point is inside a bar, returns the PlotEntity for that bar.\n * - Otherwise, gets the nearest PlotEntity by the primary direction (X for vertical, Y for horizontal),\n * breaking ties with the secondary direction.\n * Returns undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n entityNearest(queryPoint: Point): PlotEntity;\n protected _entityVisibleOnPlot(pixelPoint: Point, datum: any, index: number, dataset: Dataset): boolean;\n /**\n * Gets the Entities at a particular Point.\n *\n * @param {Point} p\n * @returns {PlotEntity[]}\n */\n entitiesAt(p: Point): PlotEntity[];\n /**\n * Gets the Entities that intersect the Bounds.\n *\n * @param {Bounds} bounds\n * @returns {PlotEntity[]}\n */\n entitiesIn(bounds: Bounds): PlotEntity[];\n /**\n * Gets the Entities that intersect the area defined by the ranges.\n *\n * @param {Range} xRange\n * @param {Range} yRange\n * @returns {PlotEntity[]}\n */\n entitiesIn(xRange: Range, yRange: Range): PlotEntity[];\n private _entitiesIntersecting(xValOrRange, yValOrRange);\n private _updateValueScale();\n protected _additionalPaint(time: number): void;\n /**\n * Makes sure the extent takes into account the widths of the bars\n */\n protected _extentsForProperty(property: string): any[];\n private _drawLabels();\n private _drawLabel(data, dataset);\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n /**\n * Computes the barPixelWidth of all the bars in the plot.\n *\n * If the position scale of the plot is a CategoryScale and in bands mode, then the rangeBands function will be used.\n * If the position scale of the plot is a QuantitativeScale, then the bar width is equal to the smallest distance between\n * two adjacent data points, padded for visualisation.\n */\n protected _getBarPixelWidth(): number;\n private _updateBarPixelWidth();\n entities(datasets?: Dataset[]): PlotEntity[];\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): Point;\n protected _uninstallScaleForKey(scale: Scale, key: string): void;\n protected _getDataToDraw(): Utils.Map;\n }\n}\ndeclare namespace Plottable.Plots {\n class Line extends XYPlot {\n private _interpolator;\n private _autorangeSmooth;\n private _croppedRenderingEnabled;\n private _downsamplingEnabled;\n /**\n * A Line Plot draws line segments starting from the first data point to the next.\n *\n * @constructor\n */\n constructor();\n x(): Plots.AccessorScaleBinding;\n x(x: number | Accessor): this;\n x(x: X | Accessor, xScale: Scale): this;\n y(): Plots.AccessorScaleBinding;\n y(y: number | Accessor): this;\n y(y: number | Accessor, yScale: Scale): this;\n autorangeMode(): string;\n autorangeMode(autorangeMode: string): this;\n /**\n * Gets whether or not the autoranging is done smoothly.\n */\n autorangeSmooth(): boolean;\n /**\n * Sets whether or not the autorange is done smoothly.\n *\n * Smooth autoranging is done by making sure lines always exit on the left / right side of the plot\n * and deactivating the nice domain feature on the scales\n */\n autorangeSmooth(autorangeSmooth: boolean): this;\n private _setScaleSnapping();\n /**\n * Gets the interpolation function associated with the plot.\n *\n * @return {string | (points: Array<[number, number]>) => string)}\n */\n interpolator(): string | ((points: Array<[number, number]>) => string);\n /**\n * Sets the interpolation function associated with the plot.\n *\n * @param {string | points: Array<[number, number]>) => string} interpolator Interpolation function\n * @return Plots.Line\n */\n interpolator(interpolator: string | ((points: Array<[number, number]>) => string)): this;\n interpolator(interpolator: \"linear\"): this;\n interpolator(interpolator: \"linear-closed\"): this;\n interpolator(interpolator: \"step\"): this;\n interpolator(interpolator: \"step-before\"): this;\n interpolator(interpolator: \"step-after\"): this;\n interpolator(interpolator: \"basis\"): this;\n interpolator(interpolator: \"basis-open\"): this;\n interpolator(interpolator: \"basis-closed\"): this;\n interpolator(interpolator: \"bundle\"): this;\n interpolator(interpolator: \"cardinal\"): this;\n interpolator(interpolator: \"cardinal-open\"): this;\n interpolator(interpolator: \"cardinal-closed\"): this;\n interpolator(interpolator: \"monotone\"): this;\n /**\n * Gets if downsampling is enabled\n *\n * When downsampling is enabled, two consecutive lines with the same slope will be merged to one line.\n */\n downsamplingEnabled(): boolean;\n /**\n * Sets if downsampling is enabled\n *\n * @returns {Plots.Line} The calling Plots.Line\n */\n downsamplingEnabled(downsampling: boolean): this;\n /**\n * Gets if croppedRendering is enabled\n *\n * When croppedRendering is enabled, lines that will not be visible in the viewport will not be drawn.\n */\n croppedRenderingEnabled(): boolean;\n /**\n * Sets if croppedRendering is enabled\n *\n * @returns {Plots.Line} The calling Plots.Line\n */\n croppedRenderingEnabled(croppedRendering: boolean): this;\n protected _createDrawer(dataset: Dataset): Drawer;\n protected _extentsForProperty(property: string): any[];\n private _getEdgeIntersectionPoints();\n protected _getResetYFunction(): (d: any, i: number, dataset: Dataset) => number;\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n /**\n * Returns the PlotEntity nearest to the query point by X then by Y, or undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n entityNearestByXThenY(queryPoint: Point): PlotEntity;\n protected _propertyProjectors(): AttributeToProjector;\n protected _constructLineProjector(xProjector: Projector, yProjector: Projector): (datum: any, index: number, dataset: Dataset) => string;\n protected _getDataToDraw(): Utils.Map;\n private _filterCroppedRendering(dataset, indices);\n private _filterDownsampling(dataset, indices);\n }\n}\ndeclare namespace Plottable.Plots {\n class Area extends Line {\n private static _Y0_KEY;\n private _lineDrawers;\n private _constantBaselineValueProvider;\n /**\n * An Area Plot draws a filled region (area) between Y and Y0.\n *\n * @constructor\n */\n constructor();\n protected _setup(): void;\n y(): Plots.AccessorScaleBinding;\n y(y: number | Accessor): this;\n y(y: number | Accessor, yScale: QuantitativeScale): this;\n /**\n * Gets the AccessorScaleBinding for Y0.\n */\n y0(): Plots.AccessorScaleBinding;\n /**\n * Sets Y0 to a constant number or the result of an Accessor.\n * If a Scale has been set for Y, it will also be used to scale Y0.\n *\n * @param {number|Accessor} y0\n * @returns {Area} The calling Area Plot.\n */\n y0(y0: number | Accessor): this;\n protected _onDatasetUpdate(): void;\n addDataset(dataset: Dataset): this;\n protected _addDataset(dataset: Dataset): this;\n protected _removeDatasetNodes(dataset: Dataset): void;\n protected _additionalPaint(): void;\n private _generateLineDrawSteps();\n private _generateLineAttrToProjector();\n protected _createDrawer(dataset: Dataset): Drawers.Area;\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _updateYScale(): void;\n protected _getResetYFunction(): Accessor;\n protected _propertyProjectors(): AttributeToProjector;\n selections(datasets?: Dataset[]): d3.Selection;\n protected _constructAreaProjector(xProjector: Projector, yProjector: Projector, y0Projector: Projector): (datum: any[], index: number, dataset: Dataset) => string;\n }\n}\ndeclare namespace Plottable.Plots {\n class ClusteredBar extends Bar {\n private _clusterOffsets;\n /**\n * A ClusteredBar Plot groups bars across Datasets based on the primary value of the bars.\n * On a vertical ClusteredBar Plot, the bars with the same X value are grouped.\n * On a horizontal ClusteredBar Plot, the bars with the same Y value are grouped.\n *\n * @constructor\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n constructor(orientation?: string);\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n private _updateClusterPosition();\n private _makeInnerScale();\n protected _getDataToDraw(): Utils.Map;\n }\n}\ndeclare namespace Plottable.Plots {\n class StackedArea extends Area {\n private _stackingResult;\n private _stackedExtent;\n private _baseline;\n private _baselineValue;\n private _baselineValueProvider;\n /**\n * @constructor\n */\n constructor();\n croppedRenderingEnabled(): boolean;\n croppedRenderingEnabled(croppedRendering: boolean): this;\n protected _getAnimator(key: string): Animator;\n protected _setup(): void;\n x(): Plots.AccessorScaleBinding;\n x(x: number | Accessor): this;\n x(x: X | Accessor, xScale: Scale): this;\n y(): Plots.AccessorScaleBinding;\n y(y: number | Accessor): this;\n y(y: number | Accessor, yScale: QuantitativeScale): this;\n /**\n * Gets if downsampling is enabled\n *\n * When downsampling is enabled, two consecutive lines with the same slope will be merged to one line.\n */\n downsamplingEnabled(): boolean;\n /**\n * Sets if downsampling is enabled\n *\n * For now, downsampling is always disabled in stacked area plot\n * @returns {Plots.StackedArea} The calling Plots.StackedArea\n */\n downsamplingEnabled(downsampling: boolean): this;\n protected _additionalPaint(): void;\n protected _updateYScale(): void;\n protected _onDatasetUpdate(): this;\n protected _updateExtentsForProperty(property: string): void;\n protected _extentsForProperty(attr: string): any[];\n private _updateStackExtentsAndOffsets();\n private _checkSameDomain(datasets, keyAccessor);\n /**\n * Given an array of Datasets and the accessor function for the key, computes the\n * set reunion (no duplicates) of the domain of each Dataset. The keys are stringified\n * before being returned.\n *\n * @param {Dataset[]} datasets The Datasets for which we extract the domain keys\n * @param {Accessor} keyAccessor The accessor for the key of the data\n * @return {string[]} An array of stringified keys\n */\n private static _domainKeys(datasets, keyAccessor);\n protected _propertyProjectors(): AttributeToProjector;\n protected _pixelPoint(datum: any, index: number, dataset: Dataset): Point;\n }\n}\ndeclare namespace Plottable.Plots {\n class StackedBar extends Bar {\n private _stackingResult;\n private _stackedExtent;\n /**\n * A StackedBar Plot stacks bars across Datasets based on the primary value of the bars.\n * On a vertical StackedBar Plot, the bars with the same X value are stacked.\n * On a horizontal StackedBar Plot, the bars with the same Y value are stacked.\n *\n * @constructor\n * @param {Scale} xScale\n * @param {Scale} yScale\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n constructor(orientation?: string);\n x(): Plots.AccessorScaleBinding;\n x(x: number | Accessor): this;\n x(x: X | Accessor, xScale: Scale): this;\n y(): Plots.AccessorScaleBinding;\n y(y: number | Accessor): this;\n y(y: Y | Accessor, yScale: Scale): this;\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n protected _onDatasetUpdate(): this;\n protected _updateExtentsForProperty(property: string): void;\n protected _extentsForProperty(attr: string): any[];\n private _updateStackExtentsAndOffsets();\n }\n}\ndeclare namespace Plottable.Plots {\n class Segment extends XYPlot {\n private static _X2_KEY;\n private static _Y2_KEY;\n /**\n * A Segment Plot displays line segments based on the data.\n *\n * @constructor\n */\n constructor();\n protected _createDrawer(dataset: Dataset): Drawers.Segment;\n protected _generateDrawSteps(): Drawers.DrawStep[];\n protected _updateExtentsForProperty(property: string): void;\n protected _filterForProperty(property: string): (datum: any, index: number, dataset: Dataset) => boolean;\n /**\n * Gets the AccessorScaleBinding for X\n */\n x(): AccessorScaleBinding;\n /**\n * Sets X to a constant value or the result of an Accessor.\n *\n * @param {X|Accessor} x\n * @returns {Plots.Segment} The calling Segment Plot.\n */\n x(x: number | Accessor): this;\n /**\n * Sets X to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {X|Accessor} x\n * @param {Scale} xScale\n * @returns {Plots.Segment} The calling Segment Plot.\n */\n x(x: X | Accessor, xScale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for X2\n */\n x2(): AccessorScaleBinding;\n /**\n * Sets X2 to a constant number or the result of an Accessor.\n * If a Scale has been set for X, it will also be used to scale X2.\n *\n * @param {number|Accessor|Y|Accessor} y2\n * @returns {Plots.Segment} The calling Segment Plot\n */\n x2(x2: number | Accessor | X | Accessor): this;\n /**\n * Gets the AccessorScaleBinding for Y\n */\n y(): AccessorScaleBinding;\n /**\n * Sets Y to a constant value or the result of an Accessor.\n *\n * @param {Y|Accessor} y\n * @returns {Plots.Segment} The calling Segment Plot.\n */\n y(y: number | Accessor): this;\n /**\n * Sets Y to a scaled constant value or scaled result of an Accessor.\n * The provided Scale will account for the values when autoDomain()-ing.\n *\n * @param {Y|Accessor} y\n * @param {Scale} yScale\n * @returns {Plots.Segment} The calling Segment Plot.\n */\n y(y: Y | Accessor, yScale: Scale): this;\n /**\n * Gets the AccessorScaleBinding for Y2.\n */\n y2(): AccessorScaleBinding;\n /**\n * Sets Y2 to a constant number or the result of an Accessor.\n * If a Scale has been set for Y, it will also be used to scale Y2.\n *\n * @param {number|Accessor|Y|Accessor} y2\n * @returns {Plots.Segment} The calling Segment Plot.\n */\n y2(y2: number | Accessor | Y | Accessor): this;\n protected _propertyProjectors(): AttributeToProjector;\n /**\n * Gets the Entities that intersect the Bounds.\n *\n * @param {Bounds} bounds\n * @returns {PlotEntity[]}\n */\n entitiesIn(bounds: Bounds): PlotEntity[];\n /**\n * Gets the Entities that intersect the area defined by the ranges.\n *\n * @param {Range} xRange\n * @param {Range} yRange\n * @returns {PlotEntity[]}\n */\n entitiesIn(xRange: Range, yRange: Range): PlotEntity[];\n private _entitiesIntersecting(xRange, yRange);\n private _lineIntersectsBox(entity, xRange, yRange, attrToProjector);\n private _lineIntersectsSegment(point1, point2, point3, point4);\n }\n}\ndeclare namespace Plottable.Plots {\n class Waterfall extends Bar {\n private static _BAR_DECLINE_CLASS;\n private static _BAR_GROWTH_CLASS;\n private static _BAR_TOTAL_CLASS;\n private static _CONNECTOR_CLASS;\n private static _CONNECTOR_AREA_CLASS;\n private static _TOTAL_KEY;\n private _connectorArea;\n private _connectorsEnabled;\n private _extent;\n private _subtotals;\n constructor();\n /**\n * Gets whether connectors are enabled.\n *\n * @returns {boolean} Whether connectors should be shown or not.\n */\n connectorsEnabled(): boolean;\n /**\n * Sets whether connectors are enabled.\n *\n * @param {boolean} enabled\n * @returns {Plots.Waterfall} The calling Waterfall Plot.\n */\n connectorsEnabled(enabled: boolean): this;\n /**\n * Gets the AccessorScaleBinding for whether a bar represents a total or a delta.\n */\n total(): Plots.AccessorScaleBinding;\n /**\n * Sets total to a constant number or the result of an Accessor\n *\n * @param {Accessor}\n * @returns {Plots.Waterfall} The calling Waterfall Plot.\n */\n total(total: Accessor): this;\n protected _additionalPaint(time: number): void;\n protected _createNodesForDataset(dataset: Dataset): Drawer;\n protected _extentsForProperty(attr: string): any[];\n protected _generateAttrToProjector(): {\n [attr: string]: (datum: any, index: number, dataset: Dataset) => any;\n };\n protected _onDatasetUpdate(): this;\n private _calculateSubtotalsAndExtent(dataset);\n private _drawConnectors();\n private _updateSubtotals();\n }\n}\ndeclare namespace Plottable {\n interface Animator {\n /**\n * Applies the supplied attributes to a d3.Selection with some animation.\n *\n * @param {d3.Selection} selection The update selection or transition selection that we wish to animate.\n * @param {AttributeToAppliedProjector} attrToAppliedProjector The set of\n * AppliedProjectors that we will use to set attributes on the selection.\n * @return {any} Animators should return the selection or\n * transition object so that plots may chain the transitions between\n * animators.\n */\n animate(selection: d3.Selection, attrToAppliedProjector: AttributeToAppliedProjector): d3.Selection | d3.Transition;\n /**\n * Given the number of elements, return the total time the animation requires\n *\n * @param {number} numberofIterations The number of elements that will be drawn\n * @returns {number}\n */\n totalTime(numberOfIterations: number): number;\n }\n}\ndeclare namespace Plottable.Animators {\n /**\n * An animator implementation with no animation. The attributes are\n * immediately set on the selection.\n */\n class Null implements Animator {\n totalTime(selection: any): number;\n animate(selection: d3.Selection, attrToAppliedProjector: AttributeToAppliedProjector): d3.Selection;\n }\n}\ndeclare namespace Plottable.Animators {\n /**\n * An Animator with easing and configurable durations and delays.\n */\n class Easing implements Animator {\n /**\n * The default starting delay of the animation in milliseconds\n */\n private static _DEFAULT_START_DELAY_MILLISECONDS;\n /**\n * The default duration of one animation step in milliseconds\n */\n private static _DEFAULT_STEP_DURATION_MILLISECONDS;\n /**\n * The default maximum start delay between each step of an animation\n */\n private static _DEFAULT_ITERATIVE_DELAY_MILLISECONDS;\n /**\n * The default maximum total animation duration\n */\n private static _DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS;\n /**\n * The default easing of the animation\n */\n private static _DEFAULT_EASING_MODE;\n private _startDelay;\n private _stepDuration;\n private _stepDelay;\n private _maxTotalDuration;\n private _easingMode;\n /**\n * Constructs the default animator\n *\n * @constructor\n */\n constructor();\n totalTime(numberOfSteps: number): number;\n animate(selection: d3.Selection, attrToAppliedProjector: AttributeToAppliedProjector): d3.Transition;\n /**\n * Gets the start delay of the animation in milliseconds.\n *\n * @returns {number} The current start delay.\n */\n startDelay(): number;\n /**\n * Sets the start delay of the animation in milliseconds.\n *\n * @param {number} startDelay The start delay in milliseconds.\n * @returns {Easing} The calling Easing Animator.\n */\n startDelay(startDelay: number): this;\n /**\n * Gets the duration of one animation step in milliseconds.\n *\n * @returns {number} The current duration.\n */\n stepDuration(): number;\n /**\n * Sets the duration of one animation step in milliseconds.\n *\n * @param {number} stepDuration The duration in milliseconds.\n * @returns {Easing} The calling Easing Animator.\n */\n stepDuration(stepDuration: number): this;\n /**\n * Gets the maximum start delay between animation steps in milliseconds.\n *\n * @returns {number} The current maximum iterative delay.\n */\n stepDelay(): number;\n /**\n * Sets the maximum start delay between animation steps in milliseconds.\n *\n * @param {number} stepDelay The maximum iterative delay in milliseconds.\n * @returns {Easing} The calling Easing Animator.\n */\n stepDelay(stepDelay: number): this;\n /**\n * Gets the maximum total animation duration constraint in milliseconds.\n *\n * If the animation time would exceed the specified time, the duration of each step\n * and the delay between each step will be reduced until the animation fits within\n * the specified time.\n *\n * @returns {number} The current maximum total animation duration.\n */\n maxTotalDuration(): number;\n /**\n * Sets the maximum total animation duration constraint in miliseconds.\n *\n * If the animation time would exceed the specified time, the duration of each step\n * and the delay between each step will be reduced until the animation fits within\n * the specified time.\n *\n * @param {number} maxTotalDuration The maximum total animation duration in milliseconds.\n * @returns {Easing} The calling Easing Animator.\n */\n maxTotalDuration(maxTotalDuration: number): this;\n /**\n * Gets the current easing mode of the animation.\n *\n * @returns {string} the current easing mode.\n */\n easingMode(): string;\n /**\n * Sets the easing mode of the animation.\n *\n * @param {string} easingMode The desired easing mode.\n * @returns {Easing} The calling Easing Animator.\n */\n easingMode(easingMode: string): this;\n /**\n * Adjust the iterative delay, such that it takes into account the maxTotalDuration constraint\n */\n private _getAdjustedIterativeDelay(numberOfSteps);\n }\n}\ndeclare namespace Plottable {\n class Dispatcher {\n protected _eventToProcessingFunction: {\n [eventName: string]: (e: Event) => any;\n };\n private _eventNameToCallbackSet;\n private _connected;\n private _hasNoCallbacks();\n private _connect();\n private _disconnect();\n protected _addCallbackForEvent(eventName: string, callback: Function): void;\n protected _removeCallbackForEvent(eventName: string, callback: Function): void;\n protected _callCallbacksForEvent(eventName: string, ...args: any[]): void;\n }\n}\ndeclare namespace Plottable.Dispatchers {\n type MouseCallback = (p: Point, event: MouseEvent) => void;\n class Mouse extends Dispatcher {\n private static _DISPATCHER_KEY;\n private _translator;\n private _lastMousePosition;\n private static _MOUSEOVER_EVENT_NAME;\n private static _MOUSEMOVE_EVENT_NAME;\n private static _MOUSEOUT_EVENT_NAME;\n private static _MOUSEDOWN_EVENT_NAME;\n private static _MOUSEUP_EVENT_NAME;\n private static _WHEEL_EVENT_NAME;\n private static _DBLCLICK_EVENT_NAME;\n /**\n * Get a Mouse Dispatcher for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n *\n * @param {SVGElement} elem\n * @return {Dispatchers.Mouse}\n */\n static getDispatcher(elem: SVGElement): Dispatchers.Mouse;\n /**\n * This constructor not be invoked directly.\n *\n * @constructor\n * @param {SVGElement} svg The root to attach to.\n */\n constructor(svg: SVGElement);\n /**\n * Registers a callback to be called when the mouse position changes.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n onMouseMove(callback: MouseCallback): this;\n /**\n * Removes a callback that would be called when the mouse position changes.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n offMouseMove(callback: MouseCallback): this;\n /**\n * Registers a callback to be called when a mousedown occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n onMouseDown(callback: MouseCallback): this;\n /**\n * Removes a callback that would be called when a mousedown occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n offMouseDown(callback: MouseCallback): this;\n /**\n * Registers a callback to be called when a mouseup occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n onMouseUp(callback: MouseCallback): this;\n /**\n * Removes a callback that would be called when a mouseup occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n offMouseUp(callback: MouseCallback): this;\n /**\n * Registers a callback to be called when a wheel event occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n onWheel(callback: MouseCallback): this;\n /**\n * Removes a callback that would be called when a wheel event occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n offWheel(callback: MouseCallback): this;\n /**\n * Registers a callback to be called when a dblClick occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n onDblClick(callback: MouseCallback): this;\n /**\n * Removes a callback that would be called when a dblClick occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n offDblClick(callback: MouseCallback): this;\n /**\n * Computes the mouse position from the given event, and if successful\n * calls all the callbacks in the provided callbackSet.\n */\n private _measureAndDispatch(event, eventName, scope?);\n eventInsideSVG(event: MouseEvent): boolean;\n /**\n * Returns the last computed mouse position in coordinate space.\n *\n * @return {Point}\n */\n lastMousePosition(): Point;\n }\n}\ndeclare namespace Plottable.Dispatchers {\n type TouchCallback = (ids: number[], idToPoint: {\n [id: number]: Point;\n }, event: TouchEvent) => void;\n class Touch extends Dispatcher {\n private static _DISPATCHER_KEY;\n private static _TOUCHSTART_EVENT_NAME;\n private static _TOUCHMOVE_EVENT_NAME;\n private static _TOUCHEND_EVENT_NAME;\n private static _TOUCHCANCEL_EVENT_NAME;\n private _translator;\n /**\n * Gets a Touch Dispatcher for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n *\n * @param {SVGElement} elem\n * @return {Dispatchers.Touch}\n */\n static getDispatcher(elem: SVGElement): Dispatchers.Touch;\n /**\n * This constructor should not be invoked directly.\n *\n * @constructor\n * @param {SVGElement} svg The root to attach to.\n */\n constructor(svg: SVGElement);\n /**\n * Registers a callback to be called when a touch starts.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n onTouchStart(callback: TouchCallback): this;\n /**\n * Removes a callback that would be called when a touch starts.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n offTouchStart(callback: TouchCallback): this;\n /**\n * Registers a callback to be called when the touch position changes.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n onTouchMove(callback: TouchCallback): this;\n /**\n * Removes a callback that would be called when the touch position changes.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n offTouchMove(callback: TouchCallback): this;\n /**\n * Registers a callback to be called when a touch ends.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n onTouchEnd(callback: TouchCallback): this;\n /**\n * Removes a callback that would be called when a touch ends.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n offTouchEnd(callback: TouchCallback): this;\n /**\n * Registers a callback to be called when a touch is cancelled.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n onTouchCancel(callback: TouchCallback): this;\n /**\n * Removes a callback that would be called when a touch is cancelled.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n offTouchCancel(callback: TouchCallback): this;\n /**\n * Computes the Touch position from the given event, and if successful\n * calls all the callbacks in the provided callbackSet.\n */\n private _measureAndDispatch(event, eventName, scope?);\n eventInsideSVG(event: TouchEvent): boolean;\n }\n}\ndeclare namespace Plottable.Dispatchers {\n type KeyCallback = (keyCode: number, event: KeyboardEvent) => void;\n class Key extends Dispatcher {\n private static _DISPATCHER_KEY;\n private static _KEYDOWN_EVENT_NAME;\n private static _KEYUP_EVENT_NAME;\n /**\n * Gets a Key Dispatcher. If one already exists it will be returned;\n * otherwise, a new one will be created.\n *\n * @return {Dispatchers.Key}\n */\n static getDispatcher(): Dispatchers.Key;\n /**\n * This constructor should not be invoked directly.\n *\n * @constructor\n */\n constructor();\n private _processKeydown(event);\n private _processKeyup(event);\n /**\n * Registers a callback to be called whenever a key is pressed.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n onKeyDown(callback: KeyCallback): this;\n /**\n * Removes the callback to be called whenever a key is pressed.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n offKeyDown(callback: KeyCallback): this;\n /** Registers a callback to be called whenever a key is released.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n onKeyUp(callback: KeyCallback): this;\n /**\n * Removes the callback to be called whenever a key is released.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n offKeyUp(callback: KeyCallback): this;\n }\n}\ndeclare namespace Plottable {\n class Interaction {\n protected _componentAttachedTo: Component;\n private _anchorCallback;\n private _isAnchored;\n private _enabled;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n /**\n * Attaches this Interaction to a Component.\n * If the Interaction was already attached to a Component, it first detaches itself from the old Component.\n *\n * @param {Component} component\n * @returns {Interaction} The calling Interaction.\n */\n attachTo(component: Component): this;\n private _connect();\n /**\n * Detaches this Interaction from the Component.\n * This Interaction can be reused.\n *\n * @param {Component} component\n * @returns {Interaction} The calling Interaction.\n */\n detachFrom(component: Component): this;\n private _disconnect();\n /**\n * Gets whether this Interaction is enabled.\n */\n enabled(): boolean;\n /**\n * Enables or disables this Interaction.\n *\n * @param {boolean} enabled Whether the Interaction should be enabled.\n * @return {Interaction} The calling Interaction.\n */\n enabled(enabled: boolean): this;\n /**\n * Translates an -coordinate-space point to Component-space coordinates.\n *\n * @param {Point} p A Point in -space coordinates.\n * @return {Point} The same location in Component-space coordinates.\n */\n protected _translateToComponentSpace(p: Point): Point;\n /**\n * Checks whether a Component-coordinate-space Point is inside the Component.\n *\n * @param {Point} p A Point in Compoennt-space coordinates.\n * @return {boolean} Whether or not the point is inside the Component.\n */\n protected _isInsideComponent(p: Point): boolean;\n }\n}\ndeclare namespace Plottable {\n type ClickCallback = (point: Point) => void;\n}\ndeclare namespace Plottable.Interactions {\n class Click extends Interaction {\n private _mouseDispatcher;\n private _touchDispatcher;\n private _clickedDown;\n private _onClickCallbacks;\n private _mouseDownCallback;\n private _mouseUpCallback;\n private _touchStartCallback;\n private _touchEndCallback;\n private _touchCancelCallback;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _handleClickDown(p);\n private _handleClickUp(p);\n /**\n * Adds a callback to be called when the Component is clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.Click} The calling Click Interaction.\n */\n onClick(callback: ClickCallback): this;\n /**\n * Removes a callback that would be called when the Component is clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.Click} The calling Click Interaction.\n */\n offClick(callback: ClickCallback): this;\n }\n}\ndeclare namespace Plottable.Interactions {\n class DoubleClick extends Interaction {\n private _mouseDispatcher;\n private _touchDispatcher;\n private _clickState;\n private _clickedDown;\n private _clickedPoint;\n private _onDoubleClickCallbacks;\n private _mouseDownCallback;\n private _mouseUpCallback;\n private _dblClickCallback;\n private _touchStartCallback;\n private _touchEndCallback;\n private _touchCancelCallback;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _handleClickDown(p);\n private _handleClickUp(p);\n private _handleDblClick();\n private _handleClickCancel();\n private static _pointsEqual(p1, p2);\n /**\n * Adds a callback to be called when the Component is double-clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.DoubleClick} The calling DoubleClick Interaction.\n */\n onDoubleClick(callback: ClickCallback): this;\n /**\n * Removes a callback that would be called when the Component is double-clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.DoubleClick} The calling DoubleClick Interaction.\n */\n offDoubleClick(callback: ClickCallback): this;\n }\n}\ndeclare namespace Plottable {\n type KeyCallback = (keyCode: number) => void;\n namespace Interactions {\n class Key extends Interaction {\n /**\n * A Key Interaction listens to key events that occur while the Component is\n * moused over.\n */\n private _positionDispatcher;\n private _keyDispatcher;\n private _keyPressCallbacks;\n private _keyReleaseCallbacks;\n private _mouseMoveCallback;\n private _downedKeys;\n private _keyDownCallback;\n private _keyUpCallback;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _handleKeyDownEvent(keyCode, event);\n private _handleKeyUpEvent(keyCode);\n /**\n * Adds a callback to be called when the key with the given keyCode is\n * pressed and the user is moused over the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n onKeyPress(keyCode: number, callback: KeyCallback): this;\n /**\n * Removes a callback that would be called when the key with the given keyCode is\n * pressed and the user is moused over the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n offKeyPress(keyCode: number, callback: KeyCallback): this;\n /**\n * Adds a callback to be called when the key with the given keyCode is\n * released if the key was pressed with the mouse inside of the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n onKeyRelease(keyCode: number, callback: KeyCallback): this;\n /**\n * Removes a callback that would be called when the key with the given keyCode is\n * released if the key was pressed with the mouse inside of the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n offKeyRelease(keyCode: number, callback: KeyCallback): this;\n }\n }\n}\ndeclare namespace Plottable {\n type PointerCallback = (point: Point) => void;\n}\ndeclare namespace Plottable.Interactions {\n class Pointer extends Interaction {\n private _mouseDispatcher;\n private _touchDispatcher;\n private _overComponent;\n private _pointerEnterCallbacks;\n private _pointerMoveCallbacks;\n private _pointerExitCallbacks;\n private _mouseMoveCallback;\n private _touchStartCallback;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _handleMouseEvent(p, e);\n private _handleTouchEvent(p, e);\n private _handlePointerEvent(p, insideSVG);\n /**\n * Adds a callback to be called when the pointer enters the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n onPointerEnter(callback: PointerCallback): this;\n /**\n * Removes a callback that would be called when the pointer enters the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n offPointerEnter(callback: PointerCallback): this;\n /**\n * Adds a callback to be called when the pointer moves within the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n onPointerMove(callback: PointerCallback): this;\n /**\n * Removes a callback that would be called when the pointer moves within the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n offPointerMove(callback: PointerCallback): this;\n /**\n * Adds a callback to be called when the pointer exits the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n onPointerExit(callback: PointerCallback): this;\n /**\n * Removes a callback that would be called when the pointer exits the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n offPointerExit(callback: PointerCallback): this;\n }\n}\ndeclare namespace Plottable.Interactions {\n class PanZoom extends Interaction {\n /**\n * The number of pixels occupied in a line.\n */\n private static _PIXELS_PER_LINE;\n private _xScales;\n private _yScales;\n private _dragInteraction;\n private _mouseDispatcher;\n private _touchDispatcher;\n private _touchIds;\n private _wheelCallback;\n private _touchStartCallback;\n private _touchMoveCallback;\n private _touchEndCallback;\n private _touchCancelCallback;\n private _minDomainExtents;\n private _maxDomainExtents;\n /**\n * A PanZoom Interaction updates the domains of an x-scale and/or a y-scale\n * in response to the user panning or zooming.\n *\n * @constructor\n * @param {QuantitativeScale} [xScale] The x-scale to update on panning/zooming.\n * @param {QuantitativeScale} [yScale] The y-scale to update on panning/zooming.\n */\n constructor(xScale?: QuantitativeScale, yScale?: QuantitativeScale);\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _handleTouchStart(ids, idToPoint, e);\n private _handlePinch(ids, idToPoint, e);\n private static _centerPoint(point1, point2);\n private static _pointDistance(point1, point2);\n private _handleTouchEnd(ids, idToPoint, e);\n private _magnifyScale(scale, magnifyAmount, centerValue);\n private _translateScale(scale, translateAmount);\n private _handleWheelEvent(p, e);\n private _constrainedZoomAmount(scale, zoomAmount);\n private _setupDragInteraction();\n private _nonLinearScaleWithExtents(scale);\n /**\n * Gets the x scales for this PanZoom Interaction.\n */\n xScales(): QuantitativeScale[];\n /**\n * Sets the x scales for this PanZoom Interaction.\n *\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n xScales(xScales: QuantitativeScale[]): this;\n /**\n * Gets the y scales for this PanZoom Interaction.\n */\n yScales(): QuantitativeScale[];\n /**\n * Sets the y scales for this PanZoom Interaction.\n *\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n yScales(yScales: QuantitativeScale[]): this;\n /**\n * Adds an x scale to this PanZoom Interaction\n *\n * @param {QuantitativeScale} An x scale to add\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n addXScale(xScale: QuantitativeScale): this;\n /**\n * Removes an x scale from this PanZoom Interaction\n *\n * @param {QuantitativeScale} An x scale to remove\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n removeXScale(xScale: QuantitativeScale): this;\n /**\n * Adds a y scale to this PanZoom Interaction\n *\n * @param {QuantitativeScale} A y scale to add\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n addYScale(yScale: QuantitativeScale): this;\n /**\n * Removes a y scale from this PanZoom Interaction\n *\n * @param {QuantitativeScale} A y scale to remove\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n removeYScale(yScale: QuantitativeScale): this;\n /**\n * Gets the minimum domain extent for the scale, specifying the minimum allowable amount\n * between the ends of the domain.\n *\n * Note that extents will mainly work on scales that work linearly like Linear Scale and Time Scale\n *\n * @param {QuantitativeScale} quantitativeScale The scale to query\n * @returns {D} The minimum domain extent for the scale.\n */\n minDomainExtent(quantitativeScale: QuantitativeScale): D;\n /**\n * Sets the minimum domain extent for the scale, specifying the minimum allowable amount\n * between the ends of the domain.\n *\n * Note that extents will mainly work on scales that work linearly like Linear Scale and Time Scale\n *\n * @param {QuantitativeScale} quantitativeScale The scale to query\n * @param {D} minDomainExtent The minimum domain extent for the scale.\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n minDomainExtent(quantitativeScale: QuantitativeScale, minDomainExtent: D): this;\n /**\n * Gets the maximum domain extent for the scale, specifying the maximum allowable amount\n * between the ends of the domain.\n *\n * Note that extents will mainly work on scales that work linearly like Linear Scale and Time Scale\n *\n * @param {QuantitativeScale} quantitativeScale The scale to query\n * @returns {D} The maximum domain extent for the scale.\n */\n maxDomainExtent(quantitativeScale: QuantitativeScale): D;\n /**\n * Sets the maximum domain extent for the scale, specifying the maximum allowable amount\n * between the ends of the domain.\n *\n * Note that extents will mainly work on scales that work linearly like Linear Scale and Time Scale\n *\n * @param {QuantitativeScale} quantitativeScale The scale to query\n * @param {D} minDomainExtent The maximum domain extent for the scale.\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n maxDomainExtent(quantitativeScale: QuantitativeScale, maxDomainExtent: D): this;\n }\n}\ndeclare namespace Plottable {\n type DragCallback = (start: Point, end: Point) => void;\n}\ndeclare namespace Plottable.Interactions {\n class Drag extends Interaction {\n private _dragging;\n private _constrainedToComponent;\n private _mouseDispatcher;\n private _touchDispatcher;\n private _dragOrigin;\n private _dragStartCallbacks;\n private _dragCallbacks;\n private _dragEndCallbacks;\n private _mouseDownCallback;\n private _mouseMoveCallback;\n private _mouseUpCallback;\n private _touchStartCallback;\n private _touchMoveCallback;\n private _touchEndCallback;\n protected _anchor(component: Component): void;\n protected _unanchor(): void;\n private _translateAndConstrain(p);\n private _startDrag(point, event);\n private _doDrag(point, event);\n private _endDrag(point, event);\n /**\n * Gets whether the Drag Interaction constrains Points passed to its\n * callbacks to lie inside its Component.\n *\n * If true, when the user drags outside of the Component, the closest Point\n * inside the Component will be passed to the callback instead of the actual\n * cursor position.\n *\n * @return {boolean}\n */\n constrainedToComponent(): boolean;\n /**\n * Sets whether the Drag Interaction constrains Points passed to its\n * callbacks to lie inside its Component.\n *\n * If true, when the user drags outside of the Component, the closest Point\n * inside the Component will be passed to the callback instead of the actual\n * cursor position.\n *\n * @param {boolean}\n * @return {Interactions.Drag} The calling Drag Interaction.\n */\n constrainedToComponent(constrainedToComponent: boolean): this;\n /**\n * Adds a callback to be called when dragging starts.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n onDragStart(callback: DragCallback): this;\n /**\n * Removes a callback that would be called when dragging starts.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n offDragStart(callback: DragCallback): this;\n /**\n * Adds a callback to be called during dragging.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n onDrag(callback: DragCallback): this;\n /**\n * Removes a callback that would be called during dragging.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n offDrag(callback: DragCallback): this;\n /**\n * Adds a callback to be called when dragging ends.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n onDragEnd(callback: DragCallback): this;\n /**\n * Removes a callback that would be called when dragging ends.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n offDragEnd(callback: DragCallback): this;\n }\n}\ndeclare namespace Plottable {\n type DragBoxCallback = (bounds: Bounds) => void;\n}\ndeclare namespace Plottable.Components {\n class DragBoxLayer extends Components.SelectionBoxLayer {\n private _dragInteraction;\n private _detectionEdgeT;\n private _detectionEdgeB;\n private _detectionEdgeL;\n private _detectionEdgeR;\n private _detectionCornerTL;\n private _detectionCornerTR;\n private _detectionCornerBL;\n private _detectionCornerBR;\n private _detectionRadius;\n private _resizable;\n private _movable;\n protected _hasCorners: boolean;\n private _dragStartCallbacks;\n private _dragCallbacks;\n private _dragEndCallbacks;\n private _disconnectInteraction;\n /**\n * Constructs a DragBoxLayer.\n *\n * A DragBoxLayer is a SelectionBoxLayer with a built-in Drag Interaction.\n * A drag gesture will set the Bounds of the box.\n * If resizing is enabled using resizable(true), the edges of box can be repositioned.\n *\n * @constructor\n */\n constructor();\n private _setUpCallbacks();\n protected _setup(): void;\n private _getResizingEdges(p);\n renderImmediately(): this;\n /**\n * Gets the detection radius of the drag box in pixels.\n */\n detectionRadius(): number;\n /**\n * Sets the detection radius of the drag box in pixels.\n *\n * @param {number} r\n * @return {DragBoxLayer} The calling DragBoxLayer.\n */\n detectionRadius(r: number): this;\n /**\n * Gets whether or not the drag box is resizable.\n */\n resizable(): boolean;\n /**\n * Sets whether or not the drag box is resizable.\n *\n * @param {boolean} canResize\n * @return {DragBoxLayer} The calling DragBoxLayer.\n */\n resizable(canResize: boolean): this;\n protected _setResizableClasses(canResize: boolean): void;\n /**\n * Gets whether or not the drag box is movable.\n */\n movable(): boolean;\n /**\n * Sets whether or not the drag box is movable.\n *\n * @param {boolean} movable\n * @return {DragBoxLayer} The calling DragBoxLayer.\n */\n movable(movable: boolean): this;\n private _setMovableClass();\n /**\n * Sets the callback to be called when dragging starts.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n onDragStart(callback: DragBoxCallback): this;\n /**\n * Removes a callback to be called when dragging starts.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n offDragStart(callback: DragBoxCallback): this;\n /**\n * Sets a callback to be called during dragging.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n onDrag(callback: DragBoxCallback): this;\n /**\n * Removes a callback to be called during dragging.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n offDrag(callback: DragBoxCallback): this;\n /**\n * Sets a callback to be called when dragging ends.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n onDragEnd(callback: DragBoxCallback): this;\n /**\n * Removes a callback to be called when dragging ends.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n offDragEnd(callback: DragBoxCallback): this;\n /**\n * Gets the internal Interactions.Drag of the DragBoxLayer.\n */\n dragInteraction(): Interactions.Drag;\n /**\n * Enables or disables the interaction and drag box.\n */\n enabled(enabled: boolean): this;\n /**\n * Gets the enabled state.\n */\n enabled(): boolean;\n destroy(): void;\n detach(): this;\n anchor(selection: d3.Selection): this;\n private _resetState();\n }\n}\ndeclare namespace Plottable.Components {\n class XDragBoxLayer extends DragBoxLayer {\n /**\n * An XDragBoxLayer is a DragBoxLayer whose size can only be set in the X-direction.\n * The y-values of the bounds() are always set to 0 and the height() of the XDragBoxLayer.\n *\n * @constructor\n */\n constructor();\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n protected _setBounds(newBounds: Bounds): void;\n protected _setResizableClasses(canResize: boolean): void;\n yScale(): QuantitativeScale;\n yScale(yScale: QuantitativeScale): this;\n yExtent(): (number | {\n valueOf(): number;\n })[];\n yExtent(yExtent: (number | {\n valueOf(): number;\n })[]): this;\n }\n}\ndeclare namespace Plottable.Components {\n class YDragBoxLayer extends DragBoxLayer {\n /**\n * A YDragBoxLayer is a DragBoxLayer whose size can only be set in the Y-direction.\n * The x-values of the bounds() are always set to 0 and the width() of the YDragBoxLayer.\n *\n * @constructor\n */\n constructor();\n computeLayout(origin?: Point, availableWidth?: number, availableHeight?: number): this;\n protected _setBounds(newBounds: Bounds): void;\n protected _setResizableClasses(canResize: boolean): void;\n xScale(): QuantitativeScale;\n xScale(xScale: QuantitativeScale): this;\n xExtent(): (number | {\n valueOf(): number;\n })[];\n xExtent(xExtent: (number | {\n valueOf(): number;\n })[]): this;\n }\n}\ndeclare namespace Plottable {\n interface DragLineCallback {\n (dragLineLayer: Components.DragLineLayer): void;\n }\n}\ndeclare namespace Plottable.Components {\n class DragLineLayer extends GuideLineLayer {\n private _dragInteraction;\n private _detectionRadius;\n private _detectionEdge;\n private _enabled;\n private _dragStartCallbacks;\n private _dragCallbacks;\n private _dragEndCallbacks;\n private _disconnectInteraction;\n constructor(orientation: string);\n protected _setup(): void;\n renderImmediately(): this;\n /**\n * Gets the detection radius of the drag line in pixels.\n */\n detectionRadius(): number;\n /**\n * Sets the detection radius of the drag line in pixels.\n *\n * @param {number} detectionRadius\n * @return {DragLineLayer} The calling DragLineLayer.\n */\n detectionRadius(detectionRadius: number): this;\n /**\n * Gets whether the DragLineLayer is enabled.\n */\n enabled(): boolean;\n /**\n * Enables or disables the DragLineLayer.\n *\n * @param {boolean} enabled\n * @return {DragLineLayer} The calling DragLineLayer.\n */\n enabled(enabled: boolean): this;\n /**\n * Sets the callback to be called when dragging starts.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n onDragStart(callback: DragLineCallback): this;\n /**\n * Removes a callback that would be called when dragging starts.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n offDragStart(callback: DragLineCallback): this;\n /**\n * Sets a callback to be called during dragging.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n onDrag(callback: DragLineCallback): this;\n /**\n * Removes a callback that would be called during dragging.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n offDrag(callback: DragLineCallback): this;\n /**\n * Sets a callback to be called when dragging ends.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n onDragEnd(callback: DragLineCallback): this;\n /**\n * Removes a callback that would be called when dragging ends.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n offDragEnd(callback: DragLineCallback): this;\n destroy(): void;\n }\n}\n" }, { "path": "cluster/benchmarks/bower_components/plottable/plottable.js", "content": "/*!\nPlottable 2.2.0 (https://github.com/palantir/plottable)\nCopyright 2014-2015 Palantir Technologies\nLicensed under MIT (https://github.com/palantir/plottable/blob/master/LICENSE)\n*/\n\n(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module unless amdModuleId is set\n define([\"d3\"], function (a0) {\n return (root['Plottable'] = factory(a0));\n });\n } else if (typeof exports === 'object') {\n // Node. Does not work with strict CommonJS, but\n // only CommonJS-like environments that support module.exports,\n // like Node.\n module.exports = factory(require(\"d3\"));\n } else {\n root['Plottable'] = factory(d3);\n }\n}(this, function (d3) {\n\nvar __extends = (this && this.__extends) || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n};\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var Math;\n (function (Math) {\n var nativeMath = window.Math;\n /**\n * Checks if x is between a and b.\n *\n * @param {number} x The value to test if in range\n * @param {number} a The beginning of the (inclusive) range\n * @param {number} b The ending of the (inclusive) range\n * @return {boolean} Whether x is in [a, b]\n */\n function inRange(x, a, b) {\n return (nativeMath.min(a, b) <= x && x <= nativeMath.max(a, b));\n }\n Math.inRange = inRange;\n /**\n * Clamps x to the range [min, max].\n *\n * @param {number} x The value to be clamped.\n * @param {number} min The minimum value.\n * @param {number} max The maximum value.\n * @return {number} A clamped value in the range [min, max].\n */\n function clamp(x, min, max) {\n return nativeMath.min(nativeMath.max(min, x), max);\n }\n Math.clamp = clamp;\n function max(array, firstArg, secondArg) {\n var accessor = typeof (firstArg) === \"function\" ? firstArg : null;\n var defaultValue = accessor == null ? firstArg : secondArg;\n /* tslint:disable:ban */\n var maxValue = accessor == null ? d3.max(array) : d3.max(array, accessor);\n /* tslint:enable:ban */\n return maxValue !== undefined ? maxValue : defaultValue;\n }\n Math.max = max;\n function min(array, firstArg, secondArg) {\n var accessor = typeof (firstArg) === \"function\" ? firstArg : null;\n var defaultValue = accessor == null ? firstArg : secondArg;\n /* tslint:disable:ban */\n var minValue = accessor == null ? d3.min(array) : d3.min(array, accessor);\n /* tslint:enable:ban */\n return minValue !== undefined ? minValue : defaultValue;\n }\n Math.min = min;\n /**\n * Returns true **only** if x is NaN\n */\n function isNaN(n) {\n return n !== n;\n }\n Math.isNaN = isNaN;\n /**\n * Returns true if the argument is a number, which is not NaN\n * Numbers represented as strings do not pass this function\n */\n function isValidNumber(n) {\n return typeof n === \"number\" && !Plottable.Utils.Math.isNaN(n) && isFinite(n);\n }\n Math.isValidNumber = isValidNumber;\n /**\n * Generates an array of consecutive, strictly increasing numbers\n * in the range [start, stop) separeted by step\n */\n function range(start, stop, step) {\n if (step === void 0) { step = 1; }\n if (step === 0) {\n throw new Error(\"step cannot be 0\");\n }\n var length = nativeMath.max(nativeMath.ceil((stop - start) / step), 0);\n var range = [];\n for (var i = 0; i < length; ++i) {\n range[i] = start + step * i;\n }\n return range;\n }\n Math.range = range;\n /**\n * Returns the square of the distance between two points\n *\n * @param {Point} p1\n * @param {Point} p2\n * @return {number} dist(p1, p2)^2\n */\n function distanceSquared(p1, p2) {\n return nativeMath.pow(p2.y - p1.y, 2) + nativeMath.pow(p2.x - p1.x, 2);\n }\n Math.distanceSquared = distanceSquared;\n function degreesToRadians(degree) {\n return degree / 360 * nativeMath.PI * 2;\n }\n Math.degreesToRadians = degreesToRadians;\n })(Math = Utils.Math || (Utils.Math = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n /**\n * Shim for ES6 map.\n * https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map\n */\n var Map = (function () {\n function Map() {\n if (typeof window.Map === \"function\") {\n this._es6Map = new window.Map();\n }\n else {\n this._keyValuePairs = [];\n }\n }\n Map.prototype.set = function (key, value) {\n if (Utils.Math.isNaN(key)) {\n throw new Error(\"NaN may not be used as a key to the Map\");\n }\n if (this._es6Map != null) {\n this._es6Map.set(key, value);\n return this;\n }\n for (var i = 0; i < this._keyValuePairs.length; i++) {\n if (this._keyValuePairs[i].key === key) {\n this._keyValuePairs[i].value = value;\n return this;\n }\n }\n this._keyValuePairs.push({ key: key, value: value });\n return this;\n };\n Map.prototype.get = function (key) {\n if (this._es6Map != null) {\n return this._es6Map.get(key);\n }\n for (var i = 0; i < this._keyValuePairs.length; i++) {\n if (this._keyValuePairs[i].key === key) {\n return this._keyValuePairs[i].value;\n }\n }\n return undefined;\n };\n Map.prototype.has = function (key) {\n if (this._es6Map != null) {\n return this._es6Map.has(key);\n }\n for (var i = 0; i < this._keyValuePairs.length; i++) {\n if (this._keyValuePairs[i].key === key) {\n return true;\n }\n }\n return false;\n };\n Map.prototype.forEach = function (callbackFn, thisArg) {\n var _this = this;\n if (this._es6Map != null) {\n var callbackWrapper = function (value, key) { return callbackFn.call(thisArg, value, key, _this); };\n this._es6Map.forEach(callbackWrapper, thisArg);\n return;\n }\n this._keyValuePairs.forEach(function (keyValuePair) {\n callbackFn.call(thisArg, keyValuePair.value, keyValuePair.key, _this);\n });\n };\n Map.prototype.delete = function (key) {\n if (this._es6Map != null) {\n return this._es6Map.delete(key);\n }\n for (var i = 0; i < this._keyValuePairs.length; i++) {\n if (this._keyValuePairs[i].key === key) {\n this._keyValuePairs.splice(i, 1);\n return true;\n }\n }\n return false;\n };\n return Map;\n }());\n Utils.Map = Map;\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n /**\n * Shim for ES6 set.\n * https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Set\n */\n var Set = (function () {\n function Set() {\n if (typeof window.Set === \"function\") {\n this._es6Set = new window.Set();\n }\n else {\n this._values = [];\n }\n this.size = 0;\n }\n Set.prototype.add = function (value) {\n if (this._es6Set != null) {\n this._es6Set.add(value);\n this.size = this._es6Set.size;\n return this;\n }\n if (!this.has(value)) {\n this._values.push(value);\n this.size = this._values.length;\n }\n return this;\n };\n Set.prototype.delete = function (value) {\n if (this._es6Set != null) {\n var deleted = this._es6Set.delete(value);\n this.size = this._es6Set.size;\n return deleted;\n }\n var index = this._values.indexOf(value);\n if (index !== -1) {\n this._values.splice(index, 1);\n this.size = this._values.length;\n return true;\n }\n return false;\n };\n Set.prototype.has = function (value) {\n if (this._es6Set != null) {\n return this._es6Set.has(value);\n }\n return this._values.indexOf(value) !== -1;\n };\n Set.prototype.forEach = function (callback, thisArg) {\n var _this = this;\n if (this._es6Set != null) {\n var callbackWrapper = function (value, value2) { return callback.call(thisArg, value, value2, _this); };\n this._es6Set.forEach(callbackWrapper, thisArg);\n return;\n }\n this._values.forEach(function (value) {\n callback.call(thisArg, value, value, _this);\n });\n };\n return Set;\n }());\n Utils.Set = Set;\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var DOM;\n (function (DOM) {\n var nativeMath = window.Math;\n /**\n * Gets the bounding box of an element.\n * @param {d3.Selection} element\n * @returns {SVGRed} The bounding box.\n */\n function elementBBox(element) {\n var bbox;\n // HACKHACK: Firefox won't correctly measure nodes with style \"display: none\" or their descendents (FF Bug 612118).\n try {\n bbox = element.node().getBBox();\n }\n catch (err) {\n bbox = { x: 0, y: 0, width: 0, height: 0 };\n }\n return bbox;\n }\n DOM.elementBBox = elementBBox;\n /**\n * Screen refresh rate which is assumed to be 60fps\n */\n DOM.SCREEN_REFRESH_RATE_MILLISECONDS = 1000 / 60;\n /**\n * Polyfill for `window.requestAnimationFrame`.\n * If the function exists, then we use the function directly.\n * Otherwise, we set a timeout on `SCREEN_REFRESH_RATE_MILLISECONDS` and then perform the function.\n *\n * @param {() => void} callback The callback to call in the next animation frame\n */\n function requestAnimationFramePolyfill(callback) {\n if (window.requestAnimationFrame != null) {\n window.requestAnimationFrame(callback);\n }\n else {\n setTimeout(callback, DOM.SCREEN_REFRESH_RATE_MILLISECONDS);\n }\n }\n DOM.requestAnimationFramePolyfill = requestAnimationFramePolyfill;\n /**\n * Calculates the width of the element.\n * The width includes the padding and the border on the element's left and right sides.\n *\n * @param {Element} element The element to query\n * @returns {number} The width of the element.\n */\n function elementWidth(element) {\n var style = window.getComputedStyle(element);\n return _parseStyleValue(style, \"width\")\n + _parseStyleValue(style, \"padding-left\")\n + _parseStyleValue(style, \"padding-right\")\n + _parseStyleValue(style, \"border-left-width\")\n + _parseStyleValue(style, \"border-right-width\");\n }\n DOM.elementWidth = elementWidth;\n /**\n * Calculates the height of the element.\n * The height includes the padding the and the border on the element's top and bottom sides.\n *\n * @param {Element} element The element to query\n * @returns {number} The height of the element\n */\n function elementHeight(element) {\n var style = window.getComputedStyle(element);\n return _parseStyleValue(style, \"height\")\n + _parseStyleValue(style, \"padding-top\")\n + _parseStyleValue(style, \"padding-bottom\")\n + _parseStyleValue(style, \"border-top-width\")\n + _parseStyleValue(style, \"border-bottom-width\");\n }\n DOM.elementHeight = elementHeight;\n function translate(selection, x, y) {\n var transformMatrix = d3.transform(selection.attr(\"transform\"));\n if (x == null) {\n return transformMatrix.translate;\n }\n y = (y == null) ? 0 : y;\n transformMatrix.translate[0] = x;\n transformMatrix.translate[1] = y;\n selection.attr(\"transform\", transformMatrix.toString());\n return selection;\n }\n DOM.translate = translate;\n /**\n * Checks if the first ClientRect overlaps the second.\n *\n * @param {ClientRect} clientRectA The first ClientRect\n * @param {ClientRect} clientRectB The second ClientRect\n * @returns {boolean} If the ClientRects overlap each other.\n */\n function clientRectsOverlap(clientRectA, clientRectB) {\n if (nativeMath.floor(clientRectA.right) <= nativeMath.ceil(clientRectB.left)) {\n return false;\n }\n if (nativeMath.ceil(clientRectA.left) >= nativeMath.floor(clientRectB.right)) {\n return false;\n }\n if (nativeMath.floor(clientRectA.bottom) <= nativeMath.ceil(clientRectB.top)) {\n return false;\n }\n if (nativeMath.ceil(clientRectA.top) >= nativeMath.floor(clientRectB.bottom)) {\n return false;\n }\n return true;\n }\n DOM.clientRectsOverlap = clientRectsOverlap;\n /**\n * Returns true if and only if innerClientRect is inside outerClientRect.\n *\n * @param {ClientRect} innerClientRect The first ClientRect\n * @param {ClientRect} outerClientRect The second ClientRect\n * @returns {boolean} If and only if the innerClientRect is inside outerClientRect.\n */\n function clientRectInside(innerClientRect, outerClientRect) {\n return (nativeMath.floor(outerClientRect.left) <= nativeMath.ceil(innerClientRect.left) &&\n nativeMath.floor(outerClientRect.top) <= nativeMath.ceil(innerClientRect.top) &&\n nativeMath.floor(innerClientRect.right) <= nativeMath.ceil(outerClientRect.right) &&\n nativeMath.floor(innerClientRect.bottom) <= nativeMath.ceil(outerClientRect.bottom));\n }\n DOM.clientRectInside = clientRectInside;\n /**\n * Retrieves the bounding svg of the input element\n *\n * @param {SVGElement} element The element to query\n * @returns {SVGElement} The bounding svg\n */\n function boundingSVG(element) {\n var ownerSVG = element.ownerSVGElement;\n if (ownerSVG != null) {\n return ownerSVG;\n }\n if (element.nodeName.toLowerCase() === \"svg\") {\n return element;\n }\n return null; // not in the DOM\n }\n DOM.boundingSVG = boundingSVG;\n var _latestClipPathId = 0;\n /**\n * Generates a ClipPath ID that is unique for this instance of Plottable\n */\n function generateUniqueClipPathId() {\n return \"plottableClipPath\" + ++_latestClipPathId;\n }\n DOM.generateUniqueClipPathId = generateUniqueClipPathId;\n /**\n * Returns true if the supplied coordinates or Ranges intersect or are contained by bbox.\n *\n * @param {number | Range} xValOrRange The x coordinate or Range to test\n * @param {number | Range} yValOrRange The y coordinate or Range to test\n * @param {SVGRect} bbox The bbox\n * @param {number} tolerance Amount by which to expand bbox, in each dimension, before\n * testing intersection\n *\n * @returns {boolean} True if the supplied coordinates or Ranges intersect or are\n * contained by bbox, false otherwise.\n */\n function intersectsBBox(xValOrRange, yValOrRange, bbox, tolerance) {\n if (tolerance === void 0) { tolerance = 0.5; }\n var xRange = _parseRange(xValOrRange);\n var yRange = _parseRange(yValOrRange);\n // SVGRects are positioned with sub-pixel accuracy (the default unit\n // for the x, y, height & width attributes), but user selections (e.g. via\n // mouse events) usually have pixel accuracy. A tolerance of half-a-pixel\n // seems appropriate.\n return bbox.x + bbox.width >= xRange.min - tolerance &&\n bbox.x <= xRange.max + tolerance &&\n bbox.y + bbox.height >= yRange.min - tolerance &&\n bbox.y <= yRange.max + tolerance;\n }\n DOM.intersectsBBox = intersectsBBox;\n /**\n * Create a Range from a number or an object with \"min\" and \"max\" defined.\n *\n * @param {any} input The object to parse\n *\n * @returns {Range} The generated Range\n */\n function _parseRange(input) {\n if (typeof (input) === \"number\") {\n var value = input;\n return { min: value, max: value };\n }\n var range = input;\n if (range instanceof Object && \"min\" in range && \"max\" in range) {\n return range;\n }\n throw new Error(\"input '\" + input + \"' can't be parsed as an Range\");\n }\n function _parseStyleValue(style, property) {\n var value = style.getPropertyValue(property);\n var parsedValue = parseFloat(value);\n return parsedValue || 0;\n }\n })(DOM = Utils.DOM || (Utils.DOM = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var Color;\n (function (Color) {\n var nativeMath = window.Math;\n /**\n * Return contrast ratio between two colors\n * Based on implementation from chroma.js by Gregor Aisch (gka) (licensed under BSD)\n * chroma.js may be found here: https://github.com/gka/chroma.js\n * License may be found here: https://github.com/gka/chroma.js/blob/master/LICENSE\n * see http://www.w3.org/TR/2008/REC-WCAG20-20081211/#contrast-ratiodef\n */\n function contrast(a, b) {\n var l1 = luminance(a) + 0.05;\n var l2 = luminance(b) + 0.05;\n return l1 > l2 ? l1 / l2 : l2 / l1;\n }\n Color.contrast = contrast;\n /**\n * Returns a brighter copy of this color. Each channel is multiplied by 0.7 ^ -factor.\n * Channel values are capped at the maximum value of 255, and the minimum value of 30.\n */\n function lightenColor(color, factor) {\n var hsl = d3.hsl(color).brighter(factor);\n return hsl.rgb().toString();\n }\n Color.lightenColor = lightenColor;\n /**\n * Gets the Hex Code of the color resulting by applying the className CSS class to the\n * colorTester selection. Returns null if the tester is transparent.\n *\n * @param {d3.Selection} colorTester The d3 selection to apply the CSS class to\n * @param {string} className The name of the class to be applied\n * @return {string} The hex code of the computed color\n */\n function colorTest(colorTester, className) {\n colorTester.classed(className, true);\n // Use regex to get the text inside the rgb parentheses\n var colorStyle = colorTester.style(\"background-color\");\n if (colorStyle === \"transparent\") {\n return null;\n }\n var rgb = /\\((.+)\\)/.exec(colorStyle)[1]\n .split(\",\")\n .map(function (colorValue) {\n var colorNumber = +colorValue;\n var hexValue = colorNumber.toString(16);\n return colorNumber < 16 ? \"0\" + hexValue : hexValue;\n });\n if (rgb.length === 4 && rgb[3] === \"00\") {\n return null;\n }\n var hexCode = \"#\" + rgb.join(\"\");\n colorTester.classed(className, false);\n return hexCode;\n }\n Color.colorTest = colorTest;\n /**\n * Return relative luminance (defined here: http://www.w3.org/TR/2008/REC-WCAG20-20081211/#relativeluminancedef)\n * Based on implementation from chroma.js by Gregor Aisch (gka) (licensed under BSD)\n * chroma.js may be found here: https://github.com/gka/chroma.js\n * License may be found here: https://github.com/gka/chroma.js/blob/master/LICENSE\n */\n function luminance(color) {\n var rgb = d3.rgb(color);\n var lum = function (x) {\n x = x / 255;\n return x <= 0.03928 ? x / 12.92 : nativeMath.pow((x + 0.055) / 1.055, 2.4);\n };\n var r = lum(rgb.r);\n var g = lum(rgb.g);\n var b = lum(rgb.b);\n return 0.2126 * r + 0.7152 * g + 0.0722 * b;\n }\n })(Color = Utils.Color || (Utils.Color = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var Array;\n (function (Array) {\n var nativeArray = window.Array;\n /**\n * Takes two arrays of numbers and adds them together\n *\n * @param {number[]} aList The first array of numbers\n * @param {number[]} bList The second array of numbers\n * @return {number[]} An array of numbers where x[i] = aList[i] + bList[i]\n */\n function add(aList, bList) {\n if (aList.length !== bList.length) {\n throw new Error(\"attempted to add arrays of unequal length\");\n }\n return aList.map(function (_, i) { return aList[i] + bList[i]; });\n }\n Array.add = add;\n /**\n * Take an array of values, and return the unique values.\n * Will work iff ∀ a, b, a.toString() == b.toString() => a == b; will break on Object inputs\n *\n * @param {T[]} values The values to find uniqueness for\n * @return {T[]} The unique values\n */\n function uniq(arr) {\n var seen = d3.set();\n var result = [];\n arr.forEach(function (x) {\n if (!seen.has(String(x))) {\n seen.add(String(x));\n result.push(x);\n }\n });\n return result;\n }\n Array.uniq = uniq;\n /**\n * @param {T[][]} a The 2D array that will have its elements joined together.\n * @return {T[]} Every array in a, concatenated together in the order they appear.\n */\n function flatten(a) {\n return nativeArray.prototype.concat.apply([], a);\n }\n Array.flatten = flatten;\n /**\n * Creates an array of length `count`, filled with value or (if value is a function), value()\n *\n * @param {T | ((index?: number) => T)} value The value to fill the array with or a value generator (called with index as arg)\n * @param {number} count The length of the array to generate\n * @return {any[]}\n */\n function createFilledArray(value, count) {\n var out = [];\n for (var i = 0; i < count; i++) {\n out[i] = typeof (value) === \"function\" ? value(i) : value;\n }\n return out;\n }\n Array.createFilledArray = createFilledArray;\n })(Array = Utils.Array || (Utils.Array = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n /**\n * A set of callbacks which can be all invoked at once.\n * Each callback exists at most once in the set (based on reference equality).\n * All callbacks should have the same signature.\n */\n var CallbackSet = (function (_super) {\n __extends(CallbackSet, _super);\n function CallbackSet() {\n _super.apply(this, arguments);\n }\n CallbackSet.prototype.callCallbacks = function () {\n var _this = this;\n var args = [];\n for (var _i = 0; _i < arguments.length; _i++) {\n args[_i - 0] = arguments[_i];\n }\n this.forEach(function (callback) {\n callback.apply(_this, args);\n });\n return this;\n };\n return CallbackSet;\n }(Utils.Set));\n Utils.CallbackSet = CallbackSet;\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var Stacking;\n (function (Stacking) {\n var nativeMath = window.Math;\n /**\n * Computes the StackingResult (value and offset) for each data point in each Dataset.\n *\n * @param {Dataset[]} datasets The Datasets to be stacked on top of each other in the order of stacking\n * @param {Accessor} keyAccessor Accessor for the key of the data\n * @param {Accessor} valueAccessor Accessor for the value of the data\n * @return {StackingResult} value and offset for each datapoint in each Dataset\n */\n function stack(datasets, keyAccessor, valueAccessor) {\n var positiveOffsets = d3.map();\n var negativeOffsets = d3.map();\n var datasetToKeyToStackedDatum = new Utils.Map();\n datasets.forEach(function (dataset) {\n var keyToStackedDatum = new Utils.Map();\n dataset.data().forEach(function (datum, index) {\n var key = normalizeKey(keyAccessor(datum, index, dataset));\n var value = +valueAccessor(datum, index, dataset);\n var offset;\n var offsetMap = (value >= 0) ? positiveOffsets : negativeOffsets;\n if (offsetMap.has(key)) {\n offset = offsetMap.get(key);\n offsetMap.set(key, offset + value);\n }\n else {\n offset = 0;\n offsetMap.set(key, value);\n }\n keyToStackedDatum.set(key, {\n value: value,\n offset: offset,\n });\n });\n datasetToKeyToStackedDatum.set(dataset, keyToStackedDatum);\n });\n return datasetToKeyToStackedDatum;\n }\n Stacking.stack = stack;\n /**\n * Computes the total extent over all data points in all Datasets, taking stacking into consideration.\n *\n * @param {StackingResult} stackingResult The value and offset information for each datapoint in each dataset\n * @oaram {Accessor} keyAccessor Accessor for the key of the data existent in the stackingResult\n * @param {Accessor} filter A filter for data to be considered when computing the total extent\n * @return {[number, number]} The total extent\n */\n function stackedExtent(stackingResult, keyAccessor, filter) {\n var extents = [];\n stackingResult.forEach(function (stackedDatumMap, dataset) {\n dataset.data().forEach(function (datum, index) {\n if (filter != null && !filter(datum, index, dataset)) {\n return;\n }\n var stackedDatum = stackedDatumMap.get(normalizeKey(keyAccessor(datum, index, dataset)));\n extents.push(stackedDatum.value + stackedDatum.offset);\n });\n });\n var maxStackExtent = Utils.Math.max(extents, 0);\n var minStackExtent = Utils.Math.min(extents, 0);\n return [nativeMath.min(minStackExtent, 0), nativeMath.max(0, maxStackExtent)];\n }\n Stacking.stackedExtent = stackedExtent;\n /**\n * Normalizes a key used for stacking\n *\n * @param {any} key The key to be normalized\n * @return {string} The stringified key\n */\n function normalizeKey(key) {\n return String(key);\n }\n Stacking.normalizeKey = normalizeKey;\n })(Stacking = Utils.Stacking || (Utils.Stacking = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var Window;\n (function (Window) {\n /**\n * Print a warning message to the console, if it is available.\n *\n * @param {string} The warnings to print\n */\n function warn(warning) {\n if (!Plottable.Configs.SHOW_WARNINGS) {\n return;\n }\n /* tslint:disable:no-console */\n if (window.console != null) {\n if (window.console.warn != null) {\n console.warn(warning);\n }\n else if (window.console.log != null) {\n console.log(warning);\n }\n }\n /* tslint:enable:no-console */\n }\n Window.warn = warn;\n /**\n * Is like setTimeout, but activates synchronously if time=0\n * We special case 0 because of an observed issue where calling setTimeout causes visible flickering.\n * We believe this is because when requestAnimationFrame calls into the paint function, as soon as that function finishes\n * evaluating, the results are painted to the screen. As a result, if we want something to occur immediately but call setTimeout\n * with time=0, then it is pushed to the call stack and rendered in the next frame, so the component that was rendered via\n * setTimeout appears out-of-sync with the rest of the plot.\n */\n function setTimeout(f, time) {\n var args = [];\n for (var _i = 2; _i < arguments.length; _i++) {\n args[_i - 2] = arguments[_i];\n }\n if (time === 0) {\n f(args);\n return -1;\n }\n else {\n return window.setTimeout(f, time, args);\n }\n }\n Window.setTimeout = setTimeout;\n /**\n * Sends a deprecation warning to the console. The warning includes the name of the deprecated method,\n * version number of the deprecation, and an optional message.\n *\n * To be used in the first line of a deprecated method.\n *\n * @param {string} callingMethod The name of the method being deprecated\n * @param {string} version The version when the tagged method became obsolete\n * @param {string?} message Optional message to be shown with the warning\n */\n function deprecated(callingMethod, version, message) {\n if (message === void 0) { message = \"\"; }\n Utils.Window.warn(\"Method \" + callingMethod + \" has been deprecated in version \" + version +\n \". Please refer to the release notes. \" + message);\n }\n Window.deprecated = deprecated;\n })(Window = Utils.Window || (Utils.Window = {}));\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Utils;\n (function (Utils) {\n var ClientToSVGTranslator = (function () {\n function ClientToSVGTranslator(svg) {\n this._svg = svg;\n this._measureRect = document.createElementNS(svg.namespaceURI, \"rect\");\n this._measureRect.setAttribute(\"class\", \"measure-rect\");\n this._measureRect.setAttribute(\"style\", \"opacity: 0; visibility: hidden;\");\n this._measureRect.setAttribute(\"width\", \"1\");\n this._measureRect.setAttribute(\"height\", \"1\");\n this._svg.appendChild(this._measureRect);\n }\n /**\n * Returns the ClientToSVGTranslator for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n */\n ClientToSVGTranslator.getTranslator = function (elem) {\n var svg = Utils.DOM.boundingSVG(elem);\n var translator = svg[ClientToSVGTranslator._TRANSLATOR_KEY];\n if (translator == null) {\n translator = new ClientToSVGTranslator(svg);\n svg[ClientToSVGTranslator._TRANSLATOR_KEY] = translator;\n }\n return translator;\n };\n /**\n * Computes the position relative to the in svg-coordinate-space.\n */\n ClientToSVGTranslator.prototype.computePosition = function (clientX, clientY) {\n // get the origin\n this._measureRect.setAttribute(\"x\", \"0\");\n this._measureRect.setAttribute(\"y\", \"0\");\n var mrBCR = this._measureRect.getBoundingClientRect();\n var origin = { x: mrBCR.left, y: mrBCR.top };\n // calculate the scale\n var sampleDistance = 100;\n this._measureRect.setAttribute(\"x\", String(sampleDistance));\n this._measureRect.setAttribute(\"y\", String(sampleDistance));\n mrBCR = this._measureRect.getBoundingClientRect();\n var testPoint = { x: mrBCR.left, y: mrBCR.top };\n // invalid measurements -- SVG might not be in the DOM\n if (origin.x === testPoint.x || origin.y === testPoint.y) {\n return null;\n }\n var scaleX = (testPoint.x - origin.x) / sampleDistance;\n var scaleY = (testPoint.y - origin.y) / sampleDistance;\n // get the true cursor position\n this._measureRect.setAttribute(\"x\", String((clientX - origin.x) / scaleX));\n this._measureRect.setAttribute(\"y\", String((clientY - origin.y) / scaleY));\n mrBCR = this._measureRect.getBoundingClientRect();\n var trueCursorPosition = { x: mrBCR.left, y: mrBCR.top };\n var scaledPosition = {\n x: (trueCursorPosition.x - origin.x) / scaleX,\n y: (trueCursorPosition.y - origin.y) / scaleY,\n };\n return scaledPosition;\n };\n /**\n * Checks whether event happened inside element.\n */\n ClientToSVGTranslator.prototype.insideSVG = function (e) {\n return Utils.DOM.boundingSVG(e.target) === this._svg;\n };\n ClientToSVGTranslator._TRANSLATOR_KEY = \"__Plottable_ClientToSVGTranslator\";\n return ClientToSVGTranslator;\n }());\n Utils.ClientToSVGTranslator = ClientToSVGTranslator;\n })(Utils = Plottable.Utils || (Plottable.Utils = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Configs;\n (function (Configs) {\n /**\n * Specifies if Plottable should show warnings.\n */\n Configs.SHOW_WARNINGS = true;\n /**\n * Specifies if Plottable should add elements to text.\n */\n Configs.ADD_TITLE_ELEMENTS = true;\n })(Configs = Plottable.Configs || (Plottable.Configs = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n Plottable.version = \"2.2.0\";\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Dataset = (function () {\n /**\n * A Dataset contains an array of data and some metadata.\n * Changes to the data or metadata will cause anything subscribed to the Dataset to update.\n *\n * @constructor\n * @param {any[]} [data=[]] The data for this Dataset.\n * @param {any} [metadata={}] An object containing additional information.\n */\n function Dataset(data, metadata) {\n if (data === void 0) { data = []; }\n if (metadata === void 0) { metadata = {}; }\n this._data = data;\n this._metadata = metadata;\n this._callbacks = new Plottable.Utils.CallbackSet();\n }\n /**\n * Adds a callback to be called when the Dataset updates.\n *\n * @param {DatasetCallback} callback.\n * @returns {Dataset} The calling Dataset.\n */\n Dataset.prototype.onUpdate = function (callback) {\n this._callbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the Dataset updates.\n *\n * @param {DatasetCallback} callback\n * @returns {Dataset} The calling Dataset.\n */\n Dataset.prototype.offUpdate = function (callback) {\n this._callbacks.delete(callback);\n return this;\n };\n Dataset.prototype.data = function (data) {\n if (data == null) {\n return this._data;\n }\n else {\n this._data = data;\n this._callbacks.callCallbacks(this);\n return this;\n }\n };\n Dataset.prototype.metadata = function (metadata) {\n if (metadata == null) {\n return this._metadata;\n }\n else {\n this._metadata = metadata;\n this._callbacks.callCallbacks(this);\n return this;\n }\n };\n return Dataset;\n }());\n Plottable.Dataset = Dataset;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var RenderPolicies;\n (function (RenderPolicies) {\n /**\n * Renders Components immediately after they are enqueued.\n * Useful for debugging, horrible for performance.\n */\n var Immediate = (function () {\n function Immediate() {\n }\n Immediate.prototype.render = function () {\n Plottable.RenderController.flush();\n };\n return Immediate;\n }());\n RenderPolicies.Immediate = Immediate;\n /**\n * The default way to render, which only tries to render every frame\n * (usually, 1/60th of a second).\n */\n var AnimationFrame = (function () {\n function AnimationFrame() {\n }\n AnimationFrame.prototype.render = function () {\n Plottable.Utils.DOM.requestAnimationFramePolyfill(Plottable.RenderController.flush);\n };\n return AnimationFrame;\n }());\n RenderPolicies.AnimationFrame = AnimationFrame;\n /**\n * Renders with `setTimeout()`.\n * Generally an inferior way to render compared to `requestAnimationFrame`,\n * but useful for browsers that don't suppoort `requestAnimationFrame`.\n */\n var Timeout = (function () {\n function Timeout() {\n this._timeoutMsec = Plottable.Utils.DOM.SCREEN_REFRESH_RATE_MILLISECONDS;\n }\n Timeout.prototype.render = function () {\n setTimeout(Plottable.RenderController.flush, this._timeoutMsec);\n };\n return Timeout;\n }());\n RenderPolicies.Timeout = Timeout;\n })(RenderPolicies = Plottable.RenderPolicies || (Plottable.RenderPolicies = {}));\n})(Plottable || (Plottable = {}));\n/**\n * The RenderController is responsible for enqueueing and synchronizing\n * layout and render calls for Components.\n *\n * Layout and render calls occur inside an animation callback\n * (window.requestAnimationFrame if available).\n *\n * RenderController.flush() immediately lays out and renders all Components currently enqueued.\n *\n * To always have immediate rendering (useful for debugging), call\n * ```typescript\n * Plottable.RenderController.setRenderPolicy(\n * new Plottable.RenderPolicies.Immediate()\n * );\n * ```\n */\nvar Plottable;\n(function (Plottable) {\n var RenderController;\n (function (RenderController) {\n var _componentsNeedingRender = new Plottable.Utils.Set();\n var _componentsNeedingComputeLayout = new Plottable.Utils.Set();\n var _animationRequested = false;\n var _isCurrentlyFlushing = false;\n var Policy;\n (function (Policy) {\n Policy.IMMEDIATE = \"immediate\";\n Policy.ANIMATION_FRAME = \"animationframe\";\n Policy.TIMEOUT = \"timeout\";\n })(Policy = RenderController.Policy || (RenderController.Policy = {}));\n var _renderPolicy = new Plottable.RenderPolicies.AnimationFrame();\n function renderPolicy(renderPolicy) {\n if (renderPolicy == null) {\n return _renderPolicy;\n }\n switch (renderPolicy.toLowerCase()) {\n case Policy.IMMEDIATE:\n _renderPolicy = new Plottable.RenderPolicies.Immediate();\n break;\n case Policy.ANIMATION_FRAME:\n _renderPolicy = new Plottable.RenderPolicies.AnimationFrame();\n break;\n case Policy.TIMEOUT:\n _renderPolicy = new Plottable.RenderPolicies.Timeout();\n break;\n default:\n Plottable.Utils.Window.warn(\"Unrecognized renderPolicy: \" + renderPolicy);\n }\n }\n RenderController.renderPolicy = renderPolicy;\n /**\n * Enqueues the Component for rendering.\n *\n * @param {Component} component\n */\n function registerToRender(component) {\n if (_isCurrentlyFlushing) {\n Plottable.Utils.Window.warn(\"Registered to render while other components are flushing: request may be ignored\");\n }\n _componentsNeedingRender.add(component);\n requestRender();\n }\n RenderController.registerToRender = registerToRender;\n /**\n * Enqueues the Component for layout and rendering.\n *\n * @param {Component} component\n */\n function registerToComputeLayout(component) {\n _componentsNeedingComputeLayout.add(component);\n _componentsNeedingRender.add(component);\n requestRender();\n }\n RenderController.registerToComputeLayout = registerToComputeLayout;\n function requestRender() {\n // Only run or enqueue flush on first request.\n if (!_animationRequested) {\n _animationRequested = true;\n _renderPolicy.render();\n }\n }\n /**\n * Renders all Components waiting to be rendered immediately\n * instead of waiting until the next frame.\n *\n * Useful to call when debugging.\n */\n function flush() {\n if (_animationRequested) {\n // Layout\n _componentsNeedingComputeLayout.forEach(function (component) { return component.computeLayout(); });\n // Top level render; Containers will put their children in the toRender queue\n _componentsNeedingRender.forEach(function (component) { return component.render(); });\n _isCurrentlyFlushing = true;\n var failed_1 = new Plottable.Utils.Set();\n _componentsNeedingRender.forEach(function (component) {\n try {\n component.renderImmediately();\n }\n catch (err) {\n // throw error with timeout to avoid interrupting further renders\n window.setTimeout(function () { throw err; }, 0);\n failed_1.add(component);\n }\n });\n _componentsNeedingComputeLayout = new Plottable.Utils.Set();\n _componentsNeedingRender = failed_1;\n _animationRequested = false;\n _isCurrentlyFlushing = false;\n }\n }\n RenderController.flush = flush;\n })(RenderController = Plottable.RenderController || (Plottable.RenderController = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Formatters;\n (function (Formatters) {\n /**\n * Creates a formatter for currency values.\n *\n * @param {number} [precision] The number of decimal places to show (default 2).\n * @param {string} [symbol] The currency symbol to use (default \"$\").\n * @param {boolean} [prefix] Whether to prepend or append the currency symbol (default true).\n *\n * @returns {Formatter} A formatter for currency values.\n */\n function currency(precision, symbol, prefix) {\n if (precision === void 0) { precision = 2; }\n if (symbol === void 0) { symbol = \"$\"; }\n if (prefix === void 0) { prefix = true; }\n var fixedFormatter = Formatters.fixed(precision);\n return function (d) {\n var formattedValue = fixedFormatter(Math.abs(d));\n if (formattedValue !== \"\") {\n if (prefix) {\n formattedValue = symbol + formattedValue;\n }\n else {\n formattedValue += symbol;\n }\n if (d < 0) {\n formattedValue = \"-\" + formattedValue;\n }\n }\n return formattedValue;\n };\n }\n Formatters.currency = currency;\n /**\n * Creates a formatter that displays exactly [precision] decimal places.\n *\n * @param {number} [precision] The number of decimal places to show (default 3).\n *\n * @returns {Formatter} A formatter that displays exactly [precision] decimal places.\n */\n function fixed(precision) {\n if (precision === void 0) { precision = 3; }\n verifyPrecision(precision);\n return function (d) { return d.toFixed(precision); };\n }\n Formatters.fixed = fixed;\n /**\n * Creates a formatter that formats numbers to show no more than\n * [maxNumberOfDecimalPlaces] decimal places. All other values are stringified.\n *\n * @param {number} [maxNumberOfDecimalPlaces] The number of decimal places to show (default 3).\n *\n * @returns {Formatter} A formatter for general values.\n */\n function general(maxNumberOfDecimalPlaces) {\n if (maxNumberOfDecimalPlaces === void 0) { maxNumberOfDecimalPlaces = 3; }\n verifyPrecision(maxNumberOfDecimalPlaces);\n return function (d) {\n if (typeof d === \"number\") {\n var multiplier = Math.pow(10, maxNumberOfDecimalPlaces);\n return String(Math.round(d * multiplier) / multiplier);\n }\n else {\n return String(d);\n }\n };\n }\n Formatters.general = general;\n /**\n * Creates a formatter that stringifies its input.\n *\n * @returns {Formatter} A formatter that stringifies its input.\n */\n function identity() {\n return function (d) { return String(d); };\n }\n Formatters.identity = identity;\n /**\n * Creates a formatter for percentage values.\n * Multiplies the input by 100 and appends \"%\".\n *\n * @param {number} [precision] The number of decimal places to show (default 0).\n *\n * @returns {Formatter} A formatter for percentage values.\n */\n function percentage(precision) {\n if (precision === void 0) { precision = 0; }\n var fixedFormatter = Formatters.fixed(precision);\n return function (d) {\n var valToFormat = d * 100;\n // Account for float imprecision\n var valString = d.toString();\n var integerPowerTen = Math.pow(10, valString.length - (valString.indexOf(\".\") + 1));\n valToFormat = parseInt((valToFormat * integerPowerTen).toString(), 10) / integerPowerTen;\n return fixedFormatter(valToFormat) + \"%\";\n };\n }\n Formatters.percentage = percentage;\n /**\n * Creates a formatter for values that displays [numberOfSignificantFigures] significant figures\n * and puts SI notation.\n *\n * @param {number} [numberOfSignificantFigures] The number of significant figures to show (default 3).\n *\n * @returns {Formatter} A formatter for SI values.\n */\n function siSuffix(numberOfSignificantFigures) {\n if (numberOfSignificantFigures === void 0) { numberOfSignificantFigures = 3; }\n verifyPrecision(numberOfSignificantFigures);\n return function (d) { return d3.format(\".\" + numberOfSignificantFigures + \"s\")(d); };\n }\n Formatters.siSuffix = siSuffix;\n /**\n * Creates a formatter for values that displays abbreviated values\n * and uses standard short scale suffixes\n * - K - thousands - 10 ^ 3\n * - M - millions - 10 ^ 6\n * - B - billions - 10 ^ 9\n * - T - trillions - 10 ^ 12\n * - Q - quadrillions - 10 ^ 15\n *\n * Numbers with a magnitude outside of (10 ^ (-precision), 10 ^ 15) are shown using\n * scientific notation to avoid creating extremely long decimal strings.\n *\n * @param {number} [precision] the number of decimal places to show (default 3)\n * @returns {Formatter} A formatter with short scale formatting\n */\n function shortScale(precision) {\n if (precision === void 0) { precision = 3; }\n verifyPrecision(precision);\n var suffixes = \"KMBTQ\";\n var exponentFormatter = d3.format(\".\" + precision + \"e\");\n var fixedFormatter = d3.format(\".\" + precision + \"f\");\n var max = Math.pow(10, (3 * (suffixes.length + 1)));\n var min = Math.pow(10, -precision);\n return function (num) {\n var absNum = Math.abs(num);\n if ((absNum < min || absNum >= max) && absNum !== 0) {\n return exponentFormatter(num);\n }\n var idx = -1;\n while (absNum >= Math.pow(1000, idx + 2) && idx < (suffixes.length - 1)) {\n idx++;\n }\n var output = \"\";\n if (idx === -1) {\n output = fixedFormatter(num);\n }\n else {\n output = fixedFormatter(num / Math.pow(1000, idx + 1)) + suffixes[idx];\n }\n // catch rounding by the underlying d3 formatter\n if ((num > 0 && output.substr(0, 4) === \"1000\") || (num < 0 && output.substr(0, 5) === \"-1000\")) {\n if (idx < suffixes.length - 1) {\n idx++;\n output = fixedFormatter(num / Math.pow(1000, idx + 1)) + suffixes[idx];\n }\n else {\n output = exponentFormatter(num);\n }\n }\n return output;\n };\n }\n Formatters.shortScale = shortScale;\n /**\n * Creates a multi time formatter that displays dates.\n *\n * @returns {Formatter} A formatter for time/date values.\n */\n function multiTime() {\n var numFormats = 8;\n // these defaults were taken from d3\n // https://github.com/mbostock/d3/wiki/Time-Formatting#format_multi\n var timeFormat = {};\n timeFormat[0] = {\n format: \".%L\",\n filter: function (d) { return d.getMilliseconds() !== 0; },\n };\n timeFormat[1] = {\n format: \":%S\",\n filter: function (d) { return d.getSeconds() !== 0; },\n };\n timeFormat[2] = {\n format: \"%I:%M\",\n filter: function (d) { return d.getMinutes() !== 0; },\n };\n timeFormat[3] = {\n format: \"%I %p\",\n filter: function (d) { return d.getHours() !== 0; },\n };\n timeFormat[4] = {\n format: \"%a %d\",\n filter: function (d) { return d.getDay() !== 0 && d.getDate() !== 1; },\n };\n timeFormat[5] = {\n format: \"%b %d\",\n filter: function (d) { return d.getDate() !== 1; },\n };\n timeFormat[6] = {\n format: \"%b\",\n filter: function (d) { return d.getMonth() !== 0; },\n };\n timeFormat[7] = {\n format: \"%Y\",\n filter: function () { return true; },\n };\n return function (d) {\n for (var i = 0; i < numFormats; i++) {\n if (timeFormat[i].filter(d)) {\n return d3.time.format(timeFormat[i].format)(d);\n }\n }\n };\n }\n Formatters.multiTime = multiTime;\n /**\n * Creates a time formatter that displays time/date using given specifier.\n *\n * List of directives can be found on: https://github.com/mbostock/d3/wiki/Time-Formatting#format\n *\n * @param {string} [specifier] The specifier for the formatter.\n *\n * @returns {Formatter} A formatter for time/date values.\n */\n function time(specifier) {\n return d3.time.format(specifier);\n }\n Formatters.time = time;\n function verifyPrecision(precision) {\n if (precision < 0 || precision > 20) {\n throw new RangeError(\"Formatter precision must be between 0 and 20\");\n }\n if (precision !== Math.floor(precision)) {\n throw new RangeError(\"Formatter precision must be an integer\");\n }\n }\n })(Formatters = Plottable.Formatters || (Plottable.Formatters = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var SymbolFactories;\n (function (SymbolFactories) {\n function circle() {\n return function (symbolSize) { return d3.svg.symbol().type(\"circle\").size(Math.PI * Math.pow(symbolSize / 2, 2))(null); };\n }\n SymbolFactories.circle = circle;\n function square() {\n return function (symbolSize) { return d3.svg.symbol().type(\"square\").size(Math.pow(symbolSize, 2))(null); };\n }\n SymbolFactories.square = square;\n function cross() {\n return function (symbolSize) { return d3.svg.symbol().type(\"cross\").size((5 / 9) * Math.pow(symbolSize, 2))(null); };\n }\n SymbolFactories.cross = cross;\n function diamond() {\n return function (symbolSize) { return d3.svg.symbol().type(\"diamond\").size(Math.tan(Math.PI / 6) * Math.pow(symbolSize, 2) / 2)(null); };\n }\n SymbolFactories.diamond = diamond;\n function triangleUp() {\n return function (symbolSize) { return d3.svg.symbol().type(\"triangle-up\").size(Math.sqrt(3) * Math.pow(symbolSize / 2, 2))(null); };\n }\n SymbolFactories.triangleUp = triangleUp;\n function triangleDown() {\n return function (symbolSize) { return d3.svg.symbol().type(\"triangle-down\").size(Math.sqrt(3) * Math.pow(symbolSize / 2, 2))(null); };\n }\n SymbolFactories.triangleDown = triangleDown;\n })(SymbolFactories = Plottable.SymbolFactories || (Plottable.SymbolFactories = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scale = (function () {\n /**\n * A Scale is a function (in the mathematical sense) that maps values from a domain to a range.\n *\n * @constructor\n */\n function Scale() {\n this._autoDomainAutomatically = true;\n this._domainModificationInProgress = false;\n this._callbacks = new Plottable.Utils.CallbackSet();\n this._includedValuesProviders = new Plottable.Utils.Set();\n }\n /**\n * Given an array of potential domain values, computes the extent of those values.\n *\n * @param {D[]} values\n * @returns {D[]} The extent of the input values.\n */\n Scale.prototype.extentOfValues = function (values) {\n return []; // this should be overwritten\n };\n Scale.prototype._getAllIncludedValues = function () {\n var _this = this;\n var providerArray = [];\n this._includedValuesProviders.forEach(function (provider) {\n var extents = provider(_this);\n providerArray = providerArray.concat(extents);\n });\n return providerArray;\n };\n Scale.prototype._getExtent = function () {\n return []; // this should be overwritten\n };\n /**\n * Adds a callback to be called when the Scale updates.\n *\n * @param {ScaleCallback} callback.\n * @returns {Scale} The calling Scale.\n */\n Scale.prototype.onUpdate = function (callback) {\n this._callbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the Scale updates.\n *\n * @param {ScaleCallback} callback.\n * @returns {Scale} The calling Scale.\n */\n Scale.prototype.offUpdate = function (callback) {\n this._callbacks.delete(callback);\n return this;\n };\n Scale.prototype._dispatchUpdate = function () {\n this._callbacks.callCallbacks(this);\n };\n /**\n * Sets the Scale's domain so that it spans the Extents of all its ExtentsProviders.\n *\n * @returns {Scale} The calling Scale.\n */\n Scale.prototype.autoDomain = function () {\n this._autoDomainAutomatically = true;\n this._setDomain(this._getExtent());\n return this;\n };\n Scale.prototype._autoDomainIfAutomaticMode = function () {\n if (this._autoDomainAutomatically) {\n this.autoDomain();\n }\n };\n /**\n * Computes the range value corresponding to a given domain value.\n *\n * @param {D} value\n * @returns {R} The range value corresponding to the supplied domain value.\n */\n Scale.prototype.scale = function (value) {\n throw new Error(\"Subclasses should override scale\");\n };\n Scale.prototype.domain = function (values) {\n if (values == null) {\n return this._getDomain();\n }\n else {\n this._autoDomainAutomatically = false;\n this._setDomain(values);\n return this;\n }\n };\n Scale.prototype._getDomain = function () {\n throw new Error(\"Subclasses should override _getDomain\");\n };\n Scale.prototype._setDomain = function (values) {\n if (!this._domainModificationInProgress) {\n this._domainModificationInProgress = true;\n this._backingScaleDomain(values);\n this._dispatchUpdate();\n this._domainModificationInProgress = false;\n }\n };\n Scale.prototype._backingScaleDomain = function (values) {\n throw new Error(\"Subclasses should override _backingDomain\");\n };\n Scale.prototype.range = function (values) {\n if (values == null) {\n return this._getRange();\n }\n else {\n this._setRange(values);\n return this;\n }\n };\n Scale.prototype._getRange = function () {\n throw new Error(\"Subclasses should override _getRange\");\n };\n Scale.prototype._setRange = function (values) {\n throw new Error(\"Subclasses should override _setRange\");\n };\n /**\n * Adds an IncludedValuesProvider to the Scale.\n *\n * @param {Scales.IncludedValuesProvider} provider\n * @returns {Scale} The calling Scale.\n */\n Scale.prototype.addIncludedValuesProvider = function (provider) {\n this._includedValuesProviders.add(provider);\n this._autoDomainIfAutomaticMode();\n return this;\n };\n /**\n * Removes the IncludedValuesProvider from the Scale.\n *\n * @param {Scales.IncludedValuesProvider} provider\n * @returns {Scale} The calling Scale.\n */\n Scale.prototype.removeIncludedValuesProvider = function (provider) {\n this._includedValuesProviders.delete(provider);\n this._autoDomainIfAutomaticMode();\n return this;\n };\n return Scale;\n }());\n Plottable.Scale = Scale;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var QuantitativeScale = (function (_super) {\n __extends(QuantitativeScale, _super);\n /**\n * A QuantitativeScale is a Scale that maps number-like values to numbers.\n * It is invertible and continuous.\n *\n * @constructor\n */\n function QuantitativeScale() {\n _super.call(this);\n this._tickGenerator = function (scale) { return scale.defaultTicks(); };\n this._padProportion = 0.05;\n this._snappingDomainEnabled = true;\n this._paddingExceptionsProviders = new Plottable.Utils.Set();\n }\n QuantitativeScale.prototype.autoDomain = function () {\n this._domainMin = null;\n this._domainMax = null;\n _super.prototype.autoDomain.call(this);\n return this;\n };\n QuantitativeScale.prototype._autoDomainIfAutomaticMode = function () {\n if (this._domainMin != null && this._domainMax != null) {\n this._setDomain([this._domainMin, this._domainMax]);\n return;\n }\n var computedExtent = this._getExtent();\n if (this._domainMin != null) {\n var maxValue = computedExtent[1];\n if (this._domainMin >= maxValue) {\n maxValue = this._expandSingleValueDomain([this._domainMin, this._domainMin])[1];\n }\n this._setDomain([this._domainMin, maxValue]);\n return;\n }\n if (this._domainMax != null) {\n var minValue = computedExtent[0];\n if (this._domainMax <= minValue) {\n minValue = this._expandSingleValueDomain([this._domainMax, this._domainMax])[0];\n }\n this._setDomain([minValue, this._domainMax]);\n return;\n }\n _super.prototype._autoDomainIfAutomaticMode.call(this);\n };\n QuantitativeScale.prototype._getExtent = function () {\n var includedValues = this._getAllIncludedValues();\n var extent = this._defaultExtent();\n if (includedValues.length !== 0) {\n var combinedExtent = [\n Plottable.Utils.Math.min(includedValues, extent[0]),\n Plottable.Utils.Math.max(includedValues, extent[1]),\n ];\n extent = this._padDomain(combinedExtent);\n }\n if (this._domainMin != null) {\n extent[0] = this._domainMin;\n }\n if (this._domainMax != null) {\n extent[1] = this._domainMax;\n }\n return extent;\n };\n /**\n * Adds a padding exception provider.\n * If one end of the domain is set to an excepted value as a result of autoDomain()-ing,\n * that end of the domain will not be padded.\n *\n * @param {Scales.PaddingExceptionProvider} provider The provider function.\n * @returns {QuantitativeScale} The calling QuantitativeScale.\n */\n QuantitativeScale.prototype.addPaddingExceptionsProvider = function (provider) {\n this._paddingExceptionsProviders.add(provider);\n this._autoDomainIfAutomaticMode();\n return this;\n };\n /**\n * Removes the padding exception provider.\n *\n * @param {Scales.PaddingExceptionProvider} provider The provider function.\n * @returns {QuantitativeScale} The calling QuantitativeScale.\n */\n QuantitativeScale.prototype.removePaddingExceptionsProvider = function (provider) {\n this._paddingExceptionsProviders.delete(provider);\n this._autoDomainIfAutomaticMode();\n return this;\n };\n QuantitativeScale.prototype.padProportion = function (padProportion) {\n if (padProportion == null) {\n return this._padProportion;\n }\n if (padProportion < 0) {\n throw new Error(\"padProportion must be non-negative\");\n }\n this._padProportion = padProportion;\n this._autoDomainIfAutomaticMode();\n return this;\n };\n QuantitativeScale.prototype._padDomain = function (domain) {\n var _this = this;\n if (domain[0].valueOf() === domain[1].valueOf()) {\n return this._expandSingleValueDomain(domain);\n }\n if (this._padProportion === 0) {\n return domain;\n }\n var p = this._padProportion / 2;\n var min = domain[0];\n var max = domain[1];\n var minExistsInExceptions = false;\n var maxExistsInExceptions = false;\n this._paddingExceptionsProviders.forEach(function (provider) {\n var values = provider(_this);\n values.forEach(function (value) {\n if (value.valueOf() === min.valueOf()) {\n minExistsInExceptions = true;\n }\n if (value.valueOf() === max.valueOf()) {\n maxExistsInExceptions = true;\n }\n });\n });\n var originalDomain = this._backingScaleDomain();\n this._backingScaleDomain(domain);\n var newMin = minExistsInExceptions ? min : this.invert(this.scale(min) - (this.scale(max) - this.scale(min)) * p);\n var newMax = maxExistsInExceptions ? max : this.invert(this.scale(max) + (this.scale(max) - this.scale(min)) * p);\n this._backingScaleDomain(originalDomain);\n if (this._snappingDomainEnabled) {\n return this._niceDomain([newMin, newMax]);\n }\n return ([newMin, newMax]);\n };\n QuantitativeScale.prototype.snappingDomainEnabled = function (snappingDomainEnabled) {\n if (snappingDomainEnabled == null) {\n return this._snappingDomainEnabled;\n }\n this._snappingDomainEnabled = snappingDomainEnabled;\n this._autoDomainIfAutomaticMode();\n return this;\n };\n QuantitativeScale.prototype._expandSingleValueDomain = function (singleValueDomain) {\n return singleValueDomain;\n };\n /**\n * Computes the domain value corresponding to a supplied range value.\n *\n * @param {number} value: A value from the Scale's range.\n * @returns {D} The domain value corresponding to the supplied range value.\n */\n QuantitativeScale.prototype.invert = function (value) {\n throw new Error(\"Subclasses should override invert\");\n };\n QuantitativeScale.prototype.domain = function (values) {\n if (values != null) {\n this._domainMin = values[0];\n this._domainMax = values[1];\n }\n return _super.prototype.domain.call(this, values);\n };\n QuantitativeScale.prototype.domainMin = function (domainMin) {\n if (domainMin == null) {\n return this.domain()[0];\n }\n this._domainMin = domainMin;\n this._autoDomainIfAutomaticMode();\n return this;\n };\n QuantitativeScale.prototype.domainMax = function (domainMax) {\n if (domainMax == null) {\n return this.domain()[1];\n }\n this._domainMax = domainMax;\n this._autoDomainIfAutomaticMode();\n return this;\n };\n QuantitativeScale.prototype.extentOfValues = function (values) {\n // HACKHACK: TS1.4 doesn't consider numbers to be Number-like (valueOf() returning number), so D can't be typed correctly\n var extent = d3.extent(values.filter(function (value) { return Plottable.Utils.Math.isValidNumber(+value); }));\n if (extent[0] == null || extent[1] == null) {\n return [];\n }\n else {\n return extent;\n }\n };\n QuantitativeScale.prototype._setDomain = function (values) {\n var isNaNOrInfinity = function (x) { return Plottable.Utils.Math.isNaN(x) || x === Infinity || x === -Infinity; };\n if (isNaNOrInfinity(values[0]) || isNaNOrInfinity(values[1])) {\n Plottable.Utils.Window.warn(\"Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring.\");\n return;\n }\n _super.prototype._setDomain.call(this, values);\n };\n /**\n * Gets the array of tick values generated by the default algorithm.\n */\n QuantitativeScale.prototype.defaultTicks = function () {\n throw new Error(\"Subclasses should override _getDefaultTicks\");\n };\n /**\n * Gets an array of tick values spanning the domain.\n *\n * @returns {D[]}\n */\n QuantitativeScale.prototype.ticks = function () {\n return this._tickGenerator(this);\n };\n /**\n * Given a domain, expands its domain onto \"nice\" values, e.g. whole\n * numbers.\n */\n QuantitativeScale.prototype._niceDomain = function (domain, count) {\n throw new Error(\"Subclasses should override _niceDomain\");\n };\n QuantitativeScale.prototype._defaultExtent = function () {\n throw new Error(\"Subclasses should override _defaultExtent\");\n };\n QuantitativeScale.prototype.tickGenerator = function (generator) {\n if (generator == null) {\n return this._tickGenerator;\n }\n else {\n this._tickGenerator = generator;\n return this;\n }\n };\n QuantitativeScale._DEFAULT_NUM_TICKS = 10;\n return QuantitativeScale;\n }(Plottable.Scale));\n Plottable.QuantitativeScale = QuantitativeScale;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var Linear = (function (_super) {\n __extends(Linear, _super);\n /**\n * @constructor\n */\n function Linear() {\n _super.call(this);\n this._d3Scale = d3.scale.linear();\n }\n Linear.prototype._defaultExtent = function () {\n return [0, 1];\n };\n Linear.prototype._expandSingleValueDomain = function (singleValueDomain) {\n if (singleValueDomain[0] === singleValueDomain[1]) {\n return [singleValueDomain[0] - 1, singleValueDomain[1] + 1];\n }\n return singleValueDomain;\n };\n Linear.prototype.scale = function (value) {\n return this._d3Scale(value);\n };\n Linear.prototype._getDomain = function () {\n return this._backingScaleDomain();\n };\n Linear.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n return this;\n }\n };\n Linear.prototype._getRange = function () {\n return this._d3Scale.range();\n };\n Linear.prototype._setRange = function (values) {\n this._d3Scale.range(values);\n };\n Linear.prototype.invert = function (value) {\n return this._d3Scale.invert(value);\n };\n Linear.prototype.defaultTicks = function () {\n return this._d3Scale.ticks(Scales.Linear._DEFAULT_NUM_TICKS);\n };\n Linear.prototype._niceDomain = function (domain, count) {\n return this._d3Scale.copy().domain(domain).nice(count).domain();\n };\n return Linear;\n }(Plottable.QuantitativeScale));\n Scales.Linear = Linear;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var ModifiedLog = (function (_super) {\n __extends(ModifiedLog, _super);\n /**\n * A ModifiedLog Scale acts as a regular log scale for large numbers.\n * As it approaches 0, it gradually becomes linear.\n * Consequently, a ModifiedLog Scale can process 0 and negative numbers.\n *\n * For x >= base, scale(x) = log(x).\n *\n * For 0 < x < base, scale(x) will become more and more\n * linear as it approaches 0.\n *\n * At x == 0, scale(x) == 0.\n *\n * For negative values, scale(-x) = -scale(x).\n *\n * The range and domain for the scale should also be set, using the\n * range() and domain() accessors, respectively.\n *\n * For `range`, provide a two-element array giving the minimum and\n * maximum of values produced when scaling.\n *\n * For `domain` provide a two-element array giving the minimum and\n * maximum of the values that will be scaled.\n *\n * @constructor\n * @param {number} [base=10]\n * The base of the log. Must be > 1.\n *\n */\n function ModifiedLog(base) {\n if (base === void 0) { base = 10; }\n _super.call(this);\n this._d3Scale = d3.scale.linear();\n this._base = base;\n this._pivot = this._base;\n this._setDomain(this._defaultExtent());\n if (base <= 1) {\n throw new Error(\"ModifiedLogScale: The base must be > 1\");\n }\n }\n /**\n * Returns an adjusted log10 value for graphing purposes. The first\n * adjustment is that negative values are changed to positive during\n * the calculations, and then the answer is negated at the end. The\n * second is that, for values less than 10, an increasingly large\n * (0 to 1) scaling factor is added such that at 0 the value is\n * adjusted to 1, resulting in a returned result of 0.\n */\n ModifiedLog.prototype._adjustedLog = function (x) {\n var negationFactor = x < 0 ? -1 : 1;\n x *= negationFactor;\n if (x < this._pivot) {\n x += (this._pivot - x) / this._pivot;\n }\n x = Math.log(x) / Math.log(this._base);\n x *= negationFactor;\n return x;\n };\n ModifiedLog.prototype._invertedAdjustedLog = function (x) {\n var negationFactor = x < 0 ? -1 : 1;\n x *= negationFactor;\n x = Math.pow(this._base, x);\n if (x < this._pivot) {\n x = (this._pivot * (x - 1)) / (this._pivot - 1);\n }\n x *= negationFactor;\n return x;\n };\n ModifiedLog.prototype.scale = function (x) {\n return this._d3Scale(this._adjustedLog(x));\n };\n ModifiedLog.prototype.invert = function (x) {\n return this._invertedAdjustedLog(this._d3Scale.invert(x));\n };\n ModifiedLog.prototype._getDomain = function () {\n return this._untransformedDomain;\n };\n ModifiedLog.prototype._setDomain = function (values) {\n this._untransformedDomain = values;\n var transformedDomain = [this._adjustedLog(values[0]), this._adjustedLog(values[1])];\n _super.prototype._setDomain.call(this, transformedDomain);\n };\n ModifiedLog.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n return this;\n }\n };\n ModifiedLog.prototype.ticks = function () {\n // Say your domain is [-100, 100] and your pivot is 10.\n // then we're going to draw negative log ticks from -100 to -10,\n // linear ticks from -10 to 10, and positive log ticks from 10 to 100.\n var middle = function (x, y, z) { return [x, y, z].sort(function (a, b) { return a - b; })[1]; };\n var min = Plottable.Utils.Math.min(this._untransformedDomain, 0);\n var max = Plottable.Utils.Math.max(this._untransformedDomain, 0);\n var negativeLower = min;\n var negativeUpper = middle(min, max, -this._pivot);\n var positiveLower = middle(min, max, this._pivot);\n var positiveUpper = max;\n var negativeLogTicks = this._logTicks(-negativeUpper, -negativeLower).map(function (x) { return -x; }).reverse();\n var positiveLogTicks = this._logTicks(positiveLower, positiveUpper);\n var linearMin = Math.max(min, -this._pivot);\n var linearMax = Math.min(max, this._pivot);\n var linearTicks = d3.scale.linear().domain([linearMin, linearMax]).ticks(this._howManyTicks(linearMin, linearMax));\n var ticks = negativeLogTicks.concat(linearTicks).concat(positiveLogTicks);\n // If you only have 1 tick, you can't tell how big the scale is.\n if (ticks.length <= 1) {\n ticks = d3.scale.linear().domain([min, max]).ticks(Scales.ModifiedLog._DEFAULT_NUM_TICKS);\n }\n return ticks;\n };\n /**\n * Return an appropriate number of ticks from lower to upper.\n *\n * This will first try to fit as many powers of this.base as it can from\n * lower to upper.\n *\n * If it still has ticks after that, it will generate ticks in \"clusters\",\n * e.g. [20, 30, ... 90, 100] would be a cluster, [200, 300, ... 900, 1000]\n * would be another cluster.\n *\n * This function will generate clusters as large as it can while not\n * drastically exceeding its number of ticks.\n */\n ModifiedLog.prototype._logTicks = function (lower, upper) {\n var _this = this;\n var nTicks = this._howManyTicks(lower, upper);\n if (nTicks === 0) {\n return [];\n }\n var startLogged = Math.floor(Math.log(lower) / Math.log(this._base));\n var endLogged = Math.ceil(Math.log(upper) / Math.log(this._base));\n var bases = d3.range(endLogged, startLogged, -Math.ceil((endLogged - startLogged) / nTicks));\n var multiples = d3.range(this._base, 1, -(this._base - 1)).map(Math.floor);\n var uniqMultiples = Plottable.Utils.Array.uniq(multiples);\n var clusters = bases.map(function (b) { return uniqMultiples.map(function (x) { return Math.pow(_this._base, b - 1) * x; }); });\n var flattened = Plottable.Utils.Array.flatten(clusters);\n var filtered = flattened.filter(function (x) { return lower <= x && x <= upper; });\n var sorted = filtered.sort(function (x, y) { return x - y; });\n return sorted;\n };\n /**\n * How many ticks does the range [lower, upper] deserve?\n *\n * e.g. if your domain was [10, 1000] and I asked _howManyTicks(10, 100),\n * I would get 1/2 of the ticks. The range 10, 100 takes up 1/2 of the\n * distance when plotted.\n */\n ModifiedLog.prototype._howManyTicks = function (lower, upper) {\n var adjustedMin = this._adjustedLog(Plottable.Utils.Math.min(this._untransformedDomain, 0));\n var adjustedMax = this._adjustedLog(Plottable.Utils.Math.max(this._untransformedDomain, 0));\n var adjustedLower = this._adjustedLog(lower);\n var adjustedUpper = this._adjustedLog(upper);\n var proportion = (adjustedUpper - adjustedLower) / (adjustedMax - adjustedMin);\n var ticks = Math.ceil(proportion * Scales.ModifiedLog._DEFAULT_NUM_TICKS);\n return ticks;\n };\n ModifiedLog.prototype._niceDomain = function (domain, count) {\n return domain;\n };\n ModifiedLog.prototype._defaultExtent = function () {\n return [0, this._base];\n };\n ModifiedLog.prototype._expandSingleValueDomain = function (singleValueDomain) {\n if (singleValueDomain[0] === singleValueDomain[1]) {\n var singleValue = singleValueDomain[0];\n if (singleValue > 0) {\n return [singleValue / this._base, singleValue * this._base];\n }\n else if (singleValue === 0) {\n return [-this._base, this._base];\n }\n else {\n return [singleValue * this._base, singleValue / this._base];\n }\n }\n return singleValueDomain;\n };\n ModifiedLog.prototype._getRange = function () {\n return this._d3Scale.range();\n };\n ModifiedLog.prototype._setRange = function (values) {\n this._d3Scale.range(values);\n };\n ModifiedLog.prototype.defaultTicks = function () {\n return this._d3Scale.ticks(Scales.ModifiedLog._DEFAULT_NUM_TICKS);\n };\n return ModifiedLog;\n }(Plottable.QuantitativeScale));\n Scales.ModifiedLog = ModifiedLog;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var Category = (function (_super) {\n __extends(Category, _super);\n /**\n * A Category Scale maps strings to numbers.\n *\n * @constructor\n */\n function Category() {\n _super.call(this);\n this._range = [0, 1];\n this._d3Scale = d3.scale.ordinal();\n var d3InnerPadding = 0.3;\n this._innerPadding = Category._convertToPlottableInnerPadding(d3InnerPadding);\n this._outerPadding = Category._convertToPlottableOuterPadding(0.5, d3InnerPadding);\n }\n Category.prototype.extentOfValues = function (values) {\n return Plottable.Utils.Array.uniq(values);\n };\n Category.prototype._getExtent = function () {\n return Plottable.Utils.Array.uniq(this._getAllIncludedValues());\n };\n Category.prototype.domain = function (values) {\n return _super.prototype.domain.call(this, values);\n };\n Category.prototype.range = function (values) {\n return _super.prototype.range.call(this, values);\n };\n Category._convertToPlottableInnerPadding = function (d3InnerPadding) {\n return 1 / (1 - d3InnerPadding) - 1;\n };\n Category._convertToPlottableOuterPadding = function (d3OuterPadding, d3InnerPadding) {\n return d3OuterPadding / (1 - d3InnerPadding);\n };\n Category.prototype._setBands = function () {\n var d3InnerPadding = 1 - 1 / (1 + this.innerPadding());\n var d3OuterPadding = this.outerPadding() / (1 + this.innerPadding());\n this._d3Scale.rangeBands(this._range, d3InnerPadding, d3OuterPadding);\n };\n /**\n * Returns the width of the range band.\n *\n * @returns {number} The range band width\n */\n Category.prototype.rangeBand = function () {\n return this._d3Scale.rangeBand();\n };\n /**\n * Returns the step width of the scale.\n *\n * The step width is the pixel distance between adjacent values in the domain.\n *\n * @returns {number}\n */\n Category.prototype.stepWidth = function () {\n return this.rangeBand() * (1 + this.innerPadding());\n };\n Category.prototype.innerPadding = function (innerPadding) {\n if (innerPadding == null) {\n return this._innerPadding;\n }\n this._innerPadding = innerPadding;\n this.range(this.range());\n this._dispatchUpdate();\n return this;\n };\n Category.prototype.outerPadding = function (outerPadding) {\n if (outerPadding == null) {\n return this._outerPadding;\n }\n this._outerPadding = outerPadding;\n this.range(this.range());\n this._dispatchUpdate();\n return this;\n };\n Category.prototype.scale = function (value) {\n // scale it to the middle\n return this._d3Scale(value) + this.rangeBand() / 2;\n };\n Category.prototype._getDomain = function () {\n return this._backingScaleDomain();\n };\n Category.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n this._setBands();\n return this;\n }\n };\n Category.prototype._getRange = function () {\n return this._range;\n };\n Category.prototype._setRange = function (values) {\n this._range = values;\n this._setBands();\n };\n return Category;\n }(Plottable.Scale));\n Scales.Category = Category;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var Color = (function (_super) {\n __extends(Color, _super);\n /**\n * A Color Scale maps string values to color hex values expressed as a string.\n *\n * @constructor\n * @param {string} [scaleType] One of \"Category10\"/\"Category20\"/\"Category20b\"/\"Category20c\".\n * (see https://github.com/mbostock/d3/wiki/Ordinal-Scales#categorical-colors)\n * If not supplied, reads the colors defined using CSS -- see plottable.css.\n */\n function Color(scaleType) {\n _super.call(this);\n var scale;\n switch (scaleType) {\n case null:\n case undefined:\n if (Color._plottableColorCache == null) {\n Color._plottableColorCache = Color._getPlottableColors();\n }\n scale = d3.scale.ordinal().range(Color._plottableColorCache);\n break;\n case \"Category10\":\n case \"category10\":\n case \"10\":\n scale = d3.scale.category10();\n break;\n case \"Category20\":\n case \"category20\":\n case \"20\":\n scale = d3.scale.category20();\n break;\n case \"Category20b\":\n case \"category20b\":\n case \"20b\":\n scale = d3.scale.category20b();\n break;\n case \"Category20c\":\n case \"category20c\":\n case \"20c\":\n scale = d3.scale.category20c();\n break;\n default:\n throw new Error(\"Unsupported ColorScale type\");\n }\n this._d3Scale = scale;\n }\n Color.prototype.extentOfValues = function (values) {\n return Plottable.Utils.Array.uniq(values);\n };\n // Duplicated from OrdinalScale._getExtent - should be removed in #388\n Color.prototype._getExtent = function () {\n return Plottable.Utils.Array.uniq(this._getAllIncludedValues());\n };\n Color.invalidateColorCache = function () {\n Color._plottableColorCache = null;\n };\n Color._getPlottableColors = function () {\n var plottableDefaultColors = [];\n var colorTester = d3.select(\"body\").append(\"plottable-color-tester\");\n var defaultColorHex = Plottable.Utils.Color.colorTest(colorTester, \"\");\n var i = 0;\n var colorHex = Plottable.Utils.Color.colorTest(colorTester, \"plottable-colors-0\");\n while (colorHex != null && i < this._MAXIMUM_COLORS_FROM_CSS) {\n if (colorHex === defaultColorHex && colorHex === plottableDefaultColors[plottableDefaultColors.length - 1]) {\n break;\n }\n plottableDefaultColors.push(colorHex);\n i++;\n colorHex = Plottable.Utils.Color.colorTest(colorTester, \"plottable-colors-\" + i);\n }\n colorTester.remove();\n return plottableDefaultColors;\n };\n /**\n * Returns the color-string corresponding to a given string.\n * If there are not enough colors in the range(), a lightened version of an existing color will be used.\n *\n * @param {string} value\n * @returns {string}\n */\n Color.prototype.scale = function (value) {\n var color = this._d3Scale(value);\n var index = this.domain().indexOf(value);\n var numLooped = Math.floor(index / this.range().length);\n var modifyFactor = Math.log(numLooped * Color._LOOP_LIGHTEN_FACTOR + 1);\n return Plottable.Utils.Color.lightenColor(color, modifyFactor);\n };\n Color.prototype._getDomain = function () {\n return this._backingScaleDomain();\n };\n Color.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n return this;\n }\n };\n Color.prototype._getRange = function () {\n return this._d3Scale.range();\n };\n Color.prototype._setRange = function (values) {\n this._d3Scale.range(values);\n };\n Color._LOOP_LIGHTEN_FACTOR = 1.6;\n // The maximum number of colors we are getting from CSS stylesheets\n Color._MAXIMUM_COLORS_FROM_CSS = 256;\n return Color;\n }(Plottable.Scale));\n Scales.Color = Color;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var Time = (function (_super) {\n __extends(Time, _super);\n /**\n * A Time Scale maps Date objects to numbers.\n *\n * @constructor\n */\n function Time() {\n _super.call(this);\n this._d3Scale = d3.time.scale();\n this.autoDomain();\n }\n /**\n * Returns an array of ticks values separated by the specified interval.\n *\n * @param {string} interval A string specifying the interval unit.\n * @param {number?} [step] The number of multiples of the interval between consecutive ticks.\n * @return {Date[]}\n */\n Time.prototype.tickInterval = function (interval, step) {\n // temporarily creats a time scale from our linear scale into a time scale so we can get access to its api\n var tempScale = d3.time.scale();\n var d3Interval = Time.timeIntervalToD3Time(interval);\n tempScale.domain(this.domain());\n tempScale.range(this.range());\n return tempScale.ticks(d3Interval, step);\n };\n Time.prototype._setDomain = function (values) {\n if (values[1] < values[0]) {\n throw new Error(\"Scale.Time domain values must be in chronological order\");\n }\n return _super.prototype._setDomain.call(this, values);\n };\n Time.prototype._defaultExtent = function () {\n return [new Date(\"1970-01-01\"), new Date(\"1970-01-02\")];\n };\n Time.prototype._expandSingleValueDomain = function (singleValueDomain) {\n var startTime = singleValueDomain[0].getTime();\n var endTime = singleValueDomain[1].getTime();\n if (startTime === endTime) {\n var startDate = new Date(startTime);\n startDate.setDate(startDate.getDate() - 1);\n var endDate = new Date(endTime);\n endDate.setDate(endDate.getDate() + 1);\n return [startDate, endDate];\n }\n return singleValueDomain;\n };\n Time.prototype.scale = function (value) {\n return this._d3Scale(value);\n };\n Time.prototype._getDomain = function () {\n return this._backingScaleDomain();\n };\n Time.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n return this;\n }\n };\n Time.prototype._getRange = function () {\n return this._d3Scale.range();\n };\n Time.prototype._setRange = function (values) {\n this._d3Scale.range(values);\n };\n Time.prototype.invert = function (value) {\n return this._d3Scale.invert(value);\n };\n Time.prototype.defaultTicks = function () {\n return this._d3Scale.ticks(Scales.Time._DEFAULT_NUM_TICKS);\n };\n Time.prototype._niceDomain = function (domain) {\n return this._d3Scale.copy().domain(domain).nice().domain();\n };\n /**\n * Transforms the Plottable TimeInterval string into a d3 time interval equivalent.\n * If the provided TimeInterval is incorrect, the default is d3.time.year\n */\n Time.timeIntervalToD3Time = function (timeInterval) {\n switch (timeInterval) {\n case Plottable.TimeInterval.second:\n return d3.time.second;\n case Plottable.TimeInterval.minute:\n return d3.time.minute;\n case Plottable.TimeInterval.hour:\n return d3.time.hour;\n case Plottable.TimeInterval.day:\n return d3.time.day;\n case Plottable.TimeInterval.week:\n return d3.time.week;\n case Plottable.TimeInterval.month:\n return d3.time.month;\n case Plottable.TimeInterval.year:\n return d3.time.year;\n default:\n throw Error(\"TimeInterval specified does not exist: \" + timeInterval);\n }\n };\n return Time;\n }(Plottable.QuantitativeScale));\n Scales.Time = Time;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var InterpolatedColor = (function (_super) {\n __extends(InterpolatedColor, _super);\n /**\n * An InterpolatedColor Scale maps numbers to color hex values, expressed as strings.\n *\n * @param {string} [scaleType=\"linear\"] One of \"linear\"/\"log\"/\"sqrt\"/\"pow\".\n */\n function InterpolatedColor(scaleType) {\n if (scaleType === void 0) { scaleType = \"linear\"; }\n _super.call(this);\n switch (scaleType) {\n case \"linear\":\n this._colorScale = d3.scale.linear();\n break;\n case \"log\":\n this._colorScale = d3.scale.log();\n break;\n case \"sqrt\":\n this._colorScale = d3.scale.sqrt();\n break;\n case \"pow\":\n this._colorScale = d3.scale.pow();\n break;\n }\n if (this._colorScale == null) {\n throw new Error(\"unknown QuantitativeScale scale type \" + scaleType);\n }\n this.range(InterpolatedColor.REDS);\n }\n InterpolatedColor.prototype.extentOfValues = function (values) {\n var extent = d3.extent(values);\n if (extent[0] == null || extent[1] == null) {\n return [];\n }\n else {\n return extent;\n }\n };\n /**\n * Generates the converted QuantitativeScale.\n */\n InterpolatedColor.prototype._d3InterpolatedScale = function () {\n return this._colorScale.range([0, 1]).interpolate(this._interpolateColors());\n };\n /**\n * Generates the d3 interpolator for colors.\n */\n InterpolatedColor.prototype._interpolateColors = function () {\n var colors = this._colorRange;\n if (colors.length < 2) {\n throw new Error(\"Color scale arrays must have at least two elements.\");\n }\n ;\n return function (a, b) {\n return function (t) {\n // Clamp t parameter to [0,1]\n t = Math.max(0, Math.min(1, t));\n // Determine indices for colors\n var tScaled = t * (colors.length - 1);\n var i0 = Math.floor(tScaled);\n var i1 = Math.ceil(tScaled);\n var frac = (tScaled - i0);\n // Interpolate in the L*a*b color space\n return d3.interpolateLab(colors[i0], colors[i1])(frac);\n };\n };\n };\n InterpolatedColor.prototype._resetScale = function () {\n this._d3Scale = this._d3InterpolatedScale();\n this._autoDomainIfAutomaticMode();\n this._dispatchUpdate();\n };\n InterpolatedColor.prototype.autoDomain = function () {\n // InterpolatedColorScales do not pad\n var includedValues = this._getAllIncludedValues();\n if (includedValues.length > 0) {\n this._setDomain([Plottable.Utils.Math.min(includedValues, 0), Plottable.Utils.Math.max(includedValues, 0)]);\n }\n return this;\n };\n InterpolatedColor.prototype.scale = function (value) {\n return this._d3Scale(value);\n };\n InterpolatedColor.prototype._getDomain = function () {\n return this._backingScaleDomain();\n };\n InterpolatedColor.prototype._backingScaleDomain = function (values) {\n if (values == null) {\n return this._d3Scale.domain();\n }\n else {\n this._d3Scale.domain(values);\n return this;\n }\n };\n InterpolatedColor.prototype._getRange = function () {\n return this._colorRange;\n };\n InterpolatedColor.prototype._setRange = function (range) {\n this._colorRange = range;\n this._resetScale();\n };\n InterpolatedColor.REDS = [\n \"#FFFFFF\",\n \"#FFF6E1\",\n \"#FEF4C0\",\n \"#FED976\",\n \"#FEB24C\",\n \"#FD8D3C\",\n \"#FC4E2A\",\n \"#E31A1C\",\n \"#B10026\",\n ];\n InterpolatedColor.BLUES = [\n \"#FFFFFF\",\n \"#CCFFFF\",\n \"#A5FFFD\",\n \"#85F7FB\",\n \"#6ED3EF\",\n \"#55A7E0\",\n \"#417FD0\",\n \"#2545D3\",\n \"#0B02E1\",\n ];\n InterpolatedColor.POSNEG = [\n \"#0B02E1\",\n \"#2545D3\",\n \"#417FD0\",\n \"#55A7E0\",\n \"#6ED3EF\",\n \"#85F7FB\",\n \"#A5FFFD\",\n \"#CCFFFF\",\n \"#FFFFFF\",\n \"#FFF6E1\",\n \"#FEF4C0\",\n \"#FED976\",\n \"#FEB24C\",\n \"#FD8D3C\",\n \"#FC4E2A\",\n \"#E31A1C\",\n \"#B10026\",\n ];\n return InterpolatedColor;\n }(Plottable.Scale));\n Scales.InterpolatedColor = InterpolatedColor;\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Scales;\n (function (Scales) {\n var TickGenerators;\n (function (TickGenerators) {\n /**\n * Creates a TickGenerator using the specified interval.\n *\n * Generates ticks at multiples of the interval while also including the domain boundaries.\n *\n * @param {number} interval\n * @returns {TickGenerator}\n */\n function intervalTickGenerator(interval) {\n if (interval <= 0) {\n throw new Error(\"interval must be positive number\");\n }\n return function (s) {\n var domain = s.domain();\n var low = Math.min(domain[0], domain[1]);\n var high = Math.max(domain[0], domain[1]);\n var firstTick = Math.ceil(low / interval) * interval;\n var numTicks = Math.floor((high - firstTick) / interval) + 1;\n var lowTicks = low % interval === 0 ? [] : [low];\n var middleTicks = Plottable.Utils.Math.range(0, numTicks).map(function (t) { return firstTick + t * interval; });\n var highTicks = high % interval === 0 ? [] : [high];\n return lowTicks.concat(middleTicks).concat(highTicks);\n };\n }\n TickGenerators.intervalTickGenerator = intervalTickGenerator;\n /**\n * Creates a TickGenerator returns only integer tick values.\n *\n * @returns {TickGenerator}\n */\n function integerTickGenerator() {\n return function (s) {\n var defaultTicks = s.defaultTicks();\n return defaultTicks.filter(function (tick, i) { return (tick % 1 === 0) || (i === 0) || (i === defaultTicks.length - 1); });\n };\n }\n TickGenerators.integerTickGenerator = integerTickGenerator;\n })(TickGenerators = Scales.TickGenerators || (Scales.TickGenerators = {}));\n })(Scales = Plottable.Scales || (Plottable.Scales = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawer = (function () {\n /**\n * A Drawer draws svg elements based on the input Dataset.\n *\n * @constructor\n * @param {Dataset} dataset The dataset associated with this Drawer\n */\n function Drawer(dataset) {\n this._cachedSelectionValid = false;\n this._dataset = dataset;\n }\n Drawer.prototype.renderArea = function (area) {\n if (area == null) {\n return this._renderArea;\n }\n this._renderArea = area;\n this._cachedSelectionValid = false;\n return this;\n };\n /**\n * Removes the Drawer and its renderArea\n */\n Drawer.prototype.remove = function () {\n if (this.renderArea() != null) {\n this.renderArea().remove();\n }\n };\n /**\n * Binds data to selection\n *\n * @param{any[]} data The data to be drawn\n */\n Drawer.prototype._bindSelectionData = function (data) {\n var dataElements = this.selection().data(data);\n dataElements.enter().append(this._svgElementName);\n dataElements.exit().remove();\n this._applyDefaultAttributes(dataElements);\n };\n Drawer.prototype._applyDefaultAttributes = function (selection) {\n if (this._className != null) {\n selection.classed(this._className, true);\n }\n };\n /**\n * Draws data using one step\n *\n * @param{AppliedDrawStep} step The step, how data should be drawn.\n */\n Drawer.prototype._drawStep = function (step) {\n var selection = this.selection();\n var colorAttributes = [\"fill\", \"stroke\"];\n colorAttributes.forEach(function (colorAttribute) {\n if (step.attrToAppliedProjector[colorAttribute] != null) {\n selection.attr(colorAttribute, step.attrToAppliedProjector[colorAttribute]);\n }\n });\n step.animator.animate(selection, step.attrToAppliedProjector);\n if (this._className != null) {\n this.selection().classed(this._className, true);\n }\n };\n Drawer.prototype._appliedProjectors = function (attrToProjector) {\n var _this = this;\n var modifiedAttrToProjector = {};\n Object.keys(attrToProjector).forEach(function (attr) {\n modifiedAttrToProjector[attr] =\n function (datum, index) { return attrToProjector[attr](datum, index, _this._dataset); };\n });\n return modifiedAttrToProjector;\n };\n /**\n * Calculates the total time it takes to use the input drawSteps to draw the input data\n *\n * @param {any[]} data The data that would have been drawn\n * @param {Drawers.DrawStep[]} drawSteps The DrawSteps to use\n * @returns {number} The total time it takes to draw\n */\n Drawer.prototype.totalDrawTime = function (data, drawSteps) {\n var delay = 0;\n drawSteps.forEach(function (drawStep, i) {\n delay += drawStep.animator.totalTime(data.length);\n });\n return delay;\n };\n /**\n * Draws the data into the renderArea using the spefic steps and metadata\n *\n * @param{any[]} data The data to be drawn\n * @param{DrawStep[]} drawSteps The list of steps, which needs to be drawn\n */\n Drawer.prototype.draw = function (data, drawSteps) {\n var _this = this;\n var appliedDrawSteps = drawSteps.map(function (dr) {\n var attrToAppliedProjector = _this._appliedProjectors(dr.attrToProjector);\n return {\n attrToAppliedProjector: attrToAppliedProjector,\n animator: dr.animator,\n };\n });\n this._bindSelectionData(data);\n this._cachedSelectionValid = false;\n var delay = 0;\n appliedDrawSteps.forEach(function (drawStep, i) {\n Plottable.Utils.Window.setTimeout(function () { return _this._drawStep(drawStep); }, delay);\n delay += drawStep.animator.totalTime(data.length);\n });\n return this;\n };\n Drawer.prototype.selection = function () {\n if (!this._cachedSelectionValid) {\n this._cachedSelection = this.renderArea().selectAll(this.selector());\n this._cachedSelectionValid = true;\n }\n return this._cachedSelection;\n };\n /**\n * Returns the CSS selector for this Drawer's visual elements.\n */\n Drawer.prototype.selector = function () {\n return this._svgElementName;\n };\n /**\n * Returns the D3 selection corresponding to the datum with the specified index.\n */\n Drawer.prototype.selectionForIndex = function (index) {\n return d3.select(this.selection()[0][index]);\n };\n return Drawer;\n }());\n Plottable.Drawer = Drawer;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Line = (function (_super) {\n __extends(Line, _super);\n function Line(dataset) {\n _super.call(this, dataset);\n this._className = \"line\";\n this._svgElementName = \"path\";\n }\n Line.prototype._applyDefaultAttributes = function (selection) {\n _super.prototype._applyDefaultAttributes.call(this, selection);\n selection.style(\"fill\", \"none\");\n };\n Line.prototype.selectionForIndex = function (index) {\n return d3.select(this.selection()[0][0]);\n };\n return Line;\n }(Plottable.Drawer));\n Drawers.Line = Line;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Area = (function (_super) {\n __extends(Area, _super);\n function Area(dataset) {\n _super.call(this, dataset);\n this._className = \"area\";\n this._svgElementName = \"path\";\n }\n Area.prototype._applyDefaultAttributes = function (selection) {\n _super.prototype._applyDefaultAttributes.call(this, selection);\n selection.style(\"stroke\", \"none\");\n };\n Area.prototype.selectionForIndex = function (index) {\n return d3.select(this.selection()[0][0]);\n };\n return Area;\n }(Plottable.Drawer));\n Drawers.Area = Area;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Rectangle = (function (_super) {\n __extends(Rectangle, _super);\n function Rectangle(dataset) {\n _super.call(this, dataset);\n this._svgElementName = \"rect\";\n }\n return Rectangle;\n }(Plottable.Drawer));\n Drawers.Rectangle = Rectangle;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Arc = (function (_super) {\n __extends(Arc, _super);\n function Arc(dataset) {\n _super.call(this, dataset);\n this._className = \"arc fill\";\n this._svgElementName = \"path\";\n }\n Arc.prototype._applyDefaultAttributes = function (selection) {\n _super.prototype._applyDefaultAttributes.call(this, selection);\n selection.style(\"stroke\", \"none\");\n };\n return Arc;\n }(Plottable.Drawer));\n Drawers.Arc = Arc;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var ArcOutline = (function (_super) {\n __extends(ArcOutline, _super);\n function ArcOutline(dataset) {\n _super.call(this, dataset);\n this._className = \"arc outline\";\n this._svgElementName = \"path\";\n }\n ArcOutline.prototype._applyDefaultAttributes = function (selection) {\n _super.prototype._applyDefaultAttributes.call(this, selection);\n selection.style(\"fill\", \"none\");\n };\n return ArcOutline;\n }(Plottable.Drawer));\n Drawers.ArcOutline = ArcOutline;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Symbol = (function (_super) {\n __extends(Symbol, _super);\n function Symbol(dataset) {\n _super.call(this, dataset);\n this._svgElementName = \"path\";\n this._className = \"symbol\";\n }\n return Symbol;\n }(Plottable.Drawer));\n Drawers.Symbol = Symbol;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Drawers;\n (function (Drawers) {\n var Segment = (function (_super) {\n __extends(Segment, _super);\n function Segment(dataset) {\n _super.call(this, dataset);\n this._svgElementName = \"line\";\n }\n return Segment;\n }(Plottable.Drawer));\n Drawers.Segment = Segment;\n })(Drawers = Plottable.Drawers || (Plottable.Drawers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Alignment = (function () {\n function Alignment() {\n }\n Alignment.TOP = \"top\";\n Alignment.BOTTOM = \"bottom\";\n Alignment.LEFT = \"left\";\n Alignment.RIGHT = \"right\";\n Alignment.CENTER = \"center\";\n return Alignment;\n }());\n Components.Alignment = Alignment;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n var Component = (function () {\n function Component() {\n this._clipPathEnabled = false;\n this._origin = { x: 0, y: 0 }; // Origin of the coordinate space for the Component.\n this._xAlignment = \"left\";\n this._yAlignment = \"top\";\n this._isSetup = false;\n this._isAnchored = false;\n this._boxes = [];\n this._isTopLevelComponent = false;\n this._cssClasses = new Plottable.Utils.Set();\n this._destroyed = false;\n this._onAnchorCallbacks = new Plottable.Utils.CallbackSet();\n this._onDetachCallbacks = new Plottable.Utils.CallbackSet();\n this._cssClasses.add(\"component\");\n }\n /**\n * Attaches the Component as a child of a given d3 Selection.\n *\n * @param {d3.Selection} selection.\n * @returns {Component} The calling Component.\n */\n Component.prototype.anchor = function (selection) {\n if (this._destroyed) {\n throw new Error(\"Can't reuse destroy()-ed Components!\");\n }\n this._isTopLevelComponent = selection.node().nodeName.toLowerCase() === \"svg\";\n if (this._isTopLevelComponent) {\n // svg node gets the \"plottable\" CSS class\n this._rootSVG = selection;\n this._rootSVG.classed(\"plottable\", true);\n // visible overflow for firefox https://stackoverflow.com/questions/5926986/why-does-firefox-appear-to-truncate-embedded-svgs\n this._rootSVG.style(\"overflow\", \"visible\");\n // HACKHACK: Safari fails to register events on the itself\n var safariBacking = this._rootSVG.select(\".\" + Component._SAFARI_EVENT_BACKING_CLASS);\n if (safariBacking.empty()) {\n this._rootSVG.append(\"rect\").classed(Component._SAFARI_EVENT_BACKING_CLASS, true).attr({\n x: 0,\n y: 0,\n width: \"100%\",\n height: \"100%\",\n }).style(\"opacity\", 0);\n }\n }\n if (this._element != null) {\n // reattach existing element\n selection.node().appendChild(this._element.node());\n }\n else {\n this._element = selection.append(\"g\");\n this._setup();\n }\n this._isAnchored = true;\n this._onAnchorCallbacks.callCallbacks(this);\n return this;\n };\n /**\n * Adds a callback to be called on anchoring the Component to the DOM.\n * If the Component is already anchored, the callback is called immediately.\n *\n * @param {ComponentCallback} callback\n * @return {Component}\n */\n Component.prototype.onAnchor = function (callback) {\n if (this._isAnchored) {\n callback(this);\n }\n this._onAnchorCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called on anchoring the Component to the DOM.\n * The callback is identified by reference equality.\n *\n * @param {ComponentCallback} callback\n * @return {Component}\n */\n Component.prototype.offAnchor = function (callback) {\n this._onAnchorCallbacks.delete(callback);\n return this;\n };\n /**\n * Creates additional elements as necessary for the Component to function.\n * Called during anchor() if the Component's element has not been created yet.\n * Override in subclasses to provide additional functionality.\n */\n Component.prototype._setup = function () {\n var _this = this;\n if (this._isSetup) {\n return;\n }\n this._cssClasses.forEach(function (cssClass) {\n _this._element.classed(cssClass, true);\n });\n this._cssClasses = new Plottable.Utils.Set();\n this._backgroundContainer = this._element.append(\"g\").classed(\"background-container\", true);\n this._addBox(\"background-fill\", this._backgroundContainer);\n this._content = this._element.append(\"g\").classed(\"content\", true);\n this._foregroundContainer = this._element.append(\"g\").classed(\"foreground-container\", true);\n this._boxContainer = this._element.append(\"g\").classed(\"box-container\", true);\n if (this._clipPathEnabled) {\n this._generateClipPath();\n }\n ;\n this._boundingBox = this._addBox(\"bounding-box\");\n this._isSetup = true;\n };\n /**\n * Given available space in pixels, returns the minimum width and height this Component will need.\n *\n * @param {number} availableWidth\n * @param {number} availableHeight\n * @returns {SpaceRequest}\n */\n Component.prototype.requestedSpace = function (availableWidth, availableHeight) {\n return {\n minWidth: 0,\n minHeight: 0,\n };\n };\n /**\n * Computes and sets the size, position, and alignment of the Component from the specified values.\n * If no parameters are supplied and the Component is a root node,\n * they are inferred from the size of the Component's element.\n *\n * @param {Point} [origin] Origin of the space offered to the Component.\n * @param {number} [availableWidth] Available width in pixels.\n * @param {number} [availableHeight] Available height in pixels.\n * @returns {Component} The calling Component.\n */\n Component.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n var _this = this;\n if (origin == null || availableWidth == null || availableHeight == null) {\n if (this._element == null) {\n throw new Error(\"anchor() must be called before computeLayout()\");\n }\n else if (this._isTopLevelComponent) {\n // we are the root node, retrieve height/width from root SVG\n origin = { x: 0, y: 0 };\n // Set width/height to 100% if not specified, to allow accurate size calculation\n // see http://www.w3.org/TR/CSS21/visudet.html#block-replaced-width\n // and http://www.w3.org/TR/CSS21/visudet.html#inline-replaced-height\n if (this._rootSVG.attr(\"width\") == null) {\n this._rootSVG.attr(\"width\", \"100%\");\n }\n if (this._rootSVG.attr(\"height\") == null) {\n this._rootSVG.attr(\"height\", \"100%\");\n }\n var elem = this._rootSVG.node();\n availableWidth = Plottable.Utils.DOM.elementWidth(elem);\n availableHeight = Plottable.Utils.DOM.elementHeight(elem);\n }\n else {\n throw new Error(\"null arguments cannot be passed to computeLayout() on a non-root node\");\n }\n }\n var size = this._sizeFromOffer(availableWidth, availableHeight);\n this._width = size.width;\n this._height = size.height;\n var xAlignProportion = Component._xAlignToProportion[this._xAlignment];\n var yAlignProportion = Component._yAlignToProportion[this._yAlignment];\n this._origin = {\n x: origin.x + (availableWidth - this.width()) * xAlignProportion,\n y: origin.y + (availableHeight - this.height()) * yAlignProportion,\n };\n this._element.attr(\"transform\", \"translate(\" + this._origin.x + \",\" + this._origin.y + \")\");\n this._boxes.forEach(function (b) { return b.attr(\"width\", _this.width()).attr(\"height\", _this.height()); });\n return this;\n };\n Component.prototype._sizeFromOffer = function (availableWidth, availableHeight) {\n var requestedSpace = this.requestedSpace(availableWidth, availableHeight);\n return {\n width: this.fixedWidth() ? Math.min(availableWidth, requestedSpace.minWidth) : availableWidth,\n height: this.fixedHeight() ? Math.min(availableHeight, requestedSpace.minHeight) : availableHeight,\n };\n };\n /**\n * Queues the Component for rendering.\n *\n * @returns {Component} The calling Component.\n */\n Component.prototype.render = function () {\n if (this._isAnchored && this._isSetup && this.width() >= 0 && this.height() >= 0) {\n Plottable.RenderController.registerToRender(this);\n }\n return this;\n };\n Component.prototype._scheduleComputeLayout = function () {\n if (this._isAnchored && this._isSetup) {\n Plottable.RenderController.registerToComputeLayout(this);\n }\n };\n /**\n * Renders the Component without waiting for the next frame.\n */\n Component.prototype.renderImmediately = function () {\n if (this._clipPathEnabled) {\n this._updateClipPath();\n }\n return this;\n };\n /**\n * Causes the Component to re-layout and render.\n *\n * This function should be called when a CSS change has occured that could\n * influence the layout of the Component, such as changing the font size.\n *\n * @returns {Component} The calling Component.\n */\n Component.prototype.redraw = function () {\n if (this._isAnchored && this._isSetup) {\n if (this._isTopLevelComponent) {\n this._scheduleComputeLayout();\n }\n else {\n this.parent().redraw();\n }\n }\n return this;\n };\n /**\n * Renders the Component to a given .\n *\n * @param {String|d3.Selection} element A selector-string for the , or a d3 selection containing an .\n * @returns {Component} The calling Component.\n */\n Component.prototype.renderTo = function (element) {\n this.detach();\n if (element != null) {\n var selection = void 0;\n if (typeof (element) === \"string\") {\n selection = d3.select(element);\n }\n else if (element instanceof Element) {\n selection = d3.select(element);\n }\n else {\n selection = element;\n }\n if (!selection.node() || selection.node().nodeName.toLowerCase() !== \"svg\") {\n throw new Error(\"Plottable requires a valid SVG to renderTo\");\n }\n this.anchor(selection);\n }\n if (this._element == null) {\n throw new Error(\"If a Component has never been rendered before, then renderTo must be given a node to render to, \" +\n \"or a d3.Selection, or a selector string\");\n }\n this.computeLayout();\n this.render();\n // flush so that consumers can immediately attach to stuff we create in the DOM\n Plottable.RenderController.flush();\n return this;\n };\n Component.prototype.xAlignment = function (xAlignment) {\n if (xAlignment == null) {\n return this._xAlignment;\n }\n xAlignment = xAlignment.toLowerCase();\n if (Component._xAlignToProportion[xAlignment] == null) {\n throw new Error(\"Unsupported alignment: \" + xAlignment);\n }\n this._xAlignment = xAlignment;\n this.redraw();\n return this;\n };\n Component.prototype.yAlignment = function (yAlignment) {\n if (yAlignment == null) {\n return this._yAlignment;\n }\n yAlignment = yAlignment.toLowerCase();\n if (Component._yAlignToProportion[yAlignment] == null) {\n throw new Error(\"Unsupported alignment: \" + yAlignment);\n }\n this._yAlignment = yAlignment;\n this.redraw();\n return this;\n };\n Component.prototype._addBox = function (className, parentElement) {\n if (this._element == null) {\n throw new Error(\"Adding boxes before anchoring is currently disallowed\");\n }\n parentElement = parentElement == null ? this._boxContainer : parentElement;\n var box = parentElement.append(\"rect\");\n if (className != null) {\n box.classed(className, true);\n }\n this._boxes.push(box);\n if (this.width() != null && this.height() != null) {\n box.attr(\"width\", this.width()).attr(\"height\", this.height());\n }\n return box;\n };\n Component.prototype._generateClipPath = function () {\n // The clip path will prevent content from overflowing its Component space.\n this._clipPathID = Plottable.Utils.DOM.generateUniqueClipPathId();\n var clipPathParent = this._boxContainer.append(\"clipPath\").attr(\"id\", this._clipPathID);\n this._addBox(\"clip-rect\", clipPathParent);\n this._updateClipPath();\n };\n Component.prototype._updateClipPath = function () {\n // HACKHACK: IE <= 9 does not respect the HTML base element in SVG.\n // They don't need the current URL in the clip path reference.\n var prefix = /MSIE [5-9]/.test(navigator.userAgent) ? \"\" : document.location.href;\n prefix = prefix.split(\"#\")[0]; // To fix cases where an anchor tag was used\n this._element.attr(\"clip-path\", \"url(\\\"\" + prefix + \"#\" + this._clipPathID + \"\\\")\");\n };\n /**\n * Checks if the Component has a given CSS class.\n *\n * @param {string} cssClass The CSS class to check for.\n */\n Component.prototype.hasClass = function (cssClass) {\n if (cssClass == null) {\n return false;\n }\n if (this._element == null) {\n return this._cssClasses.has(cssClass);\n }\n else {\n return this._element.classed(cssClass);\n }\n };\n /**\n * Adds a given CSS class to the Component.\n *\n * @param {string} cssClass The CSS class to add.\n * @returns {Component} The calling Component.\n */\n Component.prototype.addClass = function (cssClass) {\n if (cssClass == null) {\n return this;\n }\n if (this._element == null) {\n this._cssClasses.add(cssClass);\n }\n else {\n this._element.classed(cssClass, true);\n }\n return this;\n };\n /**\n * Removes a given CSS class from the Component.\n *\n * @param {string} cssClass The CSS class to remove.\n * @returns {Component} The calling Component.\n */\n Component.prototype.removeClass = function (cssClass) {\n if (cssClass == null) {\n return this;\n }\n if (this._element == null) {\n this._cssClasses.delete(cssClass);\n }\n else {\n this._element.classed(cssClass, false);\n }\n return this;\n };\n /**\n * Checks if the Component has a fixed width or if it grows to fill available space.\n * Returns false by default on the base Component class.\n */\n Component.prototype.fixedWidth = function () {\n return false;\n };\n /**\n * Checks if the Component has a fixed height or if it grows to fill available space.\n * Returns false by default on the base Component class.\n */\n Component.prototype.fixedHeight = function () {\n return false;\n };\n /**\n * Detaches a Component from the DOM. The Component can be reused.\n *\n * This should only be used if you plan on reusing the calling Component. Otherwise, use destroy().\n *\n * @returns The calling Component.\n */\n Component.prototype.detach = function () {\n this.parent(null);\n if (this._isAnchored) {\n this._element.remove();\n if (this._isTopLevelComponent) {\n this._rootSVG.select(\".\" + Component._SAFARI_EVENT_BACKING_CLASS).remove();\n }\n }\n this._isAnchored = false;\n this._onDetachCallbacks.callCallbacks(this);\n return this;\n };\n /**\n * Adds a callback to be called when the Component is detach()-ed.\n *\n * @param {ComponentCallback} callback\n * @return {Component} The calling Component.\n */\n Component.prototype.onDetach = function (callback) {\n this._onDetachCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback to be called when the Component is detach()-ed.\n * The callback is identified by reference equality.\n *\n * @param {ComponentCallback} callback\n * @return {Component} The calling Component.\n */\n Component.prototype.offDetach = function (callback) {\n this._onDetachCallbacks.delete(callback);\n return this;\n };\n Component.prototype.parent = function (parent) {\n if (parent === undefined) {\n return this._parent;\n }\n if (parent !== null && !parent.has(this)) {\n throw new Error(\"Passed invalid parent\");\n }\n this._parent = parent;\n return this;\n };\n /**\n * Removes a Component from the DOM and disconnects all listeners.\n */\n Component.prototype.destroy = function () {\n this._destroyed = true;\n this.detach();\n };\n /**\n * Gets the width of the Component in pixels.\n */\n Component.prototype.width = function () {\n return this._width;\n };\n /**\n * Gets the height of the Component in pixels.\n */\n Component.prototype.height = function () {\n return this._height;\n };\n /**\n * Gets the origin of the Component relative to its parent.\n *\n * @return {Point}\n */\n Component.prototype.origin = function () {\n return {\n x: this._origin.x,\n y: this._origin.y,\n };\n };\n /**\n * Gets the origin of the Component relative to the root .\n *\n * @return {Point}\n */\n Component.prototype.originToSVG = function () {\n var origin = this.origin();\n var ancestor = this.parent();\n while (ancestor != null) {\n var ancestorOrigin = ancestor.origin();\n origin.x += ancestorOrigin.x;\n origin.y += ancestorOrigin.y;\n ancestor = ancestor.parent();\n }\n return origin;\n };\n /**\n * Gets the Selection containing the in front of the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection}\n */\n Component.prototype.foreground = function () {\n return this._foregroundContainer;\n };\n /**\n * Gets a Selection containing a that holds the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection} content selection for the Component\n */\n Component.prototype.content = function () {\n return this._content;\n };\n /**\n * Gets the Selection containing the behind the visual elements of the Component.\n *\n * Will return undefined if the Component has not been anchored.\n *\n * @return {d3.Selection} background selection for the Component\n */\n Component.prototype.background = function () {\n return this._backgroundContainer;\n };\n Component._xAlignToProportion = {\n \"left\": 0,\n \"center\": 0.5,\n \"right\": 1,\n };\n Component._yAlignToProportion = {\n \"top\": 0,\n \"center\": 0.5,\n \"bottom\": 1,\n };\n Component._SAFARI_EVENT_BACKING_CLASS = \"safari-event-backing\";\n return Component;\n }());\n Plottable.Component = Component;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n /*\n * ComponentContainer class encapsulates Table and ComponentGroup's shared functionality.\n * It will not do anything if instantiated directly.\n */\n var ComponentContainer = (function (_super) {\n __extends(ComponentContainer, _super);\n function ComponentContainer() {\n var _this = this;\n _super.call(this);\n this._detachCallback = function (component) { return _this.remove(component); };\n }\n ComponentContainer.prototype.anchor = function (selection) {\n var _this = this;\n _super.prototype.anchor.call(this, selection);\n this._forEach(function (c) { return c.anchor(_this.content()); });\n return this;\n };\n ComponentContainer.prototype.render = function () {\n this._forEach(function (c) { return c.render(); });\n return this;\n };\n /**\n * Checks whether the specified Component is in the ComponentContainer.\n */\n ComponentContainer.prototype.has = function (component) {\n throw new Error(\"has() is not implemented on ComponentContainer\");\n };\n ComponentContainer.prototype._adoptAndAnchor = function (component) {\n component.parent(this);\n component.onDetach(this._detachCallback);\n if (this._isAnchored) {\n component.anchor(this.content());\n }\n };\n /**\n * Removes the specified Component from the ComponentContainer.\n */\n ComponentContainer.prototype.remove = function (component) {\n if (this.has(component)) {\n component.offDetach(this._detachCallback);\n this._remove(component);\n component.detach();\n this.redraw();\n }\n return this;\n };\n /**\n * Carry out the actual removal of a Component.\n * Implementation dependent on the type of container.\n *\n * @return {boolean} true if the Component was successfully removed, false otherwise.\n */\n ComponentContainer.prototype._remove = function (component) {\n return false;\n };\n /**\n * Invokes a callback on each Component in the ComponentContainer.\n */\n ComponentContainer.prototype._forEach = function (callback) {\n throw new Error(\"_forEach() is not implemented on ComponentContainer\");\n };\n /**\n * Destroys the ComponentContainer and all Components within it.\n */\n ComponentContainer.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n this._forEach(function (c) { return c.destroy(); });\n };\n return ComponentContainer;\n }(Plottable.Component));\n Plottable.ComponentContainer = ComponentContainer;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Group = (function (_super) {\n __extends(Group, _super);\n /**\n * Constructs a Group.\n *\n * A Group contains Components that will be rendered on top of each other.\n * Components added later will be rendered above Components already in the Group.\n *\n * @constructor\n * @param {Component[]} [components=[]] Components to be added to the Group.\n */\n function Group(components) {\n var _this = this;\n if (components === void 0) { components = []; }\n _super.call(this);\n this._components = [];\n this.addClass(\"component-group\");\n components.forEach(function (c) { return _this.append(c); });\n }\n Group.prototype._forEach = function (callback) {\n this.components().forEach(callback);\n };\n /**\n * Checks whether the specified Component is in the Group.\n */\n Group.prototype.has = function (component) {\n return this._components.indexOf(component) >= 0;\n };\n Group.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var requests = this._components.map(function (c) { return c.requestedSpace(offeredWidth, offeredHeight); });\n return {\n minWidth: Plottable.Utils.Math.max(requests, function (request) { return request.minWidth; }, 0),\n minHeight: Plottable.Utils.Math.max(requests, function (request) { return request.minHeight; }, 0),\n };\n };\n Group.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n var _this = this;\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n this._forEach(function (component) {\n component.computeLayout({ x: 0, y: 0 }, _this.width(), _this.height());\n });\n return this;\n };\n Group.prototype._sizeFromOffer = function (availableWidth, availableHeight) {\n return {\n width: availableWidth,\n height: availableHeight,\n };\n };\n Group.prototype.fixedWidth = function () {\n return this._components.every(function (c) { return c.fixedWidth(); });\n };\n Group.prototype.fixedHeight = function () {\n return this._components.every(function (c) { return c.fixedHeight(); });\n };\n /**\n * @return {Component[]} The Components in this Group.\n */\n Group.prototype.components = function () {\n return this._components.slice();\n };\n /**\n * Adds a Component to this Group.\n * The added Component will be rendered above Components already in the Group.\n */\n Group.prototype.append = function (component) {\n if (component != null && !this.has(component)) {\n component.detach();\n this._components.push(component);\n this._adoptAndAnchor(component);\n this.redraw();\n }\n return this;\n };\n Group.prototype._remove = function (component) {\n var removeIndex = this._components.indexOf(component);\n if (removeIndex >= 0) {\n this._components.splice(removeIndex, 1);\n return true;\n }\n return false;\n };\n return Group;\n }(Plottable.ComponentContainer));\n Components.Group = Group;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var PlotGroup = (function (_super) {\n __extends(PlotGroup, _super);\n function PlotGroup() {\n _super.apply(this, arguments);\n }\n PlotGroup.prototype.entityNearest = function (point) {\n var closestPlotEntity;\n var minDistSquared = Infinity;\n this.components().forEach(function (plot) {\n var candidatePlotEntity = plot.entityNearest(point);\n if (candidatePlotEntity == null) {\n return;\n }\n var distSquared = Plottable.Utils.Math.distanceSquared(candidatePlotEntity.position, point);\n if (distSquared <= minDistSquared) {\n minDistSquared = distSquared;\n closestPlotEntity = candidatePlotEntity;\n }\n });\n return closestPlotEntity;\n };\n /**\n * Adds a Plot to this Plot Group.\n * The added Plot will be rendered above Plots already in the Group.\n */\n PlotGroup.prototype.append = function (plot) {\n if (plot != null && !(plot instanceof Plottable.Plot)) {\n throw new Error(\"Plot Group only accepts plots\");\n }\n _super.prototype.append.call(this, plot);\n return this;\n };\n return PlotGroup;\n }(Components.Group));\n Components.PlotGroup = PlotGroup;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Axis = (function (_super) {\n __extends(Axis, _super);\n /**\n * Constructs an Axis.\n * An Axis is a visual representation of a Scale.\n *\n * @constructor\n * @param {Scale} scale\n * @param {string} orientation One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n function Axis(scale, orientation) {\n var _this = this;\n _super.call(this);\n this._endTickLength = 5;\n this._innerTickLength = 5;\n this._tickLabelPadding = 10;\n this._margin = 15;\n this._showEndTickLabels = false;\n this._annotationsEnabled = false;\n this._annotationTierCount = 1;\n if (scale == null || orientation == null) {\n throw new Error(\"Axis requires a scale and orientation\");\n }\n this._scale = scale;\n this.orientation(orientation);\n this._setDefaultAlignment();\n this.addClass(\"axis\");\n if (this._isHorizontal()) {\n this.addClass(\"x-axis\");\n }\n else {\n this.addClass(\"y-axis\");\n }\n this.formatter(Plottable.Formatters.identity());\n this._rescaleCallback = function (newScale) { return _this._rescale(); };\n this._scale.onUpdate(this._rescaleCallback);\n this._annotatedTicks = [];\n this._annotationFormatter = Plottable.Formatters.identity();\n }\n Axis.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n this._scale.offUpdate(this._rescaleCallback);\n };\n Axis.prototype._isHorizontal = function () {\n return this._orientation === \"top\" || this._orientation === \"bottom\";\n };\n Axis.prototype._computeWidth = function () {\n // to be overridden by subclass logic\n return this._maxLabelTickLength();\n };\n Axis.prototype._computeHeight = function () {\n // to be overridden by subclass logic\n return this._maxLabelTickLength();\n };\n Axis.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var requestedWidth = 0;\n var requestedHeight = 0;\n if (this._isHorizontal()) {\n requestedHeight = this._computeHeight() + this._margin;\n if (this.annotationsEnabled()) {\n var tierHeight = this._annotationMeasurer.measure().height + 2 * Axis._ANNOTATION_LABEL_PADDING;\n requestedHeight += tierHeight * this.annotationTierCount();\n }\n }\n else {\n requestedWidth = this._computeWidth() + this._margin;\n if (this.annotationsEnabled()) {\n var tierHeight = this._annotationMeasurer.measure().height + 2 * Axis._ANNOTATION_LABEL_PADDING;\n requestedWidth += tierHeight * this.annotationTierCount();\n }\n }\n return {\n minWidth: requestedWidth,\n minHeight: requestedHeight,\n };\n };\n Axis.prototype.fixedHeight = function () {\n return this._isHorizontal();\n };\n Axis.prototype.fixedWidth = function () {\n return !this._isHorizontal();\n };\n Axis.prototype._rescale = function () {\n // default implementation; subclasses may call redraw() here\n this.render();\n };\n Axis.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n if (this._isHorizontal()) {\n this._scale.range([0, this.width()]);\n }\n else {\n this._scale.range([this.height(), 0]);\n }\n return this;\n };\n Axis.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._tickMarkContainer = this.content().append(\"g\")\n .classed(Axis.TICK_MARK_CLASS + \"-container\", true);\n this._tickLabelContainer = this.content().append(\"g\")\n .classed(Axis.TICK_LABEL_CLASS + \"-container\", true);\n this._baseline = this.content().append(\"line\").classed(\"baseline\", true);\n this._annotationContainer = this.content().append(\"g\")\n .classed(\"annotation-container\", true);\n this._annotationContainer.append(\"g\").classed(\"annotation-line-container\", true);\n this._annotationContainer.append(\"g\").classed(\"annotation-circle-container\", true);\n this._annotationContainer.append(\"g\").classed(\"annotation-rect-container\", true);\n var annotationLabelContainer = this._annotationContainer.append(\"g\").classed(\"annotation-label-container\", true);\n this._annotationMeasurer = new SVGTypewriter.Measurers.Measurer(annotationLabelContainer);\n this._annotationWriter = new SVGTypewriter.Writers.Writer(this._annotationMeasurer);\n };\n /*\n * Function for generating tick values in data-space (as opposed to pixel values).\n * To be implemented by subclasses.\n */\n Axis.prototype._getTickValues = function () {\n return [];\n };\n Axis.prototype.renderImmediately = function () {\n var tickMarkValues = this._getTickValues();\n var tickMarks = this._tickMarkContainer.selectAll(\".\" + Axis.TICK_MARK_CLASS).data(tickMarkValues);\n tickMarks.enter().append(\"line\").classed(Axis.TICK_MARK_CLASS, true);\n tickMarks.attr(this._generateTickMarkAttrHash());\n d3.select(tickMarks[0][0]).classed(Axis.END_TICK_MARK_CLASS, true)\n .attr(this._generateTickMarkAttrHash(true));\n d3.select(tickMarks[0][tickMarkValues.length - 1]).classed(Axis.END_TICK_MARK_CLASS, true)\n .attr(this._generateTickMarkAttrHash(true));\n tickMarks.exit().remove();\n this._baseline.attr(this._generateBaselineAttrHash());\n if (this.annotationsEnabled()) {\n this._drawAnnotations();\n }\n else {\n this._removeAnnotations();\n }\n return this;\n };\n Axis.prototype.annotatedTicks = function (annotatedTicks) {\n if (annotatedTicks == null) {\n return this._annotatedTicks;\n }\n this._annotatedTicks = annotatedTicks;\n this.render();\n return this;\n };\n Axis.prototype.annotationFormatter = function (annotationFormatter) {\n if (annotationFormatter == null) {\n return this._annotationFormatter;\n }\n this._annotationFormatter = annotationFormatter;\n this.render();\n return this;\n };\n Axis.prototype.annotationsEnabled = function (annotationsEnabled) {\n if (annotationsEnabled == null) {\n return this._annotationsEnabled;\n }\n this._annotationsEnabled = annotationsEnabled;\n this.redraw();\n return this;\n };\n Axis.prototype.annotationTierCount = function (annotationTierCount) {\n if (annotationTierCount == null) {\n return this._annotationTierCount;\n }\n if (annotationTierCount < 0) {\n throw new Error(\"annotationTierCount cannot be negative\");\n }\n this._annotationTierCount = annotationTierCount;\n this.redraw();\n return this;\n };\n Axis.prototype._drawAnnotations = function () {\n var _this = this;\n var labelPadding = Axis._ANNOTATION_LABEL_PADDING;\n var measurements = new Plottable.Utils.Map();\n var annotatedTicks = this._annotatedTicksToRender();\n annotatedTicks.forEach(function (annotatedTick) {\n var measurement = _this._annotationMeasurer.measure(_this.annotationFormatter()(annotatedTick));\n var paddedMeasurement = { width: measurement.width + 2 * labelPadding, height: measurement.height + 2 * labelPadding };\n measurements.set(annotatedTick, paddedMeasurement);\n });\n var tierHeight = this._annotationMeasurer.measure().height + 2 * labelPadding;\n var annotationToTier = this._annotationToTier(measurements);\n var hiddenAnnotations = new Plottable.Utils.Set();\n var axisHeight = this._isHorizontal() ? this.height() : this.width();\n var axisHeightWithoutMarginAndAnnotations = this._coreSize();\n var numTiers = Math.min(this.annotationTierCount(), Math.floor((axisHeight - axisHeightWithoutMarginAndAnnotations) / tierHeight));\n annotationToTier.forEach(function (tier, annotation) {\n if (tier === -1 || tier >= numTiers) {\n hiddenAnnotations.add(annotation);\n }\n });\n var bindElements = function (selection, elementName, className) {\n var elements = selection.selectAll(\".\" + className).data(annotatedTicks);\n elements.enter().append(elementName).classed(className, true);\n elements.exit().remove();\n return elements;\n };\n var offsetF = function (d) {\n switch (_this.orientation()) {\n case \"bottom\":\n case \"right\":\n return annotationToTier.get(d) * tierHeight + axisHeightWithoutMarginAndAnnotations;\n case \"top\":\n case \"left\":\n return axisHeight - axisHeightWithoutMarginAndAnnotations - annotationToTier.get(d) * tierHeight;\n }\n };\n var positionF = function (d) { return _this._scale.scale(d); };\n var visibilityF = function (d) { return hiddenAnnotations.has(d) ? \"hidden\" : \"visible\"; };\n var secondaryPosition;\n switch (this.orientation()) {\n case \"bottom\":\n case \"right\":\n secondaryPosition = 0;\n break;\n case \"top\":\n secondaryPosition = this.height();\n break;\n case \"left\":\n secondaryPosition = this.width();\n break;\n }\n var isHorizontal = this._isHorizontal();\n bindElements(this._annotationContainer.select(\".annotation-line-container\"), \"line\", Axis.ANNOTATION_LINE_CLASS)\n .attr({\n x1: isHorizontal ? positionF : secondaryPosition,\n x2: isHorizontal ? positionF : offsetF,\n y1: isHorizontal ? secondaryPosition : positionF,\n y2: isHorizontal ? offsetF : positionF,\n visibility: visibilityF,\n });\n bindElements(this._annotationContainer.select(\".annotation-circle-container\"), \"circle\", Axis.ANNOTATION_CIRCLE_CLASS)\n .attr({\n cx: isHorizontal ? positionF : secondaryPosition,\n cy: isHorizontal ? secondaryPosition : positionF,\n r: 3,\n });\n var rectangleOffsetF = function (d) {\n switch (_this.orientation()) {\n case \"bottom\":\n case \"right\":\n return offsetF(d);\n case \"top\":\n case \"left\":\n return offsetF(d) - measurements.get(d).height;\n }\n };\n bindElements(this._annotationContainer.select(\".annotation-rect-container\"), \"rect\", Axis.ANNOTATION_RECT_CLASS)\n .attr({\n x: isHorizontal ? positionF : rectangleOffsetF,\n y: isHorizontal ? rectangleOffsetF : positionF,\n width: isHorizontal ? function (d) { return measurements.get(d).width; } : function (d) { return measurements.get(d).height; },\n height: isHorizontal ? function (d) { return measurements.get(d).height; } : function (d) { return measurements.get(d).width; },\n visibility: visibilityF,\n });\n var annotationWriter = this._annotationWriter;\n var annotationFormatter = this.annotationFormatter();\n var annotationLabels = bindElements(this._annotationContainer.select(\".annotation-label-container\"), \"g\", Axis.ANNOTATION_LABEL_CLASS);\n annotationLabels.selectAll(\".text-container\").remove();\n annotationLabels.attr({\n transform: function (d) {\n var xTranslate = isHorizontal ? positionF(d) : rectangleOffsetF(d);\n var yTranslate = isHorizontal ? rectangleOffsetF(d) : positionF(d);\n return \"translate(\" + xTranslate + \",\" + yTranslate + \")\";\n },\n visibility: visibilityF,\n })\n .each(function (annotationLabel) {\n var writeOptions = {\n selection: d3.select(this),\n xAlign: \"center\",\n yAlign: \"center\",\n textRotation: isHorizontal ? 0 : 90,\n };\n annotationWriter.write(annotationFormatter(annotationLabel), isHorizontal ? measurements.get(annotationLabel).width : measurements.get(annotationLabel).height, isHorizontal ? measurements.get(annotationLabel).height : measurements.get(annotationLabel).width, writeOptions);\n });\n };\n Axis.prototype._annotatedTicksToRender = function () {\n var _this = this;\n var scaleRange = this._scale.range();\n return Plottable.Utils.Array.uniq(this.annotatedTicks().filter(function (tick) {\n if (tick == null) {\n return false;\n }\n return Plottable.Utils.Math.inRange(_this._scale.scale(tick), scaleRange[0], scaleRange[1]);\n }));\n };\n /**\n * Retrieves the size of the core pieces.\n *\n * The core pieces include the labels, the end tick marks, the inner tick marks, and the tick label padding.\n */\n Axis.prototype._coreSize = function () {\n var relevantDimension = this._isHorizontal() ? this.height() : this.width();\n var axisHeightWithoutMargin = this._isHorizontal() ? this._computeHeight() : this._computeWidth();\n return Math.min(axisHeightWithoutMargin, relevantDimension);\n };\n Axis.prototype._annotationTierHeight = function () {\n return this._annotationMeasurer.measure().height + 2 * Axis._ANNOTATION_LABEL_PADDING;\n };\n Axis.prototype._annotationToTier = function (measurements) {\n var _this = this;\n var annotationTiers = [[]];\n var annotationToTier = new Plottable.Utils.Map();\n var dimension = this._isHorizontal() ? this.width() : this.height();\n this._annotatedTicksToRender().forEach(function (annotatedTick) {\n var position = _this._scale.scale(annotatedTick);\n var length = measurements.get(annotatedTick).width;\n if (position < 0 || position + length > dimension) {\n annotationToTier.set(annotatedTick, -1);\n return;\n }\n var tierHasCollision = function (testTier) { return annotationTiers[testTier].some(function (testTick) {\n var testPosition = _this._scale.scale(testTick);\n var testLength = measurements.get(testTick).width;\n return position + length >= testPosition && position <= testPosition + testLength;\n }); };\n var tier = 0;\n while (tierHasCollision(tier)) {\n tier++;\n if (annotationTiers.length === tier) {\n annotationTiers.push([]);\n }\n }\n annotationTiers[tier].push(annotatedTick);\n annotationToTier.set(annotatedTick, tier);\n });\n return annotationToTier;\n };\n Axis.prototype._removeAnnotations = function () {\n this._annotationContainer.selectAll(\".annotation-line\").remove();\n this._annotationContainer.selectAll(\".annotation-circle\").remove();\n this._annotationContainer.selectAll(\".annotation-rect\").remove();\n this._annotationContainer.selectAll(\".annotation-label\").remove();\n };\n Axis.prototype._generateBaselineAttrHash = function () {\n var baselineAttrHash = {\n x1: 0,\n y1: 0,\n x2: 0,\n y2: 0,\n };\n switch (this._orientation) {\n case \"bottom\":\n baselineAttrHash[\"x2\"] = this.width();\n break;\n case \"top\":\n baselineAttrHash[\"x2\"] = this.width();\n baselineAttrHash[\"y1\"] = this.height();\n baselineAttrHash[\"y2\"] = this.height();\n break;\n case \"left\":\n baselineAttrHash[\"x1\"] = this.width();\n baselineAttrHash[\"x2\"] = this.width();\n baselineAttrHash[\"y2\"] = this.height();\n break;\n case \"right\":\n baselineAttrHash[\"y2\"] = this.height();\n break;\n }\n return baselineAttrHash;\n };\n Axis.prototype._generateTickMarkAttrHash = function (isEndTickMark) {\n var _this = this;\n if (isEndTickMark === void 0) { isEndTickMark = false; }\n var tickMarkAttrHash = {\n x1: 0,\n y1: 0,\n x2: 0,\n y2: 0,\n };\n var scalingFunction = function (d) { return _this._scale.scale(d); };\n if (this._isHorizontal()) {\n tickMarkAttrHash[\"x1\"] = scalingFunction;\n tickMarkAttrHash[\"x2\"] = scalingFunction;\n }\n else {\n tickMarkAttrHash[\"y1\"] = scalingFunction;\n tickMarkAttrHash[\"y2\"] = scalingFunction;\n }\n var tickLength = isEndTickMark ? this._endTickLength : this._innerTickLength;\n switch (this._orientation) {\n case \"bottom\":\n tickMarkAttrHash[\"y2\"] = tickLength;\n break;\n case \"top\":\n tickMarkAttrHash[\"y1\"] = this.height();\n tickMarkAttrHash[\"y2\"] = this.height() - tickLength;\n break;\n case \"left\":\n tickMarkAttrHash[\"x1\"] = this.width();\n tickMarkAttrHash[\"x2\"] = this.width() - tickLength;\n break;\n case \"right\":\n tickMarkAttrHash[\"x2\"] = tickLength;\n break;\n }\n return tickMarkAttrHash;\n };\n Axis.prototype._setDefaultAlignment = function () {\n switch (this._orientation) {\n case \"bottom\":\n this.yAlignment(\"top\");\n break;\n case \"top\":\n this.yAlignment(\"bottom\");\n break;\n case \"left\":\n this.xAlignment(\"right\");\n break;\n case \"right\":\n this.xAlignment(\"left\");\n break;\n }\n };\n Axis.prototype.formatter = function (formatter) {\n if (formatter == null) {\n return this._formatter;\n }\n this._formatter = formatter;\n this.redraw();\n return this;\n };\n Axis.prototype.innerTickLength = function (length) {\n if (length == null) {\n return this._innerTickLength;\n }\n else {\n if (length < 0) {\n throw new Error(\"inner tick length must be positive\");\n }\n this._innerTickLength = length;\n this.redraw();\n return this;\n }\n };\n Axis.prototype.endTickLength = function (length) {\n if (length == null) {\n return this._endTickLength;\n }\n else {\n if (length < 0) {\n throw new Error(\"end tick length must be positive\");\n }\n this._endTickLength = length;\n this.redraw();\n return this;\n }\n };\n Axis.prototype._maxLabelTickLength = function () {\n if (this.showEndTickLabels()) {\n return Math.max(this.innerTickLength(), this.endTickLength());\n }\n else {\n return this.innerTickLength();\n }\n };\n Axis.prototype.tickLabelPadding = function (padding) {\n if (padding == null) {\n return this._tickLabelPadding;\n }\n else {\n if (padding < 0) {\n throw new Error(\"tick label padding must be positive\");\n }\n this._tickLabelPadding = padding;\n this.redraw();\n return this;\n }\n };\n Axis.prototype.margin = function (size) {\n if (size == null) {\n return this._margin;\n }\n else {\n if (size < 0) {\n throw new Error(\"margin size must be positive\");\n }\n this._margin = size;\n this.redraw();\n return this;\n }\n };\n Axis.prototype.orientation = function (orientation) {\n if (orientation == null) {\n return this._orientation;\n }\n else {\n var newOrientationLC = orientation.toLowerCase();\n if (newOrientationLC !== \"top\" &&\n newOrientationLC !== \"bottom\" &&\n newOrientationLC !== \"left\" &&\n newOrientationLC !== \"right\") {\n throw new Error(\"unsupported orientation\");\n }\n this._orientation = newOrientationLC;\n this.redraw();\n return this;\n }\n };\n Axis.prototype.showEndTickLabels = function (show) {\n if (show == null) {\n return this._showEndTickLabels;\n }\n this._showEndTickLabels = show;\n this.render();\n return this;\n };\n /**\n * The css class applied to each end tick mark (the line on the end tick).\n */\n Axis.END_TICK_MARK_CLASS = \"end-tick-mark\";\n /**\n * The css class applied to each tick mark (the line on the tick).\n */\n Axis.TICK_MARK_CLASS = \"tick-mark\";\n /**\n * The css class applied to each tick label (the text associated with the tick).\n */\n Axis.TICK_LABEL_CLASS = \"tick-label\";\n /**\n * The css class applied to each annotation line, which extends from the axis to the rect.\n */\n Axis.ANNOTATION_LINE_CLASS = \"annotation-line\";\n /**\n * The css class applied to each annotation rect, which surrounds the annotation label.\n */\n Axis.ANNOTATION_RECT_CLASS = \"annotation-rect\";\n /**\n * The css class applied to each annotation circle, which denotes which tick is being annotated.\n */\n Axis.ANNOTATION_CIRCLE_CLASS = \"annotation-circle\";\n /**\n * The css class applied to each annotation label, which shows the formatted annotation text.\n */\n Axis.ANNOTATION_LABEL_CLASS = \"annotation-label\";\n Axis._ANNOTATION_LABEL_PADDING = 4;\n return Axis;\n }(Plottable.Component));\n Plottable.Axis = Axis;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var TimeInterval;\n (function (TimeInterval) {\n TimeInterval.second = \"second\";\n TimeInterval.minute = \"minute\";\n TimeInterval.hour = \"hour\";\n TimeInterval.day = \"day\";\n TimeInterval.week = \"week\";\n TimeInterval.month = \"month\";\n TimeInterval.year = \"year\";\n })(TimeInterval = Plottable.TimeInterval || (Plottable.TimeInterval = {}));\n ;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Axes;\n (function (Axes) {\n var Time = (function (_super) {\n __extends(Time, _super);\n /**\n * Constructs a Time Axis.\n *\n * A Time Axis is a visual representation of a Time Scale.\n *\n * @constructor\n * @param {Scales.Time} scale\n * @param {string} orientation One of \"top\"/\"bottom\".\n */\n function Time(scale, orientation) {\n _super.call(this, scale, orientation);\n this._tierLabelPositions = [];\n this.addClass(\"time-axis\");\n this.tickLabelPadding(5);\n this.axisConfigurations(Time._DEFAULT_TIME_AXIS_CONFIGURATIONS);\n this.annotationFormatter(Plottable.Formatters.time(\"%a %b %d, %Y\"));\n }\n Time.prototype.tierLabelPositions = function (newPositions) {\n if (newPositions == null) {\n return this._tierLabelPositions;\n }\n else {\n if (!newPositions.every(function (pos) { return pos.toLowerCase() === \"between\" || pos.toLowerCase() === \"center\"; })) {\n throw new Error(\"Unsupported position for tier labels\");\n }\n this._tierLabelPositions = newPositions;\n this.redraw();\n return this;\n }\n };\n Time.prototype.axisConfigurations = function (configurations) {\n if (configurations == null) {\n return this._possibleTimeAxisConfigurations;\n }\n this._possibleTimeAxisConfigurations = configurations;\n this._numTiers = Plottable.Utils.Math.max(this._possibleTimeAxisConfigurations.map(function (config) { return config.length; }), 0);\n if (this._isAnchored) {\n this._setupDomElements();\n }\n var oldLabelPositions = this.tierLabelPositions();\n var newLabelPositions = [];\n for (var i = 0; i < this._numTiers; i++) {\n newLabelPositions.push(oldLabelPositions[i] || \"between\");\n }\n this.tierLabelPositions(newLabelPositions);\n this.redraw();\n return this;\n };\n /**\n * Gets the index of the most precise TimeAxisConfiguration that will fit in the current width.\n */\n Time.prototype._getMostPreciseConfigurationIndex = function () {\n var _this = this;\n var mostPreciseIndex = this._possibleTimeAxisConfigurations.length;\n this._possibleTimeAxisConfigurations.forEach(function (interval, index) {\n if (index < mostPreciseIndex && interval.every(function (tier) {\n return _this._checkTimeAxisTierConfigurationWidth(tier);\n })) {\n mostPreciseIndex = index;\n }\n });\n if (mostPreciseIndex === this._possibleTimeAxisConfigurations.length) {\n Plottable.Utils.Window.warn(\"zoomed out too far: could not find suitable interval to display labels\");\n --mostPreciseIndex;\n }\n return mostPreciseIndex;\n };\n Time.prototype.orientation = function (orientation) {\n if (orientation && (orientation.toLowerCase() === \"right\" || orientation.toLowerCase() === \"left\")) {\n throw new Error(orientation + \" is not a supported orientation for TimeAxis - only horizontal orientations are supported\");\n }\n return _super.prototype.orientation.call(this, orientation); // maintains getter-setter functionality\n };\n Time.prototype._computeHeight = function () {\n var textHeight = this._measurer.measure().height;\n this._tierHeights = [];\n for (var i = 0; i < this._numTiers; i++) {\n this._tierHeights.push(textHeight + this.tickLabelPadding() +\n ((this._tierLabelPositions[i]) === \"between\" ? 0 : this._maxLabelTickLength()));\n }\n return d3.sum(this._tierHeights);\n };\n Time.prototype._getIntervalLength = function (config) {\n var startDate = this._scale.domain()[0];\n var d3Interval = Plottable.Scales.Time.timeIntervalToD3Time(config.interval);\n var endDate = d3Interval.offset(startDate, config.step);\n if (endDate > this._scale.domain()[1]) {\n // this offset is too large, so just return available width\n return this.width();\n }\n // measure how much space one date can get\n var stepLength = Math.abs(this._scale.scale(endDate) - this._scale.scale(startDate));\n return stepLength;\n };\n Time.prototype._maxWidthForInterval = function (config) {\n return this._measurer.measure(config.formatter(Time._LONG_DATE)).width;\n };\n /**\n * Check if tier configuration fits in the current width.\n */\n Time.prototype._checkTimeAxisTierConfigurationWidth = function (config) {\n var worstWidth = this._maxWidthForInterval(config) + 2 * this.tickLabelPadding();\n return Math.min(this._getIntervalLength(config), this.width()) >= worstWidth;\n };\n Time.prototype._sizeFromOffer = function (availableWidth, availableHeight) {\n // Makes sure that the size it requires is a multiple of tier sizes, such that\n // we have no leftover tiers\n var size = _super.prototype._sizeFromOffer.call(this, availableWidth, availableHeight);\n var tierHeights = this._tierHeights.reduce(function (prevValue, currValue, index, arr) {\n return (prevValue + currValue > size.height) ? prevValue : (prevValue + currValue);\n });\n var nonCoreHeight = this.margin() + (this.annotationsEnabled() ? this.annotationTierCount() * this._annotationTierHeight() : 0);\n size.height = Math.min(size.height, tierHeights + nonCoreHeight);\n return size;\n };\n Time.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._setupDomElements();\n };\n Time.prototype._setupDomElements = function () {\n this.content().selectAll(\".\" + Time.TIME_AXIS_TIER_CLASS).remove();\n this._tierLabelContainers = [];\n this._tierMarkContainers = [];\n this._tierBaselines = [];\n this._tickLabelContainer.remove();\n this._baseline.remove();\n for (var i = 0; i < this._numTiers; ++i) {\n var tierContainer = this.content().append(\"g\").classed(Time.TIME_AXIS_TIER_CLASS, true);\n this._tierLabelContainers.push(tierContainer.append(\"g\").classed(Plottable.Axis.TICK_LABEL_CLASS + \"-container\", true));\n this._tierMarkContainers.push(tierContainer.append(\"g\").classed(Plottable.Axis.TICK_MARK_CLASS + \"-container\", true));\n this._tierBaselines.push(tierContainer.append(\"line\").classed(\"baseline\", true));\n }\n this._measurer = new SVGTypewriter.Measurers.Measurer(this._tierLabelContainers[0]);\n };\n Time.prototype._getTickIntervalValues = function (config) {\n return this._scale.tickInterval(config.interval, config.step);\n };\n Time.prototype._getTickValues = function () {\n var _this = this;\n return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce(function (ticks, config) { return ticks.concat(_this._getTickIntervalValues(config)); }, []);\n };\n Time.prototype._cleanTiers = function () {\n for (var index = 0; index < this._tierLabelContainers.length; index++) {\n this._tierLabelContainers[index].selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS).remove();\n this._tierMarkContainers[index].selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS).remove();\n this._tierBaselines[index].style(\"visibility\", \"hidden\");\n }\n };\n Time.prototype._getTickValuesForConfiguration = function (config) {\n var tickPos = this._scale.tickInterval(config.interval, config.step);\n var domain = this._scale.domain();\n var tickPosValues = tickPos.map(function (d) { return d.valueOf(); }); // can't indexOf with objects\n if (tickPosValues.indexOf(domain[0].valueOf()) === -1) {\n tickPos.unshift(domain[0]);\n }\n if (tickPosValues.indexOf(domain[1].valueOf()) === -1) {\n tickPos.push(domain[1]);\n }\n return tickPos;\n };\n Time.prototype._renderTierLabels = function (container, config, index) {\n var _this = this;\n var tickPos = this._getTickValuesForConfiguration(config);\n var labelPos = [];\n if (this._tierLabelPositions[index] === \"between\" && config.step === 1) {\n tickPos.map(function (datum, i) {\n if (i + 1 >= tickPos.length) {\n return;\n }\n labelPos.push(new Date((tickPos[i + 1].valueOf() - tickPos[i].valueOf()) / 2 + tickPos[i].valueOf()));\n });\n }\n else {\n labelPos = tickPos;\n }\n var tickLabels = container.selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS).data(labelPos, function (d) { return String(d.valueOf()); });\n var tickLabelsEnter = tickLabels.enter().append(\"g\").classed(Plottable.Axis.TICK_LABEL_CLASS, true);\n tickLabelsEnter.append(\"text\");\n var xTranslate = (this._tierLabelPositions[index] === \"center\" || config.step === 1) ? 0 : this.tickLabelPadding();\n var yTranslate;\n if (this.orientation() === \"bottom\") {\n yTranslate = d3.sum(this._tierHeights.slice(0, index + 1)) - this.tickLabelPadding();\n }\n else {\n if (this._tierLabelPositions[index] === \"center\") {\n yTranslate = this.height() - d3.sum(this._tierHeights.slice(0, index)) - this.tickLabelPadding() - this._maxLabelTickLength();\n }\n else {\n yTranslate = this.height() - d3.sum(this._tierHeights.slice(0, index)) - this.tickLabelPadding();\n }\n }\n var textSelection = tickLabels.selectAll(\"text\");\n if (textSelection.size() > 0) {\n Plottable.Utils.DOM.translate(textSelection, xTranslate, yTranslate);\n }\n tickLabels.exit().remove();\n tickLabels.attr(\"transform\", function (d) { return \"translate(\" + _this._scale.scale(d) + \",0)\"; });\n var anchor = (this._tierLabelPositions[index] === \"center\" || config.step === 1) ? \"middle\" : \"start\";\n tickLabels.selectAll(\"text\").text(config.formatter).style(\"text-anchor\", anchor);\n };\n Time.prototype._renderTickMarks = function (tickValues, index) {\n var tickMarks = this._tierMarkContainers[index].selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS).data(tickValues);\n tickMarks.enter().append(\"line\").classed(Plottable.Axis.TICK_MARK_CLASS, true);\n var attr = this._generateTickMarkAttrHash();\n var offset = this._tierHeights.slice(0, index).reduce(function (translate, height) { return translate + height; }, 0);\n if (this.orientation() === \"bottom\") {\n attr[\"y1\"] = offset;\n attr[\"y2\"] = offset + (this._tierLabelPositions[index] === \"center\" ? this.innerTickLength() : this._tierHeights[index]);\n }\n else {\n attr[\"y1\"] = this.height() - offset;\n attr[\"y2\"] = this.height() - (offset + (this._tierLabelPositions[index] === \"center\" ?\n this.innerTickLength() : this._tierHeights[index]));\n }\n tickMarks.attr(attr);\n if (this.orientation() === \"bottom\") {\n attr[\"y1\"] = offset;\n attr[\"y2\"] = offset + (this._tierLabelPositions[index] === \"center\" ? this.endTickLength() : this._tierHeights[index]);\n }\n else {\n attr[\"y1\"] = this.height() - offset;\n attr[\"y2\"] = this.height() - (offset + (this._tierLabelPositions[index] === \"center\" ?\n this.endTickLength() : this._tierHeights[index]));\n }\n d3.select(tickMarks[0][0]).attr(attr);\n d3.select(tickMarks[0][tickMarks.size() - 1]).attr(attr);\n // Add end-tick classes to first and last tick for CSS customization purposes\n d3.select(tickMarks[0][0]).classed(Plottable.Axis.END_TICK_MARK_CLASS, true);\n d3.select(tickMarks[0][tickMarks.size() - 1]).classed(Plottable.Axis.END_TICK_MARK_CLASS, true);\n tickMarks.exit().remove();\n };\n Time.prototype._renderLabellessTickMarks = function (tickValues) {\n var tickMarks = this._tickMarkContainer.selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS).data(tickValues);\n tickMarks.enter().append(\"line\").classed(Plottable.Axis.TICK_MARK_CLASS, true);\n var attr = this._generateTickMarkAttrHash();\n attr[\"y2\"] = (this.orientation() === \"bottom\") ? this.tickLabelPadding() : this.height() - this.tickLabelPadding();\n tickMarks.attr(attr);\n tickMarks.exit().remove();\n };\n Time.prototype._generateLabellessTicks = function () {\n if (this._mostPreciseConfigIndex < 1) {\n return [];\n }\n return this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex - 1][0]);\n };\n Time.prototype.renderImmediately = function () {\n var _this = this;\n this._mostPreciseConfigIndex = this._getMostPreciseConfigurationIndex();\n var tierConfigs = this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];\n this._cleanTiers();\n tierConfigs.forEach(function (config, i) {\n return _this._renderTierLabels(_this._tierLabelContainers[i], config, i);\n });\n var tierTicks = tierConfigs.map(function (config, i) {\n return _this._getTickValuesForConfiguration(config);\n });\n var baselineOffset = 0;\n for (var i = 0; i < Math.max(tierConfigs.length, 1); ++i) {\n var attr = this._generateBaselineAttrHash();\n attr[\"y1\"] += (this.orientation() === \"bottom\") ? baselineOffset : -baselineOffset;\n attr[\"y2\"] = attr[\"y1\"];\n this._tierBaselines[i].attr(attr).style(\"visibility\", \"inherit\");\n baselineOffset += this._tierHeights[i];\n }\n var labelLessTicks = [];\n var domain = this._scale.domain();\n var totalLength = this._scale.scale(domain[1]) - this._scale.scale(domain[0]);\n if (this._getIntervalLength(tierConfigs[0]) * 1.5 >= totalLength) {\n labelLessTicks = this._generateLabellessTicks();\n }\n this._renderLabellessTickMarks(labelLessTicks);\n this._hideOverflowingTiers();\n for (var i = 0; i < tierConfigs.length; ++i) {\n this._renderTickMarks(tierTicks[i], i);\n this._hideOverlappingAndCutOffLabels(i);\n }\n if (this.annotationsEnabled()) {\n this._drawAnnotations();\n }\n else {\n this._removeAnnotations();\n }\n return this;\n };\n Time.prototype._hideOverflowingTiers = function () {\n var _this = this;\n var availableHeight = this.height();\n var usedHeight = 0;\n this.content()\n .selectAll(\".\" + Time.TIME_AXIS_TIER_CLASS)\n .attr(\"visibility\", function (d, i) {\n usedHeight += _this._tierHeights[i];\n return usedHeight <= availableHeight ? \"inherit\" : \"hidden\";\n });\n };\n Time.prototype._hideOverlappingAndCutOffLabels = function (index) {\n var _this = this;\n var boundingBox = this._boundingBox.node().getBoundingClientRect();\n var isInsideBBox = function (tickBox) {\n return (Math.floor(boundingBox.left) <= Math.ceil(tickBox.left) &&\n Math.floor(boundingBox.top) <= Math.ceil(tickBox.top) &&\n Math.floor(tickBox.right) <= Math.ceil(boundingBox.left + _this.width()) &&\n Math.floor(tickBox.bottom) <= Math.ceil(boundingBox.top + _this.height()));\n };\n var visibleTickMarks = this._tierMarkContainers[index]\n .selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS)\n .filter(function (d, i) {\n var visibility = d3.select(this).style(\"visibility\");\n return visibility === \"visible\" || visibility === \"inherit\";\n });\n // We use the ClientRects because x1/x2 attributes are not comparable to ClientRects of labels\n var visibleTickMarkRects = visibleTickMarks[0].map(function (mark) { return mark.getBoundingClientRect(); });\n var visibleTickLabels = this._tierLabelContainers[index]\n .selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS)\n .filter(function (d, i) {\n var visibility = d3.select(this).style(\"visibility\");\n return visibility === \"visible\" || visibility === \"inherit\";\n });\n var lastLabelClientRect;\n visibleTickLabels.each(function (d, i) {\n var clientRect = this.getBoundingClientRect();\n var tickLabel = d3.select(this);\n var leadingTickMark = visibleTickMarkRects[i];\n var trailingTickMark = visibleTickMarkRects[i + 1];\n var isOverlappingLastLabel = (lastLabelClientRect != null && Plottable.Utils.DOM.clientRectsOverlap(clientRect, lastLabelClientRect));\n var isOverlappingLeadingTickMark = (leadingTickMark != null && Plottable.Utils.DOM.clientRectsOverlap(clientRect, leadingTickMark));\n var isOverlappingTrailingTickMark = (trailingTickMark != null && Plottable.Utils.DOM.clientRectsOverlap(clientRect, trailingTickMark));\n if (!isInsideBBox(clientRect) || isOverlappingLastLabel || isOverlappingLeadingTickMark || isOverlappingTrailingTickMark) {\n tickLabel.style(\"visibility\", \"hidden\");\n }\n else {\n lastLabelClientRect = clientRect;\n tickLabel.style(\"visibility\", \"inherit\");\n }\n });\n };\n /**\n * The CSS class applied to each Time Axis tier\n */\n Time.TIME_AXIS_TIER_CLASS = \"time-axis-tier\";\n Time._DEFAULT_TIME_AXIS_CONFIGURATIONS = [\n [\n { interval: Plottable.TimeInterval.second, step: 1, formatter: Plottable.Formatters.time(\"%I:%M:%S %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.second, step: 5, formatter: Plottable.Formatters.time(\"%I:%M:%S %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.second, step: 10, formatter: Plottable.Formatters.time(\"%I:%M:%S %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.second, step: 15, formatter: Plottable.Formatters.time(\"%I:%M:%S %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.second, step: 30, formatter: Plottable.Formatters.time(\"%I:%M:%S %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.minute, step: 1, formatter: Plottable.Formatters.time(\"%I:%M %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.minute, step: 5, formatter: Plottable.Formatters.time(\"%I:%M %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.minute, step: 10, formatter: Plottable.Formatters.time(\"%I:%M %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.minute, step: 15, formatter: Plottable.Formatters.time(\"%I:%M %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.minute, step: 30, formatter: Plottable.Formatters.time(\"%I:%M %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.hour, step: 1, formatter: Plottable.Formatters.time(\"%I %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.hour, step: 3, formatter: Plottable.Formatters.time(\"%I %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.hour, step: 6, formatter: Plottable.Formatters.time(\"%I %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.hour, step: 12, formatter: Plottable.Formatters.time(\"%I %p\") },\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%B %e, %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%a %e\") },\n { interval: Plottable.TimeInterval.month, step: 1, formatter: Plottable.Formatters.time(\"%B %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.day, step: 1, formatter: Plottable.Formatters.time(\"%e\") },\n { interval: Plottable.TimeInterval.month, step: 1, formatter: Plottable.Formatters.time(\"%B %Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.month, step: 1, formatter: Plottable.Formatters.time(\"%B\") },\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.month, step: 1, formatter: Plottable.Formatters.time(\"%b\") },\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.month, step: 3, formatter: Plottable.Formatters.time(\"%b\") },\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.month, step: 6, formatter: Plottable.Formatters.time(\"%b\") },\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 1, formatter: Plottable.Formatters.time(\"%y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 5, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 25, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 50, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 100, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 200, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 500, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n [\n { interval: Plottable.TimeInterval.year, step: 1000, formatter: Plottable.Formatters.time(\"%Y\") },\n ],\n ];\n Time._LONG_DATE = new Date(9999, 8, 29, 12, 59, 9999);\n return Time;\n }(Plottable.Axis));\n Axes.Time = Time;\n })(Axes = Plottable.Axes || (Plottable.Axes = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Axes;\n (function (Axes) {\n var Numeric = (function (_super) {\n __extends(Numeric, _super);\n /**\n * Constructs a Numeric Axis.\n *\n * A Numeric Axis is a visual representation of a QuantitativeScale.\n *\n * @constructor\n * @param {QuantitativeScale} scale\n * @param {string} orientation One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n function Numeric(scale, orientation) {\n _super.call(this, scale, orientation);\n this._tickLabelPositioning = \"center\";\n this._usesTextWidthApproximation = false;\n this.formatter(Plottable.Formatters.general());\n }\n Numeric.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._measurer = new SVGTypewriter.Measurers.Measurer(this._tickLabelContainer, Plottable.Axis.TICK_LABEL_CLASS);\n this._wrapper = new SVGTypewriter.Wrappers.Wrapper().maxLines(1);\n };\n Numeric.prototype._computeWidth = function () {\n var maxTextWidth = this._usesTextWidthApproximation ? this._computeApproximateTextWidth() : this._computeExactTextWidth();\n if (this._tickLabelPositioning === \"center\") {\n return this._maxLabelTickLength() + this.tickLabelPadding() + maxTextWidth;\n }\n else {\n return Math.max(this._maxLabelTickLength(), this.tickLabelPadding() + maxTextWidth);\n }\n };\n Numeric.prototype._computeExactTextWidth = function () {\n var _this = this;\n var tickValues = this._getTickValues();\n var textLengths = tickValues.map(function (v) {\n var formattedValue = _this.formatter()(v);\n return _this._measurer.measure(formattedValue).width;\n });\n return Plottable.Utils.Math.max(textLengths, 0);\n };\n Numeric.prototype._computeApproximateTextWidth = function () {\n var _this = this;\n var tickValues = this._getTickValues();\n var mWidth = this._measurer.measure(\"M\").width;\n var textLengths = tickValues.map(function (v) {\n var formattedValue = _this.formatter()(v);\n return formattedValue.length * mWidth;\n });\n return Plottable.Utils.Math.max(textLengths, 0);\n };\n Numeric.prototype._computeHeight = function () {\n var textHeight = this._measurer.measure().height;\n if (this._tickLabelPositioning === \"center\") {\n return this._maxLabelTickLength() + this.tickLabelPadding() + textHeight;\n }\n else {\n return Math.max(this._maxLabelTickLength(), this.tickLabelPadding() + textHeight);\n }\n };\n Numeric.prototype._getTickValues = function () {\n var scale = this._scale;\n var domain = scale.domain();\n var min = domain[0] <= domain[1] ? domain[0] : domain[1];\n var max = domain[0] >= domain[1] ? domain[0] : domain[1];\n if (min === domain[0]) {\n return scale.ticks().filter(function (i) { return i >= min && i <= max; });\n }\n else {\n return scale.ticks().filter(function (i) { return i >= min && i <= max; }).reverse();\n }\n };\n Numeric.prototype._rescale = function () {\n if (!this._isSetup) {\n return;\n }\n if (!this._isHorizontal()) {\n var reComputedWidth = this._computeWidth();\n if (reComputedWidth > this.width() || reComputedWidth < (this.width() - this.margin())) {\n this.redraw();\n return;\n }\n }\n this.render();\n };\n Numeric.prototype.renderImmediately = function () {\n var _this = this;\n _super.prototype.renderImmediately.call(this);\n var tickLabelAttrHash = {\n x: 0,\n y: 0,\n dx: \"0em\",\n dy: \"0.3em\",\n };\n var tickMarkLength = this._maxLabelTickLength();\n var tickLabelPadding = this.tickLabelPadding();\n var tickLabelTextAnchor = \"middle\";\n var labelGroupTransformX = 0;\n var labelGroupTransformY = 0;\n var labelGroupShiftX = 0;\n var labelGroupShiftY = 0;\n if (this._isHorizontal()) {\n switch (this._tickLabelPositioning) {\n case \"left\":\n tickLabelTextAnchor = \"end\";\n labelGroupTransformX = -tickLabelPadding;\n labelGroupShiftY = tickLabelPadding;\n break;\n case \"center\":\n labelGroupShiftY = tickMarkLength + tickLabelPadding;\n break;\n case \"right\":\n tickLabelTextAnchor = \"start\";\n labelGroupTransformX = tickLabelPadding;\n labelGroupShiftY = tickLabelPadding;\n break;\n }\n }\n else {\n switch (this._tickLabelPositioning) {\n case \"top\":\n tickLabelAttrHash[\"dy\"] = \"-0.3em\";\n labelGroupShiftX = tickLabelPadding;\n labelGroupTransformY = -tickLabelPadding;\n break;\n case \"center\":\n labelGroupShiftX = tickMarkLength + tickLabelPadding;\n break;\n case \"bottom\":\n tickLabelAttrHash[\"dy\"] = \"1em\";\n labelGroupShiftX = tickLabelPadding;\n labelGroupTransformY = tickLabelPadding;\n break;\n }\n }\n var tickMarkAttrHash = this._generateTickMarkAttrHash();\n switch (this.orientation()) {\n case \"bottom\":\n tickLabelAttrHash[\"x\"] = tickMarkAttrHash[\"x1\"];\n tickLabelAttrHash[\"dy\"] = \"0.95em\";\n labelGroupTransformY = tickMarkAttrHash[\"y1\"] + labelGroupShiftY;\n break;\n case \"top\":\n tickLabelAttrHash[\"x\"] = tickMarkAttrHash[\"x1\"];\n tickLabelAttrHash[\"dy\"] = \"-.25em\";\n labelGroupTransformY = tickMarkAttrHash[\"y1\"] - labelGroupShiftY;\n break;\n case \"left\":\n tickLabelTextAnchor = \"end\";\n labelGroupTransformX = tickMarkAttrHash[\"x1\"] - labelGroupShiftX;\n tickLabelAttrHash[\"y\"] = tickMarkAttrHash[\"y1\"];\n break;\n case \"right\":\n tickLabelTextAnchor = \"start\";\n labelGroupTransformX = tickMarkAttrHash[\"x1\"] + labelGroupShiftX;\n tickLabelAttrHash[\"y\"] = tickMarkAttrHash[\"y1\"];\n break;\n }\n var tickLabelValues = this._getTickValues();\n var tickLabels = this._tickLabelContainer\n .selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS)\n .data(tickLabelValues);\n tickLabels.enter().append(\"text\").classed(Plottable.Axis.TICK_LABEL_CLASS, true);\n tickLabels.exit().remove();\n tickLabels.style(\"text-anchor\", tickLabelTextAnchor)\n .style(\"visibility\", \"inherit\")\n .attr(tickLabelAttrHash)\n .text(function (s) { return _this.formatter()(s); });\n var labelGroupTransform = \"translate(\" + labelGroupTransformX + \", \" + labelGroupTransformY + \")\";\n this._tickLabelContainer.attr(\"transform\", labelGroupTransform);\n this._showAllTickMarks();\n if (!this.showEndTickLabels()) {\n this._hideEndTickLabels();\n }\n this._hideOverflowingTickLabels();\n this._hideOverlappingTickLabels();\n if (this._tickLabelPositioning === \"bottom\" ||\n this._tickLabelPositioning === \"top\" ||\n this._tickLabelPositioning === \"left\" ||\n this._tickLabelPositioning === \"right\") {\n this._hideTickMarksWithoutLabel();\n }\n return this;\n };\n Numeric.prototype._showAllTickMarks = function () {\n this._tickMarkContainer.selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS)\n .each(function () {\n d3.select(this).style(\"visibility\", \"inherit\");\n });\n };\n /**\n * Hides the Tick Marks which have no corresponding Tick Labels\n */\n Numeric.prototype._hideTickMarksWithoutLabel = function () {\n var visibleTickMarks = this._tickMarkContainer.selectAll(\".\" + Plottable.Axis.TICK_MARK_CLASS);\n var visibleTickLabels = this._tickLabelContainer\n .selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS)\n .filter(function (d, i) {\n var visibility = d3.select(this).style(\"visibility\");\n return (visibility === \"inherit\") || (visibility === \"visible\");\n });\n var labelNumbersShown = [];\n visibleTickLabels.each(function (labelNumber) { return labelNumbersShown.push(labelNumber); });\n visibleTickMarks.each(function (e, i) {\n if (labelNumbersShown.indexOf(e) === -1) {\n d3.select(this).style(\"visibility\", \"hidden\");\n }\n });\n };\n Numeric.prototype.tickLabelPosition = function (position) {\n if (position == null) {\n return this._tickLabelPositioning;\n }\n else {\n var positionLC = position.toLowerCase();\n if (this._isHorizontal()) {\n if (!(positionLC === \"left\" || positionLC === \"center\" || positionLC === \"right\")) {\n throw new Error(positionLC + \" is not a valid tick label position for a horizontal NumericAxis\");\n }\n }\n else {\n if (!(positionLC === \"top\" || positionLC === \"center\" || positionLC === \"bottom\")) {\n throw new Error(positionLC + \" is not a valid tick label position for a vertical NumericAxis\");\n }\n }\n this._tickLabelPositioning = positionLC;\n this.redraw();\n return this;\n }\n };\n Numeric.prototype.usesTextWidthApproximation = function (enable) {\n if (enable == null) {\n return this._usesTextWidthApproximation;\n }\n else {\n this._usesTextWidthApproximation = enable;\n return this;\n }\n };\n Numeric.prototype._hideEndTickLabels = function () {\n var boundingBox = this._boundingBox.node().getBoundingClientRect();\n var tickLabels = this._tickLabelContainer.selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS);\n if (tickLabels[0].length === 0) {\n return;\n }\n var firstTickLabel = tickLabels[0][0];\n if (!Plottable.Utils.DOM.clientRectInside(firstTickLabel.getBoundingClientRect(), boundingBox)) {\n d3.select(firstTickLabel).style(\"visibility\", \"hidden\");\n }\n var lastTickLabel = tickLabels[0][tickLabels[0].length - 1];\n if (!Plottable.Utils.DOM.clientRectInside(lastTickLabel.getBoundingClientRect(), boundingBox)) {\n d3.select(lastTickLabel).style(\"visibility\", \"hidden\");\n }\n };\n // Responsible for hiding any tick labels that break out of the bounding container\n Numeric.prototype._hideOverflowingTickLabels = function () {\n var boundingBox = this._boundingBox.node().getBoundingClientRect();\n var tickLabels = this._tickLabelContainer.selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS);\n if (tickLabels.empty()) {\n return;\n }\n tickLabels.each(function (d, i) {\n if (!Plottable.Utils.DOM.clientRectInside(this.getBoundingClientRect(), boundingBox)) {\n d3.select(this).style(\"visibility\", \"hidden\");\n }\n });\n };\n Numeric.prototype._hideOverlappingTickLabels = function () {\n var visibleTickLabels = this._tickLabelContainer\n .selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS)\n .filter(function (d, i) {\n var visibility = d3.select(this).style(\"visibility\");\n return (visibility === \"inherit\") || (visibility === \"visible\");\n });\n var visibleTickLabelRects = visibleTickLabels[0].map(function (label) { return label.getBoundingClientRect(); });\n var interval = 1;\n while (!this._hasOverlapWithInterval(interval, visibleTickLabelRects) && interval < visibleTickLabelRects.length) {\n interval += 1;\n }\n visibleTickLabels.each(function (d, i) {\n var tickLabel = d3.select(this);\n if (i % interval !== 0) {\n tickLabel.style(\"visibility\", \"hidden\");\n }\n });\n };\n /**\n * The method is responsible for evenly spacing the labels on the axis.\n * @return test to see if taking every `interval` recrangle from `rects`\n * will result in labels not overlapping\n *\n * For top, bottom, left, right positioning of the thicks, we want the padding\n * between the labels to be 3x, such that the label will be `padding` distance\n * from the tick and 2 * `padding` distance (or more) from the next tick\n *\n */\n Numeric.prototype._hasOverlapWithInterval = function (interval, rects) {\n var padding = this.tickLabelPadding();\n if (this._tickLabelPositioning === \"bottom\" ||\n this._tickLabelPositioning === \"top\" ||\n this._tickLabelPositioning === \"left\" ||\n this._tickLabelPositioning === \"right\") {\n padding *= 3;\n }\n for (var i = 0; i < rects.length - (interval); i += interval) {\n var currRect = rects[i];\n var nextRect = rects[i + interval];\n if (this._isHorizontal()) {\n if (currRect.right + padding >= nextRect.left) {\n return false;\n }\n }\n else {\n if (currRect.top - padding <= nextRect.bottom) {\n return false;\n }\n }\n }\n return true;\n };\n return Numeric;\n }(Plottable.Axis));\n Axes.Numeric = Numeric;\n })(Axes = Plottable.Axes || (Plottable.Axes = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Axes;\n (function (Axes) {\n var Category = (function (_super) {\n __extends(Category, _super);\n /**\n * Constructs a Category Axis.\n *\n * A Category Axis is a visual representation of a Category Scale.\n *\n * @constructor\n * @param {Scales.Category} scale\n * @param {string} [orientation=\"bottom\"] One of \"top\"/\"bottom\"/\"left\"/\"right\".\n */\n function Category(scale, orientation) {\n _super.call(this, scale, orientation);\n this._tickLabelAngle = 0;\n this.addClass(\"category-axis\");\n }\n Category.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._measurer = new SVGTypewriter.Measurers.CacheCharacterMeasurer(this._tickLabelContainer);\n this._wrapper = new SVGTypewriter.Wrappers.Wrapper();\n this._writer = new SVGTypewriter.Writers.Writer(this._measurer, this._wrapper);\n };\n Category.prototype._rescale = function () {\n return this.redraw();\n };\n Category.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var widthRequiredByTicks = this._isHorizontal() ? 0 : this._maxLabelTickLength() + this.tickLabelPadding() + this.margin();\n var heightRequiredByTicks = this._isHorizontal() ? this._maxLabelTickLength() + this.tickLabelPadding() + this.margin() : 0;\n if (this._scale.domain().length === 0) {\n return {\n minWidth: 0,\n minHeight: 0,\n };\n }\n if (this.annotationsEnabled()) {\n var tierTotalHeight = this._annotationTierHeight() * this.annotationTierCount();\n if (this._isHorizontal()) {\n heightRequiredByTicks += tierTotalHeight;\n }\n else {\n widthRequiredByTicks += tierTotalHeight;\n }\n }\n var categoryScale = this._scale;\n var measureResult = this._measureTicks(offeredWidth, offeredHeight, categoryScale, categoryScale.domain());\n return {\n minWidth: measureResult.usedWidth + widthRequiredByTicks,\n minHeight: measureResult.usedHeight + heightRequiredByTicks,\n };\n };\n Category.prototype._coreSize = function () {\n var relevantDimension = this._isHorizontal() ? this.height() : this.width();\n var relevantRequestedSpaceDimension = this._isHorizontal() ?\n this.requestedSpace(this.width(), this.height()).minHeight :\n this.requestedSpace(this.width(), this.height()).minWidth;\n var marginAndAnnotationSize = this.margin() + this._annotationTierHeight();\n var axisHeightWithoutMargin = relevantRequestedSpaceDimension - marginAndAnnotationSize;\n return Math.min(axisHeightWithoutMargin, relevantDimension);\n };\n Category.prototype._getTickValues = function () {\n return this._scale.domain();\n };\n Category.prototype.tickLabelAngle = function (angle) {\n if (angle == null) {\n return this._tickLabelAngle;\n }\n if (angle !== 0 && angle !== 90 && angle !== -90) {\n throw new Error(\"Angle \" + angle + \" not supported; only 0, 90, and -90 are valid values\");\n }\n this._tickLabelAngle = angle;\n this.redraw();\n return this;\n };\n /**\n * Measures the size of the ticks while also writing them to the DOM.\n * @param {d3.Selection} ticks The tick elements to be written to.\n */\n Category.prototype._drawTicks = function (axisWidth, axisHeight, scale, ticks) {\n var self = this;\n var xAlign;\n var yAlign;\n switch (this.tickLabelAngle()) {\n case 0:\n xAlign = { left: \"right\", right: \"left\", top: \"center\", bottom: \"center\" };\n yAlign = { left: \"center\", right: \"center\", top: \"bottom\", bottom: \"top\" };\n break;\n case 90:\n xAlign = { left: \"center\", right: \"center\", top: \"right\", bottom: \"left\" };\n yAlign = { left: \"top\", right: \"bottom\", top: \"center\", bottom: \"center\" };\n break;\n case -90:\n xAlign = { left: \"center\", right: \"center\", top: \"left\", bottom: \"right\" };\n yAlign = { left: \"bottom\", right: \"top\", top: \"center\", bottom: \"center\" };\n break;\n }\n ticks.each(function (d) {\n var bandWidth = scale.stepWidth();\n var width = self._isHorizontal() ? bandWidth : axisWidth - self._maxLabelTickLength() - self.tickLabelPadding();\n var height = self._isHorizontal() ? axisHeight - self._maxLabelTickLength() - self.tickLabelPadding() : bandWidth;\n var writeOptions = {\n selection: d3.select(this),\n xAlign: xAlign[self.orientation()],\n yAlign: yAlign[self.orientation()],\n textRotation: self.tickLabelAngle(),\n };\n self._writer.write(self.formatter()(d), width, height, writeOptions);\n });\n };\n /**\n * Measures the size of the ticks without making any (permanent) DOM\n * changes.\n *\n * @param {string[]} ticks The strings that will be printed on the ticks.\n */\n Category.prototype._measureTicks = function (axisWidth, axisHeight, scale, ticks) {\n var _this = this;\n var axisSpace = this._isHorizontal() ? axisWidth : axisHeight;\n var totalOuterPaddingRatio = 2 * scale.outerPadding();\n var totalInnerPaddingRatio = (ticks.length - 1) * scale.innerPadding();\n var expectedRangeBand = axisSpace / (totalOuterPaddingRatio + totalInnerPaddingRatio + ticks.length);\n var stepWidth = expectedRangeBand * (1 + scale.innerPadding());\n var wrappingResults = ticks.map(function (s) {\n // HACKHACK: https://github.com/palantir/svg-typewriter/issues/25\n var width = axisWidth - _this._maxLabelTickLength() - _this.tickLabelPadding(); // default for left/right\n if (_this._isHorizontal()) {\n width = stepWidth; // defaults to the band width\n if (_this._tickLabelAngle !== 0) {\n width = axisHeight - _this._maxLabelTickLength() - _this.tickLabelPadding(); // use the axis height\n }\n // HACKHACK: Wrapper fails under negative circumstances\n width = Math.max(width, 0);\n }\n // HACKHACK: https://github.com/palantir/svg-typewriter/issues/25\n var height = stepWidth; // default for left/right\n if (_this._isHorizontal()) {\n height = axisHeight - _this._maxLabelTickLength() - _this.tickLabelPadding();\n if (_this._tickLabelAngle !== 0) {\n height = axisWidth - _this._maxLabelTickLength() - _this.tickLabelPadding();\n }\n // HACKHACK: Wrapper fails under negative circumstances\n height = Math.max(height, 0);\n }\n return _this._wrapper.wrap(_this.formatter()(s), _this._measurer, width, height);\n });\n // HACKHACK: https://github.com/palantir/svg-typewriter/issues/25\n var widthFn = (this._isHorizontal() && this._tickLabelAngle === 0) ? d3.sum : Plottable.Utils.Math.max;\n var heightFn = (this._isHorizontal() && this._tickLabelAngle === 0) ? Plottable.Utils.Math.max : d3.sum;\n var textFits = wrappingResults.every(function (t) {\n return !SVGTypewriter.Utils.StringMethods.isNotEmptyString(t.truncatedText) && t.noLines === 1;\n });\n var usedWidth = widthFn(wrappingResults, function (t) { return _this._measurer.measure(t.wrappedText).width; }, 0);\n var usedHeight = heightFn(wrappingResults, function (t) { return _this._measurer.measure(t.wrappedText).height; }, 0);\n // If the tick labels are rotated, reverse usedWidth and usedHeight\n // HACKHACK: https://github.com/palantir/svg-typewriter/issues/25\n if (this._tickLabelAngle !== 0) {\n var tempHeight = usedHeight;\n usedHeight = usedWidth;\n usedWidth = tempHeight;\n }\n return {\n textFits: textFits,\n usedWidth: usedWidth,\n usedHeight: usedHeight,\n };\n };\n Category.prototype.renderImmediately = function () {\n var _this = this;\n _super.prototype.renderImmediately.call(this);\n var catScale = this._scale;\n var tickLabels = this._tickLabelContainer.selectAll(\".\" + Plottable.Axis.TICK_LABEL_CLASS).data(this._scale.domain(), function (d) { return d; });\n var getTickLabelTransform = function (d, i) {\n var innerPaddingWidth = catScale.stepWidth() - catScale.rangeBand();\n var scaledValue = catScale.scale(d) - catScale.rangeBand() / 2 - innerPaddingWidth / 2;\n var x = _this._isHorizontal() ? scaledValue : 0;\n var y = _this._isHorizontal() ? 0 : scaledValue;\n return \"translate(\" + x + \",\" + y + \")\";\n };\n tickLabels.enter().append(\"g\").classed(Plottable.Axis.TICK_LABEL_CLASS, true);\n tickLabels.exit().remove();\n tickLabels.attr(\"transform\", getTickLabelTransform);\n // erase all text first, then rewrite\n tickLabels.text(\"\");\n this._drawTicks(this.width(), this.height(), catScale, tickLabels);\n var xTranslate = this.orientation() === \"right\" ? this._maxLabelTickLength() + this.tickLabelPadding() : 0;\n var yTranslate = this.orientation() === \"bottom\" ? this._maxLabelTickLength() + this.tickLabelPadding() : 0;\n Plottable.Utils.DOM.translate(this._tickLabelContainer, xTranslate, yTranslate);\n return this;\n };\n Category.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n // When anyone calls redraw(), computeLayout() will be called\n // on everyone, including this. Since CSS or something might have\n // affected the size of the characters, clear the cache.\n this._measurer.reset();\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n if (!this._isHorizontal()) {\n this._scale.range([0, this.height()]);\n }\n return this;\n };\n return Category;\n }(Plottable.Axis));\n Axes.Category = Category;\n })(Axes = Plottable.Axes || (Plottable.Axes = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Label = (function (_super) {\n __extends(Label, _super);\n /**\n * A Label is a Component that displays a single line of text.\n *\n * @constructor\n * @param {string} [displayText=\"\"] The text of the Label.\n * @param {number} [angle=0] The angle of the Label in degrees (-90/0/90). 0 is horizontal.\n */\n function Label(displayText, angle) {\n if (displayText === void 0) { displayText = \"\"; }\n if (angle === void 0) { angle = 0; }\n _super.call(this);\n this.addClass(\"label\");\n this.text(displayText);\n this.angle(angle);\n this.xAlignment(\"center\").yAlignment(\"center\");\n this._padding = 0;\n }\n Label.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var desiredWH = this._measurer.measure(this._text);\n var desiredWidth = (this.angle() === 0 ? desiredWH.width : desiredWH.height) + 2 * this.padding();\n var desiredHeight = (this.angle() === 0 ? desiredWH.height : desiredWH.width) + 2 * this.padding();\n return {\n minWidth: desiredWidth,\n minHeight: desiredHeight,\n };\n };\n Label.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._textContainer = this.content().append(\"g\");\n this._measurer = new SVGTypewriter.Measurers.Measurer(this._textContainer);\n this._wrapper = new SVGTypewriter.Wrappers.Wrapper();\n this._writer = new SVGTypewriter.Writers.Writer(this._measurer, this._wrapper);\n this.text(this._text);\n };\n Label.prototype.text = function (displayText) {\n if (displayText == null) {\n return this._text;\n }\n else {\n if (typeof displayText !== \"string\") {\n throw new Error(\"Label.text() only takes strings as input\");\n }\n this._text = displayText;\n this.redraw();\n return this;\n }\n };\n Label.prototype.angle = function (angle) {\n if (angle == null) {\n return this._angle;\n }\n else {\n angle %= 360;\n if (angle > 180) {\n angle -= 360;\n }\n else if (angle < -180) {\n angle += 360;\n }\n if (angle === -90 || angle === 0 || angle === 90) {\n this._angle = angle;\n }\n else {\n throw new Error(angle + \" is not a valid angle for Label\");\n }\n this.redraw();\n return this;\n }\n };\n Label.prototype.padding = function (padAmount) {\n if (padAmount == null) {\n return this._padding;\n }\n else {\n padAmount = +padAmount;\n if (padAmount < 0) {\n throw new Error(padAmount + \" is not a valid padding value. Cannot be less than 0.\");\n }\n this._padding = padAmount;\n this.redraw();\n return this;\n }\n };\n Label.prototype.fixedWidth = function () {\n return true;\n };\n Label.prototype.fixedHeight = function () {\n return true;\n };\n Label.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n // HACKHACK SVGTypewriter should remove existing content - #21 on SVGTypewriter.\n this._textContainer.selectAll(\"g\").remove();\n var textMeasurement = this._measurer.measure(this._text);\n var heightPadding = Math.max(Math.min((this.height() - textMeasurement.height) / 2, this.padding()), 0);\n var widthPadding = Math.max(Math.min((this.width() - textMeasurement.width) / 2, this.padding()), 0);\n this._textContainer.attr(\"transform\", \"translate(\" + widthPadding + \",\" + heightPadding + \")\");\n var writeWidth = this.width() - 2 * widthPadding;\n var writeHeight = this.height() - 2 * heightPadding;\n var writeOptions = {\n selection: this._textContainer,\n xAlign: this.xAlignment(),\n yAlign: this.yAlignment(),\n textRotation: this.angle(),\n };\n this._writer.write(this._text, writeWidth, writeHeight, writeOptions);\n return this;\n };\n return Label;\n }(Plottable.Component));\n Components.Label = Label;\n var TitleLabel = (function (_super) {\n __extends(TitleLabel, _super);\n /**\n * @constructor\n * @param {string} [text]\n * @param {number} [angle] One of -90/0/90. 0 is horizontal.\n */\n function TitleLabel(text, angle) {\n _super.call(this, text, angle);\n this.addClass(TitleLabel.TITLE_LABEL_CLASS);\n }\n TitleLabel.TITLE_LABEL_CLASS = \"title-label\";\n return TitleLabel;\n }(Label));\n Components.TitleLabel = TitleLabel;\n var AxisLabel = (function (_super) {\n __extends(AxisLabel, _super);\n /**\n * @constructor\n * @param {string} [text]\n * @param {number} [angle] One of -90/0/90. 0 is horizontal.\n */\n function AxisLabel(text, angle) {\n _super.call(this, text, angle);\n this.addClass(AxisLabel.AXIS_LABEL_CLASS);\n }\n AxisLabel.AXIS_LABEL_CLASS = \"axis-label\";\n return AxisLabel;\n }(Label));\n Components.AxisLabel = AxisLabel;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Legend = (function (_super) {\n __extends(Legend, _super);\n /**\n * The Legend consists of a series of entries, each with a color and label taken from the Color Scale.\n *\n * @constructor\n * @param {Scale.Color} scale\n */\n function Legend(colorScale) {\n var _this = this;\n _super.call(this);\n this._padding = 5;\n this.addClass(\"legend\");\n this.maxEntriesPerRow(1);\n if (colorScale == null) {\n throw new Error(\"Legend requires a colorScale\");\n }\n this._colorScale = colorScale;\n this._redrawCallback = function (scale) { return _this.redraw(); };\n this._colorScale.onUpdate(this._redrawCallback);\n this._formatter = Plottable.Formatters.identity();\n this.xAlignment(\"right\").yAlignment(\"top\");\n this.comparator(function (a, b) {\n var formattedText = _this._colorScale.domain().slice().map(function (d) { return _this._formatter(d); });\n return formattedText.indexOf(a) - formattedText.indexOf(b);\n });\n this._symbolFactoryAccessor = function () { return Plottable.SymbolFactories.circle(); };\n this._symbolOpacityAccessor = function () { return 1; };\n }\n Legend.prototype._setup = function () {\n _super.prototype._setup.call(this);\n var fakeLegendRow = this.content().append(\"g\").classed(Legend.LEGEND_ROW_CLASS, true);\n var fakeLegendEntry = fakeLegendRow.append(\"g\").classed(Legend.LEGEND_ENTRY_CLASS, true);\n fakeLegendEntry.append(\"text\");\n this._measurer = new SVGTypewriter.Measurers.Measurer(fakeLegendRow);\n this._wrapper = new SVGTypewriter.Wrappers.Wrapper().maxLines(1);\n this._writer = new SVGTypewriter.Writers.Writer(this._measurer, this._wrapper).addTitleElement(Plottable.Configs.ADD_TITLE_ELEMENTS);\n };\n Legend.prototype.formatter = function (formatter) {\n if (formatter == null) {\n return this._formatter;\n }\n this._formatter = formatter;\n this.redraw();\n return this;\n };\n Legend.prototype.maxEntriesPerRow = function (maxEntriesPerRow) {\n if (maxEntriesPerRow == null) {\n return this._maxEntriesPerRow;\n }\n else {\n this._maxEntriesPerRow = maxEntriesPerRow;\n this.redraw();\n return this;\n }\n };\n Legend.prototype.comparator = function (comparator) {\n if (comparator == null) {\n return this._comparator;\n }\n else {\n this._comparator = comparator;\n this.redraw();\n return this;\n }\n };\n Legend.prototype.colorScale = function (colorScale) {\n if (colorScale != null) {\n this._colorScale.offUpdate(this._redrawCallback);\n this._colorScale = colorScale;\n this._colorScale.onUpdate(this._redrawCallback);\n this.redraw();\n return this;\n }\n else {\n return this._colorScale;\n }\n };\n Legend.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n this._colorScale.offUpdate(this._redrawCallback);\n };\n Legend.prototype._calculateLayoutInfo = function (availableWidth, availableHeight) {\n var _this = this;\n var textHeight = this._measurer.measure().height;\n var availableWidthForEntries = Math.max(0, (availableWidth - this._padding));\n var entryNames = this._colorScale.domain().slice().sort(function (a, b) { return _this._comparator(_this._formatter(a), _this._formatter(b)); });\n var entryLengths = d3.map();\n var untruncatedEntryLengths = d3.map();\n entryNames.forEach(function (entryName) {\n var untruncatedEntryLength = textHeight + _this._measurer.measure(_this._formatter(entryName)).width + _this._padding;\n var entryLength = Math.min(untruncatedEntryLength, availableWidthForEntries);\n entryLengths.set(entryName, entryLength);\n untruncatedEntryLengths.set(entryName, untruncatedEntryLength);\n });\n var rows = this._packRows(availableWidthForEntries, entryNames, entryLengths);\n var rowsAvailable = Math.floor((availableHeight - 2 * this._padding) / textHeight);\n if (rowsAvailable !== rowsAvailable) {\n rowsAvailable = 0;\n }\n return {\n textHeight: textHeight,\n entryLengths: entryLengths,\n untruncatedEntryLengths: untruncatedEntryLengths,\n rows: rows,\n numRowsToDraw: Math.max(Math.min(rowsAvailable, rows.length), 0),\n };\n };\n Legend.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var estimatedLayout = this._calculateLayoutInfo(offeredWidth, offeredHeight);\n var untruncatedRowLengths = estimatedLayout.rows.map(function (row) {\n return d3.sum(row, function (entry) { return estimatedLayout.untruncatedEntryLengths.get(entry); });\n });\n var longestUntruncatedRowLength = Plottable.Utils.Math.max(untruncatedRowLengths, 0);\n return {\n minWidth: this._padding + longestUntruncatedRowLength,\n minHeight: estimatedLayout.rows.length * estimatedLayout.textHeight + 2 * this._padding,\n };\n };\n Legend.prototype._packRows = function (availableWidth, entries, entryLengths) {\n var _this = this;\n var rows = [];\n var currentRow = [];\n var spaceLeft = availableWidth;\n entries.forEach(function (e) {\n var entryLength = entryLengths.get(e);\n if (entryLength > spaceLeft || currentRow.length === _this._maxEntriesPerRow) {\n rows.push(currentRow);\n currentRow = [];\n spaceLeft = availableWidth;\n }\n currentRow.push(e);\n spaceLeft -= entryLength;\n });\n if (currentRow.length !== 0) {\n rows.push(currentRow);\n }\n return rows;\n };\n /**\n * Gets the Entities (representing Legend entries) at a particular point.\n * Returns an empty array if no Entities are present at that location.\n *\n * @param {Point} p\n * @returns {Entity[]}\n */\n Legend.prototype.entitiesAt = function (p) {\n if (!this._isSetup) {\n return [];\n }\n var entities = [];\n var layout = this._calculateLayoutInfo(this.width(), this.height());\n var legendPadding = this._padding;\n var legend = this;\n this.content().selectAll(\"g.\" + Legend.LEGEND_ROW_CLASS).each(function (d, i) {\n var lowY = i * layout.textHeight + legendPadding;\n var highY = (i + 1) * layout.textHeight + legendPadding;\n var symbolY = (lowY + highY) / 2;\n var lowX = legendPadding;\n var highX = legendPadding;\n d3.select(this).selectAll(\"g.\" + Legend.LEGEND_ENTRY_CLASS).each(function (value) {\n highX += layout.entryLengths.get(value);\n var symbolX = lowX + layout.textHeight / 2;\n if (highX >= p.x && lowX <= p.x &&\n highY >= p.y && lowY <= p.y) {\n var entrySelection = d3.select(this);\n var datum = entrySelection.datum();\n entities.push({\n datum: datum,\n position: { x: symbolX, y: symbolY },\n selection: entrySelection,\n component: legend,\n });\n }\n lowX += layout.entryLengths.get(value);\n });\n });\n return entities;\n };\n Legend.prototype.renderImmediately = function () {\n var _this = this;\n _super.prototype.renderImmediately.call(this);\n var layout = this._calculateLayoutInfo(this.width(), this.height());\n var rowsToDraw = layout.rows.slice(0, layout.numRowsToDraw);\n var rows = this.content().selectAll(\"g.\" + Legend.LEGEND_ROW_CLASS).data(rowsToDraw);\n rows.enter().append(\"g\").classed(Legend.LEGEND_ROW_CLASS, true);\n rows.exit().remove();\n rows.attr(\"transform\", function (d, i) { return \"translate(0, \" + (i * layout.textHeight + _this._padding) + \")\"; });\n var entries = rows.selectAll(\"g.\" + Legend.LEGEND_ENTRY_CLASS).data(function (d) { return d; });\n var entriesEnter = entries.enter().append(\"g\").classed(Legend.LEGEND_ENTRY_CLASS, true);\n entriesEnter.append(\"path\");\n entriesEnter.append(\"g\").classed(\"text-container\", true);\n entries.exit().remove();\n var legendPadding = this._padding;\n rows.each(function (values) {\n var xShift = legendPadding;\n var entriesInRow = d3.select(this).selectAll(\"g.\" + Legend.LEGEND_ENTRY_CLASS);\n entriesInRow.attr(\"transform\", function (value, i) {\n var translateString = \"translate(\" + xShift + \", 0)\";\n xShift += layout.entryLengths.get(value);\n return translateString;\n });\n });\n entries.select(\"path\").attr(\"d\", function (d, i, j) { return _this.symbol()(d, j)(layout.textHeight * 0.6); })\n .attr(\"transform\", \"translate(\" + (layout.textHeight / 2) + \",\" + layout.textHeight / 2 + \")\")\n .attr(\"fill\", function (value) { return _this._colorScale.scale(value); })\n .attr(\"opacity\", function (d, i, j) { return _this.symbolOpacity()(d, j); })\n .classed(Legend.LEGEND_SYMBOL_CLASS, true);\n var padding = this._padding;\n var textContainers = entries.select(\"g.text-container\");\n textContainers.text(\"\"); // clear out previous results\n var self = this;\n textContainers.attr(\"transform\", \"translate(\" + layout.textHeight + \", 0)\")\n .each(function (value) {\n var container = d3.select(this);\n var maxTextLength = layout.entryLengths.get(value) - layout.textHeight - padding;\n var writeOptions = {\n selection: container,\n xAlign: \"left\",\n yAlign: \"top\",\n textRotation: 0,\n };\n self._writer.write(self._formatter(value), maxTextLength, self.height(), writeOptions);\n });\n return this;\n };\n Legend.prototype.symbol = function (symbol) {\n if (symbol == null) {\n return this._symbolFactoryAccessor;\n }\n else {\n this._symbolFactoryAccessor = symbol;\n this.render();\n return this;\n }\n };\n Legend.prototype.symbolOpacity = function (symbolOpacity) {\n if (symbolOpacity == null) {\n return this._symbolOpacityAccessor;\n }\n else if (typeof symbolOpacity === \"number\") {\n this._symbolOpacityAccessor = function () { return symbolOpacity; };\n }\n else {\n this._symbolOpacityAccessor = symbolOpacity;\n }\n this.render();\n return this;\n };\n Legend.prototype.fixedWidth = function () {\n return true;\n };\n Legend.prototype.fixedHeight = function () {\n return true;\n };\n /**\n * The css class applied to each legend row\n */\n Legend.LEGEND_ROW_CLASS = \"legend-row\";\n /**\n * The css class applied to each legend entry\n */\n Legend.LEGEND_ENTRY_CLASS = \"legend-entry\";\n /**\n * The css class applied to each legend symbol\n */\n Legend.LEGEND_SYMBOL_CLASS = \"legend-symbol\";\n return Legend;\n }(Plottable.Component));\n Components.Legend = Legend;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var InterpolatedColorLegend = (function (_super) {\n __extends(InterpolatedColorLegend, _super);\n /**\n * Creates an InterpolatedColorLegend.\n *\n * The InterpolatedColorLegend consists of a sequence of swatches that show the\n * associated InterpolatedColor Scale sampled at various points.\n * Two labels show the maximum and minimum values of the InterpolatedColor Scale.\n *\n * @constructor\n * @param {Scales.InterpolatedColor} interpolatedColorScale\n */\n function InterpolatedColorLegend(interpolatedColorScale) {\n var _this = this;\n _super.call(this);\n this._textPadding = 5;\n if (interpolatedColorScale == null) {\n throw new Error(\"InterpolatedColorLegend requires a interpolatedColorScale\");\n }\n this._scale = interpolatedColorScale;\n this._redrawCallback = function (scale) { return _this.redraw(); };\n this._scale.onUpdate(this._redrawCallback);\n this._formatter = Plottable.Formatters.general();\n this._orientation = \"horizontal\";\n this._expands = false;\n this.addClass(\"legend\");\n this.addClass(\"interpolated-color-legend\");\n }\n InterpolatedColorLegend.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n this._scale.offUpdate(this._redrawCallback);\n };\n InterpolatedColorLegend.prototype.formatter = function (formatter) {\n if (formatter === undefined) {\n return this._formatter;\n }\n this._formatter = formatter;\n this.redraw();\n return this;\n };\n InterpolatedColorLegend.prototype.expands = function (expands) {\n if (expands == null) {\n return this._expands;\n }\n this._expands = expands;\n this.redraw();\n return this;\n };\n InterpolatedColorLegend._ensureOrientation = function (orientation) {\n orientation = orientation.toLowerCase();\n if (orientation === \"horizontal\" || orientation === \"left\" || orientation === \"right\") {\n return orientation;\n }\n else {\n throw new Error(\"\\\"\" + orientation + \"\\\" is not a valid orientation for InterpolatedColorLegend\");\n }\n };\n InterpolatedColorLegend.prototype.orientation = function (orientation) {\n if (orientation == null) {\n return this._orientation;\n }\n else {\n this._orientation = InterpolatedColorLegend._ensureOrientation(orientation);\n this.redraw();\n return this;\n }\n };\n InterpolatedColorLegend.prototype.fixedWidth = function () {\n return !this.expands() || this._isVertical();\n };\n InterpolatedColorLegend.prototype.fixedHeight = function () {\n return !this.expands() || !this._isVertical();\n };\n InterpolatedColorLegend.prototype._generateTicks = function (numSwatches) {\n if (numSwatches === void 0) { numSwatches = InterpolatedColorLegend._DEFAULT_NUM_SWATCHES; }\n var domain = this._scale.domain();\n if (numSwatches === 1) {\n return [domain[0]];\n }\n var slope = (domain[1] - domain[0]) / (numSwatches - 1);\n var ticks = [];\n for (var i = 0; i < numSwatches; i++) {\n ticks.push(domain[0] + slope * i);\n }\n return ticks;\n };\n InterpolatedColorLegend.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._swatchContainer = this.content().append(\"g\").classed(\"swatch-container\", true);\n this._swatchBoundingBox = this.content().append(\"rect\").classed(\"swatch-bounding-box\", true);\n this._lowerLabel = this.content().append(\"g\").classed(InterpolatedColorLegend.LEGEND_LABEL_CLASS, true);\n this._upperLabel = this.content().append(\"g\").classed(InterpolatedColorLegend.LEGEND_LABEL_CLASS, true);\n this._measurer = new SVGTypewriter.Measurers.Measurer(this.content());\n this._wrapper = new SVGTypewriter.Wrappers.Wrapper();\n this._writer = new SVGTypewriter.Writers.Writer(this._measurer, this._wrapper);\n };\n InterpolatedColorLegend.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n var _this = this;\n var textHeight = this._measurer.measure().height;\n var padding = textHeight;\n var domain = this._scale.domain();\n var labelWidths = domain.map(function (d) { return _this._measurer.measure(_this._formatter(d)).width; });\n var desiredHeight;\n var desiredWidth;\n var numSwatches = InterpolatedColorLegend._DEFAULT_NUM_SWATCHES;\n if (this._isVertical()) {\n var longestWidth = Plottable.Utils.Math.max(labelWidths, 0);\n desiredWidth = padding + textHeight + this._textPadding + longestWidth + this._textPadding;\n desiredHeight = numSwatches * textHeight;\n }\n else {\n desiredHeight = padding + textHeight + padding;\n desiredWidth = this._textPadding + labelWidths[0] + numSwatches * textHeight\n + labelWidths[1] + this._textPadding;\n }\n return {\n minWidth: desiredWidth,\n minHeight: desiredHeight,\n };\n };\n InterpolatedColorLegend.prototype._isVertical = function () {\n return this._orientation !== \"horizontal\";\n };\n InterpolatedColorLegend.prototype.renderImmediately = function () {\n var _this = this;\n _super.prototype.renderImmediately.call(this);\n var domain = this._scale.domain();\n var text0 = this._formatter(domain[0]);\n var text0Width = this._measurer.measure(text0).width;\n var text1 = this._formatter(domain[1]);\n var text1Width = this._measurer.measure(text1).width;\n var textHeight = this._measurer.measure().height;\n var textPadding = this._textPadding;\n var upperLabelShift = { x: 0, y: 0 };\n var lowerLabelShift = { x: 0, y: 0 };\n var lowerWriteOptions = {\n selection: this._lowerLabel,\n xAlign: \"center\",\n yAlign: \"center\",\n textRotation: 0,\n };\n var upperWriteOptions = {\n selection: this._upperLabel,\n xAlign: \"center\",\n yAlign: \"center\",\n textRotation: 0,\n };\n var swatchWidth;\n var swatchHeight;\n var swatchX;\n var swatchY;\n var boundingBoxAttr = {\n x: 0,\n y: 0,\n width: 0,\n height: 0,\n };\n var padding;\n var numSwatches;\n if (this._isVertical()) {\n numSwatches = Math.floor(this.height());\n var longestTextWidth_1 = Math.max(text0Width, text1Width);\n padding = (this.width() - longestTextWidth_1 - 2 * this._textPadding) / 2;\n swatchWidth = Math.max(this.width() - padding - 2 * textPadding - longestTextWidth_1, 0);\n swatchHeight = 1;\n swatchY = function (d, i) { return _this.height() - (i + 1); };\n upperWriteOptions.yAlign = \"top\";\n upperLabelShift.y = 0;\n lowerWriteOptions.yAlign = \"bottom\";\n lowerLabelShift.y = 0;\n if (this._orientation === \"left\") {\n swatchX = function (d, i) { return textPadding + longestTextWidth_1 + textPadding; };\n upperWriteOptions.xAlign = \"right\";\n upperLabelShift.x = -(padding + swatchWidth + textPadding);\n lowerWriteOptions.xAlign = \"right\";\n lowerLabelShift.x = -(padding + swatchWidth + textPadding);\n }\n else {\n swatchX = function (d, i) { return padding; };\n upperWriteOptions.xAlign = \"left\";\n upperLabelShift.x = padding + swatchWidth + textPadding;\n lowerWriteOptions.xAlign = \"left\";\n lowerLabelShift.x = padding + swatchWidth + textPadding;\n }\n boundingBoxAttr[\"width\"] = swatchWidth;\n boundingBoxAttr[\"height\"] = numSwatches * swatchHeight;\n }\n else {\n padding = Math.max(textPadding, (this.height() - textHeight) / 2);\n numSwatches = Math.max(Math.floor(this.width() - textPadding * 4 - text0Width - text1Width), 0);\n swatchWidth = 1;\n swatchHeight = Math.max((this.height() - 2 * padding), 0);\n swatchX = function (d, i) { return Math.floor(text0Width + 2 * textPadding) + i; };\n swatchY = function (d, i) { return padding; };\n upperWriteOptions.xAlign = \"right\";\n upperLabelShift.x = -textPadding;\n lowerWriteOptions.xAlign = \"left\";\n lowerLabelShift.x = textPadding;\n boundingBoxAttr[\"y\"] = padding;\n boundingBoxAttr[\"width\"] = numSwatches * swatchWidth;\n boundingBoxAttr[\"height\"] = swatchHeight;\n }\n boundingBoxAttr[\"x\"] = swatchX(null, 0); // position of the first swatch\n this._upperLabel.text(\"\"); // clear the upper label\n this._writer.write(text1, this.width(), this.height(), upperWriteOptions);\n var upperTranslateString = \"translate(\" + upperLabelShift.x + \", \" + upperLabelShift.y + \")\";\n this._upperLabel.attr(\"transform\", upperTranslateString);\n this._lowerLabel.text(\"\"); // clear the lower label\n this._writer.write(text0, this.width(), this.height(), lowerWriteOptions);\n var lowerTranslateString = \"translate(\" + lowerLabelShift.x + \", \" + lowerLabelShift.y + \")\";\n this._lowerLabel.attr(\"transform\", lowerTranslateString);\n this._swatchBoundingBox.attr(boundingBoxAttr);\n var ticks = this._generateTicks(numSwatches);\n var swatches = this._swatchContainer.selectAll(\"rect.swatch\").data(ticks);\n var rects = swatches.enter().append(\"rect\").classed(\"swatch\", true);\n swatches.exit().remove();\n swatches.attr({\n \"fill\": function (d, i) { return _this._scale.scale(d); },\n \"width\": swatchWidth,\n \"height\": swatchHeight,\n \"x\": swatchX,\n \"y\": swatchY,\n \"shape-rendering\": \"crispEdges\",\n });\n if (Plottable.Configs.ADD_TITLE_ELEMENTS) {\n rects.append(\"title\").text(function (d) { return _this._formatter(d); });\n }\n return this;\n };\n InterpolatedColorLegend._DEFAULT_NUM_SWATCHES = 11;\n /**\n * The css class applied to the legend labels.\n */\n InterpolatedColorLegend.LEGEND_LABEL_CLASS = \"legend-label\";\n return InterpolatedColorLegend;\n }(Plottable.Component));\n Components.InterpolatedColorLegend = InterpolatedColorLegend;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Gridlines = (function (_super) {\n __extends(Gridlines, _super);\n /**\n * @constructor\n * @param {QuantitativeScale} xScale The scale to base the x gridlines on. Pass null if no gridlines are desired.\n * @param {QuantitativeScale} yScale The scale to base the y gridlines on. Pass null if no gridlines are desired.\n */\n function Gridlines(xScale, yScale) {\n var _this = this;\n if (xScale != null && !(Plottable.QuantitativeScale.prototype.isPrototypeOf(xScale))) {\n throw new Error(\"xScale needs to inherit from Scale.QuantitativeScale\");\n }\n if (yScale != null && !(Plottable.QuantitativeScale.prototype.isPrototypeOf(yScale))) {\n throw new Error(\"yScale needs to inherit from Scale.QuantitativeScale\");\n }\n _super.call(this);\n this.addClass(\"gridlines\");\n this._xScale = xScale;\n this._yScale = yScale;\n this._renderCallback = function (scale) { return _this.render(); };\n if (this._xScale) {\n this._xScale.onUpdate(this._renderCallback);\n }\n if (this._yScale) {\n this._yScale.onUpdate(this._renderCallback);\n }\n }\n Gridlines.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n if (this._xScale) {\n this._xScale.offUpdate(this._renderCallback);\n }\n if (this._yScale) {\n this._yScale.offUpdate(this._renderCallback);\n }\n return this;\n };\n Gridlines.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._xLinesContainer = this.content().append(\"g\").classed(\"x-gridlines\", true);\n this._yLinesContainer = this.content().append(\"g\").classed(\"y-gridlines\", true);\n };\n Gridlines.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n this._redrawXLines();\n this._redrawYLines();\n return this;\n };\n Gridlines.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n if (this._xScale != null) {\n this._xScale.range([0, this.width()]);\n }\n if (this._yScale != null) {\n this._yScale.range([this.height(), 0]);\n }\n return this;\n };\n Gridlines.prototype._redrawXLines = function () {\n var _this = this;\n if (this._xScale) {\n var xTicks = this._xScale.ticks();\n var getScaledXValue = function (tickVal) { return _this._xScale.scale(tickVal); };\n var xLines = this._xLinesContainer.selectAll(\"line\").data(xTicks);\n xLines.enter().append(\"line\");\n xLines.attr(\"x1\", getScaledXValue)\n .attr(\"y1\", 0)\n .attr(\"x2\", getScaledXValue)\n .attr(\"y2\", this.height())\n .classed(\"zeroline\", function (t) { return t === 0; });\n xLines.exit().remove();\n }\n };\n Gridlines.prototype._redrawYLines = function () {\n var _this = this;\n if (this._yScale) {\n var yTicks = this._yScale.ticks();\n var getScaledYValue = function (tickVal) { return _this._yScale.scale(tickVal); };\n var yLines = this._yLinesContainer.selectAll(\"line\").data(yTicks);\n yLines.enter().append(\"line\");\n yLines.attr(\"x1\", 0)\n .attr(\"y1\", getScaledYValue)\n .attr(\"x2\", this.width())\n .attr(\"y2\", getScaledYValue)\n .classed(\"zeroline\", function (t) { return t === 0; });\n yLines.exit().remove();\n }\n };\n return Gridlines;\n }(Plottable.Component));\n Components.Gridlines = Gridlines;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var Table = (function (_super) {\n __extends(Table, _super);\n /**\n * A Table combines Components in the form of a grid. A\n * common case is combining a y-axis, x-axis, and the plotted data via\n * ```typescript\n * new Table([[yAxis, plot],\n * [null, xAxis]]);\n * ```\n *\n * @constructor\n * @param {Component[][]} [rows=[]] A 2-D array of Components to be added to the Table.\n * null can be used if a cell is empty.\n */\n function Table(rows) {\n var _this = this;\n if (rows === void 0) { rows = []; }\n _super.call(this);\n this._rowPadding = 0;\n this._columnPadding = 0;\n this._rows = [];\n this._rowWeights = [];\n this._columnWeights = [];\n this._nRows = 0;\n this._nCols = 0;\n this._calculatedLayout = null;\n this.addClass(\"table\");\n rows.forEach(function (row, rowIndex) {\n row.forEach(function (component, colIndex) {\n if (component != null) {\n _this.add(component, rowIndex, colIndex);\n }\n });\n });\n }\n Table.prototype._forEach = function (callback) {\n for (var r = 0; r < this._nRows; r++) {\n for (var c = 0; c < this._nCols; c++) {\n if (this._rows[r][c] != null) {\n callback(this._rows[r][c]);\n }\n }\n }\n };\n /**\n * Checks whether the specified Component is in the Table.\n */\n Table.prototype.has = function (component) {\n for (var r = 0; r < this._nRows; r++) {\n for (var c = 0; c < this._nCols; c++) {\n if (this._rows[r][c] === component) {\n return true;\n }\n }\n }\n return false;\n };\n /**\n * Returns the Component at the specified row and column index.\n *\n * @param {number} rowIndex\n * @param {number} columnIndex\n * @returns {Component} The Component at the specified position, or null if no Component is there.\n */\n Table.prototype.componentAt = function (rowIndex, columnIndex) {\n if (rowIndex < 0 || rowIndex >= this._nRows || columnIndex < 0 || columnIndex >= this._nCols) {\n return null;\n }\n return this._rows[rowIndex][columnIndex];\n };\n ;\n /**\n * Adds a Component in the specified row and column position.\n *\n * For example, instead of calling `new Table([[a, b], [null, c]])`, you\n * could call\n * var table = new Plottable.Components.Table();\n * table.add(a, 0, 0);\n * table.add(b, 0, 1);\n * table.add(c, 1, 1);\n *\n * @param {Component} component The Component to be added.\n * @param {number} row\n * @param {number} col\n * @returns {Table} The calling Table.\n */\n Table.prototype.add = function (component, row, col) {\n if (component == null) {\n throw Error(\"Cannot add null to a table cell\");\n }\n if (!this.has(component)) {\n var currentComponent = this._rows[row] && this._rows[row][col];\n if (currentComponent != null) {\n throw new Error(\"cell is occupied\");\n }\n component.detach();\n this._nRows = Math.max(row + 1, this._nRows);\n this._nCols = Math.max(col + 1, this._nCols);\n this._padTableToSize(this._nRows, this._nCols);\n this._rows[row][col] = component;\n this._adoptAndAnchor(component);\n this.redraw();\n }\n return this;\n };\n Table.prototype._remove = function (component) {\n for (var r = 0; r < this._nRows; r++) {\n for (var c = 0; c < this._nCols; c++) {\n if (this._rows[r][c] === component) {\n this._rows[r][c] = null;\n return true;\n }\n }\n }\n return false;\n };\n Table.prototype._iterateLayout = function (availableWidth, availableHeight, isFinalOffer) {\n if (isFinalOffer === void 0) { isFinalOffer = false; }\n /*\n * Given availableWidth and availableHeight, figure out how to allocate it between rows and columns using an iterative algorithm.\n *\n * For both dimensions, keeps track of \"guaranteedSpace\", which the fixed-size components have requested, and\n * \"proportionalSpace\", which is being given to proportionally-growing components according to the weights on the table.\n * Here is how it works (example uses width but it is the same for height). First, columns are guaranteed no width, and\n * the free width is allocated to columns based on their colWeights. Then, in determineGuarantees, every component is\n * offered its column's width and may request some amount of it, which increases that column's guaranteed\n * width. If there are some components that were not satisfied with the width they were offered, and there is free\n * width that has not already been guaranteed, then the remaining width is allocated to the unsatisfied columns and the\n * algorithm runs again. If all components are satisfied, then the remaining width is allocated as proportional space\n * according to the colWeights.\n *\n * The guaranteed width for each column is monotonically increasing as the algorithm iterates. Since it is deterministic\n * and monotonically increasing, if the freeWidth does not change during an iteration it implies that no further progress\n * is possible, so the algorithm will not continue iterating on that dimension's account.\n *\n * If the algorithm runs more than 5 times, we stop and just use whatever we arrived at. It's not clear under what\n * circumstances this will happen or if it will happen at all. A message will be printed to the console if this occurs.\n *\n */\n var rows = this._rows;\n var cols = d3.transpose(this._rows);\n var availableWidthAfterPadding = availableWidth - this._columnPadding * (this._nCols - 1);\n var availableHeightAfterPadding = availableHeight - this._rowPadding * (this._nRows - 1);\n var rowWeights = Table._calcComponentWeights(this._rowWeights, rows, function (c) { return (c == null) || c.fixedHeight(); });\n var colWeights = Table._calcComponentWeights(this._columnWeights, cols, function (c) { return (c == null) || c.fixedWidth(); });\n // To give the table a good starting position to iterate from, we give the fixed-width components half-weight\n // so that they will get some initial space allocated to work with\n var heuristicColWeights = colWeights.map(function (c) { return c === 0 ? 0.5 : c; });\n var heuristicRowWeights = rowWeights.map(function (c) { return c === 0 ? 0.5 : c; });\n var colProportionalSpace = Table._calcProportionalSpace(heuristicColWeights, availableWidthAfterPadding);\n var rowProportionalSpace = Table._calcProportionalSpace(heuristicRowWeights, availableHeightAfterPadding);\n var guaranteedWidths = Plottable.Utils.Array.createFilledArray(0, this._nCols);\n var guaranteedHeights = Plottable.Utils.Array.createFilledArray(0, this._nRows);\n var freeWidth;\n var freeHeight;\n var nIterations = 0;\n var guarantees;\n var wantsWidth;\n var wantsHeight;\n while (true) {\n var offeredHeights = Plottable.Utils.Array.add(guaranteedHeights, rowProportionalSpace);\n var offeredWidths = Plottable.Utils.Array.add(guaranteedWidths, colProportionalSpace);\n guarantees = this._determineGuarantees(offeredWidths, offeredHeights, isFinalOffer);\n guaranteedWidths = guarantees.guaranteedWidths;\n guaranteedHeights = guarantees.guaranteedHeights;\n wantsWidth = guarantees.wantsWidthArr.some(function (x) { return x; });\n wantsHeight = guarantees.wantsHeightArr.some(function (x) { return x; });\n var lastFreeWidth = freeWidth;\n var lastFreeHeight = freeHeight;\n freeWidth = availableWidthAfterPadding - d3.sum(guarantees.guaranteedWidths);\n freeHeight = availableHeightAfterPadding - d3.sum(guarantees.guaranteedHeights);\n var xWeights = void 0;\n if (wantsWidth) {\n xWeights = guarantees.wantsWidthArr.map(function (x) { return x ? 0.1 : 0; });\n xWeights = Plottable.Utils.Array.add(xWeights, colWeights);\n }\n else {\n xWeights = colWeights;\n }\n var yWeights = void 0;\n if (wantsHeight) {\n yWeights = guarantees.wantsHeightArr.map(function (x) { return x ? 0.1 : 0; });\n yWeights = Plottable.Utils.Array.add(yWeights, rowWeights);\n }\n else {\n yWeights = rowWeights;\n }\n colProportionalSpace = Table._calcProportionalSpace(xWeights, freeWidth);\n rowProportionalSpace = Table._calcProportionalSpace(yWeights, freeHeight);\n nIterations++;\n var canImproveWidthAllocation = freeWidth > 0 && freeWidth !== lastFreeWidth;\n var canImproveHeightAllocation = freeHeight > 0 && freeHeight !== lastFreeHeight;\n if (!(canImproveWidthAllocation || canImproveHeightAllocation)) {\n break;\n }\n if (nIterations > 5) {\n break;\n }\n }\n // Redo the proportional space one last time, to ensure we use the real weights not the wantsWidth/Height weights\n freeWidth = availableWidthAfterPadding - d3.sum(guarantees.guaranteedWidths);\n freeHeight = availableHeightAfterPadding - d3.sum(guarantees.guaranteedHeights);\n colProportionalSpace = Table._calcProportionalSpace(colWeights, freeWidth);\n rowProportionalSpace = Table._calcProportionalSpace(rowWeights, freeHeight);\n return {\n colProportionalSpace: colProportionalSpace,\n rowProportionalSpace: rowProportionalSpace,\n guaranteedWidths: guarantees.guaranteedWidths,\n guaranteedHeights: guarantees.guaranteedHeights,\n wantsWidth: wantsWidth,\n wantsHeight: wantsHeight,\n };\n };\n Table.prototype._determineGuarantees = function (offeredWidths, offeredHeights, isFinalOffer) {\n if (isFinalOffer === void 0) { isFinalOffer = false; }\n var requestedWidths = Plottable.Utils.Array.createFilledArray(0, this._nCols);\n var requestedHeights = Plottable.Utils.Array.createFilledArray(0, this._nRows);\n var columnNeedsWidth = Plottable.Utils.Array.createFilledArray(false, this._nCols);\n var rowNeedsHeight = Plottable.Utils.Array.createFilledArray(false, this._nRows);\n this._rows.forEach(function (row, rowIndex) {\n row.forEach(function (component, colIndex) {\n var spaceRequest;\n if (component != null) {\n spaceRequest = component.requestedSpace(offeredWidths[colIndex], offeredHeights[rowIndex]);\n }\n else {\n spaceRequest = {\n minWidth: 0,\n minHeight: 0,\n };\n }\n var columnWidth = isFinalOffer ? Math.min(spaceRequest.minWidth, offeredWidths[colIndex]) : spaceRequest.minWidth;\n requestedWidths[colIndex] = Math.max(requestedWidths[colIndex], columnWidth);\n var rowHeight = isFinalOffer ? Math.min(spaceRequest.minHeight, offeredHeights[rowIndex]) : spaceRequest.minHeight;\n requestedHeights[rowIndex] = Math.max(requestedHeights[rowIndex], rowHeight);\n var componentNeedsWidth = spaceRequest.minWidth > offeredWidths[colIndex];\n columnNeedsWidth[colIndex] = columnNeedsWidth[colIndex] || componentNeedsWidth;\n var componentNeedsHeight = spaceRequest.minHeight > offeredHeights[rowIndex];\n rowNeedsHeight[rowIndex] = rowNeedsHeight[rowIndex] || componentNeedsHeight;\n });\n });\n return {\n guaranteedWidths: requestedWidths,\n guaranteedHeights: requestedHeights,\n wantsWidthArr: columnNeedsWidth,\n wantsHeightArr: rowNeedsHeight,\n };\n };\n Table.prototype.requestedSpace = function (offeredWidth, offeredHeight) {\n this._calculatedLayout = this._iterateLayout(offeredWidth, offeredHeight);\n return {\n minWidth: d3.sum(this._calculatedLayout.guaranteedWidths),\n minHeight: d3.sum(this._calculatedLayout.guaranteedHeights),\n };\n };\n Table.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n var _this = this;\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n var lastLayoutWidth = d3.sum(this._calculatedLayout.guaranteedWidths);\n var lastLayoutHeight = d3.sum(this._calculatedLayout.guaranteedHeights);\n var layout = this._calculatedLayout;\n if (lastLayoutWidth > this.width() || lastLayoutHeight > this.height()) {\n layout = this._iterateLayout(this.width(), this.height(), true);\n }\n var childYOrigin = 0;\n var rowHeights = Plottable.Utils.Array.add(layout.rowProportionalSpace, layout.guaranteedHeights);\n var colWidths = Plottable.Utils.Array.add(layout.colProportionalSpace, layout.guaranteedWidths);\n this._rows.forEach(function (row, rowIndex) {\n var childXOrigin = 0;\n row.forEach(function (component, colIndex) {\n // recursively compute layout\n if (component != null) {\n component.computeLayout({ x: childXOrigin, y: childYOrigin }, colWidths[colIndex], rowHeights[rowIndex]);\n }\n childXOrigin += colWidths[colIndex] + _this._columnPadding;\n });\n childYOrigin += rowHeights[rowIndex] + _this._rowPadding;\n });\n return this;\n };\n Table.prototype.rowPadding = function (rowPadding) {\n if (rowPadding == null) {\n return this._rowPadding;\n }\n if (!Plottable.Utils.Math.isValidNumber(rowPadding) || rowPadding < 0) {\n throw Error(\"rowPadding must be a non-negative finite value\");\n }\n this._rowPadding = rowPadding;\n this.redraw();\n return this;\n };\n Table.prototype.columnPadding = function (columnPadding) {\n if (columnPadding == null) {\n return this._columnPadding;\n }\n if (!Plottable.Utils.Math.isValidNumber(columnPadding) || columnPadding < 0) {\n throw Error(\"columnPadding must be a non-negative finite value\");\n }\n this._columnPadding = columnPadding;\n this.redraw();\n return this;\n };\n Table.prototype.rowWeight = function (index, weight) {\n if (weight == null) {\n return this._rowWeights[index];\n }\n if (!Plottable.Utils.Math.isValidNumber(weight) || weight < 0) {\n throw Error(\"rowWeight must be a non-negative finite value\");\n }\n this._rowWeights[index] = weight;\n this.redraw();\n return this;\n };\n Table.prototype.columnWeight = function (index, weight) {\n if (weight == null) {\n return this._columnWeights[index];\n }\n if (!Plottable.Utils.Math.isValidNumber(weight) || weight < 0) {\n throw Error(\"columnWeight must be a non-negative finite value\");\n }\n this._columnWeights[index] = weight;\n this.redraw();\n return this;\n };\n Table.prototype.fixedWidth = function () {\n var cols = d3.transpose(this._rows);\n return Table._fixedSpace(cols, function (c) { return (c == null) || c.fixedWidth(); });\n };\n Table.prototype.fixedHeight = function () {\n return Table._fixedSpace(this._rows, function (c) { return (c == null) || c.fixedHeight(); });\n };\n Table.prototype._padTableToSize = function (nRows, nCols) {\n for (var i = 0; i < nRows; i++) {\n if (this._rows[i] === undefined) {\n this._rows[i] = [];\n this._rowWeights[i] = null;\n }\n for (var j = 0; j < nCols; j++) {\n if (this._rows[i][j] === undefined) {\n this._rows[i][j] = null;\n }\n }\n }\n for (var j = 0; j < nCols; j++) {\n if (this._columnWeights[j] === undefined) {\n this._columnWeights[j] = null;\n }\n }\n };\n Table._calcComponentWeights = function (setWeights, componentGroups, fixityAccessor) {\n // If the row/col weight was explicitly set, then return it outright\n // If the weight was not explicitly set, then guess it using the heuristic that if all components are fixed-space\n // then weight is 0, otherwise weight is 1\n return setWeights.map(function (w, i) {\n if (w != null) {\n return w;\n }\n var fixities = componentGroups[i].map(fixityAccessor);\n var allFixed = fixities.reduce(function (a, b) { return a && b; }, true);\n return allFixed ? 0 : 1;\n });\n };\n Table._calcProportionalSpace = function (weights, freeSpace) {\n var weightSum = d3.sum(weights);\n if (weightSum === 0) {\n return Plottable.Utils.Array.createFilledArray(0, weights.length);\n }\n else {\n return weights.map(function (w) { return freeSpace * w / weightSum; });\n }\n };\n Table._fixedSpace = function (componentGroup, fixityAccessor) {\n var all = function (bools) { return bools.reduce(function (a, b) { return a && b; }, true); };\n var groupIsFixed = function (components) { return all(components.map(fixityAccessor)); };\n return all(componentGroup.map(groupIsFixed));\n };\n return Table;\n }(Plottable.ComponentContainer));\n Components.Table = Table;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n (function (PropertyMode) {\n PropertyMode[PropertyMode[\"VALUE\"] = 0] = \"VALUE\";\n PropertyMode[PropertyMode[\"PIXEL\"] = 1] = \"PIXEL\";\n })(Components.PropertyMode || (Components.PropertyMode = {}));\n var PropertyMode = Components.PropertyMode;\n ;\n var SelectionBoxLayer = (function (_super) {\n __extends(SelectionBoxLayer, _super);\n function SelectionBoxLayer() {\n var _this = this;\n _super.call(this);\n this._boxVisible = false;\n this._boxBounds = {\n topLeft: { x: 0, y: 0 },\n bottomRight: { x: 0, y: 0 },\n };\n this._xBoundsMode = PropertyMode.PIXEL;\n this._yBoundsMode = PropertyMode.PIXEL;\n this.addClass(\"selection-box-layer\");\n this._adjustBoundsCallback = function () {\n _this.render();\n };\n this._clipPathEnabled = true;\n this._xExtent = [undefined, undefined];\n this._yExtent = [undefined, undefined];\n }\n SelectionBoxLayer.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._box = this.content().append(\"g\").classed(\"selection-box\", true).remove();\n this._boxArea = this._box.append(\"rect\").classed(\"selection-area\", true);\n };\n SelectionBoxLayer.prototype._sizeFromOffer = function (availableWidth, availableHeight) {\n return {\n width: availableWidth,\n height: availableHeight,\n };\n };\n SelectionBoxLayer.prototype.bounds = function (newBounds) {\n if (newBounds == null) {\n return this._getBounds();\n }\n this._setBounds(newBounds);\n this._xBoundsMode = PropertyMode.PIXEL;\n this._yBoundsMode = PropertyMode.PIXEL;\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype._setBounds = function (newBounds) {\n var topLeft = {\n x: Math.min(newBounds.topLeft.x, newBounds.bottomRight.x),\n y: Math.min(newBounds.topLeft.y, newBounds.bottomRight.y),\n };\n var bottomRight = {\n x: Math.max(newBounds.topLeft.x, newBounds.bottomRight.x),\n y: Math.max(newBounds.topLeft.y, newBounds.bottomRight.y),\n };\n this._boxBounds = {\n topLeft: topLeft,\n bottomRight: bottomRight,\n };\n };\n SelectionBoxLayer.prototype._getBounds = function () {\n return {\n topLeft: {\n x: this._xBoundsMode === PropertyMode.PIXEL ?\n this._boxBounds.topLeft.x :\n (this._xScale == null ?\n 0 :\n Math.min(this.xScale().scale(this.xExtent()[0]), this.xScale().scale(this.xExtent()[1]))),\n y: this._yBoundsMode === PropertyMode.PIXEL ?\n this._boxBounds.topLeft.y :\n (this._yScale == null ?\n 0 :\n Math.min(this.yScale().scale(this.yExtent()[0]), this.yScale().scale(this.yExtent()[1]))),\n },\n bottomRight: {\n x: this._xBoundsMode === PropertyMode.PIXEL ?\n this._boxBounds.bottomRight.x :\n (this._xScale == null ?\n 0 :\n Math.max(this.xScale().scale(this.xExtent()[0]), this.xScale().scale(this.xExtent()[1]))),\n y: this._yBoundsMode === PropertyMode.PIXEL ?\n this._boxBounds.bottomRight.y :\n (this._yScale == null ?\n 0 :\n Math.max(this.yScale().scale(this.yExtent()[0]), this.yScale().scale(this.yExtent()[1]))),\n },\n };\n };\n SelectionBoxLayer.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n if (this._boxVisible) {\n var bounds = this.bounds();\n var t = bounds.topLeft.y;\n var b = bounds.bottomRight.y;\n var l = bounds.topLeft.x;\n var r = bounds.bottomRight.x;\n if (!(Plottable.Utils.Math.isValidNumber(t) &&\n Plottable.Utils.Math.isValidNumber(b) &&\n Plottable.Utils.Math.isValidNumber(l) &&\n Plottable.Utils.Math.isValidNumber(r))) {\n throw new Error(\"bounds have not been properly set\");\n }\n this._boxArea.attr({\n x: l, y: t, width: r - l, height: b - t,\n });\n this.content().node().appendChild(this._box.node());\n }\n else {\n this._box.remove();\n }\n return this;\n };\n SelectionBoxLayer.prototype.boxVisible = function (show) {\n if (show == null) {\n return this._boxVisible;\n }\n this._boxVisible = show;\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype.fixedWidth = function () {\n return true;\n };\n SelectionBoxLayer.prototype.fixedHeight = function () {\n return true;\n };\n SelectionBoxLayer.prototype.xScale = function (xScale) {\n if (xScale == null) {\n return this._xScale;\n }\n if (this._xScale != null) {\n this._xScale.offUpdate(this._adjustBoundsCallback);\n }\n this._xScale = xScale;\n this._xBoundsMode = PropertyMode.VALUE;\n this._xScale.onUpdate(this._adjustBoundsCallback);\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype.yScale = function (yScale) {\n if (yScale == null) {\n return this._yScale;\n }\n if (this._yScale != null) {\n this._yScale.offUpdate(this._adjustBoundsCallback);\n }\n this._yScale = yScale;\n this._yBoundsMode = PropertyMode.VALUE;\n this._yScale.onUpdate(this._adjustBoundsCallback);\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype.xExtent = function (xExtent) {\n // Explicit typing for Typescript 1.4\n if (xExtent == null) {\n return this._getXExtent();\n }\n this._setXExtent(xExtent);\n this._xBoundsMode = PropertyMode.VALUE;\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype._getXExtent = function () {\n return this._xBoundsMode === PropertyMode.VALUE ?\n this._xExtent :\n (this._xScale == null ?\n [undefined, undefined] :\n [\n this._xScale.invert(this._boxBounds.topLeft.x),\n this._xScale.invert(this._boxBounds.bottomRight.x),\n ]);\n };\n SelectionBoxLayer.prototype._setXExtent = function (xExtent) {\n this._xExtent = xExtent;\n };\n SelectionBoxLayer.prototype.yExtent = function (yExtent) {\n // Explicit typing for Typescript 1.4\n if (yExtent == null) {\n return this._getYExtent();\n }\n this._setYExtent(yExtent);\n this._yBoundsMode = PropertyMode.VALUE;\n this.render();\n return this;\n };\n SelectionBoxLayer.prototype._getYExtent = function () {\n return this._yBoundsMode === PropertyMode.VALUE ?\n this._yExtent :\n (this._yScale == null ?\n [undefined, undefined] :\n [\n this._yScale.invert(this._boxBounds.topLeft.y),\n this._yScale.invert(this._boxBounds.bottomRight.y),\n ]);\n };\n SelectionBoxLayer.prototype._setYExtent = function (yExtent) {\n this._yExtent = yExtent;\n };\n SelectionBoxLayer.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n if (this._xScale != null) {\n this.xScale().offUpdate(this._adjustBoundsCallback);\n }\n if (this._yScale != null) {\n this.yScale().offUpdate(this._adjustBoundsCallback);\n }\n };\n return SelectionBoxLayer;\n }(Plottable.Component));\n Components.SelectionBoxLayer = SelectionBoxLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var PropertyMode;\n (function (PropertyMode) {\n PropertyMode[PropertyMode[\"VALUE\"] = 0] = \"VALUE\";\n PropertyMode[PropertyMode[\"PIXEL\"] = 1] = \"PIXEL\";\n })(PropertyMode || (PropertyMode = {}));\n ;\n var GuideLineLayer = (function (_super) {\n __extends(GuideLineLayer, _super);\n function GuideLineLayer(orientation) {\n var _this = this;\n _super.call(this);\n this._mode = PropertyMode.VALUE;\n if (orientation !== GuideLineLayer.ORIENTATION_VERTICAL && orientation !== GuideLineLayer.ORIENTATION_HORIZONTAL) {\n throw new Error(orientation + \" is not a valid orientation for GuideLineLayer\");\n }\n this._orientation = orientation;\n this._clipPathEnabled = true;\n this.addClass(\"guide-line-layer\");\n if (this._isVertical()) {\n this.addClass(\"vertical\");\n }\n else {\n this.addClass(\"horizontal\");\n }\n this._scaleUpdateCallback = function () {\n _this._syncPixelPositionAndValue();\n _this.render();\n };\n }\n GuideLineLayer.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._guideLine = this.content().append(\"line\").classed(\"guide-line\", true);\n };\n GuideLineLayer.prototype._sizeFromOffer = function (availableWidth, availableHeight) {\n return {\n width: availableWidth,\n height: availableHeight,\n };\n };\n GuideLineLayer.prototype._isVertical = function () {\n return this._orientation === GuideLineLayer.ORIENTATION_VERTICAL;\n };\n GuideLineLayer.prototype.fixedWidth = function () {\n return true;\n };\n GuideLineLayer.prototype.fixedHeight = function () {\n return true;\n };\n GuideLineLayer.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n if (this.scale() != null) {\n if (this._isVertical()) {\n this.scale().range([0, this.width()]);\n }\n else {\n this.scale().range([this.height(), 0]);\n }\n }\n return this;\n };\n GuideLineLayer.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n this._syncPixelPositionAndValue();\n this._guideLine.attr({\n x1: this._isVertical() ? this.pixelPosition() : 0,\n y1: this._isVertical() ? 0 : this.pixelPosition(),\n x2: this._isVertical() ? this.pixelPosition() : this.width(),\n y2: this._isVertical() ? this.height() : this.pixelPosition(),\n });\n return this;\n };\n // sets pixelPosition() or value() based on the other, depending on which was the last one set\n GuideLineLayer.prototype._syncPixelPositionAndValue = function () {\n if (this.scale() == null) {\n return;\n }\n if (this._mode === PropertyMode.VALUE && this.value() != null) {\n this._pixelPosition = this.scale().scale(this.value());\n }\n else if (this._mode === PropertyMode.PIXEL && this.pixelPosition() != null) {\n this._value = this.scale().invert(this.pixelPosition());\n }\n };\n GuideLineLayer.prototype._setPixelPositionWithoutChangingMode = function (pixelPosition) {\n this._pixelPosition = pixelPosition;\n if (this.scale() != null) {\n this._value = this.scale().invert(this.pixelPosition());\n }\n this.render();\n };\n GuideLineLayer.prototype.scale = function (scale) {\n if (scale == null) {\n return this._scale;\n }\n var previousScale = this._scale;\n if (previousScale != null) {\n previousScale.offUpdate(this._scaleUpdateCallback);\n }\n this._scale = scale;\n this._scale.onUpdate(this._scaleUpdateCallback);\n this._syncPixelPositionAndValue();\n this.redraw();\n return this;\n };\n GuideLineLayer.prototype.value = function (value) {\n if (value == null) {\n return this._value;\n }\n this._value = value;\n this._mode = PropertyMode.VALUE;\n this._syncPixelPositionAndValue();\n this.render();\n return this;\n };\n GuideLineLayer.prototype.pixelPosition = function (pixelPosition) {\n if (pixelPosition == null) {\n return this._pixelPosition;\n }\n if (!Plottable.Utils.Math.isValidNumber(pixelPosition)) {\n throw new Error(\"pixelPosition must be a finite number\");\n }\n this._pixelPosition = pixelPosition;\n this._mode = PropertyMode.PIXEL;\n this._syncPixelPositionAndValue();\n this.render();\n return this;\n };\n GuideLineLayer.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n if (this.scale() != null) {\n this.scale().offUpdate(this._scaleUpdateCallback);\n }\n };\n GuideLineLayer.ORIENTATION_VERTICAL = \"vertical\";\n GuideLineLayer.ORIENTATION_HORIZONTAL = \"horizontal\";\n return GuideLineLayer;\n }(Plottable.Component));\n Components.GuideLineLayer = GuideLineLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Animator;\n (function (Animator) {\n Animator.MAIN = \"main\";\n Animator.RESET = \"reset\";\n })(Animator = Plots.Animator || (Plots.Animator = {}));\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plot = (function (_super) {\n __extends(Plot, _super);\n /**\n * A Plot draws some visualization of the inputted Datasets.\n *\n * @constructor\n */\n function Plot() {\n var _this = this;\n _super.call(this);\n this._dataChanged = false;\n this._animate = false;\n this._animators = {};\n this._clipPathEnabled = true;\n this.addClass(\"plot\");\n this._datasetToDrawer = new Plottable.Utils.Map();\n this._attrBindings = d3.map();\n this._attrExtents = d3.map();\n this._includedValuesProvider = function (scale) { return _this._includedValuesForScale(scale); };\n this._renderCallback = function (scale) { return _this.render(); };\n this._onDatasetUpdateCallback = function () { return _this._onDatasetUpdate(); };\n this._propertyBindings = d3.map();\n this._propertyExtents = d3.map();\n var mainAnimator = new Plottable.Animators.Easing().maxTotalDuration(Plot._ANIMATION_MAX_DURATION);\n this.animator(Plottable.Plots.Animator.MAIN, mainAnimator);\n this.animator(Plottable.Plots.Animator.RESET, new Plottable.Animators.Null());\n }\n Plot.prototype.anchor = function (selection) {\n _super.prototype.anchor.call(this, selection);\n this._dataChanged = true;\n this._updateExtents();\n return this;\n };\n Plot.prototype._setup = function () {\n var _this = this;\n _super.prototype._setup.call(this);\n this._renderArea = this.content().append(\"g\").classed(\"render-area\", true);\n this.datasets().forEach(function (dataset) { return _this._createNodesForDataset(dataset); });\n };\n Plot.prototype.destroy = function () {\n var _this = this;\n _super.prototype.destroy.call(this);\n this._scales().forEach(function (scale) { return scale.offUpdate(_this._renderCallback); });\n this.datasets([]);\n };\n Plot.prototype._createNodesForDataset = function (dataset) {\n var drawer = this._datasetToDrawer.get(dataset);\n drawer.renderArea(this._renderArea.append(\"g\"));\n return drawer;\n };\n Plot.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawer(dataset);\n };\n Plot.prototype._getAnimator = function (key) {\n if (this._animateOnNextRender()) {\n return this._animators[key] || new Plottable.Animators.Null();\n }\n else {\n return new Plottable.Animators.Null();\n }\n };\n Plot.prototype._onDatasetUpdate = function () {\n this._updateExtents();\n this._dataChanged = true;\n this.render();\n };\n Plot.prototype.attr = function (attr, attrValue, scale) {\n if (attrValue == null) {\n return this._attrBindings.get(attr);\n }\n this._bindAttr(attr, attrValue, scale);\n this.render(); // queue a re-render upon changing projector\n return this;\n };\n Plot.prototype._bindProperty = function (property, value, scale) {\n var binding = this._propertyBindings.get(property);\n var oldScale = binding != null ? binding.scale : null;\n this._propertyBindings.set(property, { accessor: d3.functor(value), scale: scale });\n this._updateExtentsForProperty(property);\n if (oldScale != null) {\n this._uninstallScaleForKey(oldScale, property);\n }\n if (scale != null) {\n this._installScaleForKey(scale, property);\n }\n };\n Plot.prototype._bindAttr = function (attr, value, scale) {\n var binding = this._attrBindings.get(attr);\n var oldScale = binding != null ? binding.scale : null;\n this._attrBindings.set(attr, { accessor: d3.functor(value), scale: scale });\n this._updateExtentsForAttr(attr);\n if (oldScale != null) {\n this._uninstallScaleForKey(oldScale, attr);\n }\n if (scale != null) {\n this._installScaleForKey(scale, attr);\n }\n };\n Plot.prototype._generateAttrToProjector = function () {\n var h = {};\n this._attrBindings.forEach(function (attr, binding) {\n var accessor = binding.accessor;\n var scale = binding.scale;\n var fn = scale ? function (d, i, dataset) { return scale.scale(accessor(d, i, dataset)); } : accessor;\n h[attr] = fn;\n });\n var propertyProjectors = this._propertyProjectors();\n Object.keys(propertyProjectors).forEach(function (key) {\n if (h[key] == null) {\n h[key] = propertyProjectors[key];\n }\n });\n return h;\n };\n Plot.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n if (this._isAnchored) {\n this._paint();\n this._dataChanged = false;\n }\n return this;\n };\n Plot.prototype.animated = function (willAnimate) {\n if (willAnimate == null) {\n return this._animate;\n }\n this._animate = willAnimate;\n return this;\n };\n Plot.prototype.detach = function () {\n _super.prototype.detach.call(this);\n // make the domain resize\n this._updateExtents();\n return this;\n };\n /**\n * @returns {Scale[]} A unique array of all scales currently used by the Plot.\n */\n Plot.prototype._scales = function () {\n var scales = [];\n this._attrBindings.forEach(function (attr, binding) {\n var scale = binding.scale;\n if (scale != null && scales.indexOf(scale) === -1) {\n scales.push(scale);\n }\n });\n this._propertyBindings.forEach(function (property, binding) {\n var scale = binding.scale;\n if (scale != null && scales.indexOf(scale) === -1) {\n scales.push(scale);\n }\n });\n return scales;\n };\n /**\n * Updates the extents associated with each attribute, then autodomains all scales the Plot uses.\n */\n Plot.prototype._updateExtents = function () {\n var _this = this;\n this._attrBindings.forEach(function (attr) { return _this._updateExtentsForAttr(attr); });\n this._propertyExtents.forEach(function (property) { return _this._updateExtentsForProperty(property); });\n this._scales().forEach(function (scale) { return scale.addIncludedValuesProvider(_this._includedValuesProvider); });\n };\n Plot.prototype._updateExtentsForAttr = function (attr) {\n // Filters should never be applied to attributes\n this._updateExtentsForKey(attr, this._attrBindings, this._attrExtents, null);\n };\n Plot.prototype._updateExtentsForProperty = function (property) {\n this._updateExtentsForKey(property, this._propertyBindings, this._propertyExtents, this._filterForProperty(property));\n };\n Plot.prototype._filterForProperty = function (property) {\n return null;\n };\n Plot.prototype._updateExtentsForKey = function (key, bindings, extents, filter) {\n var _this = this;\n var accScaleBinding = bindings.get(key);\n if (accScaleBinding == null || accScaleBinding.accessor == null) {\n return;\n }\n extents.set(key, this.datasets().map(function (dataset) { return _this._computeExtent(dataset, accScaleBinding, filter); }));\n };\n Plot.prototype._computeExtent = function (dataset, accScaleBinding, filter) {\n var accessor = accScaleBinding.accessor;\n var scale = accScaleBinding.scale;\n if (scale == null) {\n return [];\n }\n var data = dataset.data();\n if (filter != null) {\n data = data.filter(function (d, i) { return filter(d, i, dataset); });\n }\n var appliedAccessor = function (d, i) { return accessor(d, i, dataset); };\n var mappedData = data.map(appliedAccessor);\n return scale.extentOfValues(mappedData);\n };\n /**\n * Override in subclass to add special extents, such as included values\n */\n Plot.prototype._extentsForProperty = function (property) {\n return this._propertyExtents.get(property);\n };\n Plot.prototype._includedValuesForScale = function (scale) {\n var _this = this;\n if (!this._isAnchored) {\n return [];\n }\n var includedValues = [];\n this._attrBindings.forEach(function (attr, binding) {\n if (binding.scale === scale) {\n var extents = _this._attrExtents.get(attr);\n if (extents != null) {\n includedValues = includedValues.concat(d3.merge(extents));\n }\n }\n });\n this._propertyBindings.forEach(function (property, binding) {\n if (binding.scale === scale) {\n var extents = _this._extentsForProperty(property);\n if (extents != null) {\n includedValues = includedValues.concat(d3.merge(extents));\n }\n }\n });\n return includedValues;\n };\n Plot.prototype.animator = function (animatorKey, animator) {\n if (animator === undefined) {\n return this._animators[animatorKey];\n }\n else {\n this._animators[animatorKey] = animator;\n return this;\n }\n };\n /**\n * Adds a Dataset to the Plot.\n *\n * @param {Dataset} dataset\n * @returns {Plot} The calling Plot.\n */\n Plot.prototype.addDataset = function (dataset) {\n this._addDataset(dataset);\n this._onDatasetUpdate();\n return this;\n };\n Plot.prototype._addDataset = function (dataset) {\n this._removeDataset(dataset);\n var drawer = this._createDrawer(dataset);\n this._datasetToDrawer.set(dataset, drawer);\n if (this._isSetup) {\n this._createNodesForDataset(dataset);\n }\n dataset.onUpdate(this._onDatasetUpdateCallback);\n return this;\n };\n /**\n * Removes a Dataset from the Plot.\n *\n * @param {Dataset} dataset\n * @returns {Plot} The calling Plot.\n */\n Plot.prototype.removeDataset = function (dataset) {\n this._removeDataset(dataset);\n this._onDatasetUpdate();\n return this;\n };\n Plot.prototype._removeDataset = function (dataset) {\n if (this.datasets().indexOf(dataset) === -1) {\n return this;\n }\n this._removeDatasetNodes(dataset);\n dataset.offUpdate(this._onDatasetUpdateCallback);\n this._datasetToDrawer.delete(dataset);\n return this;\n };\n Plot.prototype._removeDatasetNodes = function (dataset) {\n var drawer = this._datasetToDrawer.get(dataset);\n drawer.remove();\n };\n Plot.prototype.datasets = function (datasets) {\n var _this = this;\n var currentDatasets = [];\n this._datasetToDrawer.forEach(function (drawer, dataset) { return currentDatasets.push(dataset); });\n if (datasets == null) {\n return currentDatasets;\n }\n currentDatasets.forEach(function (dataset) { return _this._removeDataset(dataset); });\n datasets.forEach(function (dataset) { return _this._addDataset(dataset); });\n this._onDatasetUpdate();\n return this;\n };\n Plot.prototype._getDrawersInOrder = function () {\n var _this = this;\n return this.datasets().map(function (dataset) { return _this._datasetToDrawer.get(dataset); });\n };\n Plot.prototype._generateDrawSteps = function () {\n return [{ attrToProjector: this._generateAttrToProjector(), animator: new Plottable.Animators.Null() }];\n };\n Plot.prototype._additionalPaint = function (time) {\n // no-op\n };\n Plot.prototype._getDataToDraw = function () {\n var dataToDraw = new Plottable.Utils.Map();\n this.datasets().forEach(function (dataset) { return dataToDraw.set(dataset, dataset.data()); });\n return dataToDraw;\n };\n Plot.prototype._paint = function () {\n var drawSteps = this._generateDrawSteps();\n var dataToDraw = this._getDataToDraw();\n var drawers = this._getDrawersInOrder();\n this.datasets().forEach(function (ds, i) { return drawers[i].draw(dataToDraw.get(ds), drawSteps); });\n var times = this.datasets().map(function (ds, i) { return drawers[i].totalDrawTime(dataToDraw.get(ds), drawSteps); });\n var maxTime = Plottable.Utils.Math.max(times, 0);\n this._additionalPaint(maxTime);\n };\n /**\n * Retrieves Selections of this Plot for the specified Datasets.\n *\n * @param {Dataset[]} [datasets] The Datasets to retrieve the Selections for.\n * If not provided, Selections will be retrieved for all Datasets on the Plot.\n * @returns {d3.Selection}\n */\n Plot.prototype.selections = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n var selections = [];\n datasets.forEach(function (dataset) {\n var drawer = _this._datasetToDrawer.get(dataset);\n if (drawer == null) {\n return;\n }\n drawer.renderArea().selectAll(drawer.selector()).each(function () {\n selections.push(this);\n });\n });\n return d3.selectAll(selections);\n };\n /**\n * Gets the Entities associated with the specified Datasets.\n *\n * @param {dataset[]} datasets The Datasets to retrieve the Entities for.\n * If not provided, returns defaults to all Datasets on the Plot.\n * @return {Plots.PlotEntity[]}\n */\n Plot.prototype.entities = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n return this._lightweightEntities(datasets).map(function (entity) { return _this._lightweightPlotEntityToPlotEntity(entity); });\n };\n Plot.prototype._lightweightEntities = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n var lightweightEntities = [];\n datasets.forEach(function (dataset) {\n var drawer = _this._datasetToDrawer.get(dataset);\n var validDatumIndex = 0;\n dataset.data().forEach(function (datum, datasetIndex) {\n var position = _this._pixelPoint(datum, datasetIndex, dataset);\n if (Plottable.Utils.Math.isNaN(position.x) || Plottable.Utils.Math.isNaN(position.y)) {\n return;\n }\n lightweightEntities.push({\n datum: datum,\n index: datasetIndex,\n dataset: dataset,\n position: position,\n component: _this,\n drawer: drawer,\n validDatumIndex: validDatumIndex,\n });\n validDatumIndex++;\n });\n });\n return lightweightEntities;\n };\n Plot.prototype._lightweightPlotEntityToPlotEntity = function (entity) {\n var plotEntity = {\n datum: entity.datum,\n position: entity.position,\n dataset: entity.dataset,\n index: entity.index,\n component: entity.component,\n selection: entity.drawer.selectionForIndex(entity.validDatumIndex),\n };\n return plotEntity;\n };\n /**\n * Returns the PlotEntity nearest to the query point by the Euclidian norm, or undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {Plots.PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n Plot.prototype.entityNearest = function (queryPoint) {\n var _this = this;\n var closestDistanceSquared = Infinity;\n var closestPointEntity;\n var entities = this._lightweightEntities();\n entities.forEach(function (entity) {\n if (!_this._entityVisibleOnPlot(entity.position, entity.datum, entity.index, entity.dataset)) {\n return;\n }\n var distanceSquared = Plottable.Utils.Math.distanceSquared(entity.position, queryPoint);\n if (distanceSquared < closestDistanceSquared) {\n closestDistanceSquared = distanceSquared;\n closestPointEntity = entity;\n }\n });\n if (closestPointEntity === undefined) {\n return undefined;\n }\n return this._lightweightPlotEntityToPlotEntity(closestPointEntity);\n };\n Plot.prototype._entityVisibleOnPlot = function (pixelPoint, datum, index, dataset) {\n return !(pixelPoint.x < 0 || pixelPoint.y < 0 ||\n pixelPoint.x > this.width() || pixelPoint.y > this.height());\n };\n Plot.prototype._uninstallScaleForKey = function (scale, key) {\n scale.offUpdate(this._renderCallback);\n scale.removeIncludedValuesProvider(this._includedValuesProvider);\n };\n Plot.prototype._installScaleForKey = function (scale, key) {\n scale.onUpdate(this._renderCallback);\n scale.addIncludedValuesProvider(this._includedValuesProvider);\n };\n Plot.prototype._propertyProjectors = function () {\n return {};\n };\n Plot._scaledAccessor = function (binding) {\n return binding.scale == null ?\n binding.accessor :\n function (d, i, ds) { return binding.scale.scale(binding.accessor(d, i, ds)); };\n };\n Plot.prototype._pixelPoint = function (datum, index, dataset) {\n return { x: 0, y: 0 };\n };\n Plot.prototype._animateOnNextRender = function () {\n return this._animate && this._dataChanged;\n };\n Plot._ANIMATION_MAX_DURATION = 600;\n return Plot;\n }(Plottable.Component));\n Plottable.Plot = Plot;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Pie = (function (_super) {\n __extends(Pie, _super);\n /**\n * @constructor\n */\n function Pie() {\n var _this = this;\n _super.call(this);\n this._labelFormatter = Plottable.Formatters.identity();\n this._labelsEnabled = false;\n this.innerRadius(0);\n this.outerRadius(function () { return Math.min(_this.width(), _this.height()) / 2; });\n this.addClass(\"pie-plot\");\n this.attr(\"fill\", function (d, i) { return String(i); }, new Plottable.Scales.Color());\n this._strokeDrawers = new Plottable.Utils.Map();\n }\n Pie.prototype._setup = function () {\n var _this = this;\n _super.prototype._setup.call(this);\n this._strokeDrawers.forEach(function (d) { return d.renderArea(_this._renderArea.append(\"g\")); });\n };\n Pie.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n this._renderArea.attr(\"transform\", \"translate(\" + this.width() / 2 + \",\" + this.height() / 2 + \")\");\n var radiusLimit = Math.min(this.width(), this.height()) / 2;\n if (this.innerRadius().scale != null) {\n this.innerRadius().scale.range([0, radiusLimit]);\n }\n if (this.outerRadius().scale != null) {\n this.outerRadius().scale.range([0, radiusLimit]);\n }\n return this;\n };\n Pie.prototype.addDataset = function (dataset) {\n _super.prototype.addDataset.call(this, dataset);\n return this;\n };\n Pie.prototype._addDataset = function (dataset) {\n if (this.datasets().length === 1) {\n Plottable.Utils.Window.warn(\"Only one dataset is supported in Pie plots\");\n return this;\n }\n this._updatePieAngles();\n var strokeDrawer = new Plottable.Drawers.ArcOutline(dataset);\n if (this._isSetup) {\n strokeDrawer.renderArea(this._renderArea.append(\"g\"));\n }\n this._strokeDrawers.set(dataset, strokeDrawer);\n _super.prototype._addDataset.call(this, dataset);\n return this;\n };\n Pie.prototype.removeDataset = function (dataset) {\n _super.prototype.removeDataset.call(this, dataset);\n return this;\n };\n Pie.prototype._removeDatasetNodes = function (dataset) {\n _super.prototype._removeDatasetNodes.call(this, dataset);\n this._strokeDrawers.get(dataset).remove();\n };\n Pie.prototype._removeDataset = function (dataset) {\n _super.prototype._removeDataset.call(this, dataset);\n this._startAngles = [];\n this._endAngles = [];\n return this;\n };\n Pie.prototype.selections = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n var allSelections = _super.prototype.selections.call(this, datasets)[0];\n datasets.forEach(function (dataset) {\n var drawer = _this._strokeDrawers.get(dataset);\n if (drawer == null) {\n return;\n }\n drawer.renderArea().selectAll(drawer.selector()).each(function () {\n allSelections.push(this);\n });\n });\n return d3.selectAll(allSelections);\n };\n Pie.prototype._onDatasetUpdate = function () {\n _super.prototype._onDatasetUpdate.call(this);\n this._updatePieAngles();\n this.render();\n };\n Pie.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Arc(dataset);\n };\n Pie.prototype.entities = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n var entities = _super.prototype.entities.call(this, datasets);\n entities.forEach(function (entity) {\n entity.position.x += _this.width() / 2;\n entity.position.y += _this.height() / 2;\n var stroke = _this._strokeDrawers.get(entity.dataset).selectionForIndex(entity.index);\n entity.selection[0].push(stroke[0][0]);\n });\n return entities;\n };\n Pie.prototype.sectorValue = function (sectorValue, scale) {\n if (sectorValue == null) {\n return this._propertyBindings.get(Pie._SECTOR_VALUE_KEY);\n }\n this._bindProperty(Pie._SECTOR_VALUE_KEY, sectorValue, scale);\n this._updatePieAngles();\n this.render();\n return this;\n };\n Pie.prototype.innerRadius = function (innerRadius, scale) {\n if (innerRadius == null) {\n return this._propertyBindings.get(Pie._INNER_RADIUS_KEY);\n }\n this._bindProperty(Pie._INNER_RADIUS_KEY, innerRadius, scale);\n this.render();\n return this;\n };\n Pie.prototype.outerRadius = function (outerRadius, scale) {\n if (outerRadius == null) {\n return this._propertyBindings.get(Pie._OUTER_RADIUS_KEY);\n }\n this._bindProperty(Pie._OUTER_RADIUS_KEY, outerRadius, scale);\n this.render();\n return this;\n };\n Pie.prototype.labelsEnabled = function (enabled) {\n if (enabled == null) {\n return this._labelsEnabled;\n }\n else {\n this._labelsEnabled = enabled;\n this.render();\n return this;\n }\n };\n Pie.prototype.labelFormatter = function (formatter) {\n if (formatter == null) {\n return this._labelFormatter;\n }\n else {\n this._labelFormatter = formatter;\n this.render();\n return this;\n }\n };\n /*\n * Gets the Entities at a particular Point.\n *\n * @param {Point} p\n * @param {PlotEntity[]}\n */\n Pie.prototype.entitiesAt = function (queryPoint) {\n var center = { x: this.width() / 2, y: this.height() / 2 };\n var adjustedQueryPoint = { x: queryPoint.x - center.x, y: queryPoint.y - center.y };\n var index = this._sliceIndexForPoint(adjustedQueryPoint);\n return index == null ? [] : [this.entities()[index]];\n };\n Pie.prototype._propertyProjectors = function () {\n var _this = this;\n var attrToProjector = _super.prototype._propertyProjectors.call(this);\n var innerRadiusAccessor = Plottable.Plot._scaledAccessor(this.innerRadius());\n var outerRadiusAccessor = Plottable.Plot._scaledAccessor(this.outerRadius());\n attrToProjector[\"d\"] = function (datum, index, ds) {\n return d3.svg.arc().innerRadius(innerRadiusAccessor(datum, index, ds))\n .outerRadius(outerRadiusAccessor(datum, index, ds))\n .startAngle(_this._startAngles[index])\n .endAngle(_this._endAngles[index])(datum, index);\n };\n return attrToProjector;\n };\n Pie.prototype._updatePieAngles = function () {\n if (this.sectorValue() == null) {\n return;\n }\n if (this.datasets().length === 0) {\n return;\n }\n var sectorValueAccessor = Plottable.Plot._scaledAccessor(this.sectorValue());\n var dataset = this.datasets()[0];\n var data = this._getDataToDraw().get(dataset);\n var pie = d3.layout.pie().sort(null).value(function (d, i) { return sectorValueAccessor(d, i, dataset); })(data);\n this._startAngles = pie.map(function (slice) { return slice.startAngle; });\n this._endAngles = pie.map(function (slice) { return slice.endAngle; });\n };\n Pie.prototype._getDataToDraw = function () {\n var dataToDraw = _super.prototype._getDataToDraw.call(this);\n if (this.datasets().length === 0) {\n return dataToDraw;\n }\n var sectorValueAccessor = Plottable.Plot._scaledAccessor(this.sectorValue());\n var ds = this.datasets()[0];\n var data = dataToDraw.get(ds);\n var filteredData = data.filter(function (d, i) { return Pie._isValidData(sectorValueAccessor(d, i, ds)); });\n dataToDraw.set(ds, filteredData);\n return dataToDraw;\n };\n Pie._isValidData = function (value) {\n return Plottable.Utils.Math.isValidNumber(value) && value >= 0;\n };\n Pie.prototype._pixelPoint = function (datum, index, dataset) {\n var scaledValueAccessor = Plottable.Plot._scaledAccessor(this.sectorValue());\n if (!Pie._isValidData(scaledValueAccessor(datum, index, dataset))) {\n return { x: NaN, y: NaN };\n }\n var innerRadius = Plottable.Plot._scaledAccessor(this.innerRadius())(datum, index, dataset);\n var outerRadius = Plottable.Plot._scaledAccessor(this.outerRadius())(datum, index, dataset);\n var avgRadius = (innerRadius + outerRadius) / 2;\n var pie = d3.layout.pie()\n .sort(null)\n .value(function (d, i) {\n var value = scaledValueAccessor(d, i, dataset);\n return Pie._isValidData(value) ? value : 0;\n })(dataset.data());\n var startAngle = pie[index].startAngle;\n var endAngle = pie[index].endAngle;\n var avgAngle = (startAngle + endAngle) / 2;\n return { x: avgRadius * Math.sin(avgAngle), y: -avgRadius * Math.cos(avgAngle) };\n };\n Pie.prototype._additionalPaint = function (time) {\n var _this = this;\n this._renderArea.select(\".label-area\").remove();\n if (this._labelsEnabled) {\n Plottable.Utils.Window.setTimeout(function () { return _this._drawLabels(); }, time);\n }\n var drawSteps = this._generateStrokeDrawSteps();\n var dataToDraw = this._getDataToDraw();\n this.datasets().forEach(function (dataset) { return _this._strokeDrawers.get(dataset).draw(dataToDraw.get(dataset), drawSteps); });\n };\n Pie.prototype._generateStrokeDrawSteps = function () {\n var attrToProjector = this._generateAttrToProjector();\n return [{ attrToProjector: attrToProjector, animator: new Plottable.Animators.Null() }];\n };\n Pie.prototype._sliceIndexForPoint = function (p) {\n var pointRadius = Math.sqrt(Math.pow(p.x, 2) + Math.pow(p.y, 2));\n var pointAngle = Math.acos(-p.y / pointRadius);\n if (p.x < 0) {\n pointAngle = Math.PI * 2 - pointAngle;\n }\n var index;\n for (var i = 0; i < this._startAngles.length; i++) {\n if (this._startAngles[i] < pointAngle && this._endAngles[i] > pointAngle) {\n index = i;\n break;\n }\n }\n if (index !== undefined) {\n var dataset = this.datasets()[0];\n var datum = dataset.data()[index];\n var innerRadius = this.innerRadius().accessor(datum, index, dataset);\n var outerRadius = this.outerRadius().accessor(datum, index, dataset);\n if (pointRadius > innerRadius && pointRadius < outerRadius) {\n return index;\n }\n }\n return null;\n };\n Pie.prototype._drawLabels = function () {\n var _this = this;\n var attrToProjector = this._generateAttrToProjector();\n var labelArea = this._renderArea.append(\"g\").classed(\"label-area\", true);\n var measurer = new SVGTypewriter.Measurers.Measurer(labelArea);\n var writer = new SVGTypewriter.Writers.Writer(measurer);\n var dataset = this.datasets()[0];\n var data = this._getDataToDraw().get(dataset);\n data.forEach(function (datum, datumIndex) {\n var value = _this.sectorValue().accessor(datum, datumIndex, dataset);\n if (!Plottable.Utils.Math.isValidNumber(value)) {\n return;\n }\n value = _this._labelFormatter(value);\n var measurement = measurer.measure(value);\n var theta = (_this._endAngles[datumIndex] + _this._startAngles[datumIndex]) / 2;\n var outerRadius = _this.outerRadius().accessor(datum, datumIndex, dataset);\n if (_this.outerRadius().scale) {\n outerRadius = _this.outerRadius().scale.scale(outerRadius);\n }\n var innerRadius = _this.innerRadius().accessor(datum, datumIndex, dataset);\n if (_this.innerRadius().scale) {\n innerRadius = _this.innerRadius().scale.scale(innerRadius);\n }\n var labelRadius = (outerRadius + innerRadius) / 2;\n var x = Math.sin(theta) * labelRadius - measurement.width / 2;\n var y = -Math.cos(theta) * labelRadius - measurement.height / 2;\n var corners = [\n { x: x, y: y },\n { x: x, y: y + measurement.height },\n { x: x + measurement.width, y: y },\n { x: x + measurement.width, y: y + measurement.height },\n ];\n var showLabel = corners.every(function (corner) {\n return Math.abs(corner.x) <= _this.width() / 2 && Math.abs(corner.y) <= _this.height() / 2;\n });\n if (showLabel) {\n var sliceIndices = corners.map(function (corner) { return _this._sliceIndexForPoint(corner); });\n showLabel = sliceIndices.every(function (index) { return index === datumIndex; });\n }\n var color = attrToProjector[\"fill\"](datum, datumIndex, dataset);\n var dark = Plottable.Utils.Color.contrast(\"white\", color) * 1.6 < Plottable.Utils.Color.contrast(\"black\", color);\n var g = labelArea.append(\"g\").attr(\"transform\", \"translate(\" + x + \",\" + y + \")\");\n var className = dark ? \"dark-label\" : \"light-label\";\n g.classed(className, true);\n g.style(\"visibility\", showLabel ? \"inherit\" : \"hidden\");\n writer.write(value, measurement.width, measurement.height, {\n selection: g,\n xAlign: \"center\",\n yAlign: \"center\",\n textRotation: 0,\n });\n });\n };\n Pie._INNER_RADIUS_KEY = \"inner-radius\";\n Pie._OUTER_RADIUS_KEY = \"outer-radius\";\n Pie._SECTOR_VALUE_KEY = \"sector-value\";\n return Pie;\n }(Plottable.Plot));\n Plots.Pie = Pie;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var XYPlot = (function (_super) {\n __extends(XYPlot, _super);\n /**\n * An XYPlot is a Plot that displays data along two primary directions, X and Y.\n *\n * @constructor\n * @param {Scale} xScale The x scale to use.\n * @param {Scale} yScale The y scale to use.\n */\n function XYPlot() {\n var _this = this;\n _super.call(this);\n this._autoAdjustXScaleDomain = false;\n this._autoAdjustYScaleDomain = false;\n this._deferredRendering = false;\n this._cachedDomainX = [null, null];\n this._cachedDomainY = [null, null];\n this.addClass(\"xy-plot\");\n this._adjustYDomainOnChangeFromXCallback = function (scale) { return _this._adjustYDomainOnChangeFromX(); };\n this._adjustXDomainOnChangeFromYCallback = function (scale) { return _this._adjustXDomainOnChangeFromY(); };\n var _deltaX = 0;\n var _deltaY = 0;\n var _scalingX = 1;\n var _scalingY = 1;\n var _lastSeenDomainX = [null, null];\n var _lastSeenDomainY = [null, null];\n var _timeoutReference = 0;\n var _deferredRenderingTimeout = 500;\n var _registerDeferredRendering = function () {\n if (_this._renderArea == null) {\n return;\n }\n _this._renderArea.attr(\"transform\", \"translate(\" + _deltaX + \", \" + _deltaY + \")\" +\n \"scale(\" + _scalingX + \", \" + _scalingY + \")\");\n clearTimeout(_timeoutReference);\n _timeoutReference = setTimeout(function () {\n _this._cachedDomainX = _lastSeenDomainX;\n _this._cachedDomainY = _lastSeenDomainY;\n _deltaX = 0;\n _deltaY = 0;\n _scalingX = 1;\n _scalingY = 1;\n _this.render();\n _this._renderArea.attr(\"transform\", \"translate(0, 0) scale(1, 1)\");\n }, _deferredRenderingTimeout);\n };\n var _lazyDomainChangeCallbackX = function (scale) {\n if (!_this._isAnchored) {\n return;\n }\n _lastSeenDomainX = scale.domain();\n _scalingX = (scale.scale(_this._cachedDomainX[1]) - scale.scale(_this._cachedDomainX[0])) /\n (scale.scale(_lastSeenDomainX[1]) - scale.scale(_lastSeenDomainX[0])) || 1;\n _deltaX = scale.scale(_this._cachedDomainX[0]) - scale.scale(_lastSeenDomainX[0]) || 0;\n _registerDeferredRendering();\n };\n var _lazyDomainChangeCallbackY = function (scale) {\n if (!_this._isAnchored) {\n return;\n }\n _lastSeenDomainY = scale.domain();\n _scalingY = (scale.scale(_this._cachedDomainY[1]) - scale.scale(_this._cachedDomainY[0])) /\n (scale.scale(_lastSeenDomainY[1]) - scale.scale(_lastSeenDomainY[0])) || 1;\n _deltaY = scale.scale(_this._cachedDomainY[0]) - scale.scale(_lastSeenDomainY[0]) * _scalingY || 0;\n _registerDeferredRendering();\n };\n this._renderCallback = function (scale) {\n if (_this.deferredRendering() && _this.x() && _this.x().scale === scale) {\n _lazyDomainChangeCallbackX(scale);\n }\n else if (_this.deferredRendering() && _this.y() && _this.y().scale === scale) {\n _lazyDomainChangeCallbackY(scale);\n }\n else {\n _this.render();\n }\n };\n }\n XYPlot.prototype.deferredRendering = function (deferredRendering) {\n if (deferredRendering == null) {\n return this._deferredRendering;\n }\n if (deferredRendering && this._isAnchored) {\n if (this.x() && this.x().scale) {\n this._cachedDomainX = this.x().scale.domain();\n }\n if (this.y() && this.y().scale) {\n this._cachedDomainY = this.y().scale.domain();\n }\n }\n this._deferredRendering = deferredRendering;\n return this;\n };\n XYPlot.prototype.x = function (x, xScale) {\n if (x == null) {\n return this._propertyBindings.get(XYPlot._X_KEY);\n }\n this._bindProperty(XYPlot._X_KEY, x, xScale);\n var width = this.width();\n if (xScale != null && width != null) {\n xScale.range([0, width]);\n }\n if (this._autoAdjustYScaleDomain) {\n this._updateYExtentsAndAutodomain();\n }\n this.render();\n return this;\n };\n XYPlot.prototype.y = function (y, yScale) {\n if (y == null) {\n return this._propertyBindings.get(XYPlot._Y_KEY);\n }\n this._bindProperty(XYPlot._Y_KEY, y, yScale);\n var height = this.height();\n if (yScale != null && height != null) {\n if (yScale instanceof Plottable.Scales.Category) {\n yScale.range([0, height]);\n }\n else {\n yScale.range([height, 0]);\n }\n }\n if (this._autoAdjustXScaleDomain) {\n this._updateXExtentsAndAutodomain();\n }\n this.render();\n return this;\n };\n XYPlot.prototype._filterForProperty = function (property) {\n if (property === \"x\" && this._autoAdjustXScaleDomain) {\n return this._makeFilterByProperty(\"y\");\n }\n else if (property === \"y\" && this._autoAdjustYScaleDomain) {\n return this._makeFilterByProperty(\"x\");\n }\n return null;\n };\n XYPlot.prototype._makeFilterByProperty = function (property) {\n var binding = this._propertyBindings.get(property);\n if (binding != null) {\n var accessor_1 = binding.accessor;\n var scale_1 = binding.scale;\n if (scale_1 != null) {\n return function (datum, index, dataset) {\n var range = scale_1.range();\n return Plottable.Utils.Math.inRange(scale_1.scale(accessor_1(datum, index, dataset)), range[0], range[1]);\n };\n }\n }\n return null;\n };\n XYPlot.prototype._uninstallScaleForKey = function (scale, key) {\n _super.prototype._uninstallScaleForKey.call(this, scale, key);\n var adjustCallback = key === XYPlot._X_KEY ? this._adjustYDomainOnChangeFromXCallback\n : this._adjustXDomainOnChangeFromYCallback;\n scale.offUpdate(adjustCallback);\n };\n XYPlot.prototype._installScaleForKey = function (scale, key) {\n _super.prototype._installScaleForKey.call(this, scale, key);\n var adjustCallback = key === XYPlot._X_KEY ? this._adjustYDomainOnChangeFromXCallback\n : this._adjustXDomainOnChangeFromYCallback;\n scale.onUpdate(adjustCallback);\n };\n XYPlot.prototype.destroy = function () {\n _super.prototype.destroy.call(this);\n if (this.x().scale) {\n this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback);\n }\n if (this.y().scale) {\n this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback);\n }\n return this;\n };\n XYPlot.prototype.autorangeMode = function (autorangeMode) {\n if (autorangeMode == null) {\n if (this._autoAdjustXScaleDomain) {\n return \"x\";\n }\n if (this._autoAdjustYScaleDomain) {\n return \"y\";\n }\n return \"none\";\n }\n switch (autorangeMode) {\n case \"x\":\n this._autoAdjustXScaleDomain = true;\n this._autoAdjustYScaleDomain = false;\n this._adjustXDomainOnChangeFromY();\n break;\n case \"y\":\n this._autoAdjustXScaleDomain = false;\n this._autoAdjustYScaleDomain = true;\n this._adjustYDomainOnChangeFromX();\n break;\n case \"none\":\n this._autoAdjustXScaleDomain = false;\n this._autoAdjustYScaleDomain = false;\n break;\n default:\n throw new Error(\"Invalid scale name '\" + autorangeMode + \"', must be 'x', 'y' or 'none'\");\n }\n return this;\n };\n XYPlot.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n var xBinding = this.x();\n var xScale = xBinding && xBinding.scale;\n if (xScale != null) {\n xScale.range([0, this.width()]);\n }\n var yBinding = this.y();\n var yScale = yBinding && yBinding.scale;\n if (yScale != null) {\n if (yScale instanceof Plottable.Scales.Category) {\n yScale.range([0, this.height()]);\n }\n else {\n yScale.range([this.height(), 0]);\n }\n }\n return this;\n };\n XYPlot.prototype._updateXExtentsAndAutodomain = function () {\n this._updateExtentsForProperty(\"x\");\n var xScale = this.x().scale;\n if (xScale != null) {\n xScale.autoDomain();\n }\n };\n XYPlot.prototype._updateYExtentsAndAutodomain = function () {\n this._updateExtentsForProperty(\"y\");\n var yScale = this.y().scale;\n if (yScale != null) {\n yScale.autoDomain();\n }\n };\n /**\n * Adjusts the domains of both X and Y scales to show all data.\n * This call does not override the autorange() behavior.\n *\n * @returns {XYPlot} The calling XYPlot.\n */\n XYPlot.prototype.showAllData = function () {\n this._updateXExtentsAndAutodomain();\n this._updateYExtentsAndAutodomain();\n return this;\n };\n XYPlot.prototype._adjustYDomainOnChangeFromX = function () {\n if (!this._projectorsReady()) {\n return;\n }\n if (this._autoAdjustYScaleDomain) {\n this._updateYExtentsAndAutodomain();\n }\n };\n XYPlot.prototype._adjustXDomainOnChangeFromY = function () {\n if (!this._projectorsReady()) {\n return;\n }\n if (this._autoAdjustXScaleDomain) {\n this._updateXExtentsAndAutodomain();\n }\n };\n XYPlot.prototype._projectorsReady = function () {\n var xBinding = this.x();\n var yBinding = this.y();\n return xBinding != null &&\n xBinding.accessor != null &&\n yBinding != null &&\n yBinding.accessor != null;\n };\n XYPlot.prototype._pixelPoint = function (datum, index, dataset) {\n var xProjector = Plottable.Plot._scaledAccessor(this.x());\n var yProjector = Plottable.Plot._scaledAccessor(this.y());\n return { x: xProjector(datum, index, dataset), y: yProjector(datum, index, dataset) };\n };\n XYPlot.prototype._getDataToDraw = function () {\n var _this = this;\n var dataToDraw = _super.prototype._getDataToDraw.call(this);\n var definedFunction = function (d, i, dataset) {\n var positionX = Plottable.Plot._scaledAccessor(_this.x())(d, i, dataset);\n var positionY = Plottable.Plot._scaledAccessor(_this.y())(d, i, dataset);\n return Plottable.Utils.Math.isValidNumber(positionX) &&\n Plottable.Utils.Math.isValidNumber(positionY);\n };\n this.datasets().forEach(function (dataset) {\n dataToDraw.set(dataset, dataToDraw.get(dataset).filter(function (d, i) { return definedFunction(d, i, dataset); }));\n });\n return dataToDraw;\n };\n XYPlot._X_KEY = \"x\";\n XYPlot._Y_KEY = \"y\";\n return XYPlot;\n }(Plottable.Plot));\n Plottable.XYPlot = XYPlot;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Rectangle = (function (_super) {\n __extends(Rectangle, _super);\n /**\n * A Rectangle Plot displays rectangles based on the data.\n * The left and right edges of each rectangle can be set with x() and x2().\n * If only x() is set the Rectangle Plot will attempt to compute the correct left and right edge positions.\n * The top and bottom edges of each rectangle can be set with y() and y2().\n * If only y() is set the Rectangle Plot will attempt to compute the correct top and bottom edge positions.\n *\n * @constructor\n * @param {Scale.Scale} xScale\n * @param {Scale.Scale} yScale\n */\n function Rectangle() {\n _super.call(this);\n this._labelsEnabled = false;\n this._label = null;\n this.animator(\"rectangles\", new Plottable.Animators.Null());\n this.addClass(\"rectangle-plot\");\n this.attr(\"fill\", new Plottable.Scales.Color().range()[0]);\n }\n Rectangle.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Rectangle(dataset);\n };\n Rectangle.prototype._generateAttrToProjector = function () {\n var _this = this;\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n // Copy each of the different projectors.\n var xAttr = Plottable.Plot._scaledAccessor(this.x());\n var x2Attr = attrToProjector[Rectangle._X2_KEY];\n var yAttr = Plottable.Plot._scaledAccessor(this.y());\n var y2Attr = attrToProjector[Rectangle._Y2_KEY];\n var xScale = this.x().scale;\n var yScale = this.y().scale;\n if (x2Attr != null) {\n attrToProjector[\"width\"] = function (d, i, dataset) { return Math.abs(x2Attr(d, i, dataset) - xAttr(d, i, dataset)); };\n attrToProjector[\"x\"] = function (d, i, dataset) { return Math.min(x2Attr(d, i, dataset), xAttr(d, i, dataset)); };\n }\n else {\n attrToProjector[\"width\"] = function (d, i, dataset) { return _this._rectangleWidth(xScale); };\n attrToProjector[\"x\"] = function (d, i, dataset) { return xAttr(d, i, dataset) - 0.5 * attrToProjector[\"width\"](d, i, dataset); };\n }\n if (y2Attr != null) {\n attrToProjector[\"height\"] = function (d, i, dataset) { return Math.abs(y2Attr(d, i, dataset) - yAttr(d, i, dataset)); };\n attrToProjector[\"y\"] = function (d, i, dataset) {\n return Math.max(y2Attr(d, i, dataset), yAttr(d, i, dataset)) - attrToProjector[\"height\"](d, i, dataset);\n };\n }\n else {\n attrToProjector[\"height\"] = function (d, i, dataset) { return _this._rectangleWidth(yScale); };\n attrToProjector[\"y\"] = function (d, i, dataset) { return yAttr(d, i, dataset) - 0.5 * attrToProjector[\"height\"](d, i, dataset); };\n }\n // Clean up the attributes projected onto the SVG elements\n delete attrToProjector[Rectangle._X2_KEY];\n delete attrToProjector[Rectangle._Y2_KEY];\n return attrToProjector;\n };\n Rectangle.prototype._generateDrawSteps = function () {\n return [{ attrToProjector: this._generateAttrToProjector(), animator: this._getAnimator(\"rectangles\") }];\n };\n Rectangle.prototype._updateExtentsForProperty = function (property) {\n _super.prototype._updateExtentsForProperty.call(this, property);\n if (property === \"x\") {\n _super.prototype._updateExtentsForProperty.call(this, \"x2\");\n }\n else if (property === \"y\") {\n _super.prototype._updateExtentsForProperty.call(this, \"y2\");\n }\n };\n Rectangle.prototype._filterForProperty = function (property) {\n if (property === \"x2\") {\n return _super.prototype._filterForProperty.call(this, \"x\");\n }\n else if (property === \"y2\") {\n return _super.prototype._filterForProperty.call(this, \"y\");\n }\n return _super.prototype._filterForProperty.call(this, property);\n };\n Rectangle.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n }\n if (xScale != null) {\n var x2Binding = this.x2();\n var x2 = x2Binding && x2Binding.accessor;\n if (x2 != null) {\n this._bindProperty(Rectangle._X2_KEY, x2, xScale);\n }\n }\n // The x and y scales should render in bands with no padding for category scales\n if (xScale instanceof Plottable.Scales.Category) {\n xScale.innerPadding(0).outerPadding(0);\n }\n return this;\n };\n Rectangle.prototype.x2 = function (x2) {\n if (x2 == null) {\n return this._propertyBindings.get(Rectangle._X2_KEY);\n }\n var xBinding = this.x();\n var xScale = xBinding && xBinding.scale;\n this._bindProperty(Rectangle._X2_KEY, x2, xScale);\n this.render();\n return this;\n };\n Rectangle.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n }\n if (yScale != null) {\n var y2Binding = this.y2();\n var y2 = y2Binding && y2Binding.accessor;\n if (y2 != null) {\n this._bindProperty(Rectangle._Y2_KEY, y2, yScale);\n }\n }\n // The x and y scales should render in bands with no padding for category scales\n if (yScale instanceof Plottable.Scales.Category) {\n yScale.innerPadding(0).outerPadding(0);\n }\n return this;\n };\n Rectangle.prototype.y2 = function (y2) {\n if (y2 == null) {\n return this._propertyBindings.get(Rectangle._Y2_KEY);\n }\n var yBinding = this.y();\n var yScale = yBinding && yBinding.scale;\n this._bindProperty(Rectangle._Y2_KEY, y2, yScale);\n this.render();\n return this;\n };\n /**\n * Gets the PlotEntities at a particular Point.\n *\n * @param {Point} point The point to query.\n * @returns {PlotEntity[]} The PlotEntities at the particular point\n */\n Rectangle.prototype.entitiesAt = function (point) {\n var attrToProjector = this._generateAttrToProjector();\n return this.entities().filter(function (entity) {\n var datum = entity.datum;\n var index = entity.index;\n var dataset = entity.dataset;\n var x = attrToProjector[\"x\"](datum, index, dataset);\n var y = attrToProjector[\"y\"](datum, index, dataset);\n var width = attrToProjector[\"width\"](datum, index, dataset);\n var height = attrToProjector[\"height\"](datum, index, dataset);\n return x <= point.x && point.x <= x + width && y <= point.y && point.y <= y + height;\n });\n };\n Rectangle.prototype.entitiesIn = function (xRangeOrBounds, yRange) {\n var dataXRange;\n var dataYRange;\n if (yRange == null) {\n var bounds = xRangeOrBounds;\n dataXRange = { min: bounds.topLeft.x, max: bounds.bottomRight.x };\n dataYRange = { min: bounds.topLeft.y, max: bounds.bottomRight.y };\n }\n else {\n dataXRange = xRangeOrBounds;\n dataYRange = yRange;\n }\n return this._entitiesIntersecting(dataXRange, dataYRange);\n };\n Rectangle.prototype._entityBBox = function (datum, index, dataset, attrToProjector) {\n return {\n x: attrToProjector[\"x\"](datum, index, dataset),\n y: attrToProjector[\"y\"](datum, index, dataset),\n width: attrToProjector[\"width\"](datum, index, dataset),\n height: attrToProjector[\"height\"](datum, index, dataset),\n };\n };\n Rectangle.prototype._entitiesIntersecting = function (xValOrRange, yValOrRange) {\n var _this = this;\n var intersected = [];\n var attrToProjector = this._generateAttrToProjector();\n this.entities().forEach(function (entity) {\n if (Plottable.Utils.DOM.intersectsBBox(xValOrRange, yValOrRange, _this._entityBBox(entity.datum, entity.index, entity.dataset, attrToProjector))) {\n intersected.push(entity);\n }\n });\n return intersected;\n };\n Rectangle.prototype.label = function (label) {\n if (label == null) {\n return this._label;\n }\n this._label = label;\n this.render();\n return this;\n };\n Rectangle.prototype.labelsEnabled = function (enabled) {\n if (enabled == null) {\n return this._labelsEnabled;\n }\n else {\n this._labelsEnabled = enabled;\n this.render();\n return this;\n }\n };\n Rectangle.prototype._propertyProjectors = function () {\n var attrToProjector = _super.prototype._propertyProjectors.call(this);\n if (this.x2() != null) {\n attrToProjector[\"x2\"] = Plottable.Plot._scaledAccessor(this.x2());\n }\n if (this.y2() != null) {\n attrToProjector[\"y2\"] = Plottable.Plot._scaledAccessor(this.y2());\n }\n return attrToProjector;\n };\n Rectangle.prototype._pixelPoint = function (datum, index, dataset) {\n var attrToProjector = this._generateAttrToProjector();\n var rectX = attrToProjector[\"x\"](datum, index, dataset);\n var rectY = attrToProjector[\"y\"](datum, index, dataset);\n var rectWidth = attrToProjector[\"width\"](datum, index, dataset);\n var rectHeight = attrToProjector[\"height\"](datum, index, dataset);\n var x = rectX + rectWidth / 2;\n var y = rectY + rectHeight / 2;\n return { x: x, y: y };\n };\n Rectangle.prototype._rectangleWidth = function (scale) {\n if (scale instanceof Plottable.Scales.Category) {\n return scale.rangeBand();\n }\n else {\n var accessor_2 = scale === this.x().scale ? this.x().accessor : this.y().accessor;\n var accessorData = d3.set(Plottable.Utils.Array.flatten(this.datasets().map(function (dataset) {\n return dataset.data().map(function (d, i) { return accessor_2(d, i, dataset).valueOf(); });\n }))).values().map(function (value) { return +value; });\n // Get the absolute difference between min and max\n var min = Plottable.Utils.Math.min(accessorData, 0);\n var max = Plottable.Utils.Math.max(accessorData, 0);\n var scaledMin = scale.scale(min);\n var scaledMax = scale.scale(max);\n return (scaledMax - scaledMin) / Math.abs(max - min);\n }\n };\n Rectangle.prototype._getDataToDraw = function () {\n var dataToDraw = new Plottable.Utils.Map();\n var attrToProjector = this._generateAttrToProjector();\n this.datasets().forEach(function (dataset) {\n var data = dataset.data().filter(function (d, i) { return Plottable.Utils.Math.isValidNumber(attrToProjector[\"x\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"y\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"width\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"height\"](d, i, dataset)); });\n dataToDraw.set(dataset, data);\n });\n return dataToDraw;\n };\n Rectangle.prototype._additionalPaint = function (time) {\n var _this = this;\n this._renderArea.selectAll(\".label-area\").remove();\n if (this._labelsEnabled && this.label() != null) {\n Plottable.Utils.Window.setTimeout(function () { return _this._drawLabels(); }, time);\n }\n };\n Rectangle.prototype._drawLabels = function () {\n var _this = this;\n var dataToDraw = this._getDataToDraw();\n this.datasets().forEach(function (dataset, i) { return _this._drawLabel(dataToDraw, dataset, i); });\n };\n Rectangle.prototype._drawLabel = function (dataToDraw, dataset, datasetIndex) {\n var _this = this;\n var attrToProjector = this._generateAttrToProjector();\n var labelArea = this._renderArea.append(\"g\").classed(\"label-area\", true);\n var measurer = new SVGTypewriter.Measurers.Measurer(labelArea);\n var writer = new SVGTypewriter.Writers.Writer(measurer);\n var xRange = this.x().scale.range();\n var yRange = this.y().scale.range();\n var xMin = Math.min.apply(null, xRange);\n var xMax = Math.max.apply(null, xRange);\n var yMin = Math.min.apply(null, yRange);\n var yMax = Math.max.apply(null, yRange);\n var data = dataToDraw.get(dataset);\n data.forEach(function (datum, datumIndex) {\n var label = \"\" + _this.label()(datum, datumIndex, dataset);\n var measurement = measurer.measure(label);\n var x = attrToProjector[\"x\"](datum, datumIndex, dataset);\n var y = attrToProjector[\"y\"](datum, datumIndex, dataset);\n var width = attrToProjector[\"width\"](datum, datumIndex, dataset);\n var height = attrToProjector[\"height\"](datum, datumIndex, dataset);\n if (measurement.height <= height && measurement.width <= width) {\n var horizontalOffset = (width - measurement.width) / 2;\n var verticalOffset = (height - measurement.height) / 2;\n x += horizontalOffset;\n y += verticalOffset;\n var xLabelRange = { min: x, max: x + measurement.width };\n var yLabelRange = { min: y, max: y + measurement.height };\n if (xLabelRange.min < xMin || xLabelRange.max > xMax || yLabelRange.min < yMin || yLabelRange.max > yMax) {\n return;\n }\n if (_this._overlayLabel(xLabelRange, yLabelRange, datumIndex, datasetIndex, dataToDraw)) {\n return;\n }\n var color = attrToProjector[\"fill\"](datum, datumIndex, dataset);\n var dark = Plottable.Utils.Color.contrast(\"white\", color) * 1.6 < Plottable.Utils.Color.contrast(\"black\", color);\n var g = labelArea.append(\"g\").attr(\"transform\", \"translate(\" + x + \",\" + y + \")\");\n var className = dark ? \"dark-label\" : \"light-label\";\n g.classed(className, true);\n writer.write(label, measurement.width, measurement.height, {\n selection: g,\n xAlign: \"center\",\n yAlign: \"center\",\n textRotation: 0,\n });\n }\n });\n };\n Rectangle.prototype._overlayLabel = function (labelXRange, labelYRange, datumIndex, datasetIndex, dataToDraw) {\n var attrToProjector = this._generateAttrToProjector();\n var datasets = this.datasets();\n for (var i = datasetIndex; i < datasets.length; i++) {\n var dataset = datasets[i];\n var data = dataToDraw.get(dataset);\n for (var j = (i === datasetIndex ? datumIndex + 1 : 0); j < data.length; j++) {\n if (Plottable.Utils.DOM.intersectsBBox(labelXRange, labelYRange, this._entityBBox(data[j], j, dataset, attrToProjector))) {\n return true;\n }\n }\n }\n return false;\n };\n Rectangle._X2_KEY = \"x2\";\n Rectangle._Y2_KEY = \"y2\";\n return Rectangle;\n }(Plottable.XYPlot));\n Plots.Rectangle = Rectangle;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Scatter = (function (_super) {\n __extends(Scatter, _super);\n /**\n * A Scatter Plot draws a symbol at each data point.\n *\n * @constructor\n */\n function Scatter() {\n _super.call(this);\n this.addClass(\"scatter-plot\");\n var animator = new Plottable.Animators.Easing();\n animator.startDelay(5);\n animator.stepDuration(250);\n animator.maxTotalDuration(Plottable.Plot._ANIMATION_MAX_DURATION);\n this.animator(Plots.Animator.MAIN, animator);\n this.attr(\"opacity\", 0.6);\n this.attr(\"fill\", new Plottable.Scales.Color().range()[0]);\n this.size(6);\n var circleSymbolFactory = Plottable.SymbolFactories.circle();\n this.symbol(function () { return circleSymbolFactory; });\n }\n Scatter.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Symbol(dataset);\n };\n Scatter.prototype.size = function (size, scale) {\n if (size == null) {\n return this._propertyBindings.get(Scatter._SIZE_KEY);\n }\n this._bindProperty(Scatter._SIZE_KEY, size, scale);\n this.render();\n return this;\n };\n Scatter.prototype.symbol = function (symbol) {\n if (symbol == null) {\n return this._propertyBindings.get(Scatter._SYMBOL_KEY);\n }\n this._propertyBindings.set(Scatter._SYMBOL_KEY, { accessor: symbol });\n this.render();\n return this;\n };\n Scatter.prototype._generateDrawSteps = function () {\n var drawSteps = [];\n if (this._animateOnNextRender()) {\n var resetAttrToProjector = this._generateAttrToProjector();\n var symbolProjector_1 = Plottable.Plot._scaledAccessor(this.symbol());\n resetAttrToProjector[\"d\"] = function (datum, index, dataset) { return symbolProjector_1(datum, index, dataset)(0); };\n drawSteps.push({ attrToProjector: resetAttrToProjector, animator: this._getAnimator(Plots.Animator.RESET) });\n }\n drawSteps.push({ attrToProjector: this._generateAttrToProjector(), animator: this._getAnimator(Plots.Animator.MAIN) });\n return drawSteps;\n };\n Scatter.prototype._entityVisibleOnPlot = function (pixelPoint, datum, index, dataset) {\n var xRange = { min: 0, max: this.width() };\n var yRange = { min: 0, max: this.height() };\n var diameter = Plottable.Plot._scaledAccessor(this.size())(datum, index, dataset);\n var translatedBbox = {\n x: pixelPoint.x - diameter,\n y: pixelPoint.y - diameter,\n width: diameter,\n height: diameter,\n };\n return Plottable.Utils.DOM.intersectsBBox(xRange, yRange, translatedBbox);\n };\n Scatter.prototype._propertyProjectors = function () {\n var propertyToProjectors = _super.prototype._propertyProjectors.call(this);\n var xProjector = Plottable.Plot._scaledAccessor(this.x());\n var yProjector = Plottable.Plot._scaledAccessor(this.y());\n var sizeProjector = Plottable.Plot._scaledAccessor(this.size());\n propertyToProjectors[\"transform\"] = function (datum, index, dataset) {\n return \"translate(\" + xProjector(datum, index, dataset) + \",\" + yProjector(datum, index, dataset) + \")\";\n };\n var symbolProjector = Plottable.Plot._scaledAccessor(this.symbol());\n propertyToProjectors[\"d\"] = function (datum, index, dataset) {\n return symbolProjector(datum, index, dataset)(sizeProjector(datum, index, dataset));\n };\n return propertyToProjectors;\n };\n Scatter.prototype.entitiesIn = function (xRangeOrBounds, yRange) {\n var dataXRange;\n var dataYRange;\n if (yRange == null) {\n var bounds = xRangeOrBounds;\n dataXRange = { min: bounds.topLeft.x, max: bounds.bottomRight.x };\n dataYRange = { min: bounds.topLeft.y, max: bounds.bottomRight.y };\n }\n else {\n dataXRange = xRangeOrBounds;\n dataYRange = yRange;\n }\n var xProjector = Plottable.Plot._scaledAccessor(this.x());\n var yProjector = Plottable.Plot._scaledAccessor(this.y());\n return this.entities().filter(function (entity) {\n var datum = entity.datum;\n var index = entity.index;\n var dataset = entity.dataset;\n var x = xProjector(datum, index, dataset);\n var y = yProjector(datum, index, dataset);\n return dataXRange.min <= x && x <= dataXRange.max && dataYRange.min <= y && y <= dataYRange.max;\n });\n };\n /**\n * Gets the Entities at a particular Point.\n *\n * @param {Point} p\n * @returns {PlotEntity[]}\n */\n Scatter.prototype.entitiesAt = function (p) {\n var xProjector = Plottable.Plot._scaledAccessor(this.x());\n var yProjector = Plottable.Plot._scaledAccessor(this.y());\n var sizeProjector = Plottable.Plot._scaledAccessor(this.size());\n return this.entities().filter(function (entity) {\n var datum = entity.datum;\n var index = entity.index;\n var dataset = entity.dataset;\n var x = xProjector(datum, index, dataset);\n var y = yProjector(datum, index, dataset);\n var size = sizeProjector(datum, index, dataset);\n return x - size / 2 <= p.x && p.x <= x + size / 2 && y - size / 2 <= p.y && p.y <= y + size / 2;\n });\n };\n Scatter._SIZE_KEY = \"size\";\n Scatter._SYMBOL_KEY = \"symbol\";\n return Scatter;\n }(Plottable.XYPlot));\n Plots.Scatter = Scatter;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Bar = (function (_super) {\n __extends(Bar, _super);\n /**\n * A Bar Plot draws bars growing out from a baseline to some value\n *\n * @constructor\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n function Bar(orientation) {\n var _this = this;\n if (orientation === void 0) { orientation = Bar.ORIENTATION_VERTICAL; }\n _super.call(this);\n this._labelFormatter = Plottable.Formatters.identity();\n this._labelsEnabled = false;\n this._hideBarsIfAnyAreTooWide = true;\n this._barPixelWidth = 0;\n this.addClass(\"bar-plot\");\n if (orientation !== Bar.ORIENTATION_VERTICAL && orientation !== Bar.ORIENTATION_HORIZONTAL) {\n throw new Error(orientation + \" is not a valid orientation for Plots.Bar\");\n }\n this._isVertical = orientation === Bar.ORIENTATION_VERTICAL;\n this.animator(\"baseline\", new Plottable.Animators.Null());\n this.attr(\"fill\", new Plottable.Scales.Color().range()[0]);\n this.attr(\"width\", function () { return _this._barPixelWidth; });\n this._labelConfig = new Plottable.Utils.Map();\n this._baselineValueProvider = function () { return [_this.baselineValue()]; };\n this._updateBarPixelWidthCallback = function () { return _this._updateBarPixelWidth(); };\n }\n Bar.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n xScale.onUpdate(this._updateBarPixelWidthCallback);\n }\n this._updateValueScale();\n return this;\n };\n Bar.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n yScale.onUpdate(this._updateBarPixelWidthCallback);\n }\n this._updateValueScale();\n return this;\n };\n /**\n * Gets the orientation of the plot\n *\n * @return \"vertical\" | \"horizontal\"\n */\n Bar.prototype.orientation = function () {\n return this._isVertical ? Bar.ORIENTATION_VERTICAL : Bar.ORIENTATION_HORIZONTAL;\n };\n Bar.prototype.render = function () {\n this._updateBarPixelWidth();\n this._updateExtents();\n _super.prototype.render.call(this);\n return this;\n };\n Bar.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Rectangle(dataset);\n };\n Bar.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._baseline = this._renderArea.append(\"line\").classed(\"baseline\", true);\n };\n Bar.prototype.baselineValue = function (value) {\n if (value == null) {\n if (this._baselineValue != null) {\n return this._baselineValue;\n }\n if (!this._projectorsReady()) {\n return 0;\n }\n var valueScale = this._isVertical ? this.y().scale : this.x().scale;\n if (!valueScale) {\n return 0;\n }\n if (valueScale instanceof Plottable.Scales.Time) {\n return new Date(0);\n }\n return 0;\n }\n this._baselineValue = value;\n this._updateValueScale();\n this.render();\n return this;\n };\n Bar.prototype.addDataset = function (dataset) {\n _super.prototype.addDataset.call(this, dataset);\n this._updateBarPixelWidth();\n return this;\n };\n Bar.prototype._addDataset = function (dataset) {\n dataset.onUpdate(this._updateBarPixelWidthCallback);\n _super.prototype._addDataset.call(this, dataset);\n return this;\n };\n Bar.prototype.removeDataset = function (dataset) {\n dataset.offUpdate(this._updateBarPixelWidthCallback);\n _super.prototype.removeDataset.call(this, dataset);\n this._updateBarPixelWidth();\n return this;\n };\n Bar.prototype._removeDataset = function (dataset) {\n dataset.offUpdate(this._updateBarPixelWidthCallback);\n _super.prototype._removeDataset.call(this, dataset);\n return this;\n };\n Bar.prototype.datasets = function (datasets) {\n if (datasets == null) {\n return _super.prototype.datasets.call(this);\n }\n _super.prototype.datasets.call(this, datasets);\n this._updateBarPixelWidth();\n return this;\n };\n Bar.prototype.labelsEnabled = function (enabled) {\n if (enabled == null) {\n return this._labelsEnabled;\n }\n else {\n this._labelsEnabled = enabled;\n this.render();\n return this;\n }\n };\n Bar.prototype.labelFormatter = function (formatter) {\n if (formatter == null) {\n return this._labelFormatter;\n }\n else {\n this._labelFormatter = formatter;\n this.render();\n return this;\n }\n };\n Bar.prototype._createNodesForDataset = function (dataset) {\n var drawer = _super.prototype._createNodesForDataset.call(this, dataset);\n drawer.renderArea().classed(Bar._BAR_AREA_CLASS, true);\n var labelArea = this._renderArea.append(\"g\").classed(Bar._LABEL_AREA_CLASS, true);\n var measurer = new SVGTypewriter.Measurers.CacheCharacterMeasurer(labelArea);\n var writer = new SVGTypewriter.Writers.Writer(measurer);\n this._labelConfig.set(dataset, { labelArea: labelArea, measurer: measurer, writer: writer });\n return drawer;\n };\n Bar.prototype._removeDatasetNodes = function (dataset) {\n _super.prototype._removeDatasetNodes.call(this, dataset);\n var labelConfig = this._labelConfig.get(dataset);\n if (labelConfig != null) {\n labelConfig.labelArea.remove();\n this._labelConfig.delete(dataset);\n }\n };\n /**\n * Returns the PlotEntity nearest to the query point according to the following algorithm:\n * - If the query point is inside a bar, returns the PlotEntity for that bar.\n * - Otherwise, gets the nearest PlotEntity by the primary direction (X for vertical, Y for horizontal),\n * breaking ties with the secondary direction.\n * Returns undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n Bar.prototype.entityNearest = function (queryPoint) {\n var _this = this;\n var minPrimaryDist = Infinity;\n var minSecondaryDist = Infinity;\n var queryPtPrimary = this._isVertical ? queryPoint.x : queryPoint.y;\n var queryPtSecondary = this._isVertical ? queryPoint.y : queryPoint.x;\n // SVGRects are positioned with sub-pixel accuracy (the default unit\n // for the x, y, height & width attributes), but user selections (e.g. via\n // mouse events) usually have pixel accuracy. We add a tolerance of 0.5 pixels.\n var tolerance = 0.5;\n var closest;\n this.entities().forEach(function (entity) {\n if (!_this._entityVisibleOnPlot(entity.position, entity.datum, entity.index, entity.dataset)) {\n return;\n }\n var primaryDist = 0;\n var secondaryDist = 0;\n var plotPt = entity.position;\n // if we're inside a bar, distance in both directions should stay 0\n var barBBox = Plottable.Utils.DOM.elementBBox(entity.selection);\n if (!Plottable.Utils.DOM.intersectsBBox(queryPoint.x, queryPoint.y, barBBox, tolerance)) {\n var plotPtPrimary = _this._isVertical ? plotPt.x : plotPt.y;\n primaryDist = Math.abs(queryPtPrimary - plotPtPrimary);\n // compute this bar's min and max along the secondary axis\n var barMinSecondary = _this._isVertical ? barBBox.y : barBBox.x;\n var barMaxSecondary = barMinSecondary + (_this._isVertical ? barBBox.height : barBBox.width);\n if (queryPtSecondary >= barMinSecondary - tolerance && queryPtSecondary <= barMaxSecondary + tolerance) {\n // if we're within a bar's secondary axis span, it is closest in that direction\n secondaryDist = 0;\n }\n else {\n var plotPtSecondary = _this._isVertical ? plotPt.y : plotPt.x;\n secondaryDist = Math.abs(queryPtSecondary - plotPtSecondary);\n }\n }\n // if we find a closer bar, record its distance and start new closest lists\n if (primaryDist < minPrimaryDist\n || primaryDist === minPrimaryDist && secondaryDist < minSecondaryDist) {\n closest = entity;\n minPrimaryDist = primaryDist;\n minSecondaryDist = secondaryDist;\n }\n });\n return closest;\n };\n Bar.prototype._entityVisibleOnPlot = function (pixelPoint, datum, index, dataset) {\n var xRange = { min: 0, max: this.width() };\n var yRange = { min: 0, max: this.height() };\n var attrToProjector = this._generateAttrToProjector();\n var barBBox = {\n x: attrToProjector[\"x\"](datum, index, dataset),\n y: attrToProjector[\"y\"](datum, index, dataset),\n width: attrToProjector[\"width\"](datum, index, dataset),\n height: attrToProjector[\"height\"](datum, index, dataset),\n };\n return Plottable.Utils.DOM.intersectsBBox(xRange, yRange, barBBox);\n };\n /**\n * Gets the Entities at a particular Point.\n *\n * @param {Point} p\n * @returns {PlotEntity[]}\n */\n Bar.prototype.entitiesAt = function (p) {\n return this._entitiesIntersecting(p.x, p.y);\n };\n Bar.prototype.entitiesIn = function (xRangeOrBounds, yRange) {\n var dataXRange;\n var dataYRange;\n if (yRange == null) {\n var bounds = xRangeOrBounds;\n dataXRange = { min: bounds.topLeft.x, max: bounds.bottomRight.x };\n dataYRange = { min: bounds.topLeft.y, max: bounds.bottomRight.y };\n }\n else {\n dataXRange = xRangeOrBounds;\n dataYRange = yRange;\n }\n return this._entitiesIntersecting(dataXRange, dataYRange);\n };\n Bar.prototype._entitiesIntersecting = function (xValOrRange, yValOrRange) {\n var intersected = [];\n this.entities().forEach(function (entity) {\n if (Plottable.Utils.DOM.intersectsBBox(xValOrRange, yValOrRange, Plottable.Utils.DOM.elementBBox(entity.selection))) {\n intersected.push(entity);\n }\n });\n return intersected;\n };\n Bar.prototype._updateValueScale = function () {\n if (!this._projectorsReady()) {\n return;\n }\n var valueScale = this._isVertical ? this.y().scale : this.x().scale;\n if (valueScale instanceof Plottable.QuantitativeScale) {\n var qscale = valueScale;\n qscale.addPaddingExceptionsProvider(this._baselineValueProvider);\n qscale.addIncludedValuesProvider(this._baselineValueProvider);\n }\n };\n Bar.prototype._additionalPaint = function (time) {\n var _this = this;\n var primaryScale = this._isVertical ? this.y().scale : this.x().scale;\n var scaledBaseline = primaryScale.scale(this.baselineValue());\n var baselineAttr = {\n \"x1\": this._isVertical ? 0 : scaledBaseline,\n \"y1\": this._isVertical ? scaledBaseline : 0,\n \"x2\": this._isVertical ? this.width() : scaledBaseline,\n \"y2\": this._isVertical ? scaledBaseline : this.height(),\n };\n this._getAnimator(\"baseline\").animate(this._baseline, baselineAttr);\n this.datasets().forEach(function (dataset) { return _this._labelConfig.get(dataset).labelArea.selectAll(\"g\").remove(); });\n if (this._labelsEnabled) {\n Plottable.Utils.Window.setTimeout(function () { return _this._drawLabels(); }, time);\n }\n };\n /**\n * Makes sure the extent takes into account the widths of the bars\n */\n Bar.prototype._extentsForProperty = function (property) {\n var _this = this;\n var extents = _super.prototype._extentsForProperty.call(this, property);\n var accScaleBinding;\n if (property === \"x\" && this._isVertical) {\n accScaleBinding = this.x();\n }\n else if (property === \"y\" && !this._isVertical) {\n accScaleBinding = this.y();\n }\n else {\n return extents;\n }\n if (!(accScaleBinding && accScaleBinding.scale && accScaleBinding.scale instanceof Plottable.QuantitativeScale)) {\n return extents;\n }\n var scale = accScaleBinding.scale;\n // To account for inverted domains\n extents = extents.map(function (extent) { return d3.extent([\n scale.invert(scale.scale(extent[0]) - _this._barPixelWidth / 2),\n scale.invert(scale.scale(extent[0]) + _this._barPixelWidth / 2),\n scale.invert(scale.scale(extent[1]) - _this._barPixelWidth / 2),\n scale.invert(scale.scale(extent[1]) + _this._barPixelWidth / 2),\n ]); });\n return extents;\n };\n Bar.prototype._drawLabels = function () {\n var _this = this;\n var dataToDraw = this._getDataToDraw();\n var labelsTooWide = false;\n this.datasets().forEach(function (dataset) { return labelsTooWide = labelsTooWide || _this._drawLabel(dataToDraw.get(dataset), dataset); });\n if (this._hideBarsIfAnyAreTooWide && labelsTooWide) {\n this.datasets().forEach(function (dataset) { return _this._labelConfig.get(dataset).labelArea.selectAll(\"g\").remove(); });\n }\n };\n Bar.prototype._drawLabel = function (data, dataset) {\n var _this = this;\n var attrToProjector = this._generateAttrToProjector();\n var labelConfig = this._labelConfig.get(dataset);\n var labelArea = labelConfig.labelArea;\n var measurer = labelConfig.measurer;\n var writer = labelConfig.writer;\n var drawLabel = function (d, i) {\n var valueAccessor = _this._isVertical ? _this.y().accessor : _this.x().accessor;\n var value = valueAccessor(d, i, dataset);\n var valueScale = _this._isVertical ? _this.y().scale : _this.x().scale;\n var scaledValue = valueScale != null ? valueScale.scale(value) : value;\n var scaledBaseline = valueScale != null ? valueScale.scale(_this.baselineValue()) : _this.baselineValue();\n var barWidth = attrToProjector[\"width\"](d, i, dataset);\n var barHeight = attrToProjector[\"height\"](d, i, dataset);\n var text = _this._labelFormatter(valueAccessor(d, i, dataset));\n var measurement = measurer.measure(text);\n var xAlignment = \"center\";\n var yAlignment = \"center\";\n var labelContainerOrigin = {\n x: attrToProjector[\"x\"](d, i, dataset),\n y: attrToProjector[\"y\"](d, i, dataset),\n };\n var containerWidth = barWidth;\n var containerHeight = barHeight;\n var labelOrigin = {\n x: labelContainerOrigin.x,\n y: labelContainerOrigin.y,\n };\n var showLabelOnBar;\n if (_this._isVertical) {\n labelOrigin.x += containerWidth / 2 - measurement.width / 2;\n var barY = attrToProjector[\"y\"](d, i, dataset);\n var effectiveBarHeight = barHeight;\n if (barY + barHeight > _this.height()) {\n effectiveBarHeight = _this.height() - barY;\n }\n else if (barY < 0) {\n effectiveBarHeight = barY + barHeight;\n }\n var offset = Bar._LABEL_VERTICAL_PADDING;\n showLabelOnBar = measurement.height + 2 * offset <= effectiveBarHeight;\n if (showLabelOnBar) {\n if (scaledValue < scaledBaseline) {\n labelContainerOrigin.y += offset;\n yAlignment = \"top\";\n labelOrigin.y += offset;\n }\n else {\n labelContainerOrigin.y -= offset;\n yAlignment = \"bottom\";\n labelOrigin.y += containerHeight - offset - measurement.height;\n }\n }\n else {\n containerHeight = barHeight + offset + measurement.height;\n if (scaledValue <= scaledBaseline) {\n labelContainerOrigin.y -= offset + measurement.height;\n yAlignment = \"top\";\n labelOrigin.y -= offset + measurement.height;\n }\n else {\n yAlignment = \"bottom\";\n labelOrigin.y += barHeight + offset;\n }\n }\n }\n else {\n labelOrigin.y += containerHeight / 2 - measurement.height / 2;\n var barX = attrToProjector[\"x\"](d, i, dataset);\n var effectiveBarWidth = barWidth;\n if (barX + barWidth > _this.width()) {\n effectiveBarWidth = _this.width() - barX;\n }\n else if (barX < 0) {\n effectiveBarWidth = barX + barWidth;\n }\n var offset = Bar._LABEL_HORIZONTAL_PADDING;\n showLabelOnBar = measurement.width + 2 * offset <= effectiveBarWidth;\n if (showLabelOnBar) {\n if (scaledValue < scaledBaseline) {\n labelContainerOrigin.x += offset;\n xAlignment = \"left\";\n labelOrigin.x += offset;\n }\n else {\n labelContainerOrigin.x -= offset;\n xAlignment = \"right\";\n labelOrigin.x += containerWidth - offset - measurement.width;\n }\n }\n else {\n containerWidth = barWidth + offset + measurement.width;\n if (scaledValue < scaledBaseline) {\n labelContainerOrigin.x -= offset + measurement.width;\n xAlignment = \"left\";\n labelOrigin.x -= offset + measurement.width;\n }\n else {\n xAlignment = \"right\";\n labelOrigin.x += barWidth + offset;\n }\n }\n }\n var labelContainer = labelArea.append(\"g\").attr(\"transform\", \"translate(\" + labelContainerOrigin.x + \", \" + labelContainerOrigin.y + \")\");\n if (showLabelOnBar) {\n labelContainer.classed(\"on-bar-label\", true);\n var color = attrToProjector[\"fill\"](d, i, dataset);\n var dark = Plottable.Utils.Color.contrast(\"white\", color) * 1.6 < Plottable.Utils.Color.contrast(\"black\", color);\n labelContainer.classed(dark ? \"dark-label\" : \"light-label\", true);\n }\n else {\n labelContainer.classed(\"off-bar-label\", true);\n }\n var hideLabel = labelOrigin.x < 0 ||\n labelOrigin.y < 0 ||\n labelOrigin.x + measurement.width > _this.width() ||\n labelOrigin.y + measurement.height > _this.height();\n labelContainer.style(\"visibility\", hideLabel ? \"hidden\" : \"inherit\");\n var writeOptions = {\n selection: labelContainer,\n xAlign: xAlignment,\n yAlign: yAlignment,\n textRotation: 0,\n };\n writer.write(text, containerWidth, containerHeight, writeOptions);\n var tooWide = _this._isVertical ? barWidth < measurement.width : barHeight < measurement.height;\n return tooWide;\n };\n var labelTooWide = data.map(drawLabel);\n return labelTooWide.some(function (d) { return d; });\n };\n Bar.prototype._generateDrawSteps = function () {\n var drawSteps = [];\n if (this._animateOnNextRender()) {\n var resetAttrToProjector = this._generateAttrToProjector();\n var primaryScale = this._isVertical ? this.y().scale : this.x().scale;\n var scaledBaseline_1 = primaryScale.scale(this.baselineValue());\n var positionAttr = this._isVertical ? \"y\" : \"x\";\n var dimensionAttr = this._isVertical ? \"height\" : \"width\";\n resetAttrToProjector[positionAttr] = function () { return scaledBaseline_1; };\n resetAttrToProjector[dimensionAttr] = function () { return 0; };\n drawSteps.push({ attrToProjector: resetAttrToProjector, animator: this._getAnimator(Plots.Animator.RESET) });\n }\n drawSteps.push({ attrToProjector: this._generateAttrToProjector(), animator: this._getAnimator(Plots.Animator.MAIN) });\n return drawSteps;\n };\n Bar.prototype._generateAttrToProjector = function () {\n // Primary scale/direction: the \"length\" of the bars\n // Secondary scale/direction: the \"width\" of the bars\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n var primaryScale = this._isVertical ? this.y().scale : this.x().scale;\n var primaryAttr = this._isVertical ? \"y\" : \"x\";\n var secondaryAttr = this._isVertical ? \"x\" : \"y\";\n var scaledBaseline = primaryScale.scale(this.baselineValue());\n var positionF = this._isVertical ? Plottable.Plot._scaledAccessor(this.x()) : Plottable.Plot._scaledAccessor(this.y());\n var widthF = attrToProjector[\"width\"];\n var originalPositionFn = this._isVertical ? Plottable.Plot._scaledAccessor(this.y()) : Plottable.Plot._scaledAccessor(this.x());\n var heightF = function (d, i, dataset) {\n return Math.abs(scaledBaseline - originalPositionFn(d, i, dataset));\n };\n attrToProjector[\"width\"] = this._isVertical ? widthF : heightF;\n attrToProjector[\"height\"] = this._isVertical ? heightF : widthF;\n attrToProjector[secondaryAttr] = function (d, i, dataset) {\n return positionF(d, i, dataset) - widthF(d, i, dataset) / 2;\n };\n attrToProjector[primaryAttr] = function (d, i, dataset) {\n var originalPos = originalPositionFn(d, i, dataset);\n // If it is past the baseline, it should start at the baselin then width/height\n // carries it over. If it's not past the baseline, leave it at original position and\n // then width/height carries it to baseline\n return (originalPos > scaledBaseline) ? scaledBaseline : originalPos;\n };\n return attrToProjector;\n };\n /**\n * Computes the barPixelWidth of all the bars in the plot.\n *\n * If the position scale of the plot is a CategoryScale and in bands mode, then the rangeBands function will be used.\n * If the position scale of the plot is a QuantitativeScale, then the bar width is equal to the smallest distance between\n * two adjacent data points, padded for visualisation.\n */\n Bar.prototype._getBarPixelWidth = function () {\n if (!this._projectorsReady()) {\n return 0;\n }\n var barPixelWidth;\n var barScale = this._isVertical ? this.x().scale : this.y().scale;\n if (barScale instanceof Plottable.Scales.Category) {\n barPixelWidth = barScale.rangeBand();\n }\n else {\n var barAccessor_1 = this._isVertical ? this.x().accessor : this.y().accessor;\n var numberBarAccessorData = d3.set(Plottable.Utils.Array.flatten(this.datasets().map(function (dataset) {\n return dataset.data().map(function (d, i) { return barAccessor_1(d, i, dataset); })\n .filter(function (d) { return d != null; })\n .map(function (d) { return d.valueOf(); });\n }))).values().map(function (value) { return +value; });\n numberBarAccessorData.sort(function (a, b) { return a - b; });\n var scaledData = numberBarAccessorData.map(function (datum) { return barScale.scale(datum); });\n var barAccessorDataPairs = d3.pairs(scaledData);\n var barWidthDimension = this._isVertical ? this.width() : this.height();\n barPixelWidth = Plottable.Utils.Math.min(barAccessorDataPairs, function (pair, i) {\n return Math.abs(pair[1] - pair[0]);\n }, barWidthDimension * Bar._SINGLE_BAR_DIMENSION_RATIO);\n barPixelWidth *= Bar._BAR_WIDTH_RATIO;\n }\n return barPixelWidth;\n };\n Bar.prototype._updateBarPixelWidth = function () {\n this._barPixelWidth = this._getBarPixelWidth();\n };\n Bar.prototype.entities = function (datasets) {\n if (datasets === void 0) { datasets = this.datasets(); }\n if (!this._projectorsReady()) {\n return [];\n }\n var entities = _super.prototype.entities.call(this, datasets);\n return entities;\n };\n Bar.prototype._pixelPoint = function (datum, index, dataset) {\n var attrToProjector = this._generateAttrToProjector();\n var rectX = attrToProjector[\"x\"](datum, index, dataset);\n var rectY = attrToProjector[\"y\"](datum, index, dataset);\n var rectWidth = attrToProjector[\"width\"](datum, index, dataset);\n var rectHeight = attrToProjector[\"height\"](datum, index, dataset);\n var x;\n var y;\n var originalPosition = (this._isVertical ? Plottable.Plot._scaledAccessor(this.y()) : Plottable.Plot._scaledAccessor(this.x()))(datum, index, dataset);\n var scaledBaseline = (this._isVertical ? this.y().scale : this.x().scale).scale(this.baselineValue());\n if (this._isVertical) {\n x = rectX + rectWidth / 2;\n y = originalPosition <= scaledBaseline ? rectY : rectY + rectHeight;\n }\n else {\n x = originalPosition >= scaledBaseline ? rectX + rectWidth : rectX;\n y = rectY + rectHeight / 2;\n }\n return { x: x, y: y };\n };\n Bar.prototype._uninstallScaleForKey = function (scale, key) {\n scale.offUpdate(this._updateBarPixelWidthCallback);\n _super.prototype._uninstallScaleForKey.call(this, scale, key);\n };\n Bar.prototype._getDataToDraw = function () {\n var dataToDraw = new Plottable.Utils.Map();\n var attrToProjector = this._generateAttrToProjector();\n this.datasets().forEach(function (dataset) {\n var data = dataset.data().filter(function (d, i) { return Plottable.Utils.Math.isValidNumber(attrToProjector[\"x\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"y\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"width\"](d, i, dataset)) &&\n Plottable.Utils.Math.isValidNumber(attrToProjector[\"height\"](d, i, dataset)); });\n dataToDraw.set(dataset, data);\n });\n return dataToDraw;\n };\n Bar.ORIENTATION_VERTICAL = \"vertical\";\n Bar.ORIENTATION_HORIZONTAL = \"horizontal\";\n Bar._BAR_WIDTH_RATIO = 0.95;\n Bar._SINGLE_BAR_DIMENSION_RATIO = 0.4;\n Bar._BAR_AREA_CLASS = \"bar-area\";\n Bar._LABEL_AREA_CLASS = \"bar-label-text-area\";\n Bar._LABEL_VERTICAL_PADDING = 5;\n Bar._LABEL_HORIZONTAL_PADDING = 5;\n return Bar;\n }(Plottable.XYPlot));\n Plots.Bar = Bar;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Line = (function (_super) {\n __extends(Line, _super);\n /**\n * A Line Plot draws line segments starting from the first data point to the next.\n *\n * @constructor\n */\n function Line() {\n _super.call(this);\n this._interpolator = \"linear\";\n this._autorangeSmooth = false;\n this._croppedRenderingEnabled = true;\n this._downsamplingEnabled = false;\n this.addClass(\"line-plot\");\n var animator = new Plottable.Animators.Easing();\n animator.stepDuration(Plottable.Plot._ANIMATION_MAX_DURATION);\n animator.easingMode(\"exp-in-out\");\n animator.maxTotalDuration(Plottable.Plot._ANIMATION_MAX_DURATION);\n this.animator(Plots.Animator.MAIN, animator);\n this.attr(\"stroke\", new Plottable.Scales.Color().range()[0]);\n this.attr(\"stroke-width\", \"2px\");\n }\n Line.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n else {\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n }\n this._setScaleSnapping();\n return this;\n }\n };\n Line.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n this._setScaleSnapping();\n return this;\n }\n };\n Line.prototype.autorangeMode = function (autorangeMode) {\n if (autorangeMode == null) {\n return _super.prototype.autorangeMode.call(this);\n }\n _super.prototype.autorangeMode.call(this, autorangeMode);\n this._setScaleSnapping();\n return this;\n };\n Line.prototype.autorangeSmooth = function (autorangeSmooth) {\n if (autorangeSmooth == null) {\n return this._autorangeSmooth;\n }\n this._autorangeSmooth = autorangeSmooth;\n this._setScaleSnapping();\n return this;\n };\n Line.prototype._setScaleSnapping = function () {\n if (this.autorangeMode() === \"x\" && this.x() && this.x().scale && this.x().scale instanceof Plottable.QuantitativeScale) {\n this.x().scale.snappingDomainEnabled(!this.autorangeSmooth());\n }\n if (this.autorangeMode() === \"y\" && this.y() && this.y().scale && this.y().scale instanceof Plottable.QuantitativeScale) {\n this.y().scale.snappingDomainEnabled(!this.autorangeSmooth());\n }\n };\n Line.prototype.interpolator = function (interpolator) {\n if (interpolator == null) {\n return this._interpolator;\n }\n this._interpolator = interpolator;\n this.render();\n return this;\n };\n Line.prototype.downsamplingEnabled = function (downsampling) {\n if (downsampling == null) {\n return this._downsamplingEnabled;\n }\n this._downsamplingEnabled = downsampling;\n return this;\n };\n Line.prototype.croppedRenderingEnabled = function (croppedRendering) {\n if (croppedRendering == null) {\n return this._croppedRenderingEnabled;\n }\n this._croppedRenderingEnabled = croppedRendering;\n this.render();\n return this;\n };\n Line.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Line(dataset);\n };\n Line.prototype._extentsForProperty = function (property) {\n var extents = _super.prototype._extentsForProperty.call(this, property);\n if (!this._autorangeSmooth) {\n return extents;\n }\n if (this.autorangeMode() !== property) {\n return extents;\n }\n if (this.autorangeMode() !== \"x\" && this.autorangeMode() !== \"y\") {\n return extents;\n }\n var edgeIntersectionPoints = this._getEdgeIntersectionPoints();\n var includedValues;\n if (this.autorangeMode() === \"y\") {\n includedValues = edgeIntersectionPoints.left.concat(edgeIntersectionPoints.right).map(function (point) { return point.y; });\n }\n else {\n includedValues = edgeIntersectionPoints.top.concat(edgeIntersectionPoints.bottom).map(function (point) { return point.x; });\n }\n return extents.map(function (extent) { return d3.extent(d3.merge([extent, includedValues])); });\n };\n Line.prototype._getEdgeIntersectionPoints = function () {\n var _this = this;\n if (!(this.y().scale instanceof Plottable.QuantitativeScale && this.x().scale instanceof Plottable.QuantitativeScale)) {\n return {\n left: [],\n right: [],\n top: [],\n bottom: [],\n };\n }\n var yScale = this.y().scale;\n var xScale = this.x().scale;\n var intersectionPoints = {\n left: [],\n right: [],\n top: [],\n bottom: [],\n };\n var leftX = xScale.scale(xScale.domain()[0]);\n var rightX = xScale.scale(xScale.domain()[1]);\n var bottomY = yScale.scale(yScale.domain()[0]);\n var topY = yScale.scale(yScale.domain()[1]);\n this.datasets().forEach(function (dataset) {\n var data = dataset.data();\n var x1, x2, y1, y2;\n var prevX, prevY, currX, currY;\n for (var i = 1; i < data.length; i++) {\n prevX = currX || xScale.scale(_this.x().accessor(data[i - 1], i - 1, dataset));\n prevY = currY || yScale.scale(_this.y().accessor(data[i - 1], i - 1, dataset));\n currX = xScale.scale(_this.x().accessor(data[i], i, dataset));\n currY = yScale.scale(_this.y().accessor(data[i], i, dataset));\n // If values crossed left edge\n if ((prevX < leftX) === (leftX <= currX)) {\n x1 = leftX - prevX;\n x2 = currX - prevX;\n y2 = currY - prevY;\n y1 = x1 * y2 / x2;\n intersectionPoints.left.push({\n x: leftX,\n y: yScale.invert(prevY + y1),\n });\n }\n // If values crossed right edge\n if ((prevX < rightX) === (rightX <= currX)) {\n x1 = rightX - prevX;\n x2 = currX - prevX;\n y2 = currY - prevY;\n y1 = x1 * y2 / x2;\n intersectionPoints.right.push({\n x: rightX,\n y: yScale.invert(prevY + y1),\n });\n }\n // If values crossed upper edge\n if ((prevY < topY) === (topY <= currY)) {\n x2 = currX - prevX;\n y1 = topY - prevY;\n y2 = currY - prevY;\n x1 = y1 * x2 / y2;\n intersectionPoints.top.push({\n x: xScale.invert(prevX + x1),\n y: topY,\n });\n }\n // If values crossed lower edge\n if ((prevY < bottomY) === (bottomY <= currY)) {\n x2 = currX - prevX;\n y1 = bottomY - prevY;\n y2 = currY - prevY;\n x1 = y1 * x2 / y2;\n intersectionPoints.bottom.push({\n x: xScale.invert(prevX + x1),\n y: bottomY,\n });\n }\n }\n ;\n });\n return intersectionPoints;\n };\n Line.prototype._getResetYFunction = function () {\n // gets the y-value generator for the animation start point\n var yDomain = this.y().scale.domain();\n var domainMax = Math.max(yDomain[0], yDomain[1]);\n var domainMin = Math.min(yDomain[0], yDomain[1]);\n // start from zero, or the closest domain value to zero\n // avoids lines zooming on from offscreen.\n var startValue = (domainMax < 0 && domainMax) || (domainMin > 0 && domainMin) || 0;\n var scaledStartValue = this.y().scale.scale(startValue);\n return function (d, i, dataset) { return scaledStartValue; };\n };\n Line.prototype._generateDrawSteps = function () {\n var drawSteps = [];\n if (this._animateOnNextRender()) {\n var attrToProjector = this._generateAttrToProjector();\n attrToProjector[\"d\"] = this._constructLineProjector(Plottable.Plot._scaledAccessor(this.x()), this._getResetYFunction());\n drawSteps.push({ attrToProjector: attrToProjector, animator: this._getAnimator(Plots.Animator.RESET) });\n }\n drawSteps.push({ attrToProjector: this._generateAttrToProjector(), animator: this._getAnimator(Plots.Animator.MAIN) });\n return drawSteps;\n };\n Line.prototype._generateAttrToProjector = function () {\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n Object.keys(attrToProjector).forEach(function (attribute) {\n if (attribute === \"d\") {\n return;\n }\n var projector = attrToProjector[attribute];\n attrToProjector[attribute] = function (data, i, dataset) {\n return data.length > 0 ? projector(data[0], i, dataset) : null;\n };\n });\n return attrToProjector;\n };\n /**\n * Returns the PlotEntity nearest to the query point by X then by Y, or undefined if no PlotEntity can be found.\n *\n * @param {Point} queryPoint\n * @returns {PlotEntity} The nearest PlotEntity, or undefined if no PlotEntity can be found.\n */\n Line.prototype.entityNearestByXThenY = function (queryPoint) {\n var _this = this;\n var minXDist = Infinity;\n var minYDist = Infinity;\n var closest;\n this.entities().forEach(function (entity) {\n if (!_this._entityVisibleOnPlot(entity.position, entity.datum, entity.index, entity.dataset)) {\n return;\n }\n var xDist = Math.abs(queryPoint.x - entity.position.x);\n var yDist = Math.abs(queryPoint.y - entity.position.y);\n if (xDist < minXDist || xDist === minXDist && yDist < minYDist) {\n closest = entity;\n minXDist = xDist;\n minYDist = yDist;\n }\n });\n return closest;\n };\n Line.prototype._propertyProjectors = function () {\n var propertyToProjectors = _super.prototype._propertyProjectors.call(this);\n propertyToProjectors[\"d\"] = this._constructLineProjector(Plottable.Plot._scaledAccessor(this.x()), Plottable.Plot._scaledAccessor(this.y()));\n return propertyToProjectors;\n };\n Line.prototype._constructLineProjector = function (xProjector, yProjector) {\n var _this = this;\n var definedProjector = function (d, i, dataset) {\n var positionX = Plottable.Plot._scaledAccessor(_this.x())(d, i, dataset);\n var positionY = Plottable.Plot._scaledAccessor(_this.y())(d, i, dataset);\n return positionX != null && !Plottable.Utils.Math.isNaN(positionX) &&\n positionY != null && !Plottable.Utils.Math.isNaN(positionY);\n };\n return function (datum, index, dataset) {\n return d3.svg.line()\n .x(function (innerDatum, innerIndex) { return xProjector(innerDatum, innerIndex, dataset); })\n .y(function (innerDatum, innerIndex) { return yProjector(innerDatum, innerIndex, dataset); })\n .interpolate(_this.interpolator())\n .defined(function (innerDatum, innerIndex) { return definedProjector(innerDatum, innerIndex, dataset); })(datum);\n };\n };\n Line.prototype._getDataToDraw = function () {\n var _this = this;\n var dataToDraw = new Plottable.Utils.Map();\n this.datasets().forEach(function (dataset) {\n var data = dataset.data();\n if (!_this._croppedRenderingEnabled && !_this._downsamplingEnabled) {\n dataToDraw.set(dataset, [data]);\n return;\n }\n var filteredDataIndices = data.map(function (d, i) { return i; });\n if (_this._croppedRenderingEnabled) {\n filteredDataIndices = _this._filterCroppedRendering(dataset, filteredDataIndices);\n }\n if (_this._downsamplingEnabled) {\n filteredDataIndices = _this._filterDownsampling(dataset, filteredDataIndices);\n }\n dataToDraw.set(dataset, [filteredDataIndices.map(function (d, i) { return data[d]; })]);\n });\n return dataToDraw;\n };\n Line.prototype._filterCroppedRendering = function (dataset, indices) {\n var _this = this;\n var xProjector = Plottable.Plot._scaledAccessor(this.x());\n var yProjector = Plottable.Plot._scaledAccessor(this.y());\n var data = dataset.data();\n var filteredDataIndices = [];\n var pointInViewport = function (x, y) {\n return Plottable.Utils.Math.inRange(x, 0, _this.width()) &&\n Plottable.Utils.Math.inRange(y, 0, _this.height());\n };\n for (var i = 0; i < indices.length; i++) {\n var currXPoint = xProjector(data[indices[i]], indices[i], dataset);\n var currYPoint = yProjector(data[indices[i]], indices[i], dataset);\n var shouldShow = pointInViewport(currXPoint, currYPoint);\n if (!shouldShow && indices[i - 1] != null && data[indices[i - 1]] != null) {\n var prevXPoint = xProjector(data[indices[i - 1]], indices[i - 1], dataset);\n var prevYPoint = yProjector(data[indices[i - 1]], indices[i - 1], dataset);\n shouldShow = shouldShow || pointInViewport(prevXPoint, prevYPoint);\n }\n if (!shouldShow && indices[i + 1] != null && data[indices[i + 1]] != null) {\n var nextXPoint = xProjector(data[indices[i + 1]], indices[i + 1], dataset);\n var nextYPoint = yProjector(data[indices[i + 1]], indices[i + 1], dataset);\n shouldShow = shouldShow || pointInViewport(nextXPoint, nextYPoint);\n }\n if (shouldShow) {\n filteredDataIndices.push(indices[i]);\n }\n }\n return filteredDataIndices;\n };\n Line.prototype._filterDownsampling = function (dataset, indices) {\n if (indices.length === 0) {\n return [];\n }\n var data = dataset.data();\n var scaledXAccessor = Plottable.Plot._scaledAccessor(this.x());\n var scaledYAccessor = Plottable.Plot._scaledAccessor(this.y());\n var filteredIndices = [indices[0]];\n var indexOnCurrentSlope = function (i, currentSlope) {\n var p1x = scaledXAccessor(data[indices[i]], indices[i], dataset);\n var p1y = scaledYAccessor(data[indices[i]], indices[i], dataset);\n var p2x = scaledXAccessor(data[indices[i + 1]], indices[i + 1], dataset);\n var p2y = scaledYAccessor(data[indices[i + 1]], indices[i + 1], dataset);\n if (currentSlope === Infinity) {\n return Math.floor(p1x) === Math.floor(p2x);\n }\n else {\n var expectedP2y = p1y + (p2x - p1x) * currentSlope;\n return Math.floor(p2y) === Math.floor(expectedP2y);\n }\n };\n for (var i = 0; i < indices.length - 1;) {\n var indexFirst = indices[i];\n var p1x = scaledXAccessor(data[indices[i]], indices[i], dataset);\n var p1y = scaledYAccessor(data[indices[i]], indices[i], dataset);\n var p2x = scaledXAccessor(data[indices[i + 1]], indices[i + 1], dataset);\n var p2y = scaledYAccessor(data[indices[i + 1]], indices[i + 1], dataset);\n var currentSlope = (Math.floor(p1x) === Math.floor(p2x)) ? Infinity : (p2y - p1y) / (p2x - p1x);\n var indexMin = indices[i];\n var minScaledValue = (currentSlope === Infinity) ? p1y : p1x;\n var indexMax = indexMin;\n var maxScaledValue = minScaledValue;\n var firstIndexOnCurrentSlope = true;\n while (i < indices.length - 1 && (firstIndexOnCurrentSlope || indexOnCurrentSlope(i, currentSlope))) {\n i++;\n firstIndexOnCurrentSlope = false;\n var currScaledValue = currentSlope === Infinity ? scaledYAccessor(data[indices[i]], indices[i], dataset) :\n scaledXAccessor(data[indices[i]], indices[i], dataset);\n if (currScaledValue > maxScaledValue) {\n maxScaledValue = currScaledValue;\n indexMax = indices[i];\n }\n if (currScaledValue < minScaledValue) {\n minScaledValue = currScaledValue;\n indexMin = indices[i];\n }\n }\n var indexLast = indices[i];\n if (indexMin !== indexFirst) {\n filteredIndices.push(indexMin);\n }\n if (indexMax !== indexMin && indexMax !== indexFirst) {\n filteredIndices.push(indexMax);\n }\n if (indexLast !== indexFirst && indexLast !== indexMin && indexLast !== indexMax) {\n filteredIndices.push(indexLast);\n }\n }\n return filteredIndices;\n };\n return Line;\n }(Plottable.XYPlot));\n Plots.Line = Line;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Area = (function (_super) {\n __extends(Area, _super);\n /**\n * An Area Plot draws a filled region (area) between Y and Y0.\n *\n * @constructor\n */\n function Area() {\n _super.call(this);\n this.addClass(\"area-plot\");\n this.y0(0); // default\n this.attr(\"fill-opacity\", 0.25);\n this.attr(\"fill\", new Plottable.Scales.Color().range()[0]);\n this._lineDrawers = new Plottable.Utils.Map();\n }\n Area.prototype._setup = function () {\n var _this = this;\n _super.prototype._setup.call(this);\n this._lineDrawers.forEach(function (d) { return d.renderArea(_this._renderArea.append(\"g\")); });\n };\n Area.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n }\n if (yScale != null) {\n var y0 = this.y0().accessor;\n if (y0 != null) {\n this._bindProperty(Area._Y0_KEY, y0, yScale);\n }\n this._updateYScale();\n }\n return this;\n };\n Area.prototype.y0 = function (y0) {\n if (y0 == null) {\n return this._propertyBindings.get(Area._Y0_KEY);\n }\n var yBinding = this.y();\n var yScale = yBinding && yBinding.scale;\n this._bindProperty(Area._Y0_KEY, y0, yScale);\n this._updateYScale();\n this.render();\n return this;\n };\n Area.prototype._onDatasetUpdate = function () {\n _super.prototype._onDatasetUpdate.call(this);\n this._updateYScale();\n };\n Area.prototype.addDataset = function (dataset) {\n _super.prototype.addDataset.call(this, dataset);\n return this;\n };\n Area.prototype._addDataset = function (dataset) {\n var lineDrawer = new Plottable.Drawers.Line(dataset);\n if (this._isSetup) {\n lineDrawer.renderArea(this._renderArea.append(\"g\"));\n }\n this._lineDrawers.set(dataset, lineDrawer);\n _super.prototype._addDataset.call(this, dataset);\n return this;\n };\n Area.prototype._removeDatasetNodes = function (dataset) {\n _super.prototype._removeDatasetNodes.call(this, dataset);\n this._lineDrawers.get(dataset).remove();\n };\n Area.prototype._additionalPaint = function () {\n var _this = this;\n var drawSteps = this._generateLineDrawSteps();\n var dataToDraw = this._getDataToDraw();\n this.datasets().forEach(function (dataset) { return _this._lineDrawers.get(dataset).draw(dataToDraw.get(dataset), drawSteps); });\n };\n Area.prototype._generateLineDrawSteps = function () {\n var drawSteps = [];\n if (this._animateOnNextRender()) {\n var attrToProjector = this._generateLineAttrToProjector();\n attrToProjector[\"d\"] = this._constructLineProjector(Plottable.Plot._scaledAccessor(this.x()), this._getResetYFunction());\n drawSteps.push({ attrToProjector: attrToProjector, animator: this._getAnimator(Plots.Animator.RESET) });\n }\n drawSteps.push({ attrToProjector: this._generateLineAttrToProjector(), animator: this._getAnimator(Plots.Animator.MAIN) });\n return drawSteps;\n };\n Area.prototype._generateLineAttrToProjector = function () {\n var lineAttrToProjector = this._generateAttrToProjector();\n lineAttrToProjector[\"d\"] = this._constructLineProjector(Plottable.Plot._scaledAccessor(this.x()), Plottable.Plot._scaledAccessor(this.y()));\n return lineAttrToProjector;\n };\n Area.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Area(dataset);\n };\n Area.prototype._generateDrawSteps = function () {\n var drawSteps = [];\n if (this._animateOnNextRender()) {\n var attrToProjector = this._generateAttrToProjector();\n attrToProjector[\"d\"] = this._constructAreaProjector(Plottable.Plot._scaledAccessor(this.x()), this._getResetYFunction(), Plottable.Plot._scaledAccessor(this.y0()));\n drawSteps.push({ attrToProjector: attrToProjector, animator: this._getAnimator(Plots.Animator.RESET) });\n }\n drawSteps.push({ attrToProjector: this._generateAttrToProjector(), animator: this._getAnimator(Plots.Animator.MAIN) });\n return drawSteps;\n };\n Area.prototype._updateYScale = function () {\n var extents = this._propertyExtents.get(\"y0\");\n var extent = Plottable.Utils.Array.flatten(extents);\n var uniqExtentVals = Plottable.Utils.Array.uniq(extent);\n var constantBaseline = uniqExtentVals.length === 1 ? uniqExtentVals[0] : null;\n var yBinding = this.y();\n var yScale = (yBinding && yBinding.scale);\n if (yScale == null) {\n return;\n }\n if (this._constantBaselineValueProvider != null) {\n yScale.removePaddingExceptionsProvider(this._constantBaselineValueProvider);\n this._constantBaselineValueProvider = null;\n }\n if (constantBaseline != null) {\n this._constantBaselineValueProvider = function () { return [constantBaseline]; };\n yScale.addPaddingExceptionsProvider(this._constantBaselineValueProvider);\n }\n };\n Area.prototype._getResetYFunction = function () {\n return Plottable.Plot._scaledAccessor(this.y0());\n };\n Area.prototype._propertyProjectors = function () {\n var propertyToProjectors = _super.prototype._propertyProjectors.call(this);\n propertyToProjectors[\"d\"] = this._constructAreaProjector(Plottable.Plot._scaledAccessor(this.x()), Plottable.Plot._scaledAccessor(this.y()), Plottable.Plot._scaledAccessor(this.y0()));\n return propertyToProjectors;\n };\n Area.prototype.selections = function (datasets) {\n var _this = this;\n if (datasets === void 0) { datasets = this.datasets(); }\n var allSelections = _super.prototype.selections.call(this, datasets)[0];\n var lineDrawers = datasets.map(function (dataset) { return _this._lineDrawers.get(dataset); })\n .filter(function (drawer) { return drawer != null; });\n lineDrawers.forEach(function (ld, i) { return allSelections.push(ld.selectionForIndex(i).node()); });\n return d3.selectAll(allSelections);\n };\n Area.prototype._constructAreaProjector = function (xProjector, yProjector, y0Projector) {\n var _this = this;\n var definedProjector = function (d, i, dataset) {\n var positionX = Plottable.Plot._scaledAccessor(_this.x())(d, i, dataset);\n var positionY = Plottable.Plot._scaledAccessor(_this.y())(d, i, dataset);\n return Plottable.Utils.Math.isValidNumber(positionX) && Plottable.Utils.Math.isValidNumber(positionY);\n };\n return function (datum, index, dataset) {\n var areaGenerator = d3.svg.area()\n .x(function (innerDatum, innerIndex) { return xProjector(innerDatum, innerIndex, dataset); })\n .y1(function (innerDatum, innerIndex) { return yProjector(innerDatum, innerIndex, dataset); })\n .y0(function (innerDatum, innerIndex) { return y0Projector(innerDatum, innerIndex, dataset); })\n .interpolate(_this.interpolator())\n .defined(function (innerDatum, innerIndex) { return definedProjector(innerDatum, innerIndex, dataset); });\n return areaGenerator(datum);\n };\n };\n Area._Y0_KEY = \"y0\";\n return Area;\n }(Plots.Line));\n Plots.Area = Area;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var ClusteredBar = (function (_super) {\n __extends(ClusteredBar, _super);\n /**\n * A ClusteredBar Plot groups bars across Datasets based on the primary value of the bars.\n * On a vertical ClusteredBar Plot, the bars with the same X value are grouped.\n * On a horizontal ClusteredBar Plot, the bars with the same Y value are grouped.\n *\n * @constructor\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n function ClusteredBar(orientation) {\n if (orientation === void 0) { orientation = Plots.Bar.ORIENTATION_VERTICAL; }\n _super.call(this, orientation);\n this._clusterOffsets = new Plottable.Utils.Map();\n }\n ClusteredBar.prototype._generateAttrToProjector = function () {\n var _this = this;\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n // the width is constant, so set the inner scale range to that\n var innerScale = this._makeInnerScale();\n var innerWidthF = function (d, i) { return innerScale.rangeBand(); };\n attrToProjector[\"width\"] = this._isVertical ? innerWidthF : attrToProjector[\"width\"];\n attrToProjector[\"height\"] = !this._isVertical ? innerWidthF : attrToProjector[\"height\"];\n var xAttr = attrToProjector[\"x\"];\n var yAttr = attrToProjector[\"y\"];\n attrToProjector[\"x\"] = this._isVertical ?\n function (d, i, ds) { return xAttr(d, i, ds) + _this._clusterOffsets.get(ds); } :\n function (d, i, ds) { return xAttr(d, i, ds); };\n attrToProjector[\"y\"] = this._isVertical ?\n function (d, i, ds) { return yAttr(d, i, ds); } :\n function (d, i, ds) { return yAttr(d, i, ds) + _this._clusterOffsets.get(ds); };\n return attrToProjector;\n };\n ClusteredBar.prototype._updateClusterPosition = function () {\n var _this = this;\n var innerScale = this._makeInnerScale();\n this.datasets().forEach(function (d, i) { return _this._clusterOffsets.set(d, innerScale.scale(String(i)) - innerScale.rangeBand() / 2); });\n };\n ClusteredBar.prototype._makeInnerScale = function () {\n var innerScale = new Plottable.Scales.Category();\n innerScale.domain(this.datasets().map(function (d, i) { return String(i); }));\n var widthProjector = Plottable.Plot._scaledAccessor(this.attr(\"width\"));\n innerScale.range([0, widthProjector(null, 0, null)]);\n return innerScale;\n };\n ClusteredBar.prototype._getDataToDraw = function () {\n this._updateClusterPosition();\n return _super.prototype._getDataToDraw.call(this);\n };\n return ClusteredBar;\n }(Plots.Bar));\n Plots.ClusteredBar = ClusteredBar;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var StackedArea = (function (_super) {\n __extends(StackedArea, _super);\n /**\n * @constructor\n */\n function StackedArea() {\n var _this = this;\n _super.call(this);\n this._baselineValue = 0;\n this.addClass(\"stacked-area-plot\");\n this.attr(\"fill-opacity\", 1);\n this._stackingResult = new Plottable.Utils.Map();\n this._stackedExtent = [];\n this._baselineValueProvider = function () { return [_this._baselineValue]; };\n this.croppedRenderingEnabled(false);\n }\n StackedArea.prototype.croppedRenderingEnabled = function (croppedRendering) {\n if (croppedRendering == null) {\n return _super.prototype.croppedRenderingEnabled.call(this);\n }\n if (croppedRendering === true) {\n // HACKHACK #3032: cropped rendering doesn't currently work correctly on StackedArea\n Plottable.Utils.Window.warn(\"Warning: Stacked Area Plot does not support cropped rendering.\");\n return this;\n }\n return _super.prototype.croppedRenderingEnabled.call(this, croppedRendering);\n };\n StackedArea.prototype._getAnimator = function (key) {\n return new Plottable.Animators.Null();\n };\n StackedArea.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._baseline = this._renderArea.append(\"line\").classed(\"baseline\", true);\n };\n StackedArea.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n }\n this._updateStackExtentsAndOffsets();\n return this;\n };\n StackedArea.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n }\n this._updateStackExtentsAndOffsets();\n return this;\n };\n StackedArea.prototype.downsamplingEnabled = function (downsampling) {\n if (downsampling == null) {\n return _super.prototype.downsamplingEnabled.call(this);\n }\n Plottable.Utils.Window.warn(\"Warning: Stacked Area Plot does not support downsampling\");\n return this;\n };\n StackedArea.prototype._additionalPaint = function () {\n var scaledBaseline = this.y().scale.scale(this._baselineValue);\n var baselineAttr = {\n \"x1\": 0,\n \"y1\": scaledBaseline,\n \"x2\": this.width(),\n \"y2\": scaledBaseline,\n };\n this._getAnimator(\"baseline\").animate(this._baseline, baselineAttr);\n };\n StackedArea.prototype._updateYScale = function () {\n var yBinding = this.y();\n var scale = (yBinding && yBinding.scale);\n if (scale == null) {\n return;\n }\n scale.addPaddingExceptionsProvider(this._baselineValueProvider);\n scale.addIncludedValuesProvider(this._baselineValueProvider);\n };\n StackedArea.prototype._onDatasetUpdate = function () {\n this._updateStackExtentsAndOffsets();\n _super.prototype._onDatasetUpdate.call(this);\n return this;\n };\n StackedArea.prototype._updateExtentsForProperty = function (property) {\n _super.prototype._updateExtentsForProperty.call(this, property);\n if ((property === \"x\" || property === \"y\") && this._projectorsReady()) {\n this._updateStackExtentsAndOffsets();\n }\n };\n StackedArea.prototype._extentsForProperty = function (attr) {\n var primaryAttr = \"y\";\n if (attr === primaryAttr) {\n return [this._stackedExtent];\n }\n else {\n return _super.prototype._extentsForProperty.call(this, attr);\n }\n };\n StackedArea.prototype._updateStackExtentsAndOffsets = function () {\n if (!this._projectorsReady()) {\n return;\n }\n var datasets = this.datasets();\n var keyAccessor = this.x().accessor;\n var valueAccessor = this.y().accessor;\n var filter = this._filterForProperty(\"y\");\n this._checkSameDomain(datasets, keyAccessor);\n this._stackingResult = Plottable.Utils.Stacking.stack(datasets, keyAccessor, valueAccessor);\n this._stackedExtent = Plottable.Utils.Stacking.stackedExtent(this._stackingResult, keyAccessor, filter);\n };\n StackedArea.prototype._checkSameDomain = function (datasets, keyAccessor) {\n var keySets = datasets.map(function (dataset) {\n return d3.set(dataset.data().map(function (datum, i) { return keyAccessor(datum, i, dataset).toString(); })).values();\n });\n var domainKeys = StackedArea._domainKeys(datasets, keyAccessor);\n if (keySets.some(function (keySet) { return keySet.length !== domainKeys.length; })) {\n Plottable.Utils.Window.warn(\"the domains across the datasets are not the same. Plot may produce unintended behavior.\");\n }\n };\n /**\n * Given an array of Datasets and the accessor function for the key, computes the\n * set reunion (no duplicates) of the domain of each Dataset. The keys are stringified\n * before being returned.\n *\n * @param {Dataset[]} datasets The Datasets for which we extract the domain keys\n * @param {Accessor} keyAccessor The accessor for the key of the data\n * @return {string[]} An array of stringified keys\n */\n StackedArea._domainKeys = function (datasets, keyAccessor) {\n var domainKeys = d3.set();\n datasets.forEach(function (dataset) {\n dataset.data().forEach(function (datum, index) {\n domainKeys.add(keyAccessor(datum, index, dataset));\n });\n });\n return domainKeys.values();\n };\n StackedArea.prototype._propertyProjectors = function () {\n var _this = this;\n var propertyToProjectors = _super.prototype._propertyProjectors.call(this);\n var yAccessor = this.y().accessor;\n var xAccessor = this.x().accessor;\n var normalizedXAccessor = function (datum, index, dataset) {\n return Plottable.Utils.Stacking.normalizeKey(xAccessor(datum, index, dataset));\n };\n var stackYProjector = function (d, i, dataset) {\n return _this.y().scale.scale(+yAccessor(d, i, dataset) + _this._stackingResult.get(dataset).get(normalizedXAccessor(d, i, dataset)).offset);\n };\n var stackY0Projector = function (d, i, dataset) {\n return _this.y().scale.scale(_this._stackingResult.get(dataset).get(normalizedXAccessor(d, i, dataset)).offset);\n };\n propertyToProjectors[\"d\"] = this._constructAreaProjector(Plottable.Plot._scaledAccessor(this.x()), stackYProjector, stackY0Projector);\n return propertyToProjectors;\n };\n StackedArea.prototype._pixelPoint = function (datum, index, dataset) {\n var pixelPoint = _super.prototype._pixelPoint.call(this, datum, index, dataset);\n var xValue = this.x().accessor(datum, index, dataset);\n var yValue = this.y().accessor(datum, index, dataset);\n var scaledYValue = this.y().scale.scale(+yValue + this._stackingResult.get(dataset).get(Plottable.Utils.Stacking.normalizeKey(xValue)).offset);\n return { x: pixelPoint.x, y: scaledYValue };\n };\n return StackedArea;\n }(Plots.Area));\n Plots.StackedArea = StackedArea;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var StackedBar = (function (_super) {\n __extends(StackedBar, _super);\n /**\n * A StackedBar Plot stacks bars across Datasets based on the primary value of the bars.\n * On a vertical StackedBar Plot, the bars with the same X value are stacked.\n * On a horizontal StackedBar Plot, the bars with the same Y value are stacked.\n *\n * @constructor\n * @param {Scale} xScale\n * @param {Scale} yScale\n * @param {string} [orientation=\"vertical\"] One of \"vertical\"/\"horizontal\".\n */\n function StackedBar(orientation) {\n if (orientation === void 0) { orientation = Plots.Bar.ORIENTATION_VERTICAL; }\n _super.call(this, orientation);\n this.addClass(\"stacked-bar-plot\");\n this._stackingResult = new Plottable.Utils.Map();\n this._stackedExtent = [];\n }\n StackedBar.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n }\n this._updateStackExtentsAndOffsets();\n return this;\n };\n StackedBar.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n }\n this._updateStackExtentsAndOffsets();\n return this;\n };\n StackedBar.prototype._generateAttrToProjector = function () {\n var _this = this;\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n var valueAttr = this._isVertical ? \"y\" : \"x\";\n var keyAttr = this._isVertical ? \"x\" : \"y\";\n var primaryScale = this._isVertical ? this.y().scale : this.x().scale;\n var primaryAccessor = this._propertyBindings.get(valueAttr).accessor;\n var keyAccessor = this._propertyBindings.get(keyAttr).accessor;\n var normalizedKeyAccessor = function (datum, index, dataset) {\n return Plottable.Utils.Stacking.normalizeKey(keyAccessor(datum, index, dataset));\n };\n var getStart = function (d, i, dataset) {\n return primaryScale.scale(_this._stackingResult.get(dataset).get(normalizedKeyAccessor(d, i, dataset)).offset);\n };\n var getEnd = function (d, i, dataset) {\n return primaryScale.scale(+primaryAccessor(d, i, dataset) +\n _this._stackingResult.get(dataset).get(normalizedKeyAccessor(d, i, dataset)).offset);\n };\n var heightF = function (d, i, dataset) {\n return Math.abs(getEnd(d, i, dataset) - getStart(d, i, dataset));\n };\n attrToProjector[this._isVertical ? \"height\" : \"width\"] = heightF;\n var attrFunction = function (d, i, dataset) {\n return +primaryAccessor(d, i, dataset) < 0 ? getStart(d, i, dataset) : getEnd(d, i, dataset);\n };\n attrToProjector[valueAttr] = function (d, i, dataset) {\n return _this._isVertical ? attrFunction(d, i, dataset) : attrFunction(d, i, dataset) - heightF(d, i, dataset);\n };\n return attrToProjector;\n };\n StackedBar.prototype._onDatasetUpdate = function () {\n this._updateStackExtentsAndOffsets();\n _super.prototype._onDatasetUpdate.call(this);\n return this;\n };\n StackedBar.prototype._updateExtentsForProperty = function (property) {\n _super.prototype._updateExtentsForProperty.call(this, property);\n if ((property === \"x\" || property === \"y\") && this._projectorsReady()) {\n this._updateStackExtentsAndOffsets();\n }\n };\n StackedBar.prototype._extentsForProperty = function (attr) {\n var primaryAttr = this._isVertical ? \"y\" : \"x\";\n if (attr === primaryAttr) {\n return [this._stackedExtent];\n }\n else {\n return _super.prototype._extentsForProperty.call(this, attr);\n }\n };\n StackedBar.prototype._updateStackExtentsAndOffsets = function () {\n if (!this._projectorsReady()) {\n return;\n }\n var datasets = this.datasets();\n var keyAccessor = this._isVertical ? this.x().accessor : this.y().accessor;\n var valueAccessor = this._isVertical ? this.y().accessor : this.x().accessor;\n var filter = this._filterForProperty(this._isVertical ? \"y\" : \"x\");\n this._stackingResult = Plottable.Utils.Stacking.stack(datasets, keyAccessor, valueAccessor);\n this._stackedExtent = Plottable.Utils.Stacking.stackedExtent(this._stackingResult, keyAccessor, filter);\n };\n return StackedBar;\n }(Plots.Bar));\n Plots.StackedBar = StackedBar;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Segment = (function (_super) {\n __extends(Segment, _super);\n /**\n * A Segment Plot displays line segments based on the data.\n *\n * @constructor\n */\n function Segment() {\n _super.call(this);\n this.addClass(\"segment-plot\");\n this.attr(\"stroke\", new Plottable.Scales.Color().range()[0]);\n this.attr(\"stroke-width\", \"2px\");\n }\n Segment.prototype._createDrawer = function (dataset) {\n return new Plottable.Drawers.Segment(dataset);\n };\n Segment.prototype._generateDrawSteps = function () {\n return [{ attrToProjector: this._generateAttrToProjector(), animator: new Plottable.Animators.Null() }];\n };\n Segment.prototype._updateExtentsForProperty = function (property) {\n _super.prototype._updateExtentsForProperty.call(this, property);\n if (property === \"x\") {\n _super.prototype._updateExtentsForProperty.call(this, \"x2\");\n }\n else if (property === \"y\") {\n _super.prototype._updateExtentsForProperty.call(this, \"y2\");\n }\n };\n Segment.prototype._filterForProperty = function (property) {\n if (property === \"x2\") {\n return _super.prototype._filterForProperty.call(this, \"x\");\n }\n else if (property === \"y2\") {\n return _super.prototype._filterForProperty.call(this, \"y\");\n }\n return _super.prototype._filterForProperty.call(this, property);\n };\n Segment.prototype.x = function (x, xScale) {\n if (x == null) {\n return _super.prototype.x.call(this);\n }\n if (xScale == null) {\n _super.prototype.x.call(this, x);\n }\n else {\n _super.prototype.x.call(this, x, xScale);\n var x2Binding = this.x2();\n var x2 = x2Binding && x2Binding.accessor;\n if (x2 != null) {\n this._bindProperty(Segment._X2_KEY, x2, xScale);\n }\n }\n return this;\n };\n Segment.prototype.x2 = function (x2) {\n if (x2 == null) {\n return this._propertyBindings.get(Segment._X2_KEY);\n }\n var xBinding = this.x();\n var xScale = xBinding && xBinding.scale;\n this._bindProperty(Segment._X2_KEY, x2, xScale);\n this.render();\n return this;\n };\n Segment.prototype.y = function (y, yScale) {\n if (y == null) {\n return _super.prototype.y.call(this);\n }\n if (yScale == null) {\n _super.prototype.y.call(this, y);\n }\n else {\n _super.prototype.y.call(this, y, yScale);\n var y2Binding = this.y2();\n var y2 = y2Binding && y2Binding.accessor;\n if (y2 != null) {\n this._bindProperty(Segment._Y2_KEY, y2, yScale);\n }\n }\n return this;\n };\n Segment.prototype.y2 = function (y2) {\n if (y2 == null) {\n return this._propertyBindings.get(Segment._Y2_KEY);\n }\n var yBinding = this.y();\n var yScale = yBinding && yBinding.scale;\n this._bindProperty(Segment._Y2_KEY, y2, yScale);\n this.render();\n return this;\n };\n Segment.prototype._propertyProjectors = function () {\n var attrToProjector = _super.prototype._propertyProjectors.call(this);\n attrToProjector[\"x1\"] = Plottable.Plot._scaledAccessor(this.x());\n attrToProjector[\"x2\"] = this.x2() == null ? Plottable.Plot._scaledAccessor(this.x()) : Plottable.Plot._scaledAccessor(this.x2());\n attrToProjector[\"y1\"] = Plottable.Plot._scaledAccessor(this.y());\n attrToProjector[\"y2\"] = this.y2() == null ? Plottable.Plot._scaledAccessor(this.y()) : Plottable.Plot._scaledAccessor(this.y2());\n return attrToProjector;\n };\n Segment.prototype.entitiesIn = function (xRangeOrBounds, yRange) {\n var dataXRange;\n var dataYRange;\n if (yRange == null) {\n var bounds = xRangeOrBounds;\n dataXRange = { min: bounds.topLeft.x, max: bounds.bottomRight.x };\n dataYRange = { min: bounds.topLeft.y, max: bounds.bottomRight.y };\n }\n else {\n dataXRange = xRangeOrBounds;\n dataYRange = yRange;\n }\n return this._entitiesIntersecting(dataXRange, dataYRange);\n };\n Segment.prototype._entitiesIntersecting = function (xRange, yRange) {\n var _this = this;\n var intersected = [];\n var attrToProjector = this._generateAttrToProjector();\n this.entities().forEach(function (entity) {\n if (_this._lineIntersectsBox(entity, xRange, yRange, attrToProjector)) {\n intersected.push(entity);\n }\n });\n return intersected;\n };\n Segment.prototype._lineIntersectsBox = function (entity, xRange, yRange, attrToProjector) {\n var _this = this;\n var x1 = attrToProjector[\"x1\"](entity.datum, entity.index, entity.dataset);\n var x2 = attrToProjector[\"x2\"](entity.datum, entity.index, entity.dataset);\n var y1 = attrToProjector[\"y1\"](entity.datum, entity.index, entity.dataset);\n var y2 = attrToProjector[\"y2\"](entity.datum, entity.index, entity.dataset);\n // check if any of end points of the segment is inside the box\n if ((xRange.min <= x1 && x1 <= xRange.max && yRange.min <= y1 && y1 <= yRange.max) ||\n (xRange.min <= x2 && x2 <= xRange.max && yRange.min <= y2 && y2 <= yRange.max)) {\n return true;\n }\n var startPoint = { x: x1, y: y1 };\n var endPoint = { x: x2, y: y2 };\n var corners = [\n { x: xRange.min, y: yRange.min },\n { x: xRange.min, y: yRange.max },\n { x: xRange.max, y: yRange.max },\n { x: xRange.max, y: yRange.min },\n ];\n var intersections = corners.filter(function (point, index) {\n if (index !== 0) {\n // return true if border formed by conecting current corner and previous corner intersects with the segment\n return _this._lineIntersectsSegment(startPoint, endPoint, point, corners[index - 1]) &&\n _this._lineIntersectsSegment(point, corners[index - 1], startPoint, endPoint);\n }\n });\n return intersections.length > 0;\n };\n Segment.prototype._lineIntersectsSegment = function (point1, point2, point3, point4) {\n /* tslint:disable no-shadowed-variable */\n var calcOrientation = function (point1, point2, point) {\n return (point2.x - point1.x) * (point.y - point2.y) - (point2.y - point1.y) * (point.x - point2.x);\n };\n /* tslint:enable no-shadowed-variable */\n // point3 and point4 are on different sides of line formed by point1 and point2\n return calcOrientation(point1, point2, point3) * calcOrientation(point1, point2, point4) < 0;\n };\n Segment._X2_KEY = \"x2\";\n Segment._Y2_KEY = \"y2\";\n return Segment;\n }(Plottable.XYPlot));\n Plots.Segment = Segment;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Plots;\n (function (Plots) {\n var Waterfall = (function (_super) {\n __extends(Waterfall, _super);\n function Waterfall() {\n _super.call(this);\n this._connectorsEnabled = false;\n this.addClass(\"waterfall-plot\");\n }\n Waterfall.prototype.connectorsEnabled = function (enabled) {\n if (enabled == null) {\n return this._connectorsEnabled;\n }\n this._connectorsEnabled = enabled;\n return this;\n };\n Waterfall.prototype.total = function (total) {\n if (total == null) {\n return this._propertyBindings.get(Waterfall._TOTAL_KEY);\n }\n this._bindProperty(Waterfall._TOTAL_KEY, total, null);\n return this;\n };\n Waterfall.prototype._additionalPaint = function (time) {\n var _this = this;\n this._connectorArea.selectAll(\"line\").remove();\n if (this._connectorsEnabled) {\n Plottable.Utils.Window.setTimeout(function () { return _this._drawConnectors(); }, time);\n }\n };\n Waterfall.prototype._createNodesForDataset = function (dataset) {\n var drawer = _super.prototype._createNodesForDataset.call(this, dataset);\n this._connectorArea = this._renderArea.append(\"g\").classed(Waterfall._CONNECTOR_AREA_CLASS, true);\n return drawer;\n };\n Waterfall.prototype._extentsForProperty = function (attr) {\n var primaryAttr = \"y\";\n if (attr === primaryAttr) {\n return [this._extent];\n }\n else {\n return _super.prototype._extentsForProperty.call(this, attr);\n }\n };\n Waterfall.prototype._generateAttrToProjector = function () {\n var _this = this;\n var attrToProjector = _super.prototype._generateAttrToProjector.call(this);\n var yScale = this.y().scale;\n var totalAccessor = Plottable.Plot._scaledAccessor(this.total());\n var yAttr = this.attr(\"y\");\n if (yAttr == null) {\n attrToProjector[\"y\"] = function (d, i, dataset) {\n var currentValue = _this.y().accessor(d, i, dataset);\n var isTotal = totalAccessor(d, i, dataset);\n if (isTotal) {\n return Math.min(yScale.scale(currentValue), yScale.scale(0));\n }\n else {\n var currentSubtotal = _this._subtotals[i];\n if (i === 0) {\n if (currentValue < 0) {\n return yScale.scale(currentSubtotal - currentValue);\n }\n else {\n return yScale.scale(currentSubtotal);\n }\n }\n var priorSubtotal = _this._subtotals[i - 1];\n if (currentSubtotal > priorSubtotal) {\n return yScale.scale(currentSubtotal);\n }\n else {\n return yScale.scale(priorSubtotal);\n }\n }\n };\n }\n var heightAttr = this.attr(\"height\");\n if (heightAttr == null) {\n attrToProjector[\"height\"] = function (d, i, dataset) {\n var isTotal = totalAccessor(d, i, dataset);\n var currentValue = _this.y().accessor(d, i, dataset);\n if (isTotal) {\n return Math.abs(yScale.scale(currentValue) - yScale.scale(0));\n }\n else {\n var currentSubtotal = _this._subtotals[i];\n if (i === 0) {\n return Math.abs(yScale.scale(currentSubtotal) - yScale.scale(currentSubtotal - currentValue));\n }\n else {\n var priorSubtotal = _this._subtotals[i - 1];\n return Math.abs(yScale.scale(currentSubtotal) - yScale.scale(priorSubtotal));\n }\n }\n };\n }\n attrToProjector[\"class\"] = function (d, i, dataset) {\n var baseClass = \"\";\n if (_this.attr(\"class\") != null) {\n baseClass = _this.attr(\"class\").accessor(d, i, dataset) + \" \";\n }\n var isTotal = totalAccessor(d, i, dataset);\n if (isTotal) {\n return baseClass + Waterfall._BAR_TOTAL_CLASS;\n }\n else {\n var delta = _this.y().accessor(d, i, dataset);\n return baseClass + (delta > 0 ? Waterfall._BAR_GROWTH_CLASS : Waterfall._BAR_DECLINE_CLASS);\n }\n };\n return attrToProjector;\n };\n Waterfall.prototype._onDatasetUpdate = function () {\n this._updateSubtotals();\n _super.prototype._onDatasetUpdate.call(this);\n return this;\n };\n Waterfall.prototype._calculateSubtotalsAndExtent = function (dataset) {\n var _this = this;\n var min = Number.MAX_VALUE;\n var max = Number.MIN_VALUE;\n var total = 0;\n var hasStarted = false;\n dataset.data().forEach(function (datum, index) {\n var currentValue = _this.y().accessor(datum, index, dataset);\n var isTotal = _this.total().accessor(datum, index, dataset);\n if (!isTotal || index === 0) {\n total += currentValue;\n }\n _this._subtotals.push(total);\n if (total < min) {\n min = total;\n }\n if (total > max) {\n max = total;\n }\n if (isTotal) {\n if (currentValue < min) {\n min = currentValue;\n }\n if (currentValue > max) {\n max = currentValue;\n }\n }\n if (!hasStarted && isTotal) {\n var startTotal = currentValue - total;\n for (var i = 0; i < _this._subtotals.length; i++) {\n _this._subtotals[i] += startTotal;\n }\n hasStarted = true;\n total += startTotal;\n min += startTotal;\n max += startTotal;\n }\n });\n this._extent = [min, max];\n };\n Waterfall.prototype._drawConnectors = function () {\n var attrToProjector = this._generateAttrToProjector();\n var dataset = this.datasets()[0];\n for (var datumIndex = 1; datumIndex < dataset.data().length; datumIndex++) {\n var prevIndex = datumIndex - 1;\n var datum = dataset.data()[datumIndex];\n var prevDatum = dataset.data()[prevIndex];\n var x = attrToProjector[\"x\"](prevDatum, prevIndex, dataset);\n var x2 = attrToProjector[\"x\"](datum, datumIndex, dataset) + attrToProjector[\"width\"](datum, datumIndex, dataset);\n var y = attrToProjector[\"y\"](datum, datumIndex, dataset);\n if ((this._subtotals[datumIndex] > 0 && this._subtotals[datumIndex] > this._subtotals[prevIndex]) ||\n (this._subtotals[datumIndex] < 0 && this._subtotals[datumIndex] >= this._subtotals[prevIndex])) {\n y = attrToProjector[\"y\"](datum, datumIndex, dataset) + attrToProjector[\"height\"](datum, datumIndex, dataset);\n }\n this._connectorArea.append(\"line\").classed(Waterfall._CONNECTOR_CLASS, true)\n .attr(\"x1\", x).attr(\"x2\", x2).attr(\"y1\", y).attr(\"y2\", y);\n }\n };\n Waterfall.prototype._updateSubtotals = function () {\n var datasets = this.datasets();\n if (datasets.length > 0) {\n var dataset = datasets[datasets.length - 1];\n this._subtotals = new Array();\n this._calculateSubtotalsAndExtent(dataset);\n }\n };\n Waterfall._BAR_DECLINE_CLASS = \"waterfall-decline\";\n Waterfall._BAR_GROWTH_CLASS = \"waterfall-growth\";\n Waterfall._BAR_TOTAL_CLASS = \"waterfall-total\";\n Waterfall._CONNECTOR_CLASS = \"connector\";\n Waterfall._CONNECTOR_AREA_CLASS = \"connector-area\";\n Waterfall._TOTAL_KEY = \"total\";\n return Waterfall;\n }(Plots.Bar));\n Plots.Waterfall = Waterfall;\n })(Plots = Plottable.Plots || (Plottable.Plots = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Animators;\n (function (Animators) {\n /**\n * An animator implementation with no animation. The attributes are\n * immediately set on the selection.\n */\n var Null = (function () {\n function Null() {\n }\n Null.prototype.totalTime = function (selection) {\n return 0;\n };\n Null.prototype.animate = function (selection, attrToAppliedProjector) {\n return selection.attr(attrToAppliedProjector);\n };\n return Null;\n }());\n Animators.Null = Null;\n })(Animators = Plottable.Animators || (Plottable.Animators = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Animators;\n (function (Animators) {\n /**\n * An Animator with easing and configurable durations and delays.\n */\n var Easing = (function () {\n /**\n * Constructs the default animator\n *\n * @constructor\n */\n function Easing() {\n this._startDelay = Easing._DEFAULT_START_DELAY_MILLISECONDS;\n this._stepDuration = Easing._DEFAULT_STEP_DURATION_MILLISECONDS;\n this._stepDelay = Easing._DEFAULT_ITERATIVE_DELAY_MILLISECONDS;\n this._maxTotalDuration = Easing._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS;\n this._easingMode = Easing._DEFAULT_EASING_MODE;\n }\n Easing.prototype.totalTime = function (numberOfSteps) {\n var adjustedIterativeDelay = this._getAdjustedIterativeDelay(numberOfSteps);\n return this.startDelay() + adjustedIterativeDelay * (Math.max(numberOfSteps - 1, 0)) + this.stepDuration();\n };\n Easing.prototype.animate = function (selection, attrToAppliedProjector) {\n var _this = this;\n var numberOfSteps = selection[0].length;\n var adjustedIterativeDelay = this._getAdjustedIterativeDelay(numberOfSteps);\n return selection.transition()\n .ease(this.easingMode())\n .duration(this.stepDuration())\n .delay(function (d, i) { return _this.startDelay() + adjustedIterativeDelay * i; })\n .attr(attrToAppliedProjector);\n };\n Easing.prototype.startDelay = function (startDelay) {\n if (startDelay == null) {\n return this._startDelay;\n }\n else {\n this._startDelay = startDelay;\n return this;\n }\n };\n Easing.prototype.stepDuration = function (stepDuration) {\n if (stepDuration == null) {\n return Math.min(this._stepDuration, this._maxTotalDuration);\n }\n else {\n this._stepDuration = stepDuration;\n return this;\n }\n };\n Easing.prototype.stepDelay = function (stepDelay) {\n if (stepDelay == null) {\n return this._stepDelay;\n }\n else {\n this._stepDelay = stepDelay;\n return this;\n }\n };\n Easing.prototype.maxTotalDuration = function (maxTotalDuration) {\n if (maxTotalDuration == null) {\n return this._maxTotalDuration;\n }\n else {\n this._maxTotalDuration = maxTotalDuration;\n return this;\n }\n };\n Easing.prototype.easingMode = function (easingMode) {\n if (easingMode == null) {\n return this._easingMode;\n }\n else {\n this._easingMode = easingMode;\n return this;\n }\n };\n /**\n * Adjust the iterative delay, such that it takes into account the maxTotalDuration constraint\n */\n Easing.prototype._getAdjustedIterativeDelay = function (numberOfSteps) {\n var stepStartTimeInterval = this.maxTotalDuration() - this.stepDuration();\n stepStartTimeInterval = Math.max(stepStartTimeInterval, 0);\n var maxPossibleIterativeDelay = stepStartTimeInterval / Math.max(numberOfSteps - 1, 1);\n return Math.min(this.stepDelay(), maxPossibleIterativeDelay);\n };\n /**\n * The default starting delay of the animation in milliseconds\n */\n Easing._DEFAULT_START_DELAY_MILLISECONDS = 0;\n /**\n * The default duration of one animation step in milliseconds\n */\n Easing._DEFAULT_STEP_DURATION_MILLISECONDS = 300;\n /**\n * The default maximum start delay between each step of an animation\n */\n Easing._DEFAULT_ITERATIVE_DELAY_MILLISECONDS = 15;\n /**\n * The default maximum total animation duration\n */\n Easing._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS = Infinity;\n /**\n * The default easing of the animation\n */\n Easing._DEFAULT_EASING_MODE = \"exp-out\";\n return Easing;\n }());\n Animators.Easing = Easing;\n })(Animators = Plottable.Animators || (Plottable.Animators = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Dispatcher = (function () {\n function Dispatcher() {\n this._eventToProcessingFunction = {};\n this._eventNameToCallbackSet = {};\n this._connected = false;\n }\n Dispatcher.prototype._hasNoCallbacks = function () {\n var eventNames = Object.keys(this._eventNameToCallbackSet);\n for (var i = 0; i < eventNames.length; i++) {\n if (this._eventNameToCallbackSet[eventNames[i]].size !== 0) {\n return false;\n }\n }\n return true;\n };\n Dispatcher.prototype._connect = function () {\n var _this = this;\n if (this._connected) {\n return;\n }\n Object.keys(this._eventToProcessingFunction).forEach(function (event) {\n var processingFunction = _this._eventToProcessingFunction[event];\n document.addEventListener(event, processingFunction);\n });\n this._connected = true;\n };\n Dispatcher.prototype._disconnect = function () {\n var _this = this;\n if (this._connected && this._hasNoCallbacks()) {\n Object.keys(this._eventToProcessingFunction).forEach(function (event) {\n var processingFunction = _this._eventToProcessingFunction[event];\n document.removeEventListener(event, processingFunction);\n });\n this._connected = false;\n }\n };\n Dispatcher.prototype._addCallbackForEvent = function (eventName, callback) {\n if (this._eventNameToCallbackSet[eventName] == null) {\n this._eventNameToCallbackSet[eventName] = new Plottable.Utils.CallbackSet();\n }\n this._eventNameToCallbackSet[eventName].add(callback);\n this._connect();\n };\n Dispatcher.prototype._removeCallbackForEvent = function (eventName, callback) {\n if (this._eventNameToCallbackSet[eventName] != null) {\n this._eventNameToCallbackSet[eventName].delete(callback);\n }\n this._disconnect();\n };\n Dispatcher.prototype._callCallbacksForEvent = function (eventName) {\n var args = [];\n for (var _i = 1; _i < arguments.length; _i++) {\n args[_i - 1] = arguments[_i];\n }\n var callbackSet = this._eventNameToCallbackSet[eventName];\n if (callbackSet != null) {\n callbackSet.callCallbacks.apply(callbackSet, args);\n }\n };\n return Dispatcher;\n }());\n Plottable.Dispatcher = Dispatcher;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Dispatchers;\n (function (Dispatchers) {\n var Mouse = (function (_super) {\n __extends(Mouse, _super);\n /**\n * This constructor not be invoked directly.\n *\n * @constructor\n * @param {SVGElement} svg The root to attach to.\n */\n function Mouse(svg) {\n var _this = this;\n _super.call(this);\n this._translator = Plottable.Utils.ClientToSVGTranslator.getTranslator(svg);\n this._lastMousePosition = { x: -1, y: -1 };\n var processMoveCallback = function (e) { return _this._measureAndDispatch(e, Mouse._MOUSEMOVE_EVENT_NAME, \"page\"); };\n this._eventToProcessingFunction[Mouse._MOUSEOVER_EVENT_NAME] = processMoveCallback;\n this._eventToProcessingFunction[Mouse._MOUSEMOVE_EVENT_NAME] = processMoveCallback;\n this._eventToProcessingFunction[Mouse._MOUSEOUT_EVENT_NAME] = processMoveCallback;\n this._eventToProcessingFunction[Mouse._MOUSEDOWN_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Mouse._MOUSEDOWN_EVENT_NAME); };\n this._eventToProcessingFunction[Mouse._MOUSEUP_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Mouse._MOUSEUP_EVENT_NAME, \"page\"); };\n this._eventToProcessingFunction[Mouse._WHEEL_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Mouse._WHEEL_EVENT_NAME); };\n this._eventToProcessingFunction[Mouse._DBLCLICK_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Mouse._DBLCLICK_EVENT_NAME); };\n }\n /**\n * Get a Mouse Dispatcher for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n *\n * @param {SVGElement} elem\n * @return {Dispatchers.Mouse}\n */\n Mouse.getDispatcher = function (elem) {\n var svg = Plottable.Utils.DOM.boundingSVG(elem);\n var dispatcher = svg[Mouse._DISPATCHER_KEY];\n if (dispatcher == null) {\n dispatcher = new Mouse(svg);\n svg[Mouse._DISPATCHER_KEY] = dispatcher;\n }\n return dispatcher;\n };\n /**\n * Registers a callback to be called when the mouse position changes.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.onMouseMove = function (callback) {\n this._addCallbackForEvent(Mouse._MOUSEMOVE_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the mouse position changes.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.offMouseMove = function (callback) {\n this._removeCallbackForEvent(Mouse._MOUSEMOVE_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a mousedown occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.onMouseDown = function (callback) {\n this._addCallbackForEvent(Mouse._MOUSEDOWN_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a mousedown occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.offMouseDown = function (callback) {\n this._removeCallbackForEvent(Mouse._MOUSEDOWN_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a mouseup occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.onMouseUp = function (callback) {\n this._addCallbackForEvent(Mouse._MOUSEUP_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a mouseup occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.offMouseUp = function (callback) {\n this._removeCallbackForEvent(Mouse._MOUSEUP_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a wheel event occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.onWheel = function (callback) {\n this._addCallbackForEvent(Mouse._WHEEL_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a wheel event occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.offWheel = function (callback) {\n this._removeCallbackForEvent(Mouse._WHEEL_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a dblClick occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.onDblClick = function (callback) {\n this._addCallbackForEvent(Mouse._DBLCLICK_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a dblClick occurs.\n *\n * @param {MouseCallback} callback\n * @return {Dispatchers.Mouse} The calling Mouse Dispatcher.\n */\n Mouse.prototype.offDblClick = function (callback) {\n this._removeCallbackForEvent(Mouse._DBLCLICK_EVENT_NAME, callback);\n return this;\n };\n /**\n * Computes the mouse position from the given event, and if successful\n * calls all the callbacks in the provided callbackSet.\n */\n Mouse.prototype._measureAndDispatch = function (event, eventName, scope) {\n if (scope === void 0) { scope = \"element\"; }\n if (scope !== \"page\" && scope !== \"element\") {\n throw new Error(\"Invalid scope '\" + scope + \"', must be 'element' or 'page'\");\n }\n if (scope === \"page\" || this.eventInsideSVG(event)) {\n var newMousePosition = this._translator.computePosition(event.clientX, event.clientY);\n if (newMousePosition != null) {\n this._lastMousePosition = newMousePosition;\n this._callCallbacksForEvent(eventName, this.lastMousePosition(), event);\n }\n }\n };\n Mouse.prototype.eventInsideSVG = function (event) {\n return this._translator.insideSVG(event);\n };\n /**\n * Returns the last computed mouse position in coordinate space.\n *\n * @return {Point}\n */\n Mouse.prototype.lastMousePosition = function () {\n return this._lastMousePosition;\n };\n Mouse._DISPATCHER_KEY = \"__Plottable_Dispatcher_Mouse\";\n Mouse._MOUSEOVER_EVENT_NAME = \"mouseover\";\n Mouse._MOUSEMOVE_EVENT_NAME = \"mousemove\";\n Mouse._MOUSEOUT_EVENT_NAME = \"mouseout\";\n Mouse._MOUSEDOWN_EVENT_NAME = \"mousedown\";\n Mouse._MOUSEUP_EVENT_NAME = \"mouseup\";\n Mouse._WHEEL_EVENT_NAME = \"wheel\";\n Mouse._DBLCLICK_EVENT_NAME = \"dblclick\";\n return Mouse;\n }(Plottable.Dispatcher));\n Dispatchers.Mouse = Mouse;\n })(Dispatchers = Plottable.Dispatchers || (Plottable.Dispatchers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Dispatchers;\n (function (Dispatchers) {\n var Touch = (function (_super) {\n __extends(Touch, _super);\n /**\n * This constructor should not be invoked directly.\n *\n * @constructor\n * @param {SVGElement} svg The root to attach to.\n */\n function Touch(svg) {\n var _this = this;\n _super.call(this);\n this._translator = Plottable.Utils.ClientToSVGTranslator.getTranslator(svg);\n this._eventToProcessingFunction[Touch._TOUCHSTART_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Touch._TOUCHSTART_EVENT_NAME, \"page\"); };\n this._eventToProcessingFunction[Touch._TOUCHMOVE_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Touch._TOUCHMOVE_EVENT_NAME, \"page\"); };\n this._eventToProcessingFunction[Touch._TOUCHEND_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Touch._TOUCHEND_EVENT_NAME, \"page\"); };\n this._eventToProcessingFunction[Touch._TOUCHCANCEL_EVENT_NAME] =\n function (e) { return _this._measureAndDispatch(e, Touch._TOUCHCANCEL_EVENT_NAME, \"page\"); };\n }\n /**\n * Gets a Touch Dispatcher for the containing elem.\n * If one already exists on that , it will be returned; otherwise, a new one will be created.\n *\n * @param {SVGElement} elem\n * @return {Dispatchers.Touch}\n */\n Touch.getDispatcher = function (elem) {\n var svg = Plottable.Utils.DOM.boundingSVG(elem);\n var dispatcher = svg[Touch._DISPATCHER_KEY];\n if (dispatcher == null) {\n dispatcher = new Touch(svg);\n svg[Touch._DISPATCHER_KEY] = dispatcher;\n }\n return dispatcher;\n };\n /**\n * Registers a callback to be called when a touch starts.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.onTouchStart = function (callback) {\n this._addCallbackForEvent(Touch._TOUCHSTART_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a touch starts.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.offTouchStart = function (callback) {\n this._removeCallbackForEvent(Touch._TOUCHSTART_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when the touch position changes.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.onTouchMove = function (callback) {\n this._addCallbackForEvent(Touch._TOUCHMOVE_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the touch position changes.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.offTouchMove = function (callback) {\n this._removeCallbackForEvent(Touch._TOUCHMOVE_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a touch ends.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.onTouchEnd = function (callback) {\n this._addCallbackForEvent(Touch._TOUCHEND_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a touch ends.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.offTouchEnd = function (callback) {\n this._removeCallbackForEvent(Touch._TOUCHEND_EVENT_NAME, callback);\n return this;\n };\n /**\n * Registers a callback to be called when a touch is cancelled.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.onTouchCancel = function (callback) {\n this._addCallbackForEvent(Touch._TOUCHCANCEL_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes a callback that would be called when a touch is cancelled.\n *\n * @param {TouchCallback} callback\n * @return {Dispatchers.Touch} The calling Touch Dispatcher.\n */\n Touch.prototype.offTouchCancel = function (callback) {\n this._removeCallbackForEvent(Touch._TOUCHCANCEL_EVENT_NAME, callback);\n return this;\n };\n /**\n * Computes the Touch position from the given event, and if successful\n * calls all the callbacks in the provided callbackSet.\n */\n Touch.prototype._measureAndDispatch = function (event, eventName, scope) {\n if (scope === void 0) { scope = \"element\"; }\n if (scope !== \"page\" && scope !== \"element\") {\n throw new Error(\"Invalid scope '\" + scope + \"', must be 'element' or 'page'\");\n }\n if (scope === \"element\" && !this.eventInsideSVG(event)) {\n return;\n }\n var touches = event.changedTouches;\n var touchPositions = {};\n var touchIdentifiers = [];\n for (var i = 0; i < touches.length; i++) {\n var touch = touches[i];\n var touchID = touch.identifier;\n var newTouchPosition = this._translator.computePosition(touch.clientX, touch.clientY);\n if (newTouchPosition != null) {\n touchPositions[touchID] = newTouchPosition;\n touchIdentifiers.push(touchID);\n }\n }\n ;\n if (touchIdentifiers.length > 0) {\n this._callCallbacksForEvent(eventName, touchIdentifiers, touchPositions, event);\n }\n };\n Touch.prototype.eventInsideSVG = function (event) {\n return this._translator.insideSVG(event);\n };\n Touch._DISPATCHER_KEY = \"__Plottable_Dispatcher_Touch\";\n Touch._TOUCHSTART_EVENT_NAME = \"touchstart\";\n Touch._TOUCHMOVE_EVENT_NAME = \"touchmove\";\n Touch._TOUCHEND_EVENT_NAME = \"touchend\";\n Touch._TOUCHCANCEL_EVENT_NAME = \"touchcancel\";\n return Touch;\n }(Plottable.Dispatcher));\n Dispatchers.Touch = Touch;\n })(Dispatchers = Plottable.Dispatchers || (Plottable.Dispatchers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Dispatchers;\n (function (Dispatchers) {\n var Key = (function (_super) {\n __extends(Key, _super);\n /**\n * This constructor should not be invoked directly.\n *\n * @constructor\n */\n function Key() {\n var _this = this;\n _super.call(this);\n this._eventToProcessingFunction[Key._KEYDOWN_EVENT_NAME] = function (e) { return _this._processKeydown(e); };\n this._eventToProcessingFunction[Key._KEYUP_EVENT_NAME] = function (e) { return _this._processKeyup(e); };\n }\n /**\n * Gets a Key Dispatcher. If one already exists it will be returned;\n * otherwise, a new one will be created.\n *\n * @return {Dispatchers.Key}\n */\n Key.getDispatcher = function () {\n var dispatcher = document[Key._DISPATCHER_KEY];\n if (dispatcher == null) {\n dispatcher = new Key();\n document[Key._DISPATCHER_KEY] = dispatcher;\n }\n return dispatcher;\n };\n Key.prototype._processKeydown = function (event) {\n this._callCallbacksForEvent(Key._KEYDOWN_EVENT_NAME, event.keyCode, event);\n };\n Key.prototype._processKeyup = function (event) {\n this._callCallbacksForEvent(Key._KEYUP_EVENT_NAME, event.keyCode, event);\n };\n /**\n * Registers a callback to be called whenever a key is pressed.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n Key.prototype.onKeyDown = function (callback) {\n this._addCallbackForEvent(Key._KEYDOWN_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes the callback to be called whenever a key is pressed.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n Key.prototype.offKeyDown = function (callback) {\n this._removeCallbackForEvent(Key._KEYDOWN_EVENT_NAME, callback);\n return this;\n };\n /** Registers a callback to be called whenever a key is released.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n Key.prototype.onKeyUp = function (callback) {\n this._addCallbackForEvent(Key._KEYUP_EVENT_NAME, callback);\n return this;\n };\n /**\n * Removes the callback to be called whenever a key is released.\n *\n * @param {KeyCallback} callback\n * @return {Dispatchers.Key} The calling Key Dispatcher.\n */\n Key.prototype.offKeyUp = function (callback) {\n this._removeCallbackForEvent(Key._KEYUP_EVENT_NAME, callback);\n return this;\n };\n Key._DISPATCHER_KEY = \"__Plottable_Dispatcher_Key\";\n Key._KEYDOWN_EVENT_NAME = \"keydown\";\n Key._KEYUP_EVENT_NAME = \"keyup\";\n return Key;\n }(Plottable.Dispatcher));\n Dispatchers.Key = Key;\n })(Dispatchers = Plottable.Dispatchers || (Plottable.Dispatchers = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interaction = (function () {\n function Interaction() {\n var _this = this;\n this._anchorCallback = function (component) { return _this._anchor(component); };\n this._enabled = true;\n }\n Interaction.prototype._anchor = function (component) {\n this._isAnchored = true;\n };\n Interaction.prototype._unanchor = function () {\n this._isAnchored = false;\n };\n /**\n * Attaches this Interaction to a Component.\n * If the Interaction was already attached to a Component, it first detaches itself from the old Component.\n *\n * @param {Component} component\n * @returns {Interaction} The calling Interaction.\n */\n Interaction.prototype.attachTo = function (component) {\n this._disconnect();\n this._componentAttachedTo = component;\n this._connect();\n return this;\n };\n Interaction.prototype._connect = function () {\n if (this.enabled() && this._componentAttachedTo != null && !this._isAnchored) {\n this._componentAttachedTo.onAnchor(this._anchorCallback);\n }\n };\n /**\n * Detaches this Interaction from the Component.\n * This Interaction can be reused.\n *\n * @param {Component} component\n * @returns {Interaction} The calling Interaction.\n */\n Interaction.prototype.detachFrom = function (component) {\n this._disconnect();\n this._componentAttachedTo = null;\n return this;\n };\n Interaction.prototype._disconnect = function () {\n if (this._isAnchored) {\n this._unanchor();\n }\n if (this._componentAttachedTo != null) {\n this._componentAttachedTo.offAnchor(this._anchorCallback);\n }\n };\n Interaction.prototype.enabled = function (enabled) {\n if (enabled == null) {\n return this._enabled;\n }\n this._enabled = enabled;\n if (this._enabled) {\n this._connect();\n }\n else {\n this._disconnect();\n }\n return this;\n };\n /**\n * Translates an -coordinate-space point to Component-space coordinates.\n *\n * @param {Point} p A Point in -space coordinates.\n * @return {Point} The same location in Component-space coordinates.\n */\n Interaction.prototype._translateToComponentSpace = function (p) {\n var origin = this._componentAttachedTo.originToSVG();\n return {\n x: p.x - origin.x,\n y: p.y - origin.y,\n };\n };\n /**\n * Checks whether a Component-coordinate-space Point is inside the Component.\n *\n * @param {Point} p A Point in Compoennt-space coordinates.\n * @return {boolean} Whether or not the point is inside the Component.\n */\n Interaction.prototype._isInsideComponent = function (p) {\n return 0 <= p.x && 0 <= p.y\n && p.x <= this._componentAttachedTo.width()\n && p.y <= this._componentAttachedTo.height();\n };\n return Interaction;\n }());\n Plottable.Interaction = Interaction;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var Click = (function (_super) {\n __extends(Click, _super);\n function Click() {\n var _this = this;\n _super.apply(this, arguments);\n this._clickedDown = false;\n this._onClickCallbacks = new Plottable.Utils.CallbackSet();\n this._mouseDownCallback = function (p) { return _this._handleClickDown(p); };\n this._mouseUpCallback = function (p) { return _this._handleClickUp(p); };\n this._touchStartCallback = function (ids, idToPoint) { return _this._handleClickDown(idToPoint[ids[0]]); };\n this._touchEndCallback = function (ids, idToPoint) { return _this._handleClickUp(idToPoint[ids[0]]); };\n this._touchCancelCallback = function (ids, idToPoint) { return _this._clickedDown = false; };\n }\n Click.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._mouseDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(component.content().node());\n this._mouseDispatcher.onMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.onMouseUp(this._mouseUpCallback);\n this._touchDispatcher = Plottable.Dispatchers.Touch.getDispatcher(component.content().node());\n this._touchDispatcher.onTouchStart(this._touchStartCallback);\n this._touchDispatcher.onTouchEnd(this._touchEndCallback);\n this._touchDispatcher.onTouchCancel(this._touchCancelCallback);\n };\n Click.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._mouseDispatcher.offMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.offMouseUp(this._mouseUpCallback);\n this._mouseDispatcher = null;\n this._touchDispatcher.offTouchStart(this._touchStartCallback);\n this._touchDispatcher.offTouchEnd(this._touchEndCallback);\n this._touchDispatcher.offTouchCancel(this._touchCancelCallback);\n this._touchDispatcher = null;\n };\n Click.prototype._handleClickDown = function (p) {\n var translatedPoint = this._translateToComponentSpace(p);\n if (this._isInsideComponent(translatedPoint)) {\n this._clickedDown = true;\n }\n };\n Click.prototype._handleClickUp = function (p) {\n var translatedPoint = this._translateToComponentSpace(p);\n if (this._clickedDown && this._isInsideComponent(translatedPoint)) {\n this._onClickCallbacks.callCallbacks(translatedPoint);\n }\n this._clickedDown = false;\n };\n /**\n * Adds a callback to be called when the Component is clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.Click} The calling Click Interaction.\n */\n Click.prototype.onClick = function (callback) {\n this._onClickCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the Component is clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.Click} The calling Click Interaction.\n */\n Click.prototype.offClick = function (callback) {\n this._onClickCallbacks.delete(callback);\n return this;\n };\n return Click;\n }(Plottable.Interaction));\n Interactions.Click = Click;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var ClickState;\n (function (ClickState) {\n ClickState[ClickState[\"NotClicked\"] = 0] = \"NotClicked\";\n ClickState[ClickState[\"SingleClicked\"] = 1] = \"SingleClicked\";\n ClickState[ClickState[\"DoubleClicked\"] = 2] = \"DoubleClicked\";\n })(ClickState || (ClickState = {}));\n ;\n var DoubleClick = (function (_super) {\n __extends(DoubleClick, _super);\n function DoubleClick() {\n var _this = this;\n _super.apply(this, arguments);\n this._clickState = ClickState.NotClicked;\n this._clickedDown = false;\n this._onDoubleClickCallbacks = new Plottable.Utils.CallbackSet();\n this._mouseDownCallback = function (p) { return _this._handleClickDown(p); };\n this._mouseUpCallback = function (p) { return _this._handleClickUp(p); };\n this._dblClickCallback = function (p) { return _this._handleDblClick(); };\n this._touchStartCallback = function (ids, idToPoint) { return _this._handleClickDown(idToPoint[ids[0]]); };\n this._touchEndCallback = function (ids, idToPoint) { return _this._handleClickUp(idToPoint[ids[0]]); };\n this._touchCancelCallback = function (ids, idToPoint) { return _this._handleClickCancel(); };\n }\n DoubleClick.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._mouseDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(component.content().node());\n this._mouseDispatcher.onMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.onMouseUp(this._mouseUpCallback);\n this._mouseDispatcher.onDblClick(this._dblClickCallback);\n this._touchDispatcher = Plottable.Dispatchers.Touch.getDispatcher(component.content().node());\n this._touchDispatcher.onTouchStart(this._touchStartCallback);\n this._touchDispatcher.onTouchEnd(this._touchEndCallback);\n this._touchDispatcher.onTouchCancel(this._touchCancelCallback);\n };\n DoubleClick.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._mouseDispatcher.offMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.offMouseUp(this._mouseUpCallback);\n this._mouseDispatcher.offDblClick(this._dblClickCallback);\n this._mouseDispatcher = null;\n this._touchDispatcher.offTouchStart(this._touchStartCallback);\n this._touchDispatcher.offTouchEnd(this._touchEndCallback);\n this._touchDispatcher.offTouchCancel(this._touchCancelCallback);\n this._touchDispatcher = null;\n };\n DoubleClick.prototype._handleClickDown = function (p) {\n var translatedP = this._translateToComponentSpace(p);\n if (this._isInsideComponent(translatedP)) {\n if (!(this._clickState === ClickState.SingleClicked) || !DoubleClick._pointsEqual(translatedP, this._clickedPoint)) {\n this._clickState = ClickState.NotClicked;\n }\n this._clickedPoint = translatedP;\n this._clickedDown = true;\n }\n };\n DoubleClick.prototype._handleClickUp = function (p) {\n var translatedP = this._translateToComponentSpace(p);\n if (this._clickedDown && DoubleClick._pointsEqual(translatedP, this._clickedPoint)) {\n this._clickState = this._clickState === ClickState.NotClicked ? ClickState.SingleClicked : ClickState.DoubleClicked;\n }\n else {\n this._clickState = ClickState.NotClicked;\n }\n this._clickedDown = false;\n };\n DoubleClick.prototype._handleDblClick = function () {\n if (this._clickState === ClickState.DoubleClicked) {\n this._onDoubleClickCallbacks.callCallbacks(this._clickedPoint);\n this._clickState = ClickState.NotClicked;\n }\n };\n DoubleClick.prototype._handleClickCancel = function () {\n this._clickState = ClickState.NotClicked;\n this._clickedDown = false;\n };\n DoubleClick._pointsEqual = function (p1, p2) {\n return p1.x === p2.x && p1.y === p2.y;\n };\n /**\n * Adds a callback to be called when the Component is double-clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.DoubleClick} The calling DoubleClick Interaction.\n */\n DoubleClick.prototype.onDoubleClick = function (callback) {\n this._onDoubleClickCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the Component is double-clicked.\n *\n * @param {ClickCallback} callback\n * @return {Interactions.DoubleClick} The calling DoubleClick Interaction.\n */\n DoubleClick.prototype.offDoubleClick = function (callback) {\n this._onDoubleClickCallbacks.delete(callback);\n return this;\n };\n return DoubleClick;\n }(Plottable.Interaction));\n Interactions.DoubleClick = DoubleClick;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var Key = (function (_super) {\n __extends(Key, _super);\n function Key() {\n var _this = this;\n _super.apply(this, arguments);\n this._keyPressCallbacks = {};\n this._keyReleaseCallbacks = {};\n this._mouseMoveCallback = function (point) { return false; }; // HACKHACK: registering a listener\n this._downedKeys = new Plottable.Utils.Set();\n this._keyDownCallback = function (keyCode, event) { return _this._handleKeyDownEvent(keyCode, event); };\n this._keyUpCallback = function (keyCode) { return _this._handleKeyUpEvent(keyCode); };\n }\n Key.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._positionDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(this._componentAttachedTo._element.node());\n this._positionDispatcher.onMouseMove(this._mouseMoveCallback);\n this._keyDispatcher = Plottable.Dispatchers.Key.getDispatcher();\n this._keyDispatcher.onKeyDown(this._keyDownCallback);\n this._keyDispatcher.onKeyUp(this._keyUpCallback);\n };\n Key.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._positionDispatcher.offMouseMove(this._mouseMoveCallback);\n this._positionDispatcher = null;\n this._keyDispatcher.offKeyDown(this._keyDownCallback);\n this._keyDispatcher.offKeyUp(this._keyUpCallback);\n this._keyDispatcher = null;\n };\n Key.prototype._handleKeyDownEvent = function (keyCode, event) {\n var p = this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());\n if (this._isInsideComponent(p) && !event.repeat) {\n if (this._keyPressCallbacks[keyCode]) {\n this._keyPressCallbacks[keyCode].callCallbacks(keyCode);\n }\n this._downedKeys.add(keyCode);\n }\n };\n Key.prototype._handleKeyUpEvent = function (keyCode) {\n if (this._downedKeys.has(keyCode) && this._keyReleaseCallbacks[keyCode]) {\n this._keyReleaseCallbacks[keyCode].callCallbacks(keyCode);\n }\n this._downedKeys.delete(keyCode);\n };\n /**\n * Adds a callback to be called when the key with the given keyCode is\n * pressed and the user is moused over the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n Key.prototype.onKeyPress = function (keyCode, callback) {\n if (!this._keyPressCallbacks[keyCode]) {\n this._keyPressCallbacks[keyCode] = new Plottable.Utils.CallbackSet();\n }\n this._keyPressCallbacks[keyCode].add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the key with the given keyCode is\n * pressed and the user is moused over the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n Key.prototype.offKeyPress = function (keyCode, callback) {\n this._keyPressCallbacks[keyCode].delete(callback);\n if (this._keyPressCallbacks[keyCode].size === 0) {\n delete this._keyPressCallbacks[keyCode];\n }\n return this;\n };\n /**\n * Adds a callback to be called when the key with the given keyCode is\n * released if the key was pressed with the mouse inside of the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n Key.prototype.onKeyRelease = function (keyCode, callback) {\n if (!this._keyReleaseCallbacks[keyCode]) {\n this._keyReleaseCallbacks[keyCode] = new Plottable.Utils.CallbackSet();\n }\n this._keyReleaseCallbacks[keyCode].add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the key with the given keyCode is\n * released if the key was pressed with the mouse inside of the Component.\n *\n * @param {number} keyCode\n * @param {KeyCallback} callback\n * @returns {Interactions.Key} The calling Key Interaction.\n */\n Key.prototype.offKeyRelease = function (keyCode, callback) {\n this._keyReleaseCallbacks[keyCode].delete(callback);\n if (this._keyReleaseCallbacks[keyCode].size === 0) {\n delete this._keyReleaseCallbacks[keyCode];\n }\n return this;\n };\n return Key;\n }(Plottable.Interaction));\n Interactions.Key = Key;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var Pointer = (function (_super) {\n __extends(Pointer, _super);\n function Pointer() {\n var _this = this;\n _super.apply(this, arguments);\n this._overComponent = false;\n this._pointerEnterCallbacks = new Plottable.Utils.CallbackSet();\n this._pointerMoveCallbacks = new Plottable.Utils.CallbackSet();\n this._pointerExitCallbacks = new Plottable.Utils.CallbackSet();\n this._mouseMoveCallback = function (p, e) { return _this._handleMouseEvent(p, e); };\n this._touchStartCallback = function (ids, idToPoint, e) { return _this._handleTouchEvent(idToPoint[ids[0]], e); };\n }\n Pointer.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._mouseDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(this._componentAttachedTo.content().node());\n this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);\n this._touchDispatcher = Plottable.Dispatchers.Touch.getDispatcher(this._componentAttachedTo.content().node());\n this._touchDispatcher.onTouchStart(this._touchStartCallback);\n };\n Pointer.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);\n this._mouseDispatcher = null;\n this._touchDispatcher.offTouchStart(this._touchStartCallback);\n this._touchDispatcher = null;\n };\n Pointer.prototype._handleMouseEvent = function (p, e) {\n var insideSVG = this._mouseDispatcher.eventInsideSVG(e);\n this._handlePointerEvent(p, insideSVG);\n };\n Pointer.prototype._handleTouchEvent = function (p, e) {\n var insideSVG = this._touchDispatcher.eventInsideSVG(e);\n this._handlePointerEvent(p, insideSVG);\n };\n Pointer.prototype._handlePointerEvent = function (p, insideSVG) {\n var translatedP = this._translateToComponentSpace(p);\n var overComponent = this._isInsideComponent(translatedP);\n if (overComponent && insideSVG) {\n if (!this._overComponent) {\n this._pointerEnterCallbacks.callCallbacks(translatedP);\n }\n this._pointerMoveCallbacks.callCallbacks(translatedP);\n }\n else if (this._overComponent) {\n this._pointerExitCallbacks.callCallbacks(translatedP);\n }\n this._overComponent = overComponent && insideSVG;\n };\n /**\n * Adds a callback to be called when the pointer enters the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.onPointerEnter = function (callback) {\n this._pointerEnterCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the pointer enters the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.offPointerEnter = function (callback) {\n this._pointerEnterCallbacks.delete(callback);\n return this;\n };\n /**\n * Adds a callback to be called when the pointer moves within the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.onPointerMove = function (callback) {\n this._pointerMoveCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the pointer moves within the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.offPointerMove = function (callback) {\n this._pointerMoveCallbacks.delete(callback);\n return this;\n };\n /**\n * Adds a callback to be called when the pointer exits the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.onPointerExit = function (callback) {\n this._pointerExitCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when the pointer exits the Component.\n *\n * @param {PointerCallback} callback\n * @return {Interactions.Pointer} The calling Pointer Interaction.\n */\n Pointer.prototype.offPointerExit = function (callback) {\n this._pointerExitCallbacks.delete(callback);\n return this;\n };\n return Pointer;\n }(Plottable.Interaction));\n Interactions.Pointer = Pointer;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var PanZoom = (function (_super) {\n __extends(PanZoom, _super);\n /**\n * A PanZoom Interaction updates the domains of an x-scale and/or a y-scale\n * in response to the user panning or zooming.\n *\n * @constructor\n * @param {QuantitativeScale} [xScale] The x-scale to update on panning/zooming.\n * @param {QuantitativeScale} [yScale] The y-scale to update on panning/zooming.\n */\n function PanZoom(xScale, yScale) {\n var _this = this;\n _super.call(this);\n this._wheelCallback = function (p, e) { return _this._handleWheelEvent(p, e); };\n this._touchStartCallback = function (ids, idToPoint, e) { return _this._handleTouchStart(ids, idToPoint, e); };\n this._touchMoveCallback = function (ids, idToPoint, e) { return _this._handlePinch(ids, idToPoint, e); };\n this._touchEndCallback = function (ids, idToPoint, e) { return _this._handleTouchEnd(ids, idToPoint, e); };\n this._touchCancelCallback = function (ids, idToPoint, e) { return _this._handleTouchEnd(ids, idToPoint, e); };\n this._xScales = new Plottable.Utils.Set();\n this._yScales = new Plottable.Utils.Set();\n this._dragInteraction = new Interactions.Drag();\n this._setupDragInteraction();\n this._touchIds = d3.map();\n this._minDomainExtents = new Plottable.Utils.Map();\n this._maxDomainExtents = new Plottable.Utils.Map();\n if (xScale != null) {\n this.addXScale(xScale);\n }\n if (yScale != null) {\n this.addYScale(yScale);\n }\n }\n PanZoom.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._dragInteraction.attachTo(component);\n this._mouseDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(this._componentAttachedTo.content().node());\n this._mouseDispatcher.onWheel(this._wheelCallback);\n this._touchDispatcher = Plottable.Dispatchers.Touch.getDispatcher(this._componentAttachedTo.content().node());\n this._touchDispatcher.onTouchStart(this._touchStartCallback);\n this._touchDispatcher.onTouchMove(this._touchMoveCallback);\n this._touchDispatcher.onTouchEnd(this._touchEndCallback);\n this._touchDispatcher.onTouchCancel(this._touchCancelCallback);\n };\n PanZoom.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._mouseDispatcher.offWheel(this._wheelCallback);\n this._mouseDispatcher = null;\n this._touchDispatcher.offTouchStart(this._touchStartCallback);\n this._touchDispatcher.offTouchMove(this._touchMoveCallback);\n this._touchDispatcher.offTouchEnd(this._touchEndCallback);\n this._touchDispatcher.offTouchCancel(this._touchCancelCallback);\n this._touchDispatcher = null;\n this._dragInteraction.detachFrom(this._componentAttachedTo);\n };\n PanZoom.prototype._handleTouchStart = function (ids, idToPoint, e) {\n for (var i = 0; i < ids.length && this._touchIds.size() < 2; i++) {\n var id = ids[i];\n this._touchIds.set(id.toString(), this._translateToComponentSpace(idToPoint[id]));\n }\n };\n PanZoom.prototype._handlePinch = function (ids, idToPoint, e) {\n var _this = this;\n if (this._touchIds.size() < 2) {\n return;\n }\n var oldPoints = this._touchIds.values();\n if (!this._isInsideComponent(this._translateToComponentSpace(oldPoints[0])) ||\n !this._isInsideComponent(this._translateToComponentSpace(oldPoints[1]))) {\n return;\n }\n var oldCornerDistance = PanZoom._pointDistance(oldPoints[0], oldPoints[1]);\n if (oldCornerDistance === 0) {\n return;\n }\n ids.forEach(function (id) {\n if (_this._touchIds.has(id.toString())) {\n _this._touchIds.set(id.toString(), _this._translateToComponentSpace(idToPoint[id]));\n }\n });\n var points = this._touchIds.values();\n var newCornerDistance = PanZoom._pointDistance(points[0], points[1]);\n if (newCornerDistance === 0) {\n return;\n }\n var magnifyAmount = oldCornerDistance / newCornerDistance;\n var normalizedPointDiffs = points.map(function (point, i) {\n return { x: (point.x - oldPoints[i].x) / magnifyAmount, y: (point.y - oldPoints[i].y) / magnifyAmount };\n });\n this.xScales().forEach(function (xScale) {\n magnifyAmount = _this._constrainedZoomAmount(xScale, magnifyAmount);\n });\n this.yScales().forEach(function (yScale) {\n magnifyAmount = _this._constrainedZoomAmount(yScale, magnifyAmount);\n });\n var constrainedPoints = oldPoints.map(function (oldPoint, i) {\n return {\n x: normalizedPointDiffs[i].x * magnifyAmount + oldPoint.x,\n y: normalizedPointDiffs[i].y * magnifyAmount + oldPoint.y,\n };\n });\n var oldCenterPoint = PanZoom._centerPoint(oldPoints[0], oldPoints[1]);\n var translateAmountX = oldCenterPoint.x - ((constrainedPoints[0].x + constrainedPoints[1].x) / 2);\n this.xScales().forEach(function (xScale) {\n _this._magnifyScale(xScale, magnifyAmount, oldCenterPoint.x);\n _this._translateScale(xScale, translateAmountX);\n });\n var translateAmountY = oldCenterPoint.y - ((constrainedPoints[0].y + constrainedPoints[1].y) / 2);\n this.yScales().forEach(function (yScale) {\n _this._magnifyScale(yScale, magnifyAmount, oldCenterPoint.y);\n _this._translateScale(yScale, translateAmountY);\n });\n };\n PanZoom._centerPoint = function (point1, point2) {\n var leftX = Math.min(point1.x, point2.x);\n var rightX = Math.max(point1.x, point2.x);\n var topY = Math.min(point1.y, point2.y);\n var bottomY = Math.max(point1.y, point2.y);\n return { x: (leftX + rightX) / 2, y: (bottomY + topY) / 2 };\n };\n PanZoom._pointDistance = function (point1, point2) {\n var leftX = Math.min(point1.x, point2.x);\n var rightX = Math.max(point1.x, point2.x);\n var topY = Math.min(point1.y, point2.y);\n var bottomY = Math.max(point1.y, point2.y);\n return Math.sqrt(Math.pow(rightX - leftX, 2) + Math.pow(bottomY - topY, 2));\n };\n PanZoom.prototype._handleTouchEnd = function (ids, idToPoint, e) {\n var _this = this;\n ids.forEach(function (id) {\n _this._touchIds.remove(id.toString());\n });\n };\n PanZoom.prototype._magnifyScale = function (scale, magnifyAmount, centerValue) {\n var magnifyTransform = function (rangeValue) { return scale.invert(centerValue - (centerValue - rangeValue) * magnifyAmount); };\n scale.domain(scale.range().map(magnifyTransform));\n };\n PanZoom.prototype._translateScale = function (scale, translateAmount) {\n var translateTransform = function (rangeValue) { return scale.invert(rangeValue + translateAmount); };\n scale.domain(scale.range().map(translateTransform));\n };\n PanZoom.prototype._handleWheelEvent = function (p, e) {\n var _this = this;\n var translatedP = this._translateToComponentSpace(p);\n if (this._isInsideComponent(translatedP)) {\n e.preventDefault();\n var deltaPixelAmount = e.deltaY * (e.deltaMode ? PanZoom._PIXELS_PER_LINE : 1);\n var zoomAmount_1 = Math.pow(2, deltaPixelAmount * .002);\n this.xScales().forEach(function (xScale) {\n zoomAmount_1 = _this._constrainedZoomAmount(xScale, zoomAmount_1);\n });\n this.yScales().forEach(function (yScale) {\n zoomAmount_1 = _this._constrainedZoomAmount(yScale, zoomAmount_1);\n });\n this.xScales().forEach(function (xScale) {\n _this._magnifyScale(xScale, zoomAmount_1, translatedP.x);\n });\n this.yScales().forEach(function (yScale) {\n _this._magnifyScale(yScale, zoomAmount_1, translatedP.y);\n });\n }\n };\n PanZoom.prototype._constrainedZoomAmount = function (scale, zoomAmount) {\n var extentIncreasing = zoomAmount > 1;\n var boundingDomainExtent = extentIncreasing ? this.maxDomainExtent(scale) : this.minDomainExtent(scale);\n if (boundingDomainExtent == null) {\n return zoomAmount;\n }\n var scaleDomain = scale.domain();\n var domainExtent = Math.abs(scaleDomain[1] - scaleDomain[0]);\n var compareF = extentIncreasing ? Math.min : Math.max;\n return compareF(zoomAmount, boundingDomainExtent / domainExtent);\n };\n PanZoom.prototype._setupDragInteraction = function () {\n var _this = this;\n this._dragInteraction.constrainedToComponent(false);\n var lastDragPoint;\n this._dragInteraction.onDragStart(function () { return lastDragPoint = null; });\n this._dragInteraction.onDrag(function (startPoint, endPoint) {\n if (_this._touchIds.size() >= 2) {\n return;\n }\n var translateAmountX = (lastDragPoint == null ? startPoint.x : lastDragPoint.x) - endPoint.x;\n _this.xScales().forEach(function (xScale) {\n _this._translateScale(xScale, translateAmountX);\n });\n var translateAmountY = (lastDragPoint == null ? startPoint.y : lastDragPoint.y) - endPoint.y;\n _this.yScales().forEach(function (yScale) {\n _this._translateScale(yScale, translateAmountY);\n });\n lastDragPoint = endPoint;\n });\n };\n PanZoom.prototype._nonLinearScaleWithExtents = function (scale) {\n return this.minDomainExtent(scale) != null && this.maxDomainExtent(scale) != null &&\n !(scale instanceof Plottable.Scales.Linear) && !(scale instanceof Plottable.Scales.Time);\n };\n PanZoom.prototype.xScales = function (xScales) {\n var _this = this;\n if (xScales == null) {\n var scales_1 = [];\n this._xScales.forEach(function (xScale) {\n scales_1.push(xScale);\n });\n return scales_1;\n }\n this._xScales = new Plottable.Utils.Set();\n xScales.forEach(function (xScale) {\n _this.addXScale(xScale);\n });\n return this;\n };\n PanZoom.prototype.yScales = function (yScales) {\n var _this = this;\n if (yScales == null) {\n var scales_2 = [];\n this._yScales.forEach(function (yScale) {\n scales_2.push(yScale);\n });\n return scales_2;\n }\n this._yScales = new Plottable.Utils.Set();\n yScales.forEach(function (yScale) {\n _this.addYScale(yScale);\n });\n return this;\n };\n /**\n * Adds an x scale to this PanZoom Interaction\n *\n * @param {QuantitativeScale} An x scale to add\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n PanZoom.prototype.addXScale = function (xScale) {\n this._xScales.add(xScale);\n return this;\n };\n /**\n * Removes an x scale from this PanZoom Interaction\n *\n * @param {QuantitativeScale} An x scale to remove\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n PanZoom.prototype.removeXScale = function (xScale) {\n this._xScales.delete(xScale);\n this._minDomainExtents.delete(xScale);\n this._maxDomainExtents.delete(xScale);\n return this;\n };\n /**\n * Adds a y scale to this PanZoom Interaction\n *\n * @param {QuantitativeScale} A y scale to add\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n PanZoom.prototype.addYScale = function (yScale) {\n this._yScales.add(yScale);\n return this;\n };\n /**\n * Removes a y scale from this PanZoom Interaction\n *\n * @param {QuantitativeScale} A y scale to remove\n * @returns {Interactions.PanZoom} The calling PanZoom Interaction.\n */\n PanZoom.prototype.removeYScale = function (yScale) {\n this._yScales.delete(yScale);\n this._minDomainExtents.delete(yScale);\n this._maxDomainExtents.delete(yScale);\n return this;\n };\n PanZoom.prototype.minDomainExtent = function (quantitativeScale, minDomainExtent) {\n if (minDomainExtent == null) {\n return this._minDomainExtents.get(quantitativeScale);\n }\n if (minDomainExtent.valueOf() < 0) {\n throw new Error(\"extent must be non-negative\");\n }\n var maxExtentForScale = this.maxDomainExtent(quantitativeScale);\n if (maxExtentForScale != null && maxExtentForScale.valueOf() < minDomainExtent.valueOf()) {\n throw new Error(\"minDomainExtent must be smaller than maxDomainExtent for the same Scale\");\n }\n if (this._nonLinearScaleWithExtents(quantitativeScale)) {\n Plottable.Utils.Window.warn(\"Panning and zooming with extents on a nonlinear scale may have unintended behavior.\");\n }\n this._minDomainExtents.set(quantitativeScale, minDomainExtent);\n return this;\n };\n PanZoom.prototype.maxDomainExtent = function (quantitativeScale, maxDomainExtent) {\n if (maxDomainExtent == null) {\n return this._maxDomainExtents.get(quantitativeScale);\n }\n if (maxDomainExtent.valueOf() <= 0) {\n throw new Error(\"extent must be positive\");\n }\n var minExtentForScale = this.minDomainExtent(quantitativeScale);\n if (minExtentForScale != null && maxDomainExtent.valueOf() < minExtentForScale.valueOf()) {\n throw new Error(\"maxDomainExtent must be larger than minDomainExtent for the same Scale\");\n }\n if (this._nonLinearScaleWithExtents(quantitativeScale)) {\n Plottable.Utils.Window.warn(\"Panning and zooming with extents on a nonlinear scale may have unintended behavior.\");\n }\n this._maxDomainExtents.set(quantitativeScale, maxDomainExtent);\n return this;\n };\n /**\n * The number of pixels occupied in a line.\n */\n PanZoom._PIXELS_PER_LINE = 120;\n return PanZoom;\n }(Plottable.Interaction));\n Interactions.PanZoom = PanZoom;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Interactions;\n (function (Interactions) {\n var Drag = (function (_super) {\n __extends(Drag, _super);\n function Drag() {\n var _this = this;\n _super.apply(this, arguments);\n this._dragging = false;\n this._constrainedToComponent = true;\n this._dragStartCallbacks = new Plottable.Utils.CallbackSet();\n this._dragCallbacks = new Plottable.Utils.CallbackSet();\n this._dragEndCallbacks = new Plottable.Utils.CallbackSet();\n this._mouseDownCallback = function (p, e) { return _this._startDrag(p, e); };\n this._mouseMoveCallback = function (p, e) { return _this._doDrag(p, e); };\n this._mouseUpCallback = function (p, e) { return _this._endDrag(p, e); };\n this._touchStartCallback = function (ids, idToPoint, e) { return _this._startDrag(idToPoint[ids[0]], e); };\n this._touchMoveCallback = function (ids, idToPoint, e) { return _this._doDrag(idToPoint[ids[0]], e); };\n this._touchEndCallback = function (ids, idToPoint, e) { return _this._endDrag(idToPoint[ids[0]], e); };\n }\n Drag.prototype._anchor = function (component) {\n _super.prototype._anchor.call(this, component);\n this._mouseDispatcher = Plottable.Dispatchers.Mouse.getDispatcher(this._componentAttachedTo.content().node());\n this._mouseDispatcher.onMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);\n this._mouseDispatcher.onMouseUp(this._mouseUpCallback);\n this._touchDispatcher = Plottable.Dispatchers.Touch.getDispatcher(this._componentAttachedTo.content().node());\n this._touchDispatcher.onTouchStart(this._touchStartCallback);\n this._touchDispatcher.onTouchMove(this._touchMoveCallback);\n this._touchDispatcher.onTouchEnd(this._touchEndCallback);\n };\n Drag.prototype._unanchor = function () {\n _super.prototype._unanchor.call(this);\n this._mouseDispatcher.offMouseDown(this._mouseDownCallback);\n this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);\n this._mouseDispatcher.offMouseUp(this._mouseUpCallback);\n this._mouseDispatcher = null;\n this._touchDispatcher.offTouchStart(this._touchStartCallback);\n this._touchDispatcher.offTouchMove(this._touchMoveCallback);\n this._touchDispatcher.offTouchEnd(this._touchEndCallback);\n this._touchDispatcher = null;\n };\n Drag.prototype._translateAndConstrain = function (p) {\n var translatedP = this._translateToComponentSpace(p);\n if (!this._constrainedToComponent) {\n return translatedP;\n }\n return {\n x: Plottable.Utils.Math.clamp(translatedP.x, 0, this._componentAttachedTo.width()),\n y: Plottable.Utils.Math.clamp(translatedP.y, 0, this._componentAttachedTo.height()),\n };\n };\n Drag.prototype._startDrag = function (point, event) {\n if (event instanceof MouseEvent && event.button !== 0) {\n return;\n }\n var translatedP = this._translateToComponentSpace(point);\n if (this._isInsideComponent(translatedP)) {\n event.preventDefault();\n this._dragging = true;\n this._dragOrigin = translatedP;\n this._dragStartCallbacks.callCallbacks(this._dragOrigin);\n }\n };\n Drag.prototype._doDrag = function (point, event) {\n if (this._dragging) {\n this._dragCallbacks.callCallbacks(this._dragOrigin, this._translateAndConstrain(point));\n }\n };\n Drag.prototype._endDrag = function (point, event) {\n if (event instanceof MouseEvent && event.button !== 0) {\n return;\n }\n if (this._dragging) {\n this._dragging = false;\n this._dragEndCallbacks.callCallbacks(this._dragOrigin, this._translateAndConstrain(point));\n }\n };\n Drag.prototype.constrainedToComponent = function (constrainedToComponent) {\n if (constrainedToComponent == null) {\n return this._constrainedToComponent;\n }\n this._constrainedToComponent = constrainedToComponent;\n return this;\n };\n /**\n * Adds a callback to be called when dragging starts.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.onDragStart = function (callback) {\n this._dragStartCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when dragging starts.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.offDragStart = function (callback) {\n this._dragStartCallbacks.delete(callback);\n return this;\n };\n /**\n * Adds a callback to be called during dragging.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.onDrag = function (callback) {\n this._dragCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called during dragging.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.offDrag = function (callback) {\n this._dragCallbacks.delete(callback);\n return this;\n };\n /**\n * Adds a callback to be called when dragging ends.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.onDragEnd = function (callback) {\n this._dragEndCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when dragging ends.\n *\n * @param {DragCallback} callback\n * @returns {Drag} The calling Drag Interaction.\n */\n Drag.prototype.offDragEnd = function (callback) {\n this._dragEndCallbacks.delete(callback);\n return this;\n };\n return Drag;\n }(Plottable.Interaction));\n Interactions.Drag = Drag;\n })(Interactions = Plottable.Interactions || (Plottable.Interactions = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var DragBoxLayer = (function (_super) {\n __extends(DragBoxLayer, _super);\n /**\n * Constructs a DragBoxLayer.\n *\n * A DragBoxLayer is a SelectionBoxLayer with a built-in Drag Interaction.\n * A drag gesture will set the Bounds of the box.\n * If resizing is enabled using resizable(true), the edges of box can be repositioned.\n *\n * @constructor\n */\n function DragBoxLayer() {\n _super.call(this);\n this._detectionRadius = 3;\n this._resizable = false;\n this._movable = false;\n this._hasCorners = true;\n this.addClass(\"drag-box-layer\");\n this._dragInteraction = new Plottable.Interactions.Drag();\n this._setUpCallbacks();\n this._dragInteraction.attachTo(this);\n this._dragStartCallbacks = new Plottable.Utils.CallbackSet();\n this._dragCallbacks = new Plottable.Utils.CallbackSet();\n this._dragEndCallbacks = new Plottable.Utils.CallbackSet();\n }\n DragBoxLayer.prototype._setUpCallbacks = function () {\n var _this = this;\n var resizingEdges;\n var topLeft;\n var bottomRight;\n var lastEndPoint;\n var DRAG_MODES = {\n newBox: 0,\n resize: 1,\n move: 2,\n };\n var mode = DRAG_MODES.newBox;\n var onDragStartCallback = function (startPoint) {\n resizingEdges = _this._getResizingEdges(startPoint);\n var bounds = _this.bounds();\n var isInsideBox = bounds.topLeft.x <= startPoint.x && startPoint.x <= bounds.bottomRight.x &&\n bounds.topLeft.y <= startPoint.y && startPoint.y <= bounds.bottomRight.y;\n if (_this.boxVisible() && (resizingEdges.top || resizingEdges.bottom || resizingEdges.left || resizingEdges.right)) {\n mode = DRAG_MODES.resize;\n }\n else if (_this.boxVisible() && _this.movable() && isInsideBox) {\n mode = DRAG_MODES.move;\n }\n else {\n mode = DRAG_MODES.newBox;\n _this._setBounds({\n topLeft: startPoint,\n bottomRight: startPoint,\n });\n if (_this._xBoundsMode === Components.PropertyMode.VALUE && _this.xScale() != null) {\n _this._setXExtent([_this.xScale().invert(startPoint.x), _this.xScale().invert(startPoint.x)]);\n }\n if (_this._yBoundsMode === Components.PropertyMode.VALUE && _this.yScale() != null) {\n _this._setYExtent([_this.yScale().invert(startPoint.y), _this.yScale().invert(startPoint.y)]);\n }\n _this.render();\n }\n _this.boxVisible(true);\n bounds = _this.bounds();\n // copy points so changes to topLeft and bottomRight don't mutate bounds\n topLeft = { x: bounds.topLeft.x, y: bounds.topLeft.y };\n bottomRight = { x: bounds.bottomRight.x, y: bounds.bottomRight.y };\n lastEndPoint = startPoint;\n _this._dragStartCallbacks.callCallbacks(bounds);\n };\n var onDragCallback = function (startPoint, endPoint) {\n switch (mode) {\n case DRAG_MODES.newBox:\n bottomRight.x = endPoint.x;\n bottomRight.y = endPoint.y;\n break;\n case DRAG_MODES.resize:\n if (resizingEdges.bottom) {\n bottomRight.y = endPoint.y;\n }\n else if (resizingEdges.top) {\n topLeft.y = endPoint.y;\n }\n if (resizingEdges.right) {\n bottomRight.x = endPoint.x;\n }\n else if (resizingEdges.left) {\n topLeft.x = endPoint.x;\n }\n break;\n case DRAG_MODES.move:\n var dx = endPoint.x - lastEndPoint.x;\n var dy = endPoint.y - lastEndPoint.y;\n topLeft.x += dx;\n topLeft.y += dy;\n bottomRight.x += dx;\n bottomRight.y += dy;\n lastEndPoint = endPoint;\n break;\n }\n _this._setBounds({\n topLeft: topLeft,\n bottomRight: bottomRight,\n });\n if (_this._xBoundsMode === Components.PropertyMode.VALUE && _this.xScale() != null) {\n _this._setXExtent([_this.xScale().invert(topLeft.x), _this.xScale().invert(bottomRight.x)]);\n }\n if (_this._yBoundsMode === Components.PropertyMode.VALUE && _this.yScale() != null) {\n _this._setYExtent([_this.yScale().invert(topLeft.y), _this.yScale().invert(bottomRight.y)]);\n }\n _this.render();\n _this._dragCallbacks.callCallbacks(_this.bounds());\n };\n var onDragEndCallback = function (startPoint, endPoint) {\n if (mode === DRAG_MODES.newBox && startPoint.x === endPoint.x && startPoint.y === endPoint.y) {\n _this.boxVisible(false);\n }\n _this._dragEndCallbacks.callCallbacks(_this.bounds());\n };\n this._dragInteraction.onDragStart(onDragStartCallback);\n this._dragInteraction.onDrag(onDragCallback);\n this._dragInteraction.onDragEnd(onDragEndCallback);\n this._disconnectInteraction = function () {\n _this._dragInteraction.offDragStart(onDragStartCallback);\n _this._dragInteraction.offDrag(onDragCallback);\n _this._dragInteraction.offDragEnd(onDragEndCallback);\n _this._dragInteraction.detachFrom(_this);\n };\n };\n DragBoxLayer.prototype._setup = function () {\n var _this = this;\n _super.prototype._setup.call(this);\n var createLine = function () { return _this._box.append(\"line\").style({\n \"opacity\": 0,\n \"stroke\": \"pink\",\n \"pointer-events\": \"visibleStroke\",\n }); };\n this._detectionEdgeT = createLine().classed(\"drag-edge-tb\", true);\n this._detectionEdgeB = createLine().classed(\"drag-edge-tb\", true);\n this._detectionEdgeL = createLine().classed(\"drag-edge-lr\", true);\n this._detectionEdgeR = createLine().classed(\"drag-edge-lr\", true);\n if (this._hasCorners) {\n var createCorner = function () { return _this._box.append(\"circle\")\n .style({\n \"opacity\": 0,\n \"fill\": \"pink\",\n \"pointer-events\": \"visibleFill\",\n }); };\n this._detectionCornerTL = createCorner().classed(\"drag-corner-tl\", true);\n this._detectionCornerTR = createCorner().classed(\"drag-corner-tr\", true);\n this._detectionCornerBL = createCorner().classed(\"drag-corner-bl\", true);\n this._detectionCornerBR = createCorner().classed(\"drag-corner-br\", true);\n }\n };\n DragBoxLayer.prototype._getResizingEdges = function (p) {\n var edges = {\n top: false,\n bottom: false,\n left: false,\n right: false,\n };\n if (!this.resizable()) {\n return edges;\n }\n var bounds = this.bounds();\n var t = bounds.topLeft.y;\n var b = bounds.bottomRight.y;\n var l = bounds.topLeft.x;\n var r = bounds.bottomRight.x;\n var rad = this._detectionRadius;\n if (l - rad <= p.x && p.x <= r + rad) {\n edges.top = (t - rad <= p.y && p.y <= t + rad);\n edges.bottom = (b - rad <= p.y && p.y <= b + rad);\n }\n if (t - rad <= p.y && p.y <= b + rad) {\n edges.left = (l - rad <= p.x && p.x <= l + rad);\n edges.right = (r - rad <= p.x && p.x <= r + rad);\n }\n return edges;\n };\n DragBoxLayer.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n if (this.boxVisible()) {\n var bounds = this.bounds();\n var t = bounds.topLeft.y;\n var b = bounds.bottomRight.y;\n var l = bounds.topLeft.x;\n var r = bounds.bottomRight.x;\n this._detectionEdgeT.attr({\n x1: l, y1: t, x2: r, y2: t,\n \"stroke-width\": this._detectionRadius * 2,\n });\n this._detectionEdgeB.attr({\n x1: l, y1: b, x2: r, y2: b,\n \"stroke-width\": this._detectionRadius * 2,\n });\n this._detectionEdgeL.attr({\n x1: l, y1: t, x2: l, y2: b,\n \"stroke-width\": this._detectionRadius * 2,\n });\n this._detectionEdgeR.attr({\n x1: r, y1: t, x2: r, y2: b,\n \"stroke-width\": this._detectionRadius * 2,\n });\n if (this._hasCorners) {\n this._detectionCornerTL.attr({ cx: l, cy: t, r: this._detectionRadius });\n this._detectionCornerTR.attr({ cx: r, cy: t, r: this._detectionRadius });\n this._detectionCornerBL.attr({ cx: l, cy: b, r: this._detectionRadius });\n this._detectionCornerBR.attr({ cx: r, cy: b, r: this._detectionRadius });\n }\n return this;\n }\n };\n DragBoxLayer.prototype.detectionRadius = function (r) {\n if (r == null) {\n return this._detectionRadius;\n }\n if (r < 0) {\n throw new Error(\"detection radius cannot be negative.\");\n }\n this._detectionRadius = r;\n this.render();\n return this;\n };\n DragBoxLayer.prototype.resizable = function (canResize) {\n if (canResize == null) {\n return this._resizable;\n }\n this._resizable = canResize;\n this._setResizableClasses(canResize);\n return this;\n };\n // Sets resizable classes. Overridden by subclasses that only resize in one dimension.\n DragBoxLayer.prototype._setResizableClasses = function (canResize) {\n if (canResize && this.enabled()) {\n this.addClass(\"x-resizable\");\n this.addClass(\"y-resizable\");\n }\n else {\n this.removeClass(\"x-resizable\");\n this.removeClass(\"y-resizable\");\n }\n };\n DragBoxLayer.prototype.movable = function (movable) {\n if (movable == null) {\n return this._movable;\n }\n this._movable = movable;\n this._setMovableClass();\n return this;\n };\n DragBoxLayer.prototype._setMovableClass = function () {\n if (this.movable() && this.enabled()) {\n this.addClass(\"movable\");\n }\n else {\n this.removeClass(\"movable\");\n }\n };\n /**\n * Sets the callback to be called when dragging starts.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.onDragStart = function (callback) {\n this._dragStartCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback to be called when dragging starts.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.offDragStart = function (callback) {\n this._dragStartCallbacks.delete(callback);\n return this;\n };\n /**\n * Sets a callback to be called during dragging.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.onDrag = function (callback) {\n this._dragCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback to be called during dragging.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.offDrag = function (callback) {\n this._dragCallbacks.delete(callback);\n return this;\n };\n /**\n * Sets a callback to be called when dragging ends.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.onDragEnd = function (callback) {\n this._dragEndCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback to be called when dragging ends.\n *\n * @param {DragBoxCallback} callback\n * @returns {DragBoxLayer} The calling DragBoxLayer.\n */\n DragBoxLayer.prototype.offDragEnd = function (callback) {\n this._dragEndCallbacks.delete(callback);\n return this;\n };\n /**\n * Gets the internal Interactions.Drag of the DragBoxLayer.\n */\n DragBoxLayer.prototype.dragInteraction = function () {\n return this._dragInteraction;\n };\n DragBoxLayer.prototype.enabled = function (enabled) {\n if (enabled == null) {\n return this._dragInteraction.enabled();\n }\n this._dragInteraction.enabled(enabled);\n this._setResizableClasses(this.resizable());\n this._setMovableClass();\n return this;\n };\n DragBoxLayer.prototype.destroy = function () {\n var _this = this;\n _super.prototype.destroy.call(this);\n this._dragStartCallbacks.forEach(function (callback) { return _this._dragCallbacks.delete(callback); });\n this._dragCallbacks.forEach(function (callback) { return _this._dragCallbacks.delete(callback); });\n this._dragEndCallbacks.forEach(function (callback) { return _this._dragEndCallbacks.delete(callback); });\n this._disconnectInteraction();\n };\n DragBoxLayer.prototype.detach = function () {\n this._resetState();\n this._dragInteraction.detachFrom(this);\n _super.prototype.detach.call(this);\n return this;\n };\n DragBoxLayer.prototype.anchor = function (selection) {\n this._dragInteraction.attachTo(this);\n _super.prototype.anchor.call(this, selection);\n return this;\n };\n DragBoxLayer.prototype._resetState = function () {\n this.bounds({\n topLeft: { x: 0, y: 0 },\n bottomRight: { x: 0, y: 0 },\n });\n };\n return DragBoxLayer;\n }(Components.SelectionBoxLayer));\n Components.DragBoxLayer = DragBoxLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var XDragBoxLayer = (function (_super) {\n __extends(XDragBoxLayer, _super);\n /**\n * An XDragBoxLayer is a DragBoxLayer whose size can only be set in the X-direction.\n * The y-values of the bounds() are always set to 0 and the height() of the XDragBoxLayer.\n *\n * @constructor\n */\n function XDragBoxLayer() {\n _super.call(this);\n this.addClass(\"x-drag-box-layer\");\n this._hasCorners = false;\n }\n XDragBoxLayer.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n // set correct bounds when width/height changes\n this._setBounds(this.bounds());\n return this;\n };\n XDragBoxLayer.prototype._setBounds = function (newBounds) {\n _super.prototype._setBounds.call(this, {\n topLeft: { x: newBounds.topLeft.x, y: 0 },\n bottomRight: { x: newBounds.bottomRight.x, y: this.height() },\n });\n };\n XDragBoxLayer.prototype._setResizableClasses = function (canResize) {\n if (canResize && this.enabled()) {\n this.addClass(\"x-resizable\");\n }\n else {\n this.removeClass(\"x-resizable\");\n }\n };\n XDragBoxLayer.prototype.yScale = function (yScale) {\n if (yScale == null) {\n return _super.prototype.yScale.call(this);\n }\n throw new Error(\"yScales cannot be set on an XDragBoxLayer\");\n };\n XDragBoxLayer.prototype.yExtent = function (yExtent) {\n if (yExtent == null) {\n return _super.prototype.yExtent.call(this);\n }\n throw new Error(\"XDragBoxLayer has no yExtent\");\n };\n return XDragBoxLayer;\n }(Components.DragBoxLayer));\n Components.XDragBoxLayer = XDragBoxLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var YDragBoxLayer = (function (_super) {\n __extends(YDragBoxLayer, _super);\n /**\n * A YDragBoxLayer is a DragBoxLayer whose size can only be set in the Y-direction.\n * The x-values of the bounds() are always set to 0 and the width() of the YDragBoxLayer.\n *\n * @constructor\n */\n function YDragBoxLayer() {\n _super.call(this);\n this.addClass(\"y-drag-box-layer\");\n this._hasCorners = false;\n }\n YDragBoxLayer.prototype.computeLayout = function (origin, availableWidth, availableHeight) {\n _super.prototype.computeLayout.call(this, origin, availableWidth, availableHeight);\n // set correct bounds when width/height changes\n this._setBounds(this.bounds());\n return this;\n };\n YDragBoxLayer.prototype._setBounds = function (newBounds) {\n _super.prototype._setBounds.call(this, {\n topLeft: { x: 0, y: newBounds.topLeft.y },\n bottomRight: { x: this.width(), y: newBounds.bottomRight.y },\n });\n };\n YDragBoxLayer.prototype._setResizableClasses = function (canResize) {\n if (canResize && this.enabled()) {\n this.addClass(\"y-resizable\");\n }\n else {\n this.removeClass(\"y-resizable\");\n }\n };\n YDragBoxLayer.prototype.xScale = function (xScale) {\n if (xScale == null) {\n return _super.prototype.xScale.call(this);\n }\n throw new Error(\"xScales cannot be set on an YDragBoxLayer\");\n };\n YDragBoxLayer.prototype.xExtent = function (xExtent) {\n if (xExtent == null) {\n return _super.prototype.xExtent.call(this);\n }\n throw new Error(\"YDragBoxLayer has no xExtent\");\n };\n return YDragBoxLayer;\n }(Components.DragBoxLayer));\n Components.YDragBoxLayer = YDragBoxLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n ;\n})(Plottable || (Plottable = {}));\nvar Plottable;\n(function (Plottable) {\n var Components;\n (function (Components) {\n var DragLineLayer = (function (_super) {\n __extends(DragLineLayer, _super);\n function DragLineLayer(orientation) {\n var _this = this;\n _super.call(this, orientation);\n this._detectionRadius = 3;\n this._enabled = true;\n this.addClass(\"drag-line-layer\");\n this.addClass(\"enabled\");\n this._dragInteraction = new Plottable.Interactions.Drag();\n this._dragInteraction.attachTo(this);\n var onLine = function (p) {\n return (_this._isVertical() &&\n _this.pixelPosition() - _this.detectionRadius() <= p.x &&\n p.x <= _this.pixelPosition() + _this.detectionRadius()) ||\n (!_this._isVertical() &&\n _this.pixelPosition() - _this.detectionRadius() <= p.y &&\n p.y <= _this.pixelPosition() + _this.detectionRadius());\n };\n var dragging = false;\n var interactionDragStartCallback = function (start) {\n if (onLine(start)) {\n dragging = true;\n _this._dragStartCallbacks.callCallbacks(_this);\n }\n };\n this._dragInteraction.onDragStart(interactionDragStartCallback);\n var interactionDragCallback = function (start, end) {\n if (dragging) {\n _this._setPixelPositionWithoutChangingMode(_this._isVertical() ? end.x : end.y);\n _this._dragCallbacks.callCallbacks(_this);\n }\n };\n this._dragInteraction.onDrag(interactionDragCallback);\n var interactionDragEndCallback = function (start, end) {\n if (dragging) {\n dragging = false;\n _this._dragEndCallbacks.callCallbacks(_this);\n }\n };\n this._dragInteraction.onDragEnd(interactionDragEndCallback);\n this._disconnectInteraction = function () {\n _this._dragInteraction.offDragStart(interactionDragStartCallback);\n _this._dragInteraction.offDrag(interactionDragCallback);\n _this._dragInteraction.offDragEnd(interactionDragEndCallback);\n _this._dragInteraction.detachFrom(_this);\n };\n this._dragStartCallbacks = new Plottable.Utils.CallbackSet();\n this._dragCallbacks = new Plottable.Utils.CallbackSet();\n this._dragEndCallbacks = new Plottable.Utils.CallbackSet();\n }\n DragLineLayer.prototype._setup = function () {\n _super.prototype._setup.call(this);\n this._detectionEdge = this.content().append(\"line\").style({\n \"opacity\": 0,\n \"stroke\": \"pink\",\n \"pointer-events\": \"visibleStroke\",\n }).classed(\"drag-edge\", true);\n };\n DragLineLayer.prototype.renderImmediately = function () {\n _super.prototype.renderImmediately.call(this);\n this._detectionEdge.attr({\n x1: this._isVertical() ? this.pixelPosition() : 0,\n y1: this._isVertical() ? 0 : this.pixelPosition(),\n x2: this._isVertical() ? this.pixelPosition() : this.width(),\n y2: this._isVertical() ? this.height() : this.pixelPosition(),\n \"stroke-width\": this._detectionRadius * 2,\n });\n return this;\n };\n DragLineLayer.prototype.detectionRadius = function (detectionRadius) {\n if (detectionRadius == null) {\n return this._detectionRadius;\n }\n if (detectionRadius < 0) {\n throw new Error(\"detection radius cannot be negative.\");\n }\n this._detectionRadius = detectionRadius;\n this.render();\n return this;\n };\n DragLineLayer.prototype.enabled = function (enabled) {\n if (enabled == null) {\n return this._enabled;\n }\n this._enabled = enabled;\n if (enabled) {\n this.addClass(\"enabled\");\n }\n else {\n this.removeClass(\"enabled\");\n }\n this._dragInteraction.enabled(enabled);\n return this;\n };\n /**\n * Sets the callback to be called when dragging starts.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.onDragStart = function (callback) {\n this._dragStartCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when dragging starts.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.offDragStart = function (callback) {\n this._dragStartCallbacks.delete(callback);\n return this;\n };\n /**\n * Sets a callback to be called during dragging.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.onDrag = function (callback) {\n this._dragCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called during dragging.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.offDrag = function (callback) {\n this._dragCallbacks.delete(callback);\n return this;\n };\n /**\n * Sets a callback to be called when dragging ends.\n * The callback will be passed the calling DragLineLayer.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.onDragEnd = function (callback) {\n this._dragEndCallbacks.add(callback);\n return this;\n };\n /**\n * Removes a callback that would be called when dragging ends.\n *\n * @param {DragLineCallback} callback\n * @returns {DragLineLayer} The calling DragLineLayer.\n */\n DragLineLayer.prototype.offDragEnd = function (callback) {\n this._dragEndCallbacks.delete(callback);\n return this;\n };\n DragLineLayer.prototype.destroy = function () {\n var _this = this;\n _super.prototype.destroy.call(this);\n this._dragStartCallbacks.forEach(function (callback) { return _this._dragStartCallbacks.delete(callback); });\n this._dragCallbacks.forEach(function (callback) { return _this._dragCallbacks.delete(callback); });\n this._dragEndCallbacks.forEach(function (callback) { return _this._dragEndCallbacks.delete(callback); });\n this._disconnectInteraction();\n };\n return DragLineLayer;\n }(Components.GuideLineLayer));\n Components.DragLineLayer = DragLineLayer;\n })(Components = Plottable.Components || (Plottable.Components = {}));\n})(Plottable || (Plottable = {}));\n\n/*!\nSVG Typewriter 0.3.0 (https://github.com/palantir/svg-typewriter)\nCopyright 2014 Palantir Technologies\nLicensed under MIT (https://github.com/palantir/svg-typewriter/blob/develop/LICENSE)\n*/\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Utils;\n (function (Utils) {\n var Methods;\n (function (Methods) {\n /**\n * Check if two arrays are equal by strict equality.\n */\n function arrayEq(a, b) {\n // Technically, null and undefined are arrays too\n if (a == null || b == null) {\n return a === b;\n }\n if (a.length !== b.length) {\n return false;\n }\n for (var i = 0; i < a.length; i++) {\n if (a[i] !== b[i]) {\n return false;\n }\n }\n return true;\n }\n Methods.arrayEq = arrayEq;\n /**\n * @param {any} a Object to check against b for equality.\n * @param {any} b Object to check against a for equality.\n *\n * @returns {boolean} whether or not two objects share the same keys, and\n * values associated with those keys. Values will be compared\n * with ===.\n */\n function objEq(a, b) {\n if (a == null || b == null) {\n return a === b;\n }\n var keysA = Object.keys(a).sort();\n var keysB = Object.keys(b).sort();\n var valuesA = keysA.map(function (k) { return a[k]; });\n var valuesB = keysB.map(function (k) { return b[k]; });\n return arrayEq(keysA, keysB) && arrayEq(valuesA, valuesB);\n }\n Methods.objEq = objEq;\n })(Methods = Utils.Methods || (Utils.Methods = {}));\n })(Utils = SVGTypewriter.Utils || (SVGTypewriter.Utils = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Utils;\n (function (Utils) {\n var DOM;\n (function (DOM) {\n function transform(s, x, y) {\n var xform = d3.transform(s.attr(\"transform\"));\n if (x == null) {\n return xform.translate;\n }\n else {\n y = (y == null) ? 0 : y;\n xform.translate[0] = x;\n xform.translate[1] = y;\n s.attr(\"transform\", xform.toString());\n return s;\n }\n }\n DOM.transform = transform;\n function getBBox(element) {\n var bbox;\n try {\n bbox = element.node().getBBox();\n }\n catch (err) {\n bbox = {\n x: 0,\n y: 0,\n width: 0,\n height: 0\n };\n }\n return bbox;\n }\n DOM.getBBox = getBBox;\n })(DOM = Utils.DOM || (Utils.DOM = {}));\n })(Utils = SVGTypewriter.Utils || (SVGTypewriter.Utils = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Utils;\n (function (Utils) {\n var Cache = (function () {\n /**\n * @constructor\n *\n * @param {string} compute The function whose results will be cached.\n * @param {(v: T, w: T) => boolean} [valueEq]\n * Used to determine if the value of canonicalKey has changed.\n * If omitted, defaults to === comparision.\n */\n function Cache(compute, valueEq) {\n if (valueEq === void 0) { valueEq = function (v, w) { return v === w; }; }\n this.cache = d3.map();\n this.compute = compute;\n this.valueEq = valueEq;\n }\n /**\n * Attempt to look up k in the cache, computing the result if it isn't\n * found.\n *\n * @param {string} k The key to look up in the cache.\n * @return {T} The value associated with k; the result of compute(k).\n */\n Cache.prototype.get = function (k) {\n if (!this.cache.has(k)) {\n this.cache.set(k, this.compute(k));\n }\n return this.cache.get(k);\n };\n /**\n * Reset the cache empty.\n *\n * @return {Cache} The calling Cache.\n */\n Cache.prototype.clear = function () {\n this.cache = d3.map();\n return this;\n };\n return Cache;\n })();\n Utils.Cache = Cache;\n })(Utils = SVGTypewriter.Utils || (SVGTypewriter.Utils = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Utils;\n (function (Utils) {\n var Tokenizer = (function () {\n function Tokenizer() {\n this.WordDividerRegExp = new RegExp(\"\\\\W\");\n this.WhitespaceRegExp = new RegExp(\"\\\\s\");\n }\n Tokenizer.prototype.tokenize = function (line) {\n var _this = this;\n return line.split(\"\").reduce(function (tokens, c) { return tokens.slice(0, -1).concat(_this.shouldCreateNewToken(tokens[tokens.length - 1], c)); }, [\"\"]);\n };\n Tokenizer.prototype.shouldCreateNewToken = function (token, newCharacter) {\n if (!token) {\n return [newCharacter];\n }\n var lastCharacter = token[token.length - 1];\n if (this.WhitespaceRegExp.test(lastCharacter) && this.WhitespaceRegExp.test(newCharacter)) {\n return [token + newCharacter];\n }\n else if (this.WhitespaceRegExp.test(lastCharacter) || this.WhitespaceRegExp.test(newCharacter)) {\n return [token, newCharacter];\n }\n else if (!(this.WordDividerRegExp.test(lastCharacter) || this.WordDividerRegExp.test(newCharacter))) {\n return [token + newCharacter];\n }\n else if (lastCharacter === newCharacter) {\n return [token + newCharacter];\n }\n else {\n return [token, newCharacter];\n }\n };\n return Tokenizer;\n })();\n Utils.Tokenizer = Tokenizer;\n })(Utils = SVGTypewriter.Utils || (SVGTypewriter.Utils = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Utils;\n (function (Utils) {\n var StringMethods;\n (function (StringMethods) {\n /**\n * Treat all sequences of consecutive whitespace as a single \" \".\n */\n function combineWhitespace(str) {\n return str.replace(/\\s+/g, \" \");\n }\n StringMethods.combineWhitespace = combineWhitespace;\n function isNotEmptyString(str) {\n return str && str.trim() !== \"\";\n }\n StringMethods.isNotEmptyString = isNotEmptyString;\n function trimStart(str, c) {\n if (!str) {\n return str;\n }\n var chars = str.split(\"\");\n var reduceFunction = c ? function (s) { return s.split(c).some(isNotEmptyString); } : isNotEmptyString;\n return chars.reduce(function (s, c) { return reduceFunction(s + c) ? s + c : s; }, \"\");\n }\n StringMethods.trimStart = trimStart;\n function trimEnd(str, c) {\n if (!str) {\n return str;\n }\n var reversedChars = str.split(\"\");\n reversedChars.reverse();\n reversedChars = trimStart(reversedChars.join(\"\"), c).split(\"\");\n reversedChars.reverse();\n return reversedChars.join(\"\");\n }\n StringMethods.trimEnd = trimEnd;\n })(StringMethods = Utils.StringMethods || (Utils.StringMethods = {}));\n })(Utils = SVGTypewriter.Utils || (SVGTypewriter.Utils = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Animators;\n (function (Animators) {\n var BaseAnimator = (function () {\n function BaseAnimator() {\n this.duration(BaseAnimator.DEFAULT_DURATION_MILLISECONDS);\n this.delay(0);\n this.easing(BaseAnimator.DEFAULT_EASING);\n this.moveX(0);\n this.moveY(0);\n }\n BaseAnimator.prototype.animate = function (selection) {\n var xForm = d3.transform(\"\");\n xForm.translate = [this.moveX(), this.moveY()];\n selection.attr(\"transform\", xForm.toString());\n xForm.translate = [0, 0];\n return this._animate(selection, { transform: xForm.toString() });\n };\n BaseAnimator.prototype._animate = function (selection, attr) {\n return selection.transition().ease(this.easing()).duration(this.duration()).delay(this.delay()).attr(attr);\n };\n BaseAnimator.prototype.duration = function (duration) {\n if (duration == null) {\n return this._duration;\n }\n else {\n this._duration = duration;\n return this;\n }\n };\n BaseAnimator.prototype.moveX = function (shift) {\n if (shift == null) {\n return this._moveX;\n }\n else {\n this._moveX = shift;\n return this;\n }\n };\n BaseAnimator.prototype.moveY = function (shift) {\n if (shift == null) {\n return this._moveY;\n }\n else {\n this._moveY = shift;\n return this;\n }\n };\n BaseAnimator.prototype.delay = function (delay) {\n if (delay == null) {\n return this._delay;\n }\n else {\n this._delay = delay;\n return this;\n }\n };\n BaseAnimator.prototype.easing = function (easing) {\n if (easing == null) {\n return this._easing;\n }\n else {\n this._easing = easing;\n return this;\n }\n };\n /**\n * The default duration of the animation in milliseconds\n */\n BaseAnimator.DEFAULT_DURATION_MILLISECONDS = 300;\n /**\n * The default easing of the animation\n */\n BaseAnimator.DEFAULT_EASING = \"exp-out\";\n return BaseAnimator;\n })();\n Animators.BaseAnimator = BaseAnimator;\n })(Animators = SVGTypewriter.Animators || (SVGTypewriter.Animators = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Animators;\n (function (Animators) {\n var UnveilAnimator = (function (_super) {\n __extends(UnveilAnimator, _super);\n function UnveilAnimator() {\n this.direction(\"bottom\");\n _super.call(this);\n }\n UnveilAnimator.prototype.direction = function (direction) {\n if (direction == null) {\n return this._direction;\n }\n else {\n if (UnveilAnimator.SupportedDirections.indexOf(direction) === -1) {\n throw new Error(\"unsupported direction - \" + direction);\n }\n this._direction = direction;\n return this;\n }\n };\n UnveilAnimator.prototype.animate = function (selection) {\n var attr = SVGTypewriter.Utils.DOM.getBBox(selection);\n var mask = selection.select(\".clip-rect\");\n mask.attr(\"width\", 0);\n mask.attr(\"height\", 0);\n switch (this._direction) {\n case \"top\":\n mask.attr(\"y\", attr.y + attr.height);\n mask.attr(\"x\", attr.x);\n mask.attr(\"width\", attr.width);\n break;\n case \"bottom\":\n mask.attr(\"y\", attr.y);\n mask.attr(\"x\", attr.x);\n mask.attr(\"width\", attr.width);\n break;\n case \"left\":\n mask.attr(\"y\", attr.y);\n mask.attr(\"x\", attr.x);\n mask.attr(\"height\", attr.height);\n break;\n case \"right\":\n mask.attr(\"y\", attr.y);\n mask.attr(\"x\", attr.x + attr.width);\n mask.attr(\"height\", attr.height);\n break;\n }\n this._animate(mask, attr);\n return _super.prototype.animate.call(this, selection);\n };\n UnveilAnimator.SupportedDirections = [\"top\", \"bottom\", \"left\", \"right\"];\n return UnveilAnimator;\n })(Animators.BaseAnimator);\n Animators.UnveilAnimator = UnveilAnimator;\n })(Animators = SVGTypewriter.Animators || (SVGTypewriter.Animators = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Animators;\n (function (Animators) {\n var OpacityAnimator = (function (_super) {\n __extends(OpacityAnimator, _super);\n function OpacityAnimator() {\n _super.apply(this, arguments);\n }\n OpacityAnimator.prototype.animate = function (selection) {\n var area = selection.select(\".text-area\");\n area.attr(\"opacity\", 0);\n var attr = {\n opacity: 1\n };\n this._animate(area, attr);\n return _super.prototype.animate.call(this, selection);\n };\n return OpacityAnimator;\n })(Animators.BaseAnimator);\n Animators.OpacityAnimator = OpacityAnimator;\n })(Animators = SVGTypewriter.Animators || (SVGTypewriter.Animators = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Wrappers;\n (function (Wrappers) {\n var Wrapper = (function () {\n function Wrapper() {\n this.maxLines(Infinity);\n this.textTrimming(\"ellipsis\");\n this.allowBreakingWords(true);\n this._tokenizer = new SVGTypewriter.Utils.Tokenizer();\n this._breakingCharacter = \"-\";\n }\n Wrapper.prototype.maxLines = function (noLines) {\n if (noLines == null) {\n return this._maxLines;\n }\n else {\n this._maxLines = noLines;\n return this;\n }\n };\n Wrapper.prototype.textTrimming = function (option) {\n if (option == null) {\n return this._textTrimming;\n }\n else {\n if (option !== \"ellipsis\" && option !== \"none\") {\n throw new Error(option + \" - unsupported text trimming option.\");\n }\n this._textTrimming = option;\n return this;\n }\n };\n Wrapper.prototype.allowBreakingWords = function (allow) {\n if (allow == null) {\n return this._allowBreakingWords;\n }\n else {\n this._allowBreakingWords = allow;\n return this;\n }\n };\n Wrapper.prototype.wrap = function (text, measurer, width, height) {\n var _this = this;\n if (height === void 0) { height = Infinity; }\n var initialWrappingResult = {\n originalText: text,\n wrappedText: \"\",\n noLines: 0,\n noBrokeWords: 0,\n truncatedText: \"\"\n };\n var state = {\n wrapping: initialWrappingResult,\n currentLine: \"\",\n availableWidth: width,\n availableLines: Math.min(Math.floor(height / measurer.measure().height), this._maxLines),\n canFitText: true\n };\n var lines = text.split(\"\\n\");\n return lines.reduce(function (state, line, i) { return _this.breakLineToFitWidth(state, line, i !== lines.length - 1, measurer); }, state).wrapping;\n };\n Wrapper.prototype.breakLineToFitWidth = function (state, line, hasNextLine, measurer) {\n var _this = this;\n if (!state.canFitText && state.wrapping.truncatedText !== \"\") {\n state.wrapping.truncatedText += \"\\n\";\n }\n var tokens = this._tokenizer.tokenize(line);\n state = tokens.reduce(function (state, token) { return _this.wrapNextToken(token, state, measurer); }, state);\n var wrappedText = SVGTypewriter.Utils.StringMethods.trimEnd(state.currentLine);\n state.wrapping.noLines += +(wrappedText !== \"\");\n if (state.wrapping.noLines === state.availableLines && this._textTrimming !== \"none\" && hasNextLine) {\n var ellipsisResult = this.addEllipsis(wrappedText, state.availableWidth, measurer);\n state.wrapping.wrappedText += ellipsisResult.wrappedToken;\n state.wrapping.truncatedText += ellipsisResult.remainingToken;\n state.canFitText = false;\n }\n else {\n state.wrapping.wrappedText += wrappedText;\n }\n state.currentLine = \"\\n\";\n return state;\n };\n Wrapper.prototype.canFitToken = function (token, width, measurer) {\n var _this = this;\n var possibleBreaks = this._allowBreakingWords ? token.split(\"\").map(function (c, i) { return (i !== token.length - 1) ? c + _this._breakingCharacter : c; }) : [token];\n return (measurer.measure(token).width <= width) || possibleBreaks.every(function (c) { return measurer.measure(c).width <= width; });\n };\n Wrapper.prototype.addEllipsis = function (line, width, measurer) {\n if (this._textTrimming === \"none\") {\n return {\n wrappedToken: line,\n remainingToken: \"\"\n };\n }\n var truncatedLine = line.substring(0).trim();\n var lineWidth = measurer.measure(truncatedLine).width;\n var ellipsesWidth = measurer.measure(\"...\").width;\n var prefix = (line.length > 0 && line[0] === \"\\n\") ? \"\\n\" : \"\";\n if (width <= ellipsesWidth) {\n var periodWidth = ellipsesWidth / 3;\n var numPeriodsThatFit = Math.floor(width / periodWidth);\n return {\n wrappedToken: prefix + \"...\".substr(0, numPeriodsThatFit),\n remainingToken: line\n };\n }\n while (lineWidth + ellipsesWidth > width) {\n truncatedLine = SVGTypewriter.Utils.StringMethods.trimEnd(truncatedLine.substr(0, truncatedLine.length - 1));\n lineWidth = measurer.measure(truncatedLine).width;\n }\n return {\n wrappedToken: prefix + truncatedLine + \"...\",\n remainingToken: SVGTypewriter.Utils.StringMethods.trimEnd(line.substring(truncatedLine.length), \"-\").trim()\n };\n };\n Wrapper.prototype.wrapNextToken = function (token, state, measurer) {\n if (!state.canFitText || state.availableLines === state.wrapping.noLines || !this.canFitToken(token, state.availableWidth, measurer)) {\n return this.finishWrapping(token, state, measurer);\n }\n var remainingToken = token;\n while (remainingToken) {\n var result = this.breakTokenToFitInWidth(remainingToken, state.currentLine, state.availableWidth, measurer);\n state.currentLine = result.line;\n remainingToken = result.remainingToken;\n if (remainingToken != null) {\n state.wrapping.noBrokeWords += +result.breakWord;\n ++state.wrapping.noLines;\n if (state.availableLines === state.wrapping.noLines) {\n var ellipsisResult = this.addEllipsis(state.currentLine, state.availableWidth, measurer);\n state.wrapping.wrappedText += ellipsisResult.wrappedToken;\n state.wrapping.truncatedText += ellipsisResult.remainingToken + remainingToken;\n state.currentLine = \"\\n\";\n return state;\n }\n else {\n state.wrapping.wrappedText += SVGTypewriter.Utils.StringMethods.trimEnd(state.currentLine);\n state.currentLine = \"\\n\";\n }\n }\n }\n return state;\n };\n Wrapper.prototype.finishWrapping = function (token, state, measurer) {\n // Token is really long, but we have a space to put part of the word.\n if (state.canFitText && state.availableLines !== state.wrapping.noLines && this._allowBreakingWords && this._textTrimming !== \"none\") {\n var res = this.addEllipsis(state.currentLine + token, state.availableWidth, measurer);\n state.wrapping.wrappedText += res.wrappedToken;\n state.wrapping.truncatedText += res.remainingToken;\n state.wrapping.noBrokeWords += +(res.remainingToken.length < token.length);\n state.wrapping.noLines += +(res.wrappedToken.length > 0);\n state.currentLine = \"\";\n }\n else {\n state.wrapping.truncatedText += token;\n }\n state.canFitText = false;\n return state;\n };\n /**\n * Breaks single token to fit current line.\n * If token contains only whitespaces then they will not be populated to next line.\n */\n Wrapper.prototype.breakTokenToFitInWidth = function (token, line, availableWidth, measurer, breakingCharacter) {\n if (breakingCharacter === void 0) { breakingCharacter = this._breakingCharacter; }\n if (measurer.measure(line + token).width <= availableWidth) {\n return {\n remainingToken: null,\n line: line + token,\n breakWord: false\n };\n }\n if (token.trim() === \"\") {\n return {\n remainingToken: \"\",\n line: line,\n breakWord: false\n };\n }\n if (!this._allowBreakingWords) {\n return {\n remainingToken: token,\n line: line,\n breakWord: false\n };\n }\n var fitTokenLength = 0;\n while (fitTokenLength < token.length) {\n if (measurer.measure(line + token.substring(0, fitTokenLength + 1) + breakingCharacter).width <= availableWidth) {\n ++fitTokenLength;\n }\n else {\n break;\n }\n }\n var suffix = \"\";\n if (fitTokenLength > 0) {\n suffix = breakingCharacter;\n }\n return {\n remainingToken: token.substring(fitTokenLength),\n line: line + token.substring(0, fitTokenLength) + suffix,\n breakWord: fitTokenLength > 0\n };\n };\n return Wrapper;\n })();\n Wrappers.Wrapper = Wrapper;\n })(Wrappers = SVGTypewriter.Wrappers || (SVGTypewriter.Wrappers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Wrappers;\n (function (Wrappers) {\n var SingleLineWrapper = (function (_super) {\n __extends(SingleLineWrapper, _super);\n function SingleLineWrapper() {\n _super.apply(this, arguments);\n }\n SingleLineWrapper.prototype.wrap = function (text, measurer, width, height) {\n var _this = this;\n if (height === void 0) { height = Infinity; }\n var lines = text.split(\"\\n\");\n if (lines.length > 1) {\n throw new Error(\"SingleLineWrapper is designed to work only on single line\");\n }\n var wrapFN = function (w) { return _super.prototype.wrap.call(_this, text, measurer, w, height); };\n var result = wrapFN(width);\n if (result.noLines < 2) {\n return result;\n }\n var left = 0;\n var right = width;\n for (var i = 0; i < SingleLineWrapper.NO_WRAP_ITERATIONS && right > left; ++i) {\n var currentWidth = (right + left) / 2;\n var currentResult = wrapFN(currentWidth);\n if (this.areSameResults(result, currentResult)) {\n right = currentWidth;\n result = currentResult;\n }\n else {\n left = currentWidth;\n }\n }\n return result;\n };\n SingleLineWrapper.prototype.areSameResults = function (one, two) {\n return one.noLines === two.noLines && one.truncatedText === two.truncatedText;\n };\n SingleLineWrapper.NO_WRAP_ITERATIONS = 5;\n return SingleLineWrapper;\n })(Wrappers.Wrapper);\n Wrappers.SingleLineWrapper = SingleLineWrapper;\n })(Wrappers = SVGTypewriter.Wrappers || (SVGTypewriter.Wrappers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Writers;\n (function (Writers) {\n var Writer = (function () {\n function Writer(measurer, wrapper) {\n this._writerID = Writer.nextID++;\n this._elementID = 0;\n this.measurer(measurer);\n if (wrapper) {\n this.wrapper(wrapper);\n }\n this.addTitleElement(false);\n }\n Writer.prototype.measurer = function (newMeasurer) {\n this._measurer = newMeasurer;\n return this;\n };\n Writer.prototype.wrapper = function (newWrapper) {\n this._wrapper = newWrapper;\n return this;\n };\n Writer.prototype.addTitleElement = function (add) {\n this._addTitleElement = add;\n return this;\n };\n Writer.prototype.writeLine = function (line, g, width, xAlign, yOffset) {\n var textEl = g.append(\"text\");\n textEl.text(line);\n var xOffset = width * Writer.XOffsetFactor[xAlign];\n var anchor = Writer.AnchorConverter[xAlign];\n textEl.attr(\"text-anchor\", anchor).classed(\"text-line\", true);\n SVGTypewriter.Utils.DOM.transform(textEl, xOffset, yOffset).attr(\"y\", \"-0.25em\");\n ;\n };\n Writer.prototype.writeText = function (text, writingArea, width, height, xAlign, yAlign) {\n var _this = this;\n var lines = text.split(\"\\n\");\n var lineHeight = this._measurer.measure().height;\n var yOffset = Writer.YOffsetFactor[yAlign] * (height - lines.length * lineHeight);\n lines.forEach(function (line, i) {\n _this.writeLine(line, writingArea, width, xAlign, (i + 1) * lineHeight + yOffset);\n });\n };\n Writer.prototype.write = function (text, width, height, options) {\n if (Writer.SupportedRotation.indexOf(options.textRotation) === -1) {\n throw new Error(\"unsupported rotation - \" + options.textRotation);\n }\n var orientHorizontally = Math.abs(Math.abs(options.textRotation) - 90) > 45;\n var primaryDimension = orientHorizontally ? width : height;\n var secondaryDimension = orientHorizontally ? height : width;\n var textContainer = options.selection.append(\"g\").classed(\"text-container\", true);\n if (this._addTitleElement) {\n textContainer.append(\"title\").text(text);\n }\n var textArea = textContainer.append(\"g\").classed(\"text-area\", true);\n var wrappedText = this._wrapper ? this._wrapper.wrap(text, this._measurer, primaryDimension, secondaryDimension).wrappedText : text;\n this.writeText(wrappedText, textArea, primaryDimension, secondaryDimension, options.xAlign, options.yAlign);\n var xForm = d3.transform(\"\");\n var xForm2 = d3.transform(\"\");\n xForm.rotate = options.textRotation;\n switch (options.textRotation) {\n case 90:\n xForm.translate = [width, 0];\n xForm2.rotate = -90;\n xForm2.translate = [0, 200];\n break;\n case -90:\n xForm.translate = [0, height];\n xForm2.rotate = 90;\n xForm2.translate = [width, 0];\n break;\n case 180:\n xForm.translate = [width, height];\n xForm2.translate = [width, height];\n xForm2.rotate = 180;\n break;\n }\n textArea.attr(\"transform\", xForm.toString());\n this.addClipPath(textContainer, xForm2);\n if (options.animator) {\n options.animator.animate(textContainer);\n }\n };\n Writer.prototype.addClipPath = function (selection, transform) {\n var elementID = this._elementID++;\n var prefix = /MSIE [5-9]/.test(navigator.userAgent) ? \"\" : document.location.href;\n prefix = prefix.split(\"#\")[0]; // To fix cases where an anchor tag was used\n var clipPathID = \"clipPath\" + this._writerID + \"_\" + elementID;\n selection.select(\".text-area\").attr(\"clip-path\", \"url(\\\"\" + prefix + \"#\" + clipPathID + \"\\\")\");\n var clipPathParent = selection.append(\"clipPath\").attr(\"id\", clipPathID);\n var bboxAttrs = SVGTypewriter.Utils.DOM.getBBox(selection.select(\".text-area\"));\n var box = clipPathParent.append(\"rect\");\n box.classed(\"clip-rect\", true).attr({\n x: bboxAttrs.x,\n y: bboxAttrs.y,\n width: bboxAttrs.width,\n height: bboxAttrs.height\n });\n };\n Writer.nextID = 0;\n Writer.SupportedRotation = [-90, 0, 180, 90];\n Writer.AnchorConverter = {\n left: \"start\",\n center: \"middle\",\n right: \"end\"\n };\n Writer.XOffsetFactor = {\n left: 0,\n center: 0.5,\n right: 1\n };\n Writer.YOffsetFactor = {\n top: 0,\n center: 0.5,\n bottom: 1\n };\n return Writer;\n })();\n Writers.Writer = Writer;\n })(Writers = SVGTypewriter.Writers || (SVGTypewriter.Writers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Measurers;\n (function (Measurers) {\n ;\n var AbstractMeasurer = (function () {\n function AbstractMeasurer(area, className) {\n this.textMeasurer = this.getTextMeasurer(area, className);\n }\n AbstractMeasurer.prototype.checkSelectionIsText = function (d) {\n return d[0][0].tagName === \"text\" || !d.select(\"text\").empty();\n };\n AbstractMeasurer.prototype.getTextMeasurer = function (area, className) {\n var _this = this;\n if (!this.checkSelectionIsText(area)) {\n var textElement = area.append(\"text\");\n if (className) {\n textElement.classed(className, true);\n }\n textElement.remove();\n return function (text) {\n area.node().appendChild(textElement.node());\n var areaDimension = _this.measureBBox(textElement, text);\n textElement.remove();\n return areaDimension;\n };\n }\n else {\n var parentNode = area.node().parentNode;\n var textSelection;\n if (area[0][0].tagName === \"text\") {\n textSelection = area;\n }\n else {\n textSelection = area.select(\"text\");\n }\n area.remove();\n return function (text) {\n parentNode.appendChild(area.node());\n var areaDimension = _this.measureBBox(textSelection, text);\n area.remove();\n return areaDimension;\n };\n }\n };\n AbstractMeasurer.prototype.measureBBox = function (d, text) {\n d.text(text);\n var bb = SVGTypewriter.Utils.DOM.getBBox(d);\n return { width: bb.width, height: bb.height };\n };\n AbstractMeasurer.prototype.measure = function (text) {\n if (text === void 0) { text = AbstractMeasurer.HEIGHT_TEXT; }\n return this.textMeasurer(text);\n };\n AbstractMeasurer.HEIGHT_TEXT = \"bqpdl\";\n return AbstractMeasurer;\n })();\n Measurers.AbstractMeasurer = AbstractMeasurer;\n })(Measurers = SVGTypewriter.Measurers || (SVGTypewriter.Measurers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Measurers;\n (function (Measurers) {\n var Measurer = (function (_super) {\n __extends(Measurer, _super);\n function Measurer(area, className, useGuards) {\n if (className === void 0) { className = null; }\n if (useGuards === void 0) { useGuards = false; }\n _super.call(this, area, className);\n this.useGuards = useGuards;\n }\n // Guards assures same line height and width of whitespaces on both ends.\n Measurer.prototype._addGuards = function (text) {\n return Measurers.AbstractMeasurer.HEIGHT_TEXT + text + Measurers.AbstractMeasurer.HEIGHT_TEXT;\n };\n Measurer.prototype.getGuardWidth = function () {\n if (this.guardWidth == null) {\n this.guardWidth = _super.prototype.measure.call(this).width;\n }\n return this.guardWidth;\n };\n Measurer.prototype._measureLine = function (line) {\n var measuredLine = this.useGuards ? this._addGuards(line) : line;\n var measuredLineDimensions = _super.prototype.measure.call(this, measuredLine);\n measuredLineDimensions.width -= this.useGuards ? (2 * this.getGuardWidth()) : 0;\n return measuredLineDimensions;\n };\n Measurer.prototype.measure = function (text) {\n var _this = this;\n if (text === void 0) { text = Measurers.AbstractMeasurer.HEIGHT_TEXT; }\n if (text.trim() === \"\") {\n return { width: 0, height: 0 };\n }\n var linesDimensions = text.trim().split(\"\\n\").map(function (line) { return _this._measureLine(line); });\n return {\n width: d3.max(linesDimensions, function (dim) { return dim.width; }),\n height: d3.sum(linesDimensions, function (dim) { return dim.height; })\n };\n };\n return Measurer;\n })(Measurers.AbstractMeasurer);\n Measurers.Measurer = Measurer;\n })(Measurers = SVGTypewriter.Measurers || (SVGTypewriter.Measurers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Measurers;\n (function (Measurers) {\n var CharacterMeasurer = (function (_super) {\n __extends(CharacterMeasurer, _super);\n function CharacterMeasurer() {\n _super.apply(this, arguments);\n }\n CharacterMeasurer.prototype._measureCharacter = function (c) {\n return _super.prototype._measureLine.call(this, c);\n };\n CharacterMeasurer.prototype._measureLine = function (line) {\n var _this = this;\n var charactersDimensions = line.split(\"\").map(function (c) { return _this._measureCharacter(c); });\n return {\n width: d3.sum(charactersDimensions, function (dim) { return dim.width; }),\n height: d3.max(charactersDimensions, function (dim) { return dim.height; })\n };\n };\n return CharacterMeasurer;\n })(Measurers.Measurer);\n Measurers.CharacterMeasurer = CharacterMeasurer;\n })(Measurers = SVGTypewriter.Measurers || (SVGTypewriter.Measurers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\n///\nvar __extends = this.__extends || function (d, b) {\n for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];\n function __() { this.constructor = d; }\n __.prototype = b.prototype;\n d.prototype = new __();\n};\nvar SVGTypewriter;\n(function (SVGTypewriter) {\n var Measurers;\n (function (Measurers) {\n var CacheCharacterMeasurer = (function (_super) {\n __extends(CacheCharacterMeasurer, _super);\n function CacheCharacterMeasurer(area, className) {\n var _this = this;\n _super.call(this, area, className);\n this.cache = new SVGTypewriter.Utils.Cache(function (c) { return _this._measureCharacterNotFromCache(c); }, SVGTypewriter.Utils.Methods.objEq);\n }\n CacheCharacterMeasurer.prototype._measureCharacterNotFromCache = function (c) {\n return _super.prototype._measureCharacter.call(this, c);\n };\n CacheCharacterMeasurer.prototype._measureCharacter = function (c) {\n return this.cache.get(c);\n };\n CacheCharacterMeasurer.prototype.reset = function () {\n this.cache.clear();\n };\n return CacheCharacterMeasurer;\n })(Measurers.CharacterMeasurer);\n Measurers.CacheCharacterMeasurer = CacheCharacterMeasurer;\n })(Measurers = SVGTypewriter.Measurers || (SVGTypewriter.Measurers = {}));\n})(SVGTypewriter || (SVGTypewriter = {}));\n\nreturn Plottable;\n\n}));\n" }, { "path": "cluster/benchmarks/dashboard_app/app.yaml", "content": "runtime: python\nenv: flex\nentrypoint: gunicorn -b :$PORT main:app\nservice: benchmarks\n\nruntime_config:\n python_version: 3\n" }, { "path": "cluster/benchmarks/dashboard_app/main.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Flask application file for benchmark dashboard.\"\"\"\nfrom collections import namedtuple\nfrom datetime import datetime\nfrom datetime import timedelta\nimport json\nimport logging\nfrom operator import itemgetter\nimport re\nimport urllib\n\nfrom flask import Flask, render_template, request\nfrom google.cloud import datastore\n\n\napp = Flask(__name__)\n\n# How much data to fetch for graphing.\n_DAYS_TO_FETCH = 90\n# Don't show a benchmark in benchmark list if it hasn't been run\n# for this many days.\n_MAX_DAYS_WITHOUT_RUN = 14\n# Arguments in this list will not be displayed on the dashboard.\n_ARGUMENTS_TO_EXCLUDE = set(\n ['job_name', 'result_storage', 'task_index'])\n\n\ndef argument_name(argument):\n \"\"\"Gets argument name from string in the form \"--arg_name=value\".\n\n Args:\n argument: String argument in the form --arg_name=value.\n\n Returns:\n String argument name.\n \"\"\"\n if len(argument) < 4 or argument[:2] != '--' or '=' not in argument:\n logging.error('Invalid argument: %s. Argument must be in the form '\n '--name=value', argument)\n return ''\n return argument[2:argument.index('=')]\n\n\n@app.route('/')\n@app.route('/')\ndef index(pattern=None):\n \"\"\"Renders index.html page with a list of benchmarks.\"\"\"\n filter_regex = None\n if pattern:\n filter_regex = re.compile(urllib.parse.unquote(pattern))\n min_time_to_lookup = datetime.now() - timedelta(days=_MAX_DAYS_WITHOUT_RUN)\n\n client = datastore.Client()\n query = client.query(kind='Test')\n query.add_filter('start', '>', min_time_to_lookup)\n\n fetched = list(query.fetch())\n test_names = {} # maps test name to encoded test name\n for fetched_result in fetched:\n if fetched_result['test'] in test_names:\n continue # already added\n if not filter_regex or re.search(pattern, fetched_result['test']):\n test_names[fetched_result['test']] = urllib.parse.quote(\n fetched_result['test'], safe='')\n\n # convert test_names to list and sort\n test_names = sorted(test_names.items(), key=itemgetter(1), reverse=True)\n\n return render_template('index.html', tests=test_names)\n\n\n@app.route('/test/')\ndef test(test_id):\n \"\"\"Renders test.html page with a graph for each benchmark entry.\"\"\"\n test_id = urllib.parse.unquote(test_id)\n min_time_to_lookup = datetime.now() - timedelta(days=2)\n client = datastore.Client()\n\n # Get most recent start time for this test\n query = client.query(kind='Test')\n query.add_filter('test', '=', test_id)\n query.order = ['-start']\n test_results = list(query.fetch(limit=1))\n if not test_results:\n return 'No data for benchmark %s' % test_id\n\n start_time = test_results[0]['start']\n\n # Get a list of entry ids\n query = client.query(kind='Entry')\n query.add_filter('test', '=', test_id)\n query.add_filter('start', '=', start_time)\n\n try:\n test_info = json.loads(test_results[0]['info'])\n except ValueError as e:\n logging.exception('Failed to parse \"info\" in test_results.', e)\n test_info = None\n arguments = []\n if (test_info and 'runConfiguration' in test_info and\n 'argument' in test_info['runConfiguration']):\n arguments = test_info['runConfiguration']['argument']\n arguments = [\n arg for arg in arguments\n if argument_name(arg) not in _ARGUMENTS_TO_EXCLUDE]\n arguments = ' '.join(arguments)\n entries = []\n Entry = namedtuple('Entry', ['id', 'latest_value'])\n for entry in query.fetch():\n info = json.loads(entry['info'])\n entries.append(Entry(entry['entry'], info['wallTime']))\n\n return render_template(\n 'test.html', test_id=test_id, entries=entries,\n latest_time=start_time.strftime('%Y-%m-%d %H:%M'),\n arguments=arguments)\n\n\n@app.route('/benchmark_data/')\ndef benchmark_data():\n \"\"\"Returns benchmark data in json format for graphing.\"\"\"\n test_id = urllib.parse.unquote(request.args.get('test'))\n entry_id = urllib.parse.unquote(request.args.get('entry'))\n min_time_to_lookup = datetime.now() - timedelta(days=_DAYS_TO_FETCH)\n\n client = datastore.Client()\n timing_query = client.query(kind='Entry')\n timing_query.add_filter('test', '=', test_id)\n timing_query.add_filter('entry', '=', entry_id)\n timing_query.add_filter('start', '>', min_time_to_lookup)\n timing_query.projection = ['start', 'timing']\n start_and_timing = [\n {'start': data['start'], 'timing': data['timing']}\n for data in timing_query.fetch()]\n start_and_timing_json = json.dumps(start_and_timing)\n return start_and_timing_json\n\n\n@app.errorhandler(500)\ndef server_error(e):\n logging.exception('An error occurred during a request.', e)\n return 'An internal error occurred.', 500\n\n\nif __name__ == '__main__':\n # This is used when running locally. Gunicorn is used to run the\n # application on Google App Engine. See entrypoint in app.yaml.\n app.run(host='127.0.0.1', port=8080, threaded=True)\n" }, { "path": "cluster/benchmarks/dashboard_app/main_test.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\nimport json\nimport main\nimport unittest\nimport urllib\n\nclass TestMain(unittest.TestCase):\n\n def testArgumentInvalidFormat(self):\n self.assertEqual('', main.argument_name(''))\n self.assertEqual('', main.argument_name('arg=val'))\n self.assertEqual('', main.argument_name('-arg=val'))\n self.assertEqual('', main.argument_name('--argval'))\n self.assertEqual('', main.argument_name('--=val'))\n self.assertEqual('', main.argument_name('--='))\n\n def testArgumentValidFormat(self):\n self.assertEqual('abc', main.argument_name('--abc=123'))\n self.assertEqual('a', main.argument_name('--a=123'))\n\n def testIndexPage(self):\n main.app.testing = True\n client = main.app.test_client()\n\n r = client.get('/')\n self.assertEqual(200, r.status_code)\n self.assertIn('sample_logged_benchmark', r.data.decode('utf-8'))\n\n def testTestPage_InvalidTest(self):\n main.app.testing = True\n client = main.app.test_client()\n\n r = client.get('/test/abc')\n self.assertEqual(200, r.status_code)\n self.assertIn('No data for benchmark', str(r.data))\n\n def testTestPage_SampleTest(self):\n main.app.testing = True\n client = main.app.test_client()\n sample_benchmark_name = '//tensorflow/examples/benchmark:sample_logged_benchmark'\n\n r = client.get(\n '/test/%252F%252Ftensorflow%252Fexamples%252Fbenchmark%253Asample_logged_benchmark')\n self.assertEqual(200, r.status_code)\n self.assertIn(\n 'Performance plots for %s' % sample_benchmark_name, str(r.data))\n\n def testFetchBenchmarkData_InvalidTest(self):\n main.app.testing = True\n client = main.app.test_client()\n\n r = client.get('/benchmark_data/?test=abc&entry=cde')\n self.assertEqual(200, r.status_code)\n self.assertEqual(b'[]', r.data)\n\n def testFetchBenchmarkData_SampleTest(self):\n main.app.testing = True\n client = main.app.test_client()\n\n encoded_benchmark_name = (\n '/test/%252F%252Ftensorflow%252Fexamples%252Fbenchmark%253Asample_logged_benchmark')\n r = client.get('/benchmark_data/?test=%s&entry=SampleBenchmark.sum_wall_time' %\n encoded_benchmark_name)\n self.assertEqual(200, r.status_code)\n self.assertEqual(b'[]', r.data)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "cluster/benchmarks/dashboard_app/requirements.txt", "content": "Flask==0.12.2\ngunicorn==19.7.1\ngoogle-cloud\n" }, { "path": "cluster/benchmarks/dashboard_app/static/css/style.css", "content": "/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n==============================================================================\n*/\n\nbody {\n font-family: roboto, sans-serif;\n}\n\nh2 {\n font-weight: 400;\n}\n\nem {\n color: #666666;\n font-size: 18px;\n font-style: normal;\n}\n\n.outer_div {\n max-width: 1000px;\n margin: 20px;\n}\n\ntable, th, td {\n border-collapse: collapse;\n border: 1px solid #d9d9d9;\n}\n\nth, td {\n padding: 15px;\n}\n\nth {\n text-align: left;\n font-weight: normal;\n}\n\nul {\n width: 100%;\n margin: 0;\n padding: 0;\n}\n\nli {\n font-size: 14px;\n background-color: white;\n list-style: none;\n border: 1px solid #d9d9d9;\n border-radius: 2px;\n margin: 10px 0 0 0;\n}\n\nli:hover {\n background-color: #eeeeee;\n}\n\nli a {\n display: inline-block;\n width: 100%;\n height: 100%;\n color: black;\n text-decoration: none;\n padding: 8px 8px;\n}\n\nsvg {\n margin-top: 20px;\n}\n\n#filter_input {\n display: block;\n width: 100%;\n font-size: 14px;\n padding: 8px 8px;\n border: 1px solid #d9d9d9;\n border-radius: 2px;\n box-sizing: border-box;\n}\n\n#filter_label, #arguments_label {\n color: #666666;\n font-size: 16px;\n}\n\n#latest_value_label {\n margin-bottom: 20px;\n}\n\nplottable .title-label text{\n font-size: 16px;\n font-family: roboto, sans-serif;\n}\n" }, { "path": "cluster/benchmarks/dashboard_app/static/js/benchmark_latency_chart.js", "content": "// Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n/**\n * @fileoverview Provides a way to create a benchmark latency chart.\n */\n\n/**\n * Constructor.\n * @param {string} svg_element_id svg element to add the chart to.\n * @param {string} test_id of the test to plot data for.\n * @param {string} entry_id of the specific test entry to plot.\n */\nvar BenchmarkLatencyChart = function(svg_element, test_id, entry_id) {\n this.svg_element = svg_element;\n this.test_id = test_id;\n this.entry_id = entry_id;\n};\n\n/**\n * Adds data to the given plots.\n */\nBenchmarkLatencyChart.prototype.addData_ = function(plot) {\n const encodedTestId = encodeURIComponent(this.test_id);\n const encodedEntryId = encodeURIComponent(this.entry_id);\n const jsonDataUrl =\n '/benchmark_data/?test=' + encodedTestId + '&entry=' + encodedEntryId\n d3.json(jsonDataUrl, function(data) {\n benchmarks = []\n for (var i = 0; i < data.length; i++) {\n const name = this.entry_id;\n const timestamp = new Date(+data[i]['start'] / 1000);\n const mean_latency = data[i]['timing'];\n benchmarks.push(\n {name: name, timestamp: timestamp,\n mean_latency: +mean_latency});\n }\n plot.addDataset(\n new Plottable.Dataset(benchmarks, {name: 'Forward'}));\n });\n};\n\n/**\n * Create the chart.\n */\nBenchmarkLatencyChart.prototype.makeChart = function() {\n const xScale = new Plottable.Scales.Time();\n const yScaleForward = new Plottable.Scales.Linear();\n\n const plot = new LatencyChart(\n this.entry_id, 'value',\n xScale, yScaleForward);\n\n this.addData_(plot);\n\n const table = new Plottable.Components.Table([[plot.table]]);\n table.renderTo(this.svg_element);\n\n plot.addTooltip();\n new Plottable.Interactions.Click()\n .attachTo(plot.linePlot)\n .onClick(function(p) {\n plot.updateForPosition(p);\n });\n};\n" }, { "path": "cluster/benchmarks/dashboard_app/templates/index.html", "content": "\n\n\n\n\n\n\n\n\n
\nFilter\n\n
    \n{% for test_name, encoded_test_name in tests %}\n
  • {{ test_name }}
    • \n{% endfor %}\n
\n
\n\n\n\n" }, { "path": "cluster/benchmarks/dashboard_app/templates/test.html", "content": "\n\n\n\n\n\n\n\n
\n

Performance plots for {{ test_id }}

\n\n{% if arguments %}\n
Arguments:
\n
{{ arguments }}
\n{% endif %}\n\n\n{% for entry in entries %}\n\n\n
Latest value: {{ entry.latest_value }} at {{ latest_time }}.
\n{% endfor %}\n\n
\n\n\n\n\n\n\n\n" }, { "path": "cluster/benchmarks/index.html", "content": "\n\n\n\n\n\n
\nSoumith benchmarks\n
\n\n\n" }, { "path": "cluster/benchmarks/js/csv_benchmark_chart.js", "content": "/**\n * @fileoverview Provides a way to create a mean latency chart based on a\n * csv file with latency data.\n */\n\n/**\n * Constructor.\n * @param {string} svg_element_id svg element to add the chart to.\n * @param {string} latency_csv_file File to read input data from. The file\n * must have lines in the following format:\n * (Forward|Forward-Backward),timestamp,num_batches,mean,sd\n */\nvar CsvLatencyChart = function(svg_element_id, latency_csv_file) {\n this.svg_element_id = svg_element_id;\n this.latency_csv_file = latency_csv_file;\n};\n\n/**\n * Adds data to the given plots.\n */\nCsvLatencyChart.prototype.addData_ = function(\n plotForward, plotForwardBackward) {\n d3.text(this.latency_csv_file, function(data) {\n data = d3.csv.parseRows(data);\n const parseDate = d3.time.format('%Y-%m-%d %H:%M:%S').parse;\n let forwardBenchmarks = [];\n let forwardBackwardBenchmarks = [];\n for (var i = 0; i < data.length; i++) {\n const name = data[i][0];\n const timestamp = data[i][1];\n const mean_latency = data[i][3];\n // Timestamp has the format: 2016-08-31 23:38:55.159320\n // However, we can't parse this date format using d3 time\n // functions, so we remove everything after the dot before parsing.\n const dateUpToSeconds = timestamp.split('.')[0]\n if (name == 'Forward') {\n forwardBenchmarks.push(\n {name: name, timestamp: parseDate(dateUpToSeconds),\n mean_latency: +mean_latency});\n } else {\n forwardBackwardBenchmarks.push(\n {name: name, timestamp: parseDate(dateUpToSeconds),\n mean_latency: +mean_latency});\n }\n }\n plotForward.addDataset(\n new Plottable.Dataset(forwardBenchmarks, {name: 'Forward'}));\n plotForwardBackward.addDataset(\n new Plottable.Dataset(\n forwardBackwardBenchmarks, {name: 'Forward-Backward'}));\n });\n};\n\n/**\n * Create the chart.\n */\nCsvLatencyChart.prototype.makeChart = function() {\n const xScale = new Plottable.Scales.Time();\n const yScaleForward = new Plottable.Scales.Linear();\n const yScaleForwardBackward = new Plottable.Scales.Linear();\n\n const plotForward = new LatencyChart(\n 'Forward pass per-batch latency', 'Mean latency (sec)',\n xScale, yScaleForward);\n const plotForwardBackward = new LatencyChart(\n 'Forward-backward pass per-batch latency', 'Mean latency (sec)',\n xScale, yScaleForwardBackward);\n\n this.addData_(plotForward, plotForwardBackward);\n\n const table = new Plottable.Components.Table([\n [plotForward.table],\n [plotForwardBackward.table]\n ]);\n table.renderTo(this.svg_element_id);\n\n plotForward.addTooltip();\n plotForwardBackward.addTooltip();\n new Plottable.Interactions.Click()\n .attachTo(plotForward.linePlot)\n .onClick(function(p) {\n plotForward.updateForPosition(p);\n plotForwardBackward.updateForPosition(p);\n });\n new Plottable.Interactions.Click()\n .attachTo(plotForwardBackward.linePlot)\n .onClick(function(p) {\n plotForward.updateForPosition(p);\n plotForwardBackward.updateForPosition(p);\n });\n};\n" }, { "path": "cluster/benchmarks/js/latency_chart.js", "content": "/**\n * @fileoverview Combines all components needed to display a line chart for\n * benchmarks.\n * @param {string} title Graph title.\n * @param {string} yLabel Label to use for the y-axis.\n * @param {Plottable.Scale} xScale X-scale for the graph.\n * @param {Plottable.Scale} yScale Y-scale for the graph.\n */\nvar LatencyChart = function(title, yLabel, xScale, yScale) {\n this.linePlot = new Plottable.Plots.Line()\n .x(function(d) { return d.timestamp; }, xScale)\n .y(function(d) { return d.mean_latency; }, yScale)\n .attr('stroke-width', 3);\n this.pointPlot = new Plottable.Plots.Scatter()\n .x(function(d) { return d.timestamp; }, xScale)\n .y(function(d) { return d.mean_latency; }, yScale)\n .size(10)\n .attr('opacity', 1)\n .addDataset(new Plottable.Dataset());\n this.guideline = new Plottable.Components.GuideLineLayer('vertical')\n .scale(xScale);\n this.xAxis = new Plottable.Axes.Time(xScale, 'bottom')\n .annotationsEnabled(true);\n this.yAxis = new Plottable.Axes.Numeric(yScale, 'left');\n this.title = new Plottable.Components.TitleLabel(title)\n .yAlignment('top').padding(10);\n this.yLabel = new Plottable.Components.AxisLabel(yLabel, '270');\n const group = new Plottable.Components.Group(\n [this.guideline, this.linePlot, this.pointPlot]);\n this.table = new Plottable.Components.Table([\n [null, null, this.title],\n [this.yLabel, this.yAxis, group],\n [null, null, this.xAxis]\n ]);\n};\n\n/**\n * Add value tooltip to this chart.\n * Note: this method must be called after the chart has been\n * rendered.\n */\nLatencyChart.prototype.addTooltip = function() {\n this.tooltip = new ValueTooltip(this.linePlot);\n};\n\n/**\n * Add data to this chart.\n * Data must be an Array with items in the form:\n * {name, timestamp, mean_latency}\n */\nLatencyChart.prototype.addDataset = function(data) {\n this.linePlot.addDataset(data);\n};\n\n/**\n * Update chart for the given position: display a guideline,\n * x-axis value indicator and show value tooltip at this position.\n * @param {Plottable.Point} position Point to update for.\n */\nLatencyChart.prototype.updateForPosition = function(position) {\n const entity = this.linePlot.entityNearest(position);\n if (typeof entity == 'undefined') {\n return;\n }\n const date = new Date(entity.datum.timestamp);\n this.guideline.value(date);\n this.pointPlot.datasets()[0].data([entity.datum]);\n if (typeof this.tooltip != 'undefined') {\n this.tooltip.update(entity.position, entity.datum.mean_latency);\n }\n this.xAxis.annotatedTicks([date]);\n};\n\n/**\n * Constructor. Creates a tooltip to display chart value.\n * @param {Plottable.Plots.Line} linePlot Plot to add the tooltip to.\n */\nvar ValueTooltip = function(linePlot) {\n this.tooltip = d3.select('body').append('div')\n .style('position', 'absolute')\n .style('z-index', '10')\n .style('visibility', 'hidden');\n this.tooltipAnchorSelection = linePlot.foreground().append('circle').attr({\n r: 3,\n opacity: 0\n });\n};\n\n/**\n * Display tooltip at the given position.\n * @param {Plottable.Point} position Point to display tooltip at.\n * @param {number} data Numeric data to show.\n */\nValueTooltip.prototype.update = function(position, data) {\n this.tooltipAnchorSelection.attr({\n cx: position.x,\n cy: position.y\n });\n const boundingRect =\n this.tooltipAnchorSelection.node().parentElement.getBoundingClientRect();\n this.tooltip.style(\n 'top',\n (boundingRect.top - document.body.getBoundingClientRect().top-10)+'px')\n .style('left',(boundingRect.left+10)+'px');\n this.tooltip.style('visibility', 'visible');\n this.tooltip.text(data.toPrecision(6));\n};\n" }, { "path": "cluster/benchmarks/scripts/Dockerfile.tf_cnn_benchmarks", "content": "FROM tensorflow/tensorflow:nightly-gpu\n\nRUN apt-get update && apt-get install -y python-pip && pip install google-cloud\nCOPY tf_cnn_benchmarks/ ./tf_cnn_benchmarks/\nRUN touch tf_cnn_benchmarks/__init__.py\nRUN mkdir ./util/\nCOPY util/ ./util/\nENTRYPOINT [\"python\", \"-m\", \"tf_cnn_benchmarks.tf_cnn_benchmarks\"]\n" }, { "path": "cluster/benchmarks/scripts/benchmark_configs.yml", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n# Distributed benchmark configs to run with continuous build.\n# For each benchmark, the following properties are supported:\n#\n# benchmark_name: (required) unique name of the benchmark to run\n# args: (optional) argument values to pass to the benchmark.\n# env_vars: (optional) environment variables to set for benchmark jobs.\n# worker_count: (required) number of worker jobs to run\n# ps_count: (required) number of ps jobs to run.\n# gpus_per_machine: (optional) number of required gpus per worker\n# (currently only supporting <= 1).\n# docker_file: (required) docker file to build a docker image for.\n# Path to the docker file should be relative to Jenkins build folder.\n# 'benchmarks' github repo will be cloned to 'benchmarks' folder.\n#\n# Example:\n# - benchmark_name: \"benchmark_alexnet\"\n# args:\n# data_format: \"NHWC\"\n# worker_count: 1\n# ps_count: 2\n# docker_file: \"benchmarks/models/Dockerfile.alexnet_distributed_test\"\n\n- benchmark_name: \"tf_cnn_benchmark_resnet50\"\n args:\n data_format: \"NHWC\"\n model: \"resnet50\"\n result_storage: \"cbuild_benchmark_datastore\"\n num_gpus: 8\n local_parameter_device: \"cpu\"\n worker_count: 2\n ps_count: 2\n gpus_per_machine: 8\n docker_file: \"benchmarks/scripts/Dockerfile.tf_cnn_benchmarks\"\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/README.md", "content": "# tf_cnn_benchmarks: High performance benchmarks\n\ntf_cnn_benchmarks contains implementations of several popular convolutional\nmodels, and is designed to be as fast as possible. tf_cnn_benchmarks supports\nboth running on a single machine or running in distributed mode across multiple\nhosts. See the [High-Performance models\nguide](https://www.tensorflow.org/performance/performance_models) for more\ninformation.\n\nThese models utilize many of the strategies in the [TensorFlow Performance\nGuide](https://www.tensorflow.org/performance/performance_guide). Benchmark\nresults can be found [here](https://www.tensorflow.org/performance/benchmarks).\n\nThese models are designed for performance. For models that have clean and\neasy-to-read implementations, see the [TensorFlow Official\nModels](https://github.com/tensorflow/models/tree/master/official).\n\n## Getting Started\n\nTo run ResNet50 with synthetic data without distortions with a single GPU, run\n\n```\npython tf_cnn_benchmarks.py --num_gpus=1 --batch_size=32 --model=resnet50 --variable_update=parameter_server\n```\n\nSome important flags are\n\n* model: Model to use, e.g. resnet50, inception3, vgg16, and alexnet.\n* num_gpus: Number of GPUs to use.\n* data_dir: Path to data to process. If not set, synthetic data is used. To\n use Imagenet data use these\n [instructions](https://github.com/tensorflow/models/tree/master/research/inception#getting-started)\n as a starting point.\n* batch_size: Batch size for each GPU.\n* variable_update: The method for managing variables: parameter_server\n ,replicated, distributed_replicated, independent\n* local_parameter_device: Device to use as parameter server: cpu or gpu.\n\nSee\n[benchmark_cnn.py](https://github.com/tensorflow/benchmarks/blob/master/scripts/tf_cnn_benchmarks/benchmark_cnn.py)\nfor the full list of flags. The `_DEFAULT_PARAMS` dict in that file contains the\nflags.\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"TensorFlow benchmark library.\n\nSee the README for more information.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport argparse\nfrom collections import namedtuple\nimport math\nimport os\nimport threading\nimport time\n\nimport numpy as np\n\nimport six\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom tensorflow.python.client import timeline\nfrom tensorflow.python.ops import data_flow_ops\nfrom tensorflow.python.platform import gfile\nfrom tensorflow.python.util import nest\nimport benchmark_storage\nimport cnn_util\nimport convnet_builder\nimport datasets\nimport variable_mgr\nfrom cnn_util import log_fn\nfrom models import model_config\n\n# _ParamSpec describes one of BenchmarkCNN's parameters. _ParamSpec is the value\n# type for _DEFAULT_PARAMS below.\n_ParamSpec = namedtuple('_ParamSpec',\n ['flag_type', 'default_value', 'description'])\n\n# _DEFAULT_PARAMS maps from each parameter's name to its _ParamSpec.\n_DEFAULT_PARAMS = {\n 'model':\n _ParamSpec('string', 'trivial', 'name of the model to run'),\n\n # The code will first check if it's running under benchmarking mode\n # or evaluation mode, depending on 'eval':\n # Under the evaluation mode, this script will read a saved model,\n # and compute the accuracy of the model against a validation dataset.\n # Additional ops for accuracy and top_k predictors are only used under\n # this mode.\n # Under the benchmarking mode, user can specify whether nor not to use\n # the forward-only option, which will only compute the loss function.\n # forward-only cannot be enabled with eval at the same time.\n 'eval':\n _ParamSpec('boolean', False, 'whether use eval or benchmarking'),\n 'eval_interval_secs':\n _ParamSpec('integer', 0,\n 'How often to run eval on saved checkpoints. Usually the '\n 'same as save_model_secs from the corresponding training '\n 'run. Pass 0 to eval only once.'),\n 'forward_only':\n _ParamSpec('boolean', False,\n 'whether use forward-only or training for benchmarking'),\n 'print_training_accuracy':\n _ParamSpec('boolean', False,\n 'whether to calculate and print training accuracy during '\n 'training'),\n 'batch_size':\n _ParamSpec('integer', 0, 'batch size per compute device'),\n 'batch_group_size':\n _ParamSpec('integer', 1,\n 'number of groups of batches processed in the image '\n 'producer.'),\n 'num_batches':\n _ParamSpec('integer', 100, 'number of batches to run, excluding '\n 'warmup'),\n 'num_warmup_batches':\n _ParamSpec('integer', None, 'number of batches to run before timing'),\n 'autotune_threshold':\n _ParamSpec('integer', None, 'The autotune threshold for the models'),\n 'num_gpus':\n _ParamSpec('integer', 1, 'the number of GPUs to run on'),\n 'gpu_indices':\n _ParamSpec('string', '', 'indices of worker GPUs in ring order'),\n 'display_every':\n _ParamSpec('integer', 10,\n 'Number of local steps after which progress is printed out'),\n 'data_dir':\n _ParamSpec('string', None,\n 'Path to dataset in TFRecord format (aka Example '\n 'protobufs). If not specified, synthetic data will be '\n 'used.'),\n 'data_name':\n _ParamSpec('string', None,\n 'Name of dataset: imagenet or cifar10. If not specified, it '\n 'is automatically guessed based on data_dir.'),\n 'resize_method':\n _ParamSpec('string', 'bilinear',\n 'Method for resizing input images: crop, nearest, bilinear, '\n 'bicubic, area, or round_robin. The `crop` mode requires '\n 'source images to be at least as large as the network input '\n 'size. The `round_robin` mode applies different resize '\n 'methods based on position in a batch in a round-robin '\n 'fashion. Other modes support any sizes and apply random '\n 'bbox distortions before resizing (even with '\n 'distortions=False).'),\n 'distortions':\n _ParamSpec('boolean', True,\n 'Enable/disable distortions during image preprocessing. '\n 'These include bbox and color distortions.'),\n 'use_datasets':\n _ParamSpec('boolean', True,\n 'Enable use of datasets for input pipeline'),\n 'cache_data':\n _ParamSpec('boolean', False,\n 'Enable use of a special datasets pipeline that reads a '\n 'single TFRecord into memory and repeats it infinitely many '\n 'times. The purpose of this flag is to make it possible '\n 'to write regression tests that are not bottlenecked by CNS '\n 'throughput.'),\n 'local_parameter_device':\n _ParamSpec('string', 'gpu',\n 'Device to use as parameter server: cpu or gpu. For '\n 'distributed training, it can affect where caching of '\n 'variables happens.'),\n 'device':\n _ParamSpec('string', 'gpu',\n 'Device to use for computation: cpu or gpu'),\n 'data_format':\n _ParamSpec('string', 'NCHW',\n 'Data layout to use: NHWC (TF native) or NCHW (cuDNN '\n 'native, requires GPU).'),\n 'num_intra_threads':\n _ParamSpec('integer', 1,\n 'Number of threads to use for intra-op parallelism. If set '\n 'to 0, the system will pick an appropriate number.'),\n 'num_inter_threads':\n _ParamSpec('integer', 0,\n 'Number of threads to use for inter-op parallelism. If set '\n 'to 0, the system will pick an appropriate number.'),\n 'trace_file':\n _ParamSpec('string', None,\n 'Enable TensorFlow tracing and write trace to this file.'),\n 'graph_file':\n _ParamSpec('string', None,\n 'Write the model\\'s graph definition to this file. Defaults '\n 'to binary format unless filename ends in `txt`.'),\n 'optimizer':\n _ParamSpec('string', 'sgd',\n 'Optimizer to use: momentum or sgd or rmsprop'),\n 'learning_rate':\n _ParamSpec('float', None, 'Initial learning rate for training.'),\n 'num_epochs_per_decay':\n _ParamSpec('float', 0,\n 'Steps after which learning rate decays. If 0, the learning '\n 'rate does not decay.'),\n 'learning_rate_decay_factor':\n _ParamSpec('float', 0,\n 'Learning rate decay factor. Decay by this factor every '\n '`num_epochs_per_decay` epochs. If 0, learning rate does '\n 'not decay.'),\n 'minimum_learning_rate':\n _ParamSpec('float', 0,\n 'The minimum learning rate. The learning rate will '\n 'never decay past this value. Requires `learning_rate`, '\n '`num_epochs_per_decay` and `learning_rate_decay_factor` to '\n 'be set.'),\n 'momentum':\n _ParamSpec('float', 0.9, 'Momentum for training.'),\n 'rmsprop_decay':\n _ParamSpec('float', 0.9, 'Decay term for RMSProp.'),\n 'rmsprop_momentum':\n _ParamSpec('float', 0.9, 'Momentum in RMSProp.'),\n 'rmsprop_epsilon':\n _ParamSpec('float', 1.0, 'Epsilon term for RMSProp.'),\n 'gradient_clip':\n _ParamSpec('float', None,\n 'Gradient clipping magnitude. Disabled by default.'),\n 'weight_decay':\n _ParamSpec('float', 0.00004, 'Weight decay factor for training.'),\n 'gpu_memory_frac_for_testing':\n _ParamSpec('float', 0,\n 'If non-zero, the fraction of GPU memory that will be used. '\n 'Useful for testing the benchmark script, as this allows '\n 'distributed mode to be run on a single machine. For '\n 'example, if there are two tasks, each can be allocated '\n '~40 percent of the memory on a single machine'),\n 'use_tf_layers':\n _ParamSpec('boolean', True,\n 'If True, use tf.layers for neural network layers. This '\n 'should not affect performance or accuracy in any way.'),\n 'tf_random_seed':\n _ParamSpec('integer', 1234,\n 'The TensorFlow random seed. Useful for debugging NaNs, as '\n 'this can be set to various values to see if the NaNs '\n 'depend on the seed.'),\n\n # Performance tuning parameters.\n 'winograd_nonfused':\n _ParamSpec('boolean', True,\n 'Enable/disable using the Winograd non-fused algorithms.'),\n 'sync_on_finish':\n _ParamSpec('boolean', False,\n 'Enable/disable whether the devices are synced after each '\n 'step.'),\n 'staged_vars':\n _ParamSpec('boolean', False,\n 'whether the variables are staged from the main '\n 'computation'),\n 'force_gpu_compatible':\n _ParamSpec('boolean', True,\n 'whether to enable force_gpu_compatible in GPU_Options'),\n 'xla':\n _ParamSpec('boolean', False, 'whether to enable XLA'),\n 'fuse_decode_and_crop':\n _ParamSpec('boolean', True,\n 'Fuse decode_and_crop for image preprocessing.'),\n 'distort_color_in_yiq':\n _ParamSpec('boolean', False,\n 'Distort color of input images in YIQ space.'),\n # Performance tuning specific to MKL.\n 'mkl':\n _ParamSpec('boolean', False, 'If true, set MKL environment variables.'),\n 'kmp_blocktime':\n _ParamSpec('integer', 30,\n 'The time, in milliseconds, that a thread should wait, '\n 'after completing the execution of a parallel region, '\n 'before sleeping'),\n 'kmp_affinity':\n _ParamSpec('string', 'granularity=fine,verbose,compact,1,0',\n 'Restricts execution of certain threads (virtual execution '\n 'units) to a subset of the physical processing units in a '\n 'multiprocessor computer.'),\n 'kmp_settings':\n _ParamSpec('integer', 1, 'If set to 1, MKL settings will be printed.'),\n\n # fp16 parameters. If use_fp16=False, no other fp16 parameters apply.\n 'use_fp16':\n _ParamSpec('boolean', False,\n 'Use 16-bit floats for certain tensors instead of 32-bit '\n 'floats. This is currently experimental.'),\n # TODO(reedwm): The default loss scale of 128 causes most models to diverge\n # on the second step with synthetic data. Changing the tf.set_random_seed\n # call to tf.set_random_seed(1235) or most other seed values causes the\n # issue not to occur.\n 'fp16_loss_scale':\n _ParamSpec('float', None,\n 'If fp16 is enabled, the loss is multiplied by this amount '\n 'right before gradients are computed, then each gradient '\n 'is divided by this amount. Mathematically, this has no '\n 'effect, but it helps avoid fp16 underflow. Set to 1 to '\n 'effectively disable.'),\n 'fp16_vars':\n _ParamSpec('boolean', False,\n 'If fp16 is enabled, also use fp16 for variables. If False, '\n 'the variables are stored in fp32 and casted to fp16 when '\n 'retrieved. Recommended to leave as False.'),\n\n # The method for managing variables:\n # parameter_server: variables are stored on a parameter server that holds\n # the master copy of the variable. In local execution, a local device\n # acts as the parameter server for each variable; in distributed\n # execution, the parameter servers are separate processes in the\n # cluster.\n # For each step, each tower gets a copy of the variables from the\n # parameter server, and sends its gradients to the param server.\n # replicated: each GPU has its own copy of the variables. To apply\n # gradients, an all_reduce algorithm or or regular cross-device\n # aggregation is used to replicate the combined gradients to all\n # towers (depending on all_reduce_spec parameter setting).\n # independent: each GPU has its own copy of the variables, and gradients\n # are not shared between towers. This can be used to check performance\n # when no data is moved between GPUs.\n # distributed_replicated: Distributed training only. Each GPU has a copy\n # of the variables, and updates its copy after the parameter servers\n # are all updated with the gradients from all servers. Only works with\n # cross_replica_sync=true. Unlike 'replicated', currently never uses\n # nccl all-reduce for replicating within a server.\n # distributed_all_reduce: Distributed training where all replicas run\n # in a single session, using all-reduce to mutally reduce the\n # gradients. Uses no parameter servers. When there is only one\n # worker, this is the same as replicated.\n 'variable_update':\n _ParamSpec('string', 'parameter_server',\n 'The method for managing variables: parameter_server, '\n 'replicated, distributed_replicated, independent, '\n 'distributed_all_reduce'),\n 'all_reduce_spec':\n _ParamSpec('string', None,\n 'A specification of the all_reduce algorithm to be used for '\n 'reducing gradients. For more details, see '\n 'parse_all_reduce_spec in variable_mgr.py. An '\n 'all_reduce_spec has BNF form:\\n'\n 'int ::= positive whole number\\n'\n 'g_int ::= int[KkMGT]?\\n'\n 'alg_spec ::= alg | alg#int\\n'\n 'range_spec ::= alg_spec | alg_spec/alg_spec\\n'\n 'spec ::= range_spec | range_spec:g_int:range_spec\\n'\n 'NOTE: not all syntactically correct constructs are '\n 'supported.\\n\\n'\n 'Examples:\\n '\n '\"xring\" == use one global ring reduction for all '\n 'tensors\\n'\n '\"pscpu\" == use CPU at worker 0 to reduce all tensors\\n'\n '\"nccl\" == use NCCL to locally reduce all tensors. '\n 'Limited to 1 worker.\\n'\n '\"nccl/xring\" == locally (to one worker) reduce values '\n 'using NCCL then ring reduce across workers.\\n'\n '\"pscpu:32k:xring\" == use pscpu algorithm for tensors of '\n 'size up to 32kB, then xring for larger tensors.'),\n\n # Distributed training parameters.\n 'job_name':\n _ParamSpec('string', '',\n 'One of \"ps\", \"worker\", \"\". Empty for local training'),\n 'ps_hosts':\n _ParamSpec('string', '', 'Comma-separated list of target hosts'),\n 'worker_hosts':\n _ParamSpec('string', '', 'Comma-separated list of target hosts'),\n 'controller_host':\n _ParamSpec('string', None, 'optional controller host'),\n 'task_index':\n _ParamSpec('integer', 0, 'Index of task within the job'),\n 'server_protocol':\n _ParamSpec('string', 'grpc', 'protocol for servers'),\n 'cross_replica_sync':\n _ParamSpec('boolean', True, ''),\n\n # Summary and Save & load checkpoints.\n 'summary_verbosity':\n _ParamSpec(\n 'integer', 0, 'Verbosity level for summary ops. '\n ' level 0: disable any summary. '\n ' level 1: small and fast ops, e.g.: learning_rate, total_loss.'\n ' level 2: medium-cost ops, e.g. histogram of all gradients.'\n ' level 3: expensive ops: images and histogram of each gradient.'),\n 'save_summaries_steps':\n _ParamSpec('integer', 0,\n 'How often to save summaries for trained models. Pass 0 to '\n 'disable summaries.'),\n 'save_model_secs':\n _ParamSpec('integer', 0,\n 'How often to save trained models. Pass 0 to disable '\n 'checkpoints.'),\n 'train_dir':\n _ParamSpec('string', None,\n 'Path to session checkpoints. Pass None to disable saving '\n 'checkpoint at the end.'),\n 'eval_dir':\n _ParamSpec('string', '/tmp/tf_cnn_benchmarks/eval',\n 'Directory where to write eval event logs.'),\n 'result_storage':\n _ParamSpec('string', None,\n 'Specifies storage option for benchmark results. None means '\n 'results won\\'t be stored. `cbuild_benchmark_datastore` '\n 'means results will be stored in cbuild datastore (note: '\n 'this option requires special permissions and meant to be '\n 'used from cbuilds).'),\n}\n\n\ndef define_flags():\n \"\"\"Define a command line flag for each _ParamSpec in _DEFAULT_PARAMS.\"\"\"\n define_flag = {\n 'boolean': tf.flags.DEFINE_boolean,\n 'float': tf.flags.DEFINE_float,\n 'integer': tf.flags.DEFINE_integer,\n 'string': tf.flags.DEFINE_string,\n }\n for name, param_spec in six.iteritems(_DEFAULT_PARAMS):\n if param_spec.flag_type not in define_flag:\n raise ValueError('Unknown flag_type %s' % param_spec.flag_type)\n else:\n define_flag[param_spec.flag_type](\n name, param_spec.default_value, param_spec.description)\n\n\nFLAGS = tf.flags.FLAGS\n\n\nclass GlobalStepWatcher(threading.Thread):\n \"\"\"A helper class for globe_step.\n\n Polls for changes in the global_step of the model, and finishes when the\n number of steps for the global run are done.\n \"\"\"\n\n def __init__(self, sess, global_step_op,\n start_at_global_step, end_at_global_step):\n threading.Thread.__init__(self)\n self.sess = sess\n self.global_step_op = global_step_op\n self.start_at_global_step = start_at_global_step\n self.end_at_global_step = end_at_global_step\n\n self.start_time = 0\n self.start_step = 0\n self.finish_time = 0\n self.finish_step = 0\n\n def run(self):\n while self.finish_time == 0:\n time.sleep(.25)\n global_step_val, = self.sess.run([self.global_step_op])\n if self.start_time == 0 and global_step_val >= self.start_at_global_step:\n # Use tf.logging.info instead of log_fn, since print (which is log_fn)\n # is not thread safe and may interleave the outputs from two parallel\n # calls to print, which can break tests.\n tf.logging.info('Starting real work at step %s at time %s' % (\n global_step_val, time.ctime()))\n self.start_time = time.time()\n self.start_step = global_step_val\n if self.finish_time == 0 and global_step_val >= self.end_at_global_step:\n tf.logging.info('Finishing real work at step %s at time %s' % (\n global_step_val, time.ctime()))\n self.finish_time = time.time()\n self.finish_step = global_step_val\n\n def done(self):\n return self.finish_time > 0\n\n def num_steps(self):\n return self.finish_step - self.start_step\n\n def elapsed_time(self):\n return self.finish_time - self.start_time\n\n\nclass CheckpointNotFoundException(Exception):\n pass\n\n\ndef get_data_type(params):\n \"\"\"Returns BenchmarkCNN's data type as determined by use_fp16.\n\n Args:\n params: Params tuple, typically created by make_params or\n make_params_from_flags.\n \"\"\"\n return tf.float16 if params.use_fp16 else tf.float32\n\n\ndef loss_function(logits, labels, aux_logits):\n \"\"\"Loss function.\"\"\"\n with tf.name_scope('xentropy'):\n cross_entropy = tf.losses.sparse_softmax_cross_entropy(\n logits=logits, labels=labels)\n loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')\n if aux_logits is not None:\n with tf.name_scope('aux_xentropy'):\n aux_cross_entropy = tf.losses.sparse_softmax_cross_entropy(\n logits=aux_logits, labels=labels)\n aux_loss = 0.4 * tf.reduce_mean(aux_cross_entropy, name='aux_loss')\n loss = tf.add_n([loss, aux_loss])\n return loss\n\n\ndef create_config_proto(params):\n \"\"\"Returns session config proto.\n\n Args:\n params: Params tuple, typically created by make_params or\n make_params_from_flags.\n \"\"\"\n config = tf.ConfigProto()\n config.allow_soft_placement = True\n config.intra_op_parallelism_threads = params.num_intra_threads\n config.inter_op_parallelism_threads = params.num_inter_threads\n config.gpu_options.force_gpu_compatible = params.force_gpu_compatible\n if params.gpu_memory_frac_for_testing > 0:\n config.gpu_options.per_process_gpu_memory_fraction = (\n params.gpu_memory_frac_for_testing)\n if params.xla:\n config.graph_options.optimizer_options.global_jit_level = (\n tf.OptimizerOptions.ON_1)\n return config\n\n\ndef get_mode_from_params(params):\n \"\"\"Returns the mode in which this script is running.\n\n Args:\n params: Params tuple, typically created by make_params or\n make_params_from_flags.\n Raises:\n ValueError: Unsupported params settings.\n \"\"\"\n if params.forward_only and params.eval:\n raise ValueError('Only one of forward_only and eval parameters is true')\n\n if params.eval:\n return 'evaluation'\n if params.forward_only:\n return 'forward-only'\n return 'training'\n\n\ndef benchmark_one_step(sess,\n fetches,\n step,\n batch_size,\n step_train_times,\n trace_filename,\n image_producer,\n params,\n summary_op=None):\n \"\"\"Advance one step of benchmarking.\"\"\"\n if trace_filename is not None and step == -1:\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n else:\n run_options = None\n run_metadata = None\n summary_str = None\n start_time = time.time()\n if summary_op is None:\n results = sess.run(fetches, options=run_options, run_metadata=run_metadata)\n else:\n (results, summary_str) = sess.run(\n [fetches, summary_op], options=run_options, run_metadata=run_metadata)\n\n if not params.forward_only:\n lossval = results['total_loss']\n else:\n lossval = 0.\n image_producer.notify_image_consumption()\n train_time = time.time() - start_time\n step_train_times.append(train_time)\n if step >= 0 and (step == 0 or (step + 1) % params.display_every == 0):\n log_str = '%i\\t%s\\t%.3f' % (\n step + 1, get_perf_timing_str(batch_size, step_train_times), lossval)\n if 'top_1_accuracy' in results:\n log_str += '\\t%.3f\\t%.3f' % (results['top_1_accuracy'],\n results['top_5_accuracy'])\n log_fn(log_str)\n if trace_filename is not None and step == -1:\n log_fn('Dumping trace to %s' % trace_filename)\n trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n with gfile.Open(trace_filename, 'w') as trace_file:\n trace_file.write(trace.generate_chrome_trace_format(show_memory=True))\n return summary_str\n\n\ndef get_perf_timing_str(batch_size, step_train_times, scale=1):\n times = np.array(step_train_times)\n speeds = batch_size / times\n speed_mean = scale * batch_size / np.mean(times)\n if scale == 1:\n speed_uncertainty = np.std(speeds) / np.sqrt(float(len(speeds)))\n speed_madstd = 1.4826 * np.median(np.abs(speeds - np.median(speeds)))\n speed_jitter = speed_madstd\n return 'images/sec: %.1f +/- %.1f (jitter = %.1f)' % (\n speed_mean, speed_uncertainty, speed_jitter)\n else:\n return 'images/sec: %.1f' % speed_mean\n\n\ndef load_checkpoint(saver, sess, ckpt_dir):\n ckpt = tf.train.get_checkpoint_state(ckpt_dir)\n if ckpt and ckpt.model_checkpoint_path:\n if os.path.isabs(ckpt.model_checkpoint_path):\n # Restores from checkpoint with absolute path.\n model_checkpoint_path = ckpt.model_checkpoint_path\n else:\n # Restores from checkpoint with relative path.\n model_checkpoint_path = os.path.join(ckpt_dir, ckpt.model_checkpoint_path)\n # Assuming model_checkpoint_path looks something like:\n # /my-favorite-path/imagenet_train/model.ckpt-0,\n # extract global_step from it.\n global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]\n if not global_step.isdigit():\n global_step = 0\n else:\n global_step = int(global_step)\n saver.restore(sess, model_checkpoint_path)\n log_fn('Successfully loaded model from %s.' % ckpt.model_checkpoint_path)\n return global_step\n else:\n raise CheckpointNotFoundException('No checkpoint file found.')\n\n\n# Params are passed to BenchmarkCNN's constructor. Params is a map from name\n# to value, with one field per key in _DEFAULT_PARAMS.\n#\n# Call make_params() or make_params_from_flags() below to construct a Params\n# tuple with default values from _DEFAULT_PARAMS, rather than constructing\n# Params directly.\nParams = namedtuple('Params', _DEFAULT_PARAMS.keys()) # pylint: disable=invalid-name\n\n\ndef make_params(**kwargs):\n \"\"\"Create a Params tuple for BenchmarkCNN from kwargs.\n\n Default values are filled in from _DEFAULT_PARAMS.\n\n Args:\n **kwargs: kwarg values will override the default values.\n Returns:\n Params namedtuple for constructing BenchmarkCNN.\n \"\"\"\n # Create a (name: default_value) map from PARAMS.\n default_kwargs = {\n name: _DEFAULT_PARAMS[name].default_value\n for name in _DEFAULT_PARAMS\n }\n return Params(**default_kwargs)._replace(**kwargs)\n\n\ndef make_params_from_flags():\n \"\"\"Create a Params tuple for BenchmarkCNN from FLAGS.\n\n Returns:\n Params namedtuple for constructing BenchmarkCNN.\n \"\"\"\n # Collect (name: value) pairs for FLAGS with matching names in\n # _DEFAULT_PARAMS.\n flag_values = {name: getattr(FLAGS, name) for name in _DEFAULT_PARAMS.keys()}\n return Params(**flag_values)\n\n\nclass BenchmarkCNN(object):\n \"\"\"Class for benchmarking a cnn network.\"\"\"\n\n def __init__(self, params):\n \"\"\"Initialize BenchmarkCNN.\n\n Args:\n params: Params tuple, typically created by make_params or\n make_params_from_flags.\n Raises:\n ValueError: Unsupported params settings.\n \"\"\"\n self.params = params\n self.dataset = datasets.create_dataset(self.params.data_dir,\n self.params.data_name)\n self.model = model_config.get_model_config(self.params.model, self.dataset)\n self.trace_filename = self.params.trace_file\n self.data_format = self.params.data_format\n self.num_batches = self.params.num_batches\n autotune_threshold = self.params.autotune_threshold if (\n self.params.autotune_threshold) else 1\n min_autotune_warmup = 5 * autotune_threshold * autotune_threshold\n self.num_warmup_batches = self.params.num_warmup_batches if (\n self.params.num_warmup_batches is not None) else max(\n 10, min_autotune_warmup)\n self.graph_file = self.params.graph_file\n self.resize_method = self.params.resize_method\n self.sync_queue_counter = 0\n self.num_gpus = self.params.num_gpus\n if self.params.gpu_indices:\n self.gpu_indices = [int(x) for x in self.params.gpu_indices.split(',')]\n else:\n self.gpu_indices = [x for x in range(self.num_gpus)]\n self.use_synthetic_gpu_images = self.dataset.use_synthetic_gpu_images()\n\n if (self.params.device == 'cpu' and self.params.data_format == 'NCHW' and\n not self.params.mkl):\n raise ValueError('device=cpu requires that data_format=NHWC')\n\n if self.params.use_tf_layers and self.params.use_fp16:\n raise ValueError('if use_fp16=true, use_tf_layers must be false.')\n\n if ((self.params.num_epochs_per_decay or\n self.params.learning_rate_decay_factor) and\n not (self.params.learning_rate and self.params.num_epochs_per_decay and\n self.params.learning_rate_decay_factor)):\n raise ValueError('If one of num_epochs_per_decay or '\n 'learning_rate_decay_factor is set, both must be set'\n 'and learning_rate must be set')\n if (self.params.minimum_learning_rate and\n not (self.params.learning_rate and self.params.num_epochs_per_decay and\n self.params.learning_rate_decay_factor)):\n raise ValueError('minimum_learning_rate requires learning_rate,'\n 'num_epochs_per_decay, and '\n 'learning_rate_decay_factor to be set')\n\n if (self.params.use_fp16 and self.params.fp16_vars and\n 'replicated' in self.params.variable_update and\n 'nccl' in self.params.all_reduce_spec):\n raise ValueError('fp16 variables are not supported with NCCL')\n\n # Use the batch size from the command line if specified, otherwise use the\n # model's default batch size. Scale the benchmark's batch size by the\n # number of GPUs.\n if self.params.batch_size > 0:\n self.model.set_batch_size(self.params.batch_size)\n self.batch_size = self.model.get_batch_size() * self.num_gpus\n self.batch_group_size = self.params.batch_group_size\n\n if self.params.use_fp16:\n self.loss_scale = (self.params.fp16_loss_scale or\n self.model.get_fp16_loss_scale())\n else:\n self.loss_scale = 1.\n\n self.job_name = self.params.job_name # \"\" for local training\n self.ps_hosts = self.params.ps_hosts.split(',')\n self.worker_hosts = self.params.worker_hosts.split(',')\n self.controller_host = self.params.controller_host\n\n if len(self.worker_hosts) > 1 and self.params.all_reduce_spec == 'nccl':\n raise ValueError('--all_reduce_spec=nccl is invalid in a '\n 'multi-worker job')\n\n # PS server is used for distributed jobs not using all-reduce.\n use_ps_server = self.job_name and (\n self.params.variable_update != 'distributed_all_reduce')\n # controller is used for distributed_all_reduce with > 1 worker.\n use_controller = (self.params.variable_update == 'distributed_all_reduce'\n and self.job_name)\n if use_controller and not self.controller_host:\n raise ValueError('When variable_update==distributed_all_reduce '\n 'controller_host must also be specified.')\n\n self.local_parameter_device_flag = self.params.local_parameter_device\n if self.job_name:\n self.task_index = self.params.task_index\n if use_controller:\n assert not use_ps_server\n self.cluster = tf.train.ClusterSpec(\n {'controller': [self.controller_host],\n 'worker': self.worker_hosts})\n else:\n assert use_ps_server\n self.cluster = tf.train.ClusterSpec({'ps': self.ps_hosts,\n 'worker': self.worker_hosts})\n\n self.server = None\n if self.job_name != 'controller':\n if not self.server:\n self.server = tf.train.Server(self.cluster, job_name=self.job_name,\n task_index=self.task_index,\n config=create_config_proto(self.params),\n protocol=self.params.server_protocol)\n\n worker_prefix = '/job:worker/task:%s' % self.task_index\n if use_ps_server:\n self.param_server_device = tf.train.replica_device_setter(\n worker_device=worker_prefix + '/cpu:0', cluster=self.cluster)\n # This device on which the queues for managing synchronization between\n # servers should be stored.\n num_ps = len(self.ps_hosts)\n self.sync_queue_devices = ['/job:ps/task:%s/cpu:0' % i\n for i in range(num_ps)]\n else:\n self.sync_queue_devices = ['/job:worker/task:0/cpu:0']\n else:\n self.task_index = 0\n self.cluster = None\n self.server = None\n worker_prefix = ''\n self.param_server_device = '/%s:0' % self.params.local_parameter_device\n self.sync_queue_devices = [self.param_server_device]\n\n # Device to use for ops that need to always run on the local worker's CPU.\n self.cpu_device = '%s/cpu:0' % worker_prefix\n\n # Device to use for ops that need to always run on the local worker's\n # compute device, and never on a parameter server device.\n self.raw_devices = [\n '%s/%s:%i' % (worker_prefix, self.params.device, i)\n for i in xrange(self.num_gpus)\n ]\n\n if (self.params.staged_vars and\n self.params.variable_update != 'parameter_server'):\n raise ValueError('staged_vars for now is only supported with '\n 'variable_update=parameter_server')\n\n if self.params.variable_update == 'parameter_server':\n if self.job_name:\n if not self.params.staged_vars:\n self.variable_mgr = variable_mgr.VariableMgrDistributedFetchFromPS(\n self)\n else:\n self.variable_mgr = (\n variable_mgr.VariableMgrDistributedFetchFromStagedPS(self))\n else:\n if not self.params.staged_vars:\n self.variable_mgr = variable_mgr.VariableMgrLocalFetchFromPS(self)\n else:\n self.variable_mgr = variable_mgr.VariableMgrLocalFetchFromStagedPS(\n self)\n elif self.params.variable_update == 'replicated':\n if self.job_name:\n raise ValueError('Invalid variable_update in distributed mode: %s' %\n self.params.variable_update)\n self.variable_mgr = variable_mgr.VariableMgrLocalReplicated(\n self, self.params.all_reduce_spec)\n elif self.params.variable_update == 'distributed_all_reduce':\n assert self.params.cross_replica_sync\n self.variable_mgr = variable_mgr.VariableMgrDistributedAllReduce(\n self, self.params.all_reduce_spec,\n 'worker' if len(self.worker_hosts) > 1 else 'localhost',\n len(self.worker_hosts))\n elif self.params.variable_update == 'distributed_replicated':\n assert self.params.cross_replica_sync\n if not self.job_name:\n raise ValueError('Invalid variable_update in local mode: %s' %\n self.params.variable_update)\n self.variable_mgr = variable_mgr.VariableMgrDistributedReplicated(self)\n elif self.params.variable_update == 'independent':\n if self.job_name:\n raise ValueError('Invalid variable_update in distributed mode: %s' %\n self.params.variable_update)\n self.variable_mgr = variable_mgr.VariableMgrIndependent(self)\n else:\n raise ValueError(\n 'Invalid variable_update: %s' % self.params.variable_update)\n\n # Device to use for running on the local worker's compute device, but\n # with variables assigned to parameter server devices.\n self.devices = self.variable_mgr.get_devices()\n if self.job_name:\n if use_ps_server:\n self.global_step_device = self.param_server_device\n else:\n self.global_step_device = '/job:worker/task:0/cpu:0'\n else:\n self.global_step_device = self.cpu_device\n\n self.image_preprocessor = self.get_image_preprocessor()\n self.init_global_step = 0\n\n def reset_devices_for_task(self, task_num, is_local=False):\n \"\"\"Used to imitate another task when building a distributed graph.\"\"\"\n worker_prefix = ('job:localhost' if is_local\n else '/job:worker/task:%s' % task_num)\n self.cpu_device = '%s/cpu:0' % worker_prefix\n self.raw_devices = ['%s/%s:%i' % (worker_prefix, self.params.device, i)\n for i in xrange(self.num_gpus)]\n self.devices = self.variable_mgr.get_devices()\n\n def raw_devices_across_tasks(self, is_local=False):\n \"\"\"Returns list of raw device names across all tasks.\"\"\"\n num_tasks = len(self.worker_hosts)\n if is_local:\n assert num_tasks == 1\n return self.raw_devices\n else:\n return ['job:worker/task%s/%s:%i' % (t, self.params.device, i)\n for t in xrange(num_tasks)\n for i in xrange(self.num_gpus)]\n\n def print_info(self):\n \"\"\"Print basic information.\"\"\"\n log_fn('Model: %s' % self.model.get_model())\n log_fn('Mode: %s' % get_mode_from_params(self.params))\n single_session = self.params.variable_update == 'distributed_all_reduce'\n log_fn('SingleSess: %s' % single_session)\n if single_session:\n device_list = self.raw_devices_across_tasks()\n num_workers = len(self.worker_hosts)\n else:\n device_list = self.raw_devices\n num_workers = 1\n batch_size = num_workers * self.batch_size\n log_fn('Batch size: %s global' % batch_size)\n log_fn(' %s per device' % (batch_size / len(device_list)))\n if self.batch_group_size > 1:\n log_fn(' %d batches per prepocessing group' %\n self.batch_group_size)\n log_fn('Devices: %s' % device_list)\n log_fn('Data format: %s' % self.data_format)\n log_fn('Optimizer: %s' % self.params.optimizer)\n log_fn('Variables: %s' % self.params.variable_update)\n if (self.params.variable_update == 'replicated' or\n self.params.variable_update == 'distributed_all_reduce'):\n log_fn('AllReduce: %s' % self.params.all_reduce_spec)\n if self.job_name:\n log_fn('Sync: %s' % self.params.cross_replica_sync)\n if self.params.staged_vars:\n log_fn('Staged vars: %s' % self.params.staged_vars)\n log_fn('==========')\n\n def run(self):\n \"\"\"Run the benchmark task assigned to this process.\n\n Returns:\n Dictionary of statistics for training or eval.\n Raises:\n ValueError: unrecognized job name.\n \"\"\"\n if self.params.job_name == 'ps':\n log_fn('Running parameter server %s' % self.task_index)\n self.server.join()\n return {}\n\n # For distributed_all_reduce with multiple workers, drive\n # from a separate controller process.\n if self.params.variable_update == 'distributed_all_reduce':\n if self.params.job_name == 'worker':\n log_fn('Starting worker %s' % self.task_index)\n self.server.join()\n return\n elif self.params.job_name and self.params.job_name != 'controller':\n raise ValueError('unrecognized job name: %s' % self.params.job_name)\n\n with tf.Graph().as_default():\n if self.params.eval:\n return self._eval_cnn()\n else:\n return self._benchmark_cnn()\n\n def _eval_cnn(self):\n \"\"\"Evaluate a model every self.params.eval_interval_secs.\n\n Returns:\n Dictionary containing eval statistics. Currently returns an empty\n dictionary.\n \"\"\"\n (image_producer_ops, enqueue_ops, fetches) = self._build_model()\n saver = tf.train.Saver(self.variable_mgr.savable_variables())\n summary_writer = tf.summary.FileWriter(self.params.eval_dir,\n tf.get_default_graph())\n target = ''\n local_var_init_op = tf.local_variables_initializer()\n variable_mgr_init_ops = [local_var_init_op]\n with tf.control_dependencies([local_var_init_op]):\n variable_mgr_init_ops.extend(self.variable_mgr.get_post_init_ops())\n local_var_init_op_group = tf.group(*variable_mgr_init_ops)\n summary_op = tf.summary.merge_all()\n while True:\n self._eval_once(\n saver, summary_writer, target, local_var_init_op_group,\n image_producer_ops, enqueue_ops, fetches, summary_op)\n if self.params.eval_interval_secs <= 0:\n break\n time.sleep(self.params.eval_interval_secs)\n return {}\n\n def _eval_once(self, saver, summary_writer, target,\n local_var_init_op_group, image_producer_ops, enqueue_ops,\n fetches, summary_op):\n \"\"\"Evaluate the model from a checkpoint using validation dataset.\"\"\"\n with tf.Session(\n target=target, config=create_config_proto(self.params)) as sess:\n if self.params.train_dir is None:\n raise ValueError('Trained model directory not specified')\n try:\n global_step = load_checkpoint(saver, sess, self.params.train_dir)\n except CheckpointNotFoundException:\n log_fn('Checkpoint not found in %s' % self.params.train_dir)\n return\n sess.run(local_var_init_op_group)\n if self.dataset.queue_runner_required():\n tf.train.start_queue_runners(sess=sess)\n image_producer = cnn_util.ImageProducer(sess, image_producer_ops,\n self.batch_group_size)\n image_producer.start()\n for i in xrange(len(enqueue_ops)):\n sess.run(enqueue_ops[:(i+1)])\n image_producer.notify_image_consumption()\n start_time = time.time()\n top_1_accuracy_sum = 0.0\n top_5_accuracy_sum = 0.0\n total_eval_count = self.num_batches * self.batch_size\n for step in xrange(self.num_batches):\n if (self.params.save_summaries_steps > 0 and\n (step + 1) % self.params.save_summaries_steps == 0):\n results, summary_str = sess.run([fetches, summary_op])\n summary_writer.add_summary(summary_str)\n else:\n results = sess.run(fetches)\n top_1_accuracy_sum += results['top_1_accuracy']\n top_5_accuracy_sum += results['top_5_accuracy']\n if (step + 1) % self.params.display_every == 0:\n duration = time.time() - start_time\n examples_per_sec = (\n self.batch_size * self.params.display_every / duration)\n log_fn('%i\\t%.1f examples/sec' % (step + 1, examples_per_sec))\n start_time = time.time()\n image_producer.notify_image_consumption()\n image_producer.done()\n precision_at_1 = top_1_accuracy_sum / self.num_batches\n recall_at_5 = top_5_accuracy_sum / self.num_batches\n summary = tf.Summary()\n summary.value.add(tag='eval/Accuracy@1', simple_value=precision_at_1)\n summary.value.add(tag='eval/Recall@5', simple_value=recall_at_5)\n summary_writer.add_summary(summary, global_step)\n log_fn('Precision @ 1 = %.4f recall @ 5 = %.4f [%d examples]' %\n (precision_at_1, recall_at_5, total_eval_count))\n\n def _benchmark_cnn(self):\n \"\"\"Run cnn in benchmark mode. When forward_only on, it forwards CNN.\n\n Returns:\n Dictionary containing training statistics (num_workers, num_steps,\n average_wall_time, images_per_sec).\n \"\"\"\n if self.params.variable_update == 'distributed_all_reduce':\n self.single_session = True\n (image_producer_ops, enqueue_ops, fetches) = (\n self._build_model_single_session())\n else:\n self.single_session = False\n (image_producer_ops, enqueue_ops, fetches) = self._build_model()\n fetches_list = nest.flatten(list(fetches.values()))\n main_fetch_group = tf.group(*fetches_list)\n execution_barrier = None\n if (not self.single_session and self.job_name and\n not self.params.cross_replica_sync):\n execution_barrier = self.add_sync_queues_and_barrier(\n 'execution_barrier_', [])\n\n global_step = tf.train.get_global_step()\n with tf.device(self.global_step_device):\n with tf.control_dependencies([main_fetch_group]):\n fetches['inc_global_step'] = global_step.assign_add(1)\n\n if ((not self.single_session) and self.job_name and\n self.params.cross_replica_sync):\n # Block all replicas until all replicas are ready for next step.\n fetches['sync_queues'] = self.add_sync_queues_and_barrier(\n 'sync_queues_step_end_', [main_fetch_group])\n\n local_var_init_op = tf.local_variables_initializer()\n variable_mgr_init_ops = [local_var_init_op]\n with tf.control_dependencies([local_var_init_op]):\n variable_mgr_init_ops.extend(self.variable_mgr.get_post_init_ops())\n local_var_init_op_group = tf.group(*variable_mgr_init_ops)\n\n summary_op = tf.summary.merge_all()\n is_chief = (not self.job_name or self.task_index == 0)\n summary_writer = None\n if (is_chief and self.params.summary_verbosity and self.params.train_dir and\n self.params.save_summaries_steps > 0):\n summary_writer = tf.summary.FileWriter(self.params.train_dir,\n tf.get_default_graph())\n\n # We want to start the benchmark timer right after a image_producer barrier\n # and avoids undesired wating times on barriers.\n if ((self.num_warmup_batches + len(enqueue_ops) - 1) %\n self.batch_group_size) != 0:\n self.num_warmup_batches = int(\n math.ceil((self.num_warmup_batches + len(enqueue_ops) - 1.0) /\n self.batch_group_size) * self.batch_group_size\n - len(enqueue_ops) + 1)\n log_fn('Round up warm up steps to %d to match batch_group_size' %\n self.num_warmup_batches)\n assert ((self.num_warmup_batches + len(enqueue_ops) - 1) %\n self.batch_group_size) == 0\n # We run the summaries in the same thread as the training operations by\n # passing in None for summary_op to avoid a summary_thread being started.\n # Running summaries and training operations in parallel could run out of\n # GPU memory.\n saver = tf.train.Saver(self.variable_mgr.savable_variables())\n ready_for_local_init_op = None\n if self.job_name and not self.single_session:\n # In distributed mode, we don't want to run local_var_init_op_group until\n # the global variables are initialized, because local_var_init_op_group\n # may use global variables (such as in distributed replicated mode). We\n # don't set this in non-distributed mode, because in non-distributed mode,\n # local_var_init_op_group may itself initialize global variables (such as\n # in replicated mode).\n ready_for_local_init_op = tf.report_uninitialized_variables(\n tf.global_variables())\n sv = tf.train.Supervisor(\n is_chief=is_chief,\n logdir=self.params.train_dir,\n ready_for_local_init_op=ready_for_local_init_op,\n local_init_op=local_var_init_op_group,\n saver=saver,\n global_step=global_step,\n summary_op=None,\n save_model_secs=self.params.save_model_secs,\n summary_writer=summary_writer)\n\n step_train_times = []\n start_standard_services = (self.params.summary_verbosity >= 1 or\n self.dataset.queue_runner_required())\n if self.job_name == 'controller':\n master_target = self.worker_hosts[0]\n else:\n master_target = self.server.target if self.server else ''\n with sv.managed_session(\n master=master_target,\n config=create_config_proto(self.params),\n start_standard_services=start_standard_services) as sess:\n image_producer = cnn_util.ImageProducer(sess, image_producer_ops,\n self.batch_group_size)\n image_producer.start()\n for i in xrange(len(enqueue_ops)):\n sess.run(enqueue_ops[:(i+1)])\n image_producer.notify_image_consumption()\n self.init_global_step, = sess.run([global_step])\n if not self.single_session:\n global_step_watcher = GlobalStepWatcher(\n sess, global_step,\n len(self.worker_hosts) * self.num_warmup_batches +\n self.init_global_step,\n len(self.worker_hosts) * (\n self.num_warmup_batches + self.num_batches) - 1)\n global_step_watcher.start()\n else:\n global_step_watcher = None\n\n if self.graph_file is not None:\n path, filename = os.path.split(self.graph_file)\n as_text = filename.endswith('txt')\n log_fn('Writing GraphDef as %s to %s' % (\n 'text' if as_text else 'binary', self.graph_file))\n tf.train.write_graph(sess.graph_def, path, filename, as_text)\n\n log_fn('Running warm up')\n local_step = -1 * self.num_warmup_batches\n\n if self.single_session or (self.params.cross_replica_sync and\n self.params.job_name):\n # In cross-replica sync mode, all workers must run the same number of\n # local steps, or else the workers running the extra step will block.\n done_fn = lambda: local_step == self.num_batches\n else:\n done_fn = global_step_watcher.done\n loop_start_time = time.time()\n while not done_fn():\n if local_step == 0:\n log_fn('Done warm up')\n if execution_barrier:\n log_fn('Waiting for other replicas to finish warm up')\n assert global_step_watcher.start_time == 0\n sess.run([execution_barrier])\n\n header_str = 'Step\\tImg/sec\\tloss'\n if self.params.print_training_accuracy or self.params.forward_only:\n header_str += '\\ttop_1_accuracy\\ttop_5_accuracy'\n log_fn(header_str)\n assert len(step_train_times) == self.num_warmup_batches\n # reset times to ignore warm up batch\n step_train_times = []\n loop_start_time = time.time()\n if (summary_writer and\n (local_step + 1) % self.params.save_summaries_steps == 0):\n fetch_summary = summary_op\n else:\n fetch_summary = None\n summary_str = benchmark_one_step(\n sess, fetches, local_step,\n self.batch_size * (len(self.worker_hosts)\n if self.single_session else 1),\n step_train_times, self.trace_filename, image_producer, self.params,\n fetch_summary)\n if summary_str is not None and is_chief:\n sv.summary_computed(sess, summary_str)\n local_step += 1\n loop_end_time = time.time()\n # Waits for the global step to be done, regardless of done_fn.\n if global_step_watcher:\n while not global_step_watcher.done():\n time.sleep(.25)\n if self.single_session:\n num_workers = len(self.worker_hosts)\n num_steps = local_step\n elapsed_time = loop_end_time - loop_start_time\n else:\n num_workers = 1\n num_steps = global_step_watcher.num_steps()\n elapsed_time = global_step_watcher.elapsed_time()\n\n average_wall_time = elapsed_time / num_steps if num_steps > 0 else 0\n images_per_sec = ((num_workers * self.batch_size) /\n average_wall_time if average_wall_time > 0 else 0)\n\n log_fn('-' * 64)\n log_fn('total images/sec: %.2f' % images_per_sec)\n log_fn('-' * 64)\n image_producer.done()\n if is_chief:\n store_benchmarks({'total_images_per_sec': images_per_sec}, self.params)\n # Save the model checkpoint.\n if self.params.train_dir is not None and is_chief:\n checkpoint_path = os.path.join(self.params.train_dir, 'model.ckpt')\n if not gfile.Exists(self.params.train_dir):\n gfile.MakeDirs(self.params.train_dir)\n sv.saver.save(sess, checkpoint_path, global_step)\n\n if execution_barrier:\n # Wait for other workers to reach the end, so this worker doesn't\n # go away underneath them.\n sess.run([execution_barrier])\n sv.stop()\n return {\n 'num_workers': num_workers,\n 'num_steps': num_steps,\n 'average_wall_time': average_wall_time,\n 'images_per_sec': images_per_sec\n }\n\n def _build_image_processing(self, shift_ratio=0):\n \"\"\"\"Build the image (pre)processing portion of the model graph.\"\"\"\n with tf.device(self.cpu_device):\n if self.params.eval:\n subset = 'validation'\n else:\n subset = 'train'\n image_producer_ops = []\n image_producer_stages = []\n images_splits, labels_splits = self.image_preprocessor.minibatch(\n self.dataset, subset=subset, use_datasets=self.params.use_datasets,\n cache_data=self.params.cache_data, shift_ratio=shift_ratio)\n images_shape = images_splits[0].get_shape()\n labels_shape = labels_splits[0].get_shape()\n for device_num in range(len(self.devices)):\n image_producer_stages.append(data_flow_ops.StagingArea(\n [images_splits[0].dtype, labels_splits[0].dtype],\n shapes=[images_shape, labels_shape]))\n for group_index in xrange(self.batch_group_size):\n if not self.use_synthetic_gpu_images:\n batch_index = group_index + device_num * self.batch_group_size\n put_op = image_producer_stages[device_num].put(\n [images_splits[batch_index], labels_splits[batch_index]])\n image_producer_ops.append(put_op)\n return (image_producer_ops, image_producer_stages)\n\n def _build_model(self):\n \"\"\"Build the TensorFlow graph.\"\"\"\n tf.set_random_seed(self.params.tf_random_seed)\n np.random.seed(4321)\n phase_train = not (self.params.eval or self.params.forward_only)\n\n log_fn('Generating model')\n losses = []\n device_grads = []\n all_logits = []\n all_top_1_ops = []\n all_top_5_ops = []\n enqueue_ops = []\n gpu_compute_stage_ops = []\n gpu_grad_stage_ops = []\n\n with tf.device(self.global_step_device):\n global_step = tf.train.get_or_create_global_step()\n\n # Build the processing and model for the worker.\n (image_producer_ops, image_producer_stages) = self._build_image_processing(\n shift_ratio=0)\n image_producer_ops = tf.group(*image_producer_ops)\n update_ops = None\n staging_delta_ops = []\n\n for device_num in range(len(self.devices)):\n with self.variable_mgr.create_outer_variable_scope(\n device_num), tf.name_scope('tower_%i' % device_num) as name_scope:\n results = self.add_forward_pass_and_gradients(\n phase_train, device_num, device_num,\n image_producer_stages[device_num],\n gpu_compute_stage_ops, gpu_grad_stage_ops)\n if phase_train:\n losses.append(results['loss'])\n device_grads.append(results['gradvars'])\n else:\n all_logits.append(results['logits'])\n if not phase_train or self.params.print_training_accuracy:\n all_top_1_ops.append(results['top_1_op'])\n all_top_5_ops.append(results['top_5_op'])\n\n if device_num == 0:\n # Retain the Batch Normalization updates operations only from the\n # first tower. These operations update the moving mean and moving\n # variance variables, which are updated (but not used) during\n # training, and used during evaluation. The moving mean and variance\n # approximate the true mean and variance across all images in the\n # dataset. Therefore, in replicated mode, these moving averages would\n # be almost identical for each tower, and so we only update and save\n # the moving averages for one tower. In parameter server mode, all\n # towers share a copy of the variables so we also only need to update\n # and save the moving averages once.\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, name_scope)\n staging_delta_ops = list(self.variable_mgr.staging_delta_ops)\n\n if self.variable_mgr.supports_staged_vars():\n for staging_ops in self.variable_mgr.staging_vars_on_devices:\n gpu_compute_stage_ops.extend(\n [put_op for _, (put_op, _) in six.iteritems(staging_ops)])\n enqueue_ops.append(tf.group(*gpu_compute_stage_ops))\n if gpu_grad_stage_ops:\n staging_delta_ops += gpu_grad_stage_ops\n if staging_delta_ops:\n enqueue_ops.append(tf.group(*(staging_delta_ops)))\n\n fetches = self._build_fetches(global_step, all_logits, losses, device_grads,\n enqueue_ops, update_ops, all_top_1_ops,\n all_top_5_ops, phase_train)\n return (image_producer_ops, enqueue_ops, fetches)\n\n def _build_fetches(self, global_step, all_logits, losses, device_grads,\n enqueue_ops, update_ops, all_top_1_ops, all_top_5_ops,\n phase_train):\n \"\"\"Complete construction of model graph, populating the fetches map.\"\"\"\n fetches = {'enqueue_ops': enqueue_ops}\n if all_top_1_ops:\n fetches['top_1_accuracy'] = tf.reduce_sum(all_top_1_ops) / self.batch_size\n if self.task_index == 0 and self.params.summary_verbosity >= 1:\n tf.summary.scalar('top_1_accuracy', fetches['top_1_accuracy'])\n if all_top_5_ops:\n fetches['top_5_accuracy'] = tf.reduce_sum(all_top_5_ops) / self.batch_size\n if self.task_index == 0 and self.params.summary_verbosity >= 1:\n tf.summary.scalar('top_5_accuracy', fetches['top_5_accuracy'])\n\n if not phase_train:\n if self.params.forward_only:\n fetches['all_logits'] = tf.concat(all_logits, 0)\n return fetches\n apply_gradient_devices, gradient_state = (\n self.variable_mgr.preprocess_device_grads(device_grads))\n\n training_ops = []\n for d, device in enumerate(apply_gradient_devices):\n with tf.device(device):\n total_loss = tf.reduce_mean(losses)\n avg_grads = self.variable_mgr.get_gradients_to_apply(d, gradient_state)\n\n gradient_clip = self.params.gradient_clip\n learning_rate = (self.params.learning_rate or\n self.model.get_learning_rate(global_step,\n self.batch_size))\n if ((not self.use_synthetic_gpu_images) and\n self.params.learning_rate and\n self.params.num_epochs_per_decay > 0 and\n self.params.learning_rate_decay_factor > 0):\n num_batches_per_epoch = (\n float(self.dataset.num_examples_per_epoch()) / self.batch_size)\n decay_steps = int(\n num_batches_per_epoch * self.params.num_epochs_per_decay)\n\n # Decay the learning rate exponentially based on the number of steps.\n learning_rate = tf.train.exponential_decay(\n self.params.learning_rate,\n global_step,\n decay_steps,\n self.params.learning_rate_decay_factor,\n staircase=True)\n\n if self.params.minimum_learning_rate != 0.:\n learning_rate = tf.maximum(learning_rate,\n self.params.minimum_learning_rate)\n\n if gradient_clip is not None:\n clipped_grads = [\n (tf.clip_by_value(grad, -gradient_clip, +gradient_clip), var)\n for grad, var in avg_grads\n ]\n else:\n clipped_grads = avg_grads\n\n learning_rate = tf.identity(learning_rate, name='learning_rate')\n if self.params.optimizer == 'momentum':\n opt = tf.train.MomentumOptimizer(\n learning_rate, self.params.momentum, use_nesterov=True)\n elif self.params.optimizer == 'sgd':\n opt = tf.train.GradientDescentOptimizer(learning_rate)\n elif self.params.optimizer == 'rmsprop':\n opt = tf.train.RMSPropOptimizer(\n learning_rate,\n self.params.rmsprop_decay,\n momentum=self.params.rmsprop_momentum,\n epsilon=self.params.rmsprop_epsilon)\n else:\n raise ValueError('Optimizer \"%s\" was not recognized',\n self.params.optimizer)\n\n self.variable_mgr.append_apply_gradients_ops(\n gradient_state, opt, clipped_grads, training_ops)\n train_op = tf.group(*(training_ops + update_ops))\n\n with tf.device(self.cpu_device):\n if self.task_index == 0 and self.params.summary_verbosity >= 1:\n tf.summary.scalar('learning_rate', learning_rate)\n tf.summary.scalar('total_loss', total_loss)\n\n if self.params.summary_verbosity >= 2:\n # Histogram of log values of all non-zero gradients.\n all_grads = []\n for grad, var in avg_grads:\n all_grads.append(tf.reshape(grad, [-1]))\n grads = tf.abs(tf.concat(all_grads, 0))\n # exclude grads with zero values.\n indices_for_non_zero_grads = tf.where(tf.not_equal(grads, 0))\n log_grads = tf.reshape(\n tf.log(tf.gather(grads, indices_for_non_zero_grads)), [-1])\n tf.summary.histogram('log_gradients', log_grads)\n\n if self.params.summary_verbosity >= 3:\n for grad, var in avg_grads:\n if grad is not None:\n tf.summary.histogram(var.op.name + '/gradients', grad)\n for var in tf.trainable_variables():\n tf.summary.histogram(var.op.name, var)\n\n fetches['train_op'] = train_op\n fetches['total_loss'] = total_loss\n return fetches\n\n def _build_model_single_session(self):\n \"\"\"Build the TensorFlow graph for multiple replicas in a single_session.\n\n Returns:\n image_producer_ops:\n enqueue_ops:\n fetches:\n\n Raises:\n ValueError: optimizer not recognized.\n\n Single session runs multiple model replicas as part of one large\n distributed graph, whose global execution is always step-synchronized.\n \"\"\"\n # verify assumptions\n assert self.params.task_index == 0\n assert not self.params.eval\n assert not self.params.forward_only\n assert not self.params.staged_vars\n\n tf.set_random_seed(self.params.tf_random_seed)\n np.random.seed(4321)\n phase_train = True\n\n log_fn('Generating model')\n losses = []\n device_grads = []\n all_logits = []\n all_top_1_ops = []\n all_top_5_ops = []\n enqueue_ops = []\n gpu_compute_stage_ops = []\n gpu_grad_stage_ops = []\n\n with tf.device(self.global_step_device):\n global_step = tf.train.get_or_create_global_step()\n\n update_ops = []\n global_image_producer_ops = []\n\n num_workers = len(self.worker_hosts)\n is_local = not self.job_name\n if is_local:\n assert num_workers == 1\n for task_num in range(num_workers):\n # Reset the devices that self.variable_mgr knows about to those\n # belonging to the next worker (task).\n self.reset_devices_for_task(task_num, is_local)\n # Build the per-worker image processing\n (image_producer_ops, image_producer_stages) = (\n self._build_image_processing(\n shift_ratio=(float(task_num) / num_workers)))\n global_image_producer_ops.extend(image_producer_ops)\n # Build the per-worker model replica.\n for rel_device_num in range(len(self.devices)):\n abs_device_num = task_num * len(self.devices) + rel_device_num\n with self.variable_mgr.create_outer_variable_scope(\n abs_device_num), tf.name_scope(\n 'task_%i_tower_%i' % (task_num, rel_device_num)) as name_scope:\n task_results = self.add_forward_pass_and_gradients(\n phase_train, rel_device_num, abs_device_num,\n image_producer_stages[rel_device_num],\n gpu_compute_stage_ops, gpu_grad_stage_ops)\n if phase_train:\n losses.append(task_results['loss'])\n device_grads.append(task_results['gradvars'])\n else:\n all_logits.append(task_results['logits'])\n if not phase_train or self.params.print_training_accuracy:\n all_top_1_ops.append(task_results['top_1_op'])\n all_top_5_ops.append(task_results['top_5_op'])\n\n if rel_device_num == 0:\n # Retain the Batch Normalization updates operations only\n # from the first tower. These operations update the moving\n # mean and moving variance variables, which are updated\n # (but not used) during training, and used during\n # evaluation. The moving mean and variance approximate the\n # true mean and variance across all images in the\n # dataset. Therefore, in replicated mode, these moving\n # averages would be almost identical for each tower, and\n # so we only update and save the moving averages for one\n # tower. In parameter server mode, all towers share a copy\n # of the variables so we also only need to update and save\n # the moving averages once.\n update_ops.extend(tf.get_collection(\n tf.GraphKeys.UPDATE_OPS, name_scope))\n assert not self.variable_mgr.staging_delta_ops\n\n enqueue_ops.append(tf.group(*gpu_compute_stage_ops))\n assert not self.variable_mgr.supports_staged_vars()\n assert not gpu_grad_stage_ops\n\n fetches = self._build_fetches(global_step, all_logits, losses,\n device_grads, enqueue_ops, update_ops,\n all_top_1_ops, all_top_5_ops, phase_train)\n global_image_producer_ops = tf.group(*global_image_producer_ops)\n return (global_image_producer_ops, enqueue_ops, fetches)\n\n def add_forward_pass_and_gradients(\n self, phase_train, rel_device_num, abs_device_num, image_producer_stage,\n gpu_compute_stage_ops, gpu_grad_stage_ops):\n \"\"\"Add ops for forward-pass and gradient computations.\"\"\"\n nclass = self.dataset.num_classes + 1\n input_data_type = get_data_type(self.params)\n data_type = get_data_type(self.params)\n if not self.use_synthetic_gpu_images:\n with tf.device(self.cpu_device):\n host_images, host_labels = image_producer_stage.get()\n images_shape = host_images.get_shape()\n labels_shape = host_labels.get_shape()\n with tf.device(self.raw_devices[rel_device_num]):\n if not self.use_synthetic_gpu_images:\n gpu_compute_stage = data_flow_ops.StagingArea(\n [host_images.dtype, host_labels.dtype],\n shapes=[images_shape, labels_shape]\n )\n # The CPU-to-GPU copy is triggered here.\n gpu_compute_stage_op = gpu_compute_stage.put(\n [host_images, host_labels])\n images, labels = gpu_compute_stage.get()\n images = tf.reshape(images, shape=images_shape)\n gpu_compute_stage_ops.append(gpu_compute_stage_op)\n else:\n # Minor hack to avoid H2D copy when using synthetic data\n image_size = self.model.get_image_size()\n image_shape = [self.batch_size // self.num_gpus, image_size, image_size,\n self.dataset.depth]\n labels_shape = [self.batch_size // self.num_gpus]\n # Synthetic image should be within [0, 255].\n images = tf.truncated_normal(\n image_shape,\n dtype=input_data_type,\n mean=127,\n stddev=60,\n name='synthetic_images')\n images = tf.contrib.framework.local_variable(\n images, name='gpu_cached_images')\n labels = tf.random_uniform(\n labels_shape, minval=0, maxval=nclass-1,\n dtype=tf.int32, name='synthetic_labels')\n\n with tf.device(self.devices[rel_device_num]):\n # Rescale from [0, 255] to [0, 2]\n images = tf.multiply(images, 1./127.5)\n # Rescale to [-1, 1]\n images = tf.subtract(images, 1.0)\n\n if self.data_format == 'NCHW':\n images = tf.transpose(images, [0, 3, 1, 2])\n if input_data_type != data_type:\n images = tf.cast(images, data_type)\n var_type = tf.float32\n if data_type == tf.float16 and self.params.fp16_vars:\n var_type = tf.float16\n network = convnet_builder.ConvNetBuilder(\n images, self.dataset.depth, phase_train, self.params.use_tf_layers,\n self.data_format, data_type, var_type)\n with tf.variable_scope('cg', custom_getter=network.get_custom_getter()):\n self.model.add_inference(network)\n # Add the final fully-connected class layer\n logits = network.affine(nclass, activation='linear')\n aux_logits = None\n if network.aux_top_layer is not None:\n with network.switch_to_aux_top_layer():\n aux_logits = network.affine(nclass, activation='linear',\n stddev=0.001)\n if data_type == tf.float16:\n # TODO(reedwm): Determine if we should do this cast here.\n logits = tf.cast(logits, tf.float32)\n if aux_logits is not None:\n aux_logits = tf.cast(aux_logits, tf.float32)\n\n results = {} # The return value\n if not phase_train or self.params.print_training_accuracy:\n top_1_op = tf.reduce_sum(\n tf.cast(tf.nn.in_top_k(logits, labels, 1), data_type))\n top_5_op = tf.reduce_sum(\n tf.cast(tf.nn.in_top_k(logits, labels, 5), data_type))\n results['top_1_op'] = top_1_op\n results['top_5_op'] = top_5_op\n\n if not phase_train:\n results['logits'] = logits\n return results\n loss = loss_function(logits, labels, aux_logits=aux_logits)\n params = self.variable_mgr.trainable_variables_on_device(rel_device_num,\n abs_device_num)\n if data_type == tf.float16 and self.params.fp16_vars:\n # fp16 reductions are very slow on GPUs, so cast to fp32 before calling\n # tf.nn.l2_loss and tf.add_n.\n # TODO(b/36217816): Once the bug is fixed, investigate if we should do\n # this reduction in fp16.\n fp32_params = (tf.cast(p, tf.float32) for p in params)\n l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in fp32_params])\n else:\n l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in params])\n weight_decay = self.params.weight_decay\n if weight_decay is not None and weight_decay != 0.:\n loss += weight_decay * l2_loss\n\n aggmeth = tf.AggregationMethod.DEFAULT\n scaled_loss = loss if self.loss_scale == 1. else loss * self.loss_scale\n grads = tf.gradients(scaled_loss, params, aggregation_method=aggmeth)\n if self.loss_scale != 1.:\n # TODO(reedwm): We could avoid these multiplications by directly\n # modifying the learning rate instead. If this is done, care must be\n # taken to ensure that this scaling method is correct, as some\n # optimizers square gradients and do other operations which might not be\n # compatible with modifying both the gradients and the learning rate.\n grads = [grad * (1. / self.loss_scale) for grad in grads]\n\n if self.params.staged_vars:\n grad_dtypes = [grad.dtype for grad in grads]\n grad_shapes = [grad.shape for grad in grads]\n grad_stage = data_flow_ops.StagingArea(grad_dtypes, grad_shapes)\n grad_stage_op = grad_stage.put(grads)\n # In general, this decouples the computation of the gradients and\n # the updates of the weights.\n # During the pipeline warm up, this runs enough training to produce\n # the first set of gradients.\n gpu_grad_stage_ops.append(grad_stage_op)\n grads = grad_stage.get()\n\n param_refs = self.variable_mgr.trainable_variables_on_device(\n rel_device_num, abs_device_num, writable=True)\n gradvars = list(zip(grads, param_refs))\n results['loss'] = loss\n results['gradvars'] = gradvars\n return results\n\n def get_image_preprocessor(self):\n \"\"\"Returns the image preprocessor to used, based on the model.\n\n Returns:\n The image preprocessor, or None if synthetic data should be used.\n \"\"\"\n image_size = self.model.get_image_size()\n input_data_type = get_data_type(self.params)\n\n shift_ratio = 0\n if self.job_name:\n # shift_ratio prevents multiple workers from processing the same batch\n # during a step\n assert self.worker_hosts\n shift_ratio = float(self.task_index) / len(self.worker_hosts)\n\n processor_class = self.dataset.get_image_preprocessor()\n assert processor_class\n return processor_class(\n image_size,\n image_size,\n self.batch_size * self.batch_group_size,\n len(self.devices) * self.batch_group_size,\n dtype=input_data_type,\n train=(not self.params.eval),\n distortions=self.params.distortions,\n resize_method=self.resize_method,\n shift_ratio=shift_ratio,\n summary_verbosity=self.params.summary_verbosity,\n distort_color_in_yiq=self.params.distort_color_in_yiq,\n fuse_decode_and_crop=self.params.fuse_decode_and_crop)\n\n def add_sync_queues_and_barrier(self, name_prefix,\n enqueue_after_list):\n \"\"\"Adds ops to enqueue on all worker queues.\n\n Args:\n name_prefix: prefixed for the shared_name of ops.\n enqueue_after_list: control dependency from ops.\n\n Returns:\n an op that should be used as control dependency before starting next step.\n \"\"\"\n self.sync_queue_counter += 1\n num_workers = self.cluster.num_tasks('worker')\n with tf.device(self.sync_queue_devices[\n self.sync_queue_counter % len(self.sync_queue_devices)]):\n sync_queues = [\n tf.FIFOQueue(num_workers, [tf.bool], shapes=[[]],\n shared_name='%s%s' % (name_prefix, i))\n for i in range(num_workers)]\n queue_ops = []\n # For each other worker, add an entry in a queue, signaling that it can\n # finish this step.\n token = tf.constant(False)\n with tf.control_dependencies(enqueue_after_list):\n for i, q in enumerate(sync_queues):\n if i == self.task_index:\n queue_ops.append(tf.no_op())\n else:\n queue_ops.append(q.enqueue(token))\n\n # Drain tokens off queue for this worker, one for each other worker.\n queue_ops.append(\n sync_queues[self.task_index].dequeue_many(len(sync_queues) - 1))\n\n return tf.group(*queue_ops)\n\n\ndef store_benchmarks(names_to_values, params):\n if params.result_storage:\n benchmark_storage.store_benchmark(names_to_values, params.result_storage)\n\n\ndef setup(params):\n \"\"\"Sets up the environment that BenchmarkCNN should run in.\n\n Args:\n params: Params tuple, typically created by make_params or\n make_params_from_flags.\n \"\"\"\n if params.winograd_nonfused:\n os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'\n else:\n os.environ.pop('TF_ENABLE_WINOGRAD_NONFUSED', None)\n if params.autotune_threshold:\n os.environ['TF_AUTOTUNE_THRESHOLD'] = str(params.autotune_threshold)\n os.environ['TF_SYNC_ON_FINISH'] = str(int(params.sync_on_finish))\n argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n # Sets environment variables for MKL\n if params.mkl:\n os.environ['KMP_BLOCKTIME'] = str(params.kmp_blocktime)\n os.environ['KMP_SETTINGS'] = str(params.kmp_settings)\n os.environ['KMP_AFFINITY'] = params.kmp_affinity\n if params.num_intra_threads > 0:\n os.environ['OMP_NUM_THREADS'] = str(params.num_intra_threads)\n\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/benchmark_storage.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Provides ways to store benchmark output.\"\"\"\n\n\ndef store_benchmark(data, storage_type=None):\n \"\"\"Store benchmark data.\n\n Args:\n data: Dictionary mapping from string benchmark name to\n numeric benchmark value.\n storage_type: (string) Specifies where to store benchmark\n result. If storage_type is\n 'cbuild_benchmark_datastore': store outputs in our continuous\n build datastore. gcloud must be setup in current environment\n pointing to the project where data will be added.\n \"\"\"\n if storage_type == 'cbuild_benchmark_datastore':\n try:\n # pylint: disable=g-import-not-at-top\n import cbuild_benchmark_storage\n # pylint: enable=g-import-not-at-top\n except ImportError:\n raise ImportError(\n 'Missing cbuild_benchmark_storage.py required for '\n 'benchmark_cloud_datastore option')\n cbuild_benchmark_storage.upload_to_benchmark_datastore(data)\n else:\n assert False, 'unknown storage_type: ' + storage_type\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/cbuild_benchmark_storage.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Provides a way to store benchmark results in GCE Datastore.\n\nDatastore client is initialized from current environment.\nData is stored using the format defined in:\n https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/test/upload_test_benchmarks_index.yaml\n\"\"\"\nfrom datetime import datetime\nimport json\nimport os\nimport sys\nfrom google.cloud import datastore\nimport six\n\n\n_TEST_NAME_ENV_VAR = 'TF_DIST_BENCHMARK_NAME'\n\n\ndef upload_to_benchmark_datastore(data, test_name=None, start_time=None):\n \"\"\"Use a new datastore.Client to upload data to datastore.\n\n Create the datastore Entities from that data and upload them to the\n datastore in a batch using the client connection.\n\n Args:\n data: Map from benchmark names to values.\n test_name: Name of this test. If not specified, name will be set either\n from TF_DIST_BENCHMARK_NAME environment variable or to default name\n 'TestBenchmark'.\n start_time: (datetime) Time to record for this test.\n\n Raises:\n ValueError: if test_name is not passed in and TF_DIST_BENCHMARK_NAME\n is not set.\n \"\"\"\n client = datastore.Client()\n\n if not test_name:\n if _TEST_NAME_ENV_VAR in os.environ:\n test_name = os.environ[_TEST_NAME_ENV_VAR]\n else:\n raise ValueError(\n 'No test name passed in for benchmarks. '\n 'Either pass a test_name to upload_to_benchmark_datastore or '\n 'set %s environment variable.' % _TEST_NAME_ENV_VAR)\n test_name = six.text_type(test_name)\n\n if not start_time:\n start_time = datetime.now()\n\n # Create one Entry Entity for each benchmark entry. The wall-clock timing is\n # the attribute to be fetched and displayed. The full entry information is\n # also stored as a non-indexed JSON blob.\n entries = []\n batch = []\n for name, value in data.items():\n e_key = client.key('Entry')\n e_val = datastore.Entity(e_key, exclude_from_indexes=['info'])\n entry_map = {'name': name, 'wallTime': value, 'iters': '1'}\n entries.append(entry_map)\n e_val.update({\n 'test': test_name,\n 'start': start_time,\n 'entry': six.text_type(name),\n 'timing': value,\n 'info': six.text_type(json.dumps(entry_map))\n })\n batch.append(e_val)\n\n # Create the Test Entity containing all the test information as a\n # non-indexed JSON blob.\n test_result = json.dumps(\n {'name': test_name,\n 'startTime': (start_time - datetime(1970, 1, 1)).total_seconds(),\n 'entries': {'entry': entries},\n 'runConfiguration': {'argument': sys.argv[1:]}})\n t_key = client.key('Test')\n t_val = datastore.Entity(t_key, exclude_from_indexes=['info'])\n t_val.update({\n 'test': test_name,\n 'start': start_time,\n 'info': six.text_type(test_result)\n })\n batch.append(t_val)\n\n # Put the whole batch of Entities in the datastore.\n client.put_multi(batch)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/cnn_util.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Utilities for CNN benchmarks.\"\"\"\nfrom __future__ import print_function\n\nimport sys\nimport threading\n\nimport tensorflow as tf\ntf.flags.DEFINE_boolean('use_python32_barrier', False,\n \"\"\"When on, use threading.Barrier at python 3.2.\"\"\")\ntf.flags.DEFINE_boolean('flush_stdout', False,\n \"\"\"When on, flush stdout everytime log_fn is called.\"\"\")\nFLAGS = tf.flags.FLAGS\n\n\ndef tensorflow_version_tuple():\n v = tf.__version__\n major, minor, patch = v.split('.')\n return (int(major), int(minor), patch)\n\n\ndef tensorflow_version():\n vt = tensorflow_version_tuple()\n return vt[0] * 1000 + vt[1]\n\n\ndef log_fn(log):\n print(log)\n if FLAGS.flush_stdout:\n sys.stdout.flush()\n\n\n# For Python 2.7 compatibility, we do not use threading.Barrier.\nclass Barrier(object):\n \"\"\"Implements a lightweight Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and simultaneously return once they have\n all made that call.\n\n # Implementaion adpoted from boost/thread/barrier.hpp\n \"\"\"\n\n def __init__(self, parties):\n \"\"\"Create a barrier, initialised to 'parties' threads.\"\"\"\n self.cond = threading.Condition(threading.Lock())\n self.parties = parties\n # Indicates the number of waiting parties.\n self.waiting = 0\n # generation is needed to deal with spurious wakeups. If self.cond.wait()\n # wakes up for other reasons, generation will force it go back to wait().\n self.generation = 0\n self.broken = False\n\n def wait(self):\n \"\"\"Wait for the barrier.\"\"\"\n with self.cond:\n # Check if the barrier has been disabled or not.\n if self.broken:\n return\n gen = self.generation\n self.waiting += 1\n if self.waiting == self.parties:\n self.waiting = 0\n self.generation += 1\n self.cond.notify_all()\n # loop because of spurious wakeups\n while gen == self.generation:\n self.cond.wait()\n\n # TODO(huangyp): Remove this method once we find a way to know which step\n # is the last barrier.\n def abort(self):\n \"\"\"Clear existing barrier and disable this barrier.\"\"\"\n with self.cond:\n if self.waiting > 0:\n self.generation += 1\n self.cond.notify_all()\n self.broken = True\n\n\nclass ImageProducer(object):\n \"\"\"An image producer that puts images into a staging area periodically.\n\n This class is useful for periodically running a set of ops, `put_ops` on a\n different thread every `batch_group_size` times.\n\n The notify_image_consumption() method is used to increment an internal counter\n so that when it is first called, `put_ops` is executed. Afterwards, every\n `batch_group_size` times notify_image_consumption() is called,\n `put_ops` is executed again. A barrier is placed so that the main thread is\n blocked until `put_ops` have been executed.\n\n The start() method is used to start the thread that runs `put_ops`.\n\n The done() method waits until the last put_ops is executed and stops the\n thread.\n\n The purpose of this class is to fill an image input pipeline every\n `batch_group_size` steps. Suppose `put_ops` supplies M images to the input\n pipeline when run, and that every step, (M/`batch_group_size`) images are\n consumed. Then, by calling notify_image_consumption() every step, images are\n supplied to the input pipeline at the same amount they are consumed.\n \"\"\"\n\n def __init__(self, sess, put_ops, batch_group_size):\n self.sess = sess\n self.num_gets = 0\n self.put_ops = put_ops\n self.batch_group_size = batch_group_size\n self.done_event = threading.Event()\n if (FLAGS.use_python32_barrier and\n sys.version_info[0] == 3 and sys.version_info[1] >= 2):\n self.put_barrier = threading.Barrier(2)\n else:\n self.put_barrier = Barrier(2)\n\n def _should_put(self):\n return self.num_gets % self.batch_group_size == 0\n\n def done(self):\n \"\"\"Stop the image producer.\"\"\"\n self.done_event.set()\n self.put_barrier.abort()\n self.thread.join()\n\n def start(self):\n \"\"\"Start the image producer.\"\"\"\n self.thread = threading.Thread(target=self._loop_producer)\n # Set daemon to true to allow Ctrl + C to terminate all threads.\n self.thread.daemon = True\n self.thread.start()\n\n def notify_image_consumption(self):\n \"\"\"Increment the counter of image_producer by 1.\n\n This should only be called by the main thread that consumes images and runs\n the model computation.\n \"\"\"\n if self._should_put():\n self.put_barrier.wait()\n self.num_gets += 1\n\n def _loop_producer(self):\n while not self.done_event.isSet():\n self.sess.run([self.put_ops])\n self.put_barrier.wait()\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/convnet_builder.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"CNN builder.\"\"\"\n\nfrom __future__ import print_function\n\nfrom collections import defaultdict\nimport contextlib\n\nimport numpy as np\n\nimport tensorflow as tf\n\nfrom tensorflow.python.layers import convolutional as conv_layers\nfrom tensorflow.python.layers import core as core_layers\nfrom tensorflow.python.layers import pooling as pooling_layers\nfrom tensorflow.python.training import moving_averages\n\n\nclass ConvNetBuilder(object):\n \"\"\"Builder of cnn net.\"\"\"\n\n def __init__(self,\n input_op,\n input_nchan,\n phase_train,\n use_tf_layers,\n data_format='NCHW',\n dtype=tf.float32,\n variable_dtype=tf.float32):\n self.top_layer = input_op\n self.top_size = input_nchan\n self.phase_train = phase_train\n self.use_tf_layers = use_tf_layers\n self.data_format = data_format\n self.dtype = dtype\n self.variable_dtype = variable_dtype\n self.counts = defaultdict(lambda: 0)\n self.use_batch_norm = False\n self.batch_norm_config = {} # 'decay': 0.997, 'scale': True}\n self.channel_pos = ('channels_last'\n if data_format == 'NHWC' else 'channels_first')\n self.aux_top_layer = None\n self.aux_top_size = 0\n\n def get_custom_getter(self):\n \"\"\"Returns a custom getter that this class's methods must be called under.\n\n All methods of this class must be called under a variable scope that was\n passed this custom getter. Example:\n\n ```python\n network = ConvNetBuilder(...)\n with tf.variable_scope('cg', custom_getter=network.get_custom_getter()):\n network.conv(...)\n # Call more methods of network here\n ```\n\n Currently, this custom getter only does anything if self.use_tf_layers is\n True. In that case, it causes variables to be stored as dtype\n self.variable_type, then casted to the requested dtype, instead of directly\n storing the variable as the requested dtype.\n \"\"\"\n def inner_custom_getter(getter, *args, **kwargs):\n \"\"\"Custom getter that forces variables to have type self.variable_type.\"\"\"\n if not self.use_tf_layers:\n return getter(*args, **kwargs)\n requested_dtype = kwargs['dtype']\n if not (requested_dtype == tf.float32 and\n self.variable_dtype == tf.float16):\n # Only change the variable dtype if doing so does not decrease variable\n # precision.\n kwargs['dtype'] = self.variable_dtype\n var = getter(*args, **kwargs)\n # This if statement is needed to guard the cast, because batch norm\n # assigns directly to the return value of this custom getter. The cast\n # makes the return value not a variable so it cannot be assigned. Batch\n # norm variables are always in fp32 so this if statement is never\n # triggered for them.\n if var.dtype.base_dtype != requested_dtype:\n var = tf.cast(var, requested_dtype)\n return var\n return inner_custom_getter\n\n @contextlib.contextmanager\n def switch_to_aux_top_layer(self):\n \"\"\"Context that construct cnn in the auxiliary arm.\"\"\"\n if self.aux_top_layer is None:\n raise RuntimeError('Empty auxiliary top layer in the network.')\n saved_top_layer = self.top_layer\n saved_top_size = self.top_size\n self.top_layer = self.aux_top_layer\n self.top_size = self.aux_top_size\n yield\n self.aux_top_layer = self.top_layer\n self.aux_top_size = self.top_size\n self.top_layer = saved_top_layer\n self.top_size = saved_top_size\n\n def get_variable(self, name, shape, dtype, cast_dtype, *args, **kwargs):\n # TODO(reedwm): Currently variables and gradients are transferred to other\n # devices and machines as type `dtype`, not `cast_dtype`. In particular,\n # this means in fp16 mode, variables are transferred as fp32 values, not\n # fp16 values, which uses extra bandwidth.\n var = tf.get_variable(name, shape, dtype, *args, **kwargs)\n return tf.cast(var, cast_dtype)\n\n def _conv2d_impl(self, input_layer, num_channels_in, filters, kernel_size,\n strides, padding, kernel_initializer):\n if self.use_tf_layers:\n return conv_layers.conv2d(input_layer, filters, kernel_size, strides,\n padding, self.channel_pos,\n kernel_initializer=kernel_initializer,\n use_bias=False)\n else:\n weights_shape = [kernel_size[0], kernel_size[1], num_channels_in, filters]\n # We use the name 'conv2d/kernel' so the variable has the same name as its\n # tf.layers equivalent. This way, if a checkpoint is written when\n # self.use_tf_layers == True, it can be loaded when\n # self.use_tf_layers == False, and vice versa.\n weights = self.get_variable('conv2d/kernel', weights_shape,\n self.variable_dtype, self.dtype,\n initializer=kernel_initializer)\n if self.data_format == 'NHWC':\n strides = [1] + strides + [1]\n else:\n strides = [1, 1] + strides\n return tf.nn.conv2d(input_layer, weights, strides, padding,\n data_format=self.data_format)\n\n def conv(self,\n num_out_channels,\n k_height,\n k_width,\n d_height=1,\n d_width=1,\n mode='SAME',\n input_layer=None,\n num_channels_in=None,\n use_batch_norm=None,\n stddev=None,\n activation='relu',\n bias=0.0):\n \"\"\"Construct a conv2d layer on top of cnn.\"\"\"\n if input_layer is None:\n input_layer = self.top_layer\n if num_channels_in is None:\n num_channels_in = self.top_size\n kernel_initializer = None\n if stddev is not None:\n kernel_initializer = tf.truncated_normal_initializer(stddev=stddev)\n name = 'conv' + str(self.counts['conv'])\n self.counts['conv'] += 1\n with tf.variable_scope(name):\n strides = [1, d_height, d_width, 1]\n if self.data_format == 'NCHW':\n strides = [strides[0], strides[3], strides[1], strides[2]]\n if mode != 'SAME_RESNET':\n conv = self._conv2d_impl(input_layer, num_channels_in, num_out_channels,\n kernel_size=[k_height, k_width],\n strides=[d_height, d_width], padding=mode,\n kernel_initializer=kernel_initializer)\n else: # Special padding mode for ResNet models\n if d_height == 1 and d_width == 1:\n conv = self._conv2d_impl(input_layer, num_channels_in,\n num_out_channels,\n kernel_size=[k_height, k_width],\n strides=[d_height, d_width], padding='SAME',\n kernel_initializer=kernel_initializer)\n else:\n rate = 1 # Unused (for 'a trous' convolutions)\n kernel_height_effective = k_height + (k_height - 1) * (rate - 1)\n pad_h_beg = (kernel_height_effective - 1) // 2\n pad_h_end = kernel_height_effective - 1 - pad_h_beg\n kernel_width_effective = k_width + (k_width - 1) * (rate - 1)\n pad_w_beg = (kernel_width_effective - 1) // 2\n pad_w_end = kernel_width_effective - 1 - pad_w_beg\n padding = [[0, 0], [pad_h_beg, pad_h_end],\n [pad_w_beg, pad_w_end], [0, 0]]\n if self.data_format == 'NCHW':\n padding = [padding[0], padding[3], padding[1], padding[2]]\n input_layer = tf.pad(input_layer, padding)\n conv = self._conv2d_impl(input_layer, num_channels_in,\n num_out_channels,\n kernel_size=[k_height, k_width],\n strides=[d_height, d_width], padding='VALID',\n kernel_initializer=kernel_initializer)\n if use_batch_norm is None:\n use_batch_norm = self.use_batch_norm\n if not use_batch_norm:\n if bias is not None:\n biases = self.get_variable('biases', [num_out_channels],\n self.variable_dtype, self.dtype,\n initializer=tf.constant_initializer(bias))\n biased = tf.reshape(\n tf.nn.bias_add(conv, biases, data_format=self.data_format),\n conv.get_shape())\n else:\n biased = conv\n else:\n self.top_layer = conv\n self.top_size = num_out_channels\n biased = self.batch_norm(**self.batch_norm_config)\n if activation == 'relu':\n conv1 = tf.nn.relu(biased)\n elif activation == 'linear' or activation is None:\n conv1 = biased\n elif activation == 'tanh':\n conv1 = tf.nn.tanh(biased)\n else:\n raise KeyError('Invalid activation type \\'%s\\'' % activation)\n self.top_layer = conv1\n self.top_size = num_out_channels\n return conv1\n\n def _pool(self,\n pool_name,\n pool_function,\n k_height,\n k_width,\n d_height,\n d_width,\n mode,\n input_layer,\n num_channels_in):\n \"\"\"Construct a pooling layer.\"\"\"\n if input_layer is None:\n input_layer = self.top_layer\n else:\n self.top_size = num_channels_in\n name = pool_name + str(self.counts[pool_name])\n self.counts[pool_name] += 1\n if self.use_tf_layers:\n pool = pool_function(\n input_layer, [k_height, k_width], [d_height, d_width],\n padding=mode,\n data_format=self.channel_pos,\n name=name)\n else:\n if self.data_format == 'NHWC':\n ksize = [1, k_height, k_width, 1]\n strides = [1, d_height, d_width, 1]\n else:\n ksize = [1, 1, k_height, k_width]\n strides = [1, 1, d_height, d_width]\n pool = tf.nn.max_pool(input_layer, ksize, strides, padding=mode,\n data_format=self.data_format, name=name)\n self.top_layer = pool\n return pool\n\n def mpool(self,\n k_height,\n k_width,\n d_height=2,\n d_width=2,\n mode='VALID',\n input_layer=None,\n num_channels_in=None):\n \"\"\"Construct a max pooling layer.\"\"\"\n return self._pool('mpool', pooling_layers.max_pooling2d, k_height, k_width,\n d_height, d_width, mode, input_layer, num_channels_in)\n\n def apool(self,\n k_height,\n k_width,\n d_height=2,\n d_width=2,\n mode='VALID',\n input_layer=None,\n num_channels_in=None):\n \"\"\"Construct an average pooling layer.\"\"\"\n return self._pool('apool', pooling_layers.average_pooling2d, k_height,\n k_width, d_height, d_width, mode, input_layer,\n num_channels_in)\n\n def reshape(self, shape, input_layer=None):\n if input_layer is None:\n input_layer = self.top_layer\n self.top_layer = tf.reshape(input_layer, shape)\n self.top_size = shape[-1] # HACK This may not always work\n return self.top_layer\n\n def affine(self,\n num_out_channels,\n input_layer=None,\n num_channels_in=None,\n bias=0.0,\n stddev=None,\n activation='relu'):\n if input_layer is None:\n input_layer = self.top_layer\n if num_channels_in is None:\n num_channels_in = self.top_size\n name = 'affine' + str(self.counts['affine'])\n self.counts['affine'] += 1\n with tf.variable_scope(name):\n init_factor = 2. if activation == 'relu' else 1.\n stddev = stddev or np.sqrt(init_factor / num_channels_in)\n kernel = self.get_variable(\n 'weights', [num_channels_in, num_out_channels],\n self.variable_dtype, self.dtype,\n initializer=tf.truncated_normal_initializer(stddev=stddev))\n biases = self.get_variable('biases', [num_out_channels],\n self.variable_dtype, self.dtype,\n initializer=tf.constant_initializer(bias))\n logits = tf.nn.xw_plus_b(input_layer, kernel, biases)\n if activation == 'relu':\n affine1 = tf.nn.relu(logits, name=name)\n elif activation == 'linear' or activation is None:\n affine1 = logits\n else:\n raise KeyError('Invalid activation type \\'%s\\'' % activation)\n self.top_layer = affine1\n self.top_size = num_out_channels\n return affine1\n\n def inception_module(self, name, cols, input_layer=None, in_size=None):\n if input_layer is None:\n input_layer = self.top_layer\n if in_size is None:\n in_size = self.top_size\n name += str(self.counts[name])\n self.counts[name] += 1\n with tf.variable_scope(name):\n col_layers = []\n col_layer_sizes = []\n for c, col in enumerate(cols):\n col_layers.append([])\n col_layer_sizes.append([])\n for l, layer in enumerate(col):\n ltype, args = layer[0], layer[1:]\n kwargs = {\n 'input_layer': input_layer,\n 'num_channels_in': in_size\n } if l == 0 else {}\n if ltype == 'conv':\n self.conv(*args, **kwargs)\n elif ltype == 'mpool':\n self.mpool(*args, **kwargs)\n elif ltype == 'apool':\n self.apool(*args, **kwargs)\n elif ltype == 'share': # Share matching layer from previous column\n self.top_layer = col_layers[c - 1][l]\n self.top_size = col_layer_sizes[c - 1][l]\n else:\n raise KeyError(\n 'Invalid layer type for inception module: \\'%s\\'' % ltype)\n col_layers[c].append(self.top_layer)\n col_layer_sizes[c].append(self.top_size)\n catdim = 3 if self.data_format == 'NHWC' else 1\n self.top_layer = tf.concat([layers[-1] for layers in col_layers], catdim)\n self.top_size = sum([sizes[-1] for sizes in col_layer_sizes])\n return self.top_layer\n\n def spatial_mean(self, keep_dims=False):\n name = 'spatial_mean' + str(self.counts['spatial_mean'])\n self.counts['spatial_mean'] += 1\n axes = [1, 2] if self.data_format == 'NHWC' else [2, 3]\n self.top_layer = tf.reduce_mean(\n self.top_layer, axes, keep_dims=keep_dims, name=name)\n return self.top_layer\n\n def dropout(self, keep_prob=0.5, input_layer=None):\n if input_layer is None:\n input_layer = self.top_layer\n else:\n self.top_size = None\n name = 'dropout' + str(self.counts['dropout'])\n with tf.variable_scope(name):\n if not self.phase_train:\n keep_prob = 1.0\n if self.use_tf_layers:\n dropout = core_layers.dropout(input_layer, 1. - keep_prob)\n else:\n dropout = tf.nn.dropout(input_layer, keep_prob)\n self.top_layer = dropout\n return dropout\n\n def _batch_norm_without_layers(self, input_layer, decay, use_scale, epsilon):\n \"\"\"Batch normalization on `input_layer` without tf.layers.\"\"\"\n # We make this function as similar as possible to the\n # tf.contrib.layers.batch_norm, to minimize the differences between using\n # layers and not using layers.\n shape = input_layer.shape\n num_channels = shape[3] if self.data_format == 'NHWC' else shape[1]\n beta = self.get_variable('beta', [num_channels], tf.float32, tf.float32,\n initializer=tf.zeros_initializer())\n if use_scale:\n gamma = self.get_variable('gamma', [num_channels], tf.float32,\n tf.float32, initializer=tf.ones_initializer())\n else:\n gamma = tf.constant(1.0, tf.float32, [num_channels])\n # For moving variables, we use tf.get_variable instead of self.get_variable,\n # since self.get_variable returns the result of tf.cast which we cannot\n # assign to.\n moving_mean = tf.get_variable('moving_mean', [num_channels],\n tf.float32,\n initializer=tf.zeros_initializer(),\n trainable=False)\n moving_variance = tf.get_variable('moving_variance', [num_channels],\n tf.float32,\n initializer=tf.ones_initializer(),\n trainable=False)\n if self.phase_train:\n bn, batch_mean, batch_variance = tf.nn.fused_batch_norm(\n input_layer, gamma, beta, epsilon=epsilon,\n data_format=self.data_format, is_training=True)\n mean_update = moving_averages.assign_moving_average(\n moving_mean, batch_mean, decay=decay, zero_debias=False)\n variance_update = moving_averages.assign_moving_average(\n moving_variance, batch_variance, decay=decay, zero_debias=False)\n tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, mean_update)\n tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, variance_update)\n else:\n bn, _, _ = tf.nn.fused_batch_norm(\n input_layer, gamma, beta, mean=moving_mean,\n variance=moving_variance, epsilon=epsilon,\n data_format=self.data_format, is_training=False)\n return bn\n\n def batch_norm(self, input_layer=None, decay=0.999, scale=False,\n epsilon=0.001):\n \"\"\"Adds a Batch Normalization layer.\"\"\"\n if input_layer is None:\n input_layer = self.top_layer\n else:\n self.top_size = None\n name = 'batchnorm' + str(self.counts['batchnorm'])\n self.counts['batchnorm'] += 1\n\n with tf.variable_scope(name) as scope:\n if self.use_tf_layers:\n bn = tf.contrib.layers.batch_norm(\n input_layer,\n decay=decay,\n scale=scale,\n epsilon=epsilon,\n is_training=self.phase_train,\n fused=True,\n data_format=self.data_format,\n scope=scope)\n else:\n bn = self._batch_norm_without_layers(input_layer, decay, scale, epsilon)\n self.top_layer = bn\n self.top_size = bn.shape[3] if self.data_format == 'NHWC' else bn.shape[1]\n self.top_size = int(self.top_size)\n return bn\n\n def lrn(self, depth_radius, bias, alpha, beta):\n \"\"\"Adds a local response normalization layer.\"\"\"\n name = 'lrn' + str(self.counts['lrn'])\n self.counts['lrn'] += 1\n self.top_layer = tf.nn.lrn(\n self.top_layer, depth_radius, bias, alpha, beta, name=name)\n return self.top_layer\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/datasets.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Benchmark dataset utilities.\n\"\"\"\n\nfrom abc import abstractmethod\nimport os\n\nimport numpy as np\nfrom six.moves import cPickle\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom tensorflow.python.platform import gfile\nimport preprocessing\n\n\nIMAGENET_NUM_TRAIN_IMAGES = 1281167\nIMAGENET_NUM_VAL_IMAGES = 50000\n\n\ndef create_dataset(data_dir, data_name):\n \"\"\"Create a Dataset instance based on data_dir and data_name.\"\"\"\n supported_datasets = {\n 'synthetic': SyntheticData,\n 'imagenet': ImagenetData,\n 'cifar10': Cifar10Data,\n }\n if not data_dir:\n data_name = 'synthetic'\n\n if data_name is None:\n for supported_name in supported_datasets:\n if supported_name in data_dir:\n data_name = supported_name\n break\n\n if data_name is None:\n raise ValueError('Could not identify name of dataset. '\n 'Please specify with --data_name option.')\n\n if data_name not in supported_datasets:\n raise ValueError('Unknown dataset. Must be one of %s', ', '.join(\n [key for key in sorted(supported_datasets.keys())]))\n\n return supported_datasets[data_name](data_dir)\n\n\nclass Dataset(object):\n \"\"\"Abstract class for cnn benchmarks dataset.\"\"\"\n\n def __init__(self, name, height=None, width=None, depth=None, data_dir=None,\n queue_runner_required=False, num_classes=1000):\n self.name = name\n self.height = height\n self.width = width\n self.depth = depth or 3\n\n self.data_dir = data_dir\n self._queue_runner_required = queue_runner_required\n self._num_classes = num_classes\n\n def tf_record_pattern(self, subset):\n return os.path.join(self.data_dir, '%s-*-of-*' % subset)\n\n def reader(self):\n return tf.TFRecordReader()\n\n @property\n def num_classes(self):\n return self._num_classes\n\n @num_classes.setter\n def num_classes(self, val):\n self._num_classes = val\n\n @abstractmethod\n def num_examples_per_epoch(self, subset):\n pass\n\n def __str__(self):\n return self.name\n\n def get_image_preprocessor(self):\n return None\n\n def queue_runner_required(self):\n return self._queue_runner_required\n\n def use_synthetic_gpu_images(self):\n return False\n\n\nclass ImagenetData(Dataset):\n \"\"\"Configuration for Imagenet dataset.\"\"\"\n\n def __init__(self, data_dir=None):\n if data_dir is None:\n raise ValueError('Data directory not specified')\n super(ImagenetData, self).__init__('imagenet', 300, 300, data_dir=data_dir)\n\n def num_examples_per_epoch(self, subset='train'):\n if subset == 'train':\n return IMAGENET_NUM_TRAIN_IMAGES\n elif subset == 'validation':\n return IMAGENET_NUM_VAL_IMAGES\n else:\n raise ValueError('Invalid data subset \"%s\"' % subset)\n\n def get_image_preprocessor(self):\n return preprocessing.RecordInputImagePreprocessor\n\n\nclass SyntheticData(Dataset):\n \"\"\"Configuration for synthetic dataset.\"\"\"\n\n def __init__(self, unused_data_dir):\n super(SyntheticData, self).__init__('synthetic')\n\n def get_image_preprocessor(self):\n return preprocessing.SyntheticImagePreprocessor\n\n def use_synthetic_gpu_images(self):\n return True\n\n\nclass Cifar10Data(Dataset):\n \"\"\"Configuration for cifar 10 dataset.\n\n It will mount all the input images to memory.\n \"\"\"\n\n def __init__(self, data_dir=None):\n if data_dir is None:\n raise ValueError('Data directory not specified')\n super(Cifar10Data, self).__init__('cifar10', 32, 32, data_dir=data_dir,\n queue_runner_required=True,\n num_classes=10)\n\n def read_data_files(self, subset='train'):\n \"\"\"Reads from data file and return images and labels in a numpy array.\"\"\"\n if subset == 'train':\n filenames = [os.path.join(self.data_dir, 'data_batch_%d' % i)\n for i in xrange(1, 6)]\n elif subset == 'validation':\n filenames = [os.path.join(self.data_dir, 'test_batch')]\n else:\n raise ValueError('Invalid data subset \"%s\"' % subset)\n\n inputs = []\n for filename in filenames:\n with gfile.Open(filename, 'r') as f:\n inputs.append(cPickle.load(f))\n # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the\n # input format.\n all_images = np.concatenate(\n [each_input['data'] for each_input in inputs]).astype(np.float32)\n all_labels = np.concatenate(\n [each_input['labels'] for each_input in inputs])\n return all_images, all_labels\n\n def num_examples_per_epoch(self, subset='train'):\n if subset == 'train':\n return 50000\n elif subset == 'validation':\n return 10000\n else:\n raise ValueError('Invalid data subset \"%s\"' % subset)\n\n def get_image_preprocessor(self):\n return preprocessing.Cifar10ImagePreprocessor\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/__init__.py", "content": "" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/alexnet_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Alexnet model configuration.\n\nReferences:\n Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton\n ImageNet Classification with Deep Convolutional Neural Networks\n Advances in Neural Information Processing Systems. 2012\n\"\"\"\n\nimport tensorflow as tf\nfrom models import model\n\n\nclass AlexnetModel(model.Model):\n \"\"\"Alexnet cnn model.\"\"\"\n\n def __init__(self):\n super(AlexnetModel, self).__init__('alexnet', 224 + 3, 512, 0.005)\n\n def add_inference(self, cnn):\n # Note: VALID requires padding the images by 3 in width and height\n cnn.conv(64, 11, 11, 4, 4, 'VALID')\n cnn.mpool(3, 3, 2, 2)\n cnn.conv(192, 5, 5)\n cnn.mpool(3, 3, 2, 2)\n cnn.conv(384, 3, 3)\n cnn.conv(384, 3, 3)\n cnn.conv(256, 3, 3)\n cnn.mpool(3, 3, 2, 2)\n cnn.reshape([-1, 256 * 6 * 6])\n cnn.affine(4096)\n cnn.dropout()\n cnn.affine(4096)\n cnn.dropout()\n\n\nclass AlexnetCifar10Model(model.Model):\n \"\"\"Alexnet cnn model for cifar datasets.\n\n The model architecture follows the one defined in the tensorflow tutorial\n model.\n\n Reference model: tensorflow/models/tutorials/image/cifar10/cifar10.py\n Paper: http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf\n \"\"\"\n\n def __init__(self):\n super(AlexnetCifar10Model, self).__init__('alexnet', 32, 128, 0.1)\n\n def add_inference(self, cnn):\n cnn.conv(64, 5, 5, 1, 1, 'SAME', stddev=5e-2)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n cnn.lrn(depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n cnn.conv(64, 5, 5, 1, 1, 'SAME', bias=0.1, stddev=5e-2)\n cnn.lrn(depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n shape = cnn.top_layer.get_shape().as_list()\n flat_dim = shape[1] * shape[2] * shape[3]\n cnn.reshape([-1, flat_dim])\n cnn.affine(384, stddev=0.04, bias=0.1)\n cnn.affine(192, stddev=0.04, bias=0.1)\n\n def get_learning_rate(self, global_step, batch_size):\n num_examples_per_epoch = 50000\n num_epochs_per_decay = 100\n decay_steps = int(num_epochs_per_decay * num_examples_per_epoch /\n batch_size)\n decay_factor = 0.1\n return tf.train.exponential_decay(\n self.learning_rate, global_step, decay_steps, decay_factor,\n staircase=True)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/densenet_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Densenet model configuration.\n\nReferences:\n \"Densely Connected Convolutional Networks\": https://arxiv.org/pdf/1608.06993\n\"\"\"\nimport numpy as np\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\nfrom models import model as model_lib\n\n\nclass DensenetCifar10Model(model_lib.Model):\n \"\"\"Densenet cnn network configuration.\"\"\"\n\n def __init__(self, model, layer_counts, growth_rate):\n self.growth_rate = growth_rate\n super(DensenetCifar10Model, self).__init__(model, 32, 64, 0.1,\n layer_counts=layer_counts)\n self.batch_norm_config = {'decay': 0.9, 'epsilon': 1e-5, 'scale': True}\n\n def dense_block(self, cnn, growth_rate):\n input_layer = cnn.top_layer\n c = cnn.batch_norm(input_layer, **self.batch_norm_config)\n c = tf.nn.relu(c)\n c = cnn.conv(growth_rate, 3, 3, 1, 1, stddev=np.sqrt(2.0/9/growth_rate),\n activation=None, input_layer=c)\n channel_index = 3 if cnn.channel_pos == 'channels_last' else 1\n cnn.top_layer = tf.concat([input_layer, c], channel_index)\n cnn.top_size += growth_rate\n\n def transition_layer(self, cnn):\n in_size = cnn.top_size\n cnn.batch_norm(**self.batch_norm_config)\n cnn.top_layer = tf.nn.relu(cnn.top_layer)\n cnn.conv(in_size, 1, 1, 1, 1, stddev=np.sqrt(2.0/9/in_size))\n cnn.apool(2, 2, 2, 2)\n\n def add_inference(self, cnn):\n if self.layer_counts is None:\n raise ValueError('Layer counts not specified for %s' % self.get_model())\n if self.growth_rate is None:\n raise ValueError('Growth rate not specified for %s' % self.get_model())\n\n cnn.conv(16, 3, 3, 1, 1, activation=None)\n # Block 1\n for _ in xrange(self.layer_counts[0]):\n self.dense_block(cnn, self.growth_rate)\n self.transition_layer(cnn)\n # Block 2\n for _ in xrange(self.layer_counts[1]):\n self.dense_block(cnn, self.growth_rate)\n self.transition_layer(cnn)\n # Block 3\n for _ in xrange(self.layer_counts[2]):\n self.dense_block(cnn, self.growth_rate)\n cnn.batch_norm(**self.batch_norm_config)\n cnn.top_layer = tf.nn.relu(cnn.top_layer)\n channel_index = 3 if cnn.channel_pos == 'channels_last' else 1\n cnn.top_size = cnn.top_layer.get_shape().as_list()[channel_index]\n cnn.spatial_mean()\n\n def get_learning_rate(self, global_step, batch_size):\n num_batches_per_epoch = int(50000 / batch_size)\n boundaries = num_batches_per_epoch * np.array([150, 225, 300],\n dtype=np.int64)\n boundaries = [x for x in boundaries]\n values = [0.1, 0.01, 0.001, 0.0001]\n return tf.train.piecewise_constant(global_step, boundaries, values)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/googlenet_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Googlenet model configuration.\n\nReferences:\n Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,\n Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich\n Going deeper with convolutions\n arXiv preprint arXiv:1409.4842 (2014)\n\"\"\"\n\nfrom models import model\n\n\nclass GooglenetModel(model.Model):\n\n def __init__(self):\n super(GooglenetModel, self).__init__('googlenet', 224, 32, 0.005)\n\n def add_inference(self, cnn):\n def inception_v1(cnn, k, l, m, n, p, q):\n cols = [[('conv', k, 1, 1)], [('conv', l, 1, 1), ('conv', m, 3, 3)],\n [('conv', n, 1, 1), ('conv', p, 5, 5)],\n [('mpool', 3, 3, 1, 1, 'SAME'), ('conv', q, 1, 1)]]\n cnn.inception_module('incept_v1', cols)\n\n cnn.conv(64, 7, 7, 2, 2)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n cnn.conv(64, 1, 1)\n cnn.conv(192, 3, 3)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n inception_v1(cnn, 64, 96, 128, 16, 32, 32)\n inception_v1(cnn, 128, 128, 192, 32, 96, 64)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n inception_v1(cnn, 192, 96, 208, 16, 48, 64)\n inception_v1(cnn, 160, 112, 224, 24, 64, 64)\n inception_v1(cnn, 128, 128, 256, 24, 64, 64)\n inception_v1(cnn, 112, 144, 288, 32, 64, 64)\n inception_v1(cnn, 256, 160, 320, 32, 128, 128)\n cnn.mpool(3, 3, 2, 2, mode='SAME')\n inception_v1(cnn, 256, 160, 320, 32, 128, 128)\n inception_v1(cnn, 384, 192, 384, 48, 128, 128)\n cnn.apool(7, 7, 1, 1, mode='VALID')\n cnn.reshape([-1, 1024])\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/inception_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Inception model configuration.\n\nIncludes multiple models: inception3, inception4, inception-resnet2.\n\nReferences:\n Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi\n Inception-v4, Inception-ResNet and the Impact of Residual Connections on\n Learning\n\n Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,\n Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich\n Going Deeper with Convolutions\n http://arxiv.org/pdf/1409.4842v1.pdf\n\n Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,\n Zbigniew Wojna\n Rethinking the Inception Architecture for Computer Vision\n arXiv preprint arXiv:1512.00567 (2015)\n\n Inception v3 model: http://arxiv.org/abs/1512.00567\n\n Inception v4 and Resnet V2 architectures: http://arxiv.org/abs/1602.07261\n\"\"\"\n\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nfrom models import model\n\n\nclass Inceptionv3Model(model.Model):\n\n def __init__(self, auxiliary=False):\n self._auxiliary = auxiliary\n super(Inceptionv3Model, self).__init__('inception3', 299, 32, 0.005)\n\n def add_inference(self, cnn):\n def inception_v3_a(cnn, n):\n cols = [[('conv', 64, 1, 1)], [('conv', 48, 1, 1), ('conv', 64, 5, 5)],\n [('conv', 64, 1, 1), ('conv', 96, 3, 3), ('conv', 96, 3, 3)],\n [('apool', 3, 3, 1, 1, 'SAME'), ('conv', n, 1, 1)]]\n cnn.inception_module('incept_v3_a', cols)\n\n def inception_v3_b(cnn):\n cols = [[('conv', 384, 3, 3, 2, 2, 'VALID')],\n [('conv', 64, 1, 1),\n ('conv', 96, 3, 3),\n ('conv', 96, 3, 3, 2, 2, 'VALID')],\n [('mpool', 3, 3, 2, 2, 'VALID')]]\n cnn.inception_module('incept_v3_b', cols)\n\n def inception_v3_c(cnn, n):\n cols = [[('conv', 192, 1, 1)],\n [('conv', n, 1, 1), ('conv', n, 1, 7), ('conv', 192, 7, 1)],\n [('conv', n, 1, 1), ('conv', n, 7, 1), ('conv', n, 1, 7),\n ('conv', n, 7, 1), ('conv', 192, 1, 7)],\n [('apool', 3, 3, 1, 1, 'SAME'), ('conv', 192, 1, 1)]]\n cnn.inception_module('incept_v3_c', cols)\n\n def inception_v3_d(cnn):\n cols = [[('conv', 192, 1, 1), ('conv', 320, 3, 3, 2, 2, 'VALID')],\n [('conv', 192, 1, 1), ('conv', 192, 1, 7), ('conv', 192, 7, 1),\n ('conv', 192, 3, 3, 2, 2, 'VALID')],\n [('mpool', 3, 3, 2, 2, 'VALID')]]\n cnn.inception_module('incept_v3_d', cols)\n\n def inception_v3_e(cnn, pooltype):\n cols = [[('conv', 320, 1, 1)], [('conv', 384, 1, 1), ('conv', 384, 1, 3)],\n [('share',), ('conv', 384, 3, 1)],\n [('conv', 448, 1, 1), ('conv', 384, 3, 3), ('conv', 384, 1, 3)],\n [('share',), ('share',), ('conv', 384, 3, 1)],\n [('mpool' if pooltype == 'max' else 'apool', 3, 3, 1, 1, 'SAME'),\n ('conv', 192, 1, 1)]]\n cnn.inception_module('incept_v3_e', cols)\n\n def incept_v3_aux(cnn):\n assert cnn.aux_top_layer is None\n cnn.aux_top_layer = cnn.top_layer\n cnn.aux_top_size = cnn.top_size\n with cnn.switch_to_aux_top_layer():\n cnn.apool(5, 5, 3, 3, mode='VALID')\n cnn.conv(128, 1, 1, mode='SAME')\n cnn.conv(768, 5, 5, mode='VALID', stddev=0.01)\n cnn.reshape([-1, 768])\n\n cnn.use_batch_norm = True\n cnn.conv(32, 3, 3, 2, 2, mode='VALID') # 299 x 299 x 3\n cnn.conv(32, 3, 3, 1, 1, mode='VALID') # 149 x 149 x 32\n cnn.conv(64, 3, 3, 1, 1, mode='SAME') # 147 x 147 x 64\n cnn.mpool(3, 3, 2, 2, mode='VALID') # 147 x 147 x 64\n cnn.conv(80, 1, 1, 1, 1, mode='VALID') # 73 x 73 x 80\n cnn.conv(192, 3, 3, 1, 1, mode='VALID') # 71 x 71 x 192\n cnn.mpool(3, 3, 2, 2, 'VALID') # 35 x 35 x 192\n inception_v3_a(cnn, 32) # 35 x 35 x 256 mixed.\n inception_v3_a(cnn, 64) # 35 x 35 x 288 mixed_1.\n inception_v3_a(cnn, 64) # 35 x 35 x 288 mixed_2\n inception_v3_b(cnn) # 17 x 17 x 768 mixed_3\n inception_v3_c(cnn, 128) # 17 x 17 x 768 mixed_4\n inception_v3_c(cnn, 160) # 17 x 17 x 768 mixed_5\n inception_v3_c(cnn, 160) # 17 x 17 x 768 mixed_6\n inception_v3_c(cnn, 192) # 17 x 17 x 768 mixed_7\n if self._auxiliary:\n incept_v3_aux(cnn) # Auxillary Head logits\n inception_v3_d(cnn) # 17 x 17 x 1280 mixed_8\n inception_v3_e(cnn, 'avg') # 8 x 8 x 2048 mixed_9\n inception_v3_e(cnn, 'max') # 8 x 8 x 2048 mixed_10\n cnn.apool(8, 8, 1, 1, 'VALID') # 8 x 8 x 2048\n cnn.reshape([-1, 2048]) # 1 x 1 x 2048\n\n\n# Stem functions\ndef inception_v4_sa(cnn):\n cols = [[('mpool', 3, 3, 2, 2, 'VALID')], [('conv', 96, 3, 3, 2, 2, 'VALID')]]\n cnn.inception_module('incept_v4_sa', cols)\n\n\ndef inception_v4_sb(cnn):\n cols = [[('conv', 64, 1, 1), ('conv', 96, 3, 3, 1, 1, 'VALID')],\n [('conv', 64, 1, 1), ('conv', 64, 7, 1), ('conv', 64, 1, 7),\n ('conv', 96, 3, 3, 1, 1, 'VALID')]]\n cnn.inception_module('incept_v4_sb', cols)\n\n\ndef inception_v4_sc(cnn):\n cols = [[('conv', 192, 3, 3, 2, 2, 'VALID')],\n [('mpool', 3, 3, 2, 2, 'VALID')]]\n cnn.inception_module('incept_v4_sc', cols)\n\n\n# Reduction functions\ndef inception_v4_ra(cnn, k, l, m, n):\n cols = [\n [('mpool', 3, 3, 2, 2, 'VALID')], [('conv', n, 3, 3, 2, 2, 'VALID')],\n [('conv', k, 1, 1), ('conv', l, 3, 3), ('conv', m, 3, 3, 2, 2, 'VALID')]\n ]\n cnn.inception_module('incept_v4_ra', cols)\n\n\ndef inception_v4_rb(cnn):\n cols = [[('mpool', 3, 3, 2, 2, 'VALID')],\n [('conv', 192, 1, 1), ('conv', 192, 3, 3, 2, 2, 'VALID')],\n [('conv', 256, 1, 1), ('conv', 256, 1, 7), ('conv', 320, 7, 1),\n ('conv', 320, 3, 3, 2, 2, 'VALID')]]\n cnn.inception_module('incept_v4_rb', cols)\n\n\nclass Inceptionv4Model(model.Model):\n\n def __init__(self):\n super(Inceptionv4Model, self).__init__('inception4', 299, 32, 0.005)\n\n def add_inference(self, cnn):\n def inception_v4_a(cnn):\n cols = [[('apool', 3, 3, 1, 1, 'SAME'), ('conv', 96, 1, 1)],\n [('conv', 96, 1, 1)], [('conv', 64, 1, 1), ('conv', 96, 3, 3)],\n [('conv', 64, 1, 1), ('conv', 96, 3, 3), ('conv', 96, 3, 3)]]\n cnn.inception_module('incept_v4_a', cols)\n\n def inception_v4_b(cnn):\n cols = [[('apool', 3, 3, 1, 1, 'SAME'), ('conv', 128, 1, 1)],\n [('conv', 384, 1, 1)],\n [('conv', 192, 1, 1), ('conv', 224, 1, 7), ('conv', 256, 7, 1)],\n [('conv', 192, 1, 1), ('conv', 192, 1, 7), ('conv', 224, 7, 1),\n ('conv', 224, 1, 7), ('conv', 256, 7, 1)]]\n cnn.inception_module('incept_v4_b', cols)\n\n def inception_v4_c(cnn):\n cols = [[('apool', 3, 3, 1, 1, 'SAME'), ('conv', 256, 1, 1)],\n [('conv', 256, 1, 1)], [('conv', 384, 1, 1), ('conv', 256, 1, 3)],\n [('share',), ('conv', 256, 3, 1)],\n [('conv', 384, 1, 1), ('conv', 448, 1, 3), ('conv', 512, 3, 1),\n ('conv', 256, 3, 1)], [('share',), ('share',), ('share',),\n ('conv', 256, 1, 3)]]\n cnn.inception_module('incept_v4_c', cols)\n\n cnn.use_batch_norm = True\n cnn.conv(32, 3, 3, 2, 2, mode='VALID')\n cnn.conv(32, 3, 3, 1, 1, mode='VALID')\n cnn.conv(64, 3, 3)\n inception_v4_sa(cnn)\n inception_v4_sb(cnn)\n inception_v4_sc(cnn)\n for _ in xrange(4):\n inception_v4_a(cnn)\n inception_v4_ra(cnn, 192, 224, 256, 384)\n for _ in xrange(7):\n inception_v4_b(cnn)\n inception_v4_rb(cnn)\n for _ in xrange(3):\n inception_v4_c(cnn)\n cnn.spatial_mean()\n cnn.dropout(0.8)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/lenet_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Lenet model configuration.\n\nReferences:\n LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner\n Gradient-based learning applied to document recognition\n Proceedings of the IEEE (1998)\n\"\"\"\n\nfrom models import model\n\n\nclass Lenet5Model(model.Model):\n\n def __init__(self):\n super(Lenet5Model, self).__init__('lenet5', 28, 32, 0.005)\n\n def add_inference(self, cnn):\n # Note: This matches TF's MNIST tutorial model\n cnn.conv(32, 5, 5)\n cnn.mpool(2, 2)\n cnn.conv(64, 5, 5)\n cnn.mpool(2, 2)\n cnn.reshape([-1, 64 * 7 * 7])\n cnn.affine(512)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Base model configuration for CNN benchmarks.\"\"\"\n\n\nclass Model(object):\n \"\"\"Base model configuration for CNN benchmarks.\"\"\"\n\n def __init__(self,\n model,\n image_size,\n batch_size,\n learning_rate,\n layer_counts=None,\n fp16_loss_scale=128):\n self.model = model\n self.image_size = image_size\n self.batch_size = batch_size\n self.default_batch_size = batch_size\n self.learning_rate = learning_rate\n self.layer_counts = layer_counts\n # TODO(reedwm) Set custom loss scales for each model instead of using the\n # default of 128.\n self.fp16_loss_scale = fp16_loss_scale\n\n def get_model(self):\n return self.model\n\n def get_image_size(self):\n return self.image_size\n\n def get_batch_size(self):\n return self.batch_size\n\n def set_batch_size(self, batch_size):\n self.batch_size = batch_size\n\n def get_default_batch_size(self):\n return self.default_batch_size\n\n def get_layer_counts(self):\n return self.layer_counts\n\n def get_fp16_loss_scale(self):\n return self.fp16_loss_scale\n\n def get_learning_rate(self, global_step, batch_size):\n del global_step\n del batch_size\n return self.learning_rate\n\n def add_inference(self, unused_cnn):\n raise ValueError('Must be implemented in derived classes')\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/model_config.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Model configurations for CNN benchmarks.\n\"\"\"\n\nfrom models import alexnet_model\nfrom models import densenet_model\nfrom models import googlenet_model\nfrom models import inception_model\nfrom models import lenet_model\nfrom models import overfeat_model\nfrom models import resnet_model\nfrom models import trivial_model\nfrom models import vgg_model\n\n\ndef get_model_config(model, dataset):\n \"\"\"Map model name to model network configuration.\"\"\"\n if 'cifar10' == dataset.name:\n return get_cifar10_model_config(model)\n if model == 'vgg11':\n mc = vgg_model.Vgg11Model()\n elif model == 'vgg16':\n mc = vgg_model.Vgg16Model()\n elif model == 'vgg19':\n mc = vgg_model.Vgg19Model()\n elif model == 'lenet':\n mc = lenet_model.Lenet5Model()\n elif model == 'googlenet':\n mc = googlenet_model.GooglenetModel()\n elif model == 'overfeat':\n mc = overfeat_model.OverfeatModel()\n elif model == 'alexnet':\n mc = alexnet_model.AlexnetModel()\n elif model == 'trivial':\n mc = trivial_model.TrivialModel()\n elif model == 'inception3':\n mc = inception_model.Inceptionv3Model()\n elif model == 'inception4':\n mc = inception_model.Inceptionv4Model()\n elif model == 'resnet50' or model == 'resnet50_v2':\n mc = resnet_model.ResnetModel(model, (3, 4, 6, 3))\n elif model == 'resnet101' or model == 'resnet101_v2':\n mc = resnet_model.ResnetModel(model, (3, 4, 23, 3))\n elif model == 'resnet152' or model == 'resnet152_v2':\n mc = resnet_model.ResnetModel(model, (3, 8, 36, 3))\n else:\n raise KeyError('Invalid model name \\'%s\\' for dataset \\'%s\\'' %\n (model, dataset.name))\n return mc\n\n\ndef get_cifar10_model_config(model):\n \"\"\"Map model name to model network configuration for cifar10 dataset.\"\"\"\n if model == 'alexnet':\n mc = alexnet_model.AlexnetCifar10Model()\n elif model == 'resnet20' or model == 'resnet20_v2':\n mc = resnet_model.ResnetCifar10Model(model, (3, 3, 3))\n elif model == 'resnet32' or model == 'resnet32_v2':\n mc = resnet_model.ResnetCifar10Model(model, (5, 5, 5))\n elif model == 'resnet44' or model == 'resnet44_v2':\n mc = resnet_model.ResnetCifar10Model(model, (7, 7, 7))\n elif model == 'resnet56' or model == 'resnet56_v2':\n mc = resnet_model.ResnetCifar10Model(model, (9, 9, 9))\n elif model == 'resnet110' or model == 'resnet110_v2':\n mc = resnet_model.ResnetCifar10Model(model, (18, 18, 18))\n elif model == 'densenet40_k12':\n mc = densenet_model.DensenetCifar10Model(model, (12, 12, 12), 12)\n elif model == 'densenet100_k12':\n mc = densenet_model.DensenetCifar10Model(model, (32, 32, 32), 12)\n elif model == 'densenet100_k24':\n mc = densenet_model.DensenetCifar10Model(model, (32, 32, 32), 24)\n else:\n raise KeyError('Invalid model name \\'%s\\' for Cifar10 DataSet.' % model)\n return mc\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/overfeat_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Overfeat model configuration.\n\nReferences:\n OverFeat: Integrated Recognition, Localization and Detection using\n Convolutional Networks\n Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus,\n Yann LeCun, 2014\n http://arxiv.org/abs/1312.6229\n\"\"\"\n\nfrom models import model\n\n\nclass OverfeatModel(model.Model):\n\n def __init__(self):\n super(OverfeatModel, self).__init__('overfeat', 231, 32, 0.005)\n\n def add_inference(self, cnn):\n # Note: VALID requires padding the images by 3 in width and height\n cnn.conv(96, 11, 11, 4, 4, mode='VALID')\n cnn.mpool(2, 2)\n cnn.conv(256, 5, 5, 1, 1, mode='VALID')\n cnn.mpool(2, 2)\n cnn.conv(512, 3, 3)\n cnn.conv(1024, 3, 3)\n cnn.conv(1024, 3, 3)\n cnn.mpool(2, 2)\n cnn.reshape([-1, 1024 * 6 * 6])\n cnn.affine(3072)\n cnn.affine(4096)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/resnet_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Resnet model configuration.\n\nReferences:\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n Deep Residual Learning for Image Recognition\n arXiv:1512.03385 (2015)\n\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n Identity Mappings in Deep Residual Networks\n arXiv:1603.05027 (2016)\n\n Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy,\n Alan L. Yuille\n DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,\n Atrous Convolution, and Fully Connected CRFs\n arXiv:1606.00915 (2016)\n\"\"\"\n\nimport numpy as np\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\nimport datasets\nfrom models import model as model_lib\n\n\ndef bottleneck_block_v1(cnn, depth, depth_bottleneck, stride):\n \"\"\"Bottleneck block with identity short-cut for ResNet v1.\n\n Args:\n cnn: the network to append bottleneck blocks.\n depth: the number of output filters for this bottleneck block.\n depth_bottleneck: the number of bottleneck filters for this block.\n stride: Stride used in the first layer of the bottleneck block.\n \"\"\"\n input_layer = cnn.top_layer\n in_size = cnn.top_size\n name_key = 'resnet_v1'\n name = name_key + str(cnn.counts[name_key])\n cnn.counts[name_key] += 1\n\n with tf.variable_scope(name):\n if depth == in_size:\n if stride == 1:\n shortcut = input_layer\n else:\n shortcut = cnn.apool(\n 1, 1, stride, stride, input_layer=input_layer,\n num_channels_in=in_size)\n else:\n shortcut = cnn.conv(\n depth, 1, 1, stride, stride, activation=None,\n use_batch_norm=True, input_layer=input_layer,\n num_channels_in=in_size, bias=None)\n cnn.conv(depth_bottleneck, 1, 1, stride, stride,\n input_layer=input_layer, num_channels_in=in_size,\n use_batch_norm=True, bias=None)\n cnn.conv(depth_bottleneck, 3, 3, 1, 1, mode='SAME_RESNET',\n use_batch_norm=True, bias=None)\n res = cnn.conv(depth, 1, 1, 1, 1, activation=None,\n use_batch_norm=True, bias=None)\n output = tf.nn.relu(shortcut + res)\n cnn.top_layer = output\n cnn.top_size = depth\n\n\ndef bottleneck_block_v2(cnn, depth, depth_bottleneck, stride):\n \"\"\"Bottleneck block with identity short-cut for ResNet v2.\n\n The main difference from v1 is that a batch norm and relu are done at the\n start of the block, instead of the end. This initial batch norm and relu is\n collectively called a pre-activation.\n\n Args:\n cnn: the network to append bottleneck blocks.\n depth: the number of output filters for this bottleneck block.\n depth_bottleneck: the number of bottleneck filters for this block.\n stride: Stride used in the first layer of the bottleneck block.\n \"\"\"\n input_layer = cnn.top_layer\n in_size = cnn.top_size\n name_key = 'resnet_v2'\n name = name_key + str(cnn.counts[name_key])\n cnn.counts[name_key] += 1\n\n preact = cnn.batch_norm()\n preact = tf.nn.relu(preact)\n with tf.variable_scope(name):\n if depth == in_size:\n if stride == 1:\n shortcut = input_layer\n else:\n shortcut = cnn.apool(\n 1, 1, stride, stride, input_layer=input_layer,\n num_channels_in=in_size)\n else:\n shortcut = cnn.conv(\n depth, 1, 1, stride, stride, activation=None, use_batch_norm=False,\n input_layer=preact, num_channels_in=in_size, bias=None)\n cnn.conv(depth_bottleneck, 1, 1, stride, stride,\n input_layer=preact, num_channels_in=in_size,\n use_batch_norm=True, bias=None)\n cnn.conv(depth_bottleneck, 3, 3, 1, 1, mode='SAME_RESNET',\n use_batch_norm=True, bias=None)\n res = cnn.conv(depth, 1, 1, 1, 1, activation=None,\n use_batch_norm=False, bias=None)\n output = shortcut + res\n cnn.top_layer = output\n cnn.top_size = depth\n\n\ndef bottleneck_block(cnn, depth, depth_bottleneck, stride, pre_activation):\n \"\"\"Bottleneck block with identity short-cut.\n\n Args:\n cnn: the network to append bottleneck blocks.\n depth: the number of output filters for this bottleneck block.\n depth_bottleneck: the number of bottleneck filters for this block.\n stride: Stride used in the first layer of the bottleneck block.\n pre_activation: use pre_activation structure used in v2 or not.\n \"\"\"\n if pre_activation:\n bottleneck_block_v2(cnn, depth, depth_bottleneck, stride)\n else:\n bottleneck_block_v1(cnn, depth, depth_bottleneck, stride)\n\n\ndef residual_block(cnn, depth, stride, pre_activation):\n \"\"\"Residual block with identity short-cut.\n\n Args:\n cnn: the network to append residual blocks.\n depth: the number of output filters for this residual block.\n stride: Stride used in the first layer of the residual block.\n pre_activation: use pre_activation structure or not.\n \"\"\"\n input_layer = cnn.top_layer\n in_size = cnn.top_size\n if in_size != depth:\n # Plan A of shortcut.\n shortcut = cnn.apool(1, 1, stride, stride,\n input_layer=input_layer,\n num_channels_in=in_size)\n padding = (depth - in_size) // 2\n if cnn.channel_pos == 'channels_last':\n shortcut = tf.pad(\n shortcut, [[0, 0], [0, 0], [0, 0], [padding, padding]])\n else:\n shortcut = tf.pad(\n shortcut, [[0, 0], [padding, padding], [0, 0], [0, 0]])\n else:\n shortcut = input_layer\n if pre_activation:\n res = cnn.batch_norm(input_layer)\n res = tf.nn.relu(res)\n else:\n res = input_layer\n cnn.conv(depth, 3, 3, stride, stride,\n input_layer=res, num_channels_in=in_size,\n use_batch_norm=True, bias=None)\n if pre_activation:\n res = cnn.conv(depth, 3, 3, 1, 1, activation=None,\n use_batch_norm=False, bias=None)\n output = shortcut + res\n else:\n res = cnn.conv(depth, 3, 3, 1, 1, activation=None,\n use_batch_norm=True, bias=None)\n output = tf.nn.relu(shortcut + res)\n cnn.top_layer = output\n cnn.top_size = depth\n\n\nclass ResnetModel(model_lib.Model):\n \"\"\"Resnet cnn network configuration.\"\"\"\n\n def __init__(self, model, layer_counts):\n default_batch_sizes = {\n 'resnet50': 64,\n 'resnet101': 32,\n 'resnet152': 32,\n 'resnet50_v2': 64,\n 'resnet101_v2': 32,\n 'resnet152_v2': 32,\n }\n batch_size = default_batch_sizes.get(model, 32)\n super(ResnetModel, self).__init__(model, 224, batch_size, 0.005,\n layer_counts)\n self.pre_activation = 'v2' in model\n\n def add_inference(self, cnn):\n if self.layer_counts is None:\n raise ValueError('Layer counts not specified for %s' % self.get_model())\n cnn.use_batch_norm = True\n cnn.batch_norm_config = {'decay': 0.997, 'epsilon': 1e-5, 'scale': True}\n cnn.conv(64, 7, 7, 2, 2, mode='SAME_RESNET', use_batch_norm=True)\n cnn.mpool(3, 3, 2, 2)\n for _ in xrange(self.layer_counts[0]):\n bottleneck_block(cnn, 256, 64, 1, self.pre_activation)\n for i in xrange(self.layer_counts[1]):\n stride = 2 if i == 0 else 1\n bottleneck_block(cnn, 512, 128, stride, self.pre_activation)\n for i in xrange(self.layer_counts[2]):\n stride = 2 if i == 0 else 1\n bottleneck_block(cnn, 1024, 256, stride, self.pre_activation)\n for i in xrange(self.layer_counts[3]):\n stride = 2 if i == 0 else 1\n bottleneck_block(cnn, 2048, 512, stride, self.pre_activation)\n if self.pre_activation:\n cnn.batch_norm()\n cnn.top_layer = tf.nn.relu(cnn.top_layer)\n cnn.spatial_mean()\n\n def get_learning_rate(self, global_step, batch_size):\n num_batches_per_epoch = (\n float(datasets.IMAGENET_NUM_TRAIN_IMAGES) / batch_size)\n boundaries = [int(num_batches_per_epoch * x) for x in [30, 60]]\n values = [0.1, 0.01, 0.001]\n return tf.train.piecewise_constant(global_step, boundaries, values)\n\n\nclass ResnetCifar10Model(model_lib.Model):\n \"\"\"Resnet cnn network configuration for Cifar 10 dataset.\n\n V1 model architecture follows the one defined in the paper:\n https://arxiv.org/pdf/1512.03385.pdf.\n\n V2 model architecture follows the one defined in the paper:\n https://arxiv.org/pdf/1603.05027.pdf.\n \"\"\"\n\n def __init__(self, model, layer_counts):\n self.pre_activation = 'v2' in model\n super(ResnetCifar10Model, self).__init__(\n model, 32, 128, 0.1, layer_counts)\n\n def add_inference(self, cnn):\n if self.layer_counts is None:\n raise ValueError('Layer counts not specified for %s' % self.get_model())\n\n cnn.use_batch_norm = True\n cnn.batch_norm_config = {'decay': 0.9, 'epsilon': 1e-5, 'scale': True}\n if self.pre_activation:\n cnn.conv(16, 3, 3, 1, 1, use_batch_norm=True)\n else:\n cnn.conv(16, 3, 3, 1, 1, activation=None, use_batch_norm=True)\n for i in xrange(self.layer_counts[0]):\n # reshape to batch_size x 16 x 32 x 32\n residual_block(cnn, 16, 1, self.pre_activation)\n for i in xrange(self.layer_counts[1]):\n # Subsampling is performed at the first convolution with a stride of 2\n stride = 2 if i == 0 else 1\n # reshape to batch_size x 32 x 16 x 16\n residual_block(cnn, 32, stride, self.pre_activation)\n for i in xrange(self.layer_counts[2]):\n stride = 2 if i == 0 else 1\n # reshape to batch_size x 64 x 8 x 8\n residual_block(cnn, 64, stride, self.pre_activation)\n if self.pre_activation:\n cnn.batch_norm()\n cnn.top_layer = tf.nn.relu(cnn.top_layer)\n cnn.spatial_mean()\n\n def get_learning_rate(self, global_step, batch_size):\n num_batches_per_epoch = int(50000 / batch_size)\n boundaries = num_batches_per_epoch * np.array([82, 123, 300],\n dtype=np.int64)\n boundaries = [x for x in boundaries]\n values = [0.1, 0.01, 0.001, 0.0002]\n return tf.train.piecewise_constant(global_step, boundaries, values)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/trivial_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Trivial model configuration.\"\"\"\n\nfrom models import model\n\n\nclass TrivialModel(model.Model):\n \"\"\"Trivial model configuration.\"\"\"\n\n def __init__(self):\n super(TrivialModel, self).__init__('trivial', 224 + 3, 32, 0.005)\n\n def add_inference(self, cnn):\n cnn.reshape([-1, 227 * 227 * 3])\n cnn.affine(1)\n cnn.affine(4096)\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/models/vgg_model.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Vgg model configuration.\n\nIncludes multiple models: vgg11, vgg16, vgg19, corresponding to\n model A, D, and E in Table 1 of [1].\n\nReferences:\n[1] Simonyan, Karen, Andrew Zisserman\n Very Deep Convolutional Networks for Large-Scale Image Recognition\n arXiv:1409.1556 (2014)\n\"\"\"\n\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nfrom models import model\n\n\ndef _construct_vgg(cnn, num_conv_layers):\n \"\"\"Build vgg architecture from blocks.\"\"\"\n assert len(num_conv_layers) == 5\n for _ in xrange(num_conv_layers[0]):\n cnn.conv(64, 3, 3)\n cnn.mpool(2, 2)\n for _ in xrange(num_conv_layers[1]):\n cnn.conv(128, 3, 3)\n cnn.mpool(2, 2)\n for _ in xrange(num_conv_layers[2]):\n cnn.conv(256, 3, 3)\n cnn.mpool(2, 2)\n for _ in xrange(num_conv_layers[3]):\n cnn.conv(512, 3, 3)\n cnn.mpool(2, 2)\n for _ in xrange(num_conv_layers[4]):\n cnn.conv(512, 3, 3)\n cnn.mpool(2, 2)\n cnn.reshape([-1, 512 * 7 * 7])\n cnn.affine(4096)\n cnn.dropout()\n cnn.affine(4096)\n cnn.dropout()\n\n\nclass Vgg11Model(model.Model):\n\n def __init__(self):\n super(Vgg11Model, self).__init__('vgg11', 224, 64, 0.005)\n\n def add_inference(self, cnn):\n _construct_vgg(cnn, [1, 1, 2, 2, 2])\n\n\nclass Vgg16Model(model.Model):\n\n def __init__(self):\n super(Vgg16Model, self).__init__('vgg16', 224, 64, 0.005)\n\n def add_inference(self, cnn):\n _construct_vgg(cnn, [2, 2, 3, 3, 3])\n\n\nclass Vgg19Model(model.Model):\n\n def __init__(self):\n super(Vgg19Model, self).__init__('vgg19', 224, 64, 0.005)\n\n def add_inference(self, cnn):\n _construct_vgg(cnn, [2, 2, 4, 4, 4])\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/preprocessing.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Image pre-processing utilities.\n\"\"\"\nimport math\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom tensorflow.contrib.data.python.ops import batching\nfrom tensorflow.contrib.data.python.ops import interleave_ops\nfrom tensorflow.contrib.image.python.ops import distort_image_ops\nfrom tensorflow.python.layers import utils\nfrom tensorflow.python.ops import data_flow_ops\nfrom tensorflow.python.platform import gfile\nimport cnn_util\n\n\ndef parse_example_proto(example_serialized):\n \"\"\"Parses an Example proto containing a training example of an image.\n\n The output of the build_image_data.py image preprocessing script is a dataset\n containing serialized Example protocol buffers. Each Example proto contains\n the following fields:\n\n image/height: 462\n image/width: 581\n image/colorspace: 'RGB'\n image/channels: 3\n image/class/label: 615\n image/class/synset: 'n03623198'\n image/class/text: 'knee pad'\n image/object/bbox/xmin: 0.1\n image/object/bbox/xmax: 0.9\n image/object/bbox/ymin: 0.2\n image/object/bbox/ymax: 0.6\n image/object/bbox/label: 615\n image/format: 'JPEG'\n image/filename: 'ILSVRC2012_val_00041207.JPEG'\n image/encoded: \n\n Args:\n example_serialized: scalar Tensor tf.string containing a serialized\n Example protocol buffer.\n\n Returns:\n image_buffer: Tensor tf.string containing the contents of a JPEG file.\n label: Tensor tf.int32 containing the label.\n bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]\n where each coordinate is [0, 1) and the coordinates are arranged as\n [ymin, xmin, ymax, xmax].\n text: Tensor tf.string containing the human-readable label.\n \"\"\"\n # Dense features in Example proto.\n feature_map = {\n 'image/encoded': tf.FixedLenFeature([], dtype=tf.string,\n default_value=''),\n 'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,\n default_value=-1),\n 'image/class/text': tf.FixedLenFeature([], dtype=tf.string,\n default_value=''),\n }\n sparse_float32 = tf.VarLenFeature(dtype=tf.float32)\n # Sparse features in Example proto.\n feature_map.update(\n {k: sparse_float32 for k in ['image/object/bbox/xmin',\n 'image/object/bbox/ymin',\n 'image/object/bbox/xmax',\n 'image/object/bbox/ymax']})\n\n features = tf.parse_single_example(example_serialized, feature_map)\n label = tf.cast(features['image/class/label'], dtype=tf.int32)\n\n xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)\n ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)\n xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)\n ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)\n\n # Note that we impose an ordering of (y, x) just to make life difficult.\n bbox = tf.concat([ymin, xmin, ymax, xmax], 0)\n\n # Force the variable number of bounding boxes into the shape\n # [1, num_boxes, coords].\n bbox = tf.expand_dims(bbox, 0)\n bbox = tf.transpose(bbox, [0, 2, 1])\n\n return features['image/encoded'], label, bbox, features['image/class/text']\n\n\ndef get_image_resize_method(resize_method, batch_position=0):\n \"\"\"Get tensorflow resize method.\n\n If method is 'round_robin', return different methods based on batch position\n in a round-robin fashion. NOTE: If the batch size is not a multiple of the\n number of methods, then the distribution of methods will not be uniform.\n\n Args:\n resize_method: (string) nearest, bilinear, bicubic, area, or round_robin.\n batch_position: position of the image in a batch. NOTE: this argument can\n be an integer or a tensor\n Returns:\n one of resize type defined in tf.image.ResizeMethod.\n \"\"\"\n resize_methods_map = {\n 'nearest': tf.image.ResizeMethod.NEAREST_NEIGHBOR,\n 'bilinear': tf.image.ResizeMethod.BILINEAR,\n 'bicubic': tf.image.ResizeMethod.BICUBIC,\n 'area': tf.image.ResizeMethod.AREA\n }\n\n if resize_method != 'round_robin':\n return resize_methods_map[resize_method]\n\n # return a resize method based on batch position in a round-robin fashion.\n resize_methods = resize_methods_map.values()\n def lookup(index):\n return resize_methods[index]\n\n def resize_method_0():\n return utils.smart_cond(batch_position % len(resize_methods) == 0,\n lambda: lookup(0), resize_method_1)\n\n def resize_method_1():\n return utils.smart_cond(batch_position % len(resize_methods) == 1,\n lambda: lookup(1), resize_method_2)\n\n def resize_method_2():\n return utils.smart_cond(batch_position % len(resize_methods) == 2,\n lambda: lookup(2), lambda: lookup(3))\n\n # NOTE(jsimsa): Unfortunately, we cannot use a single recursive function here\n # because TF would not be able to construct a finite graph.\n\n return resize_method_0()\n\n\ndef decode_jpeg(image_buffer, scope=None): # , dtype=tf.float32):\n \"\"\"Decode a JPEG string into one 3-D float image Tensor.\n\n Args:\n image_buffer: scalar string Tensor.\n scope: Optional scope for op_scope.\n Returns:\n 3-D float Tensor with values ranging from [0, 1).\n \"\"\"\n # with tf.op_scope([image_buffer], scope, 'decode_jpeg'):\n # with tf.name_scope(scope, 'decode_jpeg', [image_buffer]):\n with tf.name_scope(scope or 'decode_jpeg'):\n # Decode the string as an RGB JPEG.\n # Note that the resulting image contains an unknown height and width\n # that is set dynamically by decode_jpeg. In other words, the height\n # and width of image is unknown at compile-time.\n image = tf.image.decode_jpeg(image_buffer, channels=3,\n fancy_upscaling=False,\n dct_method='INTEGER_FAST')\n\n # image = tf.Print(image, [tf.shape(image)], 'Image shape: ')\n\n return image\n\n\ndef eval_image(image,\n height,\n width,\n batch_position,\n resize_method,\n summary_verbosity=0):\n \"\"\"Get the image for model evaluation.\n\n We preprocess the image simiarly to Slim, see\n https://github.com/tensorflow/models/blob/master/slim/preprocessing/vgg_preprocessing.py\n Validation images do not have bounding boxes, so to crop the image, we first\n resize the image such that the aspect ratio is maintained and the resized\n height and width are both at least 1.15 times `height` and `width`\n respectively. Then, we do a central crop to size (`height`, `width`).\n\n TODO(b/64579165): Determine if we should use different evaluation\n prepossessing steps.\n\n Args:\n image: 3-D float Tensor representing the image.\n height: The height of the image that will be returned.\n width: The width of the image that will be returned.\n batch_position: position of the image in a batch, which affects how images\n are distorted and resized. NOTE: this argument can be an integer or a\n tensor\n resize_method: one of the strings 'round_robin', 'nearest', 'bilinear',\n 'bicubic', or 'area'.\n summary_verbosity: Verbosity level for summary ops. Pass 0 to disable both\n summaries and checkpoints.\n Returns:\n An image of size (output_height, output_width, 3) that is resized and\n cropped as described above.\n \"\"\"\n # TODO(reedwm): Currently we resize then crop. Investigate if it's faster to\n # crop then resize.\n with tf.name_scope('eval_image'):\n if summary_verbosity >= 3:\n tf.summary.image(\n 'original_image', tf.expand_dims(image, 0))\n\n shape = tf.shape(image)\n image_height = shape[0]\n image_width = shape[1]\n image_height_float = tf.cast(image_height, tf.float32)\n image_width_float = tf.cast(image_width, tf.float32)\n\n scale_factor = 1.15\n\n # Compute resize_height and resize_width to be the minimum values such that\n # 1. The aspect ratio is maintained (i.e. resize_height / resize_width is\n # image_height / image_width), and\n # 2. resize_height >= height * `scale_factor`, and\n # 3. resize_width >= width * `scale_factor`\n max_ratio = tf.maximum(height / image_height_float,\n width / image_width_float)\n resize_height = tf.cast(image_height_float * max_ratio * scale_factor,\n tf.int32)\n resize_width = tf.cast(image_width_float * max_ratio * scale_factor,\n tf.int32)\n\n # Resize the image to shape (`resize_height`, `resize_width`)\n image_resize_method = get_image_resize_method(resize_method, batch_position)\n distorted_image = tf.image.resize_images(image,\n [resize_height, resize_width],\n image_resize_method,\n align_corners=False)\n\n # Do a central crop of the image to size (height, width).\n total_crop_height = (resize_height - height)\n crop_top = total_crop_height // 2\n total_crop_width = (resize_width - width)\n crop_left = total_crop_width // 2\n distorted_image = tf.slice(distorted_image, [crop_top, crop_left, 0],\n [height, width, 3])\n\n distorted_image.set_shape([height, width, 3])\n if summary_verbosity >= 3:\n tf.summary.image(\n 'cropped_resized_image', tf.expand_dims(distorted_image, 0))\n image = distorted_image\n return image\n\n\ndef train_image(image_buffer,\n height,\n width,\n bbox,\n batch_position,\n resize_method,\n distortions,\n scope=None,\n summary_verbosity=0,\n distort_color_in_yiq=False,\n fuse_decode_and_crop=False):\n \"\"\"Distort one image for training a network.\n\n Distorting images provides a useful technique for augmenting the data\n set during training in order to make the network invariant to aspects\n of the image that do not effect the label.\n\n Args:\n image_buffer: scalar string Tensor representing the raw JPEG image buffer.\n height: integer\n width: integer\n bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]\n where each coordinate is [0, 1) and the coordinates are arranged\n as [ymin, xmin, ymax, xmax].\n batch_position: position of the image in a batch, which affects how images\n are distorted and resized. NOTE: this argument can be an integer or a\n tensor\n resize_method: round_robin, nearest, bilinear, bicubic, or area.\n distortions: If true, apply full distortions for image colors.\n scope: Optional scope for op_scope.\n summary_verbosity: Verbosity level for summary ops. Pass 0 to disable both\n summaries and checkpoints.\n distort_color_in_yiq: distort color of input images in YIQ space.\n fuse_decode_and_crop: fuse the decode/crop operation.\n Returns:\n 3-D float Tensor of distorted image used for training.\n \"\"\"\n # with tf.op_scope([image, height, width, bbox], scope, 'distort_image'):\n # with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):\n with tf.name_scope(scope or 'distort_image'):\n # A large fraction of image datasets contain a human-annotated bounding box\n # delineating the region of the image containing the object of interest. We\n # choose to create a new bounding box for the object which is a randomly\n # distorted version of the human-annotated bounding box that obeys an\n # allowed range of aspect ratios, sizes and overlap with the human-annotated\n # bounding box. If no box is supplied, then we assume the bounding box is\n # the entire image.\n sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(\n tf.image.extract_jpeg_shape(image_buffer),\n bounding_boxes=bbox,\n min_object_covered=0.1,\n aspect_ratio_range=[0.75, 1.33],\n area_range=[0.05, 1.0],\n max_attempts=100,\n use_image_if_no_bounding_boxes=True)\n bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box\n if summary_verbosity >= 3:\n image = tf.image.decode_jpeg(image_buffer, channels=3,\n dct_method='INTEGER_FAST')\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n image_with_distorted_box = tf.image.draw_bounding_boxes(\n tf.expand_dims(image, 0), distort_bbox)\n tf.summary.image(\n 'images_with_distorted_bounding_box',\n image_with_distorted_box)\n\n # Crop the image to the specified bounding box.\n if fuse_decode_and_crop:\n offset_y, offset_x, _ = tf.unstack(bbox_begin)\n target_height, target_width, _ = tf.unstack(bbox_size)\n crop_window = tf.stack([offset_y, offset_x, target_height, target_width])\n image = tf.image.decode_and_crop_jpeg(\n image_buffer, crop_window, channels=3)\n else:\n image = tf.image.decode_jpeg(image_buffer, channels=3,\n dct_method='INTEGER_FAST')\n image = tf.slice(image, bbox_begin, bbox_size)\n\n if distortions:\n # After this point, all image pixels reside in [0,1]. Before, they were\n # uint8s in the range [0, 255].\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n\n # This resizing operation may distort the images because the aspect\n # ratio is not respected.\n image_resize_method = get_image_resize_method(resize_method, batch_position)\n if cnn_util.tensorflow_version() >= 11:\n distorted_image = tf.image.resize_images(\n image, [height, width],\n image_resize_method,\n align_corners=False)\n else:\n distorted_image = tf.image.resize_images(\n image,\n height,\n width,\n image_resize_method,\n align_corners=False)\n # Restore the shape since the dynamic slice based upon the bbox_size loses\n # the third dimension.\n distorted_image.set_shape([height, width, 3])\n if summary_verbosity >= 3:\n tf.summary.image(\n 'cropped_resized_image',\n tf.expand_dims(distorted_image, 0))\n\n # Randomly flip the image horizontally.\n distorted_image = tf.image.random_flip_left_right(distorted_image)\n\n if distortions:\n # Randomly distort the colors.\n distorted_image = distort_color(distorted_image, batch_position,\n distort_color_in_yiq=distort_color_in_yiq)\n\n # Note: This ensures the scaling matches the output of eval_image\n distorted_image *= 255\n\n if summary_verbosity >= 3:\n tf.summary.image(\n 'final_distorted_image',\n tf.expand_dims(distorted_image, 0))\n return distorted_image\n\n\ndef distort_color(image, batch_position=0, distort_color_in_yiq=False,\n scope=None):\n \"\"\"Distort the color of the image.\n\n Each color distortion is non-commutative and thus ordering of the color ops\n matters. Ideally we would randomly permute the ordering of the color ops.\n Rather then adding that level of complication, we select a distinct ordering\n of color ops based on the position of the image in a batch.\n\n Args:\n image: float32 Tensor containing single image. Tensor values should be in\n range [0, 1].\n batch_position: the position of the image in a batch. NOTE: this argument\n can be an integer or a tensor\n distort_color_in_yiq: distort color of input images in YIQ space.\n scope: Optional scope for op_scope.\n Returns:\n color-distorted image\n \"\"\"\n with tf.name_scope(scope or 'distort_color'):\n\n def distort_fn_0(image=image):\n \"\"\"Variant 0 of distort function.\"\"\"\n image = tf.image.random_brightness(image, max_delta=32. / 255.)\n if distort_color_in_yiq:\n image = distort_image_ops.random_hsv_in_yiq(\n image, lower_saturation=0.5, upper_saturation=1.5,\n max_delta_hue=0.2 * math.pi)\n else:\n image = tf.image.random_saturation(image, lower=0.5, upper=1.5)\n image = tf.image.random_hue(image, max_delta=0.2)\n image = tf.image.random_contrast(image, lower=0.5, upper=1.5)\n return image\n\n def distort_fn_1(image=image):\n \"\"\"Variant 1 of distort function.\"\"\"\n image = tf.image.random_brightness(image, max_delta=32. / 255.)\n image = tf.image.random_contrast(image, lower=0.5, upper=1.5)\n if distort_color_in_yiq:\n image = distort_image_ops.random_hsv_in_yiq(\n image, lower_saturation=0.5, upper_saturation=1.5,\n max_delta_hue=0.2 * math.pi)\n else:\n image = tf.image.random_saturation(image, lower=0.5, upper=1.5)\n image = tf.image.random_hue(image, max_delta=0.2)\n return image\n\n image = utils.smart_cond(batch_position % 2 == 0, distort_fn_0,\n distort_fn_1)\n # The random_* ops do not necessarily clamp.\n image = tf.clip_by_value(image, 0.0, 1.0)\n return image\n\n\nclass RecordInputImagePreprocessor(object):\n \"\"\"Preprocessor for images with RecordInput format.\"\"\"\n\n def __init__(self,\n height,\n width,\n batch_size,\n num_splits,\n dtype,\n train,\n distortions,\n resize_method,\n shift_ratio,\n summary_verbosity,\n distort_color_in_yiq,\n fuse_decode_and_crop):\n self.height = height\n self.width = width\n self.batch_size = batch_size\n self.num_splits = num_splits\n self.dtype = dtype\n self.train = train\n self.resize_method = resize_method\n self.shift_ratio = shift_ratio\n self.distortions = distortions\n self.distort_color_in_yiq = distort_color_in_yiq\n self.fuse_decode_and_crop = fuse_decode_and_crop\n if self.batch_size % self.num_splits != 0:\n raise ValueError(\n ('batch_size must be a multiple of num_splits: '\n 'batch_size %d, num_splits: %d') %\n (self.batch_size, self.num_splits))\n self.batch_size_per_split = self.batch_size // self.num_splits\n self.summary_verbosity = summary_verbosity\n\n def preprocess(self, image_buffer, bbox, batch_position):\n \"\"\"Preprocessing image_buffer as a function of its batch position.\"\"\"\n if self.train:\n image = train_image(image_buffer, self.height, self.width, bbox,\n batch_position, self.resize_method, self.distortions,\n None, summary_verbosity=self.summary_verbosity,\n distort_color_in_yiq=self.distort_color_in_yiq,\n fuse_decode_and_crop=self.fuse_decode_and_crop)\n else:\n image = tf.image.decode_jpeg(\n image_buffer, channels=3, dct_method='INTEGER_FAST')\n image = eval_image(image, self.height, self.width, batch_position,\n self.resize_method,\n summary_verbosity=self.summary_verbosity)\n # Note: image is now float32 [height,width,3] with range [0, 255]\n\n # image = tf.cast(image, tf.uint8) # HACK TESTING\n\n return image\n\n def parse_and_preprocess(self, value, batch_position):\n image_buffer, label_index, bbox, _ = parse_example_proto(value)\n image = self.preprocess(image_buffer, bbox, batch_position)\n return (label_index, image)\n\n def minibatch(self, dataset, subset, use_datasets, cache_data,\n shift_ratio=-1):\n if shift_ratio < 0:\n shift_ratio = self.shift_ratio\n with tf.name_scope('batch_processing'):\n # Build final results per split.\n images = [[] for _ in range(self.num_splits)]\n labels = [[] for _ in range(self.num_splits)]\n if use_datasets:\n glob_pattern = dataset.tf_record_pattern(subset)\n file_names = gfile.Glob(glob_pattern)\n if not file_names:\n raise ValueError('Found no files in --data_dir matching: {}'\n .format(glob_pattern))\n ds = tf.data.TFRecordDataset.list_files(file_names)\n ds = ds.apply(\n interleave_ops.parallel_interleave(\n tf.data.TFRecordDataset, cycle_length=10))\n if cache_data:\n ds = ds.take(1).cache().repeat()\n counter = tf.data.Dataset.range(self.batch_size)\n counter = counter.repeat()\n ds = tf.data.Dataset.zip((ds, counter))\n ds = ds.prefetch(buffer_size=self.batch_size)\n ds = ds.shuffle(buffer_size=10000)\n ds = ds.repeat()\n ds = ds.apply(\n batching.map_and_batch(\n map_func=self.parse_and_preprocess,\n batch_size=self.batch_size_per_split,\n num_parallel_batches=self.num_splits))\n ds = ds.prefetch(buffer_size=self.num_splits)\n ds_iterator = ds.make_one_shot_iterator()\n for d in xrange(self.num_splits):\n labels[d], images[d] = ds_iterator.get_next()\n\n else:\n record_input = data_flow_ops.RecordInput(\n file_pattern=dataset.tf_record_pattern(subset),\n seed=301,\n parallelism=64,\n buffer_size=10000,\n batch_size=self.batch_size,\n shift_ratio=shift_ratio,\n name='record_input')\n records = record_input.get_yield_op()\n records = tf.split(records, self.batch_size, 0)\n records = [tf.reshape(record, []) for record in records]\n for idx in xrange(self.batch_size):\n value = records[idx]\n (label, image) = self.parse_and_preprocess(value, idx)\n split_index = idx % self.num_splits\n labels[split_index].append(label)\n images[split_index].append(image)\n\n for split_index in xrange(self.num_splits):\n if not use_datasets:\n images[split_index] = tf.parallel_stack(images[split_index])\n labels[split_index] = tf.concat(labels[split_index], 0)\n images[split_index] = tf.cast(images[split_index], self.dtype)\n depth = 3\n images[split_index] = tf.reshape(\n images[split_index],\n shape=[self.batch_size_per_split, self.height, self.width, depth])\n labels[split_index] = tf.reshape(labels[split_index],\n [self.batch_size_per_split])\n return images, labels\n\n\nclass Cifar10ImagePreprocessor(object):\n \"\"\"Preprocessor for Cifar10 input images.\"\"\"\n\n def __init__(self,\n height,\n width,\n batch_size,\n num_splits,\n dtype,\n train,\n distortions,\n resize_method,\n shift_ratio,\n summary_verbosity=0,\n distort_color_in_yiq=False,\n fuse_decode_and_crop=False):\n # Process images of this size. Depending on the model configuration, the\n # size of the input layer might differ from the original size of 32 x 32.\n self.height = height or 32\n self.width = width or 32\n self.depth = 3\n self.batch_size = batch_size\n self.num_splits = num_splits\n self.dtype = dtype\n self.train = train\n self.distortions = distortions\n self.shift_ratio = shift_ratio\n del distort_color_in_yiq\n del fuse_decode_and_crop\n del resize_method\n del shift_ratio # unused, because a RecordInput is not used\n if self.batch_size % self.num_splits != 0:\n raise ValueError(\n ('batch_size must be a multiple of num_splits: '\n 'batch_size %d, num_splits: %d') %\n (self.batch_size, self.num_splits))\n self.batch_size_per_split = self.batch_size // self.num_splits\n self.summary_verbosity = summary_verbosity\n\n def _distort_image(self, image):\n \"\"\"Distort one image for training a network.\n\n Adopted the standard data augmentation scheme that is widely used for\n this dataset: the images are first zero-padded with 4 pixels on each side,\n then randomly cropped to again produce distorted images; half of the images\n are then horizontally mirrored.\n\n Args:\n image: input image.\n Returns:\n distored image.\n \"\"\"\n image = tf.image.resize_image_with_crop_or_pad(\n image, self.height + 8, self.width + 8)\n distorted_image = tf.random_crop(image,\n [self.height, self.width, self.depth])\n # Randomly flip the image horizontally.\n distorted_image = tf.image.random_flip_left_right(distorted_image)\n if self.summary_verbosity >= 3:\n tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))\n return distorted_image\n\n def _eval_image(self, image):\n \"\"\"Get the image for model evaluation.\"\"\"\n distorted_image = tf.image.resize_image_with_crop_or_pad(\n image, self.width, self.height)\n if self.summary_verbosity >= 3:\n tf.summary.image('cropped.image', tf.expand_dims(distorted_image, 0))\n return distorted_image\n\n def preprocess(self, raw_image):\n \"\"\"Preprocessing raw image.\"\"\"\n if self.summary_verbosity >= 3:\n tf.summary.image('raw.image', tf.expand_dims(raw_image, 0))\n if self.train and self.distortions:\n image = self._distort_image(raw_image)\n else:\n image = self._eval_image(raw_image)\n return image\n\n def minibatch(self, dataset, subset, use_datasets, cache_data,\n shift_ratio=-1):\n # TODO(jsimsa): Implement datasets code path\n del use_datasets, cache_data, shift_ratio\n with tf.name_scope('batch_processing'):\n all_images, all_labels = dataset.read_data_files(subset)\n all_images = tf.constant(all_images)\n all_labels = tf.constant(all_labels)\n input_image, input_label = tf.train.slice_input_producer(\n [all_images, all_labels])\n input_image = tf.cast(input_image, self.dtype)\n input_label = tf.cast(input_label, tf.int32)\n # Ensure that the random shuffling has good mixing properties.\n min_fraction_of_examples_in_queue = 0.4\n min_queue_examples = int(dataset.num_examples_per_epoch(subset) *\n min_fraction_of_examples_in_queue)\n raw_images, raw_labels = tf.train.shuffle_batch(\n [input_image, input_label], batch_size=self.batch_size,\n capacity=min_queue_examples + 3 * self.batch_size,\n min_after_dequeue=min_queue_examples)\n\n images = [[] for i in range(self.num_splits)]\n labels = [[] for i in range(self.num_splits)]\n\n # Create a list of size batch_size, each containing one image of the\n # batch. Without the unstack call, raw_images[i] would still access the\n # same image via a strided_slice op, but would be slower.\n raw_images = tf.unstack(raw_images, axis=0)\n raw_labels = tf.unstack(raw_labels, axis=0)\n for i in xrange(self.batch_size):\n split_index = i % self.num_splits\n # The raw image read from data has the format [depth, height, width]\n # reshape to the format returned by minibatch.\n raw_image = tf.reshape(raw_images[i],\n [dataset.depth, dataset.height, dataset.width])\n raw_image = tf.transpose(raw_image, [1, 2, 0])\n image = self.preprocess(raw_image)\n images[split_index].append(image)\n\n labels[split_index].append(raw_labels[i])\n\n for split_index in xrange(self.num_splits):\n images[split_index] = tf.parallel_stack(images[split_index])\n labels[split_index] = tf.parallel_stack(labels[split_index])\n return images, labels\n\n\nclass SyntheticImagePreprocessor(object):\n \"\"\"Preprocessor used for images and labels.\"\"\"\n\n def __init__(self, height, width, batch_size, num_splits,\n dtype, train, distortions, resize_method, shift_ratio,\n summary_verbosity, distort_color_in_yiq=False,\n fuse_decode_and_crop=False):\n del train, distortions, resize_method, summary_verbosity\n del distort_color_in_yiq\n del fuse_decode_and_crop\n self.batch_size = batch_size\n self.height = height\n self.width = width\n self.depth = 3\n self.dtype = dtype\n self.num_splits = num_splits\n self.shift_ratio = shift_ratio\n\n def minibatch(self, dataset, subset, use_datasets, cache_data,\n shift_ratio=-1):\n \"\"\"Get synthetic image batches.\"\"\"\n del subset, use_datasets, cache_data, shift_ratio\n input_shape = [self.batch_size, self.height, self.width, self.depth]\n images = tf.truncated_normal(\n input_shape,\n dtype=self.dtype,\n stddev=1e-1,\n name='synthetic_images')\n labels = tf.random_uniform(\n [self.batch_size],\n minval=0,\n maxval=dataset.num_classes - 1,\n dtype=tf.int32,\n name='synthetic_labels')\n # Note: This results in a H2D copy, but no computation\n # Note: This avoids recomputation of the random values, but still\n # results in a H2D copy.\n images = tf.contrib.framework.local_variable(images, name='images')\n labels = tf.contrib.framework.local_variable(labels, name='labels')\n if self.num_splits == 1:\n images_splits = [images]\n labels_splits = [labels]\n else:\n images_splits = tf.split(images, self.num_splits, 0)\n labels_splits = tf.split(labels, self.num_splits, 0)\n return images_splits, labels_splits\n\n\nclass TestImagePreprocessor(object):\n \"\"\"Preprocessor used for testing.\n\n set_fake_data() sets which images and labels will be output by minibatch(),\n and must be called before minibatch(). This allows tests to easily specify\n a set of images to use for training, without having to create any files.\n\n Queue runners must be started for this preprocessor to work.\n \"\"\"\n\n def __init__(self,\n height,\n width,\n batch_size,\n num_splits,\n dtype,\n train=None,\n distortions=None,\n resize_method=None,\n shift_ratio=0,\n summary_verbosity=0,\n distort_color_in_yiq=False,\n fuse_decode_and_crop=False):\n del height, width, train, distortions, resize_method\n del summary_verbosity, fuse_decode_and_crop, distort_color_in_yiq\n self.batch_size = batch_size\n self.num_splits = num_splits\n self.dtype = dtype\n self.expected_subset = None\n self.shift_ratio = shift_ratio\n\n def set_fake_data(self, fake_images, fake_labels):\n assert len(fake_images.shape) == 4\n assert len(fake_labels.shape) == 1\n assert fake_images.shape[0] == fake_labels.shape[0]\n assert fake_images.shape[0] % self.batch_size == 0\n self.fake_images = fake_images\n self.fake_labels = fake_labels\n\n def minibatch(self, dataset, subset, use_datasets, cache_data,\n shift_ratio=-1):\n del dataset, use_datasets, cache_data, shift_ratio\n if (not hasattr(self, 'fake_images') or\n not hasattr(self, 'fake_labels')):\n raise ValueError('Must call set_fake_data() before calling minibatch '\n 'on TestImagePreprocessor')\n if self.expected_subset is not None:\n assert subset == self.expected_subset\n\n with tf.name_scope('batch_processing'):\n image_slice, label_slice = tf.train.slice_input_producer(\n [self.fake_images, self.fake_labels],\n shuffle=False,\n name='image_slice')\n raw_images, raw_labels = tf.train.batch(\n [image_slice, label_slice], batch_size=self.batch_size,\n name='image_batch')\n images = [[] for _ in range(self.num_splits)]\n labels = [[] for _ in range(self.num_splits)]\n for i in xrange(self.batch_size):\n split_index = i % self.num_splits\n raw_image = tf.cast(raw_images[i], self.dtype)\n images[split_index].append(raw_image)\n labels[split_index].append(raw_labels[i])\n for split_index in xrange(self.num_splits):\n images[split_index] = tf.parallel_stack(images[split_index])\n labels[split_index] = tf.parallel_stack(labels[split_index])\n\n return images, labels\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Benchmark script for TensorFlow.\n\nSee the README for more information.\n\"\"\"\n\nfrom __future__ import print_function\n\n\nimport tensorflow as tf\n\nimport benchmark_cnn\nimport cnn_util\nfrom cnn_util import log_fn\n\nbenchmark_cnn.define_flags()\n\n\ndef main(extra_flags):\n # extra_flags is a list of command line arguments, excluding those defined\n # in tf.flags.FLAGS. extra_flags[0] is always the program name. It is an error\n # to supply flags not defined with tf.flags.FLAGS, so we raise an ValueError\n # in that case.\n assert len(extra_flags) >= 1\n if len(extra_flags) > 1:\n raise ValueError('Received unknown flags: %s' % extra_flags[1:])\n\n params = benchmark_cnn.make_params_from_flags()\n benchmark_cnn.setup(params)\n bench = benchmark_cnn.BenchmarkCNN(params)\n\n tfversion = cnn_util.tensorflow_version_tuple()\n log_fn('TensorFlow: %i.%i' % (tfversion[0], tfversion[1]))\n\n bench.print_info()\n bench.run()\n\n\nif __name__ == '__main__':\n tf.app.run()\n" }, { "path": "cluster/benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Defines VariableMgr and subclasses used to manage variables.\n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport collections as pycoll\nimport operator\nimport re\n\nimport tensorflow as tf\n\nfrom tensorflow.contrib import nccl\nfrom tensorflow.contrib.all_reduce.python import all_reduce\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import data_flow_ops\n\nPS_SHADOW_VAR_PREFIX = 'ps_var'\n\n\n# To be used with custom_getter on tf.get_variable.\nclass OverrideCachingDevice(object):\n \"\"\"Variable getter which caches variables on the least loaded device.\n\n Variables smaller than a certain threshold are cached on a single specific\n device, as specified in the constructor. All other variables are load balanced\n across a pool of devices, by caching each variable on the least loaded device.\n \"\"\"\n\n def __init__(self, devices, device_for_small_variables,\n small_variable_size_threshold):\n self.devices = devices\n self.sizes = [0] * len(self.devices)\n self.device_for_small_variables = device_for_small_variables\n self.small_variable_size_threshold = small_variable_size_threshold\n\n def __call__(self, getter, *args, **kwargs):\n size = tf.TensorShape(kwargs['shape']).num_elements()\n if size < self.small_variable_size_threshold:\n device_name = self.device_for_small_variables\n else:\n device_index, _ = min(enumerate(\n self.sizes), key=operator.itemgetter(1))\n device_name = self.devices[device_index]\n self.sizes[device_index] += size\n\n kwargs['caching_device'] = device_name\n var = getter(*args, **kwargs)\n return var\n\n\n# To be used with custom_getter on tf.get_variable. Ensures the created variable\n# is in LOCAL_VARIABLES and not GLOBAL_VARIBLES collection.\nclass OverrideToLocalVariableIfNotPsVar(object):\n\n # args and kwargs come from the custom_getter interface for Tensorflow\n # variables, and matches tf.get_variable's signature, with the addition of\n # 'getter' at the beginning.\n def __call__(self, getter, name, *args, **kwargs):\n if name.startswith(PS_SHADOW_VAR_PREFIX):\n return getter(*args, **kwargs)\n\n if 'collections' in kwargs:\n collections = kwargs['collections']\n if not collections:\n collections = set([tf.GraphKeys.GLOBAL_VARIABLES])\n else:\n collections = set(list(collections))\n collections.remove(tf.GraphKeys.GLOBAL_VARIABLES)\n collections.add(tf.GraphKeys.LOCAL_VARIABLES)\n kwargs['collections'] = list(collections)\n return getter(name, *args, **kwargs)\n\n\nclass ParamServerDeviceSetter(object):\n \"\"\"Helper class to assign variables on the least loaded ps-device.\"\"\"\n\n def __init__(self, worker_device, ps_devices):\n \"\"\"Initializer for ParamServerDevicSetter.\n\n Args:\n worker_device: the device to use for computer ops.\n ps_devices: a list of device to use for Variable ops. Each variable is\n assigned to the least loaded device.\n \"\"\"\n self.ps_devices = ps_devices\n self.worker_device = worker_device\n self.ps_sizes = [0] * len(self.ps_devices)\n\n def __call__(self, op):\n if op.device:\n return op.device\n if op.type not in ['Variable', 'VariableV2']:\n return self.worker_device\n\n device_index, _ = min(enumerate(\n self.ps_sizes), key=operator.itemgetter(1))\n device_name = self.ps_devices[device_index]\n var_size = op.outputs[0].get_shape().num_elements()\n self.ps_sizes[device_index] += var_size\n\n return device_name\n\n\nclass VariableMgr(object):\n \"\"\"Abstract superclass for class used by BenchmarkCnn to control variables.\n\n Functions on this class are used to control how variables are created and\n managed, and how gradients are computed and applied.\n \"\"\"\n\n def __init__(self, benchmark_cnn):\n self.benchmark_cnn = benchmark_cnn\n self.staging_delta_ops = []\n\n def each_tower_has_variables(self):\n \"\"\"Returns True if each GPU tower of the model has separate variables.\"\"\"\n assert False, 'Must be implemented in subclass'\n\n def supports_staged_vars(self):\n \"\"\"Whether staged variable management is supported.\"\"\"\n return False\n\n def create_outer_variable_scope(self, device_num):\n \"\"\"Create the tf.variable_scope around all model graph operations.\"\"\"\n del device_num # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def preprocess_device_grads(self, device_grads):\n \"\"\"Preprocess the device gradients prior to applying them.\n\n Args:\n device_grads: List of lists of (gradient, variable) tuples.\n device_grads[t][g] = (gradient, variable), where t is the index of the\n tower and g is the index of the gradient-variable pair.\n\n Returns: a tuple of (apply_gradients_devices, gradient_state).\n gradient_state is an opaque structure that should be passed to\n get_gradients_to_apply() and append_apply_gradients_ops() (in that order).\n apply_gradients_devices is a list of devices where the gradients will be\n applied with get_gradients_to_apply() and append_apply_gradients_ops().\n \"\"\"\n del device_grads # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n \"\"\"Returns the [(gradient, variable)] list to apply for device_num.\n\n Args:\n device_num: indexes into apply_gradients_devices, which was returned by an\n earlier call to preprocess_device_grads.\n gradient_state: from previous call to apply_gradients_devices.\n \"\"\"\n del device_num, gradient_state # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def append_apply_gradients_ops(\n self, gradient_state, opt, grads, training_ops):\n \"\"\"Adds training ops for grads to 'training_ops'.\n\n Args:\n gradient_state: from previous call to apply_gradients_devices.\n opt: the underlying optimizer\n grads: [(grad, var)] to apply\n training_ops: list to which to add ops\n \"\"\"\n del gradient_state # unused by this implementation\n apply_gradients_op = opt.apply_gradients(grads)\n training_ops.append(apply_gradients_op)\n\n def get_post_init_ops(self):\n \"\"\"Returns ops that should run post-initialization.\"\"\"\n return []\n\n def get_devices(self):\n \"\"\"Returns devices to use for computation; includes replica selection.\"\"\"\n assert False, 'Must be implemented in subclass'\n\n def savable_variables(self):\n \"\"\"Returns a list/dict of savable variables to pass to tf.train.Saver.\"\"\"\n return tf.global_variables()\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n \"\"\"Return the set of trainable variables on device.\n\n Args:\n rel_device_num: local worker device index.\n abs_device_num: global graph device index.\n writable: whether to get a reference to the underlying variable.\n\n Returns:\n The set of trainable vairalbes on the specified device.\n \"\"\"\n del rel_device_num, writable\n if self.each_tower_has_variables():\n params = [\n v for v in tf.trainable_variables()\n if v.name.startswith('v%s/' % abs_device_num)\n ]\n else:\n params = tf.trainable_variables()\n return params\n\n\nclass VariableMgrIndependent(VariableMgr):\n \"\"\"VariableMgr that implements the --independent mode for local jobs.\n\n Each GPU has its own copy of the variables, and gradients are\n not shared between towers. This can be used to check\n performance when no data is moved between GPUs.\n \"\"\"\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n return (self.benchmark_cnn.devices, device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n return device_grads[device_num]\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrLocalFetchFromPS(VariableMgr):\n \"\"\"VariableMgr that implements the --parameter_server mode for local jobs.\n\n Variables are stored on a parameter server. For each step, each tower gets\n a copy of the variables from the parameter server, and sends its gradients\n to the param server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return False\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v', reuse=bool(device_num))\n\n def preprocess_device_grads(self, device_grads):\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n assert device_num == 0\n device_grads = gradient_state\n return aggregate_gradients_using_copy_with_variable_colocation(\n device_grads, use_mean=True)\n\n def get_devices(self):\n raw_devices = self.benchmark_cnn.raw_devices\n if self.benchmark_cnn.local_parameter_device_flag == 'gpu':\n return [ParamServerDeviceSetter(d, raw_devices) for d in raw_devices]\n else:\n return [tf.train.replica_device_setter(\n worker_device=d, ps_device=self.benchmark_cnn.param_server_device,\n ps_tasks=1) for d in raw_devices]\n\n\nclass StagedModelVariable(object):\n \"\"\"Staging variable wrapper that decouples reads and updates.\n\n This class represents a variable through a staging buffer. Reads from this\n variable directly gets from the staging buffer. Updates are stacked into\n another staging buffer, and will be processed later.\n \"\"\"\n\n def __init__(self, real_var, var_stage_get, variable_mgr):\n \"\"\"Initializer for the model variables through a staging buffer.\n\n Args:\n real_var: the underlying real variable.\n var_stage_get: the read op from the staging buffer.\n variable_mgr: the parent variable-manager.\n \"\"\"\n self.real_var = real_var\n self.var_stage_get = var_stage_get\n self.variable_mgr = variable_mgr\n\n def _value(self):\n \"\"\"The read access of this variable. The content from the staging buffer.\"\"\"\n return self.var_stage_get\n\n def _ref(self):\n \"\"\"Return the underlying variable ref, required by tf.colocate_with.\"\"\"\n return self.real_var._ref() # pylint: disable=protected-access\n\n def read_value(self):\n \"\"\"Mimics tf.Variable.read_value().\"\"\"\n return tf.identity(self.var_stage_get, name='read')\n\n @property\n def dtype(self):\n \"\"\"Return the non-reference dtype.\"\"\"\n return self.var_stage_get.dtype\n\n def assign_sub(self, delta, name=None):\n \"\"\"Mimic the updates to the variable.\n\n Args:\n delta: is pushed into a staging buffer and will be pumped later.\n name: currently ignored; names of ops and the StagingArea are\n computed without using this pass name.\n Returns:\n The actual updates. The colocation constraint will be reapplied.\n \"\"\"\n # This parameter is ignored: the StagingArea only supports setting\n # the shared name, not the names of individual ops it uses.\n del name\n\n # colocate_with(None, True) clears the colocation constraints.\n # Push the delta into a staging buffer.\n with ops.colocate_with(None, True), tf.device(self.var_stage_get.device):\n delta_staging_area = data_flow_ops.StagingArea(\n [self.var_stage_get.dtype], shapes=[self.var_stage_get.shape])\n delta_put_op = delta_staging_area.put([delta])\n self.variable_mgr.staging_delta_ops.append(delta_put_op)\n delta_get_op = delta_staging_area.get()[0]\n # Return the actual updates. The colocation constraint will be reapplied.\n return self.real_var.assign_sub(delta_get_op)\n\n @staticmethod\n # pylint: disable=bad-staticmethod-argument,invalid-name\n def _TensorConversionFunction(self, dtype=None, name=None, as_ref=False):\n \"\"\"Utility function for converting a StagedModelVariable to a Tensor.\"\"\"\n del dtype, name # unused: this function returns the cached ref or value.\n if as_ref:\n return self._ref()\n else:\n return self._value()\n\n\nops.register_tensor_conversion_function(\n StagedModelVariable, StagedModelVariable._TensorConversionFunction) # pylint: disable=protected-access\n\n\nclass StagedVariableGetter(object):\n \"\"\"A variable getter through staging buffers on devices.\n\n Instead of a caching device, this getter tracks where the variable is used.\n And on each device, it goes through a staging buffer.\n \"\"\"\n\n def __init__(self, device_num, devices, cpu_device, variable_mgr):\n \"\"\"Initializer for StagedVariableGetter.\n\n Args:\n device_num: the current device index.\n devices: a list of all the devices to build towers.\n cpu_device: a cpu_device for this replica. If None, no cpu-caching is\n done.\n variable_mgr: the parent variable manager.\n \"\"\"\n self.device_num = device_num\n self.devices = devices\n self.cpu_device = cpu_device\n self.variable_mgr = variable_mgr\n\n def __call__(self, getter, name, *args, **kwargs):\n staging_ops = self.variable_mgr.staging_vars_on_devices[self.device_num]\n if name in staging_ops:\n put_op, get_op = staging_ops[name]\n return get_op\n real_var = getter(name, *args, **kwargs)\n shape = kwargs['shape']\n dtype = kwargs['dtype']\n trainable = kwargs['trainable']\n if self.cpu_device:\n with tf.device(self.cpu_device):\n # This helps copying the weights from the parameter to this server only\n # once.\n if name in self.variable_mgr.staged_vars_on_cpu:\n cpu_var = self.variable_mgr.staged_vars_on_cpu[name]\n else:\n cpu_var = tf.identity(real_var)\n self.variable_mgr.staged_vars_on_cpu[name] = cpu_var\n var_to_stage = cpu_var\n else:\n var_to_stage = tf.identity(real_var) # de-reference the variable.\n\n with tf.device(self.devices[self.device_num]):\n staging_area = data_flow_ops.StagingArea([dtype], shapes=[shape])\n put_op = staging_area.put([var_to_stage])\n get_op = staging_area.get()[0]\n staging_ops[name] = (put_op, get_op)\n if trainable:\n # For trainable variables, they are managed separatedly through\n # apply_gradients.\n return get_op\n else:\n # For other shadow variables, the access is decoupled through a wrapper\n # class.\n return StagedModelVariable(real_var, get_op, self.variable_mgr)\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable):\n \"\"\"Return the set of trainable variables on the specified device.\n\n Args:\n rel_device_num: local worker device index.\n abs_device_num: global graph device index.\n writable: whether the returned variables is writable or read-only.\n\n Returns:\n Return the set of trainable variables on the specified device.\n \"\"\"\n del abs_device_num\n params_refs = tf.trainable_variables()\n if writable:\n return params_refs\n params = []\n for param in params_refs:\n var_name = param.name.split(':')[0]\n _, var_get_op = self.variable_mgr.staging_vars_on_devices[rel_device_num][\n var_name]\n params.append(var_get_op)\n return params\n\n\nclass VariableMgrLocalFetchFromStagedPS(VariableMgrLocalFetchFromPS):\n \"\"\"Implements fetching a local variable through staging buffers.\n \"\"\"\n\n def __init__(self, benchmark_cnn):\n super(VariableMgrLocalFetchFromStagedPS, self).__init__(benchmark_cnn)\n # A data structure to track where the variables are used on each device.\n # Indexed by device_num and var_name, each entry stores the \"put\" and \"get\"\n # ops used for that variable on that device:\n # staging_vars_on_devices[device_num][var_name] == (put_op, get_op)\n self.staging_vars_on_devices = [dict() for _ in\n self.benchmark_cnn.raw_devices]\n\n def supports_staged_vars(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n self._custom_getter = StagedVariableGetter(\n device_num, self.benchmark_cnn.raw_devices, None, self)\n return tf.variable_scope(\n 'v', reuse=bool(device_num), custom_getter=self._custom_getter)\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n return self._custom_getter.trainable_variables_on_device(\n rel_device_num, abs_device_num, writable=writable)\n\n\nAllReduceSpecTuple = pycoll.namedtuple('AllReduceSpecTuple', 'alg shards limit')\n\n\ndef parse_general_int(s):\n \"\"\"Parse integer with power-of-2 suffix eg. 32k.\"\"\"\n mo = re.match(r'(\\d+)([KkMGT]?)$', s)\n if mo:\n i, suffix = mo.group(1, 2)\n v = int(i)\n if suffix:\n if suffix == 'K' or suffix == 'k':\n v *= 1024\n elif suffix == 'M':\n v *= (1024 * 1024)\n elif suffix == 'G':\n v *= (1024 * 1024 * 1024)\n elif suffix == 'T':\n v *= (1024 * 1024 * 1024 * 1024)\n else:\n raise ValueError('invalid integer string %s' % s)\n return v\n else:\n v = int(s)\n return v\n\n\ndef parse_all_reduce_spec(all_reduce_spec):\n \"\"\"Parse all_reduce_spec.\n\n Args:\n all_reduce_spec: a string specifying a combination of all-reduce\n algorithms to apply for gradient reduction.\n\n Returns:\n a list of AllReduceSpecTuple.\n\n Raises:\n ValueError: all_reduce_spec is not well-formed.\n\n An all_reduce_spec has BNF form:\n int ::= positive whole number\n g_int ::= int[KkMGT]?\n alg_spec ::= alg | alg#int\n range_spec ::= alg_spec | alg_spec/alg_spec\n spec ::= range_spec | range_spec:g_int:range_spec\n\n Not all syntactically correct specifications are supported.\n Examples of supported all_reduce_spec strings, with semantics explained:\n\n 'xring' == apply ring all-reduce to all tensors\n 'xring#2' == apply ring all-reduce to all tensors, using two simultaneous\n transfer rings, each operating on 1/2 of each tensor.\n 'nccl' == apply NCCL all-reduce to all tensors (only works within\n a single worker process where all devices are GPUs)\n 'nccl/xring' == apply NCCL all-reduce to all tensors within each worker\n to produce at least one full-reduced (locally) value,\n then apply ring all-reduce to one such value from each\n worker, then apply NCCL broadcast to propagate those globally\n reduced values back to every device within each worker.\n 'pscpu' == Shuffle reduce using worker CPUs as the gather devices: each\n distributed tensor is reduced by copying all instances to\n one of the worker CPUs, computing the reduction there, then\n copying back to each participating device. Tensor reductions\n are assigned to specific CPUs round-robin.\n 'psgpu#4' == Arrange all GPUs across all workers into groups of 4.\n Each distributed tensor is shuffle reduced against one\n such group of 4 GPUs, selected round-robin. That is, each\n tensor is split across 4 shards for the reduction.\n 'pscpu:2k:pscpu#2:64k:xring' == Apply single-shard pscpu to\n tensors of size <= 2048 elements, apply 2-shard pscpu to\n tensors up to size 64k elements, apply xring to larger tensors.\n 'pscpu/pscpu#2' == Use shuffle gather to locally reduce each tensor on\n the worker's CPU, then use 2-shard shuffle to reduce those\n locally reduced tensors across workers (on the worker CPUs), then\n scatter the globally reduced values locally from each worker CPU.\n \"\"\"\n range_parts = all_reduce_spec.split(':') + ['-1']\n if len(range_parts) % 2:\n raise ValueError('all_reduce_spec not well formed: %s' % all_reduce_spec)\n limit = 0\n spec = []\n alg = None\n shards = 1\n for i, range_part in enumerate(range_parts):\n if i % 2 == 1:\n try:\n limit = parse_general_int(range_part)\n spec.append(AllReduceSpecTuple(alg=alg, shards=shards, limit=limit))\n except ValueError:\n raise ValueError('all_reduce_spec (%s) contains non-integer range %s' %\n (all_reduce_spec, range_part))\n else:\n alg = range_part\n alg_parts = range_part.split('#')\n alg = alg_parts[0]\n if len(alg_parts) > 1:\n try:\n shards = int(alg_parts[1])\n except ValueError:\n raise ValueError('all_reduce_spec (%s) contains non-integer '\n 'shards %s' % all_reduce_spec, alg_parts[1])\n else:\n shards = 1\n if alg not in['nccl', 'nccl/xring', 'nccl/rechd', 'nccl/pscpu',\n 'xring', 'pscpu', 'psgpu', 'pscpu/pscpu']:\n raise ValueError('all_reduce_spec (%s) contains invalid alg %s' %\n (all_reduce_spec, alg))\n return spec\n\n\ndef build_all_reduce_device_prefixes(job_name, num_tasks):\n \"\"\"Build list of device prefix names for all_reduce.\n\n Args:\n job_name: 'worker', 'ps' or 'localhost'.\n num_tasks: number of jobs across which device names should be generated.\n\n Returns:\n A list of device name prefix strings. Each element spells out the full\n host name without adding the device.\n e.g. '/job:worker/task:0'\n \"\"\"\n if job_name != 'localhost':\n return ['/job:%s/task:%d' % (job_name, d) for d in range(0, num_tasks)]\n else:\n assert num_tasks == 1\n return ['/job:%s' % job_name]\n\n\ndef group_device_names(devices, group_size):\n \"\"\"Group device names into groups of group_size.\n\n Args:\n devices: list of strings naming devices.\n group_size: int >= 1\n\n Returns:\n list of lists of devices, where each inner list is group_size long,\n and each device appears at least once in an inner list. If\n len(devices) % group_size = 0 then each device will appear\n exactly once.\n\n Raises:\n ValueError: group_size > len(devices)\n \"\"\"\n num_devices = len(devices)\n if group_size > num_devices:\n raise ValueError('only %d devices, but group_size=%d' % (\n num_devices, group_size))\n num_groups = (num_devices // group_size) + (\n 1 if (num_devices % group_size != 0) else 0)\n groups = [[] for i in range(num_groups)]\n for i in range(0, num_groups * group_size):\n groups[i % num_groups].append(devices[i % num_devices])\n return groups\n\n\nclass VariableMgrLocalReplicated(VariableMgr):\n \"\"\"VariableMgr that implements the --replicated mode for local jobs.\n\n Each GPU has its own copy of the variables. To apply gradients,\n either a local all-reduce algorithm is applied or a regular\n cross-device aggregation is used to replicate the combined\n gradients to all towers.\n \"\"\"\n\n def __init__(self, benchmark_cnn, all_reduce_spec):\n super(VariableMgrLocalReplicated, self).__init__(benchmark_cnn)\n if all_reduce_spec:\n spec = parse_all_reduce_spec(all_reduce_spec)\n if len(spec) != 1:\n raise ValueError(\n 'replicated mode does not support hybrid all-reduce strategies')\n self._all_reduce_spec = spec[0]\n else:\n self._all_reduce_spec = None\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n if self._all_reduce_spec:\n aggregated_device_grads = sum_gradients_all_reduce(\n ['/job:localhost'], device_grads, 1,\n self._all_reduce_spec.alg,\n self._all_reduce_spec.shards, self.benchmark_cnn.gpu_indices)\n else:\n agg_grads = aggregate_gradients_using_copy_with_device_selection(\n self.benchmark_cnn, device_grads, use_mean=False)\n aggregated_device_grads = []\n for arr in device_grads:\n aggregated_device_grads.append(\n [(g, v) for (_, v), (g, _) in zip(arr, agg_grads)])\n return (self.benchmark_cnn.devices, aggregated_device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n return device_grads[device_num]\n\n def get_post_init_ops(self):\n # Copy initialized values for variables on GPU 0 to other GPUs.\n global_vars = tf.global_variables()\n var_by_name = dict([(v.name, v) for v in global_vars])\n post_init_ops = []\n for v in global_vars:\n split_name = v.name.split('/')\n # TODO(b/62630508): use more specific prefix than v or v0.\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n continue\n split_name[0] = 'v0'\n copy_from = var_by_name['/'.join(split_name)]\n post_init_ops.append(v.assign(copy_from.read_value()))\n return post_init_ops\n\n def savable_variables(self):\n \"\"\"Return the set of variables used for saving/loading the model.\"\"\"\n params = []\n for v in tf.global_variables():\n split_name = v.name.split('/')\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n params.append(v)\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrDistributedAllReduce(VariableMgr):\n \"\"\"VariableMgr that implements the --distributed_all_reduce mode.\n\n Each GPU has its own copy of the variables. To apply gradients,\n the specified all-reduce algorithm is used to reduce the gradients\n and replicate the final value to all GPUs.\n \"\"\"\n\n def __init__(self, benchmark_cnn, all_reduce_spec, job_name,\n num_workers):\n super(VariableMgrDistributedAllReduce, self).__init__(benchmark_cnn)\n if not all_reduce_spec:\n raise ValueError(\n 'distributed_all_reduce requires a non-empty all_reduce_spec')\n self._all_reduce_spec = parse_all_reduce_spec(all_reduce_spec)\n self._all_reduce_device_prefixes = build_all_reduce_device_prefixes(\n job_name, num_workers)\n self._num_workers = num_workers\n if not self._all_reduce_spec:\n raise ValueError('all_reduce_spec must be specified')\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n \"\"\"Create a scope for the named device.\n\n Args:\n device_num: index of device for variable scope. (Note that\n device_num spans all processes in cluster since a single global\n graph is used.)\n\n Returns:\n the requested variable_scope\n \"\"\"\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n remaining_grads = device_grads\n aggregated_grads = []\n for spec_tuple in self._all_reduce_spec:\n if spec_tuple.limit < 0:\n this_grads = remaining_grads\n remaining_grads = []\n else:\n (this_grads, remaining_grads) = split_grads_by_size(\n spec_tuple.limit, remaining_grads)\n if this_grads:\n range_agg_grads = sum_gradients_all_reduce(\n self._all_reduce_device_prefixes, this_grads, self._num_workers,\n spec_tuple.alg, spec_tuple.shards, self.benchmark_cnn.gpu_indices)\n if not aggregated_grads:\n aggregated_grads = range_agg_grads\n else:\n assert len(aggregated_grads) == len(range_agg_grads)\n for i in range(len(aggregated_grads)):\n aggregated_grads[i] += range_agg_grads[i]\n assert not remaining_grads\n full_device_set = []\n for grads in device_grads:\n g, v = grads[0]\n del v\n full_device_set.append(g.device)\n return (full_device_set, aggregated_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n if device_num >= len(device_grads):\n raise ValueError('device_num %d exceeds length of device_grads (%d)' %\n (device_num, len(device_grads)))\n return device_grads[device_num]\n\n def get_post_init_ops(self):\n \"\"\"Copy initialized values for variables to other devices.\"\"\"\n global_vars = tf.global_variables()\n var_by_name = dict([(v.name, v) for v in global_vars])\n post_init_ops = []\n for v in global_vars:\n split_name = v.name.split('/')\n # TODO(b/62630508): use more specific prefix than v or v0.\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n continue\n split_name[0] = 'v0'\n copy_from = var_by_name['/'.join(split_name)]\n post_init_ops.append(v.assign(copy_from.read_value()))\n return post_init_ops\n\n def savable_variables(self):\n \"\"\"Return the set of variables used for saving/loading the model.\"\"\"\n params = []\n for v in tf.global_variables():\n split_name = v.name.split('/')\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n params.append(v)\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrDistributedFetchFromPS(VariableMgr):\n \"\"\"Implements --variable_update=parameter_server mode for distributed jobs.\n\n Variables are stored on a parameter server. For each step, each tower gets\n a copy of the variables from the parameter server, and sends its gradients\n to the param server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return False\n\n def create_outer_variable_scope(self, device_num):\n if self.benchmark_cnn.local_parameter_device_flag == 'gpu':\n caching_devices = self.benchmark_cnn.raw_devices\n else:\n caching_devices = [self.benchmark_cnn.cpu_device]\n custom_getter = OverrideCachingDevice(\n caching_devices, self.benchmark_cnn.cpu_device, 1024*64)\n return tf.variable_scope('v', reuse=bool(device_num),\n custom_getter=custom_getter)\n\n def preprocess_device_grads(self, device_grads):\n # Returns (gradient_devices, gradient_state)\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n assert device_num == 0\n return aggregate_gradients_using_copy(gradient_state, use_mean=True)\n\n def get_devices(self):\n ps_strategy = tf.contrib.training.GreedyLoadBalancingStrategy(\n len(self.benchmark_cnn.ps_hosts), tf.contrib.training.byte_size_load_fn)\n return [tf.train.replica_device_setter(\n worker_device=d, cluster=self.benchmark_cnn.cluster,\n ps_strategy=ps_strategy)\n for d in self.benchmark_cnn.raw_devices]\n\n\nclass VariableMgrDistributedFetchFromStagedPS(\n VariableMgrDistributedFetchFromPS):\n \"\"\"Extends VariableMgrDistributedFetchFromPS for --staged_vars.\"\"\"\n\n def __init__(self, benchmark_cnn):\n super(VariableMgrDistributedFetchFromStagedPS, self).__init__(benchmark_cnn)\n self.staging_vars_on_devices = [dict() for _ in\n self.benchmark_cnn.raw_devices]\n self.staged_vars_on_cpu = {}\n\n def create_outer_variable_scope(self, device_num):\n self._custom_getter = StagedVariableGetter(\n device_num, self.benchmark_cnn.raw_devices,\n self.benchmark_cnn.cpu_device, self)\n return tf.variable_scope(\n 'v', reuse=bool(device_num), custom_getter=self._custom_getter)\n\n def supports_staged_vars(self):\n return True\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n return self._custom_getter.trainable_variables_on_device(\n rel_device_num, abs_device_num, writable=writable)\n\n\nclass VariableMgrDistributedReplicated(VariableMgr):\n \"\"\"VariableMgr that implements the --distributed_replicated mode.\n\n Each GPU has a copy of the variables, and updates its copy after the\n parameter servers are all updated with the gradients from all servers. Only\n works with cross_replica_sync=true. Unlike 'replicated', does not use nccl\n all-reduce for replicating within a server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope(\n 'v%s' % device_num,\n custom_getter=OverrideToLocalVariableIfNotPsVar())\n\n def preprocess_device_grads(self, device_grads):\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state # From 2nd result of preprocess_device_grads.\n\n avg_grads = aggregate_gradients_using_copy_with_device_selection(\n self.benchmark_cnn, device_grads, use_mean=True)\n\n # Make shadow variable on a parameter server for each original trainable\n # variable.\n for i, (g, v) in enumerate(avg_grads):\n my_name = PS_SHADOW_VAR_PREFIX + '/' + v.name\n if my_name.endswith(':0'): my_name = my_name[:-2]\n new_v = tf.get_variable(my_name, dtype=v.dtype.base_dtype,\n initializer=v.initial_value,\n trainable=True)\n avg_grads[i] = (g, new_v)\n return avg_grads\n\n def append_apply_gradients_ops(self, gradient_state, opt,\n grads, training_ops):\n device_grads = gradient_state # From 2nd result of preprocess_device_grads.\n\n # For each variable, apply the combined gradients for this server on\n # the parameter server, and then wait for all other servers to do\n # this.\n for i, (g, v) in enumerate(grads):\n apply_gradient_op = opt.apply_gradients([(g, v)])\n barrier = self.benchmark_cnn.add_sync_queues_and_barrier(\n 'replicate_variable_%s' % i, [apply_gradient_op])\n with tf.control_dependencies([barrier]):\n with tf.device(self.benchmark_cnn.cpu_device):\n updated_value = v.read_value()\n for my_d in range(len(self.benchmark_cnn.devices)):\n training_ops.append(\n device_grads[my_d][i][1].assign(updated_value))\n\n def _strip_port(self, s):\n if s.endswith(':0'):\n return s[:-2]\n return s\n\n def get_post_init_ops(self):\n # Copy initialized variables for variables on the parameter server\n # to the local copy of the variable.\n\n local_vars = tf.local_variables()\n local_var_by_name = dict(\n [(self._strip_port(v.name), v) for v in local_vars])\n post_init_ops = []\n for v in tf.global_variables():\n if v.name.startswith(PS_SHADOW_VAR_PREFIX + '/v0/'):\n prefix = self._strip_port(\n v.name[len(PS_SHADOW_VAR_PREFIX + '/v0'):])\n for i in range(self.benchmark_cnn.num_gpus):\n name = 'v%s%s' % (i, prefix)\n if name in local_var_by_name:\n copy_to = local_var_by_name[name]\n post_init_ops.append(copy_to.assign(v.read_value()))\n return post_init_ops\n\n def _remove_shadow_var_prefix_if_present(self, var_name):\n if var_name.startswith(PS_SHADOW_VAR_PREFIX + '/'):\n return var_name[len(PS_SHADOW_VAR_PREFIX + '/'):]\n else:\n return var_name\n\n def var_dict_name(self, v):\n return self._strip_port(self._remove_shadow_var_prefix_if_present(v.name))\n\n def savable_variables(self):\n \"\"\"Returns a list/dict of savable variables to pass to tf.train.Saver.\"\"\"\n params = {}\n for v in tf.global_variables():\n assert (v.name.startswith(PS_SHADOW_VAR_PREFIX + '/v0/') or\n v.name == 'global_step:0')\n # We store variables in the checkpoint with the shadow variable prefix\n # removed so we can evaluate checkpoints in non-distributed replicated\n # mode. The checkpoints can also be loaded for training in\n # distributed_replicated mode.\n name = self._strip_port(self._remove_shadow_var_prefix_if_present(v.name))\n params[name] = v\n for v in tf.local_variables():\n # Non-trainable variables, such as batch norm moving averages, do not have\n # corresponding global shadow variables, so we add them here. Trainable\n # local variables have corresponding global shadow variables, which were\n # added in the global variable loop above.\n if v.name.startswith('v0/') and v not in tf.trainable_variables():\n params[self._strip_port(v.name)] = v\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\ndef split_grads_by_size(threshold_size, device_grads):\n \"\"\"Break gradients into two sets according to tensor size.\n\n Args:\n threshold_size: int size cutoff for small vs large tensor.\n device_grads: List of lists of (gradient, variable) tuples. The outer\n list is over devices, the inner list is over individual gradients.\n\n Returns:\n small_grads: Subset of device_grads where shape is <= theshold_size\n elements.\n large_grads: Subset of device_grads where shape is > threshold_size\n elements.\n \"\"\"\n small_grads = []\n large_grads = []\n for dl in device_grads:\n small_dl = []\n large_dl = []\n for (g, v) in dl:\n tensor_size = g.get_shape().num_elements()\n if tensor_size <= threshold_size:\n small_dl.append([g, v])\n else:\n large_dl.append([g, v])\n if small_dl:\n small_grads.append(small_dl)\n if large_dl:\n large_grads.append(large_dl)\n return small_grads, large_grads\n\n\ndef sum_grad_and_var_all_reduce(grad_and_vars, num_workers, alg, gpu_indices,\n aux_devices=None, num_shards=1):\n \"\"\"Apply all-reduce algorithm over specified gradient tensors.\"\"\"\n # Note that each grad_and_vars looks like the following:\n # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n scaled_grads = [g for g, _ in grad_and_vars]\n if alg == 'nccl':\n summed_grads = nccl.all_sum(scaled_grads)\n elif alg == 'xring':\n summed_grads = all_reduce.build_ring_all_reduce(\n scaled_grads, num_workers, num_shards, gpu_indices, tf.add)\n elif alg == 'nccl/xring':\n summed_grads = all_reduce.build_nccl_then_ring(scaled_grads, num_shards,\n tf.add)\n elif alg == 'nccl/rechd':\n summed_grads = all_reduce.build_nccl_then_recursive_hd(scaled_grads, tf.add)\n elif alg == 'nccl/pscpu':\n summed_grads = all_reduce.build_nccl_then_shuffle(\n scaled_grads, aux_devices, tf.add, tf.add_n)\n elif alg == 'pscpu/pscpu':\n summed_grads = all_reduce.build_shuffle_then_shuffle(\n scaled_grads, aux_devices,\n # TODO(tucker): devise a way of better specifying the device set\n # for the second level.\n [aux_devices[0]],\n tf.add_n)\n elif alg in ['pscpu', 'psgpu']:\n summed_grads = all_reduce.build_shuffle_all_reduce(\n scaled_grads, aux_devices, tf.add_n)\n else:\n raise ValueError('unsupported all_reduce alg: ', alg)\n\n result = []\n for (_, v), g in zip(grad_and_vars, summed_grads):\n result.append([g, v])\n return result\n\n\ndef contains_any(haystack, needles):\n \"\"\"Tests if any needle is a substring of haystack.\n\n Args:\n haystack: a string\n needles: list of strings\n\n Returns:\n True if any element of needles is a substring of haystack,\n False otherwise.\n \"\"\"\n for n in needles:\n if n in haystack:\n return True\n return False\n\n\ndef sum_gradients_all_reduce(dev_prefixes, tower_grads, num_workers,\n alg, num_shards, gpu_indices):\n \"\"\"Apply all-reduce algorithm over specified gradient tensors.\n\n Args:\n dev_prefixes: list of prefix strings to use to generate PS device names.\n tower_grads: the gradients to reduce.\n num_workers: number of worker processes across entire job.\n alg: the all-reduce algorithm to apply.\n num_shards: alg-specific sharding factor.\n gpu_indices: indices of local GPUs in order usable for ring-reduce.\n\n Returns:\n list of reduced tensors\n \"\"\"\n alg_contains_shuffle = contains_any(alg, ['pscpu', 'psgpu'])\n new_tower_grads = []\n is_hierarchical = '/' in alg\n if 'pscpu' in alg:\n aux_devices = [prefix + '/cpu:0' for prefix in dev_prefixes]\n elif 'psgpu' in alg:\n aux_devices = [prefix + '/gpu:%d' % i for i in range(len(gpu_indices))\n for prefix in dev_prefixes]\n else:\n aux_devices = ['/job:localhost/cpu:0']\n aux_device_groups = group_device_names(\n aux_devices, num_shards if alg_contains_shuffle else 1)\n group_index = 0\n for grad_and_vars in zip(*tower_grads):\n new_tower_grads.append(sum_grad_and_var_all_reduce(\n grad_and_vars, num_workers, alg, gpu_indices,\n aux_devices if is_hierarchical else aux_device_groups[group_index],\n num_shards))\n group_index = (group_index + 1) % len(aux_device_groups)\n return [list(x) for x in zip(*new_tower_grads)]\n\n\ndef aggregate_gradients_using_copy_with_device_selection(\n benchmark_cnn, tower_grads, use_mean):\n \"\"\"Aggregate gradients, controlling device for the aggregation.\n\n Args:\n benchmark_cnn: benchmark_cnn class.\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers. For each gradient, the variable is chosen from the\n first tower.\n \"\"\"\n if benchmark_cnn.local_parameter_device_flag == 'gpu':\n avail_devices = benchmark_cnn.raw_devices\n else:\n avail_devices = [benchmark_cnn.param_server_device]\n agg_grads = []\n for i, single_grads in enumerate(zip(*tower_grads)):\n with tf.device(avail_devices[i % len(avail_devices)]):\n agg_grads.append(\n aggregate_single_gradient_using_copy(single_grads, use_mean))\n return agg_grads\n\n\ndef aggregate_gradients_using_copy_with_variable_colocation(\n tower_grads, use_mean):\n \"\"\"Aggregate gradients, colocating computation with the gradient's variable.\n\n Args:\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients. All variables\n of the same gradient across towers must be the same (that is,\n tower_grads[x][a][1] == tower_grads[y][a][1] for all indices x, y, and a)\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers.\n \"\"\"\n agg_grads = []\n for single_grads in zip(*tower_grads):\n # Note that each single_grads looks like the following:\n # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n var = single_grads[0][1]\n\n for _, v in single_grads:\n assert v == var\n\n with tf.device(var.device):\n agg_grads.append(\n aggregate_single_gradient_using_copy(single_grads, use_mean))\n return agg_grads\n\n\ndef aggregate_gradients_using_copy(tower_grads, use_mean):\n \"\"\"Calculate the average gradient for each shared variable across all towers.\n\n Note that this function provides a synchronization point across all towers.\n\n Args:\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers. For each gradient, the variable is chosen from the\n first tower.\n \"\"\"\n return [aggregate_single_gradient_using_copy(single_grads, use_mean)\n for single_grads in zip(*tower_grads)]\n\n\ndef aggregate_single_gradient_using_copy(grad_and_vars, use_mean):\n \"\"\"Calculate the average gradient for a shared variable across all towers.\n\n Note that this function provides a synchronization point across all towers.\n\n Args:\n grad_and_vars: A list or tuple of (gradient, variable) tuples. Each\n (gradient, variable) pair within the outer list represents the gradient\n of the variable calculated for a single tower, and the number of pairs\n equals the number of towers.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n The pair (average_gradient, variable) where the gradient has been averaged\n across all towers. The variable is chosen from the first tower.\n \"\"\"\n grads = [g for g, _ in grad_and_vars]\n grad = tf.add_n(grads)\n\n if use_mean and len(grads) > 1:\n grad = tf.multiply(grad, 1.0/len(grads))\n\n v = grad_and_vars[0][1]\n return grad, v\n" }, { "path": "cluster/benchmarks/scripts/util/__init__.py", "content": "" }, { "path": "cluster/benchmarks/scripts/util/benchmark_util.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Provides helper functions for distributed benchmarks running on Jenkins.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport calendar\nfrom collections import namedtuple\nimport logging\nimport os\n\nfrom google.protobuf import json_format\n\nfrom tensorflow.core.util import test_log_pb2\nfrom tensorflow.python.platform import gfile\n\n\n_OUTPUT_FILE_ENV_VAR = 'TF_DIST_BENCHMARK_RESULTS_FILE'\n_TEST_NAME_ENV_VAR = 'TF_DIST_BENCHMARK_NAME'\n\n\n# Represents a single stat_value entry where\n# - name is a unique identifier for this specific measurement.\n# - stat_value is the measurement to track (for e.g. mean time per iter).\n# - num_samples is the number of samples that stat_value is averaged over.\nStatEntry = namedtuple(\n 'StatEntry', ['name', 'stat_value', 'num_samples'])\n\n\ndef store_data_in_json(\n stat_entries, timestamp, output_file=None, test_name=None):\n \"\"\"Stores benchmark results in JSON format.\n\n Args:\n stat_entries: list of StatEntry objects.\n timestamp: (datetime) start time of the test run.\n output_file: if specified, writes benchmark results to output_file.\n Otherwise, if TF_DIST_BENCHMARK_RESULTS_FILE environment variable is set,\n writes to file specified by this environment variable. If neither\n output_file is passed in, nor TF_DIST_BENCHMARK_RESULTS_FILE is set,\n does nothing.\n test_name: benchmark name. This argument is required if\n TF_DIST_BENCHMARK_NAME environment variable is not set.\n\n Raises:\n ValueError: when neither test_name is passed in nor\n TF_DIST_BENCHMARK_NAME is set.\n \"\"\"\n test_result = test_log_pb2.TestResults(\n start_time=calendar.timegm(timestamp.timetuple()))\n if not output_file:\n if _OUTPUT_FILE_ENV_VAR not in os.environ:\n logging.warning(\n 'Skipping storing json output, since we could not determine '\n 'location to store results at. Either output_file argument or '\n '%s environment variable needs to be set.', _OUTPUT_FILE_ENV_VAR)\n return\n output_file = os.environ[_OUTPUT_FILE_ENV_VAR]\n\n if test_name is not None:\n test_result.name = test_name\n elif _TEST_NAME_ENV_VAR in os.environ:\n test_result.name = os.environ[_TEST_NAME_ENV_VAR]\n else:\n raise ValueError(\n 'Could not determine test name. test_name argument is not passed in '\n 'and TF_DIST_BENCHMARK_NAME environment variable is not set.')\n\n for stat_entry in stat_entries:\n test_result.entries.entry.add(\n name=stat_entry.name,\n iters=stat_entry.num_samples,\n wall_time=stat_entry.stat_value\n )\n json_test_results = json_format.MessageToJson(test_result)\n\n with gfile.Open(output_file, 'wb') as jsonfile:\n jsonfile.write(json_test_results)\n" }, { "path": "cluster/benchmarks/scripts/util/benchmark_util_test.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for benchmark_util.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport datetime\nimport json\nimport os\nimport tempfile\nimport unittest\n\nimport benchmark_util\n\n\nclass BenchmarkUtilTest(unittest.TestCase):\n\n def testStoreDataWithNoEntries(self):\n with tempfile.NamedTemporaryFile() as temp_file:\n timing_entries = []\n benchmark_util.store_data_in_json(\n timing_entries, datetime.date(2017, 1, 1), temp_file.name)\n with open(temp_file.name, 'r') as json_file:\n json_output = json.loads(json_file.read())\n self.assertEqual('TestBenchmark', json_output['name'])\n self.assertEqual(u'1483228800', json_output['startTime'])\n\n def testStoreDataWithEntries(self):\n with tempfile.NamedTemporaryFile() as temp_file:\n timing_entries = [benchmark_util.StatEntry('test', 0.1, 1)]\n benchmark_util.store_data_in_json(\n timing_entries, datetime.date(2017, 1, 1), temp_file.name)\n\n with open(temp_file.name, 'r') as json_file:\n json_output = json.loads(json_file.read())\n self.assertEqual(1, len(json_output['entries']['entry']))\n self.assertEqual('test', json_output['entries']['entry'][0]['name'])\n self.assertEqual(0.1, json_output['entries']['entry'][0]['wallTime'])\n self.assertEqual(u'1', json_output['entries']['entry'][0]['iters'])\n self.assertEqual(u'1483228800', json_output['startTime'])\n self.assertEqual('TestBenchmark', json_output['name'])\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "cluster/benchmarks/scripts/util/convert_csv_to_json.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Convert CSV benchmark data to JSON format.\n\nCSV benchmark data has the format:\n Description,timestamp,num_batches,time mean value,time sd\n\nJSON benchmark data in in the format of TestResults proto\nconverted to JSON.\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/test_log.proto.\n\"\"\"\nimport argparse\nimport csv\nfrom datetime import datetime\n\nimport benchmark_util\n\n\ndef get_data_from_csv(csv_reader):\n \"\"\"Creates a list of StatEntry objects based on data in CSV data.\n\n Input CSV data must be in the format:\n Description,timestamp,num_batches,time mean value,time sd\n\n Args:\n csv_reader: csv.reader instance.\n\n Returns:\n A tuple of datetime timestamp and list of benchmark_util.StatEntry objects.\n\n Raises:\n ValueError: if CSV is invalid.\n \"\"\"\n timestamp = None\n stat_entries = []\n\n for row in csv_reader:\n if len(row) != 5:\n raise ValueError('Expected 5 entries per line in the input CSV file, '\n 'but found %d entries.' % len(row))\n if '' in row:\n raise ValueError('Found empty entries in row: %s' % row)\n\n # Set timestamp based on the first line in CSV file.\n if timestamp is None:\n # Example of time formatting: 2017-06-26 02:59:29.325579\n timestamp = datetime.strptime(row[1], \"%Y-%m-%d %H:%M:%S.%f\")\n stat_entries.append(\n benchmark_util.StatEntry(row[0], float(row[3]), 1))\n return timestamp, stat_entries\n\n\ndef main():\n with open(FLAGS.input_csv_file, 'r') as csvfile:\n csv_reader = csv.reader(csvfile)\n timestamp, stat_entries = get_data_from_csv(csv_reader)\n benchmark_util.store_data_in_json(\n stat_entries, timestamp,\n output_file=FLAGS.output_json_file,\n test_name=FLAGS.test_name)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.register(\n 'type', 'bool', lambda v: v.lower() in ('true', 't', 'y', 'yes'))\n parser.add_argument(\n '--test_name', type=str, default=None, required=True,\n help='Name of the test.')\n parser.add_argument(\n '--input_csv_file', type=str, default=None, required=True,\n help='Path to the CSV file.')\n parser.add_argument(\n '--output_json_file', type=str, default=None, required=True,\n help='Path to output JSON file.')\n FLAGS, _ = parser.parse_known_args()\n main()\n\n" }, { "path": "cluster/benchmarks/scripts/util/convert_csv_to_json_test.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for convert_csv_to_json.\"\"\"\nimport csv\nimport datetime\nimport unittest\n\nimport convert_csv_to_json\n\n\nclass ConvertCsvToJsonTest(unittest.TestCase):\n\n def testSingleEntryCSV(self):\n # Description,timestamp,num_batches,time mean value,time sd\n csv_reader = csv.reader(\n ['abc,2017-06-26 02:59:29.325579,10,2.15,0.1'])\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(csv_reader)\n self.assertEqual(\n datetime.datetime(2017, 06, 26, 2, 59, 29, 325579),\n timestamp)\n self.assertEqual(1, len(stat_entries))\n self.assertEqual('abc', stat_entries[0].name)\n self.assertEqual(2.15, stat_entries[0].stat_value)\n\n def testTwoEntryCSV(self):\n # Description,timestamp,num_batches,time mean value,time sd\n csv_reader = csv.reader(\n ['abc,2017-06-26 02:59:35.425579,10,2.15,0.1',\n 'def,2017-06-26 02:59:29.325579,10,10.1,0.1'])\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(csv_reader)\n self.assertEqual(\n datetime.datetime(2017, 06, 26, 2, 59, 35, 425579),\n timestamp)\n self.assertEqual(2, len(stat_entries))\n self.assertEqual('abc', stat_entries[0].name)\n self.assertEqual(2.15, stat_entries[0].stat_value)\n self.assertEqual('def', stat_entries[1].name)\n self.assertEqual(10.1, stat_entries[1].stat_value)\n\n def testInvalidCSV_LessEntries(self):\n csv_reader = csv.reader(\n ['abc,2017-06-26 02:59:29.325579,10,2.15'])\n with self.assertRaises(ValueError):\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(\n csv_reader)\n\n def testInvalidCSV_MoreEntries(self):\n csv_reader = csv.reader(\n ['abc,2017-06-26 02:59:29.325579,10,2.15,0.1,extra_entry'])\n with self.assertRaises(ValueError):\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(\n csv_reader)\n\n def testInvalidCSV_EmptyEntry(self):\n csv_reader = csv.reader(\n [',2017-06-26 02:59:29.325579,10,2.15,0.1'])\n with self.assertRaises(ValueError):\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(\n csv_reader)\n\n def testInvalidCSV_InvalidDate(self):\n csv_reader = csv.reader(['abc,invaliddate,10,2.15,0.1'])\n with self.assertRaises(ValueError):\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(\n csv_reader)\n\n def testInvalidCSV_InvalidValue(self):\n csv_reader = csv.reader(\n ['abc,2017-06-26 02:59:29.325579,10,invalidfloat,0.1'])\n with self.assertRaises(ValueError):\n timestamp, stat_entries = convert_csv_to_json.get_data_from_csv(\n csv_reader)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "cluster/benchmarks/soumith_benchmarks.html", "content": "\n\n\n\n\n\n\n
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Alexnet

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Googlenet

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Overfeat

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VGG

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\n\n\n\n\n\n\n\n" }, { "path": "cluster/benchmarks/tools/k8s_tensorflow_lib.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Generates YAML configuration files for distributed TensorFlow workers.\n\nThe workers will be run in a Kubernetes (k8s) container cluster.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# Note: It is intentional that we do not import tensorflow in this script. The\n# machine that launches a TensorFlow k8s cluster does not have to have the\n# Python package of TensorFlow installed on it.\n\n# Worker pods will mount host volume /shared, as a convenient way to create\n# shared storage among workers during local tests.\nWORKER_POD = (\n \"\"\"apiVersion: v1\nkind: Pod\nmetadata:\n name: {name_prefix}-worker{worker_id}\n labels:\n tf-worker: \"{worker_id}\"\n name-prefix: \"{name_prefix}\"\n job: \"worker\"\nspec:\n restartPolicy: OnFailure\n containers:\n - name: tf-worker{worker_id}\n image: {docker_image}\n args: [{args}]\n ports:\n - containerPort: {port}\n env: [{env_vars}]\n resources: {{limits: {resource_limits} }}\n volumeMounts: [{volume_mounts}]\n volumes: [{volumes}]\n\"\"\")\nWORKER_SVC = (\n \"\"\"apiVersion: v1\nkind: Service\nmetadata:\n name: {name_prefix}-worker{worker_id}\n labels:\n tf-worker: \"{worker_id}\"\nspec:\n ports:\n - port: {port}\n targetPort: {port}\n selector:\n tf-worker: \"{worker_id}\"\n\"\"\")\nWORKER_LB_SVC = (\n \"\"\"apiVersion: v1\nkind: Service\nmetadata:\n name: {name_prefix}-worker{worker_id}\n labels:\n tf-worker: \"{worker_id}\"\nspec:\n type: LoadBalancer\n ports:\n - port: {port}\n selector:\n tf-worker: \"{worker_id}\"\n\"\"\")\nPARAM_SERVER_POD = (\n \"\"\"apiVersion: v1\nkind: Pod\nmetadata:\n name: {name_prefix}-ps{param_server_id}\n labels:\n tf-ps: \"{param_server_id}\"\n name-prefix: \"{name_prefix}\"\n job: \"ps\"\nspec:\n restartPolicy: OnFailure\n containers:\n - name: tf-ps{param_server_id}\n image: {docker_image}\n args: [{args}]\n ports:\n - containerPort: {port}\n env: [{env_vars}]\n volumeMounts: [{volume_mounts}]\n volumes: [{volumes}]\n\"\"\")\nPARAM_SERVER_SVC = (\n \"\"\"apiVersion: v1\nkind: Service\nmetadata:\n name: {name_prefix}-ps{param_server_id}\n labels:\n tf-ps: \"{param_server_id}\"\nspec:\n ports:\n - port: {port}\n selector:\n tf-ps: \"{param_server_id}\"\n\"\"\")\nPARAM_LB_SVC = (\"\"\"apiVersion: v1\nkind: Service\nmetadata:\n name: {name_prefix}-ps{param_server_id}\n labels:\n tf-ps: \"{param_server_id}\"\nspec:\n type: LoadBalancer\n ports:\n - port: {port}\n selector:\n tf-ps: \"{param_server_id}\"\n\"\"\")\n_ENV_VAR_TEMPLATE = '{name: \"%s\", value: \"%s\"}'\n_ARG_TEMPLATE = '\"--%s=%s\"'\n_SHARED_VOLUME_INFO = {'shared': ('/shared', '/shared')}\n_VOLUME_MOUNT_TEMPLATE = '{name: %s, mountPath: %s}'\n_VOLUME_TEMPLATE = '{name: %s, hostPath: {path: %s}}'\n_GPU_TEMPLATE = '{alpha.kubernetes.io/nvidia-gpu: %d}'\n\ndef GenerateConfig(num_workers,\n num_param_servers,\n port,\n request_load_balancer,\n docker_image,\n name_prefix,\n additional_args=None,\n env_vars=None,\n volumes=None,\n use_shared_volume=True,\n use_cluster_spec=True,\n gpu_limit=0):\n \"\"\"Generate configuration strings.\n\n Args:\n num_workers: number of worker jobs.\n num_param_servers: number of ps server jobs.\n port: GRPC server port.\n request_load_balancer: request worker0 to be exposed on a public IP\n address via an external load balancer.\n docker_image: docker image to use.\n name_prefix: name to prepend to pod job names.\n additional_args: dictionary mapping argument names to argument values\n to pass to pods.\n env_vars: dictionary of environment variables to set.\n volumes: dictionary mapping from volume name to a tuple of values\n (src_location, dst_location). This volumes will be added in addition\n to /shared volume if use_shared_volume is True.\n use_shared_volume: whether to add hostPath to /shared directory\n to the kubernetes config.\n use_cluster_spec: if true, pass --cluster_spec to worker and ps jobs.\n If false, pass --worker_hosts and --ps_hosts to worker and ps jobs.\n gpu_limit: Add a requirement to worker jobs for this many gpu's.\n\n Returns:\n Kubernetes yaml config.\n \"\"\"\n if env_vars is None:\n env_vars = {}\n env_str = ', '.join([_ENV_VAR_TEMPLATE % (name, value)\n for name, value in env_vars.items()])\n if additional_args is None:\n additional_args = {}\n config = ''\n common_args = GetCommonArgs(\n num_workers, num_param_servers, port, name_prefix, use_cluster_spec)\n\n if volumes is None:\n volumes = {}\n if use_shared_volume:\n volumes.update(_SHARED_VOLUME_INFO)\n volumes_str = ', '.join([_VOLUME_TEMPLATE % (name, location[0])\n for name, location in volumes.items()])\n volume_mounts_str = ', '.join([_VOLUME_MOUNT_TEMPLATE % (name, location[1])\n for name, location in volumes.items()])\n\n for worker in range(num_workers):\n worker_args = {\n 'job_name': 'worker',\n 'task_index': worker\n }\n worker_args.update(common_args)\n worker_args.update(additional_args)\n arg_str = ', '.join([_ARG_TEMPLATE % (name, value)\n for name, value in worker_args.items()])\n\n config += WORKER_POD.format(\n port=port,\n worker_id=worker,\n docker_image=docker_image,\n name_prefix=name_prefix,\n volume_mounts=volume_mounts_str,\n volumes=volumes_str,\n args=arg_str,\n env_vars=env_str,\n resource_limits=_GPU_TEMPLATE % gpu_limit if gpu_limit > 0 else '')\n config += '---\\n'\n if request_load_balancer:\n config += WORKER_LB_SVC.format(port=port,\n worker_id=worker,\n name_prefix=name_prefix)\n else:\n config += WORKER_SVC.format(port=port,\n worker_id=worker,\n name_prefix=name_prefix)\n config += '---\\n'\n\n for param_server in range(num_param_servers):\n ps_args = {\n 'job_name': 'ps',\n 'task_index': param_server\n }\n ps_args.update(common_args)\n ps_args.update(additional_args)\n arg_str = ', '.join([_ARG_TEMPLATE % (name, value)\n for name, value in ps_args.items()])\n config += PARAM_SERVER_POD.format(\n port=port,\n param_server_id=param_server,\n docker_image=docker_image,\n name_prefix=name_prefix,\n volume_mounts=volume_mounts_str,\n volumes=volumes_str,\n args=arg_str,\n env_vars=env_str)\n config += '---\\n'\n if request_load_balancer:\n config += PARAM_LB_SVC.format(\n port=port, param_server_id=param_server, name_prefix=name_prefix)\n else:\n config += PARAM_SERVER_SVC.format(\n port=port, param_server_id=param_server, name_prefix=name_prefix)\n config += '---\\n'\n\n return config\n\n\ndef WorkerClusterSpecString(num_workers,\n num_param_servers,\n port,\n name_prefix):\n \"\"\"Generates worker cluster spec.\"\"\"\n return ClusterSpecString(num_workers, num_param_servers, port, name_prefix)\n\n\ndef ParamServerClusterSpecString(num_workers,\n num_param_servers,\n port,\n name_prefix):\n \"\"\"Generates parameter server spec.\"\"\"\n return ClusterSpecString(num_workers, num_param_servers, port,\n name_prefix)\n\n\ndef ClusterSpecString(num_workers,\n num_param_servers,\n port,\n name_prefix):\n \"\"\"Generates general cluster spec.\"\"\"\n spec = 'worker|'\n for worker in range(num_workers):\n spec += '%s-worker%d:%d' % (name_prefix, worker, port)\n if worker != num_workers-1:\n spec += ';'\n\n spec += ',ps|'\n for param_server in range(num_param_servers):\n spec += '%s-ps%d:%d' % (name_prefix, param_server, port)\n if param_server != num_param_servers-1:\n spec += ';'\n\n return spec\n\n\ndef GetCommonArgs(num_workers,\n num_param_servers,\n port,\n name_prefix,\n use_cluster_spec):\n \"\"\"Get arguments common to both worker and ps jobs.\n\n Args:\n num_workers: number of workers.\n num_param_servers: number of ps servers.\n port: worker and ps port number.\n name_prefix: prefix to prepend to job names.\n use_cluster_spec: if true, pass --cluster_spec argument.\n If false, parse --worker_hosts and --ps_hosts arguments.\n\n Returns:\n A dictionary of argument names mapping to argument values.\n \"\"\"\n common_args = {}\n if use_cluster_spec:\n common_args['cluster_spec'] = WorkerClusterSpecString(\n num_workers,\n num_param_servers,\n port,\n name_prefix)\n else:\n common_args['worker_hosts'] = WorkerHosts(num_workers, port, name_prefix)\n common_args['ps_hosts'] = PsHosts(num_param_servers, port, name_prefix)\n return common_args\n\n\ndef WorkerHosts(num_workers, port, name_prefix):\n worker_hosts = ['%s-worker%d:%d' % (name_prefix, i, port)\n for i in range(num_workers)]\n return ','.join(worker_hosts)\n\n\ndef PsHosts(num_ps, port, name_prefix):\n ps_hosts = ['%s-ps%d:%d' % (name_prefix, i, port)\n for i in range(num_ps)]\n return ','.join(ps_hosts)\n" }, { "path": "cluster/benchmarks/tools/k8s_tensorflow_test.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for k8s_tensorflow_lib.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport unittest\n\nimport k8s_tensorflow_lib\n\n\nclass K8sTensorflowTest(unittest.TestCase):\n\n def testGenerateConfig_LoadBalancer(self):\n # Use loadbalancer\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=True,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False)\n self.assertTrue('LoadBalancer' in config)\n\n # Don't use loadbalancer\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=False,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False)\n self.assertFalse('LoadBalancer' in config)\n\n def testGenerateConfig_SharedVolume(self):\n # Use shared directory\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=False,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=True)\n self.assertTrue('/shared' in config)\n\n # Don't use shared directory\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=False,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False)\n self.assertFalse('/shared' in config)\n\n def testEnvVar(self):\n # Use loadbalancer\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=True,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False,\n env_vars={'test1': 'test1_value', 'test2': 'test2_value'})\n self.assertTrue('{name: \"test1\", value: \"test1_value\"}' in config)\n self.assertTrue('{name: \"test2\", value: \"test2_value\"}' in config)\n\n def testClusterSpec(self):\n # Use cluster_spec\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=True,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False,\n use_cluster_spec=True)\n self.assertFalse('worker_hosts' in config)\n self.assertFalse('ps_hosts' in config)\n self.assertTrue(\n '\"--cluster_spec=worker|abc-worker0:5000,ps|abc-ps0:5000\"' in config)\n\n # Don't use cluster_spec\n config = k8s_tensorflow_lib.GenerateConfig(\n num_workers=1,\n num_param_servers=1,\n port=5000,\n request_load_balancer=True,\n docker_image='test_image',\n name_prefix='abc',\n use_shared_volume=False,\n use_cluster_spec=False)\n self.assertFalse('cluster_spec' in config)\n self.assertTrue('\"--worker_hosts=abc-worker0:5000\"' in config)\n self.assertTrue('\"--ps_hosts=abc-ps0:5000\"' in config)\n\n def testWorkerHosts(self):\n self.assertEqual(\n 'test_prefix-worker0:1234',\n k8s_tensorflow_lib.WorkerHosts(1, 1234, 'test_prefix'))\n self.assertEqual(\n 'test_prefix-worker0:1234,test_prefix-worker1:1234',\n k8s_tensorflow_lib.WorkerHosts(2, 1234, 'test_prefix'))\n\n def testPsHosts(self):\n self.assertEqual(\n 'test_prefix-ps0:1234,test_prefix-ps1:1234',\n k8s_tensorflow_lib.PsHosts(2, 1234, 'test_prefix'))\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "cluster/benchmarks/tools/kubectl_util.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Utils for running, waiting and stopping benchmark jobs on kubernetes.\n\nFunctions in this file assume kubernetes jobs have 'name-prefix' and 'job'\nselectors set.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport logging\nimport subprocess\nimport time\n\n\n_KUBECTL = 'kubectl'\nWAIT_PERIOD_SECONDS = 20\n\n\nclass TimeoutError(Exception):\n pass\n\n\ndef _WaitUntil(timeout, predicate, *args):\n start_time = time.time()\n while time.time() - start_time < timeout:\n time.sleep(WAIT_PERIOD_SECONDS)\n if predicate(*args):\n return True\n return False\n\n\ndef _GetPodNames(pod_name_prefix, job_name=None):\n \"\"\"Get pod names based on the pod_name_prefix and job_name.\n\n Args:\n pod_name_prefix: value of 'name-prefix' selector.\n job_name: value of 'job' selector. If None, pod names will be\n selected only based on 'name-prefix' selector.\n\n Returns:\n List of pod names.\n \"\"\"\n pod_list_command = [\n _KUBECTL, 'get', 'pods', '-o', 'name', '-a',\n '-l', _GetJobSelector(pod_name_prefix, job_name)]\n logging.info('Command to get pod names: %s', ' '.join(pod_list_command))\n output = subprocess.check_output(pod_list_command, universal_newlines=True)\n pod_names = [name for name in output.strip().split('\\n') if name]\n logging.info('Pod names: \"%s\"', ','.join(pod_names))\n return pod_names\n\n\ndef CreatePods(pod_name, yaml_file):\n \"\"\"Creates pods based on the given kubernetes config.\n\n Args:\n pod_name: 'name-prefix' selector for the pods.\n yaml_file: kubernetes yaml config.\n\n Raises:\n TimeoutError: if jobs didn't come up for a long time.\n \"\"\"\n command = [_KUBECTL, 'create', '--filename=%s' % yaml_file]\n logging.info('Creating pods: %s', subprocess.list2cmdline(command))\n subprocess.check_call(command)\n\n if not _WaitUntil(100, _GetPodNames, pod_name):\n raise TimeoutError(\n 'Timed out waiting for %s pod to come up.' % pod_name)\n\n\ndef DeletePods(pod_name, yaml_file):\n \"\"\"Deletes pods based on the given kubernetes config.\n\n Args:\n pod_name: 'name-prefix' selector for the pods.\n yaml_file: kubernetes yaml config.\n\n Raises:\n TimeoutError: if jobs didn't terminate for a long time.\n \"\"\"\n command = [_KUBECTL, 'delete', '--filename=%s' % yaml_file]\n logging.info('Deleting pods: %s', ' '.join(command))\n subprocess.call(command)\n\n def CheckPodsAreTerminated():\n return not _GetPodNames(pod_name)\n if not _WaitUntil(100, CheckPodsAreTerminated):\n raise TimeoutError(\n 'Timed out waiting for %s pod to terminate.' % pod_name)\n\n\ndef _GetJobSelector(pod_name_prefix, job_name=None):\n selector = 'name-prefix in (%s)' % pod_name_prefix\n if job_name:\n selector += ',job in (%s)' % job_name\n return selector\n\n\ndef WaitForCompletion(pod_name_prefix, job_name='worker', timeout=2*60*60):\n \"\"\"Waits until jobs matching pod_name and job_name are terminated.\n\n Args:\n pod_name_prefix: value of 'name-prefix' selector.\n job_name: value of 'job' selector.\n timeout: how long to wait for jobs to terminate before timing out.\n\n Returns:\n True if jobs terminated with success, False otherwise.\n\n Raises:\n TimeoutError: if jobs haven't terminated after timeout.\n ValueError: if we couldn't find jobs matching pod_name and job_name.\n \"\"\"\n # Jsonpath that selects comma-separated exit codes (followed by extra comma\n # at the end).\n # If a job doesn't have an exit code yet, empty string will be returned\n # instead. For ex. output for 2 jobs where one is missing an exit code\n # and the other one has an exit code of 0 would look like: ,0,\n last_state_query = (\n 'jsonpath=\\'{range .items[*]}'\n '{.status.containerStatuses[?(@.state.terminated)]'\n '.state.terminated.exitCode},{end}\\'')\n status_command = [\n _KUBECTL, 'get', '-o', last_state_query,\n 'pods', '-l', _GetJobSelector(pod_name_prefix, job_name), '-a'\n ]\n\n exit_codes = []\n start_time = time.time()\n while time.time() - start_time < timeout:\n # Output of check_output is a string that starts and ends with '.\n output = subprocess.check_output(\n status_command, universal_newlines=True).strip('\\'')\n logging.debug('Pod status: %s', output)\n if not output:\n raise ValueError(\n 'Query did not match any data. Query: %s' % ' '.join(status_command))\n # Output will end with an extra comma. So, we remove it before splitting.\n exit_codes = output[:-1].split(',')\n if '' not in exit_codes: # fetched all exit codes\n break\n time.sleep(WAIT_PERIOD_SECONDS)\n\n if '' in exit_codes:\n raise TimeoutError(\n 'Timed out waiting for %s %s jobs to finish.' %\n (pod_name_prefix, job_name))\n _PrintLogs(pod_name_prefix, job_name)\n\n failed_job_count = sum(code != '0' for code in exit_codes)\n if failed_job_count > 0:\n logging.error('%d out of %d jobs failed. Exit codes: %s',\n failed_job_count, len(exit_codes), ','.join(exit_codes))\n return False\n return True\n\n\ndef _PrintLogs(pod_name_prefix, job_name):\n \"\"\"Prints pod logs.\n\n If a pod has been restarted, prints logs from previous run. Otherwise,\n prints the logs from current run. We print logs for pods selected\n based on pod_name_prefix and job_name.\n\n Args:\n pod_name_prefix: value of 'name-prefix' selector.\n job_name: value of 'job' selector.\n \"\"\"\n for pod_name in _GetPodNames(pod_name_prefix, job_name):\n try:\n # Get previous logs.\n logs_command = [_KUBECTL, 'logs', '-p', pod_name]\n logging.info('Command to get logs: %s', ' '.join(logs_command))\n output = subprocess.check_output(logs_command, universal_newlines=True)\n except subprocess.CalledProcessError:\n # We couldn't get previous logs, so we will try to get current logs.\n logs_command = [_KUBECTL, 'logs', pod_name]\n logging.info('Command to get logs: %s', ' '.join(logs_command))\n output = subprocess.check_output(logs_command, universal_newlines=True)\n print('%s logs:' % pod_name)\n print(output)\n" }, { "path": "cluster/benchmarks/tools/kubectl_util_test.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for kubectl_util.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport mock\nimport subprocess\nimport unittest\n\nimport kubectl_util\n\n\nkubectl_util.WAIT_PERIOD_SECONDS = 1\n\n\nclass KubectlUtilTest(unittest.TestCase):\n\n @mock.patch.object(subprocess, 'check_output')\n @mock.patch.object(subprocess, 'check_call')\n def testCreatePods(self, mock_check_call, mock_check_output):\n mock_check_output.return_value = 'nonempty'\n kubectl_util.CreatePods('test_pod', 'test.yaml')\n mock_check_call.assert_called_once_with(\n ['kubectl', 'create', '--filename=test.yaml'])\n mock_check_output.assert_called_once_with(\n ['kubectl', 'get', 'pods', '-o', 'name', '-a', '-l',\n 'name-prefix in (test_pod)'], universal_newlines=True)\n\n @mock.patch.object(subprocess, 'check_output')\n @mock.patch.object(subprocess, 'call')\n def testDeletePods(self, mock_check_call, mock_check_output):\n mock_check_output.return_value = ''\n kubectl_util.DeletePods('test_pod', 'test.yaml')\n mock_check_call.assert_called_once_with(\n ['kubectl', 'delete', '--filename=test.yaml'])\n mock_check_output.assert_called_once_with(\n ['kubectl', 'get', 'pods', '-o', 'name', '-a', '-l',\n 'name-prefix in (test_pod)'], universal_newlines=True)\n\n @mock.patch.object(subprocess, 'check_output')\n def testWaitForCompletion(self, mock_check_output):\n # Test success\n mock_check_output.return_value = '\\'0,0,\\''\n self.assertTrue(kubectl_util.WaitForCompletion('test_pod'))\n\n # Test failure\n mock_check_output.return_value = '\\'0,1,\\''\n self.assertFalse(kubectl_util.WaitForCompletion('test_pod'))\n\n # Test timeout\n with self.assertRaises(kubectl_util.TimeoutError):\n mock_check_output.return_value = '\\'0,,\\''\n kubectl_util.WaitForCompletion('test_pod', timeout=5)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "cluster/benchmarks/tools/run_distributed_benchmarks.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Builds docker images and runs benchmarks from benchmark_configs.yml file.\n\nThis script should only be run from opensource repository.\n\"\"\"\nimport argparse\nfrom datetime import datetime\nimport logging\nimport os\nfrom string import maketrans\nimport subprocess\nimport sys\n\nimport docker\nimport k8s_tensorflow_lib\nimport kubectl_util\nimport yaml\n\n\n_DOCKER_IMAGE_PATTERN = 'gcr.io/tensorflow-testing/benchmarks/%s'\n_OUTPUT_FILE_ENV_VAR = 'TF_DIST_BENCHMARK_RESULTS_FILE'\n_TEST_NAME_ENV_VAR = 'TF_DIST_BENCHMARK_NAME'\n_PORT = 5000\n\n\ndef _ConvertToValidName(name):\n \"\"\"Converts to name that we can use as a kubernetes job prefix.\n\n Args:\n name: benchmark name.\n\n Returns:\n Benchmark name that can be used as a kubernetes job prefix.\n \"\"\"\n return name.translate(maketrans('/:_', '---'))\n\n\ndef _RunBenchmark(name, yaml_file):\n \"\"\"Runs a single distributed benchmark.\n\n Args:\n name: name of the benchmark to run.\n yaml_file: path to kubernetes config file.\n \"\"\"\n kubectl_util.DeletePods(name, yaml_file)\n kubectl_util.CreatePods(name, yaml_file)\n success = kubectl_util.WaitForCompletion(name)\n kubectl_util.DeletePods(name, yaml_file)\n return success\n\n\ndef _BuildAndPushDockerImage(\n docker_client, docker_file, name, tag, push_to_gcloud=False):\n \"\"\"Builds a docker image and optionally pushes it to gcloud.\n\n Args:\n docker_client: docker.Client object.\n docker_file: Dockerfile path.\n name: name of the benchmark to build a docker image for.\n tag: tag for docker image.\n push_to_gcloud: whether to push the image to google cloud.\n\n Returns:\n Docker image identifier.\n \"\"\"\n local_docker_image_with_tag = '%s:%s' % (name, tag)\n remote_docker_image = _DOCKER_IMAGE_PATTERN % name\n remote_docker_image_with_tag = '%s:%s' % (remote_docker_image, tag)\n if FLAGS.docker_context_dir:\n docker_context = os.path.join(\n os.path.dirname(__file__), FLAGS.docker_context_dir)\n docker_file_name = docker_file\n else:\n docker_context = os.path.dirname(docker_file)\n docker_file_name = os.path.basename(docker_file)\n\n built_image = docker_client.images.build(\n path=docker_context, dockerfile=docker_file_name,\n tag=local_docker_image_with_tag,\n pull=True)\n built_image.tag(remote_docker_image, tag=tag)\n if push_to_gcloud:\n subprocess.check_call(\n ['gcloud', 'docker', '--', 'push', remote_docker_image_with_tag])\n return remote_docker_image_with_tag\n\n\ndef _GetMostRecentDockerImageFromGcloud(docker_image):\n \"\"\"Get most recent :tag for this docker_image.\n \n Args:\n docker_image: (string) docker image on Google Cloud.\n \n Returns:\n docker_image:tag if at least one tag was found for docker_image.\n Otherwise, returns None.\n \"\"\"\n tag = subprocess.check_output(\n ['gcloud', 'container', 'images', 'list-tags',\n docker_image, '--limit=1', '--format=value(tags[0])'])\n tag = tag.strip()\n if not tag:\n return None \n return '%s:%s' % (docker_image, tag)\n \n\ndef get_gpu_volume_mounts():\n \"\"\"Get volume specs to add to Kubernetes config.\n\n Returns:\n Volume specs in the format: volume_name: (hostPath, podPath). \n \"\"\"\n volume_specs = {}\n\n if FLAGS.nvidia_lib_dir:\n volume_specs['nvidia-libraries'] = (FLAGS.nvidia_lib_dir, '/usr/lib/nvidia')\n\n if FLAGS.cuda_lib_dir:\n cuda_library_files = ['libcuda.so', 'libcuda.so.1', 'libcudart.so']\n for cuda_library_file in cuda_library_files:\n lib_name = cuda_library_file.split('.')[0]\n volume_specs['cuda-libraries-%s' % lib_name] = (\n os.path.join(FLAGS.cuda_lib_dir, cuda_library_file),\n os.path.join('/usr/lib/cuda/', cuda_library_file))\n return volume_specs\n\n\nclass NoImageFoundError(Exception):\n pass\n\n\ndef main():\n config_text = open(FLAGS.benchmark_configs_file, 'r').read()\n configs = yaml.load(config_text)\n\n docker_client = docker.from_env()\n time_tag = datetime.now().strftime('%d_%m_%Y_%H_%M')\n # Create directories to store kubernetes yaml configs in.\n if not os.path.isdir(FLAGS.config_output_file_dir):\n os.makedirs(FLAGS.config_output_file_dir)\n # Keeps track of already built docker images in case multiple benchmarks\n # use the same docker image.\n benchmark_name_to_docker_image = {}\n\n # TODO(annarev): run benchmarks in parallel instead of sequentially.\n for config in configs:\n name = _ConvertToValidName(str(config['benchmark_name']))\n if name in benchmark_name_to_docker_image:\n docker_image = benchmark_name_to_docker_image[name]\n elif FLAGS.build_docker_image:\n docker_image = _BuildAndPushDockerImage(\n docker_client, config['docker_file'], name, time_tag,\n FLAGS.store_docker_image_in_gcloud)\n benchmark_name_to_docker_image[name] = docker_image\n else:\n docker_image = _GetMostRecentDockerImageFromGcloud(\n _DOCKER_IMAGE_PATTERN % name)\n if not docker_image:\n raise NoImageFoundError('No tags found for image %s.' % docker_image)\n\n env_vars = {\n _OUTPUT_FILE_ENV_VAR: os.path.join(\n FLAGS.benchmark_results_dir, name + '.json'),\n _TEST_NAME_ENV_VAR: name\n }\n gpu_count = (0 if 'gpus_per_machine' not in config\n else config['gpus_per_machine'])\n volumes = {}\n if gpu_count > 0:\n volumes = get_gpu_volume_mounts()\n env_vars['LD_LIBRARY_PATH'] = (\n '/usr/lib/cuda:/usr/lib/nvidia:/usr/lib/x86_64-linux-gnu')\n\n env_vars.update(config.get('env_vars', {}))\n args = config.get('args', {})\n kubernetes_config = k8s_tensorflow_lib.GenerateConfig(\n config['worker_count'],\n config['ps_count'],\n _PORT,\n request_load_balancer=False,\n docker_image=docker_image,\n name_prefix=name,\n additional_args=args,\n env_vars=env_vars,\n volumes=volumes,\n use_shared_volume=False,\n use_cluster_spec=False,\n gpu_limit=gpu_count)\n\n kubernetes_config_path = os.path.join(\n FLAGS.config_output_file_dir, name + '.yaml')\n with open(kubernetes_config_path, 'w') as output_config_file:\n output_config_file.write(kubernetes_config)\n\n success = _RunBenchmark(name, kubernetes_config_path)\n if not success:\n sys.exit(1)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.register(\n 'type', 'bool', lambda v: v.lower() in ('true', 't', 'y', 'yes'))\n parser.add_argument(\n '--benchmark_configs_file', type=str, default=None, required=True,\n help='YAML file with benchmark configs.')\n parser.add_argument(\n '--config_output_file_dir', type=str, default=None, required=True,\n help='Directory to write generated kubernetes configs to.')\n parser.add_argument(\n '--benchmark_results_dir', type=str, default=None, required=True,\n help='Directory to store benchmark results at.')\n parser.add_argument(\n '--docker_context_dir', type=str, default='',\n help='Directory to use as a docker context. By default, docker context '\n 'will be set to the directory containing a docker file.')\n parser.add_argument(\n '--build_docker_image', type='bool', nargs='?', const=True, default=True,\n help='Whether to build a new docker image or try to use existing one.') \n parser.add_argument(\n '--store_docker_image_in_gcloud', type='bool', nargs='?', const=True,\n default=False, help='Push docker images to google cloud.')\n parser.add_argument(\n '--cuda_lib_dir', type=str, default=None, required=False,\n help='Directory where cuda library files are located on gcloud node.')\n parser.add_argument(\n '--nvidia_lib_dir', type=str, default=None, required=False,\n help='Directory where nvidia library files are located on gcloud node.')\n FLAGS, _ = parser.parse_known_args()\n logging.basicConfig(level=logging.DEBUG)\n main()\n" }, { "path": "cluster/client_transfer_benchmark.py", "content": "#!/usr/bin/env python\n# Benchmark transferring data from TF into Python runtime\n# Macbook\n# t->p 2919.72 MB/second\n# p->t 7374.80 MB/second\n#\n# Older info\n#\n## Dependencies:\n# portpicker (pip install portpicker)\n# tcmalloc4 (sudo apt-get install google-perftools)\n# TF 0.12 (for var.read_value(), ones_initializer())\n#\n# On Linux default malloc is slow\n# sudo apt-get install google-perftools\n# export LD_PRELOAD=\"/usr/lib/libtcmalloc.so.4\"\n#\n# local session benchmark:\n# 2014 MacBook (100 iters):\n# 128MB -- 3.56 GB/s\n# 1024MB -- 1.96 GB/s\n# \n# Xeon E5-2630 v3 @ 2.40GHz (100 iters):\n# 128 MB -- 0.43 GB/s (default malloc)\n# 128 MB -- 4-6.2 GB/s (tcmalloc)\n# 1024 MB -- 4-5.97 GB/s (tcmalloc)\n#\n#\n# distributed session + profiling on Xeon:\n# python client_transfer_benchmark.py --profile\n# 116.70 MB per second\n# 151 MB per second (using AsProtoTensorContent patch)\n# google-pprof `which python` /tmp/profile.out.client --svg > /tmp/profile.client\n# google-pprof `which python` /tmp/profile.out.worker --svg > /tmp/profile.worker\n#\n#\n# Profiling feeding: on Xeon:\n# python client_transfer_benchmark.py --direction=p2t --profile\n# 1577.86 MB per second\n# 143.93 MB per second\n\n\nimport gc\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport tempfile\nimport threading\nimport time\nfrom tensorflow.python.training import training_util\nfrom tensorflow.python.platform import gfile\n\nfrom tensorflow.contrib.summary import summary_ops\nfrom tensorflow.contrib.summary import gen_summary_ops\n\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 10, \"Maximum number of additions\")\nflags.DEFINE_integer(\"warmup_iters\", 1, \"warmup iterations\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"extra logging\")\nflags.DEFINE_boolean(\"sanity_check\", False, \"run sanity check on results\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to run distributed version and \"\n \"collect CPU profile\")\nflags.DEFINE_boolean(\"in_process\", True, \"do bencharmk on in-process master\")\nflags.DEFINE_string(\"direction\", \"t2p\", \"which direction to profile, either \"\n \"tensorflow to python (t2p) or python to tensorflow (p2t)\")\nflags.DEFINE_string(\"logdir\", os.environ[\"HOME\"]+\"/efs/client_transfer_benchmark\", \"location of tensorboard events\")\n\n# internal flags, set by client\nflags.DEFINE_string(\"worker_type\", \"launcher\", \"launcher or client or worker\")\nflags.DEFINE_string(\"port\", \"12222\", \"port of master\")\nFLAGS = flags.FLAGS\n\n\nhost = \"127.0.0.1\"\ndef clusterspec():\n cluster = {\"worker\": [host+\":\"+FLAGS.port]}\n return tf.train.ClusterSpec(cluster).as_cluster_def()\n\n\ndef log(s):\n if FLAGS.verbose: print(s)\n\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n config.log_device_placement = False\n config.allow_soft_placement = False\n return config\n\n\ndef launch_distributed_service():\n port = portpicker.pick_unused_port()\n \n def launch_worker(worker_type):\n my_env = os.environ.copy()\n if not FLAGS.verbose:\n my_env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n my_env[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n if FLAGS.profile:\n my_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\n my_env[\"CPUPROFILE\"]=\"/tmp/profile.out.%s\"%(worker_type)\n\n args = [\"python\"] + sys.argv + [\"--port=\"+str(port),\n \"--worker_type=\"+worker_type]\n proc = subprocess.Popen(args, stderr=subprocess.STDOUT, env=my_env)\n log(\"worker %s pid %s\"%(worker_type, proc.pid))\n \n launch_worker(\"worker\")\n launch_worker(\"client\")\n\ndef run_benchmark(master, direction=None):\n \"\"\"Connect to master and run simple TF->Python transfer benchmark.\"\"\"\n\n\n from tensorflow.python.summary import summary as summary_lib\n from tensorflow.python import pywrap_tensorflow\n from tensorflow.python.util import compat\n from tensorflow.core.util import event_pb2\n from tensorflow.core.framework import summary_pb2\n\n\n def make_event(tag, value, step):\n event = event_pb2.Event(\n wall_time=time.time(),\n step=step,\n summary=summary_pb2.Summary(\n value=[summary_pb2.Summary.Value(\n tag=tag, simple_value=value)]))\n return event\n \n if not direction:\n os.system('mkdir -p '+FLAGS.logdir)\n\n # todo: unique filenames like with contrib.summary writer\n writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(FLAGS.logdir+'/events'))\n filename = compat.as_text(writer.FileName())\n\n training_util.get_or_create_global_step()\n sess = tf.InteractiveSession()\n step = 0\n while True:\n p_to_t = run_benchmark(master, 'p->t')\n print(\"recoridng\", p_to_t, \"to\", FLAGS.logdir)\n t_to_p = run_benchmark(master, 't->p')\n \n event = make_event('p->t', p_to_t, step)\n writer.WriteEvent(event)\n event = make_event('t->p', t_to_p, step)\n writer.WriteEvent(event)\n writer.Flush()\n step+=1\n \n writer.Close()\n return\n \n assert FLAGS.warmup_iters > 0\n gc.disable()\n \n dtype = tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n# params = tf.get_variable(\"params\", [params_size], dtype,\n# initializer=tf.ones_initializer())\n params = tf.Variable(tf.ones([params_size], dtype=dtype), name='params')\n params_read = params.read_value() # prevent caching\n params_holder = tf.placeholder(dtype)\n params_write = params.assign(params_holder)\n done_queue = create_done_queue(0)\n init_op = tf.global_variables_initializer()\n sess = tf.Session(master, config=session_config())\n sess.run(init_op)\n result = sess.run(params_read)\n \n total = 0\n for i in range(FLAGS.iters+FLAGS.warmup_iters):\n if i == FLAGS.warmup_iters:\n start_time = time.time()\n # fetch value into Python runtime\n if direction == \"t->p\":\n result = sess.run(params_read)\n if FLAGS.sanity_check:\n total += result.sum()\n print(float(total)/params_size)\n elif direction == \"p->t\":\n sess.run(params_write.op, feed_dict={params_holder: result})\n \n\n elapsed_time = time.time() - start_time\n rate = float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n print(\"%5s %.2f MB/second\" % (direction, rate))\n sess.run(done_queue.enqueue(1))\n return rate\n\ndef create_done_queue(i):\n \"\"\"Queue used to signal death for i'th worker.\"\"\"\n \n return tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue\"+\n str(i))\n\nif __name__ == '__main__':\n # run local benchmark in launcher and launch service\n if FLAGS.worker_type == \"launcher\":\n run_benchmark(\"\") # run local benchmark in launcher\n if FLAGS.profile:\n gc.collect()\n launch_distributed_service()\n\n # run distributed benchmark in client\n elif FLAGS.worker_type == \"client\":\n if not FLAGS.in_process:\n run_benchmark(\"grpc://%s:%s\"%(host, FLAGS.port))\n log(\"Killing worker.\")\n\n elif FLAGS.worker_type == \"worker\": # run tensorflow worker\n server = tf.train.Server(clusterspec(), config=session_config(),\n job_name=\"worker\",\n task_index=0)\n queue = create_done_queue(0)\n sess = tf.Session(server.target, config=session_config())\n if FLAGS.in_process:\n run_benchmark(server.target)\n\n sess.run(queue.dequeue())\n time.sleep(2) # give chance for master session.run call to return\n if FLAGS.verbose:\n print(\"Worker %s quitting.\" %(FLAGS.task_index))\n else:\n assert False, \"Unknown worker type\"\n" }, { "path": "cluster/cloud-formation-example/README.md", "content": "# TensorFlow\n\n\nCreate Stack:\n```\naws --region ap-southeast-2 cloudformation create-stack --stack-name tensorflow --template-body file://tensorflow.yaml --parameters ParameterKey=KeyName,ParameterValue=[KeyName]\n```\n\nUpdate Stack:\n```\naws --region ap-southeast-2 cloudformation update-stack --stack-name tensorflow --template-body file://tensorflow.yaml --parameters ParameterKey=KeyName,ParameterValue=[KeyName]\n```\n\nDelete Stack:\n```\naws --region ap-southeast-2 cloudformation delete-stack --stack-name tensorflow\n```\n\nDescribe Stack:\n```\naws --region ap-southeast-2 cloudformation describe-stacks --stack-name tensorflow\n```\n\n# Create DNS zone distributed.tensorflow.\nbash -x zone.sh create distributed.tensorflow. ap-southeast-2 vpc-9e314bfa\n# Launch cluster with CloudFormation\naws --region ap-southeast-2 cloudformation create-stack --stack-name tensorflow --template-body file://tensorflow.yaml --parameters ParameterKey=KeyName,ParameterValue=ytang ParameterKey=SubnetId,ParameterValue=subnet-8eaba9ea ParameterKey=VPC,ParameterValue=vpc-9e314bfa\n# Destroy cluster with CloudFormation\naws --region ap-southeast-2 cloudformation delete-stack --stack-name tensorflow\n# Delete DNS zone distributed.tensorflow.\nbash -x zone.sh delete distributed.tensorflow. ap-southeast-2 vpc-9e314bfa\n" }, { "path": "cluster/cloud-formation-example/iam.yaml", "content": "AWSTemplateFormatVersion: 2010-09-09\nDescription: TensorFlow IamInstanceProfile CloudFormation\nParameters:\n InstanceProfileName:\n Type: String\n Default: TensorFlowCloudFormation\nResources:\n Role:\n Type: AWS::IAM::Role\n Properties:\n AssumeRolePolicyDocument:\n Version: 2012-10-17\n Statement:\n Effect: Allow\n Principal:\n Service:\n - ec2.amazonaws.com\n Action:\n - sts:AssumeRole\n Policies:\n Type: AWS::IAM::Policy\n Properties:\n PolicyDocument: \n Version: 2012-10-17\n Statement:\n - \n Effect: \"Allow\"\n Action: \n - \"ec2:AssociateAddress\"\n - \"ec2:DisassociateAddress\"\n Resource: \"*\"\n PolicyName: !Join [ \"-\", [ !Ref \"AWS::StackName\", \"Policies\" ] ]\n Roles:\n - !Ref Role\n InstanceProfile:\n Type: AWS::IAM::InstanceProfile\n Properties:\n Roles:\n - !Ref Role\n InstanceProfileName: !Ref InstanceProfileName\n" }, { "path": "cluster/cloud-formation-example/tensorflow.yaml", "content": "AWSTemplateFormatVersion: 2010-09-09\nDescription: Distributed TensorFlow CloudFormation\nMappings: \n AMI:\n ap-southeast-2:\n AMI: ami-1a668878\n Init:\n tensorflow-cfn:\n Content: |\n #!/bin/bash\n #\n echo Region:$Region\n echo InstanceId:$InstanceId\n echo NetworkInterfaceId:$NetworkInterfaceId\n echo DesiredCapacity:$DesiredCapacity\n HostedZoneId=$(aws --region $Region route53 list-hosted-zones --query \"HostedZones[?Name == 'distributed.tensorflow.'].Id\" --output text | sed 's/\\/hostedzone\\///g')\n AutoScalingGroupName=$(aws --region $Region autoscaling describe-auto-scaling-instances --instance-ids $InstanceId --output text --query AutoScalingInstances[0].AutoScalingGroupName)\n Address=$(aws --region $Region ec2 describe-instances --instance-id $InstanceId --query \"Reservations[0].Instances[0].PrivateIpAddress\" --output text)\n for cycle in {1..60} ; do\n attach=$(aws --region $Region ec2 attach-network-interface --network-interface-id $NetworkInterfaceId --instance-id $InstanceId --device-index 1 --output text --query AttachmentId)\n if [[ $attach = eni-attach-* ]]; then\n for (( entry=0; entry<${DesiredCapacity}; entry++ )) ; do\n index=$(( entry ))\n name=$index.distributed.tensorflow.\n record=$(aws --region $Region route53 list-resource-record-sets --hosted-zone-id $HostedZoneId --query \"ResourceRecordSets[?Name == '$name'].ResourceRecords\" --output text)\n if [[ ! -z $record ]]; then\n value=$(aws --region $Region ec2 describe-instances --instance-id $(aws --region $Region autoscaling describe-auto-scaling-groups --auto-scaling-group-names $AutoScalingGroupName --query \"AutoScalingGroups[0].Instances[*].InstanceId\" --output text) --filters \"Name=private-ip-address,Values=$record\" \"Name=instance-state-name,Values=pending,running,stopping,stopped\" --query \"Reservations[0].Instances[0].PrivateIpAddress\" --output text)\n if [[ $record != $value ]]; then\n record=\n fi\n fi\n if [[ -z $record ]]; then\n # Found an open spot\n aws --region $Region route53 change-resource-record-sets --hosted-zone-id $HostedZoneId --change-batch \"{\\\"Changes\\\":[{\\\"Action\\\":\\\"UPSERT\\\",\\\"ResourceRecordSet\\\":{\\\"Name\\\":\\\"$name\\\",\\\"Type\\\":\\\"A\\\",\\\"TTL\\\":5,\\\"ResourceRecords\\\":[{\\\"Value\\\":\\\"$Address\\\"}]}}]}\"\n echo $index > /etc/tensorflow_index\n break\n fi\n echo record:$record\n done\n echo SUCCESS\n aws --region $Region ec2 detach-network-interface --attachment-id $attach\n exit 0\n fi\n sleep 10\n done\n echo FAILURE\n exit 1\n\nParameters: \n VPC:\n Type: String\n Default: \"\"\n SubnetId:\n Type: String\n InstanceType:\n Type: String\n Default: t2.micro\n VolumeSize:\n Type: String\n Default: 20\n DesiredCapacity:\n Type: String\n Default: 3\n IamInstanceProfile:\n Type: String\n Default: Kubernetes\n KeyName:\n Type: String\n Default: \"\"\nConditions:\n KeyName: !Equals [ !Ref KeyName, \"\" ]\nResources:\n NetworkInterface:\n Type: AWS::EC2::NetworkInterface\n Properties:\n SubnetId: !Ref SubnetId\n\n LaunchConfiguration:\n Type: AWS::AutoScaling::LaunchConfiguration\n Properties: \n KeyName: !If [ KeyName, !Ref \"AWS::NoValue\", !Ref KeyName ]\n IamInstanceProfile: !Ref IamInstanceProfile\n ImageId: !FindInMap [ AMI, !Ref \"AWS::Region\", AMI ]\n InstanceType: !Ref InstanceType\n BlockDeviceMappings:\n - DeviceName: /dev/xvda\n Ebs:\n VolumeSize: !Ref VolumeSize\n VolumeType: gp2\n UserData:\n Fn::Base64: !Sub\n - |+\n #cloud-config\n\n write_files:\n - path: /etc/tensorflow-cfn\n owner: root\n encoding: base64\n content: ${Init}\n\n runcmd:\n - set -x\n - yum install -y docker\n - sed -i \"s/^\\s*OPTIONS=\\\"/OPTIONS=\\\"--storage-driver overlay2 /\" /etc/sysconfig/docker\n - chkconfig docker on\n - service docker start\n - echo\n - docker pull tensorflow/tensorflow:1.4.0\n - echo\n - Region=$(curl -s http://169.254.169.254/latest/meta-data/placement/availability-zone | sed 's/.$//')\n - InstanceId=$(curl -s http://169.254.169.254/latest/meta-data/instance-id)\n - AmiLaunchIndex=$(curl -s http://169.254.169.254/latest/meta-data/ami-launch-index)\n - export Region=$Region && export InstanceId=$InstanceId && export NetworkInterfaceId=${NetworkInterfaceId} && export DesiredCapacity=${DesiredCapacity}\n - bash -x /etc/tensorflow-cfn\n\n output: {all: '| tee -a /var/log/cloud-config.log'}\n - { Init: { \"Fn::Base64\": !FindInMap [ Init, tensorflow-cfn, Content ] }, NetworkInterfaceId: !Ref NetworkInterface, DesiredCapacity: !Ref DesiredCapacity }\n\n AutoScalingGroup:\n Type: AWS::AutoScaling::AutoScalingGroup\n Properties:\n LaunchConfigurationName: !Ref LaunchConfiguration\n MinSize: 1\n MaxSize: !Ref DesiredCapacity\n DesiredCapacity: !Ref DesiredCapacity\n VPCZoneIdentifier:\n - !Ref SubnetId\n Tags:\n -\n Key: Name\n Value: !Ref AWS::StackName\n PropagateAtLaunch: true\n" }, { "path": "cluster/cloud-formation-example/zone.sh", "content": "set -x\nset -e\n\noption=$1\nName=$2\nRegion=$3\nVPC=$4\n\nif [[ \"$option\" == \"create\" ]]; then\naws --region $Region route53 create-hosted-zone --name $Name --vpc VPCRegion=$Region,VPCId=$VPC --caller-reference $Name.$(date \"+%F-%T\")\nexit 0\nelif [[ \"$option\" == \"delete\" ]]; then\nHostedZoneId=$(aws --region $Region route53 list-hosted-zones --query \"HostedZones[?Name == '$Name'].Id\" --output text | sed 's/\\/hostedzone\\///g')\nif [[ ! -z $HostedZoneId ]]; then\naws --region $Region route53 list-resource-record-sets \\\n --hosted-zone-id $HostedZoneId |\njq -c '.ResourceRecordSets[]' |\nwhile read -r resourcerecordset; do\n read -r name type <<<$(echo $(jq -r '.Name,.Type' <<<\"$resourcerecordset\"))\n if [ $type != \"NS\" -a $type != \"SOA\" ]; then\n aws --region $Region route53 change-resource-record-sets \\\n --hosted-zone-id $HostedZoneId \\\n --change-batch '{\"Changes\":[{\"Action\":\"DELETE\",\"ResourceRecordSet\": '\"$resourcerecordset\"' }]}' \\\n --output text --query 'ChangeInfo.Id'\n fi\ndone\n aws --region $Region route53 delete-hosted-zone --id $HostedZoneId\nfi\nexit 0\nelse\nexit 1\nfi\n" }, { "path": "cluster/connect.py", "content": "#!/usr/bin/env python\n\"\"\"\n\nScript to connect to most recent instance with containing given fragment:\nUsage:\nconnect\n-- connects to most recently launched instance\nconnect i3\n-- connects to most recently launchedn instance containing i3 in instance id\n\n\nDebugging/exploring:\n\npython\nfrom pprint import pprint\nimport boto3\nec2 = boto3.client('ec2')\nresponse = ec2.describe_instances()\nreservation=response['Reservations'][0]\ninstance = reservation['Instances'][0]\npprint(instance)\n\"\"\"\n\n# todo: allow to do ls, show tags\n# todo: handle KeyError: 'PublicIpAddress'\n\nimport boto3\nimport time\nimport sys\nimport os\nfrom datetime import datetime\nfrom operator import itemgetter\n\n\ndef toseconds(dt):\n # to invert:\n # import pytz\n # utc = pytz.UTC\n # utc.localize(datetime.fromtimestamp(seconds))\n return time.mktime(dt.utctimetuple())\n\ndef main():\n fragment = ''\n if len(sys.argv)>1:\n fragment = sys.argv[1]\n \n ec2 = boto3.client('ec2')\n response = ec2.describe_instances()\n\n instance_list = []\n for reservation in response['Reservations']:\n for instance in reservation['Instances']:\n instance_list.append((toseconds(instance['LaunchTime']), instance))\n\n import pytz\n from tzlocal import get_localzone # $ pip install tzlocal\n\n sorted_instance_list = sorted(instance_list, key=itemgetter(0))\n cmd = ''\n for (ts, instance) in reversed(sorted_instance_list):\n if fragment in instance['InstanceId']:\n \n localtime = instance['LaunchTime'].astimezone(get_localzone())\n keyname = instance.get('KeyName','none')\n print(\"Connecting to %s launched at %s with key %s\" % (instance['InstanceId'], localtime, keyname))\n cmd = \"ssh -i $HOME/Dropbox/yaroslav.pem -o StrictHostKeyChecking=no ubuntu@\"+instance['PublicIpAddress']\n break\n if not cmd:\n print(\"no instance id contains fragment '%s'\"%(fragment,))\n else:\n print(cmd)\n os.system(cmd)\n\n\n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/delete_placement_groups.py", "content": "#!/usr/bin/env python\n\n# delete all placement groups\n\nimport boto3\n\n# {'PlacementGroups': [{'GroupName': 'gpu12',\n# 'State': 'available',\n# 'Strategy': 'cluster'},\n# {'GroupName': 'gpu6', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu10', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu4', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'cnn2', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu5', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu3', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'tf', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu7', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu11', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu8', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'gpu9', 'State': 'available', 'Strategy': 'cluster'},\n# {'GroupName': 'cnn', 'State': 'available', 'Strategy': 'cluster'}],\n# 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8',\n# 'date': 'Tue, 28 Nov 2017 18:52:18 GMT',\n# 'server': 'AmazonEC2',\n# 'transfer-encoding': 'chunked',\n# 'vary': 'Accept-Encoding'},\n# 'HTTPStatusCode': 200,\n# 'RequestId': '3d7adfe7-1109-413d-9aab-2f0aeafef968',\n# 'RetryAttempts': 0}}\n\nimport boto3\nec2 = boto3.client('ec2')\n\nresult=ec2.describe_placement_groups()\n#print(result)\nfor entry in result[\"PlacementGroups\"]:\n name = entry.get('GroupName', '---')\n try:\n print(\"Deleting \"+name)\n response = ec2.delete_placement_group(GroupName=name)\n print(\"Response was %d\" %(response['ResponseMetadata']['HTTPStatusCode']))\n except Exception as e:\n print(\"Failed with %s\"%(e,))\n" }, { "path": "cluster/fill_efs.py", "content": "#!/usr/bin/env python\n\nimport numpy as np\nimport math\nimport argparse\n\nparser = argparse.ArgumentParser(description='script to fill EFS with data')\n\nparser.add_argument('--gb', type=int, default=100, metavar='N',\n help='how many GBs to dump')\nparser.add_argument('--chunk_gb', type=int, default=1, metavar='N',\n help='how many GBs to dump')\nparser.add_argument('--fn', type=str, default=\"fill\", metavar='N',\n help='filename')\nargs = parser.parse_args()\n\ndef main():\n chunk_size = args.chunk_gb*1e9\n current_size = 0\n\n file_counter = 0\n max_file_counter = int(math.ceil(args.gb/args.chunk_gb))\n while current_size < args.gb*1e9:\n fn = args.fn+\"-%05d-of-%05d\"%(file_counter, max_file_counter)\n file_counter+=1\n with open(fn, 'wb') as out:\n out.write(np.random.bytes(chunk_size))\n print(\"Wrote %5.1f GBs\"%(current_size/1e9))\n current_size+=chunk_size\n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/imagenet64/README.md", "content": "# Performance\n\nReproducing 64-GPU ImageNet performance benchmark on AWS\n\nRun this:\n\n```\npython launch.py --num_workers=8 --num_ps=4\n```\n\nThat will launch 8 gradient workers on `p2.8xlarge` instances, and 4 parameter server workers on `c5.large` instances, and stream worker output locally.\n\nFor different sizes you should see this performance:\n\n\n\n\n# Detailed instructions\n## Setup\n\nFirst create the proper environment that can run this benchmark. This means installing TensorFlow and CUDA if necessary.\n\nFor this, launch an instance on AWS, SSH into it, setup all the proper packages, and then save this instance image as an AMI to use in your experiments.\n\nStarting from Amazon Ubuntu 16.04 official AMI, might do this\n\n```\nsudo apt install -y wget python-pip git python-dev jq\nsudo apt install -y python3-pip python3-dev jq\nsudo apt install -y\nsudo apt install python3\nsudo apt install -y jq\n\ncurl -O https://repo.continuum.io/archive/Anaconda3-4.4.0-Linux-x86_64.sh\nbash ./Anaconda3-4.4.0-Linux-x86_64.sh -b -p ~/anaconda3\n\necho 'export PATH=\"/home/ubuntu/anaconda3/bin:$PATH\"' >> ~/.bashrc\necho '# comment' >> ~/.bashrc\nsource ~/.bashrc\nyes | conda update conda\n\nyes | conda create -n tf python=3.5 anaconda\nsource activate tf\npip install tf-nightly-gpu --upgrade --upgrade-strategy=only-if-needed\nyes | conda install pytorch torchvision cuda80 -c soumith\nconda install -y -c conda-forge opencv\n\ncudnn5=\"libcudnn5_5.1.10-1_cuda8.0_amd64.deb\"\ncudnn6=\"libcudnn6_6.0.21-1_cuda8.0_amd64.deb\"\ncuda_base=\"https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/\"\ncuda_ver=\"cuda-repo-ubuntu1604_8.0.61-1_amd64.deb\"\n\nwget $cuda_base/$cuda_ver\nsudo dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb\nsudo apt -y update\nsudo apt install -y \"cuda-8.0\"\n\ncode_url_base=\"https://storage.googleapis.com/studio-ed756.appspot.com/src\"\nwget $code_url_base/$cudnn5\nwget $code_url_base/$cudnn6\nsudo dpkg -i $cudnn5\nsudo dpkg -i $cudnn6\n\necho 'export LD_LIBRARY_PATH=\"$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64\"' >> ~/.bashrc\necho 'export CUDA_HOME=/usr/local/cuda' >> ~/.bashrc\necho '# comment' >> ~/.bashrc\nsource ~/.bashrc\n```\n\nNow git clone TensorFlow benchmarks directory under `git0` directory\n```\nmkdir ~/git0\ncd ~/git0\ngit clone https://github.com/tensorflow/benchmarks\n```\n\nFinally create an image from this instance,\n\n\nAfter image is created, save it's AMI id into env var, ie\n\n```\nexport AMI = 'ami-9ddb0xxx'\n```\n\n\nNow pick a security group that has SSH ports open (either pick an existing one from Security Groups tab on your console left sidebar, or create a new one)\n\nSave name of the group into env var. It is under \"Group Name\" column in the console\n\n\nsecurity-group.png\n\nSave the name of this group into environment variable\n```\nexport SECURITY_GROUP=open\n```\n\n\nNow to configure ssh keys, go through the process of SSHing into some AWS instance. As a part of this, you will create a key with corresponding `.pem` file\nLocate this file and save it's path to SSH_KEY_PATH env var\n\n```\nexport SSH_KEY_PATH=~/home.pem\n```\n\nFinally, save the name of the keypair corresponding to this file into KEY_NAME env variable (it's the \"Key pair name\" under Network & Security/Key Pairs)\n\n```\nexport KEY_NAME=mykey\n```\n\n## Running experiment\n\nNow to launch experiment with 1 gradient worker and 1 parameter server\n\n```\npip install -r requirements.txt\npython launch.py\n```\n\nAfter instances launch, you should see something like this printed on your console for each task launched (1 for ps, and 1 for gradient worker)\n\n```\n...\nTo see the output of 0: tail -f /temp/tasklogs/cnn1p3/worker/0/884ABE537587D641.stdout\n```\nExecute this command in a different console and you'll see the results streaming in real time\n\n```\n...\n2017-11-28 21:08:37.690888: I tensorflow/core/distributed_runtime/master_session.cc:1008] Start master session 158197ad72c6e80a with config: intra_op_parallelism_threads: 1 gpu_options { force_gpu_compatible: true } allow_soft_placement: true\nRunning warm up\nDone warm up\nStep\tImg/sec\tloss\n1\timages/sec: 373.4 +/- 0.0 (jitter = 0.0)\t7.924\n10\timages/sec: 369.9 +/- 4.2 (jitter = 1.3)\t7.888\n20\timages/sec: 369.6 +/- 3.0 (jitter = 1.5)\t7.871\n30\timages/sec: 371.0 +/- 2.0 (jitter = 1.9)\t7.884\n40\timages/sec: 371.5 +/- 1.5 (jitter = 1.7)\t7.902\n```\n\n\nTo launch multiple experiments, give them different run names. IE, to evaluate in parallel an experiment with 8 gradient workers, 4 ps workers, and use a specific instance type for each group, do this\n\n\n```\npython launch.py --run=cnn8 --num_workers=8 --num_ps=4\n```\n\n\nYou may also want to customize instance types to get around your limits. Specify those as follows\n```\npython launch.py --run=cnn8 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge\n```\n\nTo clean up your running experiments, use [terminate_instances.py](https://github.com/diux-dev/cluster/blob/master/terminate_instances.py) script. It's hardwired to only kill instances launched with a specific ssh key, so modify `LIMIT_TO_KEY` constant in the script to work for your instances.\n\n## Troubleshooting\n\n* Sometimes first launch will get stuck with the following error printed in worker console\n\n```\ntensorflow.python.framework.errors_impl.UnavailableError: Endpoint read failed\n```\n\nThis is due one of the workers taking too long to spin up, and connection timing out in `tf_cnn_benchmarks.py`. The solution is to simply repeat the same launch command. It will reuse existing AWS instances, so the connection should succeed the second time.\n\n* **Note for people porting to other systems**: \"variable_mgr.py\" in benchmarks is not compatible with Python3, hence the `launch.py` script overwrites it with the local `variable_mgr.py` version. If you don't use launch.py, make sure to copy this file manually.\n" }, { "path": "cluster/imagenet64/aws.py", "content": "\"\"\"Utilities to launch jobs on AWS.\n\nExample usage:\njob = aws.tf_job('myjob', 1)\ntask = job.tasks[0]\ntask.upload(__file__) # copies current script onto machine\ntask.run(\"python %s --role=worker\" % (__file__,)) # runs script and streams output locally to file in /temp\n\n\"\"\"\n\nimport argparse\nimport base64\nimport boto3\nimport os\nimport struct\nimport sys\nimport threading\nimport time\nimport yaml\nimport paramiko\n\nfrom collections import OrderedDict\nfrom pprint import pprint as pp\n\n# global settings that we don't expect to change\nDEFAULT_PORT = 3000\nLOCAL_TASKLOGDIR_PREFIX='/temp/tasklogs'\nINITIALIZE_CHECK_TIMEOUT_SEC=5\n\n# global AWS vars from environment\nAMI = os.environ['AMI']\nKEY_NAME = os.environ['KEY_NAME']\nSSH_KEY_PATH = os.environ['SSH_KEY_PATH']\nSECURITY_GROUP = os.environ['SECURITY_GROUP']\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_sec = (self.end - self.start)\n print(\"%s took %.2f seconds\"%(self.tag, interval_sec))\n\n\n\ndef _ExecuteCommandInThread(ssh_client,\n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False):\n \"\"\"Returns a thread that executes the given cmd. Non-Blocking call.\n \n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n cmd: cmd to run in the ssh_client\n stdout_file: local file to write standard output of the cmd to\n stderr_file: local file to write standard error of the cmd to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error, e.g. non-'0' exit code.\n\n returns a thread that executes the given cmd\n\n \"\"\"\n t = threading.Thread(\n target=_ExecuteCommandAndStreamOutput,\n args=(ssh_client, cmd, stdout_file, stderr_file, line_extractor,\n print_error))\n print(t.daemon)\n t.start()\n return t\n\n\ndef _StreamOutputToFile(fd, file, line_extractor, cmd=None):\n \"\"\"Stream output to local file print select content to console\n\n Streams output to a local file and if a line_extractor is passed\n uses it to determine which data is printed to the local console.\n\n \"\"\"\n def func(fd, file, line_extractor):\n with open(file, 'ab+') as f:\n if cmd:\n line = cmd + '\\n'\n f.write(line.encode('utf-8'))\n try:\n for line in iter(lambda: fd.readline(2048), ''):\n f.write(line.encode('utf-8', errors='ignore'))\n f.flush()\n if line_extractor:\n line_extractor(line)\n except UnicodeDecodeError as err:\n print('UnicodeDecodeError parsing stdout/stderr, bug in paramiko:{}'\n .format(err))\n t = threading.Thread(target=func, args=(fd, file, line_extractor))\n t.start()\n return t\n\ndef _ExecuteCommandAndStreamOutput(ssh_client,\n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False,\n ok_exit_status=[0]):\n \"\"\"Executes cmd in ssh_client. Blocking call.\n\n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n cmd: cmd to run in the ssh_client\n stdout_file: local file to write standard output of the cmd to\n stderr_file: local file to write standard error of the cmd to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error\n ok_exit_status: List of status codes that are not errors, defaults to '0'\n\n \"\"\"\n _, stdout, stderr = ssh_client.exec_command(cmd, get_pty=True)\n if stdout_file:\n t1 = _StreamOutputToFile(stdout, stdout_file, line_extractor, cmd=cmd)\n if stderr_file:\n t2 = _StreamOutputToFile(stderr, stderr_file, line_extractor)\n if stdout_file:\n t1.join()\n if stderr_file:\n t2.join()\n exit_status = stdout.channel.recv_exit_status()\n if exit_status in ok_exit_status:\n return True\n else:\n if print_error:\n print('Command execution failed! Check log. Exit Status({}):{}'.format(exit_status, cmd))\n return False\n\n\ndef lookup_aws_instances(name):\n \"\"\"Returns all AWS instances for given job.\"\"\"\n ec2 = boto3.resource('ec2')\n instances = ec2.instances.filter(\n Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])\n\n result = []\n for i in instances:\n names = []\n if i.tags:\n names = [tag['Value'] for tag in i.tags if tag['Key'] == 'Name']\n key_name = i.key_name\n\n assert len(names) <= 1\n if names:\n inst_name = names[0]\n else:\n inst_name = ''\n if inst_name == name:\n if key_name != KEY_NAME:\n print(\"name matches, but key name %s doesn't match %s, skipping\"%(key_name, KEY_NAME))\n continue\n result.append(i)\n return result\n\ndef tf_job(name, num_tasks, instance_type=None, placement_group=''):\n \"\"\"Creates TensorFlow job on AWS cluster. If job with same name already\n exist on AWS cluster, then reuse those instances instead of creating new.\n\n This requires that that job settings are identical (number of tasks/instace\n type/placement group)\n \"\"\"\n\n if instance_type is None:\n instance_type = 'c5.large'\n # assume if given job exists, it's been configured properly\n # this is a performance optimization to avoid AWS startup delay\n instances = lookup_aws_instances(name)\n if instances:\n assert len(instances) == num_tasks, (\"Found job with same name, but number\"\n \" of tasks %d doesn't match requested %d, kill job manually.\"%(len(instances), num_tasks))\n print(\"Found existing job \"+name)\n else:\n print(\"Launching new job \"+name)\n\n ec2 = boto3.resource('ec2')\n placement_arg = {'GroupName': placement_group} if placement_group else {'GroupName': ''}\n print(\"Requesting %d %s\" %(num_tasks, instance_type))\n instances = ec2.create_instances(\n ImageId=AMI,\n InstanceType=instance_type,\n MinCount=num_tasks,\n MaxCount=num_tasks,\n SecurityGroups=[SECURITY_GROUP],\n Placement=placement_arg,\n KeyName=KEY_NAME)\n \n for instance in instances:\n tag = ec2.create_tags(\n Resources=[instance.id], Tags=[{\n 'Key': 'Name',\n 'Value': name\n }])\n\n assert len(instances) == num_tasks\n print('{} Instances created'.format(len(instances)))\n \n job = Job(name, instances=instances)\n \n # todo: setup EFS logdir\n # todo: setup remote tasklogdir?\n\n return job\n\ndef terminate_job(name):\n instances = lookup_aws_instances(name)\n for i in instances:\n print(\"Killing '%s' '%s' '%s'\" %(name, i.id, i.instance_type))\n i.terminate()\n\n for i in instances:\n i.load()\n while True:\n if i.state['Name'] == 'terminated':\n break\n print(\"Waiting for %s to die, instance state is %s\"%(i.id, i.state))\n time.sleep(5)\n i.load()\n\ndef _ssh_to_host(hostname,\n ssh_key=None,\n username='ubuntu',\n retry=1):\n\n \"\"\"Create ssh connection to host\n\n Creates and returns and ssh connection to the host passed in. \n\n Args:\n hostname: host name or ip address of the system to connect to.\n retry: number of time to retry.\n ssh_key: full path to the ssk hey to use to connect.\n username: username to connect with.\n\n returns SSH client connected to host.\n\n \"\"\"\n\n k = paramiko.RSAKey.from_private_key_file(ssh_key)\n \n ssh_client = paramiko.SSHClient()\n ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n counter = retry\n while counter > 0:\n try:\n ssh_client.connect(hostname=hostname, username=username, pkey=k)\n break\n except Exception as e:\n counter = counter - 1\n print('Exception connecting to host via ssh (could be a timeout):'.format(e))\n if counter == 0:\n return None\n\n return ssh_client\n\n\nclass Job:\n def __init__(self, name, instances):\n self.name = name\n self.tasks = []\n # todo: make task_ids asignment deterministic\n for task_id, instance in enumerate(instances):\n self.tasks.append(Task(instance, self, task_id))\n\n def wait_until_ready(self):\n \"\"\"Waits until all tasks in the job are available and initialized.\"\"\"\n for task in self.tasks:\n task.wait_until_ready()\n # todo: initialization should start async in constructor instead of here\n\ndef _encode_float(value):\n ba = bytearray(struct.pack('d', value)) \n return base64.b16encode(ba).decode('ascii')\n\ndef _decode_float(b16):\n return struct.unpack('d', base64.b16decode(b16))[0]\n\nclass Task:\n def __init__(self, instance, job, task_id):\n self.instance = instance\n self.job = job\n self.id = task_id\n self.initialized = False\n self.local_tasklogdir = '%s/%s/%s' %(LOCAL_TASKLOGDIR_PREFIX, self.job.name,\n self.id)\n self.last_stdout = None # path of last stdout file location\n self.last_stderr = None # path of last stderr file location\n\n\n def wait_until_ready(self):\n while not self.initialized:\n self.initialize()\n if self.initialized:\n break\n print(\"Not initialized, retrying in %d seconds\"%(INITIALIZE_CHECK_TIMEOUT_SEC))\n time.sleep(INITIALIZE_CHECK_TIMEOUT_SEC)\n self.connect_instructions = ''\n \n\n def initialize(self):\n # todo: do we need to wait until public_ip is available?\n assert self.public_ip\n # todo: this sometimes fails because public_ip is not ready\n # add query/wait\n self.ssh_client = _ssh_to_host(self.public_ip, SSH_KEY_PATH)\n if self.ssh_client is None:\n print(\"SSH into %s:%s failed\" %(self.job.name, self.id,))\n return\n \n # this blocks until instance is up\n self.initialized = True\n \n def run_sync(self, cmd):\n \"\"\"Runs given cmd in the task, returns stdout/stderr as strings.\n Because it blocks until cmd is done, use it for short cmds.\"\"\"\n # TODO: run doesn't preserve tty\n # find paramiko recipe to use tty and use that\n stdin, stdout, stderr = self.ssh_client.exec_command(cmd)\n stdout_str = stdout.read().decode('ascii')\n stderr_str = stderr.read().decode('ascii')\n return stdout_str, stderr_str\n\n def _setup_tasklogdir(self):\n if not os.path.exists(self.local_tasklogdir):\n os.system('mkdir -p '+self.local_tasklogdir)\n \n def run(self, cmd, mirror_output=False):\n \"\"\"Runs given command in the task, streams stdout/stderr to local files.\"\"\"\n\n assert self.initialized, (\"Trying to run command on task that's not \"\n \"initialized\")\n \n self._setup_tasklogdir()\n # todo: switch from encoded floats to integer micros\n print(\"---\", cmd)\n timestamp = _encode_float(time.time())\n stdout_fn = \"%s/%s.stdout\"%(self.local_tasklogdir, timestamp)\n stderr_fn = \"%s/%s.stderr\"%(self.local_tasklogdir, timestamp)\n self.last_stdout = stdout_fn\n self.last_stderr = stderr_fn\n\n if mirror_output:\n def line_extractor(line):\n print(line)\n else:\n line_extractor = None\n \n _ExecuteCommandInThread(ssh_client=self.ssh_client,\n cmd=cmd,\n stdout_file=stdout_fn,\n stderr_file=stderr_fn,\n line_extractor=line_extractor)\n\n def upload(self, local_file, remote_file=None):\n \"\"\"Uploads file to remote instance. If location not specified, dumps it\n in default directory with same name.\"\"\"\n self.wait_until_ready()\n\n # TODO: self.ssh_client is sometimes None\n sftp = self.ssh_client.open_sftp()\n if remote_file is None:\n remote_file = os.path.basename(local_file)\n sftp.put(local_file, remote_file)\n\n def _upload_directory(self, local_directory, remote_directory):\n pass\n \n @property\n def public_ip(self):\n self.instance.load()\n return self.instance.public_ip_address\n\n @property\n def port(self):\n return DEFAULT_PORT\n\n @property\n def ip(self): # private ip\n self.instance.load()\n return self.instance.private_ip_address\n" }, { "path": "cluster/imagenet64/launch.py", "content": "#!/usr/bin/env python\n\n# ImageNet experiments\n# 1 worker, 1 ps: 1 gpu/machine\n# python launch_async_adder.py --cluster=aws --run=gpu\n# worker 0 log:\n# 1\timages/sec: 52.5 +/- 0.0 (jitter = 0.0)\t8.128\n# 10\timages/sec: 52.3 +/- 0.0 (jitter = 0.1)\t8.093\n# 20\timages/sec: 52.2 +/- 0.0 (jitter = 0.1)\t8.132\n# 30\timages/sec: 52.2 +/- 0.0 (jitter = 0.2)\t8.023\n\n# 1 worker, 1 ps: 8 gpus/machine\n# python launch_async_adder.py --cluster=aws --run=gpu2\n# worker 0 log:\n# 1\timages/sec: 391.0 +/- 0.0 (jitter = 0.0)\t8.070\n# 10\timages/sec: 391.7 +/- 0.5 (jitter = 2.1)\t7.985\n# 20\timages/sec: 391.0 +/- 0.3 (jitter = 1.1)\t7.969\n# 30\timages/sec: 390.5 +/- 0.3 (jitter = 1.0)\t7.958\n# 40\timages/sec: 389.9 +/- 0.3 (jitter = 1.4)\t7.990\n\n# 2 workers, 1 ps\n# python launch_async_adder.py --cluster=aws --run=gpu4 --num_workers=2 --num_ps=1\n# worker 0 log:\n# 1\timages/sec: 385.2 +/- 0.0 (jitter = 0.0)\t8.014\n# 10\timages/sec: 383.4 +/- 0.5 (jitter = 1.1)\t7.928\n# 20\timages/sec: 383.3 +/- 0.4 (jitter = 2.1)\t7.910\n# 30\timages/sec: 382.6 +/- 0.6 (jitter = 2.2)\t7.884\n# 40\timages/sec: 381.9 +/- 0.5 (jitter = 2.4)\t7.914\n\n# 5 workers, 1 ps\n# ./launch_async_adder.py --run=gpu5 --cluster=aws --num_workers=5 --num_ps=4 \\\n# --worker_type=p2.8xlarge --ps_type=c5.large\n# worker 0 log:\n# 1\timages/sec: 384.1 +/- 0.0 (jitter = 0.0)\t7.943\n# 10\timages/sec: 381.8 +/- 3.4 (jitter = 2.0)\t7.868\n# 20\timages/sec: 382.6 +/- 1.8 (jitter = 3.3)\t7.861\n# 30\timages/sec: 382.7 +/- 1.2 (jitter = 3.1)\t7.854\n#\n#\n# 8 workers, 4 ps\n# ./launch_async_adder.py --run=cnn --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge\n#\n# worker 0 log:\n# 1\timages/sec: 389.6 +/- 0.0 (jitter = 0.0)\t7.903\n# 10\timages/sec: 388.4 +/- 0.7 (jitter = 1.2)\t7.867\n# 20\timages/sec: 388.3 +/- 0.4 (jitter = 0.7)\t7.851\n# 30\timages/sec: 387.9 +/- 0.3 (jitter = 1.6)\t7.863\n#\n#\n# after enabling placement groups\n# python ./launch_async_adder.py --run=cnn2 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge\n# worker 0 log:\n# 1\timages/sec: 368.4 +/- 0.0 (jitter = 0.0)\t7.925\n# 10\timages/sec: 368.7 +/- 0.8 (jitter = 1.3)\t7.880\n# 20\timages/sec: 368.5 +/- 0.5 (jitter = 0.8)\t7.873\n#\n#\n# after disabling placement groups again\n# python ./launch_async_adder.py --run=cnn2 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge --disable_placement\n# 1\timages/sec: 371.8 +/- 0.0 (jitter = 0.0)\t7.928\n# 10\timages/sec: 370.0 +/- 0.6 (jitter = 1.6)\t7.886\n# 20\timages/sec: 369.1 +/- 0.4 (jitter = 2.1)\t7.874\n# 30\timages/sec: 369.0 +/- 0.3 (jitter = 1.6)\t7.886\n# 40\timages/sec: 368.4 +/- 0.3 (jitter = 1.6)\t7.914\n\n\n# If given job name exists, it will assume\n# it has correct number of instances (tasks), and reuse those instances.\n\n# without placement group\n# python launch.py --run=cnn8 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge --disable_placement\n# 1\timages/sec: 373.4 +/- 0.0 (jitter = 0.0)\t7.924\n# 10\timages/sec: 369.9 +/- 4.2 (jitter = 1.3)\t7.888\n# 20\timages/sec: 369.6 +/- 3.0 (jitter = 1.5)\t7.871\n# 30\timages/sec: 371.0 +/- 2.0 (jitter = 1.9)\t7.884\n# 40\timages/sec: 371.5 +/- 1.5 (jitter = 1.7)\t7.902\n# 50\timages/sec: 370.6 +/- 1.5 (jitter = 1.9)\t7.866\n\n# TODO: this can fail with\n# tensorflow.python.framework.errors_impl.UnavailableError: Endpoint read failed\n\nimport base64\nimport json\nimport os\nimport pickle\nimport sys\nimport tensorflow as tf\nimport time\n\nfrom collections import defaultdict\n\nimport aws\n\nLOCAL_LOGDIR_PREFIX='/temp/logs'\n\nflags = tf.flags\nflags.DEFINE_string('run', 'default',\n 'tag used to keep track of machines in this experiment')\nflags.DEFINE_integer(\"num_workers\", 1, \"number of gradient workers\")\nflags.DEFINE_integer(\"num_ps\", 1, \"number of ps workers\")\n\n# todo: remove\nflags.DEFINE_string('worker_type', 'p2.8xlarge',\n 'instance type to use for gradient workers')\nflags.DEFINE_string('ps_type', 'c5.large', 'instance type to use for '\n 'ps workers')\n\n# started getting following error on placement groups, dsiable for now\n# The placement group 'xyz' is in use in another availability zone: null\nflags.DEFINE_boolean('disable_placement', True, 'disable placement groups')\n\nFLAGS = flags.FLAGS\n\ndef ossystem(cmd):\n print(cmd)\n os.system(cmd)\n\n# todo, move into aws.py\nclass AWSInstance(object):\n\n def __init__(self, instance, ssh_key='', name='', username='ubuntu',\n tags=None):\n # assert instance is aws instance\n self.aws_instance = instance\n self.ssh_key = ssh_key\n self.username = username\n if name:\n self.SetNameTag(name)\n if tags:\n for key, value in tags.items():\n self.SetCustomTag(key, value)\n\n self.opened_ssh_client = []\n\n def __del__(self):\n self.CleanSshClient()\n\n def WaitUntilReady(self):\n self.aws_instance.wait_until_running()\n # Sometimes the wait doesn't work, wait longer and check status\n client = boto3.client('ec2')\n while True:\n res = client.describe_instance_status(\n InstanceIds=[self.aws_instance.instance_id])\n try:\n if (res['InstanceStatuses'][0]['InstanceStatus']['Status'] == 'ok' and\n res['InstanceStatuses'][0]['SystemStatus']['Status'] == 'ok'):\n break\n except:\n print('instance has no status')\n time.sleep(30)\n\n self.aws_instance.load()\n self.hostname = self.aws_instance.public_dns_name\n\n def CreateSshClient(self):\n assert self.hostname is not None\n ssh_client = util.SshToHost(self.hostname, ssh_key=self.ssh_key, username=self.username)\n self.opened_ssh_client.append(ssh_client)\n return ssh_client\n\n def reuse_ssh_client(self):\n assert self.hostname is not None\n if not hasattr(self, 'ssh_client') or self.ssh_client == None:\n self.ssh_client = util.SshToHost(self.hostname, ssh_key=self.ssh_key, username=self.username)\n return self.ssh_client\n\n def CleanSshClient(self):\n if hasattr(self, 'ssh_client'):\n self.opened_ssh_client.append(self.ssh_client)\n for ssh_client in self.opened_ssh_client:\n try:\n ssh_client.close()\n except:\n pass\n self.opened_ssh_client = []\n self.ssh_client = None\n\n @property\n def state(self):\n return self.aws_instance.state.get('Name', None)\n\n def SetNameTag(self, name='tf'):\n ec2 = boto3.resource('ec2')\n self.tag = ec2.create_tags(\n Resources=[self.aws_instance.id], Tags=[{\n 'Key': 'Name',\n 'Value': name\n }])\n\n def SetCustomTag(self, key, value):\n ec2 = boto3.resource('ec2')\n self.tag = ec2.create_tags(\n Resources=[self.aws_instance.id], Tags=[{\n 'Key': key,\n 'Value': value\n }])\n\n def Start(self):\n self.aws_instance.start()\n\n def Stop(self):\n self.CleanSshClient()\n self.aws_instance.stop()\n\n def StopAndWaitUntilStopped(self):\n self.Stop()\n self.aws_instance.wait_until_stopped()\n\n def Terminate(self):\n self.CleanSshClient()\n self.aws_instance.terminate()\n\n def TerminateAndWaitUntilTerminated(self):\n self.Terminate()\n self.aws_instance.wait_until_terminated()\n\n @property\n def instance_id(self):\n return self.aws_instance.instance_id\n\n def ExecuteCommandAndWait(self, cmd, print_error=False):\n util.ExecuteCommandAndWait(\n self.reuse_ssh_client(), cmd, print_error=print_error)\n\n def ExecuteCommandAndReturnStdout(self, cmd):\n return util.ExecuteCommandAndReturnStdout(self.reuse_ssh_client(), cmd)\n\n # TODO: rename to stdout_fn\n def ExecuteCommandAndStreamOutput(self, \n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False,\n ok_exit_status=[0]):\n \n return util.ExecuteCommandAndStreamOutput(self.reuse_ssh_client(),\n cmd,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n line_extractor=line_extractor,\n print_error=print_error,\n ok_exit_status=ok_exit_status) \n\n def ExecuteCommandInThread(self,\n command,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False):\n ssh_client = self.CreateSshClient()\n return util.ExecuteCommandInThread(\n ssh_client,\n command,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n line_extractor=line_extractor,\n print_error=print_error)\n\n def RetrieveFile(self, remote_file, local_file):\n sftp_client = self.reuse_ssh_client().open_sftp()\n sftp_client.get(remote_file, local_file)\n sftp_client.close()\n\n def UploadFile(self, local_file, remote_file):\n sftp_client = self.reuse_ssh_client().open_sftp()\n sftp_client.put(local_file, remote_file)\n sftp_client.close()\n\ndef CreateAwsInstances(num_instances=1,\n image_id='',\n instance_type='t1.micro',\n key_name='',\n ssh_key='',\n instance_tag='tf',\n security_group='default',\n placement_group='',\n tags=None):\n ec2 = boto3.resource('ec2')\n if placement_group:\n MaybeCreatePlacementGroup(name=placement_group)\n aws_instances = ec2.create_instances(\n ImageId=image_id,\n InstanceType=instance_type,\n MinCount=num_instances,\n MaxCount=num_instances,\n SecurityGroups=[security_group],\n Placement={'GroupName': placement_group},\n KeyName=key_name)\n else:\n aws_instances = ec2.create_instances(\n ImageId=image_id,\n InstanceType=instance_type,\n MinCount=num_instances,\n MaxCount=num_instances,\n SecurityGroups=[security_group],\n KeyName=key_name)\n assert len(aws_instances) == num_instances\n print('{} Instances created'.format(len(aws_instances)))\n instances = [\n AWSInstance(instance, ssh_key, instance_tag, tags=tags) for instance in aws_instances\n ]\n\n return instances\n\n\ndef setup_local_logdir(run):\n logdir = LOCAL_LOGDIR_PREFIX + '/' + run\n os.system('rm -Rf '+logdir)\n os.system('mkdir -p '+logdir)\n return logdir\n\ndef launch_job_aws(name, replicas):\n # todo: rename instance_tag to name\n instances = cluster_aws.CreateAwsInstances(num_instances=num_instances,\n image_id=AMI,\n key_name=KEY_NAME,\n ssh_key=KEY_PATH,\n security_group=SECURITY_GROUP,\n instance_tag=name,\n placement_group='',\n instance_type=INSTANCE_TYPE)\n job = AwsJob()\n\n\n\ndef tf_config_cmd(full_cluster_spec, task_spec):\n task_type = task_spec['type']\n task_id = task_spec['index']\n print(\"Task id is %r\"%(task_id,))\n host = full_cluster_spec[task_type][task_id]\n\n # every worker needs its own location\n sparse_cluster_spec = defaultdict(dict)\n sparse_cluster_spec[task_type][task_id] = host\n\n # worker workers know about all ps workers\n if task_type == 'worker':\n sparse_cluster_spec['ps'] = full_cluster_spec['ps']\n\n # ps workers know about all worker workers\n if task_type == 'ps':\n pass\n sparse_cluster_spec['worker'] = full_cluster_spec['worker']\n #sparse_cluster_spec['worker'] = {0: full_cluster_spec['worker'][0]}\n\n sparse_cluster_config = {'cluster': sparse_cluster_spec,\n 'task': task_spec}\n print(\"Cluster config for %s %s is %s\"%(task_type, task_id,\n sparse_cluster_spec))\n json_string = json.dumps(sparse_cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n\n pickle_string = pickle.dumps(sparse_cluster_config)\n pickle_string_encoded = base64.b16encode(pickle_string)\n pickle_string_encoded = pickle_string_encoded.decode('ascii')\n export_command = \"export TF_PICKLE_BASE16=%s\"%(pickle_string_encoded,)\n return export_command\n\ndef launch_aws():\n ps_job = aws.tf_job(FLAGS.run+'-ps', FLAGS.num_ps)\n worker_job = aws.tf_job(FLAGS.run+'-worker', FLAGS.num_workers, placement_group='tf')\n tb_job = aws.tf_job(FLAGS.run+'-tb', 1, placement_group='tf')\n\n # wait for everything to come up\n\n # todo: private IP's may be known before instances are ready\n ps_job.wait_for_ready()\n worker_job.wait_for_ready()\n \n # TODO: orchestration may be easier if I save server spec to a predictable\n # location on AWS rather than passing it to each worker through command-line\n \n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n # launch parameter server tasks\n task_type = 'ps' \n for task in ps_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(generate_tf_env_setup_cmd(cluster_spec, task_spec))\n task.run(PS_CMD)\n\n # launch worker tasks\n task_type = 'worker' # task type can also be \"chief\", overlapping with worker\n for task in worker_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(generate_tf_env_setup_cmd(cluster_spec, task_spec))\n task.run(WORKER_CMD)\n\n # launch tensorboard visualizer\n task = tb_job.tasks[0]\n task.run('tensorboard --port=%d --logdir=%s'%(task.port, logdir))\n\n \n\nclass Instance:\n # todo: move inside instance\n \n def tf_env_setup(self, cluster_spec, task_spec):\n cluster_config = {'cluster': cluster_spec, 'task': task_spec}\n json_string = json.dumps(cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n export_command = \"export TF_CONFIG_BASE16=%s\"%(json_string_encoded,)\n self.run(export_command)\n\n# TODO: rename .ip to get_ip()\n\ndef launch_local():\n ps_job = launch_job_tmux('ps', FLAGS.num_ps)\n worker_job = launch_job_tmux('worker', FLAGS.num_workers)\n tb_job = launch_job_tmux('tb', 1)\n\n logdir = setup_local_logdir(FLAGS.run)\n\n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n \n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n # launch parameter server tasks\n task_type = 'ps' \n for task in ps_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.tf_env_setup(cluster_spec, task_spec)\n task.run(PS_CMD)\n\n # launch worker tasks\n task_type = 'worker' # task type can also be \"chief\", overlapping with worker\n for task in worker_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.tf_env_setup(cluster_spec, task_spec)\n task.run(WORKER_CMD)\n\n # launch tensorboard visualizer\n task = tb_job.tasks[0]\n task.run('tensorboard --port=%d --logdir=%s'%(task.port, logdir))\n\n\ndef cnn_launcher():\n \"\"\"Experiment launcher.\"\"\"\n\n import boto3\n ec2 = boto3.client('ec2')\n \n if not FLAGS.disable_placement:\n placement_group = FLAGS.run\n try:\n response = ec2.create_placement_group(GroupName=placement_group,\n Strategy='cluster')\n except Exception as e:\n if 'Duplicate' in e.response['Error']['Code']:\n print(\"Warning, placement group %s already exists, skipping\" %(placement_group,))\n print(\"Got message \"+str(e))\n else:\n placement_group = ''\n\n ps_job = aws.tf_job(FLAGS.run+'/ps', FLAGS.num_ps,\n instance_type=FLAGS.ps_type,\n placement_group=placement_group)\n worker_job = aws.tf_job(FLAGS.run+'/worker', FLAGS.num_workers,\n instance_type=FLAGS.worker_type,\n placement_group=placement_group)\n\n ps_job.wait_until_ready()\n worker_job.wait_until_ready()\n\n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n\n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n ps_hosts_str = ','.join(ps_hosts)\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n worker_hosts_str = ','.join(worker_hosts)\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n setup_cmd = \"source ~/.bashrc && export PATH=~/anaconda3/bin:$PATH && source activate py2 && cd ~/git0/benchmarks/scripts/tf_cnn_benchmarks\"\n\n # kill previous running processes in case we are reusing instances\n for task in ps_job.tasks:\n task.run(\"killall python\")\n for task in worker_job.tasks:\n task.run(\"killall python\")\n\n time.sleep(5)\n\n # launch parameter server tasks\n task_type = 'ps'\n cmds = []\n ps_cmd_tmpl = \"CUDA_VISIBLE_DEVICES='' python tf_cnn_benchmarks.py --local_parameter_device=gpu --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=ps --task_index=%(task_index)s\"\n for task in ps_job.tasks:\n cmds = []\n task_spec = {'type': task_type, 'index': task.id}\n cmds.append(setup_cmd)\n cmds.append(tf_config_cmd(cluster_spec, task_spec))\n task.upload(\"variable_mgr.py\",\n \"/home/ubuntu/Dropbox/git0/benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\")\n cmds.append(ps_cmd_tmpl % {\"worker_hosts\": worker_hosts_str,\n \"ps_hosts\": ps_hosts_str,\n \"job_name\": task_type,\n \"task_index\": task.id})\n task.run(' && '.join(cmds))\n print(\"To see the output: tail -f %s\" %(task.last_stdout))\n\n # launch worker tasks\n task_type = 'worker'\n cmds = []\n worker_cmd_tmpl = \"python tf_cnn_benchmarks.py --data_format=NCHW --batch_size=64 --num_batches=1000 --model=resnet50 --optimizer=sgd --variable_update=distributed_replicated --cross_replica_sync=True --local_parameter_device=gpu --num_gpus=8 --nodistortions --display_every=10 --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=worker --task_index=%(task_index)s\"\n\n for task in worker_job.tasks:\n cmds = []\n task_spec = {'type': task_type, 'index': task.id}\n cmds.append(setup_cmd)\n\n cmds.append(tf_config_cmd(cluster_spec, task_spec))\n task.upload(\"variable_mgr.py\",\n \"/home/ubuntu/Dropbox/git0/benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\")\n cmds.append(worker_cmd_tmpl % {\"worker_hosts\": worker_hosts_str,\n \"ps_hosts\": ps_hosts_str,\n \"job_name\": task_type,\n \"task_index\": task.id})\n task.run(' && '.join(cmds))\n print(\"To see the output of %s: tail -f %s\" %(task.id,\n task.last_stdout))\n\n\ndef main():\n os.system('rm -Rf data') # todo: remove\n cnn_launcher()\n \n\nif __name__=='__main__':\n main()\n \n" }, { "path": "cluster/imagenet64/requirements.txt", "content": "boto3\nparamiko\npyyaml\ntensorflow-gpu==1.4\n" }, { "path": "cluster/imagenet64/variable_mgr.py", "content": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Defines VariableMgr and subclasses used to manage variables.\n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport collections as pycoll\nimport operator\nimport re\n\nimport tensorflow as tf\n\nfrom tensorflow.contrib import nccl\nfrom tensorflow.contrib.all_reduce.python import all_reduce\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import data_flow_ops\n\nPS_SHADOW_VAR_PREFIX = 'ps_var'\n\n\n# To be used with custom_getter on tf.get_variable.\nclass OverrideCachingDevice(object):\n \"\"\"Variable getter which caches variables on the least loaded device.\n\n Variables smaller than a certain threshold are cached on a single specific\n device, as specified in the constructor. All other variables are load balanced\n across a pool of devices, by caching each variable on the least loaded device.\n \"\"\"\n\n def __init__(self, devices, device_for_small_variables,\n small_variable_size_threshold):\n self.devices = devices\n self.sizes = [0] * len(self.devices)\n self.device_for_small_variables = device_for_small_variables\n self.small_variable_size_threshold = small_variable_size_threshold\n\n def __call__(self, getter, *args, **kwargs):\n size = tf.TensorShape(kwargs['shape']).num_elements()\n if size < self.small_variable_size_threshold:\n device_name = self.device_for_small_variables\n else:\n device_index, _ = min(enumerate(\n self.sizes), key=operator.itemgetter(1))\n device_name = self.devices[device_index]\n self.sizes[device_index] += size\n\n kwargs['caching_device'] = device_name\n var = getter(*args, **kwargs)\n return var\n\n\n# To be used with custom_getter on tf.get_variable. Ensures the created variable\n# is in LOCAL_VARIABLES and not GLOBAL_VARIBLES collection.\nclass OverrideToLocalVariableIfNotPsVar(object):\n\n # args and kwargs come from the custom_getter interface for Tensorflow\n # variables, and matches tf.get_variable's signature, with the addition of\n # 'getter' at the beginning.\n def __call__(self, getter, name, *args, **kwargs):\n if name.startswith(PS_SHADOW_VAR_PREFIX):\n return getter(*args, **kwargs)\n\n if 'collections' in kwargs:\n collections = kwargs['collections']\n if not collections:\n collections = set([tf.GraphKeys.GLOBAL_VARIABLES])\n else:\n collections = set(list(collections))\n collections.remove(tf.GraphKeys.GLOBAL_VARIABLES)\n collections.add(tf.GraphKeys.LOCAL_VARIABLES)\n kwargs['collections'] = list(collections)\n return getter(name, *args, **kwargs)\n\n\nclass ParamServerDeviceSetter(object):\n \"\"\"Helper class to assign variables on the least loaded ps-device.\"\"\"\n\n def __init__(self, worker_device, ps_devices):\n \"\"\"Initializer for ParamServerDevicSetter.\n\n Args:\n worker_device: the device to use for computer ops.\n ps_devices: a list of device to use for Variable ops. Each variable is\n assigned to the least loaded device.\n \"\"\"\n self.ps_devices = ps_devices\n self.worker_device = worker_device\n self.ps_sizes = [0] * len(self.ps_devices)\n\n def __call__(self, op):\n if op.device:\n return op.device\n if op.type not in ['Variable', 'VariableV2']:\n return self.worker_device\n\n device_index, _ = min(enumerate(\n self.ps_sizes), key=operator.itemgetter(1))\n device_name = self.ps_devices[device_index]\n var_size = op.outputs[0].get_shape().num_elements()\n self.ps_sizes[device_index] += var_size\n\n return device_name\n\n\nclass VariableMgr(object):\n \"\"\"Abstract superclass for class used by BenchmarkCnn to control variables.\n\n Functions on this class are used to control how variables are created and\n managed, and how gradients are computed and applied.\n \"\"\"\n\n def __init__(self, benchmark_cnn):\n self.benchmark_cnn = benchmark_cnn\n self.staging_delta_ops = []\n\n def each_tower_has_variables(self):\n \"\"\"Returns True if each GPU tower of the model has separate variables.\"\"\"\n assert False, 'Must be implemented in subclass'\n\n def supports_staged_vars(self):\n \"\"\"Whether staged variable management is supported.\"\"\"\n return False\n\n def create_outer_variable_scope(self, device_num):\n \"\"\"Create the tf.variable_scope around all model graph operations.\"\"\"\n del device_num # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def preprocess_device_grads(self, device_grads):\n \"\"\"Preprocess the device gradients prior to applying them.\n\n Args:\n device_grads: List of lists of (gradient, variable) tuples.\n device_grads[t][g] = (gradient, variable), where t is the index of the\n tower and g is the index of the gradient-variable pair.\n\n Returns: a tuple of (apply_gradients_devices, gradient_state).\n gradient_state is an opaque structure that should be passed to\n get_gradients_to_apply() and append_apply_gradients_ops() (in that order).\n apply_gradients_devices is a list of devices where the gradients will be\n applied with get_gradients_to_apply() and append_apply_gradients_ops().\n \"\"\"\n del device_grads # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n \"\"\"Returns the [(gradient, variable)] list to apply for device_num.\n\n Args:\n device_num: indexes into apply_gradients_devices, which was returned by an\n earlier call to preprocess_device_grads.\n gradient_state: from previous call to apply_gradients_devices.\n \"\"\"\n del device_num, gradient_state # unused by this implementation\n assert False, 'Must be implemented in subclass'\n\n def append_apply_gradients_ops(\n self, gradient_state, opt, grads, training_ops):\n \"\"\"Adds training ops for grads to 'training_ops'.\n\n Args:\n gradient_state: from previous call to apply_gradients_devices.\n opt: the underlying optimizer\n grads: [(grad, var)] to apply\n training_ops: list to which to add ops\n \"\"\"\n del gradient_state # unused by this implementation\n apply_gradients_op = opt.apply_gradients(grads)\n training_ops.append(apply_gradients_op)\n\n def get_post_init_ops(self):\n \"\"\"Returns ops that should run post-initialization.\"\"\"\n return []\n\n def get_devices(self):\n \"\"\"Returns devices to use for computation; includes replica selection.\"\"\"\n assert False, 'Must be implemented in subclass'\n\n def savable_variables(self):\n \"\"\"Returns a list/dict of savable variables to pass to tf.train.Saver.\"\"\"\n return tf.global_variables()\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n \"\"\"Return the set of trainable variables on device.\n\n Args:\n rel_device_num: local worker device index.\n abs_device_num: global graph device index.\n writable: whether to get a reference to the underlying variable.\n\n Returns:\n The set of trainable vairalbes on the specified device.\n \"\"\"\n del rel_device_num, writable\n if self.each_tower_has_variables():\n params = [\n v for v in tf.trainable_variables()\n if v.name.startswith('v%s/' % abs_device_num)\n ]\n else:\n params = tf.trainable_variables()\n return params\n\n\nclass VariableMgrIndependent(VariableMgr):\n \"\"\"VariableMgr that implements the --independent mode for local jobs.\n\n Each GPU has its own copy of the variables, and gradients are\n not shared between towers. This can be used to check\n performance when no data is moved between GPUs.\n \"\"\"\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n return (self.benchmark_cnn.devices, device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n return device_grads[device_num]\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrLocalFetchFromPS(VariableMgr):\n \"\"\"VariableMgr that implements the --parameter_server mode for local jobs.\n\n Variables are stored on a parameter server. For each step, each tower gets\n a copy of the variables from the parameter server, and sends its gradients\n to the param server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return False\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v', reuse=bool(device_num))\n\n def preprocess_device_grads(self, device_grads):\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n assert device_num == 0\n device_grads = gradient_state\n return aggregate_gradients_using_copy_with_variable_colocation(\n device_grads, use_mean=True)\n\n def get_devices(self):\n raw_devices = self.benchmark_cnn.raw_devices\n if self.benchmark_cnn.local_parameter_device_flag == 'gpu':\n return [ParamServerDeviceSetter(d, raw_devices) for d in raw_devices]\n else:\n return [tf.train.replica_device_setter(\n worker_device=d, ps_device=self.benchmark_cnn.param_server_device,\n ps_tasks=1) for d in raw_devices]\n\n\nclass StagedModelVariable(object):\n \"\"\"Staging variable wrapper that decouples reads and updates.\n\n This class represents a variable through a staging buffer. Reads from this\n variable directly gets from the staging buffer. Updates are stacked into\n another staging buffer, and will be processed later.\n \"\"\"\n\n def __init__(self, real_var, var_stage_get, variable_mgr):\n \"\"\"Initializer for the model variables through a staging buffer.\n\n Args:\n real_var: the underlying real variable.\n var_stage_get: the read op from the staging buffer.\n variable_mgr: the parent variable-manager.\n \"\"\"\n self.real_var = real_var\n self.var_stage_get = var_stage_get\n self.variable_mgr = variable_mgr\n\n def _value(self):\n \"\"\"The read access of this variable. The content from the staging buffer.\"\"\"\n return self.var_stage_get\n\n def _ref(self):\n \"\"\"Return the underlying variable ref, required by tf.colocate_with.\"\"\"\n return self.real_var._ref() # pylint: disable=protected-access\n\n def read_value(self):\n \"\"\"Mimics tf.Variable.read_value().\"\"\"\n return tf.identity(self.var_stage_get, name='read')\n\n @property\n def dtype(self):\n \"\"\"Return the non-reference dtype.\"\"\"\n return self.var_stage_get.dtype\n\n def assign_sub(self, delta, name=None):\n \"\"\"Mimic the updates to the variable.\n\n Args:\n delta: is pushed into a staging buffer and will be pumped later.\n name: currently ignored; names of ops and the StagingArea are\n computed without using this pass name.\n Returns:\n The actual updates. The colocation constraint will be reapplied.\n \"\"\"\n # This parameter is ignored: the StagingArea only supports setting\n # the shared name, not the names of individual ops it uses.\n del name\n\n # colocate_with(None, True) clears the colocation constraints.\n # Push the delta into a staging buffer.\n with ops.colocate_with(None, True), tf.device(self.var_stage_get.device):\n delta_staging_area = data_flow_ops.StagingArea(\n [self.var_stage_get.dtype], shapes=[self.var_stage_get.shape])\n delta_put_op = delta_staging_area.put([delta])\n self.variable_mgr.staging_delta_ops.append(delta_put_op)\n delta_get_op = delta_staging_area.get()[0]\n # Return the actual updates. The colocation constraint will be reapplied.\n return self.real_var.assign_sub(delta_get_op)\n\n @staticmethod\n # pylint: disable=bad-staticmethod-argument,invalid-name\n def _TensorConversionFunction(self, dtype=None, name=None, as_ref=False):\n \"\"\"Utility function for converting a StagedModelVariable to a Tensor.\"\"\"\n del dtype, name # unused: this function returns the cached ref or value.\n if as_ref:\n return self._ref()\n else:\n return self._value()\n\n\nops.register_tensor_conversion_function(\n StagedModelVariable, StagedModelVariable._TensorConversionFunction) # pylint: disable=protected-access\n\n\nclass StagedVariableGetter(object):\n \"\"\"A variable getter through staging buffers on devices.\n\n Instead of a caching device, this getter tracks where the variable is used.\n And on each device, it goes through a staging buffer.\n \"\"\"\n\n def __init__(self, device_num, devices, cpu_device, variable_mgr):\n \"\"\"Initializer for StagedVariableGetter.\n\n Args:\n device_num: the current device index.\n devices: a list of all the devices to build towers.\n cpu_device: a cpu_device for this replica. If None, no cpu-caching is\n done.\n variable_mgr: the parent variable manager.\n \"\"\"\n self.device_num = device_num\n self.devices = devices\n self.cpu_device = cpu_device\n self.variable_mgr = variable_mgr\n\n def __call__(self, getter, name, *args, **kwargs):\n staging_ops = self.variable_mgr.staging_vars_on_devices[self.device_num]\n if name in staging_ops:\n put_op, get_op = staging_ops[name]\n return get_op\n real_var = getter(name, *args, **kwargs)\n shape = kwargs['shape']\n dtype = kwargs['dtype']\n trainable = kwargs['trainable']\n if self.cpu_device:\n with tf.device(self.cpu_device):\n # This helps copying the weights from the parameter to this server only\n # once.\n if name in self.variable_mgr.staged_vars_on_cpu:\n cpu_var = self.variable_mgr.staged_vars_on_cpu[name]\n else:\n cpu_var = tf.identity(real_var)\n self.variable_mgr.staged_vars_on_cpu[name] = cpu_var\n var_to_stage = cpu_var\n else:\n var_to_stage = tf.identity(real_var) # de-reference the variable.\n\n with tf.device(self.devices[self.device_num]):\n staging_area = data_flow_ops.StagingArea([dtype], shapes=[shape])\n put_op = staging_area.put([var_to_stage])\n get_op = staging_area.get()[0]\n staging_ops[name] = (put_op, get_op)\n if trainable:\n # For trainable variables, they are managed separatedly through\n # apply_gradients.\n return get_op\n else:\n # For other shadow variables, the access is decoupled through a wrapper\n # class.\n return StagedModelVariable(real_var, get_op, self.variable_mgr)\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable):\n \"\"\"Return the set of trainable variables on the specified device.\n\n Args:\n rel_device_num: local worker device index.\n abs_device_num: global graph device index.\n writable: whether the returned variables is writable or read-only.\n\n Returns:\n Return the set of trainable variables on the specified device.\n \"\"\"\n del abs_device_num\n params_refs = tf.trainable_variables()\n if writable:\n return params_refs\n params = []\n for param in params_refs:\n var_name = param.name.split(':')[0]\n _, var_get_op = self.variable_mgr.staging_vars_on_devices[rel_device_num][\n var_name]\n params.append(var_get_op)\n return params\n\n\nclass VariableMgrLocalFetchFromStagedPS(VariableMgrLocalFetchFromPS):\n \"\"\"Implements fetching a local variable through staging buffers.\n \"\"\"\n\n def __init__(self, benchmark_cnn):\n super(VariableMgrLocalFetchFromStagedPS, self).__init__(benchmark_cnn)\n # A data structure to track where the variables are used on each device.\n # Indexed by device_num and var_name, each entry stores the \"put\" and \"get\"\n # ops used for that variable on that device:\n # staging_vars_on_devices[device_num][var_name] == (put_op, get_op)\n self.staging_vars_on_devices = [dict() for _ in\n self.benchmark_cnn.raw_devices]\n\n def supports_staged_vars(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n self._custom_getter = StagedVariableGetter(\n device_num, self.benchmark_cnn.raw_devices, None, self)\n return tf.variable_scope(\n 'v', reuse=bool(device_num), custom_getter=self._custom_getter)\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n return self._custom_getter.trainable_variables_on_device(\n rel_device_num, abs_device_num, writable=writable)\n\n\nAllReduceSpecTuple = pycoll.namedtuple('AllReduceSpecTuple', 'alg shards limit')\n\n\ndef parse_general_int(s):\n \"\"\"Parse integer with power-of-2 suffix eg. 32k.\"\"\"\n mo = re.match(r'(\\d+)([KkMGT]?)$', s)\n if mo:\n i, suffix = mo.group(1, 2)\n v = int(i)\n if suffix:\n if suffix == 'K' or suffix == 'k':\n v *= 1024\n elif suffix == 'M':\n v *= (1024 * 1024)\n elif suffix == 'G':\n v *= (1024 * 1024 * 1024)\n elif suffix == 'T':\n v *= (1024 * 1024 * 1024 * 1024)\n else:\n raise ValueError('invalid integer string %s' % s)\n return v\n else:\n v = int(s)\n return v\n\n\ndef parse_all_reduce_spec(all_reduce_spec):\n \"\"\"Parse all_reduce_spec.\n\n Args:\n all_reduce_spec: a string specifying a combination of all-reduce\n algorithms to apply for gradient reduction.\n\n Returns:\n a list of AllReduceSpecTuple.\n\n Raises:\n ValueError: all_reduce_spec is not well-formed.\n\n An all_reduce_spec has BNF form:\n int ::= positive whole number\n g_int ::= int[KkMGT]?\n alg_spec ::= alg | alg#int\n range_spec ::= alg_spec | alg_spec/alg_spec\n spec ::= range_spec | range_spec:g_int:range_spec\n\n Not all syntactically correct specifications are supported.\n Examples of supported all_reduce_spec strings, with semantics explained:\n\n 'xring' == apply ring all-reduce to all tensors\n 'xring#2' == apply ring all-reduce to all tensors, using two simultaneous\n transfer rings, each operating on 1/2 of each tensor.\n 'nccl' == apply NCCL all-reduce to all tensors (only works within\n a single worker process where all devices are GPUs)\n 'nccl/xring' == apply NCCL all-reduce to all tensors within each worker\n to produce at least one full-reduced (locally) value,\n then apply ring all-reduce to one such value from each\n worker, then apply NCCL broadcast to propagate those globally\n reduced values back to every device within each worker.\n 'pscpu' == Shuffle reduce using worker CPUs as the gather devices: each\n distributed tensor is reduced by copying all instances to\n one of the worker CPUs, computing the reduction there, then\n copying back to each participating device. Tensor reductions\n are assigned to specific CPUs round-robin.\n 'psgpu#4' == Arrange all GPUs across all workers into groups of 4.\n Each distributed tensor is shuffle reduced against one\n such group of 4 GPUs, selected round-robin. That is, each\n tensor is split across 4 shards for the reduction.\n 'pscpu:2k:pscpu#2:64k:xring' == Apply single-shard pscpu to\n tensors of size <= 2048 elements, apply 2-shard pscpu to\n tensors up to size 64k elements, apply xring to larger tensors.\n 'pscpu/pscpu#2' == Use shuffle gather to locally reduce each tensor on\n the worker's CPU, then use 2-shard shuffle to reduce those\n locally reduced tensors across workers (on the worker CPUs), then\n scatter the globally reduced values locally from each worker CPU.\n \"\"\"\n range_parts = all_reduce_spec.split(':') + ['-1']\n if len(range_parts) % 2:\n raise ValueError('all_reduce_spec not well formed: %s' % all_reduce_spec)\n limit = 0\n spec = []\n alg = None\n shards = 1\n for i, range_part in enumerate(range_parts):\n if i % 2 == 1:\n try:\n limit = parse_general_int(range_part)\n spec.append(AllReduceSpecTuple(alg=alg, shards=shards, limit=limit))\n except ValueError:\n raise ValueError('all_reduce_spec (%s) contains non-integer range %s' %\n (all_reduce_spec, range_part))\n else:\n alg = range_part\n alg_parts = range_part.split('#')\n alg = alg_parts[0]\n if len(alg_parts) > 1:\n try:\n shards = int(alg_parts[1])\n except ValueError:\n raise ValueError('all_reduce_spec (%s) contains non-integer '\n 'shards %s' % all_reduce_spec, alg_parts[1])\n else:\n shards = 1\n if alg not in['nccl', 'nccl/xring', 'nccl/rechd', 'nccl/pscpu',\n 'xring', 'pscpu', 'psgpu', 'pscpu/pscpu']:\n raise ValueError('all_reduce_spec (%s) contains invalid alg %s' %\n (all_reduce_spec, alg))\n return spec\n\n\ndef build_all_reduce_device_prefixes(job_name, num_tasks):\n \"\"\"Build list of device prefix names for all_reduce.\n\n Args:\n job_name: 'worker', 'ps' or 'localhost'.\n num_tasks: number of jobs across which device names should be generated.\n\n Returns:\n A list of device name prefix strings. Each element spells out the full\n host name without adding the device.\n e.g. '/job:worker/task:0'\n \"\"\"\n if job_name != 'localhost':\n return ['/job:%s/task:%d' % (job_name, d) for d in range(0, num_tasks)]\n else:\n assert num_tasks == 1\n return ['/job:%s' % job_name]\n\n\ndef group_device_names(devices, group_size):\n \"\"\"Group device names into groups of group_size.\n\n Args:\n devices: list of strings naming devices.\n group_size: int >= 1\n\n Returns:\n list of lists of devices, where each inner list is group_size long,\n and each device appears at least once in an inner list. If\n len(devices) % group_size = 0 then each device will appear\n exactly once.\n\n Raises:\n ValueError: group_size > len(devices)\n \"\"\"\n num_devices = len(devices)\n if group_size > num_devices:\n raise ValueError('only %d devices, but group_size=%d' % (\n num_devices, group_size))\n num_groups = (num_devices // group_size) + (\n 1 if (num_devices % group_size != 0) else 0)\n groups = [[] for i in range(num_groups)]\n for i in range(0, num_groups * group_size):\n groups[i % num_groups].append(devices[i % num_devices])\n return groups\n\n\nclass VariableMgrLocalReplicated(VariableMgr):\n \"\"\"VariableMgr that implements the --replicated mode for local jobs.\n\n Each GPU has its own copy of the variables. To apply gradients,\n either a local all-reduce algorithm is applied or a regular\n cross-device aggregation is used to replicate the combined\n gradients to all towers.\n \"\"\"\n\n def __init__(self, benchmark_cnn, all_reduce_spec):\n super(VariableMgrLocalReplicated, self).__init__(benchmark_cnn)\n if all_reduce_spec:\n spec = parse_all_reduce_spec(all_reduce_spec)\n if len(spec) != 1:\n raise ValueError(\n 'replicated mode does not support hybrid all-reduce strategies')\n self._all_reduce_spec = spec[0]\n else:\n self._all_reduce_spec = None\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n if self._all_reduce_spec:\n aggregated_device_grads = sum_gradients_all_reduce(\n ['/job:localhost'], device_grads, 1,\n self._all_reduce_spec.alg,\n self._all_reduce_spec.shards, self.benchmark_cnn.gpu_indices)\n else:\n agg_grads = aggregate_gradients_using_copy_with_device_selection(\n self.benchmark_cnn, device_grads, use_mean=False)\n aggregated_device_grads = []\n for arr in device_grads:\n aggregated_device_grads.append(\n [(g, v) for (_, v), (g, _) in zip(arr, agg_grads)])\n return (self.benchmark_cnn.devices, aggregated_device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n return device_grads[device_num]\n\n def get_post_init_ops(self):\n # Copy initialized values for variables on GPU 0 to other GPUs.\n global_vars = tf.global_variables()\n var_by_name = dict([(v.name, v) for v in global_vars])\n post_init_ops = []\n for v in global_vars:\n split_name = v.name.split('/')\n # TODO(b/62630508): use more specific prefix than v or v0.\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n continue\n split_name[0] = 'v0'\n copy_from = var_by_name['/'.join(split_name)]\n post_init_ops.append(v.assign(copy_from.read_value()))\n return post_init_ops\n\n def savable_variables(self):\n \"\"\"Return the set of variables used for saving/loading the model.\"\"\"\n params = []\n for v in tf.global_variables():\n split_name = v.name.split('/')\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n params.append(v)\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrDistributedAllReduce(VariableMgr):\n \"\"\"VariableMgr that implements the --distributed_all_reduce mode.\n\n Each GPU has its own copy of the variables. To apply gradients,\n the specified all-reduce algorithm is used to reduce the gradients\n and replicate the final value to all GPUs.\n \"\"\"\n\n def __init__(self, benchmark_cnn, all_reduce_spec, job_name,\n num_workers):\n super(VariableMgrDistributedAllReduce, self).__init__(benchmark_cnn)\n if not all_reduce_spec:\n raise ValueError(\n 'distributed_all_reduce requires a non-empty all_reduce_spec')\n self._all_reduce_spec = parse_all_reduce_spec(all_reduce_spec)\n self._all_reduce_device_prefixes = build_all_reduce_device_prefixes(\n job_name, num_workers)\n self._num_workers = num_workers\n if not self._all_reduce_spec:\n raise ValueError('all_reduce_spec must be specified')\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n \"\"\"Create a scope for the named device.\n\n Args:\n device_num: index of device for variable scope. (Note that\n device_num spans all processes in cluster since a single global\n graph is used.)\n\n Returns:\n the requested variable_scope\n \"\"\"\n return tf.variable_scope('v%s' % device_num)\n\n def preprocess_device_grads(self, device_grads):\n remaining_grads = device_grads\n aggregated_grads = []\n for spec_tuple in self._all_reduce_spec:\n if spec_tuple.limit < 0:\n this_grads = remaining_grads\n remaining_grads = []\n else:\n (this_grads, remaining_grads) = split_grads_by_size(\n spec_tuple.limit, remaining_grads)\n if this_grads:\n range_agg_grads = sum_gradients_all_reduce(\n self._all_reduce_device_prefixes, this_grads, self._num_workers,\n spec_tuple.alg, spec_tuple.shards, self.benchmark_cnn.gpu_indices)\n if not aggregated_grads:\n aggregated_grads = range_agg_grads\n else:\n assert len(aggregated_grads) == len(range_agg_grads)\n for i in range(len(aggregated_grads)):\n aggregated_grads[i] += range_agg_grads[i]\n assert not remaining_grads\n full_device_set = []\n for grads in device_grads:\n g, v = grads[0]\n del v\n full_device_set.append(g.device)\n return (full_device_set, aggregated_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state\n if device_num >= len(device_grads):\n raise ValueError('device_num %d exceeds length of device_grads (%d)' %\n (device_num, len(device_grads)))\n return device_grads[device_num]\n\n def get_post_init_ops(self):\n \"\"\"Copy initialized values for variables to other devices.\"\"\"\n global_vars = tf.global_variables()\n var_by_name = dict([(v.name, v) for v in global_vars])\n post_init_ops = []\n for v in global_vars:\n split_name = v.name.split('/')\n # TODO(b/62630508): use more specific prefix than v or v0.\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n continue\n split_name[0] = 'v0'\n copy_from = var_by_name['/'.join(split_name)]\n post_init_ops.append(v.assign(copy_from.read_value()))\n return post_init_ops\n\n def savable_variables(self):\n \"\"\"Return the set of variables used for saving/loading the model.\"\"\"\n params = []\n for v in tf.global_variables():\n split_name = v.name.split('/')\n if split_name[0] == 'v0' or not v.name.startswith('v'):\n params.append(v)\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\nclass VariableMgrDistributedFetchFromPS(VariableMgr):\n \"\"\"Implements --variable_update=parameter_server mode for distributed jobs.\n\n Variables are stored on a parameter server. For each step, each tower gets\n a copy of the variables from the parameter server, and sends its gradients\n to the param server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return False\n\n def create_outer_variable_scope(self, device_num):\n if self.benchmark_cnn.local_parameter_device_flag == 'gpu':\n caching_devices = self.benchmark_cnn.raw_devices\n else:\n caching_devices = [self.benchmark_cnn.cpu_device]\n custom_getter = OverrideCachingDevice(\n caching_devices, self.benchmark_cnn.cpu_device, 1024*64)\n return tf.variable_scope('v', reuse=bool(device_num),\n custom_getter=custom_getter)\n\n def preprocess_device_grads(self, device_grads):\n # Returns (gradient_devices, gradient_state)\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n assert device_num == 0\n return aggregate_gradients_using_copy(gradient_state, use_mean=True)\n\n def get_devices(self):\n ps_strategy = tf.contrib.training.GreedyLoadBalancingStrategy(\n len(self.benchmark_cnn.ps_hosts), tf.contrib.training.byte_size_load_fn)\n return [tf.train.replica_device_setter(\n worker_device=d, cluster=self.benchmark_cnn.cluster,\n ps_strategy=ps_strategy)\n for d in self.benchmark_cnn.raw_devices]\n\n\nclass VariableMgrDistributedFetchFromStagedPS(\n VariableMgrDistributedFetchFromPS):\n \"\"\"Extends VariableMgrDistributedFetchFromPS for --staged_vars.\"\"\"\n\n def __init__(self, benchmark_cnn):\n super(VariableMgrDistributedFetchFromStagedPS, self).__init__(benchmark_cnn)\n self.staging_vars_on_devices = [dict() for _ in\n self.benchmark_cnn.raw_devices]\n self.staged_vars_on_cpu = {}\n\n def create_outer_variable_scope(self, device_num):\n self._custom_getter = StagedVariableGetter(\n device_num, self.benchmark_cnn.raw_devices,\n self.benchmark_cnn.cpu_device, self)\n return tf.variable_scope(\n 'v', reuse=bool(device_num), custom_getter=self._custom_getter)\n\n def supports_staged_vars(self):\n return True\n\n def trainable_variables_on_device(self, rel_device_num, abs_device_num,\n writable=False):\n return self._custom_getter.trainable_variables_on_device(\n rel_device_num, abs_device_num, writable=writable)\n\n\nclass VariableMgrDistributedReplicated(VariableMgr):\n \"\"\"VariableMgr that implements the --distributed_replicated mode.\n\n Each GPU has a copy of the variables, and updates its copy after the\n parameter servers are all updated with the gradients from all servers. Only\n works with cross_replica_sync=true. Unlike 'replicated', does not use nccl\n all-reduce for replicating within a server.\n \"\"\"\n\n def each_tower_has_variables(self):\n return True\n\n def create_outer_variable_scope(self, device_num):\n return tf.variable_scope(\n 'v%s' % device_num,\n custom_getter=OverrideToLocalVariableIfNotPsVar())\n\n def preprocess_device_grads(self, device_grads):\n return ([self.benchmark_cnn.param_server_device], device_grads)\n\n def get_gradients_to_apply(self, device_num, gradient_state):\n device_grads = gradient_state # From 2nd result of preprocess_device_grads.\n\n avg_grads = aggregate_gradients_using_copy_with_device_selection(\n self.benchmark_cnn, device_grads, use_mean=True)\n\n # Make shadow variable on a parameter server for each original trainable\n # variable.\n for i, (g, v) in enumerate(avg_grads):\n my_name = PS_SHADOW_VAR_PREFIX + '/' + v.name\n if my_name.endswith(':0'): my_name = my_name[:-2]\n new_v = tf.get_variable(my_name, dtype=v.dtype.base_dtype,\n initializer=v.initial_value,\n trainable=True)\n avg_grads[i] = (g, new_v)\n return avg_grads\n\n def append_apply_gradients_ops(self, gradient_state, opt,\n grads, training_ops):\n device_grads = gradient_state # From 2nd result of preprocess_device_grads.\n\n # For each variable, apply the combined gradients for this server on\n # the parameter server, and then wait for all other servers to do\n # this.\n for i, (g, v) in enumerate(grads):\n apply_gradient_op = opt.apply_gradients([(g, v)])\n barrier = self.benchmark_cnn.add_sync_queues_and_barrier(\n 'replicate_variable_%s' % i, [apply_gradient_op])\n with tf.control_dependencies([barrier]):\n with tf.device(self.benchmark_cnn.cpu_device):\n updated_value = v.read_value()\n for my_d in range(len(self.benchmark_cnn.devices)):\n training_ops.append(\n device_grads[my_d][i][1].assign(updated_value))\n\n def _strip_port(self, s):\n if s.endswith(':0'):\n return s[:-2]\n return s\n\n def get_post_init_ops(self):\n # Copy initialized variables for variables on the parameter server\n # to the local copy of the variable.\n\n local_vars = tf.local_variables()\n local_var_by_name = dict(\n [(self._strip_port(v.name), v) for v in local_vars])\n post_init_ops = []\n for v in tf.global_variables():\n if v.name.startswith(PS_SHADOW_VAR_PREFIX + '/v0/'):\n prefix = self._strip_port(\n v.name[len(PS_SHADOW_VAR_PREFIX + '/v0'):])\n for i in range(self.benchmark_cnn.num_gpus):\n name = 'v%s%s' % (i, prefix)\n if name in local_var_by_name:\n copy_to = local_var_by_name[name]\n post_init_ops.append(copy_to.assign(v.read_value()))\n return post_init_ops\n\n def _remove_shadow_var_prefix_if_present(self, var_name):\n if var_name.startswith(PS_SHADOW_VAR_PREFIX + '/'):\n return var_name[len(PS_SHADOW_VAR_PREFIX + '/'):]\n else:\n return var_name\n\n def var_dict_name(self, v):\n return self._strip_port(self._remove_shadow_var_prefix_if_present(v.name))\n\n def savable_variables(self):\n \"\"\"Returns a list/dict of savable variables to pass to tf.train.Saver.\"\"\"\n params = {}\n for v in tf.global_variables():\n assert (v.name.startswith(PS_SHADOW_VAR_PREFIX + '/v0/') or\n v.name == 'global_step:0')\n # We store variables in the checkpoint with the shadow variable prefix\n # removed so we can evaluate checkpoints in non-distributed replicated\n # mode. The checkpoints can also be loaded for training in\n # distributed_replicated mode.\n name = self._strip_port(self._remove_shadow_var_prefix_if_present(v.name))\n params[name] = v\n for v in tf.local_variables():\n # Non-trainable variables, such as batch norm moving averages, do not have\n # corresponding global shadow variables, so we add them here. Trainable\n # local variables have corresponding global shadow variables, which were\n # added in the global variable loop above.\n if v.name.startswith('v0/') and v not in tf.trainable_variables():\n params[self._strip_port(v.name)] = v\n return params\n\n def get_devices(self):\n return self.benchmark_cnn.raw_devices\n\n\ndef split_grads_by_size(threshold_size, device_grads):\n \"\"\"Break gradients into two sets according to tensor size.\n\n Args:\n threshold_size: int size cutoff for small vs large tensor.\n device_grads: List of lists of (gradient, variable) tuples. The outer\n list is over devices, the inner list is over individual gradients.\n\n Returns:\n small_grads: Subset of device_grads where shape is <= theshold_size\n elements.\n large_grads: Subset of device_grads where shape is > threshold_size\n elements.\n \"\"\"\n small_grads = []\n large_grads = []\n for dl in device_grads:\n small_dl = []\n large_dl = []\n for (g, v) in dl:\n tensor_size = g.get_shape().num_elements()\n if tensor_size <= threshold_size:\n small_dl.append([g, v])\n else:\n large_dl.append([g, v])\n if small_dl:\n small_grads.append(small_dl)\n if large_dl:\n large_grads.append(large_dl)\n return small_grads, large_grads\n\n\ndef sum_grad_and_var_all_reduce(grad_and_vars, num_workers, alg, gpu_indices,\n aux_devices=None, num_shards=1):\n \"\"\"Apply all-reduce algorithm over specified gradient tensors.\"\"\"\n # Note that each grad_and_vars looks like the following:\n # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n scaled_grads = [g for g, _ in grad_and_vars]\n if alg == 'nccl':\n summed_grads = nccl.all_sum(scaled_grads)\n elif alg == 'xring':\n summed_grads = all_reduce.build_ring_all_reduce(\n scaled_grads, num_workers, num_shards, gpu_indices, tf.add)\n elif alg == 'nccl/xring':\n summed_grads = all_reduce.build_nccl_then_ring(scaled_grads, num_shards,\n tf.add)\n elif alg == 'nccl/rechd':\n summed_grads = all_reduce.build_nccl_then_recursive_hd(scaled_grads, tf.add)\n elif alg == 'nccl/pscpu':\n summed_grads = all_reduce.build_nccl_then_shuffle(\n scaled_grads, aux_devices, tf.add, tf.add_n)\n elif alg == 'pscpu/pscpu':\n summed_grads = all_reduce.build_shuffle_then_shuffle(\n scaled_grads, aux_devices,\n # TODO(tucker): devise a way of better specifying the device set\n # for the second level.\n [aux_devices[0]],\n tf.add_n)\n elif alg in ['pscpu', 'psgpu']:\n summed_grads = all_reduce.build_shuffle_all_reduce(\n scaled_grads, aux_devices, tf.add_n)\n else:\n raise ValueError('unsupported all_reduce alg: ', alg)\n\n result = []\n for (_, v), g in zip(grad_and_vars, summed_grads):\n result.append([g, v])\n return result\n\n\ndef contains_any(haystack, needles):\n \"\"\"Tests if any needle is a substring of haystack.\n\n Args:\n haystack: a string\n needles: list of strings\n\n Returns:\n True if any element of needles is a substring of haystack,\n False otherwise.\n \"\"\"\n for n in needles:\n if n in haystack:\n return True\n return False\n\n\ndef sum_gradients_all_reduce(dev_prefixes, tower_grads, num_workers,\n alg, num_shards, gpu_indices):\n \"\"\"Apply all-reduce algorithm over specified gradient tensors.\n\n Args:\n dev_prefixes: list of prefix strings to use to generate PS device names.\n tower_grads: the gradients to reduce.\n num_workers: number of worker processes across entire job.\n alg: the all-reduce algorithm to apply.\n num_shards: alg-specific sharding factor.\n gpu_indices: indices of local GPUs in order usable for ring-reduce.\n\n Returns:\n list of reduced tensors\n \"\"\"\n alg_contains_shuffle = contains_any(alg, ['pscpu', 'psgpu'])\n new_tower_grads = []\n is_hierarchical = '/' in alg\n if 'pscpu' in alg:\n aux_devices = [prefix + '/cpu:0' for prefix in dev_prefixes]\n elif 'psgpu' in alg:\n aux_devices = [prefix + '/gpu:%d' % i for i in range(len(gpu_indices))\n for prefix in dev_prefixes]\n else:\n aux_devices = ['/job:localhost/cpu:0']\n aux_device_groups = group_device_names(\n aux_devices, num_shards if alg_contains_shuffle else 1)\n group_index = 0\n for grad_and_vars in zip(*tower_grads):\n new_tower_grads.append(sum_grad_and_var_all_reduce(\n grad_and_vars, num_workers, alg, gpu_indices,\n aux_devices if is_hierarchical else aux_device_groups[group_index],\n num_shards))\n group_index = (group_index + 1) % len(aux_device_groups)\n return [list(x) for x in zip(*new_tower_grads)]\n\n\ndef aggregate_gradients_using_copy_with_device_selection(\n benchmark_cnn, tower_grads, use_mean):\n \"\"\"Aggregate gradients, controlling device for the aggregation.\n\n Args:\n benchmark_cnn: benchmark_cnn class.\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers. For each gradient, the variable is chosen from the\n first tower.\n \"\"\"\n if benchmark_cnn.local_parameter_device_flag == 'gpu':\n avail_devices = benchmark_cnn.raw_devices\n else:\n avail_devices = [benchmark_cnn.param_server_device]\n agg_grads = []\n for i, single_grads in enumerate(zip(*tower_grads)):\n with tf.device(avail_devices[i % len(avail_devices)]):\n agg_grads.append(\n aggregate_single_gradient_using_copy(single_grads, use_mean))\n return agg_grads\n\n\ndef aggregate_gradients_using_copy_with_variable_colocation(\n tower_grads, use_mean):\n \"\"\"Aggregate gradients, colocating computation with the gradient's variable.\n\n Args:\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients. All variables\n of the same gradient across towers must be the same (that is,\n tower_grads[x][a][1] == tower_grads[y][a][1] for all indices x, y, and a)\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers.\n \"\"\"\n agg_grads = []\n for single_grads in zip(*tower_grads):\n # Note that each single_grads looks like the following:\n # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n var = single_grads[0][1]\n\n for _, v in single_grads:\n assert v == var\n\n with tf.device(var.device):\n agg_grads.append(\n aggregate_single_gradient_using_copy(single_grads, use_mean))\n return agg_grads\n\n\ndef aggregate_gradients_using_copy(tower_grads, use_mean):\n \"\"\"Calculate the average gradient for each shared variable across all towers.\n\n Note that this function provides a synchronization point across all towers.\n\n Args:\n tower_grads: List of lists of (gradient, variable) tuples. The outer list\n is over towers. The inner list is over individual gradients.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n List of pairs of (gradient, variable) where the gradient has been averaged\n across all towers. For each gradient, the variable is chosen from the\n first tower.\n \"\"\"\n return [aggregate_single_gradient_using_copy(single_grads, use_mean)\n for single_grads in zip(*tower_grads)]\n\n\ndef aggregate_single_gradient_using_copy(grad_and_vars, use_mean):\n \"\"\"Calculate the average gradient for a shared variable across all towers.\n\n Note that this function provides a synchronization point across all towers.\n\n Args:\n grad_and_vars: A list or tuple of (gradient, variable) tuples. Each\n (gradient, variable) pair within the outer list represents the gradient\n of the variable calculated for a single tower, and the number of pairs\n equals the number of towers.\n use_mean: if True, mean is taken, else sum of gradients is taken.\n Returns:\n The pair (average_gradient, variable) where the gradient has been averaged\n across all towers. The variable is chosen from the first tower.\n \"\"\"\n grads = [g for g, _ in grad_and_vars]\n grad = tf.add_n(grads)\n\n if use_mean and len(grads) > 1:\n grad = tf.multiply(grad, 1.0/len(grads))\n\n v = grad_and_vars[0][1]\n return grad, v\n" }, { "path": "cluster/instance_info.py", "content": "from collections import defaultdict\n\nimport boto3\n\n\"\"\"\nA tool for retrieving basic information from the running EC2 instances.\n\"\"\"\n\n# Connect to EC2\nec2 = boto3.resource('ec2')\n\n# Get information for all running instances\nrunning_instances = ec2.instances.filter(Filters=[{\n 'Name': 'instance-state-name',\n 'Values': ['running']}])\n\nec2info = defaultdict()\nfor instance in running_instances:\n for tag in instance.tags or []:\n if 'Name'in tag['Key']:\n name = tag['Value']\n if name != 'tf':\n continue\n # Add instance info to a dictionary \n ec2info[instance.id] = {\n 'Name': name,\n 'Type': instance.instance_type,\n 'State': instance.state['Name'],\n 'Private IP': instance.private_ip_address,\n 'Public IP': instance.public_ip_address,\n 'Launch Time': instance.launch_time\n }\n\nattributes = ['Name', 'Type', 'State', 'Private IP', 'Public IP', 'Launch Time']\nfor instance_id, instance in ec2info.items():\n for key in attributes:\n print(\"{0}: {1}\".format(key, instance[key]))\n print(\"------\")\n \n" }, { "path": "cluster/launch_async_adder.py", "content": "#!/usr/bin/env python\n\n# ImageNet experiments\n# 1 worker, 1 ps: 1 gpu/machine\n# python launch_async_adder.py --cluster=aws --run=gpu\n# worker 0 log:\n# 1\timages/sec: 52.5 +/- 0.0 (jitter = 0.0)\t8.128\n# 10\timages/sec: 52.3 +/- 0.0 (jitter = 0.1)\t8.093\n# 20\timages/sec: 52.2 +/- 0.0 (jitter = 0.1)\t8.132\n# 30\timages/sec: 52.2 +/- 0.0 (jitter = 0.2)\t8.023\n\n# 1 worker, 1 ps: 8 gpus/machine\n# python launch_async_adder.py --cluster=aws --run=gpu2\n# worker 0 log:\n# 1\timages/sec: 391.0 +/- 0.0 (jitter = 0.0)\t8.070\n# 10\timages/sec: 391.7 +/- 0.5 (jitter = 2.1)\t7.985\n# 20\timages/sec: 391.0 +/- 0.3 (jitter = 1.1)\t7.969\n# 30\timages/sec: 390.5 +/- 0.3 (jitter = 1.0)\t7.958\n# 40\timages/sec: 389.9 +/- 0.3 (jitter = 1.4)\t7.990\n\n# 2 workers, 1 ps\n# python launch_async_adder.py --cluster=aws --run=gpu4 --num_workers=2 --num_ps=1\n# worker 0 log:\n# 1\timages/sec: 385.2 +/- 0.0 (jitter = 0.0)\t8.014\n# 10\timages/sec: 383.4 +/- 0.5 (jitter = 1.1)\t7.928\n# 20\timages/sec: 383.3 +/- 0.4 (jitter = 2.1)\t7.910\n# 30\timages/sec: 382.6 +/- 0.6 (jitter = 2.2)\t7.884\n# 40\timages/sec: 381.9 +/- 0.5 (jitter = 2.4)\t7.914\n\n# 5 workers, 1 ps\n# ./launch_async_adder.py --run=gpu5 --cluster=aws --num_workers=5 --num_ps=4 \\\n# --worker_type=p2.8xlarge --ps_type=c5.large\n# worker 0 log:\n# 1\timages/sec: 384.1 +/- 0.0 (jitter = 0.0)\t7.943\n# 10\timages/sec: 381.8 +/- 3.4 (jitter = 2.0)\t7.868\n# 20\timages/sec: 382.6 +/- 1.8 (jitter = 3.3)\t7.861\n# 30\timages/sec: 382.7 +/- 1.2 (jitter = 3.1)\t7.854\n#\n#\n# 8 workers, 4 ps\n# ./launch_async_adder.py --run=cnn --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge\n#\n# worker 0 log:\n# 1\timages/sec: 389.6 +/- 0.0 (jitter = 0.0)\t7.903\n# 10\timages/sec: 388.4 +/- 0.7 (jitter = 1.2)\t7.867\n# 20\timages/sec: 388.3 +/- 0.4 (jitter = 0.7)\t7.851\n# 30\timages/sec: 387.9 +/- 0.3 (jitter = 1.6)\t7.863\n#\n#\n# after enabling placement groups\n# python ./launch_async_adder.py --run=cnn2 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge\n# worker 0 log:\n# 1\timages/sec: 368.4 +/- 0.0 (jitter = 0.0)\t7.925\n# 10\timages/sec: 368.7 +/- 0.8 (jitter = 1.3)\t7.880\n# 20\timages/sec: 368.5 +/- 0.5 (jitter = 0.8)\t7.873\n#\n#\n# after disabling placement groups again\n# python ./launch_async_adder.py --run=cnn2 --num_workers=8 --num_ps=4 --worker_type=p2.8xlarge --ps_type=c5.2xlarge --disable_placement\n# 1\timages/sec: 371.8 +/- 0.0 (jitter = 0.0)\t7.928\n# 10\timages/sec: 370.0 +/- 0.6 (jitter = 1.6)\t7.886\n# 20\timages/sec: 369.1 +/- 0.4 (jitter = 2.1)\t7.874\n# 30\timages/sec: 369.0 +/- 0.3 (jitter = 1.6)\t7.886\n# 40\timages/sec: 368.4 +/- 0.3 (jitter = 1.6)\t7.914\n\n\n# Works either with remote or local instances.\n# Local instances are tmux sessions. Each session has separate window\n# corresponding to the task.\n\n# Remote instances are on AWS. If given job name exists, it will assume\n# it has correct number of instances (tasks), and reuse those instances.\n\n# Job naming:\n# test-ps (2 instances)\n# test-worker (3 instances)\n# test-tb (tensorboard process)\n\n# Locally\n# tmux session test-ps\n# tmux session test-worker\n\n# todo: utility to scp directory to amazon machine\n\n# TODO: check if instance limits are satisfiable ahead of time, crash early if\n# not (need iam:GetAccountSummary permission)\n#\n# Stop failure that happens when cluster init is triggered before instances\n# are ready, currently stack trace as below\n# self.stop(close_summary_writer=close_summary_writer)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py\", line 820, in stop\n# ignore_live_threads=ignore_live_threads)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py\", line 387, in join\n# six.reraise(*self._exc_info_to_raise)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py\", line 981, in managed_session\n# start_standard_services=start_standard_services)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py\", line 726, in prepare_or_wait_for_session\n# max_wait_secs=max_wait_secs)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py\", line 400, in wait_for_session\n# sess)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py\", line 481, in _try_run_local_init_op\n# is_ready_for_local_init, msg = self._model_ready_for_local_init(sess)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py\", line 466, in _model_ready_for_local_init\n# \"Model not ready for local init\")\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py\", line 508, in _ready\n# ready_value = sess.run(op)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py\", line 889, in run\n# run_metadata_ptr)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py\", line 1120, in _run\n# feed_dict_tensor, options, run_metadata)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py\", line 1317, in _do_run\n# options, run_metadata)\n# File \"/home/ubuntu/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py\", line 1336, in _do_call\n# raise type(e)(node_def, op, message)\n# tensorflow.python.framework.errors_impl.UnavailableError: Endpoint read failed\n\nimport base64\nimport json\nimport os\nimport portpicker\nimport shlex\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport threading\nimport time\nfrom collections import defaultdict\nimport pickle\n\nmodule_path=os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, module_path+'/tf-tools/benchmark/runner')\nimport cluster_aws\n\nimport aws\nimport tmux\n\n# TODO: stop launcher script from quitting when ssh command returns\n# (remove daemon thread designation)\n\n# todo: robustness to worker restarts\n\n# todo, way to do \"tail -f\" to show logs from single task\n\n\n################################################################################\n# User-specific AWS config\nAMI = 'ami-9ddb0fe5'\nKEY_NAME = 'yaroslav' # AWS key-name to use\nKEY_PATH = os.environ['HOME']+'/d/yaroslav.pem' # location of .pem file on\n # local filesystem\nSECURITY_GROUP = 'open' # security group for all instances\n################################################################################\n\n\nLOCAL_LOGDIR_PREFIX='/temp/logs'\nEFS_LOGDIR_PREFIX='/efs/logs'\n\n# TODO: replace tmux_name with just name\nflags = tf.flags\nflags.DEFINE_string('run', 'default',\n 'tag used to keep track of machines in this experiment')\nflags.DEFINE_integer(\"num_workers\", 1, \"number of gradient workers\")\nflags.DEFINE_integer(\"num_ps\", 1, \"number of ps workers\")\n#flags.DEFINE_boolean(\"verbose\", False, \"whether to have verbose logging\")\nflags.DEFINE_string(\"tmux_name\", \"async_adder\", \"name to use for tmux session\")\n#flags.DEFINE_string('localdir_prefix', '/temp/stdout',\n# 'where to mirror worker logs locally')\n#flags.DEFINE_string('logdir_prefix', '/efs/logs',\n# 'where to dump EFS logs')\nflags.DEFINE_integer('remote_worker_port', 3333, 'port to use for '\n 'remote connections')\nflags.DEFINE_integer('remote_tb_port', 6006, 'port to use for '\n 'tensorboard service')\nflags.DEFINE_string(\"cluster\", \"aws\", \"where to run (aws or local)\")\nflags.DEFINE_boolean(\"kill\", False, \"brings down experiment\")\nflags.DEFINE_string('worker_type', 'p2.8xlarge', 'instance type to use for workers')\nflags.DEFINE_string('ps_type', 'c5.large', 'instance type to use for ps')\nflags.DEFINE_boolean('disable_placement', False, 'disable placement groups')\n\nFLAGS = flags.FLAGS\n\n\nWORKER_CMD='python ./async_adder.py' # todo: path robustness?\nPS_CMD='python ./async_adder.py'\n\n\n# TODO: worker/ps logs are different, make sure different worker tasks\n# are actually writing to different logs\n\n# todo: sometimes get 'endpoint read failed', due to AWS not bringing up\n# all instances yet perhaps\ndef ossystem(cmd):\n print(cmd)\n os.system(cmd)\n\n# todo: factor out into tmux_lib\n\n\ndef setup_local_logdir(run):\n logdir = LOCAL_LOGDIR_PREFIX + '/' + run\n os.system('rm -Rf '+logdir)\n os.system('mkdir -p '+logdir)\n return logdir\n\ndef setup_remote_logdir(run):\n logdir = EFS_LOGDIR_PREFIX + '/' + run\n # TODO: make these mkdir's work\n #os.system('rm -Rf '+logdir)\n #os.system('mkdir -p '+logdir)\n return logdir\n\ninitialized_windows = set()\ndef run_in_window(window, cmd_list):\n \"\"\"Runs command in tmux window, initializing tmux session and window if\n necessary. cmd_list is list of args\"\"\"\n\n if isinstance(cmd_list, str):\n cmd_list = [cmd_list]\n\n assert isinstance(cmd_list, list)\n \n global initialized_windows\n def run(cmd):\n ossystem(\"tmux send-keys -t {} '{}' Enter\".format(window, cmd))\n\n # if nothing initialized, restart session\n if not initialized_windows:\n ossystem('tmux kill-session -t ' + FLAGS.tmux_name)\n # -d starts new session in detached mode\n # since can't start windowless tmux, start with dummy window and rename\n ossystem('tmux new-session -s %s -n %s -d '% (FLAGS.tmux_name, \"blargh\"))\n \n if not window in initialized_windows:\n if not initialized_windows:\n ossystem('tmux rename-window -t blargh '+window)\n else:\n ossystem(\"tmux new-window -t {} -n {}\".format(FLAGS.tmux_name, window))\n\n initialized_windows.add(window)\n \n for cmd in cmd_list:\n run(cmd)\n\n# all instances->tasks\ndef launch_job_tmux(role, num_tasks):\n \n job_name = FLAGS.run + '-'+role\n DEFAULT_NAME = 'blargh'\n ossystem('tmux kill-session -t ' + job_name)\n\n # todo: move tmux initialization into localjob init\n # TODO: don't need default name\n ossystem('tmux new-session -s %s -n %s -d '% (job_name, DEFAULT_NAME))\n ossystem('tmux rename-window -t %s %s '%(DEFAULT_NAME, '0'))\n for task_id in range(1, num_tasks):\n ossystem(\"tmux new-window -t {} -n {}\".format(job_name, task_id))\n\n job = LocalJob(job_name, num_tasks)\n # setup environment\n for task in job.tasks:\n task.run('source activate sep22')\n \n return job\n\n\nclass LocalJob:\n def __init__(self, name, num_tasks):\n self.name = name\n self.num_tasks = num_tasks\n self.tasks = []\n for task_id in range(num_tasks):\n self.tasks.append(LocalTask(self, task_id))\n\n\nclass LocalTask: # same as Instance\n \"\"\"Local tasks interacts with tmux session where session name is derived\n from job name, and windows are task ids.\"\"\"\n\n def __init__(self, job, task_id):\n self.job = job\n self.ip = '127.0.0.1' # hostname/ip address\n self.id = task_id\n self.port = portpicker.pick_unused_port()\n\n def run(self, cmd):\n window = self.job.name+\":\"+str(self.id)\n ossystem(\"tmux send-keys -t {} '{}' Enter\".format(window, cmd))\n\n\n def tf_env_setup(self, full_cluster_spec, task_spec):\n # full cluster config\n # todo: not needed\n # cluster_config = {'cluster': cluster_spec, 'task': task_spec}\n\n task_type = task_spec['type']\n task_id = task_spec['index']\n print(\"Task id is %r\"%(task_id,))\n host = full_cluster_spec[task_type][task_id]\n\n # every worker needs its own location\n sparse_cluster_spec = defaultdict(dict)\n sparse_cluster_spec[task_type][task_id] = host\n \n # worker workers know about all ps workers\n if task_type == 'worker':\n sparse_cluster_spec['ps'] = full_cluster_spec['ps']\n \n # ps workers know about all worker workers\n if task_type == 'ps':\n pass\n sparse_cluster_spec['worker'] = full_cluster_spec['worker']\n #sparse_cluster_spec['worker'] = {0: full_cluster_spec['worker'][0]}\n\n sparse_cluster_config = {'cluster': sparse_cluster_spec,\n 'task': task_spec}\n print(\"Cluster config for %s %s is %s\"%(task_type, task_id,\n sparse_cluster_spec))\n json_string = json.dumps(sparse_cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n export_command = \"export TF_CONFIG_BASE16=%s\"%(json_string_encoded,)\n self.run(export_command)\n\n # json has problem with sparse clusterspec (0 can't be key, only \"0\")\n # therefore also dump clusterspec as pickle object\n pickle_string = pickle.dumps(sparse_cluster_config)\n pickle_string_encoded = base64.b16encode(pickle_string)\n pickle_string_encoded = pickle_string_encoded.decode('ascii')\n export_command = \"export TF_PICKLE_BASE16=%s\"%(pickle_string_encoded,)\n self.run(export_command)\n \n logdir = LOCAL_LOGDIR_PREFIX + '/' + FLAGS.run\n self.run(\"export LOGDIR=\"+logdir)\n\n\ndef select_window(window):\n \"\"\"select the window to be in the foreground\"\"\"\n ossystem('tmux select-window -t %s:%s'% (FLAGS.tmux_name, window))\n\ndef launch_job_aws(name, replicas):\n\n # todo: rename instance_tag to name\n instances = cluster_aws.CreateAwsInstances(num_instances=num_instances,\n image_id=AMI,\n key_name=KEY_NAME,\n ssh_key=KEY_PATH,\n security_group=SECURITY_GROUP,\n instance_tag=name,\n placement_group='',\n instance_type=INSTANCE_TYPE)\n job = AwsJob()\n\n\n\ndef tf_config_cmd(full_cluster_spec, task_spec):\n task_type = task_spec['type']\n task_id = task_spec['index']\n print(\"Task id is %r\"%(task_id,))\n host = full_cluster_spec[task_type][task_id]\n\n # every worker needs its own location\n sparse_cluster_spec = defaultdict(dict)\n sparse_cluster_spec[task_type][task_id] = host\n\n # worker workers know about all ps workers\n if task_type == 'worker':\n sparse_cluster_spec['ps'] = full_cluster_spec['ps']\n\n # ps workers know about all worker workers\n if task_type == 'ps':\n pass\n sparse_cluster_spec['worker'] = full_cluster_spec['worker']\n #sparse_cluster_spec['worker'] = {0: full_cluster_spec['worker'][0]}\n\n sparse_cluster_config = {'cluster': sparse_cluster_spec,\n 'task': task_spec}\n print(\"Cluster config for %s %s is %s\"%(task_type, task_id,\n sparse_cluster_spec))\n json_string = json.dumps(sparse_cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n\n pickle_string = pickle.dumps(sparse_cluster_config)\n pickle_string_encoded = base64.b16encode(pickle_string)\n pickle_string_encoded = pickle_string_encoded.decode('ascii')\n export_command = \"export TF_PICKLE_BASE16=%s\"%(pickle_string_encoded,)\n return export_command\n\nclass BasicAwsJob():\n def __init__(name, num_tasks):\n pass\n\n \n \ndef launch_aws():\n ps_job = aws.tf_job(FLAGS.run+'-ps', FLAGS.num_ps)\n worker_job = aws.tf_job(FLAGS.run+'-worker', FLAGS.num_workers, placement_group='tf')\n tb_job = aws.tf_job(FLAGS.run+'-tb', 1, placement_group='tf')\n\n # wait for everything to come up\n\n # todo: private IP's may be known before instances are ready\n ps_job.wait_for_ready()\n worker_job.wait_for_ready()\n \n # TODO: orchestration may be easier if I save server spec to a predictable\n # location on AWS rather than passing it to each worker through command-line\n \n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n # launch parameter server tasks\n task_type = 'ps' \n for task in ps_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(generate_tf_env_setup_cmd(cluster_spec, task_spec))\n task.run(PS_CMD)\n\n # launch worker tasks\n task_type = 'worker' # task type can also be \"chief\", overlapping with worker\n for task in worker_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(generate_tf_env_setup_cmd(cluster_spec, task_spec))\n task.run(WORKER_CMD)\n\n # launch tensorboard visualizer\n task = tb_job.tasks[0]\n task.run('tensorboard --port=%d --logdir=%s'%(task.port, logdir))\n\n \n\nclass Instance:\n # todo: move inside instance\n \n def tf_env_setup(self, cluster_spec, task_spec):\n cluster_config = {'cluster': cluster_spec, 'task': task_spec}\n json_string = json.dumps(cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n export_command = \"export TF_CONFIG_BASE16=%s\"%(json_string_encoded,)\n self.run(export_command)\n\n# TODO: rename .ip to get_ip()\n\ndef launch_local():\n ps_job = launch_job_tmux('ps', FLAGS.num_ps)\n worker_job = launch_job_tmux('worker', FLAGS.num_workers)\n tb_job = launch_job_tmux('tb', 1)\n\n logdir = setup_local_logdir(FLAGS.run)\n\n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n \n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n # launch parameter server tasks\n task_type = 'ps' \n for task in ps_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.tf_env_setup(cluster_spec, task_spec)\n task.run(PS_CMD)\n\n # launch worker tasks\n task_type = 'worker' # task type can also be \"chief\", overlapping with worker\n for task in worker_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.tf_env_setup(cluster_spec, task_spec)\n task.run(WORKER_CMD)\n\n # launch tensorboard visualizer\n task = tb_job.tasks[0]\n task.run('tensorboard --port=%d --logdir=%s'%(task.port, logdir))\n\n\n# def launch_remote():\n# ps_job = AwsJob('ps', 1)\n# worker_job = launch_instances_aws('worker', 1)\n# tb_job = launch_instances_aws('tb', 1)[0]\n\n# # Orchestration, every worker needs to know their own role (task_spec)\n# # and role->ip mapping of all machines (cluster_spec)\n# # This information is saved as base16 encoded dictionary in env var\n# # and loaded in the task script\n# PORT = 3000\n# ps_hosts = [\"%s:%d\"%(i.ip, PORT) for i in ps_instances]\n# worker_hosts = [\"%s:%d\"%(i.ip, PORT) for i in worker_instances]\n# cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n# def tf_config_setup():\n# task_spec = {'type': task_type, 'index': task_id}\n# cluster_config = {'cluster': cluster_spec, 'task': task_spec}\n# json_string = json.dumps(cluster_config)\n# json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n# json_string_encoded = json_string_encoded.decode('ascii')\n# export_command = \"export TF_CONFIG_BASE16=%s\"%(json_string_encoded,)\n# print('export command')\n# print(repr(export_command))\n# return export_command\n\n# task_type = 'ps'\n# for task_id, instance in enumerate(ps_instances):\n# instance.run(tf_config_setup())\n# instance.run(PS_CMD)\n\n# task_type = 'worker'\n# for task_id, instance in enumerate(worker_instances):\n# instance.run(tf_config_setup())\n# instance.run(WORKER_CMD)\n\n# tb_instance.run('tensorboard --port=%d --logdir=%s'%(port, logdir))\n\ndef cnn_launcher():\n \"\"\"Experiment launcher.\"\"\"\n if FLAGS.cluster == 'local':\n if FLAGS.kill:\n tmux.kill_job(\"ps\")\n tmux.kill_job(\"worker\")\n tmux.kill_job(\"tb\")\n return\n \n logdir = setup_local_logdir(FLAGS.run)\n ps_job = tmux.tf_job('ps', FLAGS.num_ps)\n worker_job = tmux.tf_job('worker', FLAGS.num_workers)\n tb_job = tmux.tf_job('tb', 1)\n\n\n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n\n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n # launch parameter server tasks\n task_type = 'ps' \n for task in ps_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(tf_config_cmd(cluster_spec, task_spec))\n task.run(\"export LOGDIR=\"+logdir)\n task.run(PS_CMD)\n\n # launch worker tasks\n task_type = 'worker'\n for task in worker_job.tasks:\n task_spec = {'type': task_type, 'index': task.id}\n task.run(tf_config_cmd(cluster_spec, task_spec))\n task.run(\"export LOGDIR=\"+logdir)\n task.run(WORKER_CMD)\n\n # launch tensorboard visualizer\n task = tb_job.tasks[0]\n task.run(\"export LOGDIR=\"+logdir)\n task.run('tensorboard --port=%d --logdir=$LOGDIR')\n elif FLAGS.cluster == 'aws':\n\n # create placement group, same as run name\n import boto3\n ec2 = boto3.client('ec2')\n \n if not FLAGS.disable_placement:\n placement_group = FLAGS.run\n try:\n response = ec2.create_placement_group(GroupName=placement_group,\n Strategy='cluster')\n except Exception as e:\n if 'Duplicate' in e.response['Error']['Code']:\n print(\"Warning, placement group %s already exists, skipping\" %(placement_group,))\n print(\"Got message \"+str(e))\n else:\n placement_group = ''\n\n\n # out of instances for c5.large\n logdir = setup_remote_logdir(FLAGS.run)\n ps_job = aws.tf_job(FLAGS.run+'/ps', FLAGS.num_ps,\n instance_type=FLAGS.ps_type,\n placement_group=placement_group)\n worker_job = aws.tf_job(FLAGS.run+'/worker', FLAGS.num_workers,\n instance_type=FLAGS.worker_type,\n placement_group=placement_group)\n\n ps_job.wait_until_ready()\n worker_job.wait_until_ready()\n # tb_job = aws.tf_job('tb', 1, instance_type='m2.micro')\n\n # Orchestration: every worker needs to know:\n # 1. their own role (task_spec), ie {type: worker, index: 0}\n # 2. role->ip mapping of all machines (cluster_spec), ie\n # {\"worker\": [\"localhost:24724\"], \"ps\": [\"localhost:15960\"]}}\n\n ps_hosts = [\"%s:%d\"%(task.ip, task.port) for task in ps_job.tasks]\n ps_hosts_str = ','.join(ps_hosts)\n worker_hosts = [\"%s:%d\"%(task.ip, task.port) for task in worker_job.tasks]\n worker_hosts_str = ','.join(worker_hosts)\n cluster_spec = {'worker': worker_hosts, 'ps': ps_hosts}\n\n setup_cmd = \"source ~/.bashrc && export PATH=~/anaconda3/bin:$PATH && source activate py2 && cd ~/git0/benchmarks/scripts/tf_cnn_benchmarks\"\n\n\n for task in ps_job.tasks:\n task.run(\"killall python\")\n for task in worker_job.tasks:\n task.run(\"killall python\")\n\n time.sleep(5)\n \n # launch parameter server tasks\n task_type = 'ps'\n cmds = []\n ps_cmd_tmpl = \"CUDA_VISIBLE_DEVICES='' python tf_cnn_benchmarks.py --local_parameter_device=gpu --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=ps --task_index=%(task_index)s\"\n for task in ps_job.tasks:\n cmds = []\n task_spec = {'type': task_type, 'index': task.id}\n cmds.append(setup_cmd)\n cmds.append(tf_config_cmd(cluster_spec, task_spec))\n cmds.append(\"export LOGDIR=\"+logdir)\n # task.upload(\"./benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py\")\n task.upload(\"./benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\",\n \"/home/ubuntu/Dropbox/git0/benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\")\n cmds.append(ps_cmd_tmpl % {\"worker_hosts\": worker_hosts_str,\n \"ps_hosts\": ps_hosts_str,\n \"job_name\": task_type,\n \"task_index\": task.id})\n task.run(' && '.join(cmds))\n print(\"To see the output: tail -f %s\" %(task.last_stdout))\n\n # launch worker tasks\n task_type = 'worker'\n cmds = []\n worker_cmd_tmpl = \"python tf_cnn_benchmarks.py --data_format=NCHW --batch_size=64 --num_batches=1000 --model=resnet50 --optimizer=sgd --variable_update=distributed_replicated --cross_replica_sync=True --local_parameter_device=gpu --num_gpus=8 --nodistortions --display_every=10 --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=worker --task_index=%(task_index)s\"\n\n for task in worker_job.tasks:\n cmds = []\n task_spec = {'type': task_type, 'index': task.id}\n cmds.append(setup_cmd)\n\n cmds.append(tf_config_cmd(cluster_spec, task_spec))\n cmds.append(\"export LOGDIR=\"+logdir)\n task.upload(\"./benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\",\n \"/home/ubuntu/Dropbox/git0/benchmarks/scripts/tf_cnn_benchmarks/variable_mgr.py\")\n # task.upload(\"./benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py\")\n cmds.append(worker_cmd_tmpl % {\"worker_hosts\": worker_hosts_str,\n \"ps_hosts\": ps_hosts_str,\n \"job_name\": task_type,\n \"task_index\": task.id})\n task.run(' && '.join(cmds))\n print(\"To see the output of %s: tail -f %s\" %(task.id,\n task.last_stdout))\n\n\n print(\"Sleeping\")\n time.sleep(1000)\n\n # TODO: are ssh calls blocking?\n \n # launch tensorboard visualizer\n # task = tb_job.tasks[0]\n # cmds = []\n # cmds.append(\"export LOGDIR=\"+logdir)\n # cmds.append('tensorboard --port=%d --logdir=$LOGDIR' %(task.port))\n # task.run(' && '.join(cmds))\n \n \n\ndef main():\n os.system('rm -Rf data') # todo: remove\n cnn_launcher()\n \n\nif __name__=='__main__':\n main()\n \n" }, { "path": "cluster/launch_micro.py", "content": "#!/usr/bin/env python\n# Launch single instance\n\nfrom collections import OrderedDict\nfrom pprint import pprint as pp\nimport argparse\nimport boto3\nimport os\nimport sys\nimport time\n#import util as myutil\nimport yaml\n\nfrom myutil import timeit\n\nmodule_path=os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, module_path+'/tf-tools/benchmark/runner')\nimport cluster_aws\n\n# TODO: cluster_aws util shadows our util\n\n\n\nEXPNAME='exp3' # TODO: remove\n\nparser = argparse.ArgumentParser(description='ImageNet experiment')\nparser.add_argument('--name', type=str, default='default',\n help=('tag used to keep track of machines in this '\n 'experiment'))\nparser.add_argument('--launch', action='store_true', default=False,\n help='launch new instances (instead of reusing)')\nparser.add_argument('--kill', action='store_true', default=False,\n help='kill instances')\nparser.add_argument('--ami', type=str, default='ami-9ddb0fe5',\n help='AMI to use for launching instances')\nparser.add_argument('--key-name', type=str, default='yaroslav',\n help='AWS key-name to use')\nparser.add_argument('--key-path', type=str,\n default=os.environ['HOME']+'/d/yaroslav.pem',\n help='location of .pem file on local filesystem')\nparser.add_argument('--instance-type', type=str,\n default='t2.micro',\n help='instance type to use')\nparser.add_argument('--security-group', type=str,\n default='open',\n help='which security group to use for instances')\nparser.add_argument('--localdir_prefix', type=str, default='/temp/logs',\n help='where to mirror worker logs locally')\nparser.add_argument('--logdir_prefix', type=str, default='/efs/logs',\n help='where to dump EFS logs')\nparser.add_argument('--port', type=int, default=3333,\n help='default port to use for all connections')\n\nargs = parser.parse_args()\n\ndef main():\n localdir=args.localdir_prefix+'/'+args.name\n logdir=args.logdir_prefix+'/'+args.name\n \n os.system('rm -Rf '+localdir)\n os.system('mkdir -p '+localdir)\n\n # TODO: automatically decide whether to launch or connect to existing\n # TODO: implement killing\n if args.launch:\n print(\"Creating new instances\")\n tags = {'iam': os.environ['USER']}\n with timeit('create_instances'):\n instances = cluster_aws.CreateAwsInstances(num_instances=1,\n image_id=args.ami,\n key_name=args.key_name,\n ssh_key=args.key_path,\n security_group=args.security_group,\n instance_tag=args.name,\n placement_group='',\n instance_type=args.instance_type,\n tags=tags)\n else:\n print(\"Reusing existing instances\")\n instances = cluster_aws.LookupAwsInstances(instance_tag=args.name,\n ssh_key=args.key_path)\n assert len(instances) == 1, \"%d instances found\" % (len(instances),)\n\n with timeit('connect'):\n for i,instance in enumerate(instances):\n print(\"Connecting to instance %d, %s\" % (i, instance.instance_id))\n instance.WaitUntilReady()\n\n instance = instances[0]\n\n # TODO: mount at /efs instead of ~/efs\n setup_cmd = \"\"\"\nsudo apt-get install nfs-common -y\nEFS_ID=fs-ab2b8102\nEFS_REGION=us-west-2\nsudo mkdir -p /efs\nsudo mount -t nfs -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 $EFS_ID.efs.$EFS_REGION.amazonaws.com:/ /efs\"\"\"\n\n setup_cmds = setup_cmd.strip().split('\\n')\n cmd = ' && '.join(setup_cmds)\n i = 0\n fn_out = localdir + '/out-%02d'%(i,)\n fn_err = localdir + '/err-%02d'%(i,)\n\n print(cmd)\n def p(line): print(line)\n instance.ExecuteCommandAndStreamOutput(cmd, fn_out, fn_err, p)\n\n \nif __name__=='__main__':\n main()\n" }, { "path": "cluster/launch_ray.py", "content": "#!/usr/bin/env python\n# Launch single instance, \n\nfrom collections import OrderedDict\nfrom pprint import pprint as pp\nimport argparse\nimport boto3\nimport os\nimport sys\nimport time\n#import util as myutil\nimport yaml\n\nfrom myutil import timeit\n\nmodule_path=os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, module_path+'/tf-tools/benchmark/runner')\nimport cluster_aws\n\nparser = argparse.ArgumentParser(description='ImageNet experiment')\nparser.add_argument('--name', type=str, default='ray',\n help=('tag used to keep track of machines in this '\n 'experiment'))\nparser.add_argument('--launch', action='store_true', default=False,\n help='launch new instances (instead of reusing)')\nparser.add_argument('--kill', action='store_true', default=False,\n help='kill instances')\nparser.add_argument('--ami', type=str, default='ami-9ddb0fe5',\n help='AMI to use for launching instances')\nparser.add_argument('--key-name', type=str, default='yaroslav',\n help='AWS key-name to use')\nparser.add_argument('--key-path', type=str,\n default=os.environ['HOME']+'/d/yaroslav.pem',\n help='location of .pem file on local filesystem')\nparser.add_argument('--instance-type', type=str,\n default='p3.16xlarge',\n help='instance type to use')\nparser.add_argument('--instance-number', type=int,\n default=2,\n help='instance type to use')\nparser.add_argument('--security-group', type=str,\n default='open',\n help='which security group to use for instances')\nparser.add_argument('--localdir_prefix', type=str, default='/temp/logs',\n help='where to mirror worker logs locally')\nparser.add_argument('--logdir_prefix', type=str, default='/efs/logs',\n help='where to dump EFS logs')\nparser.add_argument('--port', type=int, default=3333,\n help='default port to use for all connections')\n\nargs = parser.parse_args()\n\ndef main():\n localdir=args.localdir_prefix+'/'+args.name\n logdir=args.logdir_prefix+'/'+args.name\n \n os.system('rm -Rf '+localdir)\n os.system('mkdir -p '+localdir)\n\n # TODO: automatically decide whether to launch or connect to existing\n # TODO: implement killing\n if args.launch:\n print(\"Creating new instances\")\n tags = {'iam': os.environ['USER']}\n with timeit('create_instances'):\n instances = cluster_aws.CreateAwsInstances(num_instances=args.instance_number,\n image_id=args.ami,\n key_name=args.key_name,\n ssh_key=args.key_path,\n security_group=args.security_group,\n instance_tag=args.name,\n placement_group='',\n instance_type=args.instance_type,\n tags=tags)\n else:\n print(\"Reusing existing instances\")\n instances = cluster_aws.LookupAwsInstances(instance_tag=args.name,\n ssh_key=args.key_path)\n assert len(instances) == args.instance_number, \"%d instances found\" % (len(instances),)\n\n with timeit('connect'):\n for i,instance in enumerate(instances):\n print(\"Connecting to instance %d, %s\" % (i, instance.instance_id))\n instance.WaitUntilReady()\n\n instance = instances[0]\n\n setup_cmd = \"\"\"\nsudo apt-get install nfs-common -y\nEFS_ID=fs-ab2b8102\nEFS_REGION=us-west-2\nsudo mkdir -p /efs\nsudo mount -t nfs -o nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2 $EFS_ID.efs.$EFS_REGION.amazonaws.com:/ /efs\"\"\"\n\n setup_cmds = setup_cmd.strip().split('\\n')\n cmd = ' && '.join(setup_cmds)\n i = 0\n fn_out = localdir + '/out-%02d'%(i,)\n fn_err = localdir + '/err-%02d'%(i,)\n\n print(cmd)\n def p(line): print(line)\n instance.ExecuteCommandAndStreamOutput(cmd, fn_out, fn_err, p)\n\n \nif __name__=='__main__':\n main()\n" }, { "path": "cluster/launch_simple_tf.py", "content": "# simple example of launching tensorflow job\n\nimport time\nimport tensorflow as tf\n\nflags = tf.flags\nflags.DEFINE_string(\"role\", \"launcher\", \"either launcher or worker\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_integer(\"iters_per_step\", 10, \"number of additions per step\")\nflags.DEFINE_string(\"cluster\", \"aws\", \"where to run (aws or local)\")\nFLAGS = flags.FLAGS\n\n \ndef main():\n if FLAGS.role == \"launcher\":\n launcher()\n elif FLAGS.role == \"worker\":\n worker()\n else:\n assert False, \"Unknown role \"+FLAGS.role\n\n\ndef launcher(do_local=False):\n if FLAGS.cluster == 'local':\n import tmux\n job = tmux.tf_job('myjob', 1)\n elif FLAGS.cluster == 'aws':\n import aws\n job = aws.tf_job('myjob', 1)\n else:\n assert False, \"Unknown cluster \"+FLAGS.cluster\n\n task = job.tasks[0]\n task.upload(__file__) # copies current script onto machine\n setup_cmd = (\"source ~/.bashrc && export PATH=~/anaconda3/bin:$PATH && \"\n \"source activate tf\")\n task.run(\"%s && python %s --role=worker\" % (setup_cmd, __file__,))\n \n print(\"To see the output: tail -f %s\" %(task.last_stdout))\n print(\"To interact with the task, do \"+task.connect_instructions)\n \n\ndef worker():\n \"\"\"Worker script that runs on AWS machine. Adds vectors of ones forever,\n prints MB/s.\"\"\"\n \n def session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n config.operation_timeout_in_ms = 10*1000 # abort after 10 seconds\n return config\n\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k floats\n dtype=tf.float32\n val = tf.ones((), dtype=dtype)\n vals = tf.fill([params_size], val)\n params = tf.Variable(vals)\n update = params.assign_add(vals)\n \n sess = tf.Session(config=session_config())\n sess.run(params.initializer)\n \n while True:\n start_time = time.perf_counter()\n for i in range(FLAGS.iters_per_step):\n sess.run(update.op)\n\n elapsed_time = time.perf_counter() - start_time\n rate = float(FLAGS.iters_per_step)*FLAGS.data_mb/elapsed_time\n print('%.2f MB/s'%(rate,)) \n\n \nif __name__=='__main__':\n main()\n" }, { "path": "cluster/local_distributed_benchmark.py", "content": "\"\"\"Benchmark tensorflow distributed by adding vector of ones on worker2\nto variable on worker1 as fast as possible.\nOn 2014 macbook, TensorFlow 0.10 this shows\nLocal rate: 2175.28 MB per second\nDistributed rate: 107.13 MB per second\n\"\"\"\n\nimport subprocess\nimport tensorflow as tf\nimport time\nimport sys\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 10, \"Maximum number of additions\")\nflags.DEFINE_integer(\"data_mb\", 100, \"size of vector in MBs\")\nflags.DEFINE_string(\"port1\", \"12224\", \"port of worker1\")\nflags.DEFINE_string(\"port2\", \"12225\", \"port of worker2\")\nflags.DEFINE_string(\"task\", \"\", \"internal use\")\nFLAGS = flags.FLAGS\n\n# setup local cluster from flags\nhost = \"127.0.0.1:\"\ncluster = {\"worker\": [host+FLAGS.port1, host+FLAGS.port2]}\nclusterspec = tf.train.ClusterSpec(cluster).as_cluster_def()\n\ndef default_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n config.log_device_placement = False\n config.allow_soft_placement = False\n return config\n\ndef create_graph(device1, device2):\n \"\"\"Create graph that keeps variable on device1 and\n vector of ones/addition op on device2\"\"\"\n \n tf.reset_default_graph()\n dtype=tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n\n with tf.device(device1):\n params = tf.get_variable(\"params\", [params_size], dtype,\n initializer=tf.zeros_initializer)\n with tf.device(device2):\n # constant node gets placed on device1 because of simple_placer\n # update = tf.constant(1, shape=[params_size], dtype=dtype)\n update = tf.get_variable(\"update\", [params_size], dtype,\n initializer=tf.ones_initializer)\n add_op = params.assign_add(update)\n \n init_op = tf.initialize_all_variables()\n return init_op, add_op\n\ndef run_benchmark(sess, init_op, add_op):\n \"\"\"Returns MB/s rate of addition.\"\"\"\n \n sess.run(init_op)\n sess.run(add_op.op) # warm-up\n start_time = time.time()\n for i in range(FLAGS.iters):\n # change to add_op.op to make faster\n sess.run(add_op)\n elapsed_time = time.time() - start_time\n return float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n\n\ndef run_benchmark_local():\n ops = create_graph(None, None)\n sess = tf.Session(config=default_config())\n return run_benchmark(sess, *ops)\n\n\ndef run_benchmark_distributed():\n ops = create_graph(\"/job:worker/task:0\", \"/job:worker/task:1\")\n\n # launch distributed service\n def runcmd(cmd): subprocess.Popen(cmd, shell=True, stderr=subprocess.STDOUT)\n runcmd(\"python %s --task=0\"%(sys.argv[0]))\n runcmd(\"python %s --task=1\"%(sys.argv[0]))\n time.sleep(1)\n\n sess = tf.Session(\"grpc://\"+host+FLAGS.port1, config=default_config())\n return run_benchmark(sess, *ops)\n \nif __name__=='__main__':\n if not FLAGS.task:\n\n rate1 = run_benchmark_local()\n rate2 = run_benchmark_distributed()\n\n print(\"Adding data in %d MB chunks\" %(FLAGS.data_mb))\n print(\"Local rate: %.2f MB per second\" %(rate1,))\n print(\"Distributed rate: %.2f MB per second\" %(rate2,))\n\n else: # Launch TensorFlow server\n server = tf.train.Server(clusterspec, config=default_config(),\n job_name=\"worker\",\n task_index=int(FLAGS.task))\n server.join()\n" }, { "path": "cluster/myutil.py", "content": "from pprint import pprint as pp\nimport yaml\n#import util\nimport boto3\nfrom collections import OrderedDict\nimport time\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_sec = (self.end - self.start)\n print(\"%s took %.2f seconds\"%(self.tag, interval_sec))\n\ndef get_instance_ip_map():\n \"\"\"Return instance_id->private_ip map for all running instances.\"\"\"\n \n ec2 = boto3.resource('ec2')\n\n # Get information for all running instances\n running_instances = ec2.instances.filter(Filters=[{\n 'Name': 'instance-state-name',\n 'Values': ['running']}])\n\n ec2info = OrderedDict()\n for instance in running_instances:\n name = ''\n for tag in instance.tags or []:\n if 'Name' in tag['Key']:\n name = tag['Value']\n ec2info[instance.id] = instance.private_ip_address\n \n return ec2info\n" }, { "path": "cluster/ray_add.py", "content": "# To benchmark READ throughput, run the following.\n#\n# python async_sgd_benchmark.py --num-workers=10 --num-parameter-servers=10 --data-size=100000000 --read\n#\n# 10 workers, 10 parameter servers, 100MB objects, 64 cores:\n# read throughput: reaches 29GB/s (after warm up).\n#\n# 1 worker, 1 parameter server, 100MB objects, 64 cores:\n# read throughput: reaches 3GB/s\n# ------------------------------------------------------------------------------\n#\n# To benchmark WRITE throughput, run the following.\n#\n# python async_sgd_benchmark.py --num-workers=10 --num-parameter-servers=10 --data-size=100000000\n#\n# 10 workers, 10 parameter servers, 100MB objects, 64 cores:\n# write throughput: reaches 15GB/s (after warm up).\n#\n# 1 worker, 1 parameter server, 100MB objects, 64 cores:\n# write throughput: 1GB/s\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport numpy as np\nimport time\n\nimport ray\n\nparser = argparse.ArgumentParser(description='Benchmark asynchronous '\n 'training.')\nparser.add_argument('--num-workers', default=4, type=int,\n help='The number of workers to use.')\nparser.add_argument('--num-parameter-servers', default=4, type=int,\n help='The number of parameter servers to use.')\nparser.add_argument('--data-size', default=1000000, type=int,\n help='The size of the data to use.')\nparser.add_argument('--read', action='store_true',\n help='measure read throughput, the default is to measure '\n 'write throughput')\n\n\nargs = parser.parse_args()\n\n@ray.remote\nclass ParameterServer(object):\n def __init__(self, data_size, read):\n self.data_size = data_size\n self.read = read\n self.value = np.zeros(data_size, dtype=np.uint8)\n self.times = []\n\n def push(self, value):\n if not self.read:\n self.update_times()\n self.value += value\n\n def pull(self):\n if self.read:\n self.update_times()\n return self.value\n\n def update_times(self):\n self.times.append(time.time())\n if len(self.times) > 100:\n self.times = self.times[-100:]\n\n def get_throughput(self):\n return (self.data_size * (len(self.times) - 1) /\n (self.times[-1] - self.times[0]))\n\n\n@ray.remote\ndef worker_task(data_size, read, *parameter_servers):\n while True:\n if read:\n # Get the current value from the parameter server.\n values = ray.get([ps.pull.remote() for ps in parameter_servers])\n else:\n # Push an update to the parameter server.\n ray.get([ps.push.remote(np.zeros(data_size, dtype=np.uint8))\n for ps in parameter_servers])\n\n\nray.init(num_workers=args.num_workers, redirect_output=True)\n\nparameter_servers = [ParameterServer.remote(args.data_size, args.read)\n for _ in range(args.num_parameter_servers)]\n\n# Let the parameter servers start up.\ntime.sleep(3)\n\n# Start some training tasks.\nworker_tasks = [worker_task.remote(args.data_size, args.read,\n *parameter_servers)\n for _ in range(args.num_workers)]\n\nwhile True:\n time.sleep(2)\n throughput = (ray.get(parameter_servers[0].get_throughput.remote()) *\n args.num_parameter_servers)\n if args.read:\n print('Read throughput is {}MB/s.'.format(throughput / 1e6))\n else:\n print('Write throughput is {}MB/s.'.format(throughput / 1e6))\n" }, { "path": "cluster/simple_distributed.py", "content": "#!/usr/bin/env python\n# Launches \n\nimport gc\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport tempfile\nimport threading\nimport time\nfrom tensorflow.python.training import training_util\nfrom tensorflow.python.platform import gfile\n\nfrom tensorflow.contrib.summary import summary_ops\nfrom tensorflow.contrib.summary import gen_summary_ops\n\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 100000, \"Maximum number of additions\")\nflags.DEFINE_integer(\"warmup_iters\", 1, \"warmup iterations\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"extra logging\")\nflags.DEFINE_boolean(\"sanity_check\", False, \"run sanity check on results\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to run distributed version and \"\n \"collect CPU profile\")\nflags.DEFINE_boolean(\"in_process\", True, \"do bencharmk on in-process master\")\nflags.DEFINE_string(\"direction\", \"t2p\", \"which direction to profile, either \"\n \"tensorflow to python (t2p) or python to tensorflow (p2t)\")\nflags.DEFINE_string(\"logdir\", os.environ[\"HOME\"]+\"/efs/client_transfer_benchmark\", \"location of tensorboard events\")\n\n# internal flags, set by client\nflags.DEFINE_string(\"worker_type\", \"launcher\", \"launcher or client or worker\")\nflags.DEFINE_string(\"port\", \"12222\", \"port of master\")\nFLAGS = flags.FLAGS\n\n\nhost = \"127.0.0.1\"\ndef clusterspec():\n cluster = {\"worker\": [host+\":\"+FLAGS.port]}\n return tf.train.ClusterSpec(cluster).as_cluster_def()\n\n\ndef log(s):\n if FLAGS.verbose: print(s)\n\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n config.log_device_placement = False\n config.allow_soft_placement = False\n return config\n\n\ndef launch_distributed_service():\n port = portpicker.pick_unused_port()\n \n def launch_worker(worker_type):\n my_env = os.environ.copy()\n if not FLAGS.verbose:\n my_env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n my_env[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n if FLAGS.profile:\n my_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\n my_env[\"CPUPROFILE\"]=\"/tmp/profile.out.%s\"%(worker_type)\n\n args = [\"python\"] + sys.argv + [\"--port=\"+str(port),\n \"--worker_type=\"+worker_type]\n proc = subprocess.Popen(args, stderr=subprocess.STDOUT, env=my_env)\n log(\"worker %s pid %s\"%(worker_type, proc.pid))\n \n launch_worker(\"worker\")\n launch_worker(\"client\")\n\ndef run_benchmark(master, direction=None):\n \"\"\"Connect to master and run simple TF->Python transfer benchmark.\"\"\"\n\n\n from tensorflow.python.summary import summary as summary_lib\n from tensorflow.python import pywrap_tensorflow\n from tensorflow.python.util import compat\n from tensorflow.core.util import event_pb2\n from tensorflow.core.framework import summary_pb2\n\n\n def make_event(tag, value, step):\n event = event_pb2.Event(\n wall_time=time.time(),\n step=step,\n summary=summary_pb2.Summary(\n value=[summary_pb2.Summary.Value(\n tag=tag, simple_value=value)]))\n return event\n \n if not direction:\n os.system('mkdir -p '+FLAGS.logdir)\n\n # todo: unique filenames like with contrib.summary writer\n writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(FLAGS.logdir+'/events'))\n filename = compat.as_text(writer.FileName())\n\n training_util.get_or_create_global_step()\n sess = tf.InteractiveSession()\n step = 0\n while True:\n p_to_t = run_benchmark(master, 'p->t')\n print(\"recoridng\", p_to_t, \"to\", FLAGS.logdir)\n t_to_p = run_benchmark(master, 't->p')\n \n event = make_event('p->t', p_to_t, step)\n writer.WriteEvent(event)\n event = make_event('t->p', t_to_p, step)\n writer.WriteEvent(event)\n writer.Flush()\n step+=1\n \n writer.Close()\n return\n \n assert FLAGS.warmup_iters > 0\n gc.disable()\n \n dtype = tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n# params = tf.get_variable(\"params\", [params_size], dtype,\n# initializer=tf.ones_initializer())\n params = tf.Variable(tf.ones([params_size], dtype=dtype), name='params')\n params_read = params.read_value() # prevent caching\n params_holder = tf.placeholder(dtype)\n params_write = params.assign(params_holder)\n done_queue = create_done_queue(0)\n init_op = tf.global_variables_initializer()\n sess = tf.Session(master, config=session_config())\n sess.run(init_op)\n result = sess.run(params_read)\n \n total = 0\n for i in range(FLAGS.iters+FLAGS.warmup_iters):\n if i == FLAGS.warmup_iters:\n start_time = time.time()\n # fetch value into Python runtime\n if direction == \"t->p\":\n result = sess.run(params_read)\n if FLAGS.sanity_check:\n total += result.sum()\n print(float(total)/params_size)\n elif direction == \"p->t\":\n sess.run(params_write.op, feed_dict={params_holder: result})\n \n\n elapsed_time = time.time() - start_time\n rate = float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n print(\"%5s %.2f MB/second\" % (direction, rate))\n sess.run(done_queue.enqueue(1))\n return rate\n\ndef create_done_queue(i):\n \"\"\"Queue used to signal death for i'th worker.\"\"\"\n \n return tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue\"+\n str(i))\n\nif __name__ == '__main__':\n # run local benchmark in launcher and launch service\n if FLAGS.worker_type == \"launcher\":\n run_benchmark(\"\") # run local benchmark in launcher\n if FLAGS.profile:\n gc.collect()\n launch_distributed_service()\n\n # run distributed benchmark in client\n elif FLAGS.worker_type == \"client\":\n if not FLAGS.in_process:\n run_benchmark(\"grpc://%s:%s\"%(host, FLAGS.port))\n log(\"Killing worker.\")\n\n elif FLAGS.worker_type == \"worker\": # run tensorflow worker\n server = tf.train.Server(clusterspec(), config=session_config(),\n job_name=\"worker\",\n task_index=0)\n queue = create_done_queue(0)\n sess = tf.Session(server.target, config=session_config())\n if FLAGS.in_process:\n run_benchmark(server.target)\n\n sess.run(queue.dequeue())\n time.sleep(2) # give chance for master session.run call to return\n if FLAGS.verbose:\n print(\"Worker %s quitting.\" %(FLAGS.task_index))\n else:\n assert False, \"Unknown worker type\"\n" }, { "path": "cluster/terminate_instances.py", "content": "#!/usr/bin/env python\n\"\"\"\n\nScript to kill all instances matching given prefix.\n\nUsage:\n\n./terminate_instances.py gpu # terminates all instances matching \"gpu*\"\n\"\"\"\n\nimport boto3\nimport time\nimport sys\nimport os\n\nLIMIT_TO_KEY = 'yaroslav' # only touch instances launched with this key,\n # set to '' to remove restriction\n\ndef main():\n prefix = sys.argv[1]\n\n ec2 = boto3.client('ec2')\n response = ec2.describe_instances()\n\n def get_name(instance_response):\n names = [entry['Value'] for entry in instance_response.get('Tags',[]) if\n entry['Key']=='Name']\n if not names:\n names = ['']\n assert len(names)==1\n return names[0]\n\n instance_list = []\n for reservation in response['Reservations']:\n for instance_response in reservation['Instances']:\n instance_list.append((get_name(instance_response),\n instance_response))\n\n instances_to_kill = []\n for (name, instance_response) in instance_list:\n if not name.startswith(prefix):\n continue\n key = instance_response.get('KeyName', '')\n if LIMIT_TO_KEY and LIMIT_TO_KEY != key:\n print(\"instance %s matches but key %s doesn't match desired key %s, \"\n \"skipping\" %(name, key, LIMIT_TO_KEY))\n continue\n state = instance_response['State']['Name']\n if state == 'terminated':\n continue\n instances_to_kill.append((instance_response['InstanceId'],\n name,\n instance_response['AmiLaunchIndex'],\n state))\n\n for (instance_id, name, task_id, state) in instances_to_kill:\n print(\"%s:%s %s\"%(name, task_id, state))\n\n answer = input(\"%d instances found, terminate? (Y/n) \" % (\n len(instances_to_kill)))\n if not answer:\n answer = \"y\"\n if answer.lower() == \"y\":\n instance_ids = [record[0] for record in instances_to_kill]\n response = ec2.terminate_instances(InstanceIds=instance_ids)\n print(\"Terminating, got response: %s\", response)\n else:\n print(\"Didn't get y, doing nothing\")\n \n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/test_aws.py", "content": "# simple example of launching tensorflow job\n\nimport aws\nimport os\nimport sys\nimport time\nimport tensorflow as tf\nimport boto3\n\nflags = tf.flags\nflags.DEFINE_string(\"role\", \"launcher\", \"either launcher or worker\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_integer(\"iters_per_step\", 10, \"number of additions per step\")\nFLAGS = flags.FLAGS\n\nmodule_path=os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, module_path+'/tf-tools/benchmark/runner')\nimport cluster_aws as toby_aws\n\n\ndef test_new_job():\n name = \"testjob\"\n instances = toby_aws.LookupAwsInstances(instance_tag=name)\n assert not instances, \"Instances already exist, kill them first\"\n\n job = aws.tf_job(name, 2)\n instances = toby_aws.LookupAwsInstances(instance_tag=name)\n assert len(instances) == 2\n\ndef test_terminate_job():\n aws.terminate_job(\"testjob\")\n\n\ndef test_reuse_job():\n name = \"testjob\"\n job = aws.tf_job(name, 2)\n\ndef test_send_file():\n name = \"testjob\"\n job = aws.tf_job(name, 4)\n job.wait_until_ready()\n task0 = job.tasks[0]\n secret_word = \"testfile3\"\n os.system(\"echo '%s' > upload_test.txt\"%(secret_word,))\n task0.upload('upload_test.txt')\n stdout,stderr = task0.run_sync(\"cat upload_test.txt\")\n print(stdout) # => testfile2\n assert stdout.strip() == secret_word\n\ndef test_upload_directory():\n pass\n\ndef test_stream_output():\n name = \"testjob\"\n job = aws.tf_job(name, 4)\n job.wait_until_ready()\n task = job.tasks[0]\n task.run('cd Dropbox && ls') \n time.sleep(0.5) # async ... todo: expose thread and join instead of sleep?\n os.system('cat '+task.last_stdout)\n\n\ndef main():\n # test_terminate_job()\n # test_new_job()\n # test_reuse_job()\n # test_send_file()\n test_stream_output()\n \nif __name__=='__main__':\n main()\n" }, { "path": "cluster/tf-tools/.gitignore", "content": "__pycache__\n.DS_Store\n" }, { "path": "cluster/tf-tools/benchmark/multi_gpu/advanced_tweaks_compare.sh", "content": "# Showing NCHW vs NHWC, NCCL and paramater server GPU vs CPU\n_NUM_GPUS=1,2,8\nLOG_FOLDER=advanced_tests\n\n# PS GPU vs. CPU NHWC\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --ps_server gpu --variable_update send_recv --data_format NHWC --log_folder_prefix ${LOG_FOLDER} --framework tensorflow\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --ps_server cpu --variable_update send_recv --data_format NHWC --log_folder_prefix ${LOG_FOLDER} --framework tensorflow\n\n# NCHW vs NHWC (GPU PS)\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --ps_server gpu --data_format NCHW --variable_update send_recv --log_folder_prefix ${LOG_FOLDER} --framework tensorflow\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --ps_server gpu --data_format NHWC --variable_update send_recv --log_folder_prefix ${LOG_FOLDER} --framework tensorflow\n\n# Add NCCL to NCHW (GPU PS)\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --ps_server gpu --data_format NCHW --variable_update replicated --log_folder_prefix ${LOG_FOLDER} --framework tensorflow\n\n\n" }, { "path": "cluster/tf-tools/benchmark/multi_gpu/image_classification_bench_tests.sh", "content": "# Runs tests for an 8 GPU server\n_NUM_GPUS=1,2,4,8\n# Inception v3\n./test_runner.sh --model inception3 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --framework tensorflow\n./test_runner.sh --model inception3 --num_batches 4 --batch_size 32 --gpus ${_NUM_GPUS} --framework mxnet\n\n\n# Resnet-50\n./test_runner.sh --model resnet50 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --framework tensorflow\n./test_runner.sh --model resnet50 --num_batches 4 --batch_size 32 --gpus ${_NUM_GPUS} --framework mxnet\n\n\n# Resnet-152\n./test_runner.sh --model resnet152 --num_batches 100 --batch_size 32 --gpus ${_NUM_GPUS} --framework tensorflow\n./test_runner.sh --model resnet152 --num_batches 4 --batch_size 32 --gpus ${_NUM_GPUS} --framework mxnet\n\n\n# AlexNet (OWT)\n# AlexNet script is broken on MXNet.\n#./test_runner.sh --model alexnet --num_batches 4 --batch_size 512 --gpus ${_NUM_GPUS} --framework mxnet\n./test_runner.sh --model alexnet --num_batches 100 --batch_size 512 --gpus ${_NUM_GPUS} --framework tensorflow\n./test_runner.sh --model alexnet --num_batches 100 --batch_size 128 --gpus ${_NUM_GPUS} --framework tensorflow\n" }, { "path": "cluster/tf-tools/benchmark/multi_gpu/stats_monitor.sh", "content": "#!/bin/bash\n\n\n# Get all nvidia-smi data worth having\n# There is no historical data so calling this after a run\n# when the GPU may no long be throttled is of no value.\n\nKEEP_LOOP=true\nLOG_FULL_PATH=\"./monitor_log.txt\"\nLOG_SUMMARY_FULL_PATH=\"./log_summary.txt\"\n\n\nwhile [[ $# -gt -0 ]]; do\n key=\"$1\"\n# echo $key\n# echo $2\n case $key in\n --log_full_path)\n LOG_FULL_PATH=\"$2\" # location to log raw monitoring logs, e.g. nvidia-smi\n shift\n ;;\n --log_summary_full_path)\n LOG_SUMMARY_FULL_PATH=\"$2\" # Format of the data NHWC or NCHW (NVIDIA)\n shift\n ;;\n *)\n echo \"Unknown flag: $key\"\n ;;\n esac\n shift # past argument or value\ndone\n\n\n\n\nMAX_SLOWDOWN_GPUS=0\n# Handle CTRL-C or other term signal, log the max number of GPUs that showed\n# Errors, for now that means \"HW Slowdown: Active\"\nfunction summarizeCleanup {\n echo \"Max GPUs throttled: ${MAX_SLOWDOWN_GPUS}\"\n echo \"Max GPUs throttled: ${MAX_SLOWDOWN_GPUS}\" >> $LOG_SUMMARY_FULL_PATH\n exit;\n}\n\n# catch being asked to end\ntrap summarizeCleanup SIGINT SIGTERM\n\n# Log nvidia-smi data forever (until killed externally) and track when HW Slowdown: Active occures \n# which indicates overheating and likely lower clock.\nwhile [ \"$KEEP_LOOP\" = \"true\" ]; do\n\n RESULT=$(nvidia-smi -q -d UTILIZATION,CLOCK,PERFORMANCE | tee -a ${LOG_FULL_PATH} | \\\ngrep -E 'HW Slowdown' | awk '!/Not Active/ {count++} END{print count}')\n \n # Handle result being blank. Likely a better way using awk above\n if [ \"$RESULT\" = \"\" ]; then\n RESULT=0\n fi \n\n if [ \"$RESULT\" -gt \"$MAX_SLOWDOWN_GPUS\" ]; then\n \tMAX_SLOWDOWN_GPUS=$RESULT\n \techo \"$MAX_SLOWDOWN_GPUS GPU(s) with slowdown\"\n fi\n\n \n # 10 second seem to be reasonable.\n sleep 10\n\ndone\n\n" }, { "path": "cluster/tf-tools/benchmark/multi_gpu/test_runner.sh", "content": "#!/bin/bash\n\n# Set defaults to best performance for most scenarios and most used values\nGPUS_PER_HOST=1\nDATA_FORMAT=NCHW #other option is NHWC (NCHW is cuDNN default format, will not work on CPU)\nBATCH_SIZE=32\nMODEL=inception3\nDISPLAY_EVERY=10 #Display after every X steps.\nNUM_BATCHES=100 # 4 or 5 is good for mxnet (their synthetic test does 40 batches per epoch)\nMEM_TEST=false\nVARIABLE_UPDATE=replicated #send-recv works on CPU and GPU. For NVIDIA GPU use replicated, which uses nccl.\nPS_SERVER=gpu # cpu often performs similar to gpu but GPU is recommended unless there is a memory constraint.\nFRAMEWORK=tensorflow #m xnet is also supported\n\n# Directories for github repos\nSRC_ROOT=\"${HOME}/src\" \nTIMESTAMP=$(date +\"%Y%m%d_%H%M%S\")\n# Only used for mxnet\nNUM_LAYERS=50\n\n#Run with Real data\nSYNTHETIC_DATA=true\nDATA_DIR=/usr/local/google/home/tobyboyd/imagenet\n\n# Genearl Config\nLOG_FOLDER_PREFIX=logs # Useful for grouping logs\n\n# Stats monitor\nMONITOR_STATS=true\n\nwhile [[ $# -gt -0 ]]; do\n key=\"$1\"\n echo $key\n echo $2\n case $key in\n --gpus)\n GPUS_PER_HOST=\"$2\" # which GPU configs to run , e.g. 1,2,4,8 (4 tests one for eaah number of GPUs)\n shift\n ;;\n --data_format)\n DATA_FORMAT=\"$2\" # Format of the data NHWC or NCHW (NVIDIA)\n shift\n ;;\n --num_batches)\n NUM_BATCHES=\"$2\" # Number of batches. Note that mxnet uses epochs which is 40 per num_batches\n shift\n ;;\n --batch_size) # Size of the batch to run\n BATCH_SIZE=\"$2\"\n shift\n ;;\n --display_every_step)\n DISPLAY_EVERY=\"$2\" # how often to display batch timings, 10 is reasonable.\n shift\n ;;\n --variable_update)\n VARIABLE_UPDATE=\"$2\" # Set how to update variables\n shift\n ;;\n --mem_test)\n MEM_TEST=\"$2\" # set to true to run a mem-test\n shift\n ;;\n --ps_server)\n PS_SERVER=\"$2\" # where to run the parameter server or where to place them cpu/gpu\n shift\n ;;\n --framework)\n FRAMEWORK=\"$2\" # framework to use, mxnet or tensorflow\n shift\n ;;\n --model)\n MODEL=\"$2\" # name of the model to run\n shift\n ;;\n --src_root)\n MODEL=\"$2\" # Folder where the github repos are found\n shift\n ;;\n --log_folder_prefix)\n LOG_FOLDER_PREFIX=\"$2\" # prefix for the log folder to help with organization\n shift\n ;;\n --data_dir)\n DATA_DIR=\"$2\" # prefix for the log folder to help with organization\n SYNTHETIC_DATA=false\n shift\n ;;\n --synthetic)\n SYNTHETIC_DATA=\"$2\" # prefix for the log folder to help with organization\n shift\n ;;\n --monitor_stats)\n MONITOR_STATS=\"$2\" # on by default set to false to turn off\n shift\n ;;\n *)\n echo \"Unknown flag: $key\"\n ;;\n esac\n shift # past argument or value\ndone\n\n#Convert values for non-tensorflow platforms\nif [ \"${FRAMEWORK}\" = \"mxnet\" ]; then\n #Cheap trick to align GPU index MXnet uses 0 base\n # TODO: make work for multiple GPU, eg. 1,2,4,8\n #GPUS=$((GPUS-1))\n\n IMAGE_SHAPE=not_set\n case $MODEL in\n inception3)\n IMAGE_SHAPE=\"3,299,299\"\n MODEL=inception-v3\n ;;\n resnet50)\n IMAGE_SHAPE=\"3,224,224\"\n MODEL=resnet\n ;;\n resnet152)\n IMAGE_SHAPE=\"3,224,224\"\n MODEL=resnet\n NUM_LAYERS=152\n ;;\n alexnet)\n IMAGE_SHAPE=\"3,224,224\"\n # MXNet has alenet and googlenet mixed up in their repo\n MODEL=alexnet\n ;;\n *)\n echo \"Unknown mxnet model: $MODEL\" # unknown option\n ;;\n esac \nfi\n\nconvertToMXNetGPU () \n{\n local mxnet_gpu=0\n case $1 in\n 1)\n mxnet_gpu=\"0\"\n ;;\n 2)\n mxnet_gpu=\"0,1\" \n ;;\n 4)\n mxnet_gpu=\"0,1,2,3\"\n ;;\n 8)\n mxnet_gpu=\"0,1,2,3,4,5,6,7\" \n ;;\n *)\n echo \"Unknown GPU string $1\"\n exit 1;\n ;;\n esac\n\n echo $mxnet_gpu\n}\n\n# Set log folder name and links to tools folders\nLOG_FOLDER=\"${LOG_FOLDER_PREFIX}/${TIMESTAMP}_${FRAMEWORK}_${MODEL}_${BATCH_SIZE}\"\nMXNET_BENCHMARK_DIRECTORY=\"${SRC_ROOT}/mxnet/example/image-classification\"\nTF_BENCHMARK_DIRECTORY=\"${HOME}/tf_cnn_bench\"\n\n\necho \"##### Config #####\"\necho \"GPU configs to test: $GPUS\"\necho \"Parameter server: ${PS_SERVER}\"\necho \"data_format: $DATA_FORMAT\"\necho \"batch_size: $BATCH_SIZE\"\necho \"variable_update: $VARIABLE_UPDATE\"\necho \"batch_num: $NUM_BATCHES\"\necho \"mem_test: $MEM_TEST\"\necho \"framework: $FRAMEWORK\"\necho \"log folder: $LOG_FOLDER\"\necho \"src root: $SRC_ROOT\"\n\n#make log folder\necho \"Make log folder:${LOG_FOLDER}\"\nmkdir -p $LOG_FOLDER\n\nGPUS=${GPUS_PER_HOST//,/$'\\n'} # change the commas to white space\n\n# PID for the stats monitor\nSTATS_MONITOR_PID=0\n# Handle CTRL-C or other term signal, log the max number of GPUs that showed\n# Errors, for now that means \"HW Slowdown: Active\"\nfunction cleanup {\n # kill stats monitor incase it is running using all methods\n if [ \"$STATS_MONITOR_PID\" -gt \"0\" ]; then \n kill $STATS_MONITOR_PID\n wait $STATS_MONITOR_PID\n pgrep \"stats_monitor\" | xargs kill >/dev/null 2>&1\n exit;\n fi\n}\n\n# catch being asked to end\ntrap cleanup SIGINT SIGTERM\n\n# This is a little lame but double check stats_monitor was killed\npgrep \"stats_monitor\" | xargs kill >/dev/null 2>&1\n\n\n# Execute the benchmark on each GPU config, e.g. 1, 2, 4 and then 8 GPUs. \nif [ \"$MEM_TEST\" = \"false\" ]; then\n for gpu in $GPUS; do\n if [ \"${FRAMEWORK}\" = \"mxnet\" ]; then\n gpu=$(convertToMXNetGPU $gpu)\n BENCH_EXEC=\"python ${MXNET_BENCHMARK_DIRECTORY}/train_imagenet.py --gpus ${gpu} --network ${MODEL} --batch-size ${BATCH_SIZE} \\\n--image-shape ${IMAGE_SHAPE} --num-epochs ${NUM_BATCHES} --kv-store device --benchmark 1 --disp-batches ${DISPLAY_EVERY} \\\n--num-layers ${NUM_LAYERS}\"\n else \n BENCH_EXEC=\"python ${TF_BENCHMARK_DIRECTORY}/tf_cnn_benchmarks.py --model=${MODEL} --batch_size=${BATCH_SIZE} --num_batches=${NUM_BATCHES} \\\n--num_gpus=${gpu} --data_format=${DATA_FORMAT} --display_every=${DISPLAY_EVERY} --weak_scaling=true \\\n--parameter_server=${PS_SERVER} --device=gpu --variable_update=${VARIABLE_UPDATE}\"\n if [ \"$SYNTHETIC_DATA\" = \"false\" ]; then\n BENCH_EXEC=\"${BENCH_EXEC} --data_dir=${DATA_DIR} --nodistortions --data_name=imagenet\"\n fi\n fi\n \n # run stats monitor (wathch for overheading cards)\n if [ \"$MONITOR_STATS\" = \"true\" ]; then\n echo \"Start stats monitor...\"\n ./stats_monitor.sh --log_full_path $LOG_FOLDER/${gpu}_full_log.txt --log_summary_full_path $LOG_FOLDER/${gpu}_log_summary.txt &\n STATS_MONITOR_PID=$! \n fi\n\n echo \"Command to run: $BENCH_EXEC\"\n $BENCH_EXEC 2>&1 | tee $LOG_FOLDER/${gpu}.txt\n \n if [ \"$STATS_MONITOR_PID\" -gt \"0\" ]; then \n echo \"Stats monitor: stopping\"\n kill $STATS_MONITOR_PID\n echo \"Wait for stats monitor to stop: ${STATS_MONITOR_PID}\"\n wait $STATS_MONITOR_PID\n echo \"Stats monitor: stopped\"\n fi\n\n if [ \"${FRAMEWORK}\" = \"mxnet\" ]; then\n # average last 5 entries (not the most elegant string of commands)\n grep \"samples/sec\" $LOG_FOLDER/${gpu}.txt | awk '{print $5}' | tail -5 | \\\nawk '{ sum += $1; n++ } END { if (n > 0) print \"Images/sec Last 5 Results:\" sum / n }' 2>&1 | tee -a $LOG_FOLDER/${gpu}.txt \n fi\n\n done\n\n echo \"$BENCH_EXEC\" >> $LOG_FOLDER/result.txt\n grep \"^total images/sec\" $LOG_FOLDER/*.txt 2>&1 | tee -a $LOG_FOLDER/result.txt\n # Gather up summary log messages into botto of results file\n cat $LOG_FOLDER/${gpu}_log_summary.txt >> $LOG_FOLDER/result.txt\n\nfi\n\n\n# Find where program fails due to memory errors by brute force\n# Suggestion is to run 100 batches as a standard\nif [ \"$MEM_TEST\" = \"true\" ]; then\n BATCH_ARRAY=(${BATCH_SIZE//,/$'\\n'}) # Turn batch_size into an array expected LOW[0] HIGH[1]\n \n # convert $BATCH_SIZE into BATCH_SIZES, BATCH_SIZE expected to be a range, e.g. 200,300\n BIN_SEARCH_HIGH=${BATCH_ARRAY[1]}\n BIN_SEARCH_LOW=${BATCH_ARRAY[0]}\n BIN_SEARCH_RUN=$BIN_SEARCH_HIGH\n RESULT=0\n RESULT_TEXT=\"Never OOM, try a larger HIGH batch size\"\n RESULT_NEVER_PASS=true\n GPU_CONVERSION=false\n #Distance moved will always be negative when moving down from OOM\n while [ $(($BIN_SEARCH_LOW + 1)) -lt $BIN_SEARCH_HIGH ]; do\n echo \"Test batch size ${BIN_SEARCH_RUN}\" \n if [ \"${FRAMEWORK}\" = \"mxnet\" ]; then\n #do not convert on every loop\n if [ \"${GPU_CONVERSION}\" = \"false\" ]; then\n GPUS=$(convertToMXNetGPU $GPUS)\n GPU_CONVERSION=true\n fi\n\n BENCH_EXEC=\"python ${MXNET_BENCHMARK_DIRECTORY}/train_imagenet.py --gpus ${GPUS} --network ${MODEL} --batch-size ${BIN_SEARCH_RUN} \\\n--image-shape ${IMAGE_SHAPE} --num-epochs ${NUM_BATCHES} --kv-store device --benchmark 1 --disp-batches ${DISPLAY_EVERY} --num-layers ${NUM_LAYERS}\"\n OOM_LOG_MSG=\"cudaMalloc failed: out of memory\"\n else\n BENCH_EXEC=\"python ${TF_BENCHMARK_DIRECTORY}/tf_cnn_benchmarks.py --model=${MODEL} --batch_size=${BIN_SEARCH_RUN} --num_batches=${NUM_BATCHES} \\\n--num_gpus=${GPUS} --data_format=${DATA_FORMAT} --display_every=${DISPLAY_EVERY} --weak_scaling=true \\\n--parameter_server=${PS_SERVER} --device=gpu --variable_update=${VARIABLE_UPDATE}\"\n OOM_LOG_MSG=\"OOM when allocating tensor\"\n fi\n\n echo \"Command to run: $BENCH_EXEC\"\n echo \"$BENCH_EXEC\" >> $LOG_FOLDER/result.txt\n $BENCH_EXEC 2>&1 | tee $LOG_FOLDER/${GPUS}_${BIN_SEARCH_RUN}.txt\n ## Check of OOM occured\n echo \"Test batch size ${BIN_SEARCH_RUN}\" \n ## Check of OOM occured\n if grep -q \"${OOM_LOG_MSG}\" $LOG_FOLDER/${GPUS}_${BIN_SEARCH_RUN}.txt\n then\n echo \"Out of Memory at batchsize=${BIN_SEARCH_RUN}\" | tee -a $LOG_FOLDER/result.txt\n RESULT=$BIN_SEARCH_RUN \n BIN_SEARCH_HIGH=$BIN_SEARCH_RUN\n BIN_SEARCH_RUN=$(bc <<< \"${BIN_SEARCH_HIGH}-(${BIN_SEARCH_HIGH}-${BIN_SEARCH_LOW})/2\")\n RESULT_TEXT=\"oom\"\n else\n # Not OOM keep tryig to find first OOM\n echo \"Not OOM at batchsize=${BIN_SEARCH_RUN}\" | tee -a $LOG_FOLDER/result.txt \n BIN_SEARCH_LOW=$BIN_SEARCH_RUN\n BIN_SEARCH_RUN=$(bc <<< \"${BIN_SEARCH_LOW}+(${BIN_SEARCH_HIGH}-${BIN_SEARCH_LOW})/2\")\n RESULT_NEVER_PASS=false\n fi\n BIN_SEARCH_LAST=$BIN_SEARCH_RUN\n echo next move distance: $BIN_DISTANCE\n done\n\n if [ $RESULT_NEVER_PASS = \"true\" ]; then\n echo \"Test was always OOM try a lower batch size\" | tee -a $LOG_FOLDER/result.txt\n else\n echo \"Final result batch-size: $RESULT ${RESULT_TEXT}\" | tee -a $LOG_FOLDER/result.txt\n fi\n \nfi \n\n" }, { "path": "cluster/tf-tools/benchmark/multi_gpu/unit_test_stats_monitor.sh", "content": "#!/bin/bash\n\n./monitor_nvidia.sh --log_full_path ./full_log.txt --log_summary_full_path ./log_summary.txt &\n\nNVIDIA_MONITOR=$!\n\necho \"Log monitor pid ${NVIDIA_MONITOR}\"\n\nsleep 12\n\nkill $NVIDIA_MONITOR\nwait $NVIDIA_MONITOR\necho \"Success: ${NVIDIA_MONITOR} is not longer running\" \n\n# put this in any script to hard kill monitor_nvidida\necho \"Test killing with pgrep\"\n./monitor_nvidia.sh --log_full_path ./full_log --log_summary_full_path ./log_summary &\nNVIDIA_MONITOR_2=$!\n\nsleep 12\n\necho \"Log monitor pid ${NVIDIA_MONITOR_2}\"\necho \"kill with pgrep\"\npgrep \"monitor_nvidia\" | xargs kill\n\necho \"Wait until dead\"\nwait $NVIDIA_MONITOR_2\necho \"Process is dead: Test successful\"\n\n" }, { "path": "cluster/tf-tools/benchmark/runner/cluster_aws.py", "content": "import boto3\nimport os\nimport time\nimport util\nfrom contextlib import contextmanager\n\n\nclass AWSInstance(object):\n\n def __init__(self, instance, ssh_key='', name='', username='ubuntu'):\n # assert instance is aws instance\n self.aws_instance = instance\n self.ssh_key = ssh_key\n self.username = username\n if name:\n self.SetNameTag(name)\n self.opened_ssh_client = []\n\n def __del__(self):\n self.CleanSshClient()\n\n def WaitUntilReady(self):\n self.aws_instance.wait_until_running()\n # Sometimes the wait doesn't work, wait longer and check status\n client = boto3.client('ec2')\n while True:\n res = client.describe_instance_status(\n InstanceIds=[self.aws_instance.instance_id])\n try:\n if (res['InstanceStatuses'][0]['InstanceStatus']['Status'] == 'ok' and\n res['InstanceStatuses'][0]['SystemStatus']['Status'] == 'ok'):\n break\n except:\n print('instance has no status')\n time.sleep(30)\n\n self.aws_instance.load()\n self.hostname = self.aws_instance.public_dns_name\n\n def CreateSshClient(self):\n assert self.hostname is not None\n ssh_client = util.SshToHost(self.hostname, ssh_key=self.ssh_key, username=self.username)\n self.opened_ssh_client.append(ssh_client)\n return ssh_client\n\n def reuse_ssh_client(self):\n assert self.hostname is not None\n if not hasattr(self, 'ssh_client') or self.ssh_client == None:\n self.ssh_client = util.SshToHost(self.hostname, ssh_key=self.ssh_key, username=self.username)\n return self.ssh_client\n\n def CleanSshClient(self):\n if hasattr(self, 'ssh_client'):\n self.opened_ssh_client.append(self.ssh_client)\n for ssh_client in self.opened_ssh_client:\n try:\n ssh_client.close()\n except:\n pass\n self.opened_ssh_client = []\n self.ssh_client = None\n\n @property\n def state(self):\n return self.aws_instance.state.get('Name', None)\n\n def SetNameTag(self, name='tf'):\n ec2 = boto3.resource('ec2')\n self.tag = ec2.create_tags(\n Resources=[self.aws_instance.id], Tags=[{\n 'Key': 'Name',\n 'Value': name\n }])\n\n def Start(self):\n self.aws_instance.start()\n\n def Stop(self):\n self.CleanSshClient()\n self.aws_instance.stop()\n\n def StopAndWaitUntilStopped(self):\n self.Stop()\n self.aws_instance.wait_until_stopped()\n\n def Terminate(self):\n self.CleanSshClient()\n self.aws_instance.terminate()\n\n def TerminateAndWaitUntilTerminated(self):\n self.Terminate()\n self.aws_instance.wait_until_terminated()\n\n @property\n def instance_id(self):\n return self.aws_instance.instance_id\n\n def ExecuteCommandAndWait(self, cmd, print_error=False):\n util.ExecuteCommandAndWait(\n self.reuse_ssh_client(), cmd, print_error=print_error)\n\n def ExecuteCommandAndReturnStdout(self, cmd):\n return util.ExecuteCommandAndReturnStdout(self.reuse_ssh_client(), cmd)\n\n def ExecuteCommandAndStreamOutput(self, \n cmd,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False,\n ok_exit_status=[0]):\n \n return util.ExecuteCommandAndStreamOutput(self.reuse_ssh_client(),\n cmd,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n line_extractor=line_extractor,\n print_error=print_error,\n ok_exit_status=ok_exit_status) \n\n def ExecuteCommandInThread(self,\n command,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False):\n ssh_client = self.CreateSshClient()\n return util.ExecuteCommandInThread(\n ssh_client,\n command,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n line_extractor=line_extractor,\n print_error=print_error)\n\n def RetrieveFile(self, remote_file, local_file):\n sftp_client = self.reuse_ssh_client().open_sftp()\n sftp_client.get(remote_file, local_file)\n sftp_client.close()\n\n def UploadFile(self, local_file, remote_file):\n sftp_client = self.reuse_ssh_client().open_sftp()\n sftp_client.put(local_file, remote_file)\n sftp_client.close()\n\n\ndef MaybeCreatePlacementGroup(name='tf_bm'):\n client = boto3.client('ec2')\n try:\n client.describe_placement_groups(GroupNames=[name])\n except boto3.exceptions.botocore.exceptions.ClientError as e:\n res = client.create_placement_group(GroupName=name, Strategy='cluster')\n\n counter = 0\n while True:\n try:\n res = client.describe_placement_groups(GroupNames=[name])\n if res['PlacementGroups'][0]['State'] == 'available':\n break\n except:\n pass\n counter = counter + 1\n if counter >= 10:\n print('Failed to create placement group %s' % name)\n time.sleep(10)\n\n\ndef DeletePlacementGroup(name='tf_bm'):\n client = boto3.client('ec2')\n try:\n client.describe_placement_groups(GroupNames=[name])\n except boto3.exceptions.botocore.exceptions.ClientError as e:\n print(\"Placement group %s doesn't exit.\" % name)\n return\n\n # Not sure whether delete_placement_group would throw or not.\n res = client.delete_placement_group(GroupName=name)\n if res['ResponseMetadata']['HTTPStatusCode'] != 200:\n print('Failed to delete placement group %s' % name)\n\n\n\n\ndef CreateAwsInstances(num_instances=1,\n image_id='',\n instance_type='t1.micro',\n key_name='',\n ssh_key='',\n instance_tag='tf',\n security_group='default',\n placement_group=''):\n ec2 = boto3.resource('ec2')\n if placement_group:\n MaybeCreatePlacementGroup(name=placement_group)\n aws_instances = ec2.create_instances(\n ImageId=image_id,\n InstanceType=instance_type,\n MinCount=num_instances,\n MaxCount=num_instances,\n SecurityGroups=[security_group],\n Placement={'GroupName': placement_group},\n KeyName=key_name)\n else:\n aws_instances = ec2.create_instances(\n ImageId=image_id,\n InstanceType=instance_type,\n MinCount=num_instances,\n MaxCount=num_instances,\n SecurityGroups=[security_group],\n KeyName=key_name)\n assert len(aws_instances) == num_instances\n print('{} Instances created'.format(len(aws_instances)))\n instances = [\n AWSInstance(instance, ssh_key, instance_tag) for instance in aws_instances\n ]\n\n return instances\n\n\ndef LookupAwsInstances(image_id=None,\n state=None,\n instance_tag=None,\n placement_group=None,\n ssh_key=None):\n\n def FillOneFilter(key, values):\n f = {}\n f['Name'] = key\n f['Values'] = values\n return f\n\n filters = []\n if image_id is not None:\n filters.append(FillOneFilter('image-id', [image_id]))\n if state is not None:\n filters.append(FillOneFilter('instance-state-name', [state.lower()]))\n if instance_tag is not None:\n filters.append(FillOneFilter('tag:Name', [instance_tag]))\n if placement_group is not None:\n filters.append(FillOneFilter('placement-group-name', [placement_group]))\n\n ec2 = boto3.resource('ec2')\n instances = ec2.instances.filter(Filters=filters)\n\n return [\n AWSInstance(instance, ssh_key) for instance in instances\n if instance.state.get('Name') != 'terminated'\n ]\n\n\n@contextmanager\ndef AwsInstances(num_instances=1,\n image_id='',\n instance_type='',\n key_name='',\n ssh_key='',\n security_group='default',\n instance_tag='',\n placement_group='bm_group',\n close_behavior=None):\n try:\n instances_created = False\n instances = CreateAwsInstances(\n num_instances=num_instances,\n image_id=image_id,\n instance_type=instance_type,\n key_name=key_name,\n ssh_key=ssh_key,\n instance_tag=instance_tag,\n security_group=security_group,\n placement_group=placement_group)\n instances_created = True\n\n for instance in instances:\n print('Waiting for instance({}) to be ready.'.format(\n instance.instance_id))\n instance.WaitUntilReady()\n print('All {} instances ready!!!'.format(len(instances)))\n yield instances\n finally:\n if not instances_created:\n return\n if close_behavior is not None:\n for instance in instances:\n if close_behavior == 'terminate':\n instance.Terminate()\n elif close_behavior == 'stop':\n instance.Stop()\n if placement_group and close_behavior == 'terminate':\n DeletePlacementGroup(placement_group)\n\n\n@contextmanager\ndef ReuseAwsInstances(image_id=None,\n state=None,\n instance_tag=None,\n placement_group=None,\n ssh_key=None,\n close_behavior=None):\n try:\n instances = LookupAwsInstances(\n image_id=image_id,\n instance_tag=instance_tag,\n state=state,\n placement_group=placement_group,\n ssh_key=ssh_key)\n\n if len(instances) == 0:\n raise ValueError('No instances found for instance_tag={} or image_id={}'.\n format(instance_tag, image_id))\n\n for instance in instances:\n if instance.state.lower() != 'running':\n print('Current instance({}) state:{}, trying to start.'.format(\n instance.instance_id, instance.state))\n instance.Start()\n for instance in instances:\n print('Waiting for instance({}) to be ready.'.format(\n instance.instance_id))\n instance.WaitUntilReady()\n print('All {} instances ready!!!'.format(len(instances)))\n yield instances\n finally:\n if close_behavior is not None:\n for instance in instances:\n if close_behavior == 'terminate':\n instance.Terminate()\n elif close_behavior == 'stop':\n instance.Stop()\n if placement_group and close_behavior == 'terminate':\n DeletePlacementGroup(placement_group)\n" }, { "path": "cluster/tf-tools/benchmark/runner/command_builder.py", "content": "import os\nimport sys\nimport six\n\ndef BuildDistributedCommandWorker(run_config, worker_hosts, ps_hosts,\n task_index):\n \"\"\"Build command to start distributed worker.\"\"\"\n\n run_script = 'python tf_cnn_benchmarks.py'\n # Build command line\n run_cmd_list = []\n\n # Arguments with no name change that are passed through from the configs.\n pass_through_args = [\n 'data_format', 'batch_size', 'num_batches', 'model', 'data_dir',\n 'optimizer', 'learning_rate', 'sync_on_finish', 'weight_decay',\n 'data_name', 'use_nccl', 'variable_update', 'num_intra_threads',\n 'num_inter_threads', 'mkl', 'num_warmup_batches', 'forward_only',\n 'kmp_blocktime', 'device', 'staged_vars', 'staged_grads', 'cross_replica_sync'\n ]\n\n for arg in pass_through_args:\n if arg in run_config:\n run_cmd_list.append('--{}={}'.format(arg, run_config[arg]))\n\n if 'ps_server' in run_config:\n run_cmd_list.append(\n '--local_parameter_device={}'.format(run_config['ps_server']))\n\n if 'gpus' in run_config:\n run_cmd_list.append('--num_gpus={}'.format(run_config['gpus']))\n\n # Setting the train_dir results in summaries and checkpoints\n # being written unless turned off.\n if 'train_dir' in run_config and task_index == 0:\n run_cmd_list.append('--train_dir={}'.format(run_config['train_dir']))\n if 'save_model_secs' in run_config:\n run_cmd_list.append(\n '--save_model_secs={}'.format(run_config['save_model_secs']))\n else:\n # Defaults to saving every 10 minutes (10*60).\n run_cmd_list.append('--save_model_secs=600')\n\n if 'summary_verbosity' in run_config:\n run_cmd_list.append(\n '--summary_verbosity={}'.format(run_config['summary_verbosity']))\n else:\n # Defaults to 1 for basic summaries\n run_cmd_list.append('--summary_verbosity=1')\n\n if 'save_summaries_steps' in run_config:\n run_cmd_list.append('--save_summaries_steps={}'.format(\n run_config['save_summaries_steps']))\n else:\n # This is local not global steps.\n run_cmd_list.append('--save_summaries_steps=500')\n\n # Set to pickup training from a checkpoint\n if 'pretrain_dir' in run_config and task_index == 0:\n run_cmd_list.append('--pretrain_dir={}'.format(run_config['pretrain_dir']))\n\n # Forces no distortions, which is the most common for benchmarks.\n run_cmd_list.append('--nodistortions')\n if 'display_every' in run_config:\n run_cmd_list.append(\n '--display_every={}'.format(run_config['display_every']))\n else:\n run_cmd_list.append('--display_every=10')\n\n if ps_hosts:\n # Set flags needed for running in distributed mode.\n run_cmd_list.append('--worker_hosts=' + worker_hosts)\n run_cmd_list.append('--ps_hosts=' + ps_hosts)\n run_cmd_list.append('--job_name=worker')\n run_cmd_list.append('--task_index={}'.format(task_index))\n\n run_cmd = '{} {}'.format(run_script, ' '.join(run_cmd_list))\n\n return run_cmd\n\n\ndef BuildDistributedCommandPS(run_config, worker_hosts, ps_hosts, task_index):\n \"\"\"Build command to start distributed parameter server.\n\n \"\"\"\n print('Build Distributed Parameter Run Command')\n\n run_script = \"CUDA_VISIBLE_DEVICES='' python tf_cnn_benchmarks.py\"\n # Build command line\n run_cmd_list = []\n\n if 'sync_on_finish' in run_config:\n run_cmd_list.append(\n '--sync_on_finish={}'.format(run_config['sync_on_finish']))\n\n run_cmd_list.append('--local_parameter_device=' + run_config['ps_server'])\n run_cmd_list.append('--worker_hosts=' + worker_hosts)\n run_cmd_list.append('--ps_hosts=' + ps_hosts)\n run_cmd_list.append('--job_name=ps')\n run_cmd_list.append('--task_index={}'.format(task_index))\n\n run_cmd = '{} {}'.format(run_script, ' '.join(run_cmd_list))\n\n return run_cmd\n\n\ndef WorkerUtil(workers):\n \"\"\"Handles different entries options for workers and ps_servers\"\"\"\n if (type(workers) is int):\n if workers == 0:\n return '0'\n else:\n return ','.join(str(n) for n in range(workers))\n else:\n return workers\n\n\ndef GpuDecode(raw_gpu_input):\n \"\"\"Handles different entries options for workers and ps_servers\"\"\"\n if type(raw_gpu_input) is int:\n return str(raw_gpu_input)\n else:\n return raw_gpu_input.split(',')\n\n\ndef LoadYamlRunConfig(full_config, debug_level):\n \"\"\"Processes config file into list of configs\n\n Reads the config made up of repeating 'run_configs' The first first config as\n is treated as the base. Each config entry after the first is merged with the\n base (first) config. The idea being the first config is the base and the\n subsequent configs are variations that override the base config\n\n Additionally, multiple configs are created based on the the following fields:\n 'model' (list of models to test), gpu (list of number of GPUs to test), and\n repeat (number of times to run the test).\n\n Args:\n full_config: full run_config normally loaded from yaml\n debug_level: controls level of output\n \"\"\"\n\n # base config that subsequent configs merge with\n base_config = None\n configs = []\n for config in full_config['run_configs']:\n if base_config is None:\n base_config = config\n else:\n # merges config with base config\n base = base_config.copy()\n base.update(config)\n config = base\n\n # Copy root settings into config. Anything\n # at the root with override anything in run_configs\n for k, v in six.iteritems(full_config):\n if k != 'run_configs':\n base_config[k] = v\n\n # breaks configs up to run each model in a seperate config\n for model in config['models']:\n #print('Model {}'.format(model))\n model_config = config.copy()\n model_config['model'] = model\n\n # Multiple configs if gpu count is comma delimited\n if 'gpus' in config:\n gpus = config['gpus']\n gpu_list = GpuDecode(gpus)\n for gpu in gpu_list:\n gpu_config = model_config.copy()\n gpu_config['raw_gpu'] = gpus\n gpu_config['gpus'] = gpu\n # Check if the test should be repeated\n if gpu_config.get('repeat') is not None:\n repeat = int(gpu_config['repeat'])\n for i in range(repeat):\n # Creates copy so each one can have an index, e.g. 'copy'\n repeat_model_config = gpu_config.copy()\n repeat_model_config['copy'] = i\n configs.append(repeat_model_config)\n else:\n # Add config to list\n configs.append(gpu_config)\n if debug_level > 0:\n print('Config:{} \\n{}'.format(gpu_config['name'], gpu_config))\n # Testing with CPU\n else:\n if model_config.get('repeat') is not None:\n repeat = int(model_config['repeat'])\n for i in range(repeat):\n # Creates copy so each one can have an index, e.g. 'copy'\n repeat_model_config = model_config.copy()\n repeat_model_config['copy'] = i\n configs.append(repeat_model_config)\n else:\n configs.append(model_config)\n if debug_level > 0:\n print('Config:{} \\n{}'.format(model_config['name'], model_config))\n return configs\n" }, { "path": "cluster/tf-tools/benchmark/runner/configs/aws/multi_server.yaml", "content": "# Run config\ncloud_type: aws\n\ntf_url: tensorflow-gpu\n\n# Shared with AWS and GCE\ninstance_tag: tf-monster\ninstance_type: p2.8xlarge\ninstance_force_reuse: False\ninstance_ami: ami-xxxxxxx\ninstance_count: 8\n#instance_on_finish: stop\n\n# As of May 2017 this config matches what was published on tf.org.\n# For batch-size 32, 4 ps_servers is the right setting for 8 workers\nrun_configs:\n - name: distributed\n workers: 8\n ps_servers: 8\n gpus: 8\n models: ['resnet50']\n ps_server: gpu\n data_format: NCHW\n variable_update: distributed_replicated\n log_folder: results\n framework: tensorflow\n num_batches: 100\n batch_size: 64\n repeat: 5\n cross_replica_sync: True\n optimizer: sgd\n\n\n####\n# Full run 32 GPUs down to 1 GPU with ps_servers tuned for resnet50\n#######\n\n - name: distributed\n workers: 4\n ps_servers: 4\n\n - name: distributed\n workers: 2\n ps_servers: 2\n\n - name: distributed\n workers: 1\n ps_servers: 1\n\n - name: distributed\n workers: '0'\n ps_servers: '0'\n gpus: 1\n" }, { "path": "cluster/tf-tools/benchmark/runner/configs/aws/yaroslav.yaml", "content": "# Run config\ncloud_type: aws\n\ntf_url: tensorflow-gpu\n\ninstance_tag: yaroslav\ninstance_type: p2.xlarge\ninstance_force_reuse: False\ninstance_ami: ami-60df1418\ninstance_count: 8\n#instance_on_finish: stop\n\nrun_configs:\n - name: distributed\n workers: 1\n ps_servers: 1\n gpus: 1\n models: ['resnet50']\n ps_server: gpu\n data_format: NCHW\n variable_update: distributed_replicated\n log_folder: results\n framework: tensorflow\n num_batches: 100\n batch_size: 64\n repeat: 1\n cross_replica_sync: True\n optimizer: sgd\n\n" }, { "path": "cluster/tf-tools/benchmark/runner/instance_info.py", "content": "from collections import defaultdict\n\nimport boto3\n\n\"\"\"\nA tool for retrieving basic information from the running EC2 instances.\n\"\"\"\n\n# Connect to EC2\nec2 = boto3.resource('ec2')\n\n# Get information for all running instances\nrunning_instances = ec2.instances.filter(Filters=[{\n 'Name': 'instance-state-name',\n 'Values': ['running']}])\n\nec2info = defaultdict()\nfor instance in running_instances:\n for tag in instance.tags or []:\n if 'Name'in tag['Key']:\n name = tag['Value']\n if name != 'tf':\n continue\n # Add instance info to a dictionary \n ec2info[instance.id] = {\n 'Name': name,\n 'Type': instance.instance_type,\n 'State': instance.state['Name'],\n 'Private IP': instance.private_ip_address,\n 'Public IP': instance.public_ip_address,\n 'Launch Time': instance.launch_time\n }\n\nattributes = ['Name', 'Type', 'State', 'Private IP', 'Public IP', 'Launch Time']\nfor instance_id, instance in ec2info.items():\n for key in attributes:\n print(\"{0}: {1}\".format(key, instance[key]))\n print(\"------\")\n \n" }, { "path": "cluster/tf-tools/benchmark/runner/launch_experiment.py", "content": "# launch imagenet experiment\n\nfrom command_builder import *\nfrom pprint import pprint as pp\nimport yaml\nimport cluster_aws\nimport util\nimport boto3\n\nfrom collections import OrderedDict\nimport time\n\nimport argparse\nparser = argparse.ArgumentParser(description='ImageNet experiment')\n\nparser.add_argument('--launch', action='store_true', default=False,\n help='launch new instances (instead of reusing)')\nparser.add_argument('--num-workers', type=int, default=1,\n help='number of workers')\nparser.add_argument('--num-ps', type=int, default=1,\n help='number of parameter servers')\nparser.add_argument('--ami', type=str, default='ami-9ddb0fe5',\n help='AMI to use for launching instances')\nparser.add_argument('--key-name', type=str, default='yaroslav',\n help='AWS key-name to use')\nparser.add_argument('--key-path', type=str,\n default=os.environ['HOME']+'/d/yaroslav.pem',\n help='location of .pem file on local filesystem')\nparser.add_argument('--instance-type', type=str,\n default='p2.8xlarge',\n help='instance type to use')\nparser.add_argument('--security-group', type=str,\n default='open',\n help='which security group to use for instances')\nparser.add_argument('--tag', type=str, default='tf',\n help=('tag used to keep track of machines in this '\n 'experiment'))\nparser.add_argument('--logdir', type=str, default='/tmp/tf',\n help='where to dump worker logs')\nparser.add_argument('--port', type=int, default=3333,\n help='default port to use for all connections')\n\nargs = parser.parse_args()\n\n\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_sec = (self.end - self.start)\n print(\"%s took %.2f seconds\"%(self.tag, interval_sec))\n\n\ndef get_instance_ip_map():\n \"\"\"Return instance_id->private_ip map for all running instances.\"\"\"\n \n ec2 = boto3.resource('ec2')\n\n # Get information for all running instances\n running_instances = ec2.instances.filter(Filters=[{\n 'Name': 'instance-state-name',\n 'Values': ['running']}])\n\n ec2info = OrderedDict()\n for instance in running_instances:\n name = ''\n for tag in instance.tags or []:\n if 'Name' in tag['Key']:\n name = tag['Value']\n ec2info[instance.id] = instance.private_ip_address\n \n return ec2info\n\n\ndef main():\n num_instances = args.num_workers + args.num_ps\n os.system('rm -Rf '+args.logdir)\n os.system('mkdir -p '+args.logdir)\n\n # TODO: add these commands (running manually for now)\n # sudo nvidia-persistenced\n # sudo nvidia-smi --auto-boost-default=0\n # sudo nvidia-smi -ac 2505,875 # p2\n \n if args.launch:\n print(\"Creating new instances\")\n with timeit('create_instances'):\n instances = cluster_aws.CreateAwsInstances(num_instances=num_instances,\n image_id=args.ami,\n key_name=args.key_name,\n ssh_key=args.key_path,\n security_group=args.security_group,\n instance_tag=args.tag,\n placement_group='',\n instance_type=args.instance_type)\n else:\n # TODO: better control of retrieved instances\n print(\"Reusing existing instances\")\n instances = cluster_aws.LookupAwsInstances(instance_tag=args.tag,\n ssh_key=args.key_path)\n assert len(instances) >= num_instances\n\n # todo: deterministic worker sort\n with timeit('connect'):\n for i,instance in enumerate(instances):\n if i >= num_instances:\n break\n print(\"Connecting to instance %d, %s\" % (i, instance.instance_id))\n instance.WaitUntilReady()\n\n\n worker_instances = instances[:args.num_workers]\n ps_instances = instances[args.num_workers:args.num_workers+args.num_ps]\n\n instance_ip_map = get_instance_ip_map()\n \n worker_host_fragments = []\n for instance in worker_instances:\n assert instance.instance_id in instance_ip_map\n worker_host_str = '%s:%d'%(instance_ip_map[instance.instance_id], args.port)\n worker_host_fragments.append(worker_host_str)\n worker_hosts = ','.join(worker_host_fragments)\n \n ps_host_fragments = []\n for instance in ps_instances:\n assert instance.instance_id in instance_ip_map\n ps_host_str = '%s:%d'%(instance_ip_map[instance.instance_id], args.port)\n ps_host_fragments.append(ps_host_str)\n ps_hosts = ','.join(ps_host_fragments)\n\n\n line_extractor = util.ExtractErrorToConsole\n\n setup_cmd = \"source ~/.bashrc && export PATH=~/anaconda3/bin:$PATH && source activate py2 && cd ~/git0/benchmarks/scripts/tf_cnn_benchmarks\"\n \n worker_cmd_tmpl = \"python tf_cnn_benchmarks.py --data_format=NCHW --batch_size=64 --num_batches=1000 --model=resnet50 --optimizer=sgd --variable_update=distributed_replicated --cross_replica_sync=True --local_parameter_device=gpu --num_gpus=1 --nodistortions --display_every=10 --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=worker --task_index=%(task_index)s\"\n\n # job_name = 'worker'\n for i, instance in enumerate(worker_instances):\n worker_cmd = worker_cmd_tmpl % {'worker_hosts': worker_hosts, 'ps_hosts': ps_hosts, 'task_index': i}\n cmd = setup_cmd + \" && \" + worker_cmd\n # print(cmd)\n fn_out = args.logdir + '/worker_out-%02d'%(i,)\n fn_err = args.logdir + '/worker_err-%02d'%(i,)\n #ssh_client = instance.reuse_ssh_client()\n result = instance.ExecuteCommandInThread(cmd,\n stdout_file=fn_out,\n stderr_file=fn_err,\n line_extractor=line_extractor)\n print(\"worker %d started\" %(i,))\n\n ps_cmd_tmpl = \"CUDA_VISIBLE_DEVICES='' python tf_cnn_benchmarks.py --local_parameter_device=gpu --worker_hosts=%(worker_hosts)s --ps_hosts=%(ps_hosts)s --job_name=ps --task_index=%(task_index)s\"\n job_name = 'ps'\n for i, instance in enumerate(ps_instances):\n ps_cmd = ps_cmd_tmpl % {'worker_hosts': worker_hosts, 'ps_hosts': ps_hosts,\n 'task_index': i}\n cmd = setup_cmd + \" && \" + ps_cmd\n # print(cmd)\n fn_out = args.logdir + '/ps_out-%02d'%(i,)\n fn_err = args.logdir + '/ps_err-%02d'%(i,)\n #ssh_client = instance.reuse_ssh_client()\n result = instance.ExecuteCommandInThread(cmd,\n stdout_file=fn_out,\n stderr_file=fn_err,\n line_extractor=line_extractor)\n print(\"parameter server %d started \" %(i,))\n\n time.sleep(10000)\n \nif __name__=='__main__':\n main()\n" }, { "path": "cluster/tf-tools/benchmark/runner/test_cluster_aws.py", "content": "from command_builder import *\nfrom pprint import pprint as pp\nimport yaml\nimport cluster_aws\n\nfrom collections import OrderedDict\nimport time\n\nAMI='ami-60df1418' # cuda 8\nAMI='ami-9ddb0fe5' # boyd base\nKEY_NAME='yaroslav'\nKEY_FILE=os.environ['HOME']+'/d/yaroslav.pem'\nSECURITY_GROUP='open'\n#INSTANCE_TYPE='g3.16xlarge'\nINSTANCE_TYPE='p2.8xlarge'\nTAG='tf'\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_sec = (self.end - self.start)\n print(\"%s took %.2f seconds\"%(self.tag, interval_sec))\n\ndef test_two_machine():\n \n\n\ndef main():\n FIRST_TIME = False\n \n if FIRST_TIME:\n with timeit('create_instances'):\n instances = cluster_aws.CreateAwsInstances(num_instances=2,\n image_id=AMI,\n key_name=KEY_NAME,\n ssh_key=KEY_FILE,\n security_group=SECURITY_GROUP,\n instance_tag=TAG,\n placement_group='',\n instance_type=INSTANCE_TYPE)\n else:\n instances = cluster_aws.LookupAwsInstances(instance_tag=TAG,\n ssh_key=KEY_FILE)\n # Exception connecting to host via ssh (could be a timeout):\n\n\n \n with timeit('connect'):\n instance = instances[0]\n instance.WaitUntilReady()\n \n\n def line_extractor(line):\n return True\n \n instance.ExecuteCommandAndStreamOutput('mkdir 43',\n stdout_file='/tmp/output')\n instance.ExecuteCommandAndStreamOutput('ls', stdout_file='/tmp/output')\n\n import pdb; pdb.set_trace()\n\n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/tf-tools/benchmark/runner/test_command_builder.py", "content": "from command_builder import *\nfrom pprint import pprint as pp\nimport yaml\n\ndef main():\n\n \n with open('configs/aws/yaroslav.yaml') as stream:\n config_yaml = yaml.load(stream)\n\n configs = LoadYamlRunConfig(config_yaml, 1)\n# pp(configs)\n\n config = configs[0]\n\n worker_hosts = ['1','2']\n worker_hosts_str = ','.join(worker_hosts)\n ps_hosts = ['a','b']\n ps_hosts_str = ','.join(ps_hosts)\n for i,worker in enumerate(worker_hosts):\n print(BuildDistributedCommandWorker(config, worker_hosts_str, ps_hosts_str, i))\n \n for i,worker in enumerate(ps_hosts):\n print(BuildDistributedCommandPS(config, worker_hosts_str, ps_hosts_str, i))\n \n \n\n \n\nif __name__=='__main__':\n main()\n" }, { "path": "cluster/tf-tools/benchmark/runner/util.py", "content": "#import exceptions\nimport functools\nimport logging\nimport os\nimport paramiko\nimport numpy\nimport sys\nimport threading\nimport time\n\ndef ExtractErrorToConsole(line):\n \"\"\"Prints errors found in output to console\n \n Add checks to ensure any errors or info from the call of interest are \n shown in the console to improve speed of identifying issues, e.g. socket \n already used on non worker_0. \n\n \"\"\"\n # tf_cnn_bench error lines start with E\n if line.find('E') == 0:\n print(line.rstrip('\\n'))\n return\n\n # Tensorflow Errors often look liked 'E tensorflow'\n if line.find('E tensorflow') != -1:\n print(line.rstrip('\\n'))\n return\n\n # A little noisy but useful\n # Print number of devices found. AWS often has\n # a busted GPU\n if line.find('DMA: ') != -1:\n print(line.rstrip('\\n'))\n return\n\ndef ExtractToStdout(line):\n print(line.rstrip('\\n'))\n\n\ndef ExtractImagePerSecond(line):\n if 'images/sec:' in line:\n print(line.rstrip('\\n'))\n\n\ndef ExecuteCommandAndWait(ssh_client, command, print_error=True, ok_exit_status=[0]):\n _, stdout, stderr = ssh_client.exec_command(command, get_pty=True)\n exit_status = stdout.channel.recv_exit_status()\n if exit_status in ok_exit_status:\n return True\n else:\n if print_error:\n print('Error({}) executing command:{}'.format(exit_status, command))\n print(stdout.read())\n return False\n\n\ndef ExecuteCommandAndReturnStdout(ssh_client, command):\n _, stdout, stderr = ssh_client.exec_command(command, get_pty=True)\n exit_status = stdout.channel.recv_exit_status()\n return stdout.read()\n\n\ndef _StreamOutputToFile(fd, file, line_extractor, command=None):\n \"\"\"Stream output to local file print select content to console\n\n Streams output to a local file and if a line_extractor is passed\n uses it to determine which data is printed to the local console.\n\n \"\"\"\n def func(fd, file, line_extractor):\n with open(file, 'ab+') as f:\n if command:\n line = command + '\\n'\n f.write(line.encode('utf-8'))\n try:\n for line in iter(lambda: fd.readline(2048), ''):\n f.write(line.encode('utf-8', errors='ignore'))\n f.flush()\n if line_extractor:\n line_extractor(line)\n except UnicodeDecodeError as err:\n print('UnicodeDecodeError parsing stdout/stderr, bug in paramiko:{}'\n .format(err))\n t = threading.Thread(target=func, args=(fd, file, line_extractor))\n t.start()\n return t\n\n\ndef ExecuteCommandAndStreamOutput(ssh_client,\n command,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False,\n ok_exit_status=[0]):\n \"\"\"Executes command in ssh_client. Blocking call.\n\n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n command: command to run in the ssh_client\n stdout_file: local file to write standard output of the command to\n stderr_file: local file to write standard error of the command to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error\n ok_exit_status: List of status codes that are not errors, defaults to '0'\n\n \"\"\"\n _, stdout, stderr = ssh_client.exec_command(command, get_pty=True)\n if stdout_file:\n t1 = _StreamOutputToFile(stdout, stdout_file, line_extractor, command=command)\n if stderr_file:\n t2 = _StreamOutputToFile(stderr, stderr_file, line_extractor)\n if stdout_file:\n t1.join()\n if stderr_file:\n t2.join()\n exit_status = stdout.channel.recv_exit_status()\n if exit_status in ok_exit_status:\n return True\n else:\n if print_error:\n print('Command execution failed! Check log. Exit Status({}):{}'.format(exit_status, command))\n return False\n\n\ndef ExecuteCommandInThread(ssh_client,\n command,\n stdout_file=None,\n stderr_file=None,\n line_extractor=None,\n print_error=False):\n \"\"\"Returns a thread that executes the given command. Non-Blocking call.\n\n \n\n Args:\n ssh_client: ssh client setup to connect to the server to run the tests on\n command: command to run in the ssh_client\n stdout_file: local file to write standard output of the command to\n stderr_file: local file to write standard error of the command to\n line_extractor: method to call on each line to determine if the line\n should be printed to the local console.\n print_error: True to print output if there is an error, e.g. non-'0' exit code.\n\n returns a thread that executes the given command\n\n \"\"\"\n t = threading.Thread(\n target=ExecuteCommandAndStreamOutput,\n args=(ssh_client, command, stdout_file, stderr_file, line_extractor,\n print_error))\n t.start()\n return t\n\n\ndef SshToHost(hostname,\n retry=10,\n ssh_key=os.path.join(os.environ['HOME'], 'd/yaroslav.pem'),\n password=None,\n username='ubuntu'):\n\n \"\"\"Create ssh connection to host\n\n Creates and returns and ssh connection to the host passed in. \n\n Args:\n hostname: host name or ip address of the system to connect to.\n retry: number of time to retry.\n ssh_key: full path to the ssk hey to use to connect.\n username: username to connect with.\n\n returns SSH client connected to host.\n\n \"\"\"\n\n # If logging is needed\n # paramiko.util.log_to_file(\"/tmp/paramiko.log\")\n k = None\n if ssh_key: \n k = paramiko.RSAKey.from_private_key_file(ssh_key)\n \n ssh_client = paramiko.SSHClient()\n ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n counter = retry\n while counter > 0:\n try:\n if password: \n ssh_client.connect(hostname=hostname, username=username, password=password)\n else:\n ssh_client.connect(hostname=hostname, username=username, pkey=k)\n break\n except Exception as e:\n counter = counter - 1\n print('Exception connecting to host via ssh (could be a timeout):'.format(e))\n if counter == 0:\n print('Got impatient with retrying ssh to host. Time to give up.')\n return None\n\n return ssh_client\n" }, { "path": "cluster/tf-tools/install/aws_amzlinux.md", "content": "# Install for Amazon Linux TensorFlow + CUDA\nThis install was created on the latest Amazon Linux image as of 12-FEB-2017.\nThe end results is a system that can build and run TensorFlow. To save space\ncreate one machine to do builds following these instructions and then another \nAMI to run tensorflow without the source code, bazel and development kit items.\nMost of the tools are necessary to run TensorFlow but I collected some of the\ncommand from other locatios and thus some of the libs and tools installed may\nnot actually be needed. I also install a few tools I use.\n\n## Which AMI and size\nPick the most recent AWS Amazon Linux image. I chose 20GB to be 100% sure \neverything would fit for a \"build box\". I also plan to run other frameworks.\nIf building something to just run TensorFlow, 8GB is likely enough. \n\n## Is this perfect?\nNot remotely. I try to keep notes each time I do it and it gets better each\ntime. I also sometimes forget to copy the commands back to this file. If it\nis not exact it will get you very close. \n\n## Fun Tip (to save money)\nStart the instance on a m4 instance. Skip the NVIDIA driver install. Everything\nelse will work. Then save off as an AMI and launch as a p2 instance. A few\nnotes:\n* m4 instances do not have avx2. Cross compile will work but the binary\n cannot be tested. Just compile without avx2 to test.\n* Compiling with CUDA works but will not run on m4, because it does not have\n an NVIDIA card. \n* After moving to p2, make sure to install the NVIDIA driver\n* This might not save money, it does for me becuase I end up keeping an m4 \n instance as a buid box. So I can build gcc 4.8.x. \n\n## Install\nSome [hints](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/accelerated-computing-instances.html#optimize_gpu)\nfrom Amazon. I did not have good results turning off auto-boost.\n\n```bash\n\n#######################################################\n# The basics, do this even if skipping other sections\n######################################################\nsudo yum update -y\nsudo yum erase nvidia cuda\n\nsudo yum install -y gcc kernel-devel-`uname -r`\nsudo yum install python-numpy gcc-c++ git\n\nOnly needed for Bazel to build TF, skip if not buildig a build machine\nsudo yum remove java-1.7.0-openjdk\nsudo yum install java-1.8.0 java-1.8.0-openjdk-devel\n\n*Setup pip (python package manager)*\nsudo pip install --upgrade pip\n# Make a symbolic link so sudo can see pip (cheap hack)\ncd /usr/bin\nsudo ln -s /usr/local/bin/pip\n\n# Upgrade some python packages\npip install awscli --upgrade --user\nsudo pip install wheel\nsudo pip install numpy\n\n############################################\n# Installing NVIDIA with CUDA and cuDNN\n#############################################\n# Install NVIDIA Driver\n# This driver. I ended up installing 367.48 from the CUDA download.\nwget http://us.download.nvidia.com/XFree86/Linux-x86_64/352.99/NVIDIA-Linux-x86_64-352.99.run\nsudo ./NVIDIA-Linux-x86_64-352.99.run\nsudo reboot\n\n# Install CUDA\n# usr a newer version if desired\nwget https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda_8.0.44_linux-run\n./cuda_8.0.44_linux-run --extract=/home/ec2-user/\nsudo ./cuda-linux64-rel-8.0.44-21122537.run\n\n# Install CuDNN\n# Download CuDNN from NVIDIA (get the Linux package not deb packages)\n# Need to sign up to down load from NVIDIA, I would share a link if I could\n# this step is annoying\nwget \ntar zxf cudnn-8.0-linux-x64-v5.1.tgz\ncd cuda\n\n# Copy files into CUDA directorys\nsudo cp -P include/cudnn.h /usr/local/cuda-8.0/include/\nsudo cp -P lib64/libcudnn* /usr/local/cuda-8.0/lib64/\nsudo chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*\n\n# Setup Profile with CUDA environment variables\nAdd to ~.bash_profile\n#CUDA Setup\nexport CUDA_HOME=/usr/local/cuda\nexport CUDA_ROOT=/usr/local/cuda\nPATH=$PATH:$CUDA_ROOT/bin\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64\nsource .bash_profile\n\n############################################\n# Installing TensorFlow from source\n#############################################\n# Download bazel get latest but the java install is fine\n\n# set java home\nexport JAVA_HOME=/etc/alternatives/java_sdk\nwget https://github.com/bazelbuild/bazel/releases/download/0.4.4/bazel-0.4.4-jdk7-installer-linux-x86_64.sh\n# install to your $HOME/bin directory (find if you are not sharing)\n./bazel-0.4.4-jdk7-installer-linux-x86_64.sh --user\n\n\n# Build Tensorflow\ncd ~\nmkdir src\ncd src\ngit clone https://github.com/tensorflow/tensorflow\ncd tensorflow\n\n./configure\nDefaults with exception of\nY Cuda 8.0 \ncompute 3.7 (max for K80 assuming not using g.xx instances)\n\n# Works for p2 AWS instances with broadwell CPUs.\n# https://gcc.gnu.org/onlinedocs/gcc-4.8.5/gcc/i386-and-x86-64-Options.html#i386-and-x86-64-Options\n# core-avx2 is the best option for hazwel with gcc 4.8.3\nbazel build --copt=-march=core-avx2 --config=cuda //tensorflow/tools/pip_package:build_pip_package\n# or to build for the native machine\nbazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package\n\n# Build the pip package\nbazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\n\n# Install\ncd /tmp/tensorflow_pkg\nsudo pip install --upgrade --force-reinstall \n\n# Do a quick Test and watch for any warnings, e.g. AVX FMA not compiled in\n# Watch that the GPUs start up, assuming GPUs were desired\npython src/tensorflow/tensorflow/examples/tutorials/mnist/mnist_softmax.py\n\n\n################################\n# Optional\n################################\n\n# ldcofig. There are some scenarios where .bash_profile does not seem to work\n# I often set the following when running benchmark tests so I know\n# cuda is always being picked up and reduces odd errors.\n\nsudo vi /etc/ld.so.conf\n/usr/local/cuda/lib64\n/usr/local/cuda/extras/CUPTI/lib64\n# Load new config\nsudo ldconfig\n\n# Verify config \nldconfig -p\n\n\n```\n\n\n\n" }, { "path": "cluster/tf-tools/install/aws_ubuntu16_04.md", "content": "# Install TensorFlow + CUDA on Ubuntu 16.04 on AWS\nThis install was done on AWS using Ubuntu 16.04 LTS Starting with m4.2xlarge to\nsave money during compile time. Just using a p2.1xlarge instance is likely\neasier an cost effective. \n\nPython 2.7, the default with Unbuntu 16.04\n\n## Objective\nInstall NVIDIA drivers, compile tensorflow and then make an AMI that can be\nlaunched on a p2.xLarge instance with NVIDIA GPUs (K80s) that support CUDA 8.0.\ng2 instances will not work as they only support up to CUDA 7.5.\n\nNote: This script is not intended to be the fastest approach. Some of the\nsteps could be turned into one-liners vs. manual work.\n\n## VM Instance settings\nIf compiling TF from source start with 15GB. To be extra safe and if there is\na desire to install other frameworks 20-30GB is a good size. 30GB is more than\nenough in my expeience and 20GB should work. If trying to keep space usage to a\nminimum, it should be possible to have an 8-10GB VM and remember to delete\nNVIDIA downloads after they are used.\n\n\n```bash\n############################################\n# Install NVIDIA Driver\n########################\n# The best approach is to install the Ubuntu version of the driver. Do not\n# install the driver included in the CUDA package unless necessary. \nsudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt-get update && sudo apt-get upgrade\n# Check https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa to see the\n# latest drivers available\nsudo apt-get install nvidia-378\n\n############################################\n# Install basic packages needed for TensorFlow and generally needed\n########################\nsudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev gfortran python-matplotlib libblas-dev liblapack-dev libatlas-base-dev python-dev python-pydot linux-headers-generic linux-image-extra-virtual unzip python-numpy swig python-pandas python-sklearn unzip wget pkg-config zip g++ zlib1g-dev libcurl3-dev\nsudo apt-get install libcupti-dev bc\n\n# AWS EFS Driver (to mount drives from EFS)\nsudo apt-get -y install nfs-common\n\n# Install Python package manager\nsudo pip install -U pip\nsudo pip install --upgrade pip\nsudo pip install wheel numpy\n\n############################################\n# Install CUDA using packages rather than the script install.\n########################\n# If a new version of CUDA is out, get the link from NVIDIA's site.\n\nwget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run\n./cuda_8.0.61_375.26_linux-run --extract=/home/ubuntu/\nsudo ./cuda-linux64-rel-8.0.61-21551265.run\n\n############################################\n# Install CuDNN\n########################\n# Download CuDNN from NVIDIA (get the Linux package not deb packages)\n# scp to ec2 instance\n# Cannot provide direct download due to needing to signup to get CuDNN.\ntar zxf cudnn-8.0-linux-x64-v5.1.tgz\n\n# Copy files into CUDA directories\nsudo cp -P include/cudnn.h /usr/local/cuda-8.0/include/\nsudo cp -P lib64/libcudnn* /usr/local/cuda-8.0/lib64/\nsudo chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*\n\n# Setup Profile with CUDA environment variables\nAdd to ~profile\n#CUDA Setup\nexport CUDA_HOME=/usr/local/cuda\nexport CUDA_ROOT=/usr/local/cuda\nPATH=$PATH:$CUDA_ROOT/bin\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64:$CUDA_ROOT/extras/CUPTI/lib64\nsource .profile\n\n############################################\n# Install Bazel\n########################\necho \"deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8\" | sudo tee /etc/apt/sources.list.d/bazel.list\ncurl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -\nsudo apt-get update && sudo apt-get install bazel\nsudo apt-get upgrade bazel\n\n############################################\n# Install TensorFlow\n########################\ngit clone https://github.com/tensorflow/tensorflow\n\n./configure\n# Defaults with exception of\n# Y Cuda / 8.0 / Compute 3.7 for K80s.\n\n# Works for m2 and p2 instances and compiles in AVX2 optimizations.\nbazel build -c opt --copt=-march=\"haswell\" --config=cuda //tensorflow/tools/pip_package:build_pip_package\n\n# Build the pip package\nbazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\n\n# Install\npip install --upgrade --force-reinstall /tmp/tensorflow_pkg tensorflow*\n\n###########################\n# Optional\n#####################\n\n# This removes a service that is not needed and looks, although it does not,\n# to take up 100% CPU at times via top.\nsudo apt-get remove gstreamer1.0\n\n###########################\n# Enhanced Networking\n# http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/enhanced-networking-ena.html#enhanced-networking-ena-ubuntu\n#####################\nsudo apt-get update && sudo apt-get upgrade -y\nsudo apt-get install -y build-essential dkms\n\n# Get amazon drivers from github and build\ngit clone https://github.com/amzn/amzn-drivers\nsudo mv amzn-drivers /usr/src/amzn-drivers-1.0.0\nsudo touch /usr/src/amzn-drivers-1.0.0/dkms.conf\nsudo vim /usr/src/amzn-drivers-1.0.0/dkms.conf\n# Add the following\nPACKAGE_NAME=\"ena\"\nPACKAGE_VERSION=\"1.0.0\"\nCLEAN=\"make -C kernel/linux/ena clean\"\nMAKE=\"make -C kernel/linux/ena/ BUILD_KERNEL=${kernelver}\"\nBUILT_MODULE_NAME[0]=\"ena\"\nBUILT_MODULE_LOCATION=\"kernel/linux/ena\"\nDEST_MODULE_LOCATION[0]=\"/updates\"\nDEST_MODULE_NAME[0]=\"ena\"\nAUTOINSTALL=\"yes\"\n\n# Compile and install\nsudo dkms add -m amzn-drivers -v 1.0.0\nsudo dkms build -m amzn-drivers -v 1.0.0\nsudo dkms install -m amzn-drivers -v 1.0.0\nsudo update-initramfs -c -k all\n\n# Verify it is installed. Not specific to look for just make sure an error\n# is not returned\nmodinfo ena\n\n# Enable on the instances (once images the AMI should always have it enabled on startup)\n# Stop the instance using the console or the cli below. Then set the attribute and restart.\n# Upgrade awscli (--ena-support is kind of new) suggest NOT using apt-get\nsudo pip install --upgrade awscli\n\naws ec2 stop-instances --instance-id i-xxxxxxxxxx\naws ec2 modify-instance-attribute --instance-id i-xxxxxxxx --ena-support\n\n# ldcofig. There are some scenarios where .bash_profile does not seem to work\n# I often set the following when running benchmark tests so I know\n# cuda is always being picked up and reduces odd errors.\n\nsudo vi /etc/ld.so.conf\n/usr/local/cuda/lib64\n/usr/local/cuda/extras/CUPTI/lib64\n# Load new config\nsudo ldconfig\n\n# Verify config \nldconfig -p\n\n```\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "cluster/tmux.py", "content": "\n#import util as myutil\nfrom collections import OrderedDict\nfrom collections import defaultdict\nfrom pprint import pprint as pp\nimport argparse\nimport base64\nimport base64\nimport boto3\nimport json\nimport os\nimport os\nimport pickle\nimport portpicker\nimport shlex\nimport struct\nimport subprocess\nimport sys\nimport sys\nimport tensorflow as tf\nimport threading\nimport threading\nimport time\nimport time\nimport yaml\n\nLOCAL_TASKLOGDIR_PREFIX='/temp/tasklogs'\nLOCAL_LOGDIR_PREFIX='/temp/logs'\n\ndef _setup_logdir(job_name):\n \"\"\"Creates appropriate logdir for given job.\"\"\"\n run_name = job_name.rsplit('-',1)[0] # somerun-11-ps -> somerun-11\n logdir = '/temp/logs/'+run_name\n os.system('rm -Rf '+logdir)\n os.system('mkdir -p '+logdir)\n return logdir\n\n\n\ndef _ossystem(cmd):\n print(cmd)\n os.system(cmd)\n\ndef kill_job(name):\n \"\"\"Simple local TensorFlow job launcher.\"\"\"\n \n _ossystem('tmux kill-session -t ' + name)\n\ndef tf_job(name, num_tasks):\n \"\"\"Simple local TensorFlow job launcher.\"\"\"\n \n DEFAULT_NAME = '0'\n _ossystem('tmux kill-session -t ' + name)\n\n # TODO: don't need default name\n tmux_windows = [name+\":\"+str(0)]\n _ossystem('tmux new-session -s %s -n %s -d '% (name, DEFAULT_NAME))\n _ossystem('tmux rename-window -t %s %s '%(DEFAULT_NAME, '0'))\n for task_id in range(1, num_tasks):\n _ossystem(\"tmux new-window -t {} -n {}\".format(name, task_id))\n tmux_windows.append(name+\":\"+str(task_id))\n\n # todo: remove num_tasks\n job = Job(name, num_tasks, tmux_windows)\n # setup environment\n for task in job.tasks:\n task.run('source activate sep22')\n\n # todo: logdir is shared across jobs, so set it up in experiment launcher\n _setup_logdir(name)\n job.logdir = _setup_logdir(name)\n\n return job\n\n\nclass Job:\n def __init__(self, name, num_tasks, tmux_windows):\n self.name = name\n # self.num_tasks = num_tasks\n self.tasks = []\n for task_id, tmux_window in enumerate(tmux_windows):\n self.tasks.append(Task(tmux_window, self, task_id))\n\n\nclass Task:\n \"\"\"Local tasks interacts with tmux session where session name is derived\n from job name, and windows are task ids.\"\"\"\n\n def __init__(self, tmux_window, job, task_id):\n self.tmux_window = tmux_window\n self.job = job\n self.ip = '127.0.0.1' # hostname/ip address\n self.id = task_id\n self.port = portpicker.pick_unused_port()\n self.connect_instructions = 'tmux a -t '+self.tmux_window\n\n self.last_stdout = '' # compatiblity with aws.py:Task\n self.last_stderr = ''\n\n def run(self, cmd):\n _ossystem(\"tmux send-keys -t {} '{}' Enter\".format(self.tmux_window, cmd))\n\n def upload(self, cmd): # compatiblity with aws.py:Task\n pass\n\n def tf_env_setup(self, full_cluster_spec, task_spec):\n # full cluster config\n # todo: not needed\n # cluster_config = {'cluster': cluster_spec, 'task': task_spec}\n\n task_type = task_spec['type']\n task_id = task_spec['index']\n print(\"Task id is %r\"%(task_id,))\n host = full_cluster_spec[task_type][task_id]\n\n # every worker needs its own location\n sparse_cluster_spec = defaultdict(dict)\n sparse_cluster_spec[task_type][task_id] = host\n \n # worker workers know about all ps workers\n if task_type == 'worker':\n sparse_cluster_spec['ps'] = full_cluster_spec['ps']\n \n # ps workers know about all worker workers\n if task_type == 'ps':\n pass\n sparse_cluster_spec['worker'] = full_cluster_spec['worker']\n #sparse_cluster_spec['worker'] = {0: full_cluster_spec['worker'][0]}\n\n sparse_cluster_config = {'cluster': sparse_cluster_spec,\n 'task': task_spec}\n print(\"Cluster config for %s %s is %s\"%(task_type, task_id,\n sparse_cluster_spec))\n json_string = json.dumps(sparse_cluster_config)\n json_string_encoded = base64.b16encode(json_string.encode('ascii'))\n json_string_encoded = json_string_encoded.decode('ascii')\n export_command = \"export TF_CONFIG_BASE16=%s\"%(json_string_encoded,)\n self.run(export_command)\n\n # json has problem with sparse clusterspec (0 can't be key, only \"0\")\n # therefore also dump clusterspec as pickle object\n pickle_string = pickle.dumps(sparse_cluster_config)\n pickle_string_encoded = base64.b16encode(pickle_string)\n pickle_string_encoded = pickle_string_encoded.decode('ascii')\n export_command = \"export TF_PICKLE_BASE16=%s\"%(pickle_string_encoded,)\n self.run(export_command)\n \n logdir = LOCAL_LOGDIR_PREFIX + '/' + FLAGS.run\n self.run(\"export LOGDIR=\"+logdir)\n" }, { "path": "cluster/upload_test.txt", "content": "testfile3\n" }, { "path": "conditional_backprop.py", "content": "# Example of conditionally enabling backprop based on a variable.\n# variable \"switches\" determines which entries of \"y\" will be backpropagated\n# through.\n#\n# IE, switches.assign([1,0]) enables backprop through first value but not\n# second.\n#\n# Running it you should see following on stdout:\n# Value 2.0, gradient 2.0\n# Value 2.0, gradient 0.0\n# Value 2.0, gradient 1.0\n\nimport tensorflow as tf\n\ndef conditional_backprop(do_backprop, tensor):\n do_backprop = tf.Print(do_backprop, [do_backprop], \"switch query\")\n t = tf.cond(tf.cast(do_backprop, tf.bool),\n lambda: tf.Print(tensor, [0],\n \"backprop enabled for \"+tensor.op.name),\n lambda: tf.zeros_like(tensor))\n y = t + tf.stop_gradient(tensor - t)\n return y\n\nx = tf.ones((), name=\"x\")\ny0 = tf.add(x, 0, name=\"y0\")\ny1 = tf.add(x, 0, name=\"y1\")\n\nswitches = tf.Variable(tf.ones((2)))\ndoit = tf.constant(True)\nyy0 = conditional_backprop(switches[0], y0)\nyy1 = conditional_backprop(switches[1], y1)\ny = tf.stack([yy0, yy1], name=\"y\")\n\nz = tf.reduce_sum(y)\n\ngrad = tf.gradients(z, [x])[0]\n\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\nprint(\"Value %.1f, gradient %.1f\"%tuple(sess.run([z, grad])))\n\nsess.run(switches.assign([0,0]))\nprint(\"Value %.1f, gradient %.1f\"%tuple(sess.run([z, grad])))\n\nsess.run(switches.assign([1,0]))\nprint(\"Value %.1f, gradient %.1f\"%tuple(sess.run([z, grad])))\n" }, { "path": "configure_tf.sh", "content": "#!/usr/bin/expect -d\n# Helper script that uses expect to automatically go through all configure\n# steps using the defaults for all options except\n# XLA: y\n# CUDA: y\n# compute capability: 3.5,5.0,6.0,6.1\nspawn ./configure\nexpect \"Please specify the location of python*\"\nsend \"\\r\"\nexpect \"Please specify optimization flags to use during compilation when bazel option*\"\nsend \"\\r\"\nexpect \"Do you wish to use jemalloc*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with Google Cloud Platform*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with Hadoop File System support*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with the XLA*\"\nsend \"y\\r\"\nexpect \"Please input the desired Python library*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with OpenCL*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with CUDA*\"\nsend \"y\\r\"\nexpect \"Please specify which gcc should*\"\nsend \"\\r\"\nexpect \"Please specify the CUDA SDK version you want to use*\"\nsend \"\\r\"\nexpect \"Please specify the location where CUDA toolkit*\"\nsend \"\\r\"\nexpect \"Please specify the Cudnn version*\"\nsend \"\\r\"\nexpect \"Please specify the location where cuDNN\"\nsend \"\\r\"\nexpect \"lease specify a list of comma-separated Cuda compute\"\nsend \"3.5,5.2,6.0,6.1\\r\"\nset timeout 120\nexpect eof\n" }, { "path": "configure_tf_cpu.sh", "content": "#!/usr/bin/expect -d\n# Helper script that uses expect to automatically go through all configure\n# steps using the defaults for all options except\n# XLA: y\n# CUDA: n\nspawn ./configure\nexpect \"Please specify the location of python*\"\nsend \"\\r\"\nexpect \"Please specify optimization flags to use during compilation when bazel option*\"\nsend \"\\r\"\nexpect \"Do you wish to use jemalloc*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with Google Cloud Platform*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with Hadoop File System support*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with the XLA*\"\nsend \"y\\r\"\nexpect \"Please input the desired Python library*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with OpenCL*\"\nsend \"\\r\"\nexpect \"Do you wish to build TensorFlow with CUDA*\"\nsend \"\\r\"\nset timeout 120\nexpect eof\n" }, { "path": "danjar_peek.py", "content": "import tensorflow as tf\nfrom tensorflow.python.client import timeline\n\n\nclass Queue(tf.FIFOQueue):\n\n def __init__(self, capacity):\n s = ()\n d = tf.int32\n super().__init__(capacity - 1, [d], [s])\n self._first = tf.get_variable(name=\"var1\",\n initializer=tf.ones_initializer(),\n shape=s, dtype=d, use_resource=False)\n self._size = tf.get_variable(name=\"size\", shape=(),\n initializer=tf.zeros_initializer(),\n dtype=tf.int32, use_resource=False)\n \n def peek(self):\n return self._first.read_value()\n\n def enqueue(self, element):\n super_ = super()\n def first():\n assigns = [self._first.assign(element)]\n with tf.control_dependencies(assigns):\n return tf.constant(0)\n \n def other():\n with tf.control_dependencies([super_.enqueue(element)]):\n return tf.constant(0)\n \n with tf.control_dependencies([self._size.assign_add(1)]):\n dummy = tf.cond(tf.equal(self._size, 0), first, other)\n return tf.identity(dummy)\n\n\nqueue = Queue(10)\nqueue_peek = queue.peek()\nprint(\"Peek op is \"+str(queue_peek))\n\nqueue_init = queue.enqueue(tf.constant(-2))\n\n\nprint(tf.get_default_graph().as_graph_def())\nfor i in range(20):\n sess = tf.Session()\n sess.run(tf.global_variables_initializer())\n sess.run(queue_init)\n print(\"queue size\", sess.run(queue.size()))\n sess.run(queue.close())\n\n# print(\"Printing queue\")\n# while True:\n# print(sess.run(queue.dequeue()))\n\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_options.output_partition_graphs = True\n run_metadata = tf.RunMetadata()\n #import pdb; pdb.set_trace()\n # queue_peek, \n result = sess.run(queue_peek, run_metadata=run_metadata,\n options=run_options)\n\n tl = timeline.Timeline(run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format()\n with open('timeline-%d.json'%(i,), 'w') as f:\n f.write(ctf)\n with open('stepstats-%d.json'%(i,), 'w') as f:\n f.write(str(run_metadata))\n\n print(result, end=' ')\n \n# Expected: 1 1 1 1 1 1 1 1 1 1\n# Actual: 0 1 0 0 1 1 0 0 0 1\n" }, { "path": "distributed/README.md", "content": "TF distributed tools\n " }, { "path": "distributed/benchmark_grpc_recv.py", "content": "# Dependencies:\n# portpicker (pip install portpicker)\n# tcmalloc4 (sudo apt-get install google-perftools)\n# TF 0.12\n#\n#\n# Benchmarks on Xeon E5-2630 v3 @ 2.40GHz\n#\n# export LD_PRELOAD=/usr/lib/libtcmalloc.so.4\n# python benchmark_grpc_recv.py --data_mb=128\n# Local rate: 15214.63 MB/s\n# Distributed rate: 569.27 MB/s\n\n# python benchmark_grpc_recv.py --data_mb=256\n# Local rate: 15461.36 MB/s\n# Distributed rate: 362.97 MB/s\n#\n# python benchmark_grpc_recv.py --data_mb=512\n# Local rate: 15451.36 MB/s\n# Distributed rate: 202.46 MB/s\n#\n# python benchmark_grpc_recv.py --data_mb=768\n# Local rate: 16046.28 MB/s\n# Distributed rate: 142.19 MB/s\n#\n# python benchmark_grpc_recv.py --data_mb=1024\n# Local rate: 14546.77 MB/s\n# Distributed rate: 106.29 MB/s\n#\n# Generating profile:\n#\n# rm /tmp/profile*\n# python benchmark_grpc_recv.py --data_mb=512 --profile\n# export p=/tmp/profile.out.0_27680\n# google-pprof `which python` $p --svg > /tmp/profile.0.svg\n# export p=/tmp/profile.out.1_27683\n# google-pprof `which python` $p --svg > /tmp/profile.1.svg\n\n\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport threading\nimport time\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 10, \"Maximum number of additions\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"whether to have verbose logging\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to collect CPU profile\")\n\n# internal flags, set by client\nflags.DEFINE_string(\"task_index\", \"\", \"# of current task\")\nflags.DEFINE_string(\"port0\", \"12222\", \"port of worker1, used as master\")\nflags.DEFINE_string(\"port1\", \"12223\", \"port of worker2\")\nFLAGS = flags.FLAGS\n\n# setup local cluster from flags\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n graph_options = tf.GraphOptions(optimizer_options=optimizer_options)\n config = tf.ConfigProto(graph_options=graph_options,\n intra_op_parallelism_threads=10,\n inter_op_parallelism_threads=10)\n\n\nhost = \"127.0.0.1\"\ndef clusterspec():\n cluster = {\"worker\": [host+\":\"+FLAGS.port0, host+\":\"+FLAGS.port1]}\n return tf.train.ClusterSpec(cluster).as_cluster_def()\n \n \ndef create_graph(device0, device1):\n \"\"\"Create graph that keeps var1 on device0, var2 on device1 and adds them\"\"\"\n \n tf.reset_default_graph()\n dtype=tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n\n with tf.device(device0):\n var1 = tf.get_variable(\"var1\", [params_size], dtype,\n initializer=tf.ones_initializer())\n with tf.device(device1):\n var2 = tf.get_variable(\"var2\", [params_size], dtype,\n initializer=tf.ones_initializer())\n add_op = var1.assign_add(var2)\n \n init_op = tf.global_variables_initializer()\n return init_op, add_op\n\ndef create_done_queue(i):\n \"\"\"Queue used to signal death for i'th worker.\"\"\"\n \n with tf.device(\"/job:worker/task:%s\" % (i)):\n return tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue\"+\n str(i))\n\ndef run_benchmark(sess, init_op, add_op):\n \"\"\"Returns MB/s rate of addition.\"\"\"\n \n sess.run(init_op)\n sess.run(add_op.op) # warm-up\n start_time = time.time()\n for i in range(FLAGS.iters):\n sess.run(add_op.op)\n \n elapsed_time = time.time() - start_time\n return float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n\n\ndef run_benchmark_local():\n ops = create_graph(None, None)\n sess = tf.Session(config=session_config())\n return run_benchmark(sess, *ops)\n\n\ndef run_benchmark_distributed():\n ops = create_graph(\"/job:worker/task:0\", \"/job:worker/task:1\")\n queues = [create_done_queue(0), create_done_queue(1)]\n\n # launch distributed service\n\n\n port0, port1 = [portpicker.pick_unused_port() for _ in range(2)]\n flags = \" \".join(sys.argv) # pass parent flags to children\n \n def run_worker(w):\n my_env = os.environ.copy()\n if not FLAGS.verbose:\n my_env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n my_env[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n if FLAGS.profile:\n my_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\n my_env[\"CPUPROFILE\"]=\"/tmp/profile.out.%s\"%(w)\n cmd = \"python %s --task=%d --port0=%s --port1=%s\"%(flags, w, port0, port1)\n subprocess.Popen(cmd, shell=True, stderr=subprocess.STDOUT,\n env=my_env)\n \n run_worker(0)\n run_worker(1)\n\n sess = tf.Session(\"grpc://%s:%s\"%(host, port0), config=session_config())\n rate = run_benchmark(sess, *ops)\n\n # bring down workers\n if FLAGS.verbose:\n print(\"Killing workers.\")\n sess.run(queues[1].enqueue(1))\n sess.run(queues[0].enqueue(1)) # bring down master last\n \n return rate\n\nif __name__=='__main__':\n if not FLAGS.task_index:\n\n rate1 = run_benchmark_local()\n rate2 = run_benchmark_distributed()\n\n if FLAGS.verbose:\n print(\"Adding data in %d MB chunks\" %(FLAGS.data_mb))\n print(\"Local rate: %.2f MB/s\" %(rate1,))\n print(\"Distributed rate: %.2f MB/s\" %(rate2,))\n\n else: # Launch TensorFlow server\n server = tf.train.Server(clusterspec(), config=session_config(),\n job_name=\"worker\",\n task_index=int(FLAGS.task_index))\n queue = create_done_queue(FLAGS.task_index)\n sess = tf.Session(server.target, config=session_config())\n sess.run(queue.dequeue())\n time.sleep(1) # give chance for master session.run call to return\n if FLAGS.verbose:\n print(\"Worker %s quitting.\" %(FLAGS.task_index))\n" }, { "path": "distributed/client_transfer_benchmark.py", "content": "# Benchmark transferring data from TF into Python runtime\n#\n## Dependencies:\n# portpicker (pip install portpicker)\n# tcmalloc4 (sudo apt-get install google-perftools)\n# TF 0.12 (for var.read_value(), ones_initializer())\n#\n# On Linux default malloc is slow\n# sudo apt-get install google-perftools\n# export LD_PRELOAD=\"/usr/lib/libtcmalloc.so.4\"\n#\n# local session benchmark:\n# 2014 MacBook (100 iters):\n# 128MB -- 3.56 GB/s\n# 1024MB -- 1.96 GB/s\n# \n# Xeon E5-2630 v3 @ 2.40GHz (100 iters):\n# 128 MB -- 0.43 GB/s (default malloc)\n# 128 MB -- 4-6.2 GB/s (tcmalloc)\n# 1024 MB -- 4-5.97 GB/s (tcmalloc)\n#\n#\n# distributed session + profiling on Xeon:\n# python client_transfer_benchmark.py --profile\n# 116.70 MB per second\n# 151 MB per second (using AsProtoTensorContent patch)\n# google-pprof `which python` /tmp/profile.out.client --svg > /tmp/profile.client\n# google-pprof `which python` /tmp/profile.out.worker --svg > /tmp/profile.worker\n#\n#\n# Profiling feeding: on Xeon:\n# python client_transfer_benchmark.py --direction=p2t --profile\n# 1577.86 MB per second\n# 143.93 MB per second\n\n# Profiling on c4 instance with tf1.4rc1\n# python3 client_transfer_benchmark.py\n# 2692.30 MB per second\n# python3 client_transfer_benchmark.py --direction=p2t\n# 7012.22 MB per second\n\n\nimport gc\nimport os\nimport portpicker\nimport subprocess\nimport sys\nimport tensorflow as tf\nimport threading\nimport time\n\nflags = tf.flags\nflags.DEFINE_integer(\"iters\", 10, \"Maximum number of additions\")\nflags.DEFINE_integer(\"warmup_iters\", 1, \"warmup iterations\")\nflags.DEFINE_integer(\"data_mb\", 128, \"size of vector in MBs\")\nflags.DEFINE_boolean(\"verbose\", False, \"extra logging\")\nflags.DEFINE_boolean(\"sanity_check\", False, \"run sanity check on results\")\nflags.DEFINE_boolean(\"profile\", False, \"whether to run distributed version and \"\n \"collect CPU profile\")\nflags.DEFINE_boolean(\"in_process\", True, \"do bencharmk on in-process master\")\nflags.DEFINE_string(\"direction\", \"t2p\", \"which direction to profile, either \"\n \"tensorflow to python (t2p) or python to tensorflow (p2t)\")\n\n# internal flags, set by client\nflags.DEFINE_string(\"worker_type\", \"launcher\", \"launcher or client or worker\")\nflags.DEFINE_string(\"port\", \"12222\", \"port of master\")\nFLAGS = flags.FLAGS\n\n\nhost = \"127.0.0.1\"\ndef clusterspec():\n cluster = {\"worker\": [host+\":\"+FLAGS.port]}\n return tf.train.ClusterSpec(cluster).as_cluster_def()\n\n\ndef log(s):\n if FLAGS.verbose: print(s)\n\n\ndef session_config():\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n config = tf.ConfigProto(\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options))\n config.log_device_placement = False\n config.allow_soft_placement = False\n return config\n\n\ndef launch_distributed_service():\n port = portpicker.pick_unused_port()\n \n def launch_worker(worker_type):\n my_env = os.environ.copy()\n if not FLAGS.verbose:\n my_env[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n my_env[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n if FLAGS.profile:\n my_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\n my_env[\"CPUPROFILE\"]=\"/tmp/profile.out.%s\"%(worker_type)\n\n args = [\"python\"] + sys.argv + [\"--port=\"+str(port),\n \"--worker_type=\"+worker_type]\n proc = subprocess.Popen(args, stderr=subprocess.STDOUT, env=my_env)\n log(\"worker %s pid %s\"%(worker_type, proc.pid))\n \n launch_worker(\"worker\")\n launch_worker(\"client\")\n\ndef run_benchmark(master):\n \"\"\"Connect to master and run simple TF->Python transfer benchmark.\"\"\"\n \n assert FLAGS.warmup_iters > 0\n gc.disable()\n \n dtype = tf.int32\n params_size = 250*1000*FLAGS.data_mb # 1MB is 250k integers\n params = tf.get_variable(\"params\", [params_size], dtype,\n initializer=tf.ones_initializer())\n params_read = params.read_value() # prevent caching\n params_holder = tf.placeholder(dtype)\n params_write = params.assign(params_holder)\n done_queue = create_done_queue(0)\n init_op = tf.global_variables_initializer()\n sess = tf.Session(master, config=session_config())\n sess.run(init_op)\n result = sess.run(params_read)\n \n total = 0\n for i in range(FLAGS.iters+FLAGS.warmup_iters):\n if i == FLAGS.warmup_iters:\n start_time = time.time()\n # fetch value into Python runtime\n if FLAGS.direction == \"t2p\":\n result = sess.run(params_read)\n if FLAGS.sanity_check:\n total += result.sum()\n print(float(total)/params_size)\n elif FLAGS.direction == \"p2t\":\n sess.run(params_write.op, feed_dict={params_holder: result})\n \n\n elapsed_time = time.time() - start_time\n rate = float(FLAGS.iters)*FLAGS.data_mb/elapsed_time\n print(\"%.2f MB per second\" % (rate))\n sess.run(done_queue.enqueue(1))\n\ndef create_done_queue(i):\n \"\"\"Queue used to signal death for i'th worker.\"\"\"\n \n return tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue\"+\n str(i))\n\nif __name__ == '__main__':\n # run local benchmark in launcher and launch service\n if FLAGS.worker_type == \"launcher\":\n run_benchmark(\"\") # run local benchmark in launcher\n if FLAGS.profile:\n gc.collect()\n launch_distributed_service()\n\n # run distributed benchmark in client\n elif FLAGS.worker_type == \"client\":\n if not FLAGS.in_process:\n run_benchmark(\"grpc://%s:%s\"%(host, FLAGS.port))\n log(\"Killing worker.\")\n\n elif FLAGS.worker_type == \"worker\": # run tensorflow worker\n server = tf.train.Server(clusterspec(), config=session_config(),\n job_name=\"worker\",\n task_index=0)\n queue = create_done_queue(0)\n sess = tf.Session(server.target, config=session_config())\n if FLAGS.in_process:\n run_benchmark(server.target)\n\n sess.run(queue.dequeue())\n time.sleep(2) # give chance for master session.run call to return\n if FLAGS.verbose:\n print(\"Worker %s quitting.\" %(FLAGS.task_index))\n else:\n assert False, \"Unknown worker type\"\n" }, { "path": "double_memory_bug.py", "content": "# Troubleshooting\n# https://github.com/tensorflow/tensorflow/issues/13433#issuecomment-351722017\n\nimport tensorflow as tf\nimport numpy as np\n\ndef sessrun(*args, **kwargs):\n \"\"\"Helper to do sess.run and save run_metadata\"\"\"\n global sess, run_metadata\n \n run_metadata = tf.RunMetadata()\n\n kwargs['options'] = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n kwargs['run_metadata'] = run_metadata\n result = sess.run(*args, **kwargs)\n first_entry = args[0]\n # have to do this because sess.run(tensor) is same as sess.run([tensor]) \n if isinstance(first_entry, list):\n if len(first_entry) == 0 and len(args) == 1:\n return None\n first_entry = first_entry[0]\n\nimport urllib.request\nresponse = urllib.request.urlopen(\"https://raw.githubusercontent.com/yaroslavvb/chain_constant_memory/master/mem_util.py\")\nopen(\"mem_util.py\", \"wb\").write(response.read())\n\nimport mem_util\n\n\ndtype = tf.float32\ndtype_size = 4 # bytes\n#shape = (1000,1000*1000)\nshape = (100, 1000*1000)\ntotal_size = np.prod(shape)*dtype_size\nprint(\"Variable with %.1f GB\" %(total_size/1e9,))\nw = tf.Variable(tf.random_uniform(shape,dtype=dtype),dtype=dtype)\nsess = tf.Session()\nsessrun(tf.global_variables_initializer())\nprint(sess.run(w[0,0]))\n\nmem_util.print_memory_timeline(run_metadata)\n" }, { "path": "dynamic_stitch_gpu.py", "content": "# from https://github.com/tensorflow/tensorflow/issues/7251\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n\nimport tensorflow as tf\nfrom tensorflow.python.client.timeline import Timeline\n\nwith tf.device(\"/gpu:0\"):\n x = tf.ones(100, name=\"x\")\n idxs = tf.range(100)\n\n for i in range(10):\n y = tf.identity(x, name=\"identity-\"+str(i))\n x = tf.dynamic_stitch([idxs, idxs], [x, y], name=\"stitch-\"+str(i))\n\nconfig = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\nsess = tf.InteractiveSession(config=config)\nmetadata = tf.RunMetadata()\nsess.run(x, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE,\n output_partition_graphs=True),\n run_metadata=metadata)\n\ntimeline = Timeline(metadata.step_stats)\nwith open(\"dynamic_stitch_gpu_profile.json\", \"w\") as f:\n f.write(timeline.generate_chrome_trace_format())\nwith open(\"dynamic_stitch_gpu_profile.pbtxt\", \"w\") as f:\n f.write(str(metadata))\n" }, { "path": "dynamic_stitch_gpu_profile.pbtxt", "content": "step_stats {\n dev_stats {\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n node_stats {\n node_name: \"_SOURCE\"\n all_start_micros: 1486162184520805\n op_start_rel_micros: 18\n op_end_rel_micros: 19\n all_end_rel_micros: 63\n memory {\n allocator_name: \"cpu\"\n }\n timeline_label: \"_SOURCE = NoOp()\"\n scheduled_micros: 1486162184520748\n }\n }\n dev_stats {\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n node_stats {\n node_name: \"_SOURCE\"\n all_start_micros: 1486162184520807\n op_start_rel_micros: 11352\n op_end_rel_micros: 11403\n all_end_rel_micros: 11412\n memory {\n allocator_name: \"gpu_bfc\"\n }\n timeline_label: \"_SOURCE = NoOp()\"\n scheduled_micros: 1486162184520733\n }\n node_stats {\n node_name: \"x\"\n all_start_micros: 1486162184532227\n op_start_rel_micros: 2\n op_end_rel_micros: 5\n all_end_rel_micros: 24\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 2\n ptr: 1108325958912\n }\n }\n }\n timeline_label: \"x = Const()\"\n scheduled_micros: 1486162184532219\n }\n node_stats {\n node_name: \"range/start\"\n all_start_micros: 1486162184532254\n op_start_rel_micros: 1\n op_end_rel_micros: 3\n all_end_rel_micros: 7\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_INT32\n shape {\n }\n allocation_description {\n requested_bytes: 4\n allocated_bytes: 256\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 2\n ptr: 1120319569920\n }\n }\n }\n timeline_label: \"range/start = Const()\"\n scheduled_micros: 1486162184532251\n }\n node_stats {\n node_name: \"range/limit\"\n all_start_micros: 1486162184532263\n op_end_rel_micros: 2\n all_end_rel_micros: 5\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_INT32\n shape {\n }\n allocation_description {\n requested_bytes: 4\n allocated_bytes: 256\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 3\n ptr: 1120319570176\n }\n }\n }\n timeline_label: \"range/limit = Const()\"\n scheduled_micros: 1486162184532261\n }\n node_stats {\n node_name: \"range/delta\"\n all_start_micros: 1486162184532269\n op_end_rel_micros: 2\n all_end_rel_micros: 4\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_INT32\n shape {\n }\n allocation_description {\n requested_bytes: 4\n allocated_bytes: 256\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 4\n ptr: 1120319570432\n }\n }\n }\n timeline_label: \"range/delta = Const()\"\n scheduled_micros: 1486162184532267\n }\n node_stats {\n node_name: \"identity-0\"\n all_start_micros: 1486162184532312\n op_start_rel_micros: 1\n op_end_rel_micros: 3\n all_end_rel_micros: 6\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 2\n ptr: 1108325958912\n }\n }\n }\n timeline_label: \"identity-0 = Identity(x)\"\n scheduled_micros: 1486162184532291\n }\n node_stats {\n node_name: \"range\"\n all_start_micros: 1486162184532322\n op_start_rel_micros: 1\n op_end_rel_micros: 660\n all_end_rel_micros: 676\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cpu_pool\"\n total_bytes: 400\n }\n output {\n tensor_description {\n dtype: DT_INT32\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocator_name: \"cpu_pool\"\n ptr: 140413138685984\n }\n }\n }\n timeline_label: \"range = Range(range/start, range/limit, range/delta)\"\n scheduled_micros: 1486162184532318\n }\n node_stats {\n node_name: \"stitch-0\"\n all_start_micros: 1486162184533623\n op_start_rel_micros: 3\n op_end_rel_micros: 56\n all_end_rel_micros: 70\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 7\n has_single_reference: true\n ptr: 1120319571712\n }\n }\n }\n timeline_label: \"stitch-0 = DynamicStitch(range, range, x/_1, identity-0/_3)\"\n scheduled_micros: 1486162184533619\n }\n node_stats {\n node_name: \"identity-1\"\n all_start_micros: 1486162184533805\n op_start_rel_micros: 3\n op_end_rel_micros: 9\n all_end_rel_micros: 20\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 3\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-1 = Identity(stitch-0/_5)\"\n scheduled_micros: 1486162184533800\n }\n node_stats {\n node_name: \"stitch-1\"\n all_start_micros: 1486162184533952\n op_start_rel_micros: 3\n op_end_rel_micros: 19\n all_end_rel_micros: 30\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 9\n has_single_reference: true\n ptr: 1120319571200\n }\n }\n }\n timeline_label: \"stitch-1 = DynamicStitch(range, range, stitch-0, identity-1/_7)\"\n scheduled_micros: 1486162184533948\n }\n node_stats {\n node_name: \"identity-2\"\n all_start_micros: 1486162184534078\n op_start_rel_micros: 3\n op_end_rel_micros: 7\n all_end_rel_micros: 16\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 4\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-2 = Identity(stitch-1/_9)\"\n scheduled_micros: 1486162184534073\n }\n node_stats {\n node_name: \"stitch-2\"\n all_start_micros: 1486162184534207\n op_start_rel_micros: 3\n op_end_rel_micros: 20\n all_end_rel_micros: 28\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 11\n has_single_reference: true\n ptr: 1120319571712\n }\n }\n }\n timeline_label: \"stitch-2 = DynamicStitch(range, range, stitch-1, identity-2/_11)\"\n scheduled_micros: 1486162184534204\n }\n node_stats {\n node_name: \"identity-3\"\n all_start_micros: 1486162184534325\n op_start_rel_micros: 2\n op_end_rel_micros: 7\n all_end_rel_micros: 15\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 5\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-3 = Identity(stitch-2/_13)\"\n scheduled_micros: 1486162184534321\n }\n node_stats {\n node_name: \"stitch-3\"\n all_start_micros: 1486162184534446\n op_start_rel_micros: 3\n op_end_rel_micros: 24\n all_end_rel_micros: 38\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 13\n has_single_reference: true\n ptr: 1120319571200\n }\n }\n }\n timeline_label: \"stitch-3 = DynamicStitch(range, range, stitch-2, identity-3/_15)\"\n scheduled_micros: 1486162184534442\n }\n node_stats {\n node_name: \"identity-4\"\n all_start_micros: 1486162184534600\n op_start_rel_micros: 4\n op_end_rel_micros: 11\n all_end_rel_micros: 23\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 6\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-4 = Identity(stitch-3/_17)\"\n scheduled_micros: 1486162184534594\n }\n node_stats {\n node_name: \"stitch-4\"\n all_start_micros: 1486162184534773\n op_start_rel_micros: 4\n op_end_rel_micros: 26\n all_end_rel_micros: 38\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 15\n has_single_reference: true\n ptr: 1120319571712\n }\n }\n }\n timeline_label: \"stitch-4 = DynamicStitch(range, range, stitch-3, identity-4/_19)\"\n scheduled_micros: 1486162184534768\n }\n node_stats {\n node_name: \"identity-5\"\n all_start_micros: 1486162184534903\n op_start_rel_micros: 3\n op_end_rel_micros: 9\n all_end_rel_micros: 18\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 7\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-5 = Identity(stitch-4/_21)\"\n scheduled_micros: 1486162184534899\n }\n node_stats {\n node_name: \"stitch-5\"\n all_start_micros: 1486162184535024\n op_start_rel_micros: 2\n op_end_rel_micros: 17\n all_end_rel_micros: 26\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 17\n has_single_reference: true\n ptr: 1120319571200\n }\n }\n }\n timeline_label: \"stitch-5 = DynamicStitch(range, range, stitch-4, identity-5/_23)\"\n scheduled_micros: 1486162184535020\n }\n node_stats {\n node_name: \"identity-6\"\n all_start_micros: 1486162184535134\n op_start_rel_micros: 2\n op_end_rel_micros: 5\n all_end_rel_micros: 12\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 8\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-6 = Identity(stitch-5/_25)\"\n scheduled_micros: 1486162184535130\n }\n node_stats {\n node_name: \"stitch-6\"\n all_start_micros: 1486162184535240\n op_start_rel_micros: 2\n op_end_rel_micros: 17\n all_end_rel_micros: 26\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 19\n has_single_reference: true\n ptr: 1120319571712\n }\n }\n }\n timeline_label: \"stitch-6 = DynamicStitch(range, range, stitch-5, identity-6/_27)\"\n scheduled_micros: 1486162184535236\n }\n node_stats {\n node_name: \"identity-7\"\n all_start_micros: 1486162184535346\n op_start_rel_micros: 1\n op_end_rel_micros: 4\n all_end_rel_micros: 10\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 9\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-7 = Identity(stitch-6/_29)\"\n scheduled_micros: 1486162184535343\n }\n node_stats {\n node_name: \"stitch-7\"\n all_start_micros: 1486162184535436\n op_start_rel_micros: 2\n op_end_rel_micros: 15\n all_end_rel_micros: 23\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 21\n has_single_reference: true\n ptr: 1120319571200\n }\n }\n }\n timeline_label: \"stitch-7 = DynamicStitch(range, range, stitch-6, identity-7/_31)\"\n scheduled_micros: 1486162184535434\n }\n node_stats {\n node_name: \"identity-8\"\n all_start_micros: 1486162184535536\n op_start_rel_micros: 2\n op_end_rel_micros: 4\n all_end_rel_micros: 10\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 10\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-8 = Identity(stitch-7/_33)\"\n scheduled_micros: 1486162184535534\n }\n node_stats {\n node_name: \"stitch-8\"\n all_start_micros: 1486162184535627\n op_start_rel_micros: 1\n op_end_rel_micros: 15\n all_end_rel_micros: 23\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cuda_host_bfc\"\n total_bytes: 512\n peak_bytes: 512\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"cuda_host_bfc\"\n allocation_id: 23\n has_single_reference: true\n ptr: 1120319571712\n }\n }\n }\n timeline_label: \"stitch-8 = DynamicStitch(range, range, stitch-7, identity-8/_35)\"\n scheduled_micros: 1486162184535625\n }\n node_stats {\n node_name: \"identity-9\"\n all_start_micros: 1486162184535725\n op_start_rel_micros: 2\n op_end_rel_micros: 5\n all_end_rel_micros: 11\n memory {\n allocator_name: \"gpu_bfc\"\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocated_bytes: 512\n allocator_name: \"gpu_bfc\"\n allocation_id: 11\n ptr: 1108325959424\n }\n }\n }\n timeline_label: \"identity-9 = Identity(stitch-8/_37)\"\n scheduled_micros: 1486162184535723\n }\n node_stats {\n node_name: \"stitch-9\"\n all_start_micros: 1486162184535882\n op_start_rel_micros: 2\n op_end_rel_micros: 20\n all_end_rel_micros: 32\n memory {\n allocator_name: \"gpu_bfc\"\n }\n memory {\n allocator_name: \"cpu_pool\"\n total_bytes: 400\n }\n output {\n tensor_description {\n dtype: DT_FLOAT\n shape {\n dim {\n size: 100\n }\n }\n allocation_description {\n requested_bytes: 400\n allocator_name: \"cpu_pool\"\n ptr: 140413136625696\n }\n }\n }\n timeline_label: \"stitch-9 = DynamicStitch(range, range, stitch-8, identity-9/_39)\"\n scheduled_micros: 1486162184535878\n }\n }\n dev_stats {\n device: \"/gpu:0/memcpy\"\n node_stats {\n node_name: \"edge_9_x:MEMCPYDtoH\"\n all_start_micros: 1486162184532468\n op_end_rel_micros: 2\n all_end_rel_micros: 2\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_10_identity-0:MEMCPYDtoH\"\n all_start_micros: 1486162184533568\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_11_stitch-0:MEMCPYHtoD\"\n all_start_micros: 1486162184533767\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_15_identity-1:MEMCPYDtoH\"\n all_start_micros: 1486162184533906\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_16_stitch-1:MEMCPYHtoD\"\n all_start_micros: 1486162184534040\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_20_identity-2:MEMCPYDtoH\"\n all_start_micros: 1486162184534165\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_21_stitch-2:MEMCPYHtoD\"\n all_start_micros: 1486162184534290\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_25_identity-3:MEMCPYDtoH\"\n all_start_micros: 1486162184534409\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_26_stitch-3:MEMCPYHtoD\"\n all_start_micros: 1486162184534564\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_30_identity-4:MEMCPYDtoH\"\n all_start_micros: 1486162184534721\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_31_stitch-4:MEMCPYHtoD\"\n all_start_micros: 1486162184534872\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_35_identity-5:MEMCPYDtoH\"\n all_start_micros: 1486162184534987\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_36_stitch-5:MEMCPYHtoD\"\n all_start_micros: 1486162184535104\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_40_identity-6:MEMCPYDtoH\"\n all_start_micros: 1486162184535206\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_41_stitch-6:MEMCPYHtoD\"\n all_start_micros: 1486162184535318\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_45_identity-7:MEMCPYDtoH\"\n all_start_micros: 1486162184535409\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_46_stitch-7:MEMCPYHtoD\"\n all_start_micros: 1486162184535509\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_50_identity-8:MEMCPYDtoH\"\n all_start_micros: 1486162184535601\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_51_stitch-8:MEMCPYHtoD\"\n all_start_micros: 1486162184535699\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_55_identity-9:MEMCPYDtoH\"\n all_start_micros: 1486162184535816\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n }\n dev_stats {\n device: \"/gpu:0/stream:14\"\n node_stats {\n node_name: \"edge_9_x:MEMCPYDtoH\"\n all_start_micros: 1486162184532468\n op_end_rel_micros: 2\n all_end_rel_micros: 2\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_10_identity-0:MEMCPYDtoH\"\n all_start_micros: 1486162184533568\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_15_identity-1:MEMCPYDtoH\"\n all_start_micros: 1486162184533906\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_20_identity-2:MEMCPYDtoH\"\n all_start_micros: 1486162184534165\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_25_identity-3:MEMCPYDtoH\"\n all_start_micros: 1486162184534409\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_30_identity-4:MEMCPYDtoH\"\n all_start_micros: 1486162184534721\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_35_identity-5:MEMCPYDtoH\"\n all_start_micros: 1486162184534987\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_40_identity-6:MEMCPYDtoH\"\n all_start_micros: 1486162184535206\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_45_identity-7:MEMCPYDtoH\"\n all_start_micros: 1486162184535409\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_50_identity-8:MEMCPYDtoH\"\n all_start_micros: 1486162184535601\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n node_stats {\n node_name: \"edge_55_identity-9:MEMCPYDtoH\"\n all_start_micros: 1486162184535816\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYDtoH 400 bytes (Device to Pinned)\"\n }\n }\n dev_stats {\n device: \"/gpu:0/stream:13\"\n node_stats {\n node_name: \"edge_11_stitch-0:MEMCPYHtoD\"\n all_start_micros: 1486162184533767\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_16_stitch-1:MEMCPYHtoD\"\n all_start_micros: 1486162184534040\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_21_stitch-2:MEMCPYHtoD\"\n all_start_micros: 1486162184534290\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_26_stitch-3:MEMCPYHtoD\"\n all_start_micros: 1486162184534564\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_31_stitch-4:MEMCPYHtoD\"\n all_start_micros: 1486162184534872\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_36_stitch-5:MEMCPYHtoD\"\n all_start_micros: 1486162184535104\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_41_stitch-6:MEMCPYHtoD\"\n all_start_micros: 1486162184535318\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_46_stitch-7:MEMCPYHtoD\"\n all_start_micros: 1486162184535509\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n node_stats {\n node_name: \"edge_51_stitch-8:MEMCPYHtoD\"\n all_start_micros: 1486162184535699\n op_end_rel_micros: 1\n all_end_rel_micros: 1\n timeline_label: \"MEMCPYHtoD 400 bytes (Pinned to Device)\"\n }\n }\n}\npartition_graphs {\n node {\n name: \"x\"\n op: \"Const\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"dtype\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_FLOAT\n tensor_shape {\n dim {\n size: 100\n }\n }\n float_val: 1.0\n }\n }\n }\n }\n node {\n name: \"x/_0\"\n op: \"_Send\"\n input: \"x\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_9_x\"\n }\n }\n }\n node {\n name: \"x/_1\"\n op: \"_HostRecv\"\n input: \"^x/_0\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_9_x\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-0\"\n op: \"Identity\"\n input: \"x\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-0/_2\"\n op: \"_Send\"\n input: \"identity-0\"\n device: 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i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_11_stitch-0\"\n }\n }\n }\n node {\n name: \"stitch-0/_5\"\n op: \"_Recv\"\n input: \"^stitch-0/_4\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_11_stitch-0\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-1\"\n op: \"Identity\"\n input: \"stitch-0/_5\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-1/_6\"\n op: \"_Send\"\n input: \"identity-1\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_15_identity-1\"\n }\n }\n }\n node {\n name: \"identity-1/_7\"\n op: \"_HostRecv\"\n input: \"^identity-1/_6\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_15_identity-1\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-1\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-0\"\n input: \"identity-1/_7\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-1/_8\"\n op: \"_HostSend\"\n input: \"stitch-1\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_16_stitch-1\"\n }\n }\n }\n node {\n name: \"stitch-1/_9\"\n op: \"_Recv\"\n input: \"^stitch-1/_8\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_16_stitch-1\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-2\"\n op: \"Identity\"\n input: \"stitch-1/_9\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-2/_10\"\n op: \"_Send\"\n input: \"identity-2\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_20_identity-2\"\n }\n }\n }\n node {\n name: \"identity-2/_11\"\n op: \"_HostRecv\"\n input: \"^identity-2/_10\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_20_identity-2\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-2\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-1\"\n input: \"identity-2/_11\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-2/_12\"\n op: \"_HostSend\"\n input: \"stitch-2\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_21_stitch-2\"\n }\n }\n }\n node {\n name: \"stitch-2/_13\"\n op: \"_Recv\"\n input: \"^stitch-2/_12\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_21_stitch-2\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-3\"\n op: \"Identity\"\n input: \"stitch-2/_13\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-3/_14\"\n op: \"_Send\"\n input: \"identity-3\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_25_identity-3\"\n }\n }\n }\n node {\n name: \"identity-3/_15\"\n op: \"_HostRecv\"\n input: \"^identity-3/_14\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_25_identity-3\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-3\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-2\"\n input: \"identity-3/_15\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-3/_16\"\n op: \"_HostSend\"\n input: \"stitch-3\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_26_stitch-3\"\n }\n }\n }\n node {\n name: \"stitch-3/_17\"\n op: \"_Recv\"\n input: \"^stitch-3/_16\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_26_stitch-3\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-4\"\n op: \"Identity\"\n input: \"stitch-3/_17\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-4/_18\"\n op: \"_Send\"\n input: \"identity-4\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_30_identity-4\"\n }\n }\n }\n node {\n name: \"identity-4/_19\"\n op: \"_HostRecv\"\n input: \"^identity-4/_18\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_30_identity-4\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-4\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-3\"\n input: \"identity-4/_19\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-4/_20\"\n op: \"_HostSend\"\n input: \"stitch-4\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_31_stitch-4\"\n }\n }\n }\n node {\n name: \"stitch-4/_21\"\n op: \"_Recv\"\n input: \"^stitch-4/_20\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_31_stitch-4\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-5\"\n op: \"Identity\"\n input: \"stitch-4/_21\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-5/_22\"\n op: \"_Send\"\n input: \"identity-5\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_35_identity-5\"\n }\n }\n }\n node {\n name: \"identity-5/_23\"\n op: \"_HostRecv\"\n input: \"^identity-5/_22\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_35_identity-5\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-5\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-4\"\n input: \"identity-5/_23\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-5/_24\"\n op: \"_HostSend\"\n input: \"stitch-5\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_36_stitch-5\"\n }\n }\n }\n node {\n name: \"stitch-5/_25\"\n op: \"_Recv\"\n input: \"^stitch-5/_24\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_36_stitch-5\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-6\"\n op: \"Identity\"\n input: \"stitch-5/_25\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-6/_26\"\n op: \"_Send\"\n input: \"identity-6\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_40_identity-6\"\n }\n }\n }\n node {\n name: \"identity-6/_27\"\n op: \"_HostRecv\"\n input: \"^identity-6/_26\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_40_identity-6\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-6\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-5\"\n input: \"identity-6/_27\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-6/_28\"\n op: \"_HostSend\"\n input: \"stitch-6\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_41_stitch-6\"\n }\n }\n }\n node {\n name: \"stitch-6/_29\"\n op: \"_Recv\"\n input: \"^stitch-6/_28\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_41_stitch-6\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-7\"\n op: \"Identity\"\n input: \"stitch-6/_29\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-7/_30\"\n op: \"_Send\"\n input: \"identity-7\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_45_identity-7\"\n }\n }\n }\n node {\n name: \"identity-7/_31\"\n op: \"_HostRecv\"\n input: \"^identity-7/_30\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_45_identity-7\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-7\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-6\"\n input: \"identity-7/_31\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-7/_32\"\n op: \"_HostSend\"\n input: \"stitch-7\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_46_stitch-7\"\n }\n }\n }\n node {\n name: \"stitch-7/_33\"\n op: \"_Recv\"\n input: \"^stitch-7/_32\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_46_stitch-7\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-8\"\n op: \"Identity\"\n input: \"stitch-7/_33\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-8/_34\"\n op: \"_Send\"\n input: \"identity-8\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_50_identity-8\"\n }\n }\n }\n node {\n name: \"identity-8/_35\"\n op: \"_HostRecv\"\n input: \"^identity-8/_34\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_50_identity-8\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-8\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-7\"\n input: \"identity-8/_35\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-8/_36\"\n op: \"_HostSend\"\n input: \"stitch-8\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_51_stitch-8\"\n }\n }\n }\n node {\n name: \"stitch-8/_37\"\n op: \"_Recv\"\n input: \"^stitch-8/_36\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_51_stitch-8\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-9\"\n op: \"Identity\"\n input: \"stitch-8/_37\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"identity-9/_38\"\n op: \"_Send\"\n input: \"identity-9\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_55_identity-9\"\n }\n }\n }\n node {\n name: \"identity-9/_39\"\n op: \"_HostRecv\"\n input: \"^identity-9/_38\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_55_identity-9\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-9\"\n op: \"DynamicStitch\"\n input: \"range\"\n input: \"range\"\n input: \"stitch-8\"\n input: \"identity-9/_39\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"N\"\n value {\n i: 2\n }\n }\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"stitch-9/_40\"\n op: \"_HostSend\"\n input: \"stitch-9\"\n device: \"/job:localhost/replica:0/task:0/gpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_61_stitch-9\"\n }\n }\n }\n versions {\n producer: 21\n }\n}\npartition_graphs {\n node {\n name: \"stitch-9/_41\"\n op: \"_Recv\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"client_terminated\"\n value {\n b: false\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/gpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 1\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"edge_61_stitch-9\"\n }\n }\n attr {\n key: \"tensor_type\"\n value {\n type: DT_FLOAT\n }\n }\n }\n node {\n name: \"_send_stitch-9_0\"\n op: \"_Send\"\n input: \"stitch-9/_41\"\n device: \"/job:localhost/replica:0/task:0/cpu:0\"\n attr {\n key: \"T\"\n value {\n type: DT_FLOAT\n }\n }\n attr {\n key: \"client_terminated\"\n value {\n b: true\n }\n }\n attr {\n key: \"recv_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device\"\n value {\n s: \"/job:localhost/replica:0/task:0/cpu:0\"\n }\n }\n attr {\n key: \"send_device_incarnation\"\n value {\n i: 6194878103004924795\n }\n }\n attr {\n key: \"tensor_name\"\n value {\n s: \"stitch-9:0\"\n }\n }\n }\n versions {\n producer: 21\n }\n}\n" }, { "path": "eager_lbfgs/.ipynb_checkpoints/performance-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# l-BFGS performance: Eager vs PyTorch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```\\n\",\n \"python run_experiment.py\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note (this is only for small batch sizes, multiple runs caused out of CUDA memory in Pytorch, need to figure out how to isolate runs better)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mgKjZy5Ykmj+WB2U4cO4Qp348aynX7U0k44M6NPnz6cfvrpVK9enXPO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kNhBjBc0jBJ/UhOJE9r1+Y5YH8ASTuShIKPD5mZVUhhoRARjcBk4A5g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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"name = \\\"short\\\"\\n\",\n \"sizes = np.loadtxt(\\\"data/\\\"+name+\\\"_batch.csv\\\")\\n\",\n \"eager_loss = np.loadtxt(\\\"data/\\\"+name+\\\"_eager_loss.csv\\\")\\n\",\n \"pytorch_loss = np.loadtxt(\\\"data/\\\"+name+\\\"_pytorch_loss.csv\\\")\\n\",\n \"eager_time = np.loadtxt(\\\"data/\\\"+name+\\\"_eager_time.csv\\\")\\n\",\n \"pytorch_time = np.loadtxt(\\\"data/\\\"+name+\\\"_pytorch_time.csv\\\")\\n\",\n \"\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(sizes, eager_loss, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(sizes, pytorch_loss, \\\"b.\\\", label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('batch')\\n\",\n \"plt.ylabel('loss')\\n\",\n \"plt.title(\\\"Loss comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(sizes, eager_time, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(sizes, pytorch_time, label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('batch')\\n\",\n \"plt.ylabel('seconds')\\n\",\n \"plt.title(\\\"Time comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Smaller batch size over time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"eager_time = [ 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]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ha9esHll9ufbzSIo9t76ikRa4b1q69uPV1z0tL0+r33dNyGUEg1Z7PW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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(dpi=50)\\n\",\n \"plt.plot(eager_time, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(pytorch_time, label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('iter')\\n\",\n \"plt.ylabel('millis')\\n\",\n \"plt.title(\\\"Time comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.4\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "eager_lbfgs/common_gd.py", "content": "import argparse\nparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n\nparser.add_argument('--batch-size', type=int, default=60000, metavar='N',\n help='input batch size for training')\nparser.add_argument('--iters', type=int, default=100, metavar='N',\n help='number of iterations to run for (default: 20)')\nparser.add_argument('--lr', type=float, default=1.0, metavar='LR',\n help='learning rate (default: 1.0)')\nparser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\nparser.add_argument('--hidden-size', type=int, default=196, metavar='H',\n help='hidden size')\nparser.add_argument('--visible-size', type=int, default=784, metavar='V',\n help='visible-size')\nparser.add_argument('--gd', action='store_true', default=False,\n help='force run of gradient descent instead of lbfgs')\nparser.add_argument('--history', type=int, default=100, metavar='V',\n help='history buffer for lbfgs')\nargs = parser.parse_args()\n" }, { "path": "eager_lbfgs/data/short_batch.csv", "content": "100\n200\n300\n" }, { "path": "eager_lbfgs/data/short_eager_batch.csv", "content": "10\n100\n" }, { "path": "eager_lbfgs/data/short_eager_loss.csv", "content": "1.125071197748184204e-03\n1.720546046271920204e-03\n2.242934657260775566e-03\n" }, { "path": "eager_lbfgs/data/short_eager_time.csv", "content": "9.806975307874381542e-01\n9.339727419428527355e-01\n9.292591358534991741e-01\n" }, { "path": "eager_lbfgs/data/short_pytorch_loss.csv", "content": "1.125177601352334023e-03\n1.720896689221262932e-03\n2.242802875116467476e-03\n" }, { "path": "eager_lbfgs/data/short_pytorch_time.csv", "content": "2.150501497089862823e-01\n2.058924520388245583e-01\n1.908177738077938557e-01\n" }, { "path": "eager_lbfgs/eager_lbfgs.py", "content": "import util as u\n\nimport tensorflow as tf\nimport numpy as np\nimport time\n\nfrom tensorflow.contrib.eager.python import tfe\ntfe.enable_eager_execution()\n\ndef dot(a, b):\n \"\"\"Dot product function since TensorFlow doesn't have one.\"\"\"\n return tf.reduce_sum(a*b)\n\ndef verbose_func(s):\n print(s)\n\nfinal_loss = None\ntimes = []\ndef lbfgs(opfunc, x, config, state, do_verbose):\n \"\"\"port of lbfgs.lua, using TensorFlow eager mode.\n \"\"\"\n\n global final_loss, times\n \n maxIter = config.maxIter or 20\n maxEval = config.maxEval or maxIter*1.25\n tolFun = config.tolFun or 1e-5\n tolX = config.tolX or 1e-9\n nCorrection = config.nCorrection or 100\n lineSearch = config.lineSearch\n lineSearchOpts = config.lineSearchOptions\n learningRate = config.learningRate or 1\n isverbose = config.verbose or False\n\n # verbose function\n if isverbose:\n verbose = verbose_func\n else:\n verbose = lambda x: None\n\n # evaluate initial f(x) and df/dx\n f, g = opfunc(x)\n\n f_hist = [f]\n currentFuncEval = 1\n state.funcEval = state.funcEval + 1\n p = g.shape[0]\n\n # check optimality of initial point\n tmp1 = tf.abs(g)\n if tf.reduce_sum(tmp1) <= tolFun:\n verbose(\"optimality condition below tolFun\")\n return x, f_hist\n\n # optimize for a max of maxIter iterations\n nIter = 0\n times = []\n while nIter < maxIter:\n start_time = time.time()\n \n # keep track of nb of iterations\n nIter = nIter + 1\n state.nIter = state.nIter + 1\n\n ############################################################\n ## compute gradient descent direction\n ############################################################\n if state.nIter == 1:\n d = -g\n old_dirs = []\n old_stps = []\n Hdiag = 1\n else:\n # do lbfgs update (update memory)\n y = g - g_old\n s = d*t\n ys = dot(y, s)\n \n if ys > 1e-10:\n # updating memory\n if len(old_dirs) == nCorrection:\n # shift history by one (limited-memory)\n del old_dirs[0]\n del old_stps[0]\n\n # store new direction/step\n old_dirs.append(s)\n old_stps.append(y)\n\n # update scale of initial Hessian approximation\n Hdiag = ys/dot(y, y)\n\n # compute the approximate (L-BFGS) inverse Hessian \n # multiplied by the gradient\n k = len(old_dirs)\n\n # need to be accessed element-by-element, so don't re-type tensor:\n ro = [0]*nCorrection\n for i in range(k):\n ro[i] = 1/dot(old_stps[i], old_dirs[i])\n \n\n # iteration in L-BFGS loop collapsed to use just one buffer\n # need to be accessed element-by-element, so don't re-type tensor:\n al = [0]*nCorrection\n\n q = -g\n for i in range(k-1, -1, -1):\n al[i] = dot(old_dirs[i], q) * ro[i]\n q = q - al[i]*old_stps[i]\n\n # multiply by initial Hessian\n r = q*Hdiag\n for i in range(k):\n be_i = dot(old_stps[i], r) * ro[i]\n r += (al[i]-be_i)*old_dirs[i]\n \n d = r\n # final direction is in r/d (same object)\n\n g_old = g\n f_old = f\n \n ############################################################\n ## compute step length\n ############################################################\n # directional derivative\n gtd = dot(g, d)\n\n # check that progress can be made along that direction\n if gtd > -tolX:\n verbose(\"Can not make progress along direction.\")\n break\n\n # reset initial guess for step size\n if state.nIter == 1:\n tmp1 = tf.abs(g)\n t = min(1, 1/tf.reduce_sum(tmp1))\n else:\n t = learningRate\n\n\n # optional line search: user function\n lsFuncEval = 0\n if lineSearch and isinstance(lineSearch) == types.FunctionType:\n # perform line search, using user function\n f,g,x,t,lsFuncEval = lineSearch(opfunc,x,t,d,f,g,gtd,lineSearchOpts)\n f_hist.append(f)\n else:\n # no line search, simply move with fixed-step\n x += t*d\n \n if nIter != maxIter:\n # re-evaluate function only if not in last iteration\n # the reason we do this: in a stochastic setting,\n # no use to re-evaluate that function here\n f, g = opfunc(x)\n \n lsFuncEval = 1\n f_hist.append(f)\n\n\n # update func eval\n currentFuncEval = currentFuncEval + lsFuncEval\n state.funcEval = state.funcEval + lsFuncEval\n\n ############################################################\n ## check conditions\n ############################################################\n if nIter == maxIter:\n break\n\n if currentFuncEval >= maxEval:\n # max nb of function evals\n verbose('max nb of function evals')\n break\n\n tmp1 = tf.abs(g)\n if tf.reduce_sum(tmp1) <=tolFun:\n # check optimality\n verbose('optimality condition below tolFun')\n break\n \n tmp1 = tf.abs(d*t)\n if tf.reduce_sum(tmp1) <= tolX:\n # step size below tolX\n verbose('step size below tolX')\n break\n\n if tf.abs(f-f_old) < tolX:\n # function value changing less than tolX\n verbose('function value changing less than tolX'+str(tf.abs(f-f_old)))\n break\n\n if do_verbose:\n print(\"Step %3d loss %6.5f msec %6.3f\"%(nIter, f.numpy(), u.last_time()))\n u.record_time()\n times.append(u.last_time())\n\n if nIter == maxIter - 1:\n final_loss = f.numpy()\n\n\n # save state\n state.old_dirs = old_dirs\n state.old_stps = old_stps\n state.Hdiag = Hdiag\n state.g_old = g_old\n state.f_old = f_old\n state.t = t\n state.d = d\n\n return x, f_hist, currentFuncEval\n\n# dummy/Struct gives Lua-like struct object with 0 defaults\nclass dummy(object):\n pass\n\nclass Struct(dummy):\n def __getattribute__(self, key):\n if key == '__dict__':\n return super(dummy, self).__getattribute__('__dict__')\n return self.__dict__.get(key, 0)\n\nW_flat = None\ndef benchmark(batch_size, iters, seed=1, cuda=True, history=100, verbose=False):\n global final_loss, W_flat\n tf.set_random_seed(seed)\n np.random.seed(seed)\n \n images = tf.constant(u.get_mnist_images(batch_size).T)\n images = images[:batch_size]\n if cuda:\n images = images.gpu()\n data = images\n\n if cuda:\n device='/gpu:0'\n else:\n device=''\n\n device_ctx = tf.device(device)\n device_ctx.__enter__()\n\n visible_size = 28*28\n hidden_size = 196\n initial_val = tf.zeros([visible_size*hidden_size])\n if W_flat is None:\n W_flat = tfe.Variable(initial_val, name='W_flat')\n W_flat.assign(initial_val)\n \n \n def loss_fn(w_flat):\n w = tf.reshape(w_flat, [visible_size, hidden_size])\n x = tf.matmul(data, w)\n x = tf.sigmoid(x)\n x = tf.matmul(x, w, transpose_b=True)\n x = tf.sigmoid(x)\n return tf.reduce_mean(tf.square(x-data))\n\n value_and_gradients_fn = tfe.value_and_gradients_function(loss_fn)\n def opfunc(x): # returns (value, gradient)\n value, grads = value_and_gradients_fn(x)\n return value, grads[0]\n \n # initialize weights\n W_flat.assign(u.ng_init(visible_size, hidden_size).flatten())\n\n state = Struct()\n config = Struct()\n config.maxIter = iters\n config.nCorrection = history\n config.verbose = True\n x, f_hist, currentFuncEval = lbfgs(opfunc, W_flat, config, state, verbose)\n\n if verbose:\n u.summarize_time()\n\n s = ','.join([\"%f\"%(n,) for n in times[2:]])\n print('{', s,'}')\n \n return final_loss\n\n\ndef main():\n import common_gd\n args = common_gd.args\n args.cuda = not args.no_cuda and (tfe.num_gpus() > 0)\n\n print(benchmark(batch_size=args.batch_size, iters=args.iters, seed=args.seed, cuda=args.cuda, history=args.history, verbose=True))\n\nif __name__=='__main__':\n main()\n \n" }, { "path": "eager_lbfgs/performance.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# l-BFGS performance: Eager vs PyTorch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```\\n\",\n \"python run_experiment.py\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note (this is only for small batch sizes, multiple runs caused out of CUDA memory in Pytorch, need to figure out how to isolate runs better)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mgKjZy5Ykmj+WB2U4cO4Qp348aynX7U0k44M6NPnz6cfvrpVK9enXPO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kNhBjBc0jBJ/UhOJE9r1+Y5YH8ASTuShIKPD5mZVUhhoRARjcBk4A5g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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"name = \\\"short\\\"\\n\",\n \"sizes = np.loadtxt(\\\"data/\\\"+name+\\\"_batch.csv\\\")\\n\",\n \"eager_loss = np.loadtxt(\\\"data/\\\"+name+\\\"_eager_loss.csv\\\")\\n\",\n \"pytorch_loss = np.loadtxt(\\\"data/\\\"+name+\\\"_pytorch_loss.csv\\\")\\n\",\n \"eager_time = np.loadtxt(\\\"data/\\\"+name+\\\"_eager_time.csv\\\")\\n\",\n \"pytorch_time = np.loadtxt(\\\"data/\\\"+name+\\\"_pytorch_time.csv\\\")\\n\",\n \"\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(sizes, eager_loss, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(sizes, pytorch_loss, \\\"b.\\\", label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('batch')\\n\",\n \"plt.ylabel('loss')\\n\",\n \"plt.title(\\\"Loss comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(sizes, eager_time, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(sizes, pytorch_time, label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('batch')\\n\",\n \"plt.ylabel('seconds')\\n\",\n \"plt.title(\\\"Time comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Smaller batch size over time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"eager_time = [ 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]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ha9esHll9ufbzSIo9t76ikRa4b1q69uPV1z0tL0+r33dNyGUEg1Z7PW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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(dpi=50)\\n\",\n \"plt.plot(eager_time, \\\"r-.\\\", label=\\\"eager\\\")\\n\",\n \"plt.plot(pytorch_time, label=\\\"pytorch\\\", )\\n\",\n \"plt.xlabel('iter')\\n\",\n \"plt.ylabel('millis')\\n\",\n \"plt.title(\\\"Time comparison\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.4\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "eager_lbfgs/pytorch_lbfgs.py", "content": "import util as u\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.autograd import Variable\nimport numpy as np\n\n# todo: make images global\n\nstep = 0\nfinal_loss = None\n\ndef benchmark(batch_size, iters, seed=1, cuda=True, history=100, verbose=False):\n global step, final_loss\n \n step = 0\n final_loss = None\n\n torch.manual_seed(seed)\n np.random.seed(seed)\n if cuda:\n torch.cuda.manual_seed(seed)\n\n visible_size = 28*28\n hidden_size = 196\n \n images = torch.Tensor(u.get_mnist_images(batch_size).T)\n images = images[:batch_size]\n if cuda:\n images = images.cuda()\n data = Variable(images)\n\n class Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.encoder = nn.Parameter(torch.rand(visible_size, hidden_size))\n\n def forward(self, input):\n x = input.view(-1, visible_size)\n x = torch.sigmoid(torch.mm(x, self.encoder))\n x = torch.sigmoid(torch.mm(x, torch.transpose(self.encoder, 0, 1)))\n return x.view_as(input)\n\n # initialize model and weights\n model = Net()\n model.encoder.data = torch.Tensor(u.ng_init(visible_size,\n hidden_size))\n if cuda:\n model.cuda()\n \n model.train()\n optimizer = optim.LBFGS(model.parameters(), max_iter=iters, history_size=history, lr=1.0)\n\n times = []\n def closure():\n global step, final_loss\n optimizer.zero_grad()\n output = model(data)\n loss = F.mse_loss(output, data)\n if verbose:\n loss0 = loss.data[0]\n times.append(u.last_time())\n print(\"Step %3d loss %6.5f msec %6.3f\"%(step, loss0, u.last_time()))\n step+=1\n if step == iters:\n final_loss = loss.data[0]\n loss.backward()\n u.record_time()\n return loss\n \n optimizer.step(closure)\n\n output = model(data)\n loss = F.mse_loss(output, data)\n loss0 = loss.data[0]\n\n if verbose:\n u.summarize_time()\n\n # print(times)\n s = ','.join([\"%f\"%(n,) for n in times[2:]])\n print('{', s,'}')\n \n return final_loss\n\n\n\ndef main():\n import common_gd\n args = common_gd.args\n args.cuda = not args.no_cuda and torch.cuda.is_available()\n\n print(benchmark(batch_size=args.batch_size, iters=args.iters, seed=args.seed, cuda = args.cuda, history=args.history, verbose=True))\n\nif __name__=='__main__':\n main()\n" }, { "path": "eager_lbfgs/run_experiment.py", "content": "# compare timing for variety of batch-sizes\n# TODO: make PyTorch not run out of memory\n\nimport tensorflow as tf\nimport eager_lbfgs\nimport pytorch_lbfgs\nimport numpy as np\nimport util as u\n\nimport time\nimport sys\nimport os\n\ndef run_experiment(iters, name):\n\n #batch_sizes = [1, 10, 100, 1000, 10000, 60000]\n batch_sizes = [100, 200, 300]\n\n eager_stats = []\n pytorch_stats = []\n\n def benchmark(f):\n # do whole run once for pre-warming\n f()\n import gc; gc.collect()\n start_time = time.perf_counter()\n final_loss = f()\n elapsed_time = time.perf_counter() - start_time\n return final_loss, elapsed_time\n\n for batch_size in batch_sizes:\n def eager_run():\n return eager_lbfgs.benchmark(batch_size=batch_size, iters=iters)\n eager_stats.append(benchmark(eager_run))\n def pytorch_run():\n return pytorch_lbfgs.benchmark(batch_size=batch_size, iters=iters)\n pytorch_stats.append(benchmark(pytorch_run))\n\n print(eager_stats)\n print(pytorch_stats)\n # pytorch_losses\n # pytorch_times\n # pytorch_sizes\n \n eager_stats = np.array(eager_stats)\n pytorch_stats = np.array(pytorch_stats)\n u.dump(batch_sizes, name+\"_batch.csv\")\n \n u.dump(eager_stats[:,0], name+\"_eager_loss.csv\")\n u.dump(eager_stats[:,1], name+\"_eager_time.csv\")\n \n u.dump(pytorch_stats[:,0], name+\"_pytorch_loss.csv\")\n u.dump(pytorch_stats[:,1], name+\"_pytorch_time.csv\")\n\n \nif __name__=='__main__':\n if len(sys.argv)<2:\n print(\"Running short comparison\")\n run_experiment(51, \"short\")\n else:\n print(\"Running long comparison\")\n run_experiment(101, \"long\")\n \n" }, { "path": "eager_lbfgs/torch_lbfgs.lua", "content": "--[[ An implementation of L-BFGS, heavily inspired by minFunc (Mark Schmidt)\n\nThis implementation of L-BFGS relies on a user-provided line\nsearch function (state.lineSearch). If this function is not\nprovided, then a simple learningRate is used to produce fixed\nsize steps. Fixed size steps are much less costly than line\nsearches, and can be useful for stochastic problems.\n\nThe learning rate is used even when a line search is provided.\nThis is also useful for large-scale stochastic problems, where\nopfunc is a noisy approximation of f(x). In that case, the learning\nrate allows a reduction of confidence in the step size.\n\nARGS:\n\n- `opfunc` : a function that takes a single input (X), the point of\n evaluation, and returns f(X) and df/dX\n- `x` : the initial point\n- `state` : a table describing the state of the optimizer; after each\n call the state is modified\n- `state.maxIter` : Maximum number of iterations allowed\n- `state.maxEval` : Maximum number of function evaluations\n- `state.tolFun` : Termination tolerance on the first-order optimality\n- `state.tolX` : Termination tol on progress in terms of func/param changes\n- `state.lineSearch` : A line search function\n- `state.learningRate` : If no line search provided, then a fixed step size is used\n\nRETURN:\n- `x*` : the new `x` vector, at the optimal point\n- `f` : a table of all function values:\n `f[1]` is the value of the function before any optimization and\n `f[#f]` is the final fully optimized value, at `x*`\n\n(Clement Farabet, 2012)\n]]\nfunction optim.lbfgs(opfunc, x, config, state)\n -- get/update state\n local config = config or {}\n local state = state or config\n local maxIter = tonumber(config.maxIter) or 20\n local maxEval = tonumber(config.maxEval) or maxIter*1.25\n local tolFun = config.tolFun or 1e-5\n local tolX = config.tolX or 1e-9\n local nCorrection = config.nCorrection or 100\n local lineSearch = config.lineSearch\n local lineSearchOpts = config.lineSearchOptions\n local learningRate = config.learningRate or 1\n local isverbose = config.verbose or false\n\n state.funcEval = state.funcEval or 0\n state.nIter = state.nIter or 0\n\n -- verbose function\n local verbose\n if isverbose then\n verbose = function(...) print(' ', ...) end\n else\n verbose = function() end\n end\n\n -- import some functions\n local abs = math.abs\n local min = math.min\n\n -- evaluate initial f(x) and df/dx\n local f,g = opfunc(x)\n local f_hist = {f}\n local currentFuncEval = 1\n state.funcEval = state.funcEval + 1\n local p = g:size(1)\n\n -- check optimality of initial point\n state.tmp1 = state.tmp1 or g.new(g:size()):zero(); local tmp1 = state.tmp1\n tmp1:copy(g):abs()\n if tmp1:sum() <= tolFun then\n -- optimality condition below tolFun\n verbose('optimality condition below tolFun')\n return x,f_hist\n end\n\n if not state.dir_bufs then\n -- reusable buffers for y's and s's, and their histories\n verbose('creating recyclable direction/step/history buffers')\n state.dir_bufs = state.dir_bufs or g.new(nCorrection+1, p):split(1)\n state.stp_bufs = state.stp_bufs or g.new(nCorrection+1, p):split(1)\n for i=1,#state.dir_bufs do\n state.dir_bufs[i] = state.dir_bufs[i]:squeeze(1)\n state.stp_bufs[i] = state.stp_bufs[i]:squeeze(1)\n end\n end\n\n -- variables cached in state (for tracing)\n local d = state.d\n local t = state.t\n local old_dirs = state.old_dirs\n local old_stps = state.old_stps\n local Hdiag = state.Hdiag\n local g_old = state.g_old\n local f_old = state.f_old\n\n -- optimize for a max of maxIter iterations\n local nIter = 0\n while nIter < maxIter do\n -- keep track of nb of iterations\n nIter = nIter + 1\n state.nIter = state.nIter + 1\n\n ------------------------------------------------------------\n -- compute gradient descent direction\n ------------------------------------------------------------\n if state.nIter == 1 then\n d = g:clone():mul(-1) -- -g\n old_dirs = {}\n old_stps = {}\n Hdiag = 1\n else\n -- do lbfgs update (update memory)\n local y = table.remove(state.dir_bufs) -- pop\n local s = table.remove(state.stp_bufs)\n y:add(g, -1, g_old) -- g - g_old\n s:mul(d, t) -- d*t\n local ys = y:dot(s) -- y*s\n if ys > 1e-10 then\n -- updating memory\n if #old_dirs == nCorrection then\n -- shift history by one (limited-memory)\n local removed1 = table.remove(old_dirs, 1)\n local removed2 = table.remove(old_stps, 1)\n table.insert(state.dir_bufs, removed1)\n table.insert(state.stp_bufs, removed2)\n end\n\n -- store new direction/step\n table.insert(old_dirs, s)\n table.insert(old_stps, y)\n\n -- update scale of initial Hessian approximation\n Hdiag = ys / y:dot(y) -- (y*y)\n else\n -- put y and s back into the buffer pool\n table.insert(state.dir_bufs, y)\n table.insert(state.stp_bufs, s)\n end\n\n -- compute the approximate (L-BFGS) inverse Hessian\n -- multiplied by the gradient\n local k = #old_dirs\n\n -- need to be accessed element-by-element, so don't re-type tensor:\n state.ro = state.ro or torch.Tensor(nCorrection); local ro = state.ro\n for i = 1,k do\n ro[i] = 1 / old_stps[i]:dot(old_dirs[i])\n end\n\n -- iteration in L-BFGS loop collapsed to use just one buffer\n local q = tmp1 -- reuse tmp1 for the q buffer\n -- need to be accessed element-by-element, so don't re-type tensor:\n state.al = state.al or torch.zeros(nCorrection) local al = state.al\n\n q:mul(g, -1) -- -g\n for i = k,1,-1 do\n al[i] = old_dirs[i]:dot(q) * ro[i]\n q:add(-al[i], old_stps[i])\n end\n\n -- multiply by initial Hessian\n r = d -- share the same buffer, since we don't need the old d\n r:mul(q, Hdiag) -- q[1] * Hdiag\n for i = 1,k do\n local be_i = old_stps[i]:dot(r) * ro[i]\n r:add(al[i]-be_i, old_dirs[i])\n end\n -- final direction is in r/d (same object)\n end\n g_old = g_old or g:clone()\n g_old:copy(g)\n f_old = f\n\n ------------------------------------------------------------\n -- compute step length\n ------------------------------------------------------------\n -- directional derivative\n local gtd = g:dot(d) -- g * d\n\n -- check that progress can be made along that direction\n if gtd > -tolX then\n break\n end\n\n -- reset initial guess for step size\n if state.nIter == 1 then\n tmp1:copy(g):abs()\n t = min(1,1/tmp1:sum()) * learningRate\n else\n t = learningRate\n end\n\n -- optional line search: user function\n local lsFuncEval = 0\n if lineSearch and type(lineSearch) == 'function' then\n -- perform line search, using user function\n f,g,x,t,lsFuncEval = lineSearch(opfunc,x,t,d,f,g,gtd,lineSearchOpts)\n table.insert(f_hist, f)\n else\n -- no line search, simply move with fixed-step\n x:add(t,d)\n if nIter ~= maxIter then\n -- re-evaluate function only if not in last iteration\n -- the reason we do this: in a stochastic setting,\n -- no use to re-evaluate that function here\n f,g = opfunc(x)\n lsFuncEval = 1\n table.insert(f_hist, f)\n end\n end\n\n -- update func eval\n currentFuncEval = currentFuncEval + lsFuncEval\n state.funcEval = state.funcEval + lsFuncEval\n\n ------------------------------------------------------------\n -- check conditions\n ------------------------------------------------------------\n if nIter == maxIter then\n -- no use to run tests\n verbose('reached max number of iterations')\n break\n end\n\n if currentFuncEval >= maxEval then\n -- max nb of function evals\n verbose('max nb of function evals')\n break\n end\n\n tmp1:copy(g):abs()\n if tmp1:sum() <= tolFun then\n -- check optimality\n verbose('optimality condition below tolFun')\n break\n end\n\n tmp1:copy(d):mul(t):abs()\n if tmp1:sum() <= tolX then\n -- step size below tolX\n verbose('step size below tolX')\n break\n end\n\n if abs(f-f_old) < tolX then\n -- function value changing less than tolX\n verbose('function value changing less than tolX')\n break\n end\n end\n\n -- save state\n state.old_dirs = old_dirs\n state.old_stps = old_stps\n state.Hdiag = Hdiag\n state.g_old = g_old\n state.f_old = f_old\n state.t = t\n state.d = d\n\n -- return optimal x, and history of f(x)\n return x,f_hist,currentFuncEval\nend\n" }, { "path": "eager_lbfgs/util.py", "content": "#!/usr/bin/env python\nimport socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n# for line profiling\ntry:\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\ndefault_tf_dtype = tf.float32\ndefault_np_dtype = np.float32\ndefault_dtype = default_tf_dtype\n\nUSE_MKL_SVD=True # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n if np.__config__.get_info(\"lapack_mkl_info\") is None:\n print(\"No MKL detected :(\")\n\n\nfrom scipy import linalg\n\ndef check_mkl():\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n print(\"Using MKL\")\n \nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n \"\"\"Sets args to be reused across several modules. Access as\n util.args.somesetting \"\"\"\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n #print(\"step: %.2f\"%(global_time_list[-1]*1000))\n\ndef last_time():\n \"\"\"Returns last interval records in millis.\"\"\"\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n # delete first large interval if exists\n if time_list and time_list[0]>3600*10:\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\n np.mean(time_list)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n# work-around for graph_editor.copy_with_input_replacements scaling\n# quadratically with size of the graph\nfrom tensorflow.contrib.graph_editor import transform\noriginal_assign_renamed_collections_handler = transform.assign_renamed_collections_handler\ndef dummy_collections_handler(info, elem, elem_): pass\ndef disable_collections_handler():\n transform.assign_renamed_collections_handler = dummy_collections_handler\ndef enable_collections_handler():\n transform.assign_renamed_collections_handler = original_assign_renamed_collections_handler\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__.endswith('Tensor') or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n logger = u.get_last_logger(skip_existence_check=True)\n if logger:\n newtag = 'time/'+self.tag\n # since tensorboard doesn't allow hierarchical tags, merge init times\n if newtag.startswith('time/init'):\n newtag = newtag.replace('time/init', 'timeinit')\n logger(newtag, interval_ms)\n\n\nglobal_record_dict = OrderedDict()\ndef record(tag, stat):\n global global_record_dict\n global_record_dict.setdefault(tag, []).append(stat)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False, use_resource=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n self.use_resource = use_resource\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n if not self.use_resource:\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n else:\n from tensorflow.python.ops import resource_variable_ops as rr\n self.cached = SvdTuple(\n rr.ResourceVariable(self.init.s, name=name+\"_s\"),\n rr.ResourceVariable(self.init.u, name=name+\"_u\"),\n rr.ResourceVariable(self.init.v, name=name+\"_v\"),\n rr.ResourceVariable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n\n if not use_resource:\n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n else:\n self.holder = self.init\n \n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n\n @profile\n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger(skip_existence_check=False):\n \"\"\"Returns last logger, if skip_existence_check is set, doesn't\n throw error if logger doesn't exist.\"\"\"\n global global_last_logger\n if not skip_existence_check:\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n interval_ms = 1000*(new_timestamp - self.last_timestamp)\n self.summary.value.add(tag='time/step',\n simple_value=interval_ms)\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n # hack, remove\n return tf.get_default_session()\n global sess\n assert sess\n return sess\n\ndef get_default_graph():\n global sess\n assert sess\n return sess.graph\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n\ndef run(fetches):\n return u.eval(fetches)\n \ntimeline_counter = 0\nrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\ndef traced_run(fetches):\n \"\"\"Runs fetches, dumps timeline files in current directory.\"\"\"\n global sess\n assert sess\n global timeline_counter\n run_metadata = tf.RunMetadata()\n\n root = os.getcwd()+\"/data\"\n from tensorflow.python.client import timeline\n\n results = sess.run(fetches,\n options=run_options,\n run_metadata=run_metadata);\n tl = timeline.Timeline(step_stats=run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format(show_memory=True,\n show_dataflow=False)\n open(root+\"/timeline_%d.json\"%(timeline_counter,), \"w\").write(ctf)\n open(root+\"/stepstats_%d.pbtxt\"%(timeline_counter,), \"w\").write(str(\n run_metadata.step_stats))\n timeline_counter+=1\n return results \n\ndef get_mnist_images(max_images=0, fold='train'):\n \"\"\"Returns mnist images, batch dimension last.\"\"\"\n \n import gzip\n from tensorflow.contrib.learn.python.learn.datasets import base\n import numpy\n \n def extract_images(f):\n \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\n Args:\n f: A file object that can be passed into a gzip reader.\n Returns:\n data: A 4D uint8 numpy array [index, y, x, depth].\n Raises:\n ValueError: If the bytestream does not start with 2051.\n \"\"\"\n # print('Extracting', f.name) # todo: remove\n with gzip.GzipFile(fileobj=f) as bytestream:\n magic = _read32(bytestream)\n if magic != 2051:\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\n (magic, f.name))\n num_images = _read32(bytestream)\n if max_images:\n num_images = max_images\n rows = _read32(bytestream)\n cols = _read32(bytestream)\n buf = bytestream.read(rows * cols * num_images)\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\n data = data.reshape(num_images, rows, cols, 1)\n return data\n\n def _read32(bytestream):\n dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]\n\n if fold == 'train': # todo: rename\n TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'\n elif fold == 'test':\n TRAIN_IMAGES = 't10k-images-idx3-ubyte.gz'\n else:\n assert False, 'unknown fold %s'%(fold)\n \n source_url = 'https://storage.googleapis.com/cvdf-datasets/mnist/'\n local_file = base.maybe_download(TRAIN_IMAGES, '/tmp',\n source_url + TRAIN_IMAGES)\n train_images = extract_images(open(local_file, 'rb'))\n dsize = train_images.shape[0]\n if fold == 'train':\n if not max_images:\n dsize == 60000\n else:\n dsize = max_images\n assert dsize <= 60000\n else:\n if not max_images:\n dsize == 60000\n else:\n dsize = max_images\n assert dsize <= 10000\n\n train_images = train_images.reshape(dsize, 28**2).T.astype(np.float64)/255\n train_images = np.ascontiguousarray(train_images)\n return train_images.astype(default_np_dtype)\n\nregularizer_cache = {}\ndef cachedGpuIdentityRegularizer(n, Lambda):\n global regularizer_cache\n\n n = int(n)\n if (n, Lambda) not in regularizer_cache:\n numpy_diag = Lambda*np.diag(np.ones([n]))\n numpy_diag = numpy_diag.astype(default_np_dtype)\n with tf.device(\"/gpu:0\"):\n regularizer_cache[(n, Lambda)] = tf.constant(numpy_diag)\n \n return regularizer_cache[(n, Lambda)]\n\n# helper utilities\ndef ng_init(s1, s2): # uniform weight init from Ng UFLDL\n r = np.sqrt(6) / np.sqrt(s1 + s2 + 1)\n flat = np.random.random(s1*s2)*2*r-r\n return flat.reshape([s1, s2]).astype(default_np_dtype)\n\n\nif __name__=='__main__':\n run_all_tests(sys.modules[__name__])\n" }, { "path": "enqueue_many_test.py", "content": "import os, sys\nimport numpy as np\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\nimport tensorflow as tf\n\ndef create_session():\n config = tf.ConfigProto(log_device_placement=False)\n config.operation_timeout_in_ms=5000 # terminate on long hangs\n config.gpu_options.per_process_gpu_memory_fraction=0.3 # don't hog all vRAM\n sess = tf.InteractiveSession(\"\", config=config)\n return sess\n\nimport time\nimport threading\nimport os\nos.environ['PYTHONUNBUFFERED'] = 'True'\n\n\nfrom google.protobuf.internal import api_implementation\nassert api_implementation._default_implementation_type == 'cpp'\n\n\nfrom tensorflow.python.client import timeline\n\ntf.reset_default_graph()\n\nreverse = False\nif len(sys.argv)>1:\n assert sys.argv[1] == 'reverse'\n reverse = True\n \nn = 10**6\ndtype = tf.int32\nqueue = tf.FIFOQueue(capacity=2*n, dtypes=[dtype], shapes=[()])\nzeros = tf.Variable(tf.zeros((n), name=\"0\", dtype=dtype))\nones = tf.Variable(tf.ones((n), name=\"1\", dtype=dtype))\nenqueue_zeros = queue.enqueue_many(zeros, name=\"zeros\")\nenqueue_ones = queue.enqueue_many(ones, name=\"ones\")\nsess = create_session()\nsess.run(tf.global_variables_initializer())\n\nstart_time0 = time.time()\nrun_metadatas = []\ndef run_op(op):\n start_time = time.time()\n print(\"%10.2f ms: starting op %s\\n\" % ((start_time-start_time0)*1000, op.name), flush=True, end='')\n \n options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n sess.run(op, options=options, run_metadata=run_metadata)\n end_time = time.time()\n print(\"%10.2f ms: ending op %s\\n\" % ((end_time-start_time0)*1000, op.name), flush=True, end='')\n run_metadatas.append(run_metadata)\n\n\n\nthreads = [threading.Thread(group=None, target=run_op, args=(op,)) for op in (enqueue_zeros, enqueue_ones)]\nif reverse:\n threads.reverse()\n \nfor t in threads:\n t.start()\n\n# wait for threads to finish\nfor t in threads:\n t.join()\n\n# generate merged timeline\nmerged_metadata = tf.RunMetadata()\nfor run_metadata in run_metadatas:\n merged_metadata.MergeFrom(run_metadata)\n\ntl = timeline.Timeline(merged_metadata.step_stats)\nctf = tl.generate_chrome_trace_format()\nwith open(sys.argv[0]+'_%s_timeline.json'%(reverse), 'w') as f:\n f.write(ctf)\n\nassert sess.run(queue.size()) == 2*n\nresult = sess.run(queue.dequeue_many(2*n))\npadding = np.array([0])\n\ndiffs = np.concatenate([padding, result])-np.concatenate([result, padding])\nprint(\"Interleaving detected: %s\" % (abs(diffs).sum()>2))\n" }, { "path": "enqueue_many_test_singlerun.py", "content": "# Test multiple enqueue many in single .run call\nimport os, sys\nimport numpy as np\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\nimport tensorflow as tf\n\ndef create_session():\n config = tf.ConfigProto(log_device_placement=False)\n config.operation_timeout_in_ms=5000 # terminate on long hangs\n config.gpu_options.per_process_gpu_memory_fraction=0.3 # don't hog all vRAM\n sess = tf.InteractiveSession(\"\", config=config)\n return sess\n\nimport time\nimport threading\nimport os\nos.environ['PYTHONUNBUFFERED'] = 'True'\n\n\nfrom google.protobuf.internal import api_implementation\nassert api_implementation._default_implementation_type == 'cpp'\n\n\nfrom tensorflow.python.client import timeline\ntf.reset_default_graph()\n\nreverse = False\nif len(sys.argv)>1:\n assert sys.argv[1] == 'reverse'\n reverse = True\n \nn = 10**6\ndtype = tf.int32\nqueue = tf.FIFOQueue(capacity=2*n, dtypes=[dtype], shapes=[()])\nzeros = tf.Variable(tf.zeros((n), name=\"0\", dtype=dtype))\nones = tf.Variable(tf.ones((n), name=\"1\", dtype=dtype))\nenqueue_zeros = queue.enqueue_many(zeros, name=\"zeros\")\nenqueue_ones = queue.enqueue_many(ones, name=\"ones\")\nsess = create_session()\nsess.run(tf.global_variables_initializer())\n\nop = tf.group(enqueue_zeros, enqueue_ones)\n\nstart_time0 = time.time()\nrun_metadatas = []\ndef run_op(op):\n start_time = time.time()\n print(\"%10.2f ms: starting op %s\\n\" % ((start_time-start_time0)*1000, op.name), flush=True, end='')\n \n options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n sess.run(op, options=options, run_metadata=run_metadata)\n end_time = time.time()\n print(\"%10.2f ms: ending op %s\\n\" % ((end_time-start_time0)*1000, op.name), flush=True, end='')\n run_metadatas.append(run_metadata)\n\n\n\nthreads = [threading.Thread(group=None, target=run_op, args=(op,))]\n \nfor t in threads:\n t.start()\n\n# wait for threads to finish\nfor t in threads:\n t.join()\n\n# generate merged timeline\nmerged_metadata = tf.RunMetadata()\nfor run_metadata in run_metadatas:\n merged_metadata.MergeFrom(run_metadata)\n\ntl = timeline.Timeline(merged_metadata.step_stats)\nctf = tl.generate_chrome_trace_format()\nwith open(sys.argv[0]+'_timeline.json', 'w') as f:\n f.write(ctf)\n\nassert sess.run(queue.size()) == 2*n\nresult = sess.run(queue.dequeue_many(2*n))\npadding = np.array([0])\n\ndiffs = np.concatenate([padding, result])-np.concatenate([result, padding])\nprint(\"Interleaving detected: %s\" % (abs(diffs).sum()>2))\n" }, { "path": "ericyue-slowreader/benchmark-batch-noqueuerunners-timeline.json", "content": "{\n \"traceEvents\": [\n {\n \"ph\": \"M\",\n \"pid\": 0,\n \"name\": \"process_name\",\n \"args\": {\n \"name\": \"Allocators\"\n }\n },\n {\n \"ph\": \"M\",\n \"pid\": 1,\n \"name\": \"process_name\",\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/cpu:0 Compute\"\n }\n },\n {\n \"ph\": \"M\",\n \"pid\": 2,\n \"name\": \"process_name\",\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/cpu:0 Tensors\"\n }\n },\n {\n \"dur\": 2,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"NoOp\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"NoOp\",\n \"name\": \"_SOURCE\"\n },\n \"ts\": 1484608787726014,\n \"tid\": 0\n },\n {\n \"dur\": 3,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"shuffle_batch/n\"\n },\n \"ts\": 1484608787726020,\n \"tid\": 0\n },\n {\n \"dur\": 5,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"RandomShuffleQueue\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"RandomShuffleQueue\",\n \"name\": \"shuffle_batch/random_shuffle_queue\"\n },\n \"ts\": 1484608787726023,\n \"tid\": 1\n },\n {\n \"dur\": 7,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"ParseExample/Const\"\n },\n \"ts\": 1484608787726024,\n \"tid\": 0\n },\n {\n \"dur\": 2,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"ParseExample/ParseExample/names\"\n },\n \"ts\": 1484608787726032,\n \"tid\": 0\n },\n {\n \"dur\": 2,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"ParseExample/ParseExample/sparse_keys_0\"\n },\n \"ts\": 1484608787726035,\n \"tid\": 0\n },\n {\n \"dur\": 1,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"ParseExample/ParseExample/sparse_keys_1\"\n },\n \"ts\": 1484608787726038,\n \"tid\": 0\n },\n {\n \"dur\": 2,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"Const\",\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"name\": \"ParseExample/ParseExample/dense_keys_0\"\n },\n \"ts\": 1484608787726040,\n \"tid\": 0\n },\n {\n \"dur\": 97,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"QueueDequeueMany\",\n \"ph\": \"X\",\n \"args\": {\n \"input1\": \"shuffle_batch/n\",\n \"op\": \"QueueDequeueMany\",\n \"name\": \"shuffle_batch\",\n \"input0\": \"shuffle_batch/random_shuffle_queue\"\n },\n \"ts\": 1484608787726029,\n \"tid\": 1\n },\n {\n \"pid\": 1,\n \"cat\": \"DataFlow\",\n \"name\": \"shuffle_batch/n\",\n \"ph\": \"s\",\n \"id\": 0,\n \"ts\": 1484608787726023,\n \"tid\": 0\n },\n {\n \"pid\": 1,\n \"cat\": \"DataFlow\",\n \"name\": \"shuffle_batch/n\",\n \"ph\": \"t\",\n \"id\": 0,\n \"ts\": 1484608787726029,\n \"tid\": 1\n },\n {\n \"dur\": 115,\n \"pid\": 1,\n \"cat\": \"Op\",\n \"name\": \"ParseExample\",\n \"ph\": \"X\",\n \"args\": {\n \"input2\": \"ParseExample/ParseExample/sparse_keys_0\",\n \"input3\": \"ParseExample/ParseExample/sparse_keys_1\",\n \"input4\": \"ParseExample/ParseExample/dense_keys_0\",\n \"name\": \"ParseExample/ParseExample\",\n \"input5\": \"ParseExample/Const\",\n \"input1\": \"ParseExample/ParseExample/names\",\n \"op\": \"ParseExample\",\n \"input0\": \"shuffle_batch\"\n },\n \"ts\": 1484608787726132,\n \"tid\": 0\n },\n {\n \"pid\": 1,\n \"cat\": \"DataFlow\",\n \"name\": \"shuffle_batch\",\n \"ph\": \"s\",\n \"id\": 1,\n \"ts\": 1484608787726126,\n \"tid\": 1\n },\n {\n \"pid\": 1,\n \"cat\": \"DataFlow\",\n \"name\": \"shuffle_batch\",\n \"ph\": \"t\",\n \"id\": 1,\n \"ts\": 1484608787726132,\n \"tid\": 0\n }\n ]\n}" }, { "path": "ericyue-slowreader/benchmark-batch-noqueuerunners.profile", "content": "\n\n\n\n\n\n\n\n/home/yaroslav/.conda/envs/starter-agent3/bin/python; 2254 samples\n\n\nLegend\n/home/yaroslav/.conda/envs/starter-agent3/bin/python\nTotal samples: 2254\nFocusing on: 2254\nDropped nodes with <= 11 abs(samples)\nDropped edges with <= 2 samples\n\n\nN1\n\nclone\n0 (0.0%)\nof 1354 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(2.8%)\n\n\nN3->N58\n\n\n61\n\n\nN79\n\ntensorflow\nFIFOQueue\nDequeueLocked\n3 (0.1%)\nof 32 (1.4%)\n\n\nN3->N79\n\n\n32\n\n\nN4->N3\n\n\n1074\n\n\nN4->N4\n\n\n188\n\n\nN4->N13\n\n\n29\n\n\nN5->N3\n\n\n1309\n\n\nN6->N6\n\n\n20\n\n\nN7\n\ntensorflow\nDevice\nComputeAsync\n1 (0.0%)\nof 937 (41.6%)\n\n\nN6->N7\n\n\n937\n\n\nN32\n\ntc_new\n130 (5.8%)\nof 191 (8.5%)\n\n\nN6->N32\n\n\n9\n\n\nN69\n\ntensorflow\nExecutorState\nPropagateOutputs\n3 (0.1%)\nof 44 (2.0%)\n\n\nN6->N69\n\n\n6\n\n\nN72\n\ntensorflow\nTensor\nIsInitialized\n27 (1.2%)\nof 40 (1.8%)\n\n\nN6->N72\n\n\n8\n\n\nN73\n\ntensorflow\nTensor\n~Tensor\n27 (1.2%)\nof 40 (1.8%)\n\n\nN6->N73\n\n\n14\n\n\nN8\n\ntensorflow\nQueueOpKernel\nComputeAsync\n1 (0.0%)\nof 934 (41.4%)\n\n\nN7->N8\n\n\n934\n\n\nN9\n\ntensorflow\nDequeueOp\nComputeAsync\n7 (0.3%)\nof 647 (28.7%)\n\n\nN8->N9\n\n\n647\n\n\nN8->N32\n\n\n4\n\n\nN38\n\ntensorflow\nEnqueueManyOp\nComputeAsync\n0 (0.0%)\nof 144 (6.4%)\n\n\nN8->N38\n\n\n144\n\n\nN48\n\ntensorflow\nGetResourceFromContext\n2 (0.1%)\nof 115 (5.1%)\n\n\nN8->N48\n\n\n115\n\n\nN78\n\nstd\nbasic_string\nbasic_string\n7 (0.3%)\nof 32 (1.4%)\n\n\nN8->N78\n\n\n10\n\n\nN9->N9\n\n\n7\n\n\nN10\n\ntensorflow\nFIFOQueue\nTryDequeue\n17 (0.8%)\nof 604 (26.8%)\n\n\nN9->N10\n\n\n604\n\n\nN9->N13\n\n\n26\n\n\nN10->N10\n\n\n17\n\n\nN11\n\ntensorflow\nQueueBase\nFlushUnlocked\n16 (0.7%)\nof 560 (24.8%)\n\n\nN10->N11\n\n\n416\n\n\nN10->N13\n\n\n41\n\n\nN10->N32\n\n\n17\n\n\nN10->N40\n\n\n67\n\n\nN61\n\n__GI___pthread_mutex_lock\n56 (2.5%)\n\n\nN10->N61\n\n\n8\n\n\nN70\n\n__GI___pthread_mutex_unlock\n4 (0.2%)\nof 42 (1.9%)\n\n\nN10->N70\n\n\n3\n\n\nN11->N3\n\n\n101\n\n\nN11->N11\n\n\n16\n\n\nN12\n\ntensorflow\nQueueBase\nTryAttemptLocked\n12 (0.5%)\nof 362 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(2.0%)\n\n\nN15->N67\n\n\n39\n\n\nN16->N15\n\n\n2864\n\n\nN16->N16\n\n\n5\n\n\nN17->N15\n\n\n1579\n\n\nN18\n\nfast_function\n(inline)\n0 (0.0%)\nof 312 (13.8%)\n\n\nN17->N18\n\n\n1908\n\n\nN37\n\nPyCFunction_Call\n0 (0.0%)\nof 175 (7.8%)\n\n\nN17->N37\n\n\n111\n\n\nN41\n\ndo_call\n(inline)\n1 (0.0%)\nof 122 (5.4%)\n\n\nN17->N41\n\n\n169\n\n\nN18->N16\n\n\n1908\n\n\nN19\n\nPyEval_EvalCode\n0 (0.0%)\nof 310 (13.8%)\n\n\nN19->N14\n\n\n413\n\n\nN20\n\n_L_lock_909\n0 (0.0%)\nof 290 (12.9%)\n\n\nN21\n\n__lll_lock_wait\n290 (12.9%)\n\n\nN20->N21\n\n\n289\n\n\nN21->N21\n\n\n289\n\n\nN22\n\n_L_unlock_722\n0 (0.0%)\nof 278 (12.3%)\n\n\nN23\n\n__lll_unlock_wake\n278 (12.3%)\n\n\nN22->N23\n\n\n278\n\n\nN23->N23\n\n\n278\n\n\nN24\n\nPyObject_Call\n1 (0.0%)\nof 267 (11.8%)\n\n\nN25\n\nfunction_call\n0 (0.0%)\nof 264 (11.7%)\n\n\nN24->N25\n\n\n531\n\n\nN24->N37\n\n\n135\n\n\nN44\n\nmethod_call\n2 (0.1%)\nof 119 (5.3%)\n\n\nN24->N44\n\n\n167\n\n\nN50\n\ntype_call\n1 (0.0%)\nof 111 (4.9%)\n\n\nN24->N50\n\n\n153\n\n\nN25->N14\n\n\n536\n\n\nN26\n\nPy_Main\n0 (0.0%)\nof 210 (9.3%)\n\n\nN34\n\nrun_file\n(inline)\n0 (0.0%)\nof 190 (8.4%)\n\n\nN26->N34\n\n\n190\n\n\nN27\n\nmain\n0 (0.0%)\nof 210 (9.3%)\n\n\nN27->N26\n\n\n210\n\n\nN28\n\n__libc_start_main\n0 (0.0%)\nof 209 (9.3%)\n\n\nN28->N27\n\n\n209\n\n\nN29\n\n_start\n0 (0.0%)\nof 208 (9.2%)\n\n\nN29->N28\n\n\n208\n\n\nN30\n\nrun_mod\n(inline)\n0 (0.0%)\nof 193 (8.6%)\n\n\nN30->N19\n\n\n192\n\n\nN31\n\nPyRun_FileExFlags\n0 (0.0%)\nof 191 (8.5%)\n\n\nN31->N30\n\n\n191\n\n\nN32->N32\n\n\n128\n\n\nN56\n\ntcmalloc\nThreadCache\nFetchFromCentralCache\n0 (0.0%)\nof 68 (3.0%)\n\n\nN32->N56\n\n\n61\n\n\nN33\n\nPyRun_SimpleFileExFlags\n0 (0.0%)\nof 190 (8.4%)\n\n\nN33->N31\n\n\n190\n\n\nN34->N33\n\n\n190\n\n\nN35->N24\n\n\n72\n\n\nN35->N37\n\n\n319\n\n\nN36->N36\n\n\n91\n\n\nN53\n\ntcmalloc\nThreadCache\nListTooLong\n0 (0.0%)\nof 83 (3.7%)\n\n\nN36->N53\n\n\n81\n\n\nN42\n\nbuiltin_exec\n0 (0.0%)\nof 119 (5.3%)\n\n\nN37->N42\n\n\n220\n\n\nN47\n\nbuiltin___import__\n0 (0.0%)\nof 117 (5.2%)\n\n\nN37->N47\n\n\n247\n\n\nN39\n\ntensorflow\nFIFOQueue\nTryEnqueueMany\n0 (0.0%)\nof 144 (6.4%)\n\n\nN38->N39\n\n\n144\n\n\nN39->N11\n\n\n144\n\n\nN40->N13\n\n\n153\n\n\nN40->N40\n\n\n5\n\n\nN41->N24\n\n\n152\n\n\nN41->N37\n\n\n9\n\n\nN43\n\nbuiltin_exec_impl\n(inline)\n0 (0.0%)\nof 119 (5.3%)\n\n\nN42->N43\n\n\n223\n\n\nN43->N19\n\n\n219\n\n\nN44->N24\n\n\n164\n\n\nN45\n\nPyImport_ImportModuleLevelObject\n0 (0.0%)\nof 117 (5.2%)\n\n\nN46\n\n_PyObject_CallMethodIdObjArgs\n0 (0.0%)\nof 117 (5.2%)\n\n\nN45->N46\n\n\n255\n\n\nN46->N24\n\n\n261\n\n\nN47->N45\n\n\n248\n\n\nN59\n\ntensorflow\nTensor\nTensor\n7 (0.3%)\nof 60 (2.7%)\n\n\nN48->N59\n\n\n6\n\n\nN48->N61\n\n\n5\n\n\nN77\n\ntensorflow\nResourceMgr\nDoLookup\n9 (0.4%)\nof 33 (1.5%)\n\n\nN48->N77\n\n\n33\n\n\nN57\n\ntensorflow\nOpKernelContext\nallocate_persistent\n5 (0.2%)\nof 66 (2.9%)\n\n\nN49->N57\n\n\n66\n\n\nN80\n\ntensorflow\nQueueBase\nCopySliceToElement\n9 (0.4%)\nof 32 (1.4%)\n\n\nN49->N80\n\n\n32\n\n\nN52\n\nslot_tp_init\n0 (0.0%)\nof 94 (4.2%)\n\n\nN50->N52\n\n\n131\n\n\nN51->N24\n\n\n134\n\n\nN52->N24\n\n\n129\n\n\nN54\n\ntcmalloc\nThreadCache\nReleaseToCentralCache\n33 (1.5%)\nof 83 (3.7%)\n\n\nN53->N54\n\n\n83\n\n\nN54->N54\n\n\n33\n\n\nN62\n\ntcmalloc\nCentralFreeList\nInsertRange\n0 (0.0%)\nof 51 (2.3%)\n\n\nN54->N62\n\n\n50\n\n\nN55\n\ntcmalloc\nCentralFreeList\nRemoveRange\n2 (0.1%)\nof 68 (3.0%)\n\n\nN60\n\ntcmalloc\nCentralFreeList\nFetchFromSpans\n55 (2.4%)\nof 58 (2.6%)\n\n\nN55->N60\n\n\n57\n\n\nN56->N55\n\n\n68\n\n\nN57->N57\n\n\n5\n\n\nN57->N59\n\n\n5\n\n\nN63\n\ntensorflow\nOpKernelContext\nallocate_tensor\n2 (0.1%)\nof 51 (2.3%)\n\n\nN57->N63\n\n\n51\n\n\nN58->N58\n\n\n3\n\n\nN58->N69\n\n\n38\n\n\nN59->N32\n\n\n6\n\n\nN59->N59\n\n\n7\n\n\nN76\n\ntensorflow\nBuffer\nBuffer\n3 (0.1%)\nof 34 (1.5%)\n\n\nN59->N76\n\n\n34\n\n\nN60->N60\n\n\n54\n\n\nN61->N61\n\n\n54\n\n\nN64\n\ntcmalloc\nCentralFreeList\nReleaseListToSpans\n4 (0.2%)\nof 50 (2.2%)\n\n\nN62->N64\n\n\n50\n\n\nN63->N59\n\n\n43\n\n\nN63->N72\n\n\n3\n\n\nN64->N64\n\n\n4\n\n\nN65\n\ntcmalloc\nCentralFreeList\nReleaseToSpans\n45 (2.0%)\nof 46 (2.0%)\n\n\nN64->N65\n\n\n46\n\n\nN65->N65\n\n\n45\n\n\nN68\n\n__sched_yield\n44 (2.0%)\n\n\nN66->N68\n\n\n44\n\n\nN67->N67\n\n\n13\n\n\nN68->N68\n\n\n44\n\n\nN69->N61\n\n\n11\n\n\nN69->N69\n\n\n3\n\n\nN71\n\n__pthread_mutex_unlock_usercnt\n(inline)\n38 (1.7%)\nof 42 (1.9%)\n\n\nN70->N71\n\n\n80\n\n\nN71->N70\n\n\n42\n\n\nN72->N72\n\n\n26\n\n\nN72->N73\n\n\n6\n\n\nN73->N36\n\n\n5\n\n\nN73->N73\n\n\n21\n\n\nN74\n\ntensorflow\nOpKernel\nInputRange\n8 (0.4%)\nof 37 (1.6%)\n\n\nN74->N36\n\n\n3\n\n\nN74->N74\n\n\n7\n\n\nN75\n\nstd\n_Hashtable\nfind\n23 (1.0%)\nof 34 (1.5%)\n\n\nN74->N75\n\n\n16\n\n\nN74->N78\n\n\n9\n\n\nN75->N75\n\n\n23\n\n\nN76->N76\n\n\n3\n\n\nN77->N61\n\n\n5\n\n\nN77->N75\n\n\n3\n\n\nN77->N77\n\n\n9\n\n\nN78->N78\n\n\n7\n\n\nN79->N79\n\n\n3\n\n\nN80->N80\n\n\n9\n\n\n\n" }, { "path": "ericyue-slowreader/benchmark-batch-noqueuerunners.py", "content": "# range size is 1000000\n# range queue 900000, batch queue 100000, 81510.55 per second\n# range d 900000, batch d 100000\n# range queue 800000, batch queue 200000, 71304.15 per second\n# range d -100000, batch d 100000\n# range queue 700000, batch queue 300000, 65481.16 per second\n#\n# When using enqueue_many=False\n# range size is 1000000\n# range queue 999000, batch queue 1000, 11485.93 per second\n# range d 999000, batch d 1000\n# range queue 998000, batch queue 2000, 13163.80 per second\n# range d -1000, batch d 1000\n# ange queue 997000, batch queue 3000, 13048.20 per second\n\nimport tensorflow as tf\nimport time, os, sys\nfrom tensorflow.python.client import timeline\n\ndump_timeline = False\nenqueue_many = True\nenqueue_many_size = 1000\nsteps_to_validate = 200\nepoch_number = 2\nthread_number = 2\nbatch_size = 100\n\ncapacity = 2*10**6\n# don't use too high of limit, 10**9 hangs (overflows to negative in TF?)\na_queue = tf.train.range_input_producer(limit=10**3, num_epochs=2000,\n capacity=capacity, shuffle=False)\n\n# manually run the queue runner for a bit\nconfig = tf.ConfigProto(log_device_placement=False)\nconfig.operation_timeout_in_ms=5000 # terminate on long hangs\nsess = tf.InteractiveSession(\"\", config=config)\nsess.run(tf.global_variables_initializer())\nsess.run(tf.local_variables_initializer())\n\n\na_queue_qr = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)[0]\nfor i in range(1000):\n sess.run(a_queue_qr.enqueue_ops)\n\n\n# check the sizes\nrange_size_node = \"input_producer/fraction_of_2000000_full/fraction_of_2000000_full_Size:0\"\n\n# size gives raw size rather than number of batches\nbatch_size_node = \"batch/fifo_queue_Size:0\"\n\nprint(\"range size is \", sess.run(range_size_node))\n\n# now create batch and run it manually\n# use size of 2 or get TypeError: 'Tensor' object is not iterable.\n# (possibly singleton list get auto-packed into a single Tensor)\nif enqueue_many:\n a_batch = []\n for i in range(enqueue_many_size):\n a_batch.append(a_queue.dequeue())\n \n b_batch = tf.train.batch([a_batch], batch_size=batch_size,\n capacity=capacity, enqueue_many=enqueue_many)\n \nelse:\n [b, _] = tf.train.batch([a_queue.dequeue()]*2, batch_size=batch_size,\n capacity=capacity, enqueue_many=enqueue_many)\n\n\nstart_time = time.time()\nold_range_size, old_batch_size = (0, 0)\n\nbatch_qr = [qr for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS) if qr.name.startswith(\"batch\")][0]\n\nfor i in range(10):\n for i in range(100): # put some elements on queue\n sess.run(batch_qr.enqueue_ops)\n\n if dump_timeline == True:\n run_metadata = tf.RunMetadata()\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n\n sess.run(batch_qr.enqueue_ops, run_metadata=run_metadata,\n options=run_options)\n trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n trace_fn = '/tmp/ericyue/process.timeline.json'\n graph_fn = '/tmp/ericyue/process.graph.pbtxt'\n metadata_fn = '/tmp/ericyue/process.metadata.pbtxt'\n open(trace_fn, 'w').write(trace.generate_chrome_trace_format())\n open(graph_fn, 'w').write(str(tf.get_default_graph().as_graph_def()))\n open(metadata_fn, 'w').write(str(run_metadata))\n\n sys.exit()\n\n \n new_range_size, new_batch_size = sess.run([range_size_node, batch_size_node])\n \n new_time = time.time()\n rate = (new_batch_size-old_batch_size)/(new_time-start_time)\n print(\"range queue %d, batch queue %d, %.2f per second\"%(new_range_size,\n new_batch_size,\n rate))\n print(\"range d %d, batch d %d\" %(new_range_size - old_range_size,\n new_batch_size - old_batch_size))\n start_time = time.time()\n old_range_size, old_batch_size = new_range_size, new_batch_size \n\n\n" }, { "path": "ericyue-slowreader/benchmark-batch.py", "content": "# measure the speed at which batches can be made\n# Only 14k \n# range queue 1996115, batch queue 3885, 6455.13 per second\n# range d 404771, batch d 3229\n# range queue 1988698, batch queue 11302, 14735.80 per second\n# range d -7417, batch d 7417\n# range queue 1981384, batch queue 18616, 14620.57 per second\n# range d -7314, batch d 7314\n# range queue 1974016, batch queue 25984, 14662.89 per second\n\nimport tensorflow as tf\nimport time\n\n\nsteps_to_validate = 200\nepoch_number = 2\nthread_number = 2\nbatch_size = 100\n\ncapacity = 2*10**6\n# don't use too high of limit, 10**9 hangs (overflows to negative in TF?)\na_queue = tf.train.range_input_producer(limit=10**3, num_epochs=2000,\n capacity=capacity, shuffle=False)\n\n# manually run the queue runner for a bit\nconfig = tf.ConfigProto(log_device_placement=False)\nconfig.operation_timeout_in_ms=5000 # terminate on long hangs\nsess = tf.InteractiveSession(\"\", config=config)\nsess.run(tf.global_variables_initializer())\nsess.run(tf.local_variables_initializer())\n\n\na_queue_qr = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)[0]\nfor i in range(1000):\n sess.run(a_queue_qr.enqueue_ops)\n\n\n# check the size\nrange_size_node = \"input_producer/fraction_of_2000000_full/fraction_of_2000000_full_Size:0\"\n\n# size gives raw size rather than number of batches\nbatch_size_node = \"batch/fifo_queue_Size:0\"\n\nprint(\"range size is \", sess.run(range_size_node))\n\n# now create batch and run it manually\n# use size of 2 or get TypeError: 'Tensor' object is not iterable.\n# (possibly singleton list get auto-packed into a single Tensor)\n[b, _] = tf.train.batch([a_queue.dequeue()]*2, batch_size=batch_size,\n capacity=capacity)\n\n\ntf.train.start_queue_runners()\nstart_time = time.time()\nold_range_size, old_batch_size = (0, 0)\nwhile True:\n new_range_size, new_batch_size = sess.run([range_size_node, batch_size_node])\n \n new_time = time.time()\n rate = (new_batch_size-old_batch_size)/(new_time-start_time)\n print(\"range queue %d, batch queue %d, %.2f per second\"%(new_range_size,\n new_batch_size,\n rate))\n print(\"range d %d, batch d %d\" %(new_range_size - old_range_size,\n new_batch_size - old_batch_size))\n start_time = time.time()\n old_range_size, old_batch_size = new_range_size, new_batch_size \n time.sleep(0.5)\n\n\n\ndef let_queue_repopulate(size_tensor, min_elements=100000, sleep_delay=0.5):\n \"\"\"Wait until queue has enough elements.\"\"\"\n size2 = \"input_producer/fraction_of_2000000_full/fraction_of_2000000_full_Size:0\"\n while sess.run(size_tensor) < min_elements:\n print(\"Size1: %d, size2: %d\" %tuple(sess.run([size_tensor, size2])))\n time.sleep(sleep_delay)\n\nstep = 0\nstart_time = time.time()\nwhile True:\n step+=1\n let_queue_repopulate(size_tensor=batch_size_node)\n sess.run(b.op)\n if step % steps_to_validate == 0:\n end_time = time.time()\n sec = (end_time - start_time)\n print(\"[{}] time[{:6.2f}] step[{:10d}] speed[{:6d}]\".format(\n str(end_time).split(\".\")[0],sec, step,\n int((steps_to_validate*batch_size)/sec)\n ))\n start_time = end_time\n" }, { "path": "ericyue-slowreader/benchmark-reader.py", "content": "# [1484609202] time[ 0.01] step[ 20] speed[360350]\n# [1484609202] time[ 0.00] step[ 40] speed[1129322]\n# [1484609202] time[ 0.00] step[ 60] speed[546168]\n# [1484609202] time[ 0.00] step[ 80] speed[709696]\n# [1484609202] time[ 0.00] step[ 100] speed[1112399]\n# [1484609202] time[ 0.00] step[ 120] speed[1506033]\n\nimport tensorflow as tf\nimport time\n\nfilename_queue = tf.train.string_input_producer([\"./data.zlib\"],\n shuffle=False,\n seed = int(time.time()))\n\nreader = tf.TFRecordReader(options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB))\n_, serialized_example = reader.read(filename_queue)\n\nreader = tf.TFRecordReader(options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB))\n_, serialized_example = reader.read(filename_queue)\n\nsess = tf.InteractiveSession()\ntf.train.start_queue_runners()\n\nbatch_size = 100\nsteps_to_validate = 20\n\nstep = 0\nstart_time = time.time()\nwhile True:\n step+=1\n sess.run(serialized_example.op)\n if step % steps_to_validate == 0:\n end_time = time.time()\n sec = (end_time - start_time)\n print(\"[{}] time[{:6.2f}] step[{:10d}] speed[{:6d}]\".format(\n str(end_time).split(\".\")[0],sec, step,\n int((steps_to_validate*batch_size)/sec)\n ))\n start_time = end_time\n" }, { "path": "ericyue-slowreader/benchmark-synthetic-batch.py", "content": "# [1484611992] time[ 0.00] step[ 420] speed[613695]\n# [1484611992] time[ 0.00] step[ 440] speed[501141]\n# [1484611992] time[ 0.01] step[ 460] speed[351428]\n# [1484611992] time[ 0.00] step[ 480] speed[450032]\n# [1484611993] time[ 0.14] step[ 500] speed[ 14419]\n# [1484611993] time[ 0.15] step[ 520] speed[ 13662]\n# [1484611993] time[ 0.14] step[ 540] speed[ 13960]\n# [1484611993] time[ 0.15] step[ 560] speed[ 13069]\n\nimport tensorflow as tf\nimport time\n\n\nsteps_to_validate = 200\nepoch_number = 2\nthread_number = 2\nbatch_size = 100\n\ncapacity = 2*10**6\n# don't use too high of limit, 10**9 hangs (overflows to negative in TF?)\na_queue = tf.train.range_input_producer(limit=10**3, capacity=capacity)\n\n# use size of 2 or get TypeError: 'Tensor' object is not iterable.\n# (possibly singleton list get auto-packed into a single Tensor)\n[b, _] = tf.train.batch([a_queue.dequeue()]*2, batch_size=100,\n capacity=capacity)\n\n\nconfig = tf.ConfigProto(log_device_placement=True)\nconfig.operation_timeout_in_ms=5000 # terminate on long hangs\nsess = tf.InteractiveSession(\"\", config=config)\n\ntf.train.start_queue_runners()\n\ndef let_queue_repopulate(size_tensor, min_elements=100000, sleep_delay=0.5):\n \"\"\"Wait until queue has enough elements.\"\"\"\n size2 = \"input_producer/fraction_of_2000000_full/fraction_of_2000000_full_Size:0\"\n while sess.run(size_tensor) < min_elements:\n print(\"Size1: %d, size2: %d\" %tuple(sess.run([size_tensor, size2])))\n time.sleep(sleep_delay)\n\nstep = 0\nstart_time = time.time()\nwhile True:\n step+=1\n let_queue_repopulate(size_tensor=\"batch/fifo_queue_Size:0\")\n sess.run(b.op)\n if step % steps_to_validate == 0:\n end_time = time.time()\n sec = (end_time - start_time)\n print(\"[{}] time[{:6.2f}] step[{:10d}] speed[{:6d}]\".format(\n str(end_time).split(\".\")[0],sec, step,\n int((steps_to_validate*batch_size)/sec)\n ))\n start_time = end_time\n" }, { "path": "ericyue-slowreader/benchmark-synthetic.py", "content": "# [1484615767] time[ 0.31] step[ 2000] speed[652222]\n# [1484615767] time[ 0.31] step[ 4000] speed[654197]\n# [1484615768] time[ 0.30] step[ 6000] speed[661347]\n# [1484615768] time[ 0.30] step[ 8000] speed[662600]\n#\n# with_dequeu_many = False\n# [1484614505] time[ 0.97] step[ 2000] speed[205131]\n# [1484614506] time[ 0.96] step[ 4000] speed[208224]\n# [1484614507] time[ 0.96] step[ 6000] speed[208984]\n# [1484614508] time[ 0.95] step[ 8000] speed[209907]\n\nimport tensorflow as tf\nimport time\n\n# try benchmarking\nsteps_to_validate = 2000\nepoch_number = 2\nthread_number = 2\nbatch_size = 100\nuse_dequeue_many = True\n\n\n# don't use too high of limit, 10**9 hangs (overflows to negative in TF?)\na_queue = tf.train.range_input_producer(limit=10**3, capacity=1000, shuffle=False)\n#a_queue = tf.train.string_input_producer([\"hello\"])\n\n\n# use an op that guarantees batch_size dequeues\nif use_dequeue_many:\n a_batch = a_queue.dequeue_many(n=batch_size)\n a_batch_op = a_batch.op\nelse:\n # otherwise just do batch_size dequeue ops\n a = a_queue.dequeue()\n a_batch = [a+i for i in range(batch_size)]\n a_batch_op = tf.group(*a_batch)\n\nconfig = tf.ConfigProto(log_device_placement=False)\nconfig.operation_timeout_in_ms=5000 # terminate on long hangs\nsess = tf.InteractiveSession(\"\", config=config)\n\ntf.train.start_queue_runners()\n\nstep = 0\nstart_time = time.time()\nwhile True:\n step+=1\n sess.run(a_batch_op)\n if step % steps_to_validate == 0:\n end_time = time.time()\n sec = (end_time - start_time)\n print(\"[{}] time[{:6.2f}] step[{:10d}] speed[{:6d}]\".format(\n str(end_time).split(\".\")[0],sec, step,\n int((steps_to_validate*batch_size)/sec)\n ))\n start_time = end_time\n" }, { "path": "ericyue-slowreader/benchmark.py", "content": "# On 32-core machine.\n\n# [2017-01-17 10:45:35] time[ 1.84] step[ 200] speed[ 10887]\n# [2017-01-17 10:45:37] time[ 2.02] step[ 400] speed[ 9896]\n# [2017-01-17 10:45:39] time[ 1.93] step[ 600] speed[ 10362]\n# [2017-01-17 10:45:41] time[ 2.05] step[ 800] speed[ 9737]\n# [2017-01-17 10:45:43] time[ 1.94] step[ 1000] speed[ 10322]\n# [2017-01-17 10:45:45] time[ 1.84] step[ 1200] speed[ 10883]\n#\n# after turning on enqueue_many=True\n# [2017-01-17 10:45:15] time[ 0.28] step[ 200] speed[ 71383]\n# [2017-01-17 10:45:15] time[ 0.16] step[ 400] speed[122375]\n# [2017-01-17 10:45:15] time[ 0.17] step[ 600] speed[119758]\n# [2017-01-17 10:45:16] time[ 0.18] step[ 800] speed[108752]\n# [2017-01-17 10:45:16] time[ 0.18] step[ 1000] speed[113386]\n# [2017-01-17 10:45:16] time[ 0.17] step[ 1200] speed[118286]\n\nimport datetime\nimport pytz\nfrom tensorflow.contrib.session_bundle import exporter\nfrom tensorflow.python.client import timeline\nimport glob\nimport json\nimport time\nimport math\nimport numpy as np\nimport os\nimport tensorflow as tf\n\nsteps_to_validate = 200\nepoch_number = 2\nthread_number = 2\nbatch_size = 100\nmin_after_dequeue = 1000\ncapacity = thread_number * batch_size + min_after_dequeue\nenqueue_many = False\nenqueue_many_size = 1000\n\n# on macos, doing more than 1k threads fails with\n# libc++abi.dylib: libc++abi.dylib: terminating with uncaught exception of type std::__1::system_error: thread constructor failed: Resource temporarily unavailableterminating with uncaught exception of type std::__1::system_error: thread constructor failed: Resource temporarily unavailable\n\n\nfilename_queue = tf.train.string_input_producer(\n [\"./data.zlib\"],\n shuffle=True,\n seed = int(time.time()),\n num_epochs=epoch_number)\n\ndef read_and_decode(filename_queue):\n reader = tf.TFRecordReader(options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB))\n _, serialized_example = reader.read(filename_queue)\n return serialized_example\n\nif enqueue_many:\n reader = tf.TFRecordReader(options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB))\n queue_batch = []\n for i in range(enqueue_many_size):\n _, serialized_example = reader.read(filename_queue)\n queue_batch.append(serialized_example)\n batch_serialized_example = tf.train.shuffle_batch(\n [queue_batch],\n batch_size=batch_size,\n num_threads=thread_number,\n capacity=capacity,\n min_after_dequeue=min_after_dequeue,\n enqueue_many=True)\n \nelse:\n serialized_example = read_and_decode(filename_queue)\n batch_serialized_example = tf.train.shuffle_batch(\n [serialized_example],\n batch_size=batch_size,\n num_threads=thread_number,\n capacity=capacity,\n min_after_dequeue=min_after_dequeue)\nfeatures = tf.parse_example(\n batch_serialized_example,\n features={\n \"label\": tf.FixedLenFeature([], tf.float32),\n \"ids\": tf.VarLenFeature(tf.int64),\n \"values\": tf.VarLenFeature(tf.float32),\n })\n\nbatch_labels = features[\"label\"]\nbatch_ids = features[\"ids\"]\nbatch_values = features[\"values\"]\n\ninit_op = tf.global_variables_initializer()\n\nsess = tf.Session()\nrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\nrun_metadata = tf.RunMetadata()\nsess.run(init_op,options=run_options, run_metadata=run_metadata)\nsess.run(tf.local_variables_initializer(),options=run_options, run_metadata=run_metadata)\n\ncoord = tf.train.Coordinator()\nthreads = tf.train.start_queue_runners(coord=coord, sess=sess)\n#time.sleep(10)\n\nstart_time = datetime.datetime.now(pytz.timezone('Asia/Shanghai'))\nstep = 1\ntry:\n while not coord.should_stop():\n f1,f2,f3 = sess.run([batch_ids,batch_values,batch_labels])\n step +=1\n if step % steps_to_validate == 0:\n end_time = datetime.datetime.now(pytz.timezone('Asia/Shanghai'))\n sec = (end_time - start_time).total_seconds()\n print(\"[{}] time[{:6.2f}] step[{:10d}] speed[{:6d}]\".format(\n str(end_time).split(\".\")[0],sec, step,\n int((steps_to_validate*batch_size)/sec)\n ))\n start_time = end_time\n if step > 10000:\n break\n\n\nexcept tf.errors.OutOfRangeError:\n print(\"Done training after reading all data\")\nfinally:\n coord.request_stop()\n print(\"coord stopped\")\n\ncoord.join(threads)\n\ntl = timeline.Timeline(run_metadata.step_stats)\nctf = tl.generate_chrome_trace_format()\nwith open('timeline.json', 'w') as f:\n f.write(ctf)\nprint(\"all done\")\n" }, { "path": "ericyue-slowreader/profile-batch.py", "content": "# script for getting cpu profile of queue runners\n# \n# sudo apt-get install google-perftools\n# LD_PRELOAD has to be set in a forked script, otherwise shell will\n# overwrite the profile file\n\nimport os, sys, subprocess\n\nmy_env = os.environ.copy()\nmy_env[\"LD_PRELOAD\"]=\"/usr/lib/libtcmalloc_and_profiler.so.4\"\nmy_env[\"CPUPROFILE\"]=\"/tmp/profile-yue/profile\"\n\nargs = [\"python\", \"benchmark-batch-noqueuerunners.py\"]\nproc = subprocess.Popen(args, stderr=subprocess.STDOUT, env=my_env)\nprint(\"Done\")\n\n" }, { "path": "free_gpus.py", "content": "#!/usr/bin/env python\n# Parse nvidia-smi for pids and kill all GPU users\n# Tested on nvidia-smi 370.23\nimport os, re, sys, subprocess\nimport pwd\n\nfrom collections import defaultdict\n\ndef tokenize(cmd):\n if isinstance(cmd, list):\n return cmd\n if isinstance(cmd, bytes):\n cmd = cmd.decode(\"ascii\")\n if isinstance(cmd, str):\n cmd = cmd.split(None)\n return cmd\n\n\ndef run_command(cmd):\n \"\"\"Run command, return output as string.\"\"\"\n\n output = subprocess.Popen(cmd, stdout=subprocess.PIPE,\n shell=True).communicate()[0]\n return output.decode(\"ascii\")\n\n\ndef run_shell(cmd):\n \"\"\"Runs shell command, returns list of outputted lines\n with newlines stripped.\"\"\"\n\n cmd = tokenize(cmd)\n p = subprocess.Popen(cmd, stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n (stdout, stderr) = p.communicate()\n stdout = stdout.decode(\"ascii\") # turn into string to make Python3 happy\n lines = stdout.split('\\n')\n stripped_lines = []\n for l in lines:\n stripped_line = l.strip()\n if l:\n stripped_lines.append(stripped_line)\n return stripped_lines\n\n\ndef run_shell_background(cmd_orig):\n \"\"\"Runs shell command in background, returns pid.\"\"\"\n\n cmd = tokenize(cmd_orig)\n p = subprocess.Popen(cmd, close_fds=True)\n print(\"[%d] %s \" % (p.pid, cmd_orig))\n\n\ndef get_pid_gpu_map():\n \"\"\"Returns map of GPU id to memory allocated on that GPU.\"\"\"\n\n output = run_command(\"nvidia-smi\")\n gpu_output = output[output.find(\"GPU Memory\"):]\n # lines of the form\n # | 0 8734 C python 11705MiB |\n regex = re.compile(r\"[|]\\s+?(?P\\d+)\\D+?(?P\\d+).+[ ]\"\n \"(?P\\d+)MiB\")\n rows = gpu_output.split(\"\\n\")\n pids = []\n pid_gpu_map = defaultdict(list)\n for row in gpu_output.split(\"\\n\"):\n m = regex.search(row)\n if not m:\n continue\n pid = int(m.group(\"pid\"))\n gpu_id = int(m.group(\"gpu_id\"))\n print(\"pid %s using gpu %s\"%(pid, gpu_id))\n pid_gpu_map[pid].append(gpu_id)\n return pid_gpu_map\n\ndef kill_pids(pids_to_kill):\n pids = []\n for pid_to_kill in pids_to_kill:\n pid = run_shell_background(\"sudo kill -9 \"+str(pid_to_kill))\n pids.append(pid)\n return pids\n\n\ndef owner(pid):\n '''Return username of UID of process pid'''\n UID = 1\n EUID = 2\n for ln in open('/proc/%d/status' % pid):\n if ln.startswith('Uid:'):\n uid = int(ln.split()[UID])\n return pwd.getpwuid(uid).pw_name\n \nif __name__ == '__main__':\n pid_gpu_map = get_pid_gpu_map()\n print(\"%10s %10s %s\" %(\"pid\", \"username\", \"gpu\"))\n for pid in pid_gpu_map:\n print(\"%10s %10s %s\" %(pid, owner(pid), pid_gpu_map[pid]))\n answer = input(\"kill these? (Y/n) \")\n if not answer:\n answer = \"y\"\n if answer.lower() == \"y\":\n pids = kill_pids(pid_gpu_map.keys())\nelse:\n print(\"Didn't get y, doing nothing\")\n" }, { "path": "github_pyfunc_slowness.py", "content": "# Example of py_func slowing down future computations\n# On Mac\n# time 1 0.007195033016614616\n# time 2 0.0070790809113532305\n# time 3 0.008019614033401012\n#\n# On Xeon V3:\n# time 1 0.011401358991861343\n# time 2 0.011637557297945023\n# time 3 0.012380894273519516\n#\n# On Mac without MKL installed:\n# time 1 0.011707969009876251\n# time 2 0.011970046092756093\n# time 3 0.011933871079236269\n\nimport numpy as np\nimport scipy\nimport scipy.linalg\nimport tensorflow as tf\nimport timeit\nsess = tf.Session()\na = np.random.random((300, 300))\na = a.dot(a.T)\nbest_time = np.inf\nfor i in range(10):\n s = timeit.default_timer()\n scipy.linalg.eigh(a)\n e = timeit.default_timer()\n if e - s < best_time:\n best_time = e - s\nprint(\"time 1\", best_time)\n \nnp.linalg.svd(np.random.randn(2, 300))\n \nbest_time = np.inf\nfor i in range(10):\n s = timeit.default_timer()\n scipy.linalg.eigh(a)\n e = timeit.default_timer()\n if e - s < best_time:\n best_time = e - s\nprint(\"time 2\", best_time)\n\nret = tf.py_func(np.linalg.svd, [np.random.randn(2, 300)], [tf.float64, tf.float64, tf.float64])\nsess.run(ret)\n\nbest_time = np.inf\nfor i in range(10):\n s = timeit.default_timer()\n scipy.linalg.eigh(a)\n e = timeit.default_timer()\n if e - s < best_time:\n best_time = e - s\nprint(\"time 3\", best_time)\n" }, { "path": "gpu-memory-transfer.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[name: \\\"/cpu:0\\\"\\n\",\n \"device_type: \\\"CPU\\\"\\n\",\n \"memory_limit: 268435456\\n\",\n \"locality {\\n\",\n \"}\\n\",\n \"incarnation: 12472952657034743275\\n\",\n \", name: \\\"/gpu:0\\\"\\n\",\n \"device_type: \\\"GPU\\\"\\n\",\n \"memory_limit: 11992055808\\n\",\n \"locality {\\n\",\n \" bus_id: 1\\n\",\n \"}\\n\",\n \"incarnation: 11654170773547241061\\n\",\n \"physical_device_desc: \\\"device: 0, name: TITAN X (Pascal), pci bus id: 0000:07:00.0\\\"\\n\",\n \"]\\n\",\n \"0.12.head\\n\",\n \"0.12.1-1934-g27fca7d-dirty\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import time\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]=\\\"3\\\"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from tensorflow.contrib import graph_editor as ge\\n\",\n \"from tensorflow.python.client import device_lib\\n\",\n \"print(device_lib.list_local_devices())\\n\",\n \"print(tf.__version__)\\n\",\n \"print(tf.__git_version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Benchmark transfering data from CPU to GPU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Transfer rate gpu->cpu = 3.38 GB/sec, 2.96 ms/call\\n\",\n \"Transfer rate cpu->gpu = 3.31 GB/sec, 3.02 ms/call\\n\"\n ]\n }\n ],\n \"source\": [\n \"size_mbs = 10\\n\",\n \"n = 250*1000*size_mbs # n of float32 entries\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" gpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \"with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" cpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \"\\n\",\n \"gpu_to_cpu = cpu_var.assign(gpu_var)\\n\",\n \"cpu_to_gpu = gpu_var.assign(cpu_var)\\n\",\n \"\\n\",\n \"# turn off graph-rewriting\\n\",\n \"config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \"sess = tf.Session(config=config)\\n\",\n \"\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"def benchmark(op, iters):\\n\",\n \" start_time = time.time()\\n\",\n \" for i in range(iters):\\n\",\n \" sess.run(gpu_to_cpu.op)\\n\",\n \" end_time = time.time()\\n\",\n \" return float(end_time - start_time)\\n\",\n \"\\n\",\n \"iters = 1000\\n\",\n \"gpu_to_cpu_time = benchmark(gpu_to_cpu, iters)\\n\",\n \"cpu_to_gpu_time = benchmark(cpu_to_gpu, iters)\\n\",\n \"data_volume_mbs = iters*size_mbs\\n\",\n \"\\n\",\n \"transfer_rate_mbs = data_volume_mbs/gpu_to_cpu_time\\n\",\n \"print(\\\"Transfer rate gpu->cpu = %.2f GB/sec, %.2f ms/call\\\" %(transfer_rate_mbs/1000,1000*gpu_to_cpu_time/iters))\\n\",\n \"\\n\",\n \"transfer_rate_mbs = data_volume_mbs/cpu_to_gpu_time\\n\",\n \"print(\\\"Transfer rate cpu->gpu = %.2f GB/sec, %.2f ms/call\\\" %(transfer_rate_mbs/1000,1000*cpu_to_gpu_time/iters))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Fair benchmark transfering data from CPU to GPU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Transfer rate gpu->cpu = 3.27 GB/sec, 3.06 ms/call\\n\",\n \"Transfer rate cpu->gpu = 3.35 GB/sec, 2.98 ms/call\\n\"\n ]\n }\n ],\n \"source\": [\n \"size_mbs = 10\\n\",\n \"n = 250*1000*size_mbs # n of float32 entries\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" gpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \" gpu_var0 = tf.Variable(tf.random_uniform((n,)))\\n\",\n \"with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" cpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \"\\n\",\n \"cpu_to_gpu = gpu_var.assign(cpu_var)\\n\",\n \"gpu_to_gpu = gpu_var.assign(gpu_var0)\\n\",\n \"\\n\",\n \"# turn off graph-rewriting\\n\",\n \"config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \"sess = tf.Session(config=config)\\n\",\n \"\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"def benchmark(op, iters):\\n\",\n \" start_time = time.time()\\n\",\n \" for i in range(iters):\\n\",\n \" sess.run(gpu_to_cpu.op)\\n\",\n \" end_time = time.time()\\n\",\n \" return float(end_time - start_time)\\n\",\n \"\\n\",\n \"iters = 1000\\n\",\n \"gpu_to_cpu_time = benchmark(gpu_to_gpu, iters)\\n\",\n \"cpu_to_gpu_time = benchmark(cpu_to_gpu, iters)\\n\",\n \"data_volume_mbs = iters*size_mbs\\n\",\n \"\\n\",\n \"transfer_rate_mbs = data_volume_mbs/gpu_to_cpu_time\\n\",\n \"print(\\\"Transfer rate gpu->cpu = %.2f GB/sec, %.2f ms/call\\\" %(transfer_rate_mbs/1000,1000*gpu_to_cpu_time/iters))\\n\",\n \"\\n\",\n \"transfer_rate_mbs = data_volume_mbs/cpu_to_gpu_time\\n\",\n \"print(\\\"Transfer rate cpu->gpu = %.2f GB/sec, %.2f ms/call\\\" %(transfer_rate_mbs/1000,1000*cpu_to_gpu_time/iters))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"35-27\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Find cross-over point for mul\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def benchmark_mul_gpu(size_mbs, iters=10):\\n\",\n \" n = 250*1000*size_mbs # n of float32 entries\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"Find time needed to multiply two vectors on GPU.\\\"\\\"\\\"\\n\",\n \" with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" var0 = tf.Variable(tf.random_uniform((n,)))\\n\",\n \" var1 = tf.Variable(tf.random_uniform((n,)))\\n\",\n \" op = tf.multiply(var0, var1)\\n\",\n \" config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" sess = tf.Session(config=config)\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" for i in range(iters):\\n\",\n \" if i == 1: # skip 0 as the warm-up iteration\\n\",\n \" start_time = time.time()\\n\",\n \" sess.run(op.op)\\n\",\n \" end_time = time.time()\\n\",\n \" return (end_time - start_time)/(iters-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mbs = range(10, 100, 10)\\n\",\n \"times_mul = [benchmark_mul_gpu(m) for m in mbs]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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SvHZgZJoVcXBmZ00Bu5NBolCpGIlJZXUli093AHc1VS2HOgDAAzyEzv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\\nMD6sAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(mbs, times_mul)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def benchmark_transfer_gpu(size_mbs, iters=10):\\n\",\n \" n = 250*1000*size_mbs # n of float32 entries\\n\",\n \" with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" gpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \" gpu_var0 = tf.Variable(tf.random_uniform((n,)))\\n\",\n \" with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" cpu_var = tf.Variable(tf.random_uniform((n,)))\\n\",\n \"\\n\",\n \" cpu_to_gpu = gpu_var.assign(cpu_var)\\n\",\n \" gpu_to_gpu = gpu_var.assign(gpu_var0)\\n\",\n \"\\n\",\n \" # turn off graph-rewriting\\n\",\n \" config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" sess = tf.Session(config=config)\\n\",\n \"\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" def benchmark(op, iters):\\n\",\n \" for i in range(iters):\\n\",\n \" if i == 1: # skip 0 as the warm-up iteration\\n\",\n \" start_time = time.time()\\n\",\n \" sess.run(op)\\n\",\n \" end_time = time.time()\\n\",\n \" return float(end_time - start_time)/(iters-1)\\n\",\n \"\\n\",\n \" gpu_to_gpu_time = benchmark(gpu_to_gpu.op, iters)\\n\",\n \" cpu_to_gpu_time = benchmark(cpu_to_gpu.op, iters)\\n\",\n \" overhead = cpu_to_gpu_time - gpu_to_gpu_time\\n\",\n \" return overhead\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mbs = range(10, 100, 10)\\n\",\n \"times_transfer = [benchmark_transfer_gpu(m) for m in mbs]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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M9Cv8PijkYyTAlBRAL3aOKbvnD6jXFHIzFIKyGY2TgzW2ZmlWa22y/F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\\nRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(mbs, times_mul, mbs, times_transfer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Find cross-over point for concat\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def benchmark_concat_gpu(size_mbs, iters=10):\\n\",\n \" n = 250*1000*size_mbs # n of float32 entries\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"Find time needed to multiply two vectors on GPU.\\\"\\\"\\\"\\n\",\n \" with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" var0 = tf.Variable(tf.random_uniform((n//2,)))\\n\",\n \" var1 = tf.Variable(tf.random_uniform(((n+1)//2,)))\\n\",\n \" op = tf.concat_v2(values=[var0, var1], axis=0)\\n\",\n \" config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" sess = tf.Session(config=config)\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" for i in range(iters):\\n\",\n \" if i == 1: # skip 0 as the warm-up iteration\\n\",\n \" start_time = time.time()\\n\",\n \" sess.run(op.op)\\n\",\n \" end_time = time.time()\\n\",\n \" return (end_time - start_time)/(iters-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mbs = range(10, 100, 10)\\n\",\n \"times_concat = [benchmark_concat_gpu(m) for m in mbs]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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6/CvKfgqCug64C4q5E0UyCISFA98U3XAXDkZXFXIzFIKRDMbIyZLTSz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\\nEEREBID/D4Yvk5eCajr1AAAAAElFTkSuQmCC\\n\",\n 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Dk5+fDRwvG2b0xtMnfTXMZnjqdvi7482OfBsPri2clIjknm9h6389C8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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(mbs, times_concat, label=\\\"concat\\\")\\n\",\n \"plt.plot(mbs, times_mul, label=\\\"mul\\\")\\n\",\n \"plt.plot(mbs, times_transfer, label=\\\"transfer\\\")\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"30px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "gpu_oom.py", "content": "# Example of catching GPU OOM error\n# http://stackoverflow.com/questions/41942538/tensorflow-gpu-memory-error-try-except-not-catching-the-error\n\nimport tensorflow as tf\n\ntry:\n with tf.device(\"gpu:0\"):\n a = tf.Variable(tf.ones((10000, 10000)))\n sess = tf.Session()\n sess.run(tf.initialize_all_variables())\nexcept:\n print(\"Caught error\")\n import pdb; pdb.set_trace()\n" }, { "path": "graph_template.py", "content": "\"\"\"Helpers to replicate computation specified as part of existing graph.\"\"\"\n\n\n# helper to allow @profile decorators even when no line profiler\nimport builtins\ntry:\n builtins.profile\nexcept AttributeError:\n def profile(func): return func\n builtins.profile = profile\n\nfrom collections import OrderedDict\nfrom pprint import pprint\nfrom tensorflow.core.framework import attr_value_pb2\nfrom tensorflow.python.framework import function\nfrom tensorflow.python.ops import gen_math_ops\nimport inspect\nimport numpy as np\nimport contextlib\nimport os, sys\nimport tensorflow as tf\nimport tensorflow.contrib.graph_editor as ge\nimport toposort\nimport whitening_util as u\n\nclass GraphTemplate:\n \"\"\"Represents TensorFlow Graph that can be applied to new outputs.\n \n Example:\n\n Define some computation with a input, b output, apply it to new input (c)\n a = tf.ones((2,))\n b = tf.square(a)\n t = GraphTemplate([a], [b])\n c = tf.zeros((2,))\n \n [d] = t.apply([c])\n\n Restrictions on computation used to create template:\n - no control flow\n - no variables\n - no colocation constraints to nodes outside of template\n - do not span device boundaries (not enforced)\n\n Also note that:\n surrounding name_scope is ignored when instantiating template\n \"\"\"\n \n def __init__(self, inputs, outputs, within_ops=None):\n assert isinstance(inputs, (list, tuple))\n assert isinstance(outputs, (list, tuple))\n\n g = tf.get_default_graph()\n self.g = g\n\n # special handling for Variable objects because graph_editor only\n # supports Tensor objects. Instead of replacing Variable with new inputs\n # replace all of its read endpoints.\n # \n # Find all Identity ops connected to variable op, replace them all in\n # tandem on apply.\n # [tensor, tensor, variable] becomes\n # [tensor, tensor, (var_read1, var_read2)]\n\n new_inputs = []\n for input in inputs:\n if isinstance(input, tf.Variable):\n # find all the read endpoints of the variable\n read_tensors = []\n for consumer in input.op.outputs[0].consumers():\n if consumer.type == 'Identity':\n read_tensors.append(consumer.outputs[0])\n new_inputs.append(read_tensors)\n else:\n new_inputs.append(input)\n inputs = new_inputs\n \n self.inputs = inputs\n self.input_ops = [tensor.op for tensor in flatten1(inputs)]\n self.outputs = outputs\n\n # only support 1 output for now, need extra logic in apply\n for output_ts in outputs:\n num_siblings = len(output_ts.op.outputs)-1\n assert num_siblings == 0\n\n # obtain part of graph that's needed\n output_ops = [ts.op for ts in self.outputs]\n\n self.ops = ge.get_backward_walk_ops(output_ops,\n inclusive=True,\n stop_at_ts=flatten1(self.inputs),\n within_ops=within_ops)\n\n # workaround for https://github.com/tensorflow/tensorflow/issues/9978\n clear_original_ops(self.ops)\n\n @profile\n def apply(self, new_inputs, update_colocation_groups=True):\n assert len(new_inputs) == len(self.inputs)\n g = tf.get_default_graph() # todo: make that member variable\n\n new_inputs2 = []\n # replace variable inputs with their read endpoint\n for input in new_inputs:\n if isinstance(input, tf.Variable):\n new_inputs2.append(input.read_value())\n else:\n new_inputs2.append(input)\n new_inputs = new_inputs2\n \n replacements = OrderedDict()\n for old_input_ts, new_input_ts in zip(self.inputs, new_inputs):\n # shape/dtype checks\n if isinstance(old_input_ts, (list, tuple)):\n reference_ts = old_input_ts[0]\n else:\n reference_ts = old_input_ts\n assert reference_ts.get_shape() == new_input_ts.get_shape()\n assert reference_ts.dtype == new_input_ts.dtype\n\n # Variable with multiple read endpoints, replace all of them with\n # new input tensor\n if isinstance(old_input_ts, (list, tuple)):\n for sub_input in old_input_ts:\n replacements[sub_input] = new_input_ts\n # regular Tensor\n else:\n replacements[old_input_ts] = new_input_ts\n\n\n # sanity checks\n # copying Variables is confusing because they don't get added\n # to GLOBAL_VARIABLES collection hence escape global initialization\n # therefore forbit it\n for op in self.ops:\n if op.type.startswith('Variable'): # 'VariableV2' or 'Variable'\n assert False, \"Can't copy variables\"\n\n\n # TODO: remove this\n def summarize_ts(ts):\n from collections import Counter\n type_counter = Counter()\n ops = set([tensor.op for tensor in ts])\n print Counter([op.type for op in ops]).most_common(10)\n\n\n\n \n sgv = ge.sgv(self.ops)\n # import pdb; pdb.set_trace()\n copied_sgv, info = ge.copy_with_input_replacements(sgv,\n replacements)\n\n\n # converting between Python bytes and unicode\n def to_bytes(s): return s.encode('ascii')\n def from_bytes(s): return s.decode('ascii')\n\n # fix colocation constraints to point to copied ops\n # see https://github.com/tensorflow/tensorflow/issues/9925\n if update_colocation_groups:\n new_ops = [info._transformed_ops[op] for op in self.ops]\n for new_op in new_ops:\n assert len(new_op.colocation_groups()) == 1\n colocation_group = new_op.colocation_groups()[0]\n assert colocation_group.startswith(b'loc:@')\n colocated_with_name = from_bytes(colocation_group[len(b'loc:@'):])\n\n # if there were no colocation constraints, the op gets colocated with\n # itself (default colocation group), ignore that constraint\n if colocated_with_name == new_op.name:\n continue\n\n colocation_op = g.get_operation_by_name(colocated_with_name)\n if colocation_op in info._transformed_ops:\n new_colocation_op = info._transformed_ops[colocation_op]\n else:\n assert colocation_op in self.input_ops\n colocation_op_idx = self.input_ops.index(colocation_op)\n new_colocation_op = new_inputs[colocation_op_idx].op\n\n # overwrite existing _class attribute with new colocation constraints\n new_colocation_groups = [b'loc:@'+to_bytes(new_colocation_op.name)]\n new_op.node_def.attr[\"_class\"].CopyFrom(attr_value_pb2.AttrValue(\n list=attr_value_pb2.AttrValue.ListValue(s=new_colocation_groups)))\n \n # place new ops on device from current device context\n device = get_current_device()\n if device:\n for op in info._transformed_ops.values():\n op._set_device(device)\n \n new_outputs = []\n for old_output_ts in self.outputs:\n new_output_op = info._transformed_ops[old_output_ts.op]\n new_output_ts = new_output_op.outputs[0]\n new_outputs.append(new_output_ts)\n \n return new_outputs\n\n\ndef clear_original_ops(ops):\n for op in ops:\n op._original_op = None\n\n \ndef tf_ops_to_graph(ops):\n \"\"\"Creates op->children dictionary from list of TensorFlow ops.\"\"\"\n\n def flatten(l): return [item for sublist in l for item in sublist]\n def children(op): return flatten(tensor.consumers() for tensor in op.outputs)\n return {op: set(children(op)) for op in ops}\n\n\ndef ops_in_toposorted_order(ops):\n \"\"\"Produces ops in deterministic order such that children are executed\n after parents\"\"\"\n\n graph_dict = tf_ops_to_graph(ops)\n toposorted = toposort.toposort(graph_dict)\n ops = []\n # toposort assumes children are dependencies, reverse order\n for op_set in reversed(list(toposorted)):\n ops.extend(sorted(op_set, key=lambda op: op.name))\n return ops\n\nclass _DeviceCaptureOp(object):\n def __init__(self):\n self.device = None\n def _set_device(self, device):\n self.device = device\n\ndef get_current_device():\n \"\"\"Returns device string of current graph context.\"\"\"\n \n g = tf.get_default_graph()\n op = _DeviceCaptureOp()\n g._apply_device_functions(op)\n return op.device\n\ndef flatten1(list_of_lists):\n \"\"\"Flattens list going down at most 1 level.\"\"\"\n \n new_list = []\n for l in list_of_lists:\n if isinstance(l, (list, tuple)):\n new_list.extend(l)\n else:\n new_list.append(l)\n\n return new_list\n\n\ndef count_gpus():\n from tensorflow.python.client import device_lib\n count = 0\n for device in device_lib.list_local_devices():\n if device.device_type == \"GPU\":\n count+=1\n return count\n\ndef current_function_name():\n import inspect\n return inspect.stack()[1][0].f_code.co_name\n\n\n\ndef check_equal(a, b):\n a = np.asarray(a)\n b = np.asarray(b)\n np.testing.assert_allclose(a, b)\n\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Captures any ops added to the default graph within this block.\"\"\"\n #outer_graph = tf.get_default_graph()\n from tensorflow.python.framework import ops\n old_create_op = ops.Graph.create_op\n op_list = []\n def new_create_op(graph_object, op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True):\n # todo: remove keyword args\n op = old_create_op(graph_object, op_type=op_type, inputs=inputs, dtypes=dtypes, input_types=input_types, name=name, attrs=attrs, op_def=op_def, compute_shapes=compute_shapes, compute_device=compute_device)\n op_list.append(op)\n return op\n ops.Graph.create_op = new_create_op\n yield op_list\n\ndef capture_ops_test():\n a = tf.ones((), name=\"a\")\n with capture_ops() as captured:\n b = tf.ones((), name=\"b\")\n assert [op.name for op in captured] == [\"b\"]\n\ndef graph_template_test():\n tf.reset_default_graph()\n a = tf.ones((2,))\n b = tf.square(a)\n\n t = GraphTemplate([a], [b])\n c = tf.zeros((2,))\n \n [d] = t.apply([c])\n sess = tf.InteractiveSession()\n np.testing.assert_equal(sess.run(d), [0, 0])\n\n\ndef graph_devices_test():\n tf.reset_default_graph()\n with tf.device(\"/cpu:0\"):\n a = tf.ones((2,))\n b = tf.square(a)\n\n t = GraphTemplate([a], [b])\n \n with tf.device(\"/cpu:1\"):\n c = tf.zeros((2,))\n [d] = t.apply([c])\n graph_def_str = str(tf.get_default_graph().as_graph_def())\n assert 'cpu:1' in graph_def_str.lower()\n\n\ndef variables_test():\n a = tf.Variable(1)\n b = tf.square(a)\n c = tf.Variable(2)\n t = GraphTemplate([a], [b])\n [d] = t.apply([c])\n sess = tf.InteractiveSession()\n sess.run(tf.global_variables_initializer())\n assert d.eval() == 4\n\ndef multi_variables_test():\n tf.reset_default_graph()\n a = tf.Variable(1)\n b = a.read_value()\n c = a.read_value()\n d = b+c\n t = GraphTemplate([a], [d])\n e = tf.Variable(2)\n [f] = t.apply([e])\n sess = tf.InteractiveSession()\n sess.run(tf.global_variables_initializer())\n assert d.eval() == 2\n assert f.eval() == 4\n\n\ndef colocate_test():\n g = tf.get_default_graph()\n with tf.device('/cpu:0'):\n a = tf.ones((), name='a')\n with tf.get_default_graph().colocate_with(a):\n b = tf.add(a, 1, name='b')\n t = GraphTemplate([a], [b])\n [b2] = t.apply([tf.zeros(())])\n sess = tf.InteractiveSession()\n sess.run(tf.global_variables_initializer())\n assert b2.eval() == 1\n\n # make sure colocation is with new op (named zeros)\n assert b2.op.colocation_groups()[0] == b'loc:@zeros'\n\n\ndef between_devices_copy_test():\n g = tf.get_default_graph()\n config = tf.ConfigProto(device_count={\"CPU\": 2},\n inter_op_parallelism_threads=2,\n intra_op_parallelism_threads=1)\n sess = tf.InteractiveSession(config=config)\n\n with tf.device('/cpu:0'):\n a1 = tf.ones(())\n with g.colocate_with(a1):\n b1 = tf.square(a1)\n\n t = GraphTemplate([a1], [b1])\n\n with tf.device('/cpu:1'):\n a2 = tf.zeros(())\n [b2] = t.apply([a2])\n \n assert b2.device.lower().endswith('cpu:1')\n\n\ndef optimization_test():\n from tensorflow.python.ops import gen_math_ops\n def fast_sum(tensor, name=None):\n return gen_math_ops._sum(input=tensor,\n reduction_indices=[],\n keep_dims=False,\n name=name)\n tf.reset_default_graph()\n g = tf.get_default_graph()\n config = tf.ConfigProto(device_count={\"CPU\": 2},\n inter_op_parallelism_threads=2,\n intra_op_parallelism_threads=1)\n sess = tf.InteractiveSession(config=config)\n params1 = tf.Variable(1, dtype=np.float32, name=\"params\")\n\n temp = fast_sum(params1, name=\"sum_temp\")\n cost1 = tf.square(temp, name=\"cost1\")\n gradients1 = tf.gradients([cost1], [params1])\n \n templ = GraphTemplate([params1, cost1], gradients1)\n \n\n params2 = tf.Variable(1, dtype=np.float32, name=\"params2\")\n cost2 = tf.square(fast_sum(params2))\n gradients2 = templ.apply([params2, cost2])\n train_op2 = params2.assign_sub(0.5*gradients2[0])\n\n sess.run(tf.global_variables_initializer())\n\n sess.run(train_op2)\n (cost1_, cost2_, params1_, params2_) = sess.run([cost1, cost2, params1,\n params2])\n assert cost1_ == 1.0\n assert cost2_ == 0.0\n assert params1_ == 1.0\n assert params2_ == 0.0\n\n\ndef multidevice_shared_params_test():\n if count_gpus() < 2:\n print(\"Not enough GPUs, skipping %s\"%(current_function_name()))\n return\n \n inputs = []\n params = tf.Variable(1, dtype=np.float32, name=\"params\")\n costs = []\n grads = []\n for i in range(2):\n with tf.device(\"/gpu:%d\"%(i,)):\n if i == 0:\n x = tf.zeros((), dtype=np.float32)\n cost = tf.square(tf.reduce_sum(params-x), name=\"cost\")\n [gradient] = tf.gradients([cost], [params])\n templ = GraphTemplate([params, x, cost], [gradient])\n inputs.append(x)\n costs.append(cost)\n grads.append(gradient)\n else:\n x = tf.zeros((), dtype=np.float32)\n cost = tf.square(tf.reduce_sum(params-x), name=\"cost\")\n [grad] = templ.apply([params, x, cost])\n inputs.append(x)\n costs.append(cost)\n grads.append(grad)\n \n # make train op\n total_grad = tf.add_n(grads)\n train_op = params.assign_sub(0.25*total_grad)\n sess = tf.InteractiveSession()\n sess.run(tf.global_variables_initializer())\n sess.run(train_op)\n costs0 = np.asarray(sess.run(costs))\n assert np.linalg.norm(costs0-[0, 0])<1e-7\n\ndef multidevice_separate_params_test():\n if count_gpus() < 2:\n print(\"Not enough GPUs, skipping %s\"%(current_function_name()))\n return\n \n inputs = []\n params_list = []\n costs = []\n grads = []\n train_ops = []\n for i in range(2):\n with tf.device(\"/gpu:%d\"%(i,)):\n if i == 0:\n x = tf.zeros((), dtype=np.float32)\n params = tf.Variable(1, dtype=np.float32, name=\"params\")\n cost = tf.square(tf.reduce_sum(params-x), name=\"cost\")\n [grad] = tf.gradients([cost], [params])\n templ = GraphTemplate([params, x, cost], [grad])\n inputs.append(x)\n costs.append(cost)\n grads.append(grad)\n params_list.append(params)\n train_ops.append(params.assign_sub(0.5*grad))\n else:\n x = tf.zeros((), dtype=np.float32)\n params = tf.Variable(1, dtype=np.float32, name=\"params\")\n cost = tf.square(tf.reduce_sum(params-x), name=\"cost\")\n [grad] = templ.apply([params, x, cost])\n inputs.append(x)\n costs.append(cost)\n grads.append(grad)\n params_list.append(params)\n train_ops.append(params.assign_sub(0.5*grad))\n \n # make train op\n sess = tf.InteractiveSession()\n sess.run(tf.global_variables_initializer())\n\n check_equal(sess.run([costs, params_list, grads]),\n [[1.0, 1.0], [1.0, 1.0], [2.0, 2.0]])\n sess.run(train_ops[0])\n\n check_equal(sess.run([costs, params_list, grads]),\n [[0.0, 1.0], [0.0, 1.0], [0.0, 2.0]])\n\n sess.run(train_ops[1])\n\n check_equal(sess.run([costs, params_list, grads]),\n [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]])\n\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n tf.reset_default_graph()\n func()\n print(module.__name__+\" tests passed.\")\n\nif __name__=='__main__':\n # run_all_tests(sys.modules[__name__])\n capture_ops_test()\n" }, { "path": "imagenet15-scratch.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# ImageNet in 15 minutes notes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def downloadFile(URL=None):\\n\",\n \" import httplib2\\n\",\n \" h = httplib2.Http(\\\".cache\\\")\\n\",\n \" resp, content = h.request(URL, \\\"GET\\\")\\n\",\n \" return content\\n\",\n \"log_url=\\\"https://s3.amazonaws.com/yaroslavvb/logs/data/training/nv/2018-07-27_07-19-baseline-single-ami12-w8/full.log\\\"\\n\",\n \"log=downloadFile(log_url).decode('ascii')\\n\",\n \"import re\\n\",\n \"times_ms = [1000*float(x) for x in re.findall(\\\"Time ([.0-9]+)\\\", log)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Timing data for original run\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"python launch_nv.py --name $name --ami-name=\\\"$ami\\\" --zone=$zone --num-tasks 1 --attach-volume imagenet_high_perf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Pd/uvtz7n5OtPwDo5BxwAR3v93dnwJOICQsnwEws0HAScB4d5/u7nOA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\\n1qj9gMeAycB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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(times_ms)\\n\",\n \"plt.ylabel('ms')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"Batch-size 192, nvidia-smi around 85\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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2HT6etFWqvl4cTMTjSze81slZktN7Mrzezfita52Mz6im7XFq0z1szO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\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(times_ms[:100])\\n\",\n \"plt.ylabel('ms')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# scratch\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Get in/out bytes from /proc/net/dev\\n\",\n \"def network_bytes():\\n\",\n \" import subprocess\\n\",\n \" proc = subprocess.Popen(['cat', '/proc/net/dev'], stdout=subprocess.PIPE)\\n\",\n \" stdout,stderr = proc.communicate()\\n\",\n \" stdout=stdout.decode('ascii')\\n\",\n \"\\n\",\n \" recv_bytes = 0\\n\",\n \" transmit_bytes = 0\\n\",\n \" lines=stdout.strip().split('\\\\n')\\n\",\n \" lines = lines[2:] # strip header\\n\",\n \" for line in lines[2:]:\\n\",\n \" line = line.strip()\\n\",\n \" # ignore loopback interface\\n\",\n \" if line.startswith('lo'):\\n\",\n \" continue\\n\",\n \" toks = line.split()\\n\",\n \"\\n\",\n \" recv_bytes += int(toks[1])\\n\",\n \" transmit_bytes += int(toks[9])\\n\",\n \" return recv_bytes, transmit_bytes\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(0, 0)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"network_bytes()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"yaro = [\\n\",\n \" '--lr-sched', '0.14,0.47,0.78,0.95',\\n\",\n \" '--epochs', 45,\\n\",\n \" '--lr', 0.4,\\n\",\n \" '--dist-url', 'file:///home/ubuntu/data/file.sync', # single instances are faster with file sync\\n\",\n \" '--init-bn0',\\n\",\n \" '--batch-sched', 64,\\n\",\n \" '--num-tasks', 1\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['--num-tasks']\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[v for v in yaro if v=='--num-tasks']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['--lr-sched',\\n\",\n \" '0.14,0.47,0.78,0.95',\\n\",\n \" '--epochs',\\n\",\n \" 45,\\n\",\n \" '--lr',\\n\",\n \" 0.4,\\n\",\n \" '--dist-url',\\n\",\n \" 'file:///home/ubuntu/data/file.sync',\\n\",\n \" '--init-bn0',\\n\",\n \" '--batch-sched',\\n\",\n \" 64,\\n\",\n \" '--num-tasks',\\n\",\n \" 1]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"yaro\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"12\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(yaro)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"del yaro[12], yaro[11]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['--lr-sched',\\n\",\n \" '0.14,0.47,0.78,0.95',\\n\",\n \" '--epochs',\\n\",\n \" 45,\\n\",\n \" '--lr',\\n\",\n \" 0.4,\\n\",\n \" '--dist-url',\\n\",\n \" 'file:///home/ubuntu/data/file.sync',\\n\",\n \" '--init-bn0',\\n\",\n \" '--batch-sched',\\n\",\n \" 64]\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"yaro\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"30px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "input_benchmarks/convert_to_records.py", "content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Converts MNIST data to TFRecords file format with Example protos.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport tensorflow as tf\nfrom tensorflow.contrib.learn.python.learn.datasets import mnist\n\n\nSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\n\nTRAIN_IMAGES = 'train-images-idx3-ubyte.gz' # MNIST filenames\nTRAIN_LABELS = 'train-labels-idx1-ubyte.gz'\nTEST_IMAGES = 't10k-images-idx3-ubyte.gz'\nTEST_LABELS = 't10k-labels-idx1-ubyte.gz'\n\n\ntf.app.flags.DEFINE_string('directory', '/tmp/data',\n 'Directory to download data files and write the '\n 'converted result')\ntf.app.flags.DEFINE_integer('validation_size', 5000,\n 'Number of examples to separate from the training '\n 'data for the validation set.')\nFLAGS = tf.app.flags.FLAGS\n\n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef convert_to(data_set, name):\n images = data_set.images\n labels = data_set.labels\n num_examples = data_set.num_examples\n\n if images.shape[0] != num_examples:\n raise ValueError('Images size %d does not match label size %d.' %\n (images.shape[0], num_examples))\n rows = images.shape[1]\n cols = images.shape[2]\n depth = images.shape[3]\n\n filename = os.path.join(FLAGS.directory, name + '.tfrecords')\n print('Writing', filename)\n writer = tf.python_io.TFRecordWriter(filename)\n for index in range(num_examples):\n image_raw = images[index].tostring()\n example = tf.train.Example(features=tf.train.Features(feature={\n 'height': _int64_feature(rows),\n 'width': _int64_feature(cols),\n 'depth': _int64_feature(depth),\n 'label': _int64_feature(int(labels[index])),\n 'image_raw': _bytes_feature(image_raw)}))\n writer.write(example.SerializeToString())\n writer.close()\n\n\ndef main(argv):\n # Get the data.\n data_sets = mnist.read_data_sets(FLAGS.directory,\n dtype=tf.uint8,\n reshape=False)\n\n # Convert to Examples and write the result to TFRecords.\n convert_to(data_sets.train, 'train')\n convert_to(data_sets.validation, 'validation')\n convert_to(data_sets.test, 'test')\n\n\nif __name__ == '__main__':\n tf.app.run()\n" }, { "path": "input_benchmarks/fully_connected_feed.py", "content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Trains and Evaluates the MNIST network using a feed dictionary.\"\"\"\n# pylint: disable=missing-docstring\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os.path\nimport time\n\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nfrom tensorflow.examples.tutorials.mnist import mnist\n\n\n# Basic model parameters as external flags.\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')\nflags.DEFINE_integer('max_steps', 2000, 'Number of steps to run trainer.')\nflags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')\nflags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')\nflags.DEFINE_integer('batch_size', 100, 'Batch size. '\n 'Must divide evenly into the dataset sizes.')\nflags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.')\nflags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '\n 'for unit testing.')\n\n\ndef placeholder_inputs(batch_size):\n \"\"\"Generate placeholder variables to represent the input tensors.\n\n These placeholders are used as inputs by the rest of the model building\n code and will be fed from the downloaded data in the .run() loop, below.\n\n Args:\n batch_size: The batch size will be baked into both placeholders.\n\n Returns:\n images_placeholder: Images placeholder.\n labels_placeholder: Labels placeholder.\n \"\"\"\n # Note that the shapes of the placeholders match the shapes of the full\n # image and label tensors, except the first dimension is now batch_size\n # rather than the full size of the train or test data sets.\n images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,\n mnist.IMAGE_PIXELS))\n labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))\n return images_placeholder, labels_placeholder\n\n\ndef fill_feed_dict(data_set, images_pl, labels_pl):\n \"\"\"Fills the feed_dict for training the given step.\n\n A feed_dict takes the form of:\n feed_dict = {\n : ,\n ....\n }\n\n Args:\n data_set: The set of images and labels, from input_data.read_data_sets()\n images_pl: The images placeholder, from placeholder_inputs().\n labels_pl: The labels placeholder, from placeholder_inputs().\n\n Returns:\n feed_dict: The feed dictionary mapping from placeholders to values.\n \"\"\"\n # Create the feed_dict for the placeholders filled with the next\n # `batch size` examples.\n images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,\n FLAGS.fake_data)\n feed_dict = {\n images_pl: images_feed,\n labels_pl: labels_feed,\n }\n return feed_dict\n\n\ndef do_eval(sess,\n eval_correct,\n images_placeholder,\n labels_placeholder,\n data_set):\n \"\"\"Runs one evaluation against the full epoch of data.\n\n Args:\n sess: The session in which the model has been trained.\n eval_correct: The Tensor that returns the number of correct predictions.\n images_placeholder: The images placeholder.\n labels_placeholder: The labels placeholder.\n data_set: The set of images and labels to evaluate, from\n input_data.read_data_sets().\n \"\"\"\n # And run one epoch of eval.\n true_count = 0 # Counts the number of correct predictions.\n steps_per_epoch = data_set.num_examples // FLAGS.batch_size\n num_examples = steps_per_epoch * FLAGS.batch_size\n for step in xrange(steps_per_epoch):\n feed_dict = fill_feed_dict(data_set,\n images_placeholder,\n labels_placeholder)\n true_count += sess.run(eval_correct, feed_dict=feed_dict)\n precision = true_count / num_examples\n print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' %\n (num_examples, true_count, precision))\n\n\ndef run_training():\n \"\"\"Train MNIST for a number of steps.\"\"\"\n # Get the sets of images and labels for training, validation, and\n # test on MNIST.\n data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)\n\n # Tell TensorFlow that the model will be built into the default Graph.\n with tf.Graph().as_default():\n # Generate placeholders for the images and labels.\n images_placeholder, labels_placeholder = placeholder_inputs(\n FLAGS.batch_size)\n\n # Build a Graph that computes predictions from the inference model.\n logits = mnist.inference(images_placeholder,\n FLAGS.hidden1,\n FLAGS.hidden2)\n\n # Add to the Graph the Ops for loss calculation.\n loss = mnist.loss(logits, labels_placeholder)\n\n # Add to the Graph the Ops that calculate and apply gradients.\n train_op = mnist.training(loss, FLAGS.learning_rate)\n\n # Add the Op to compare the logits to the labels during evaluation.\n eval_correct = mnist.evaluation(logits, labels_placeholder)\n\n # Build the summary operation based on the TF collection of Summaries.\n summary_op = tf.merge_all_summaries()\n\n # Add the variable initializer Op.\n init = tf.initialize_all_variables()\n\n # Create a saver for writing training checkpoints.\n saver = tf.train.Saver()\n\n # Create a session for running Ops on the Graph.\n sess = tf.Session()\n\n # Instantiate a SummaryWriter to output summaries and the Graph.\n summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)\n\n # And then after everything is built:\n\n # Run the Op to initialize the variables.\n sess.run(init)\n\n # Start the training loop.\n run_metadata = tf.RunMetadata()\n\n for step in xrange(FLAGS.max_steps):\n start_time = time.time()\n\n # Fill a feed dictionary with the actual set of images and labels\n # for this particular training step.\n feed_dict = fill_feed_dict(data_sets.train,\n images_placeholder,\n labels_placeholder)\n\n # Run one step of the model. The return values are the activations\n # from the `train_op` (which is discarded) and the `loss` Op. To\n # inspect the values of your Ops or variables, you may include them\n # in the list passed to sess.run() and the value tensors will be\n # returned in the tuple from the call.\n \n if step == 500:\n _, loss_value = sess.run([train_op, loss],\n options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),\n run_metadata=run_metadata,\n feed_dict=feed_dict)\n from tensorflow.python.client import timeline\n trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n trace_file = open('timeline.feed.json', 'w')\n trace_file.write(trace.generate_chrome_trace_format())\n\n _, loss_value = sess.run([train_op, loss],\n feed_dict=feed_dict)\n\n duration = time.time() - start_time\n\n # Write the summaries and print an overview fairly often.\n if step % 100 == 0:\n # Print status to stdout.\n print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))\n # Update the events file.\n summary_str = sess.run(summary_op, feed_dict=feed_dict)\n summary_writer.add_summary(summary_str, step)\n summary_writer.flush()\n\n # Save a checkpoint and evaluate the model periodically.\n if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:\n checkpoint_file = os.path.join(FLAGS.train_dir, 'checkpoint')\n saver.save(sess, checkpoint_file, global_step=step)\n # Evaluate against the training set.\n print('Training Data Eval:')\n do_eval(sess,\n eval_correct,\n images_placeholder,\n labels_placeholder,\n data_sets.train)\n # Evaluate against the validation set.\n print('Validation Data Eval:')\n do_eval(sess,\n eval_correct,\n images_placeholder,\n labels_placeholder,\n data_sets.validation)\n # Evaluate against the test set.\n print('Test Data Eval:')\n do_eval(sess,\n eval_correct,\n images_placeholder,\n labels_placeholder,\n data_sets.test)\n\n\ndef main(_):\n run_training()\n\n\nif __name__ == '__main__':\n tf.app.run()\n" }, { "path": "input_benchmarks/fully_connected_preloaded_var.py", "content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Trains the MNIST network using preloaded data stored in a variable.\n\nRun using bazel:\n\nbazel run -c opt \\\n <...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded_var\n\nor, if installed via pip:\n\ncd tensorflow/examples/how_tos/reading_data\npython fully_connected_preloaded_var.py\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport tensorflow as tf\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nfrom tensorflow.examples.tutorials.mnist import mnist\n\n\n# Basic model parameters as external flags.\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')\nflags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.')\nflags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')\nflags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')\nflags.DEFINE_integer('batch_size', 100, 'Batch size. '\n 'Must divide evenly into the dataset sizes.')\nflags.DEFINE_string('train_dir', '/tmp/data',\n 'Directory to put the training data.')\nflags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '\n 'for unit testing.')\n\n\ndef run_training():\n \"\"\"Train MNIST for a number of epochs.\"\"\"\n # Get the sets of images and labels for training, validation, and\n # test on MNIST.\n data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)\n\n # Tell TensorFlow that the model will be built into the default Graph.\n with tf.Graph().as_default():\n with tf.name_scope('input'):\n # Input data\n images_initializer = tf.placeholder(\n dtype=data_sets.train.images.dtype,\n shape=data_sets.train.images.shape)\n labels_initializer = tf.placeholder(\n dtype=data_sets.train.labels.dtype,\n shape=data_sets.train.labels.shape)\n input_images = tf.Variable(\n images_initializer, trainable=False, collections=[])\n input_labels = tf.Variable(\n labels_initializer, trainable=False, collections=[])\n\n image, label = tf.train.slice_input_producer(\n [input_images, input_labels], shuffle=False,\n num_epochs=FLAGS.num_epochs)\n label = tf.cast(label, tf.int32)\n images, labels = tf.train.batch(\n [image, label], batch_size=FLAGS.batch_size, num_threads=10)\n\n import pdb; pdb.set_trace()\n # Build a Graph that computes predictions from the inference model.\n logits = mnist.inference(images, FLAGS.hidden1, FLAGS.hidden2)\n\n # Add to the Graph the Ops for loss calculation.\n loss = mnist.loss(logits, labels)\n\n # Add to the Graph the Ops that calculate and apply gradients.\n train_op = mnist.training(loss, FLAGS.learning_rate)\n\n # Add the Op to compare the logits to the labels during evaluation.\n eval_correct = mnist.evaluation(logits, labels)\n\n # Build the summary operation based on the TF collection of Summaries.\n # summary_op = tf.merge_all_summaries()\n summary_op = tf.no_op()\n\n # Create a saver for writing training checkpoints.\n saver = tf.train.Saver()\n\n # Create the op for initializing variables.\n init_op = tf.initialize_all_variables()\n\n # Create a session for running Ops on the Graph.\n sess = tf.Session()\n\n # Run the Op to initialize the variables.\n sess.run(init_op)\n sess.run(tf.initialize_local_variables())\n sess.run(input_images.initializer,\n feed_dict={images_initializer: data_sets.train.images})\n sess.run(input_labels.initializer,\n feed_dict={labels_initializer: data_sets.train.labels})\n\n # Instantiate a SummaryWriter to output summaries and the Graph.\n summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)\n\n # Start input enqueue threads.\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n # And then after everything is built, start the training loop.\n try:\n step = 0\n run_metadata = tf.RunMetadata()\n\n while not coord.should_stop():\n start_time = time.time()\n\n if step == 100:\n _, loss_value = sess.run([train_op, loss],\n options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),\n run_metadata=run_metadata)\n from tensorflow.python.client import timeline\n trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n trace_file = open('timeline.var.json', 'w')\n trace_file.write(trace.generate_chrome_trace_format())\n\n # Run one step of the model.\n _, loss_value = sess.run([train_op, loss])\n\n duration = time.time() - start_time\n\n # Write the summaries and print an overview fairly often.\n if step % 100 == 0:\n # Print status to stdout.\n print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,\n duration))\n # Update the events file.\n summary_str = sess.run(summary_op)\n summary_writer.add_summary(summary_str, step)\n step += 1\n\n # Save a checkpoint periodically.\n if (step + 1) % 1000 == 0:\n print('Saving')\n saver.save(sess, FLAGS.train_dir, global_step=step)\n\n step += 1\n except tf.errors.OutOfRangeError:\n print('Saving')\n saver.save(sess, FLAGS.train_dir, global_step=step)\n print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))\n finally:\n # When done, ask the threads to stop.\n coord.request_stop()\n\n # Wait for threads to finish.\n coord.join(threads)\n sess.close()\n\n\ndef main(_):\n run_training()\n\n\nif __name__ == '__main__':\n tf.app.run()\n" }, { "path": "input_benchmarks/fully_connected_reader.py", "content": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Train and Eval the MNIST network.\n\nThis version is like fully_connected_feed.py but uses data converted\nto a TFRecords file containing tf.train.Example protocol buffers.\nSee tensorflow/g3doc/how_tos/reading_data.md#reading-from-files\nfor context.\n\nYOU MUST run convert_to_records before running this (but you only need to\nrun it once).\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os.path\nimport time\nimport tensorflow as tf\n\nfrom tensorflow.examples.tutorials.mnist import mnist\n\n\n# Basic model parameters as external flags.\nflags = tf.app.flags\nFLAGS = flags.FLAGS\nflags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')\nflags.DEFINE_integer('num_epochs', 1, 'Number of epochs to run trainer.')\nflags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')\nflags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')\nflags.DEFINE_integer('batch_size', 100, 'Batch size.')\nflags.DEFINE_string('train_dir', '/tmp/data',\n 'Directory with the training data.')\n\n# Constants used for dealing with the files, matches convert_to_records.\nTRAIN_FILE = 'train.tfrecords'\nVALIDATION_FILE = 'validation.tfrecords'\n\n\ndef read_and_decode(filename_queue):\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n features = tf.parse_single_example(\n serialized_example,\n # Defaults are not specified since both keys are required.\n features={\n 'image_raw': tf.FixedLenFeature([], tf.string),\n 'label': tf.FixedLenFeature([], tf.int64),\n })\n\n # Convert from a scalar string tensor (whose single string has\n # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape\n # [mnist.IMAGE_PIXELS].\n image = tf.decode_raw(features['image_raw'], tf.uint8)\n image.set_shape([mnist.IMAGE_PIXELS])\n\n # OPTIONAL: Could reshape into a 28x28 image and apply distortions\n # here. Since we are not applying any distortions in this\n # example, and the next step expects the image to be flattened\n # into a vector, we don't bother.\n\n # Convert from [0, 255] -> [-0.5, 0.5] floats.\n image = tf.cast(image, tf.float32) * (1. / 255) - 0.5\n\n # Convert label from a scalar uint8 tensor to an int32 scalar.\n label = tf.cast(features['label'], tf.int32)\n\n return image, label\n\n\ndef inputs(train, batch_size, num_epochs):\n \"\"\"Reads input data num_epochs times.\n\n Args:\n train: Selects between the training (True) and validation (False) data.\n batch_size: Number of examples per returned batch.\n num_epochs: Number of times to read the input data, or 0/None to\n train forever.\n\n Returns:\n A tuple (images, labels), where:\n * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]\n in the range [-0.5, 0.5].\n * labels is an int32 tensor with shape [batch_size] with the true label,\n a number in the range [0, mnist.NUM_CLASSES).\n Note that an tf.train.QueueRunner is added to the graph, which\n must be run using e.g. tf.train.start_queue_runners().\n \"\"\"\n if not num_epochs: num_epochs = None\n filename = os.path.join(FLAGS.train_dir,\n TRAIN_FILE if train else VALIDATION_FILE)\n\n with tf.name_scope('input'):\n filename_queue = tf.train.string_input_producer(\n [filename], num_epochs=num_epochs)\n\n # Even when reading in multiple threads, share the filename\n # queue.\n image, label = read_and_decode(filename_queue)\n\n # Shuffle the examples and collect them into batch_size batches.\n # (Internally uses a RandomShuffleQueue.)\n # We run this in two threads to avoid being a bottleneck.\n images, sparse_labels = tf.train.batch(\n [image, label], batch_size=batch_size, num_threads=10, capacity=60000)\n\n return images, sparse_labels\n\n\ndef run_training():\n \"\"\"Train MNIST for a number of steps.\"\"\"\n\n # Tell TensorFlow that the model will be built into the default Graph.\n with tf.Graph().as_default():\n # Input images and labels.\n images, labels = inputs(train=True, batch_size=FLAGS.batch_size,\n num_epochs=FLAGS.num_epochs)\n\n # Build a Graph that computes predictions from the inference model.\n logits = mnist.inference(images,\n FLAGS.hidden1,\n FLAGS.hidden2)\n\n # Add to the Graph the loss calculation.\n loss = mnist.loss(logits, labels)\n\n # Add to the Graph operations that train the model.\n train_op = mnist.training(loss, FLAGS.learning_rate)\n\n # The op for initializing the variables.\n init_op = tf.group(tf.initialize_all_variables(),\n tf.initialize_local_variables())\n\n # Create a session for running operations in the Graph.\n sess = tf.Session()\n\n # Initialize the variables (the trained variables and the\n # epoch counter).\n sess.run(init_op)\n\n # Start input enqueue threads.\n print(\"Queue runners: %s\" %([qr.name for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)]))\n\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n # waiting for queue to get loaded\n time.sleep(15)\n run_metadata = tf.RunMetadata()\n\n try:\n step = 0\n while not coord.should_stop():\n start_time = time.time()\n\n # Run one step of the model. The return values are\n # the activations from the `train_op` (which is\n # discarded) and the `loss` op. To inspect the values\n # of your ops or variables, you may include them in\n # the list passed to sess.run() and the value tensors\n # will be returned in the tuple from the call.\n if step == 500:\n _, loss_value = sess.run([train_op, loss],\n options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),\n run_metadata=run_metadata)\n with open(\"run_metadata.pbtxt\", \"w\") as out:\n out.write(str(run_metadata))\n \n from tensorflow.python.client import timeline\n trace = timeline.Timeline(step_stats=run_metadata.step_stats)\n trace_file = open('timeline.reader-1thread.json', 'w')\n trace_file.write(trace.generate_chrome_trace_format())\n else:\n _, loss_value = sess.run([train_op, loss])\n\n duration = time.time() - start_time\n\n # Print an overview fairly often.\n if step % 100 == 0:\n print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,\n duration))\n step += 1\n except tf.errors.OutOfRangeError:\n print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))\n finally:\n # When done, ask the threads to stop.\n coord.request_stop()\n\n # Wait for threads to finish.\n coord.join(threads)\n sess.close()\n\n\ndef main(_):\n run_training()\n\n\nif __name__ == '__main__':\n tf.app.run()\n" }, { "path": "input_benchmarks/timeline.feed.json", "content": "{\n \"traceEvents\": [\n {\n \"ph\": \"M\",\n \"pid\": 0,\n \"args\": {\n \"name\": \"Allocators\"\n },\n \"name\": \"process_name\"\n },\n {\n \"ph\": \"M\",\n \"pid\": 1,\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/cpu:0 Compute\"\n },\n \"name\": \"process_name\"\n },\n {\n \"ph\": \"M\",\n \"pid\": 2,\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/cpu:0 Tensors\"\n },\n \"name\": \"process_name\"\n },\n {\n \"ph\": \"M\",\n \"pid\": 3,\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/gpu:0 Compute\"\n },\n \"name\": \"process_name\"\n },\n {\n \"ph\": \"M\",\n \"pid\": 4,\n \"args\": {\n \"name\": \"/job:localhost/replica:0/task:0/gpu:0 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{\n \"cat\": \"DataFlow\",\n \"pid\": 1,\n \"ph\": \"s\",\n \"id\": 46,\n \"ts\": 1475624953528391,\n \"tid\": 1,\n \"name\": \"hidden1/weights\"\n },\n {\n \"cat\": \"DataFlow\",\n \"pid\": 1,\n \"ph\": \"t\",\n \"id\": 46,\n \"ts\": 1475624953541015,\n \"tid\": 0,\n \"name\": \"hidden1/weights\"\n },\n {\n \"dur\": 2,\n \"cat\": \"Op\",\n \"pid\": 1,\n \"ph\": \"X\",\n \"args\": {\n \"input2\": \"^GradientDescent/update_hidden2/weights/ApplyGradientDescent\",\n \"op\": \"NoOp\",\n \"input3\": \"^GradientDescent/update_hidden2/biases/ApplyGradientDescent\",\n \"input4\": \"^GradientDescent/update_softmax_linear/weights/ApplyGradientDescent\",\n \"input1\": \"^GradientDescent/update_hidden1/biases/ApplyGradientDescent\",\n \"input5\": \"^GradientDescent/update_softmax_linear/biases/ApplyGradientDescent\",\n \"name\": \"GradientDescent/update\",\n \"input0\": \"^GradientDescent/update_hidden1/weights/ApplyGradientDescent\"\n },\n \"ts\": 1475624953541082,\n \"tid\": 0,\n \"name\": \"NoOp\"\n },\n {\n \"dur\": 4,\n \"cat\": \"Op\",\n \"pid\": 1,\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"Const\",\n \"input0\": \"^GradientDescent/update\",\n \"name\": \"GradientDescent/value\"\n },\n \"ts\": 1475624953541087,\n \"tid\": 0,\n \"name\": \"Const\"\n },\n {\n \"dur\": 3,\n \"cat\": \"Op\",\n \"pid\": 1,\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"AssignAdd\",\n \"input0\": \"global_step\",\n \"input1\": \"GradientDescent/value\",\n \"name\": \"GradientDescent\"\n },\n \"ts\": 1475624953541094,\n \"tid\": 0,\n \"name\": \"AssignAdd\"\n },\n {\n \"cat\": \"DataFlow\",\n \"pid\": 1,\n \"ph\": \"s\",\n \"id\": 47,\n \"ts\": 1475624953528456,\n \"tid\": 7,\n \"name\": \"global_step\"\n },\n {\n \"cat\": \"DataFlow\",\n \"pid\": 1,\n \"ph\": \"t\",\n \"id\": 47,\n \"ts\": 1475624953541094,\n \"tid\": 0,\n \"name\": \"global_step\"\n },\n {\n \"dur\": 5,\n \"cat\": \"Op\",\n \"pid\": 1,\n \"ph\": \"X\",\n \"args\": {\n \"op\": \"NoOp\",\n \"name\": \"_SINK\"\n },\n \"ts\": 1475624953541099,\n \"tid\": 0,\n \"name\": \"NoOp\"\n }\n ]\n}" }, { "path": "inverse_segfault.py", "content": "# requirements: MKL, scipy, gzip\n\n#!/usr/bin/env python\nimport socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=False # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n\n\nfrom scipy import linalg\n\n# TODO: speed-up tests by reusing session\n\n\ndef load_MNIST_images(filename):\n \"\"\"\n returns a 28x28x[number of MNIST images] matrix containing\n the raw MNIST images\n :param filename: input data file\n \"\"\"\n with open(filename, \"r\") as f:\n magic = np.fromfile(f, dtype=np.dtype('>i4'), count=1)\n\n num_images = int(np.fromfile(f, dtype=np.dtype('>i4'), count=1))\n num_rows = int(np.fromfile(f, dtype=np.dtype('>i4'), count=1))\n num_cols = int(np.fromfile(f, dtype=np.dtype('>i4'), count=1))\n\n images = np.fromfile(f, dtype=np.ubyte)\n images = images.reshape((num_images, num_rows * num_cols)).transpose()\n images = images.astype(np.float64) / 255\n\n f.close()\n\n return images\n\n\nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n print(\"Step: \", global_time_list[-1])\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n# work-around for graph_editor.copy_with_input_replacements scaling\n# quadratically with size of the graph\nfrom tensorflow.contrib.graph_editor import transform\noriginal_assign_renamed_collections_handler = transform.assign_renamed_collections_handler\ndef dummy_collections_handler(info, elem, elem_): pass\ndef disable_collections_handler():\n transform.assign_renamed_collections_handler = dummy_collections_handler\ndef enable_collections_handler():\n transform.assign_renamed_collections_handler = original_assign_renamed_collections_handler\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__.endswith('Tensor') or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n logger = u.get_last_logger(skip_existence_check=True)\n if logger:\n newtag = 'time/'+self.tag\n # since tensorboard doesn't allow hierarchical tags, merge init times\n if newtag.startswith('time/init'):\n newtag = newtag.replace('time/init', 'timeinit')\n logger(newtag, interval_ms)\n\n\nglobal_record_dict = OrderedDict()\ndef record(tag, stat):\n global global_record_dict\n global_record_dict.setdefault(tag, []).append(stat)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False, use_resource=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n self.use_resource = use_resource\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n if not self.use_resource:\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n else:\n from tensorflow.python.ops import resource_variable_ops as rr\n self.cached = SvdTuple(\n rr.ResourceVariable(self.init.s, name=name+\"_s\"),\n rr.ResourceVariable(self.init.u, name=name+\"_u\"),\n rr.ResourceVariable(self.init.v, name=name+\"_v\"),\n rr.ResourceVariable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n\n if not use_resource:\n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n else:\n self.holder = self.init\n \n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger(skip_existence_check=False):\n \"\"\"Returns last logger, if skip_existence_check is set, doesn't\n throw error if logger doesn't exist.\"\"\"\n global global_last_logger\n if not skip_existence_check:\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n interval_ms = 1000*(new_timestamp - self.last_timestamp)\n self.summary.value.add(tag='time/step',\n simple_value=interval_ms)\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n # hack, remove\n return tf.get_default_session()\n global sess\n assert sess\n return sess\n\ndef get_default_graph():\n global sess\n assert sess\n return sess.graph\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n\ndef get_mnist_images():\n import gzip\n from tensorflow.contrib.learn.python.learn.datasets import base\n import numpy\n \n def extract_images(f):\n \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\n Args:\n f: A file object that can be passed into a gzip reader.\n Returns:\n data: A 4D uint8 numpy array [index, y, x, depth].\n Raises:\n ValueError: If the bytestream does not start with 2051.\n \"\"\"\n print('Extracting', f.name)\n with gzip.GzipFile(fileobj=f) as bytestream:\n magic = _read32(bytestream)\n if magic != 2051:\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\n (magic, f.name))\n num_images = _read32(bytestream)\n rows = _read32(bytestream)\n cols = _read32(bytestream)\n buf = bytestream.read(rows * cols * num_images)\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\n data = data.reshape(num_images, rows, cols, 1)\n return data\n\n def _read32(bytestream):\n dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]\n\n TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'\n source_url = 'https://storage.googleapis.com/cvdf-datasets/mnist/'\n local_file = base.maybe_download(TRAIN_IMAGES, '/tmp',\n source_url + TRAIN_IMAGES)\n train_images = extract_images(open(local_file, 'rb'))\n train_images = train_images.reshape(60000, 28**2).T.astype(np.float64)/255\n return train_images\n\n# helper utilities\ndef W_uniform(s1, s2): # uniform weight init from Ng UFLDL\n r = np.sqrt(6) / np.sqrt(s1 + s2 + 1)\n return np.random.random(2*s2*s1)*2*r-r\n\n\n\nimport numpy as np\nimport scipy\nimport tensorflow as tf\nimport time\n\n\n# fix for https://github.com/tensorflow/tensorflow/issues/13351\nfrom tensorflow.python.ops import variables\ndef passthrough(obj, value): return value\ntry:\n variables.Variable._build_initializer_expr=passthrough\nexcept: # older versions of TF don't have this\n pass\n\n# for line profiling\ntry:\n profile # throws an exception when profile isn't defined\nexcept NameError:\n profile = lambda x: x # if it's not defined simply ignore the decorator.\n\n\n\n@profile\ndef main():\n np.random.seed(0)\n tf.set_random_seed(0)\n \n dtype = np.float32\n\n train_images = u.get_mnist_images()\n \n dsize = 10000\n patches = train_images[:,:dsize].astype(dtype);\n fs = [dsize, 28*28, 196, 28*28]\n\n\n # values from deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial\n X0=patches\n lambda_=3e-3\n rho=tf.constant(0.1, dtype=dtype)\n beta=3\n W0f = u.W_uniform(fs[2],fs[3])\n\n def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]\n dsize = f(-1)\n n = len(fs) - 2\n\n # helper to create variables with numpy or TF initial value\n init_dict = {} # {var_placeholder: init_value}\n vard = {} # {var: util.VarInfo}\n def init_var(val, name, trainable=False, noinit=False):\n if isinstance(val, tf.Tensor):\n collections = [] if noinit else None\n var = tf.Variable(val, name=name, collections=collections)\n else:\n val = np.array(val)\n assert u.is_numeric, \"Unknown type\"\n holder = tf.placeholder(dtype, shape=val.shape, name=name+\"_holder\")\n var = tf.Variable(holder, name=name, trainable=trainable)\n init_dict[holder] = val\n var_p = tf.placeholder(var.dtype, var.shape)\n var_setter = var.assign(var_p)\n vard[var] = u.VarInfo(var_setter, var_p)\n return var\n\n lr = init_var(0.2, \"lr\")\n \n Wf = init_var(W0f, \"Wf\", True)\n Wf_copy = init_var(W0f, \"Wf_copy\", True)\n W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes\n X = init_var(X0, \"X\")\n W.insert(0, X)\n\n def sigmoid(x):\n return tf.sigmoid(x)\n \n def d_sigmoid(y):\n return y*(1-y)\n \n def kl(x, y):\n return x * tf.log(x / y) + (1 - x) * tf.log((1 - x) / (1 - y))\n def d_kl(x, y):\n return (1-x)/(1-y) - x/y\n \n # A[i] = activations needed to compute gradient of W[i]\n # A[n+1] = network output\n A = [None]*(n+2)\n\n fail_node = tf.Print(0, [0], \"fail, this must never run\")\n with tf.control_dependencies([fail_node]):\n A[0] = u.Identity(dsize, dtype=dtype)\n A[1] = W[0]\n for i in range(1, n+1):\n A[i+1] = sigmoid(W[i] @ A[i])\n \n # reconstruction error and sparsity error\n err = (A[3] - A[1])\n rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize\n\n # B[i] = backprops needed to compute gradient of W[i]\n # B2[i] = backprops from sampled labels needed for natural gradient\n B = [None]*(n+1)\n B2 = [None]*(n+1)\n B[n] = err*d_sigmoid(A[n+1])\n sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)\n sampled_labels = init_var(sampled_labels_live, \"sampled_labels\", noinit=True)\n B2[n] = sampled_labels*d_sigmoid(A[n+1])\n for i in range(n-1, -1, -1):\n backprop = t(W[i+1]) @ B[i+1]\n backprop2 = t(W[i+1]) @ B2[i+1]\n B[i] = backprop*d_sigmoid(A[i+1])\n B2[i] = backprop2*d_sigmoid(A[i+1])\n\n # dW[i] = gradient of W[i]\n dW = [None]*(n+1)\n pre_dW = [None]*(n+1) # preconditioned dW\n pre_dW_stable = [None]*(n+1) # preconditioned stable dW\n\n cov_A = [None]*(n+1) # covariance of activations[i]\n cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]\n vars_svd_A = [None]*(n+1)\n vars_svd_B2 = [None]*(n+1)\n for i in range(1,n+1):\n cov_A[i] = init_var(A[i]@t(A[i])/dsize, \"cov_A%d\"%(i,))\n cov_B2[i] = init_var(B2[i]@t(B2[i])/dsize, \"cov_B2%d\"%(i,))\n vars_svd_A[i] = u.SvdWrapper(cov_A[i],\"svd_A_%d\"%(i,), do_inverses=True)\n vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],\"svd_B2_%d\"%(i,), do_inverses=True)\n# whitened_A = u.regularized_inverse2(vars_svd_A[i],L=lambda_) @ A[i]\n# whitened_B = u.regularized_inverse2(vars_svd_B2[i],L=lambda_) @ B[i]\n whitened_A = u.regularized_inverse(cov_A[i], lambda_) @ A[i]\n whitened_B = u.regularized_inverse(cov_B2[i], lambda_) @ B[i]\n pre_dW[i] = (whitened_B @ t(whitened_A))/dsize\n dW[i] = (B[i] @ t(A[i]))/dsize\n\n # Loss function\n reconstruction = u.L2(err) / (2 * dsize)\n\n loss = reconstruction\n\n grad_live = u.flatten(dW[1:])\n pre_grad_live = u.flatten(pre_dW[1:]) # fisher preconditioned gradient\n grad = init_var(grad_live, \"grad\")\n pre_grad = init_var(pre_grad_live, \"pre_grad\")\n\n update_params_op = Wf.assign(Wf-lr*pre_grad).op\n save_params_op = Wf_copy.assign(Wf).op\n pre_grad_dot_grad = tf.reduce_sum(pre_grad*grad)\n grad_norm = tf.reduce_sum(grad*grad)\n pre_grad_norm = u.L2(pre_grad)\n\n def dump_svd_info(step):\n \"\"\"Dump singular values and gradient values in those coordinates.\"\"\"\n for i in range(1, n+1):\n svd = vars_svd_A[i]\n s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])\n util.dump(s0, \"A_%d_%d\"%(i, step))\n A0 = A[i].eval()\n At0 = v0.T @ A0\n util.dump(A0 @ A0.T, \"Acov_%d_%d\"%(i, step))\n util.dump(At0 @ At0.T, \"Atcov_%d_%d\"%(i, step))\n util.dump(s0, \"As_%d_%d\"%(i, step))\n\n for i in range(1, n+1):\n svd = vars_svd_B2[i]\n s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])\n util.dump(s0, \"B2_%d_%d\"%(i, step))\n B0 = B[i].eval()\n Bt0 = v0.T @ B0\n util.dump(B0 @ B0.T, \"Bcov_%d_%d\"%(i, step))\n util.dump(Bt0 @ Bt0.T, \"Btcov_%d_%d\"%(i, step))\n util.dump(s0, \"Bs_%d_%d\"%(i, step)) \n \n def advance_batch():\n sess.run(sampled_labels.initializer) # new labels for next call\n\n def update_covariances():\n ops_A = [cov_A[i].initializer for i in range(1, n+1)]\n ops_B2 = [cov_B2[i].initializer for i in range(1, n+1)]\n sess.run(ops_A+ops_B2)\n\n def update_svds():\n vars_svd_A[2].update()\n vars_svd_B2[2].update()\n vars_svd_B2[1].update()\n\n def init_svds():\n \"\"\"Initialize our SVD to identity matrices.\"\"\"\n ops = []\n for i in range(1, n+1):\n ops.extend(vars_svd_A[i].init_ops)\n ops.extend(vars_svd_B2[i].init_ops)\n sess = tf.get_default_session()\n sess.run(ops)\n \n init_op = tf.global_variables_initializer()\n\n from tensorflow.core.protobuf import rewriter_config_pb2\n \n rewrite_options = rewriter_config_pb2.RewriterConfig(\n disable_model_pruning=True,\n constant_folding=rewriter_config_pb2.RewriterConfig.OFF,\n memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL)\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options,\n rewrite_options=rewrite_options)\n config = tf.ConfigProto(graph_options=graph_options)\n\n sess = tf.InteractiveSession(config=config)\n sess.run(Wf.initializer, feed_dict=init_dict)\n sess.run(X.initializer, feed_dict=init_dict)\n advance_batch()\n update_covariances()\n init_svds()\n sess.run(init_op, feed_dict=init_dict) # initialize everything else\n \n print(\"Running training.\")\n u.reset_time()\n\n step_lengths = [] # keep track of learning rates\n losses = []\n \n # adaptive line search parameters\n alpha=0.3 # acceptable fraction of predicted decrease\n beta=0.8 # how much to shrink when violation\n growth_rate=1.05 # how much to grow when too conservative\n \n def update_cov_A(i):\n sess.run(cov_A[i].initializer)\n def update_cov_B2(i):\n sess.run(cov_B2[i].initializer)\n\n # only update whitening matrix of input activations in the beginning\n vars_svd_A[1].update()\n\n for step in range(40): \n update_covariances()\n update_svds()\n\n sess.run(grad.initializer)\n sess.run(pre_grad.initializer)\n \n lr0, loss0 = sess.run([lr, loss])\n update_params_op.run()\n loss1 = loss.eval()\n advance_batch()\n\n \n losses.append(loss0)\n step_lengths.append(lr0)\n\n print(\"Step %d loss %.2f\"%(step, loss0))\n u.record_time()\n\n assert losses[-1]<5\n u.summarize_time()\n print(\"Test passed\")\n\n\nif __name__=='__main__':\n main()\n" }, { "path": "keras_autoencoder/keras_large.py", "content": "#!/usr/bin/env python\n# Large MNIST autoencoder\nimport argparse\nimport os\nimport time\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-b', '--batch_size', type=int, default=128, help='batchsize to use')\nargs = parser.parse_args()\n\nfrom keras import optimizers\nfrom weightnorm import SGDWithWeightnorm\nfrom weightnorm import AdamWithWeightnorm\n\nimport util as u\nimport numpy as np\nimport scipy\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras import metrics\nimport numpy\nimport sys\n\nprefix = \"keras_large\" # large batch\nepochs = 20000\n# 2 sec per epoch, 12 hours\n\n\nfrom keras import optimizers\nimport load_MNIST\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras import callbacks\nimport numpy\n\n\nclass TestCallback(callbacks.Callback):\n def __init__(self, data_train, data_test, fn):\n print(\"Creating callback\")\n self.data_train = data_train\n self.data_test = data_test\n self.losses_train = []\n self.losses_test = []\n self.times = []\n self.fn = fn\n\n def on_epoch_end(self, epoch, logs={}):\n global start_time\n x_test, y_test = self.data_test\n x_train, y_train = self.data_train\n pixel_loss_test, acc1 = self.model.evaluate(x_test, y_test, verbose=0)\n pixel_loss_train, acc2 = self.model.evaluate(x_train, y_train, verbose=0)\n loss_test = pixel_loss_test*28*28\n loss_train = pixel_loss_train*28*28\n self.losses_train.append(loss_train)\n self.losses_test.append(loss_test)\n \n import socket\n host = socket.gethostname().split('.',1)[0]\n outfn = 'data/%s_%s'%(host, self.fn)\n\n if epoch == 0:\n os.system(\"rm -f \"+outfn)\n with open(outfn, \"a\") as myfile:\n elapsed = time.time()-start_time\n print('\\n%d sec: Loss train: %.2f'%(elapsed,loss_train))\n print('%d sec: Loss test: %.2f'%(elapsed,loss_test))\n myfile.write('%d, %f, %f, %f\\n'%(epoch, elapsed, loss_train, loss_test))\n\nstart_time = 0\nif __name__=='__main__':\n numpy.random.seed(0)\n \n dsize = 10000\n\n\n from keras.datasets import mnist\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\n X_train = X_train.astype(np.float32)\n X_train = X_train.reshape((X_train.shape[0], -1))\n X_train = X_train[:dsize,:]\n X_test = X_test.astype(np.float32)\n X_test = X_test.reshape((X_test.shape[0], -1))\n X_train /= 255\n X_test /= 255\n\n optimizer = AdamWithWeightnorm()\n\n model = Sequential()\n model.add(Dense(1024, input_dim=28*28, activation='relu'))\n model.add(Dense(1024, activation='relu'))\n model.add(Dense(1024, activation='relu'))\n model.add(Dense(196, activation='relu'))\n model.add(Dense(1024, activation='relu'))\n model.add(Dense(1024, activation='relu'))\n model.add(Dense(1024, activation='relu'))\n model.add(Dense(28*28, activation='relu'))\n model.compile(loss='mean_squared_error', optimizer=optimizer,\n metrics=[metrics.mean_squared_error])\n # nb_epochs in older version\n cb = TestCallback((X_train, X_train), (X_test,X_test), prefix)\n\n start_time = time.time()\n result = model.fit(X_train, X_train, validation_data=(X_test, X_test), \n batch_size=args.batch_size,\n nb_epoch=epochs,\n callbacks=[cb])\n\n acc_hist = np.asarray(result.history['mean_squared_error'])*28*28 # avg pixel loss->avg image image loss\n u.dump(acc_hist, \"%s_losses.csv\"%(prefix,))\n u.dump(cb.losses, \"%s_vlosses.csv\"%(prefix,))\n\n" }, { "path": "keras_autoencoder/util.py", "content": "import socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=True # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n\n\nfrom scipy import linalg\n\n# TODO: speed-up tests by reusing session\n\nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__ == 'Tensor' or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n logger = u.get_last_logger()\n logger('time/'+self.tag, interval_ms)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n \n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n\n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger():\n global global_last_logger\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n self.summary.value.add(tag='time/step',\n simple_value=(new_timestamp-self.last_timestamp))\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n # hack, remove\n return tf.get_default_session()\n global sess\n assert sess\n return sess\n\ndef get_default_graph():\n global sess\n assert sess\n return sess.graph\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n \nif __name__=='__main__':\n run_all_tests(sys.modules[__name__])\n" }, { "path": "keras_autoencoder/weightnorm.py", "content": "from keras import backend as K\nfrom keras.optimizers import SGD,Adam\nimport tensorflow as tf\n\n# adapted from keras.optimizers.SGD\nclass SGDWithWeightnorm(SGD):\n def get_updates(self, params, constraints, loss):\n grads = self.get_gradients(loss, params)\n self.updates = []\n\n lr = self.lr\n if self.initial_decay > 0:\n lr *= (1. / (1. + self.decay * self.iterations))\n self.updates .append(K.update_add(self.iterations, 1))\n\n # momentum\n shapes = [K.get_variable_shape(p) for p in params]\n moments = [K.zeros(shape) for shape in shapes]\n self.weights = [self.iterations] + moments\n for p, g, m in zip(params, grads, moments):\n\n # if a weight tensor (len > 1) use weight normalized parameterization\n ps = K.get_variable_shape(p)\n if len(ps) > 1:\n\n # get weight normalization parameters\n V, V_norm, V_scaler, g_param, grad_g, grad_V = get_weightnorm_params_and_grads(p, g)\n\n # momentum container for the 'g' parameter\n V_scaler_shape = K.get_variable_shape(V_scaler)\n m_g = K.zeros(V_scaler_shape)\n\n # update g parameters\n v_g = self.momentum * m_g - lr * grad_g # velocity\n self.updates.append(K.update(m_g, v_g))\n if self.nesterov:\n new_g_param = g_param + self.momentum * v_g - lr * grad_g\n else:\n new_g_param = g_param + v_g\n\n # update V parameters\n v_v = self.momentum * m - lr * grad_V # velocity\n self.updates.append(K.update(m, v_v))\n if self.nesterov:\n new_V_param = V + self.momentum * v_v - lr * grad_V\n else:\n new_V_param = V + v_v\n\n # if there are constraints we apply them to V, not W\n if p in constraints:\n c = constraints[p]\n new_V_param = c(new_V_param)\n\n # wn param updates --> W updates\n add_weightnorm_param_updates(self.updates, new_V_param, new_g_param, p, V_scaler)\n\n else: # normal SGD with momentum\n v = self.momentum * m - lr * g # velocity\n self.updates.append(K.update(m, v))\n\n if self.nesterov:\n new_p = p + self.momentum * v - lr * g\n else:\n new_p = p + v\n\n # apply constraints\n if p in constraints:\n c = constraints[p]\n new_p = c(new_p)\n\n self.updates.append(K.update(p, new_p))\n return self.updates\n\n# adapted from keras.optimizers.Adam\nclass AdamWithWeightnorm(Adam):\n def get_updates(self, params, constraints, loss):\n grads = self.get_gradients(loss, params)\n self.updates = [K.update_add(self.iterations, 1)]\n\n lr = self.lr\n if self.initial_decay > 0:\n lr *= (1. / (1. + self.decay * self.iterations))\n\n t = self.iterations + 1\n lr_t = lr * K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t))\n\n shapes = [K.get_variable_shape(p) for p in params]\n ms = [K.zeros(shape) for shape in shapes]\n vs = [K.zeros(shape) for shape in shapes]\n self.weights = [self.iterations] + ms + vs\n\n for p, g, m, v in zip(params, grads, ms, vs):\n\n # if a weight tensor (len > 1) use weight normalized parameterization\n # this is the only part changed w.r.t. keras.optimizers.Adam\n ps = K.get_variable_shape(p)\n if len(ps)>1:\n\n # get weight normalization parameters\n V, V_norm, V_scaler, g_param, grad_g, grad_V = get_weightnorm_params_and_grads(p, g)\n\n # Adam containers for the 'g' parameter\n V_scaler_shape = K.get_variable_shape(V_scaler)\n m_g = K.zeros(V_scaler_shape)\n v_g = K.zeros(V_scaler_shape)\n\n # update g parameters\n m_g_t = (self.beta_1 * m_g) + (1. - self.beta_1) * grad_g\n v_g_t = (self.beta_2 * v_g) + (1. - self.beta_2) * K.square(grad_g)\n new_g_param = g_param - lr_t * m_g_t / (K.sqrt(v_g_t) + self.epsilon)\n self.updates.append(K.update(m_g, m_g_t))\n self.updates.append(K.update(v_g, v_g_t))\n\n # update V parameters\n m_t = (self.beta_1 * m) + (1. - self.beta_1) * grad_V\n v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(grad_V)\n new_V_param = V - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)\n self.updates.append(K.update(m, m_t))\n self.updates.append(K.update(v, v_t))\n\n # if there are constraints we apply them to V, not W\n if p in constraints:\n c = constraints[p]\n new_V_param = c(new_V_param)\n\n # wn param updates --> W updates\n add_weightnorm_param_updates(self.updates, new_V_param, new_g_param, p, V_scaler)\n\n else: # do optimization normally\n m_t = (self.beta_1 * m) + (1. - self.beta_1) * g\n v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)\n p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)\n\n self.updates.append(K.update(m, m_t))\n self.updates.append(K.update(v, v_t))\n\n new_p = p_t\n # apply constraints\n if p in constraints:\n c = constraints[p]\n new_p = c(new_p)\n self.updates.append(K.update(p, new_p))\n return self.updates\n\n\ndef get_weightnorm_params_and_grads(p, g):\n ps = K.get_variable_shape(p)\n\n # construct weight scaler: V_scaler = g/||V||\n V_scaler_shape = (ps[-1],) # assumes we're using tensorflow!\n V_scaler = K.ones(V_scaler_shape) # init to ones, so effective parameters don't change\n\n # get V parameters = ||V||/g * W\n norm_axes = [i for i in range(len(ps) - 1)]\n V = p / tf.reshape(V_scaler, [1] * len(norm_axes) + [-1])\n\n # split V_scaler into ||V|| and g parameters\n V_norm = tf.sqrt(tf.reduce_sum(tf.square(V), norm_axes))\n g_param = V_scaler * V_norm\n\n # get grad in V,g parameters\n grad_g = tf.reduce_sum(g * V, norm_axes) / V_norm\n grad_V = tf.reshape(V_scaler, [1] * len(norm_axes) + [-1]) * \\\n (g - tf.reshape(grad_g / V_norm, [1] * len(norm_axes) + [-1]) * V)\n\n return V, V_norm, V_scaler, g_param, grad_g, grad_V\n\n\ndef add_weightnorm_param_updates(updates, new_V_param, new_g_param, W, V_scaler):\n ps = K.get_variable_shape(new_V_param)\n norm_axes = [i for i in range(len(ps) - 1)]\n\n # update W and V_scaler\n new_V_norm = tf.sqrt(tf.reduce_sum(tf.square(new_V_param), norm_axes))\n new_V_scaler = new_g_param / new_V_norm\n new_W = tf.reshape(new_V_scaler, [1] * len(norm_axes) + [-1]) * new_V_param\n updates.append(K.update(W, new_W))\n updates.append(K.update(V_scaler, new_V_scaler))\n\n\n# data based initialization for a given Keras model\ndef data_based_init(model, input):\n\n # input can be dict, numpy array, or list of numpy arrays\n if type(input) is dict:\n feed_dict = input\n elif type(input) is list:\n feed_dict = {tf_inp: np_inp for tf_inp,np_inp in zip(model.inputs,input)}\n else:\n feed_dict = {model.inputs[0]: input}\n\n # add learning phase if required\n if model.uses_learning_phase and K.learning_phase() not in feed_dict:\n feed_dict.update({K.learning_phase(): 1})\n\n # get all layer name, output, weight, bias tuples\n layer_output_weight_bias = []\n for l in model.layers:\n if hasattr(l, 'W') and hasattr(l, 'b'):\n assert(l.built)\n layer_output_weight_bias.append( (l.name,l.get_output_at(0),l.W,l.b) ) # if more than one node, only use the first\n\n # iterate over our list and do data dependent init\n sess = K.get_session()\n for l,o,W,b in layer_output_weight_bias:\n print('Performing data dependent initialization for layer ' + l)\n m,v = tf.nn.moments(o, [i for i in range(len(o.get_shape())-1)])\n s = tf.sqrt(v + 1e-10)\n updates = tf.group(W.assign(W/tf.reshape(s,[1]*(len(W.get_shape())-1)+[-1])), b.assign((b-m)/s))\n sess.run(updates, feed_dict)\n" }, { "path": "khatri_rao_benchmark.py", "content": "# 0.6 - 0.8 for 10x10 khatri rao\n# After improvement: 0.02 seconds\nimport tensorflow as tf\nimport util as u\nimport time\nimport os\nimport sys\n\n\ndef benchmark_construct(dims, iters, dtype):\n A = tf.ones((dims, dims), dtype=dtype)\n B = tf.ones((dims, dims), dtype=dtype)\n prods = []\n time0 = time.time()\n for i in range(iters):\n prods.append(u.khatri_rao(A,B))\n elapsed = time.time() - time0\n print(\"Constructed %d x %d kr %d times in %.2f seconds\"%(A.shape[0], B.shape[0], iters, elapsed))\n \ndef benchmark_execute(dims, iters, dtype):\n A = tf.random_uniform((dims, dims), dtype=dtype)\n B = tf.random_uniform((dims, dims), dtype=dtype)\n prods = []\n for i in range(iters):\n prods.append(u.khatri_rao(A,B))\n elapsed_times = []\n sess = tf.Session()\n elapsed_times = []\n u.reset_time()\n for i in range(10):\n time0 = time.time()\n sess.run(tf.group(*prods))\n elapsed_times.append(time.time()-time0)\n u.record_time()\n u.summarize_time()\n\n\nif __name__ == '__main__':\n dims = 10\n iters = 10\n dtype = tf.float32\n benchmark_construct(dims, iters, dtype)\n benchmark_execute(dims, iters, dtype)\n \n" }, { "path": "lazy_dog.py", "content": "# Overfit GPT model to \"the quick brown fox\"\n#\n# 906.45 -- the a , \" he said . \" i 'm not\n# 310.08 -- the i - \" \" i 'm not going to\n# 134.41 -- the i - \" \" i 'm not a child\n# 30.41 -- the i - \" \" i 'm not going to\n# 8.07 -- the quick , \" he said , \" i 'm not\n# 3.61 -- the quick quick quick steps , and then the quick quick\n# 2.15 -- the quick quick quick jumps over the low fence jumps over\n# 1.41 -- the quick fox jumps over the lazy dog jumps over the\n# 1.13 -- the quick fox jumps over the lazy dog jumps over the\n# 1.05 -- the quick quick brown fox jumps over the lazy dog jumps\n# 1.02 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.01 -- the quick jumps over the lazy dog jumps over the lazy\n# 1.02 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.13 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.02 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.00 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.01 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.00 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.00 -- the quick brown fox jumps over the lazy dog jumps over\n# 1.00 -- the quick brown fox jumps over the lazy dog jumps over\n\n\nimport math\nimport torch\nfrom pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel\n\n\ndef argmax(t):\n return int(torch.argmax(t).detach().numpy())\n \ndef decode(start_tokens, length=10):\n result = []\n context = torch.ones(1, 0, dtype=torch.long)\n for start_token in start_tokens:\n new_token = torch.full((1, 1), start_token, dtype=torch.long)\n context = torch.cat((context, new_token), dim=1)\n result.append(tokenizer.convert_ids_to_tokens([start_token])[0])\n\n with torch.no_grad():\n for i in range(length):\n logits = model(context) # batch_size x 1\n predicted_id = argmax(logits[0,-1])\n predicted_word = tokenizer.convert_ids_to_tokens([predicted_id])[0]\n tokenizer.convert_ids_to_tokens([])\n if predicted_word.endswith(''):\n predicted_word = predicted_word[:-len('')]\n result.append(predicted_word)\n\n predicted_id_batch = torch.tensor([[predicted_id]])\n context = torch.cat((context, predicted_id_batch), dim=1)\n\n result = ' '.join(result)\n result = result.replace('\\n', ' ')\n return result\n\n\ndef main():\n global tokenizer, model\n\n train_dataset = 'the quick brown fox jumps over the lazy dog'\n tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n tokenized = [tokenizer.tokenize(train_dataset)]\n\n # [[481, 2279, 2507, 8573, 11670, 715, 481, 8447, 2585]]\n encoded = [tokenizer.convert_tokens_to_ids(t) for t in tokenized]\n model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')\n\n optimizer = torch.optim.SGD(model.parameters(), lr = 0.001, momentum=0.9)\n\n \n batch = torch.tensor(encoded)\n\n start_words = ['the']\n start_tokens = [tokenizer.convert_tokens_to_ids(w) for w in start_words]\n \n for i in range(20):\n loss = model(input_ids=batch, lm_labels=batch)\n perplexity = math.exp(loss.item())\n print('%5.2f -- %s'%(perplexity, decode(start_tokens)))\n\n loss.backward()\n optimizer.step()\n optimizer.zero_grad()\n \n\nif __name__=='__main__':\n main()\n \n" }, { "path": "linalg-benchmark/README.md", "content": "Linear algebra benchmark:\n\nNeed Python 3.6, pip, wget\n\nRun standalone:\n```\npip install -r https://raw.githubusercontent.com/yaroslavvb/stuff/master/linalg-benchmark/requirements.txt\nwget https://raw.githubusercontent.com/yaroslavvb/stuff/master/linalg-benchmark/benchmark.py\npython benchmark.py\n```\n\nRun on AWS:\n```\npip install ncluster\npython launch_aws.py\n```\n\nResults for SVD of 1534 x 1534 matrix:\n\n```\n2015 MacBook Pro\nnumpy default min: 445.74, median: 480.14, mean: 491.55\nnumpy gesvd min: 2123.99, median: 2551.53, mean: 2552.81\nnumpy gesdd min: 584.27, median: 691.75, mean: 686.09\nTF CPU min: 2115.46, median: 2157.92, mean: 2228.17\nTF GPU no GPU detected\nPyTorch CPU min: 2468.93, median: 3316.59, mean: 3295.61\nPyTorch GPU no GPU detected\n\nr5.large\nnumpy default min: 1538.54, median: 1546.51, mean: 1573.60\nnumpy gesvd min: 33868.77, median: 33902.69, mean: 33965.48\nnumpy gesdd min: 1521.05, median: 1550.09, mean: 1549.20\nTF CPU ...\nTF GPU no GPU detected\nPyTorch CPU min: 3995.89, median: 4348.14, mean: 4364.10\nPyTorch GPU no GPU detected\n\nc5.9xlarge\nnumpy default min: 294.77, median: 295.98, mean: 365.78\nnumpy gesvd min: 1012.98, median: 1014.93, mean: 1015.95\nnumpy gesdd min: 295.27, median: 296.04, mean: 296.12\nTF CPU min: 1115.32, median: 1118.19, mean: 1122.77\nTF GPU no GPU detected\nPyTorch CPU min: 867.44, median: 967.25, mean: 965.24\nPyTorch GPU no GPU detected\n\nc5.18xlarge\nnumpy default min: 342.08, median: 343.94, mean: 346.90\nnumpy gesvd min: 954.54, median: 956.79, mean: 958.16\nnumpy gesdd min: 346.61, median: 348.00, mean: 348.26\nTF CPU min: 1165.11, median: 1170.42, mean: 1174.21\nTF GPU no GPU detected\nPyTorch CPU min: 1004.49, median: 1091.94, mean: 1087.53\nPyTorch GPU no GPU detected\n\np3.16xlarge\nnumpy default min: 341.51, median: 342.65, mean: 408.34\nnumpy gesvd min: 1264.59, median: 1265.48, mean: 1266.08\nnumpy gesdd min: 341.22, median: 341.69, mean: 342.37\nTF CPU min: 1279.98, median: 1285.51, mean: 1292.32\nTF GPU min: 6962.91, median: 7006.48, mean: 8967.89\nPyTorch CPU min: 1048.54, median: 1226.51, mean: 1269.30\nPyTorch GPU min: 506.14, median: 511.30, mean: 513.09\n\nResults for i3.metal\nnumpy default min: 266.03, median: 266.51, mean: 298.61\nnumpy gesvd min: 1324.47, median: 1326.57, mean: 1327.88\nnumpy gesdd min: 268.66, median: 269.29, mean: 269.50\nTF CPU min: 1086.69, median: 1090.78, mean: 1101.72\nTF GPU no GPU detected\nPyTorch CPU min: 1169.54, median: 1283.56, mean: 1279.05\nPyTorch GPU no GPU detected\n\nResults for t3.2xlarge\nnumpy default min: 993.98, median: 1010.18, mean: 1055.33\nnumpy gesvd min: 3493.65, median: 3518.31, mean: 3517.02\nnumpy gesdd min: 992.74, median: 1013.63, mean: 1010.28\nTF CPU min: 2118.30, median: 2159.09, mean: 2159.78\nTF GPU no GPU detected\nPyTorch CPU min: 2990.92, median: 3372.94, mean: 3385.87\nPyTorch GPU no GPU detected\n\nResults for m5.24xlarge\nnumpy default min: 353.14, median: 355.11, mean: 404.81\nnumpy gesvd min: 1103.44, median: 1105.18, mean: 1105.80\nnumpy gesdd min: 349.03, median: 350.66, mean: 350.80\nTF CPU min: 1156.68, median: 1160.73, mean: 1165.63\nTF GPU no GPU detected\nPyTorch CPU min: 1087.01, median: 1129.60, mean: 1143.58\nPyTorch GPU no GPU detected\n\n```" }, { "path": "linalg-benchmark/bad_matrix.py", "content": "from scipy import linalg # for svd\nimport urllib.request\nimport numpy as np\n\nurl=\"https://storage.googleapis.com/tensorflow-community-wheels/svd_in\"\nresponse = urllib.request.urlopen(url)\nbody = response.read()\nprint(\"Read %d bytes\"%(len(body),))\nassert len(body) == 15366400\nopen(\"svd_in\", \"wb\").write(body)\n\ndtype = np.float32\nmatrix0 = np.genfromtxt('svd_in',\n delimiter= \",\").astype(dtype)\nassert matrix0.shape == (784, 784)\nu, s, v = linalg.svd(matrix0)\nprint(\"matrix0 any NaNs: %s\"% (np.isnan(matrix0).any(),))\nprint(\"u had NaNs: %s\"% (np.isnan(u).any(),))\n" }, { "path": "linalg-benchmark/benchmark.py", "content": "\"\"\"\nRunning on I7/GTX 1080\n\nMKL version b'Intel(R) Math Kernel Library Version 2017.0.3 Product Build 20170413 for Intel(R) 64 architecture applications'\nTF version: b'v1.3.0-rc1-3233-g07bf1d3'\nTF url: https://github.com/tensorflow/tensorflow/commit/07bf1d3\nPyTorch version 0.2.0_4\nTiming in ms for 1534 x 1534 SVD of type \nnumpy default min: 328.32, median: 328.76, mean: 343.25\nnumpy gesvd min: 1424.12, median: 1425.25, mean: 1447.09\nnumpy gesdd min: 243.74, median: 243.98, mean: 244.40\nTF CPU min: 965.73, median: 1118.91, mean: 1089.15\nTF GPU min: 5525.59, median: 5726.54, mean: 5987.72\nPyTorch CPU min: 1241.66, median: 1372.49, mean: 1389.59\nPyTorch GPU min: 450.84, median: 455.17, mean: 471.32\n\nNotes:\n352->328 on c5.18\nnumactl --cpunodebind=0 --membind=0 python benchmark.py\n\n352->283 on c5.18\nOMP_NUM_THREADS=16\n\n \n\n\"\"\"\n\nimport scipy\nfrom scipy import linalg # for svd\nimport numpy as np\nimport os\nimport sys\nimport time\nimport torch\nimport tensorflow as tf\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # nospam\n\nHAVE_GPU = tf.test.is_gpu_available()\nNUM_RUNS = 11\ndtype = np.float32\nN = int(os.environ.get('LINALG_BENCHMARK_N', 1534))\n\n\ndef main():\n global sess\n\n if np.__config__.get_info(\"lapack_mkl_info\"):\n print(\"MKL version\", get_mkl_version())\n else:\n print(\"not using MKL\")\n\n print(\"TF version: \", tf.__git_version__, get_tensorflow_version_url())\n print(\"PyTorch version\", torch.version.__version__)\n\n print(\"Scipy version: \", scipy.version.full_version)\n print(\"Numpy version: \", np.version.full_version)\n # print(\"Python version: \", sys.version)\n print_cpu_info()\n\n np_data = np.random.random((N, N)).astype(dtype)\n print(\"Timing in ms for %d x %d SVD\" % (N, N))\n\n def func():\n linalg.svd(np_data)\n\n benchmark(\"numpy default\", func)\n if 'LINALG_BENCHMARK_SHORT' in os.environ:\n return\n\n def func():\n linalg.svd(np_data, lapack_driver='gesvd')\n\n benchmark(\"numpy gesvd\", func)\n\n def func():\n linalg.svd(np_data, lapack_driver='gesdd')\n\n benchmark(\"numpy gesdd\", func)\n\n def func():\n torch.svd(torch.rand((N, N)))\n\n benchmark(\"PyTorch CPU\", func)\n\n def func():\n torch.svd(torch.rand((N, N)).cuda())\n torch.cuda.synchronize()\n\n benchmark(\"PyTorch GPU\", func)\n\n # do TensorFlow last because:\n # 1. it hogs all GPU memory by default\n # 2. if it runs out of GPU memory, instead of throwing exception it crashes\n # the entire process with something like\n # F Check failed: cusolverDnCreate(&cusolver_dn_handle) == CUSOLVER_STATUS_SUCCESS Failed to create cuSolverDN instance.\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n # have to assign to variable, otherwise TF optimizes it out\n with tf.device(\"/cpu:0\"):\n variable = tf.Variable(tf.zeros((N, N)), dtype=dtype)\n data = tf.random_uniform((N, N), dtype=dtype)\n # s/u/v convention https://github.com/tensorflow/tensorflow/pull/13850\n s, u, v = tf.svd(data)\n svd_assign = variable.assign(u)\n\n def func():\n sess.run(svd_assign)\n\n benchmark(\"TF CPU\", func)\n\n with tf.device(\"/gpu:0\"):\n variable = tf.Variable(tf.zeros((N, N)), dtype=dtype)\n data = tf.random_uniform((N, N), dtype=dtype)\n s, u, v = tf.svd(data)\n svd_assign = variable.assign(u)\n\n def func():\n sess.run(svd_assign)\n\n benchmark(\"TF GPU\", func)\n\n\ndef get_tensorflow_version_url():\n version = tf.__version__\n commit = tf.__git_version__\n # commit looks like this\n # 'v1.0.0-65-g4763edf-dirty'\n commit = commit.replace(\"'\", \"\")\n if commit.endswith('-dirty'):\n dirty = True\n commit = commit[:-len('-dirty')]\n commit = commit.rsplit('-g', 1)[1]\n url = 'https://github.com/tensorflow/tensorflow/commit/' + commit\n return url\n\n\ndef get_mkl_version():\n import ctypes\n import numpy as np\n\n # this recipe only works on Linux\n try:\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n except:\n return 'unknown'\n\n\ntimeline_counter = 0\nrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n\n\ndef traced_run(fetches):\n \"\"\"Runs fetches, dumps timeline files in current directory.\"\"\"\n\n from tensorflow.python.client import timeline\n global sess\n\n global timeline_counter\n run_metadata = tf.RunMetadata()\n\n results = sess.run(fetches,\n options=run_options,\n run_metadata=run_metadata)\n tl = timeline.Timeline(step_stats=run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format(show_memory=True,\n show_dataflow=False)\n open(\"timeline_%d.json\" % (timeline_counter,), \"w\").write(ctf)\n open(\"stepstats_%d.pbtxt\" % (timeline_counter,), \"w\").write(str(\n run_metadata.step_stats))\n timeline_counter += 1\n return results\n\n\ndef benchmark(message, func):\n if 'gpu' in message.lower() and not HAVE_GPU:\n print(f\"{message:<20} no GPU detected\")\n return\n time_list = []\n try:\n for i in range(NUM_RUNS):\n start_time = time.perf_counter()\n func()\n time_list.append(time.perf_counter() - start_time)\n\n time_list = 1000 * np.array(time_list) # get seconds, convert to ms\n if len(time_list) > 0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\" % (d,) for d in time_list[:10]]\n result = f\"min: {min:8.2f}, median: {median:8.2f}, mean: {np.mean(time_list):8.2f}\"\n else:\n result = \"empty\"\n print(f\"{message:<20} {result}\")\n except Exception as e:\n print(f\"{message:<20} failed with {e}\")\n sys.stdout.flush()\n\n\ndef print_cpu_info():\n ver = 'unknown'\n try:\n for l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n ver = l\n break\n except:\n pass\n\n # core counts from https://stackoverflow.com/a/23378780/419116\n print(\"CPU version: \", ver)\n sys.stdout.write(\"CPU logical cores: \")\n sys.stdout.flush()\n os.system(\n \"echo $([ $(uname) = 'Darwin' ] && sysctl -n hw.logicalcpu_max || lscpu -p | egrep -v '^#' | wc -l)\")\n sys.stdout.write(\"CPU physical cores: \")\n sys.stdout.flush()\n os.system(\n \"echo $([ $(uname) = 'Darwin' ] && sysctl -n hw.physicalcpu_max || lscpu -p | egrep -v '^#' | sort -u -t, -k 2,4 | wc -l)\")\n\n # get mapping of logical cores to physical sockets\n import re\n socket_re = re.compile(\n \".*?processor.*?(?P\\d+).*?physical id.*?(?P\\d+).*?power\",\n flags=re.S)\n from collections import defaultdict\n socket_dict = defaultdict(list)\n try:\n for cpu, socket in socket_re.findall(open('/proc/cpuinfo').read()):\n socket_dict[socket].append(cpu)\n except FileNotFoundError:\n pass\n print(\"CPU physical sockets: \", len(socket_dict))\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "linalg-benchmark/environment.yml", "content": "name: benchmark\nchannels:\n- anaconda\n- pytorch\ndependencies:\n- python=3.6\n- mkl\n- pytorch\n- scipy\n- numpy\n- pip:\n - tensorflow-gpu\n" }, { "path": "linalg-benchmark/get_cores_per_socket.py", "content": "#!/usr/bin/env python\n\"\"\"Simple script to parse cpuinfo and generate command to limit to a single physical socket\"\"\"\n\nimport re\nsocket_re = re.compile(\".*?processor.*?(?P\\d+).*?physical id.*?(?P\\d+).*?power\", flags=re.S)\nfrom collections import defaultdict\nsocket_dict = defaultdict(list)\nfor cpu, socket in socket_re.findall(open('/proc/cpuinfo').read()):\n socket_dict[socket].append(cpu)\n\n\nfor socket,cpus in socket_dict.items():\n print('to set to socket', socket)\n print('export GOMP_CPU_AFFINITY=%s'%(','.join(cpus)))\n" }, { "path": "linalg-benchmark/launch.py", "content": "#!/usr/bin/env python\n# Run linalg benchmark on AWS\n\nimport argparse\nimport ncluster\nncluster.set_backend('aws')\n\nimport threading\n\nparser = argparse.ArgumentParser(description='launch')\nparser.add_argument('--instances', default='p3.16xlarge, c5.18xlarge, c5.9xlarge, m5.24xlarge, i3.metal, g3.16xlarge')\nparser.add_argument('--image', default=\"Deep Learning AMI (Amazon Linux) Version 15.0\")\nparser.add_argument('--N', default='')\nparser.add_argument('--short', action='store_true', help='short version of benchmark')\nargs = parser.parse_args()\n\nresults = {}\ndef launch(instance):\n \"\"\"Run benchmark on given instance type.\"\"\"\n task = ncluster.make_task('benchmark-'+instance, instance_type=instance, image_name=args.image)\n task.upload('benchmark.py')\n task.run('source activate tensorflow_p36')\n task.run('pip install torch')\n task.run('export CUDA_VISIBLE_DEVICES=0')\n if args.N:\n task.run(f'export LINALG_BENCHMARK_N={args.N}')\n if args.short:\n task.run(f'export LINALG_BENCHMARK_SHORT={args.N}')\n \n stdout, stderr = task.run_with_output('python benchmark.py')\n print('='*80)\n print(instance)\n print(stdout)\n\n\ndef main():\n # launch \n threads = []\n for instance in args.instances.split(','):\n instance = instance.strip()\n thread = threading.Thread(target=launch, args=[instance])\n thread.start()\n threads.append(thread)\n for thread in threads:\n thread.join()\n\n\n\nif __name__=='__main__':\n main()\n" }, { "path": "linalg-benchmark/launch_tensorflow_svd_crash.py", "content": "#!/usr/bin/env python\n# Run crashing TensorFlow SVD example\n\nimport ncluster\nncluster.set_backend('aws')\n\nimport argparse\nparser = argparse.ArgumentParser(description='launch')\nparser.add_argument('--instance', default='c5.9xlarge')\nparser.add_argument('--image', default=\"Deep Learning AMI (Amazon Linux) Version 13.0\")\nargs = parser.parse_args()\n\ndef main():\n task = ncluster.make_task(instance_type=args.instance,\n image_name=args.image)\n task.run('source activate tensorflow_p36')\n task.upload('tensorflow_svd_crash.py')\n stdout, stderr = task.run_with_output('python tensorflow_svd_crash.py')\n print(stdout, stderr)\n\nif __name__=='__main__':\n main()\n" }, { "path": "linalg-benchmark/requirements.txt", "content": "# mkl is conda only\nnumpy\nscipy\ntensorflow-gpu\ntorch\n" }, { "path": "linalg-benchmark/results.txt", "content": "================================================================================\n2015 MacBook Pro\n\nMKL version unknown\nTF version: v1.1.0-rc0-61-g1ec6ed5 https://github.com/tensorflow/tensorflow/commit/1ec6ed5\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.15.1\nPython version: 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:07:29) \n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\nCPU version: unknown\nCPU logical cores\n8\nCPU physical cores\n4\nCPU physical sockets: 0\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 445.74, median: 480.14, mean: 491.55\nnumpy gesvd min: 2123.99, median: 2551.53, mean: 2552.81\nnumpy gesdd min: 584.27, median: 691.75, mean: 686.09\nTF CPU min: 2115.46, median: 2157.92, mean: 2228.17\nTF GPU no GPU detected\nPyTorch CPU min: 2468.93, median: 3316.59, mean: 3295.61\nPyTorch GPU no GPU detected\n\n================================================================================\nResults for c5.9xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz\nCPU logical cores: 36\nCPU physical cores: 18\nCPU physical sockets: 1\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 284.66, median: 285.98, mean: 288.92\nnumpy gesvd min: 904.89, median: 906.37, mean: 906.35\nnumpy gesdd min: 284.46, median: 285.97, mean: 285.74\nPyTorch CPU min: 906.29, median: 1007.70, mean: 990.27\nPyTorch GPU no GPU detected\nTF CPU min: 1104.72, median: 1109.49, mean: 1117.22\nTF GPU no GPU detected\n\n================================================================================\nResults for m5.24xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz\nCPU logical cores: 96\nCPU physical cores: 48\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 354.46, median: 355.40, mean: 358.34\nnumpy gesvd min: 1041.56, median: 1045.46, mean: 1045.10\nnumpy gesdd min: 359.50, median: 360.73, mean: 361.08\nPyTorch CPU min: 981.22, median: 1137.35, mean: 1146.42\nPyTorch GPU no GPU detected\nTF CPU min: 1144.89, median: 1152.68, mean: 1159.44\nTF GPU no GPU detected\n\n================================================================================\nResults for c5.18xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz\nCPU logical cores: 72\nCPU physical cores: 36\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 345.62, median: 346.57, mean: 349.31\nnumpy gesvd min: 1001.29, median: 1005.24, mean: 1005.30\nnumpy gesdd min: 344.10, median: 345.17, mean: 345.64\nPyTorch CPU min: 967.46, median: 1077.40, mean: 1064.79\nPyTorch GPU no GPU detected\nTF CPU min: 1161.04, median: 1165.75, mean: 1173.67\nTF GPU no GPU detected\n\n================================================================================\nResults for i3.metal\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz\nCPU logical cores: 72\nCPU physical cores: 36\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 265.95, median: 266.75, mean: 268.89\nnumpy gesvd min: 1330.13, median: 1332.27, mean: 1332.34\nnumpy gesdd min: 265.25, median: 265.88, mean: 266.33\nPyTorch CPU min: 1057.18, median: 1287.02, mean: 1278.18\nPyTorch GPU no GPU detected\nTF CPU min: 1087.11, median: 1090.07, mean: 1099.65\nTF GPU no GPU detected\n\n================================================================================\nResults for t3.2xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz\nCPU logical cores: 8\nCPU physical cores: 4\nCPU physical sockets: 1\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 996.43, median: 1009.60, mean: 1014.57\nnumpy gesvd min: 3488.97, median: 3507.59, mean: 3509.89\nnumpy gesdd min: 993.81, median: 1005.34, mean: 1005.88\nPyTorch CPU min: 3269.99, median: 3560.03, mean: 3511.52\nPyTorch GPU no GPU detected\nTF CPU min: 2088.35, median: 2135.06, mean: 2137.88\nTF GPU no GPU detected\n\n================================================================================\nResults for p3.16xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz\nCPU logical cores: 64\nCPU physical cores: 32\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 341.24, median: 342.67, mean: 345.49\nnumpy gesvd min: 1331.62, median: 1332.64, mean: 1333.16\nnumpy gesdd min: 341.19, median: 341.50, mean: 341.67\nPyTorch CPU min: 1190.90, median: 1314.86, mean: 1331.97\nPyTorch GPU min: 470.67, median: 476.39, mean: 777.78\nTF CPU min: 1275.53, median: 1298.05, mean: 1297.57\nTF GPU min: 7037.22, median: 7098.86, mean: 7126.14\n\n\n\n\n\n================================================================================\nr5.large\n\n\nr5.large\nnumpy default min: 1538.54, median: 1546.51, mean: 1573.60\nnumpy gesvd min: 33868.77, median: 33902.69, mean: 33965.48\nnumpy gesdd min: 1521.05, median: 1550.09, mean: 1549.20\nTF CPU min: ---\nTF GPU no GPU detected\nPyTorch CPU min: 3995.89, median: 4348.14, mean: 4364.10\nPyTorch GPU no GPU detected\n\n================================================================================\nResults for c5.18xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz\nCPU logical cores: 72\nCPU physical cores: 36\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\n\nnumpy default min: 347.58, median: 348.98, mean: 351.79\nnumpy gesvd min: 1105.08, median: 1109.61, mean: 1109.05\nnumpy gesdd min: 352.56, median: 353.81, mean: 354.36\nTF CPU min: 1168.50, median: 1170.80, mean: 1175.00\nTF GPU no GPU detected\nPyTorch CPU min: 917.79, median: 1072.18, mean: 1063.07\nPyTorch GPU no GPU detected\n\n================================================================================\n\nResults for p3.16xlarge\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name\t: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz\nCPU logical cores: 64\nCPU physical cores: 32\nCPU physical sockets: 2\nTiming in ms for 1534 x 1534 SVD\nnumpy default min: 341.51, median: 342.65, mean: 408.34\nnumpy gesvd min: 1264.59, median: 1265.48, mean: 1266.08\nnumpy gesdd min: 341.22, median: 341.69, mean: 342.37\nTF CPU min: 1279.98, median: 1285.51, mean: 1292.32\nTF GPU min: 6962.91, median: 7006.48, mean: 8967.89\nPyTorch CPU min: 1048.54, median: 1226.51, mean: 1269.30\nPyTorch GPU min: 506.14, median: 511.30, mean: 513.09\n\n================================================================================\n\nMKL version b'Intel(R) Math Kernel Library Version 2018.0.1 Product Build 20171007 for Intel(R) 64 architecture applications'\nTF version: v1.10.0-4-g0e53c66f33 https://github.com/tensorflow/tensorflow/commit/0e53c66f33\nPyTorch version 0.4.1\nScipy version: 1.1.0\nNumpy version: 1.14.2\nCPU version: model name : Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz\nCPU logical cores: 72\nCPU physical cores: 36\nCPU physical sockets: 2\nTiming in ms for 6000 x 6000 SVD\nnumpy default min: 8693.95, median: 8710.50, mean: 8742.25\nnumpy gesvd min: 20328.73, median: 20365.47, mean: 20361.31\nnumpy gesdd min: 8686.14, median: 8698.31, mean: 8702.77\nPyTorch CPU min: 19831.50, median: 20389.32, mean: 20488.78\nPyTorch GPU no GPU detected\nTF CPU min: 74721.57, median: 74980.07, mean: 74959.97\nTF GPU no GPU detected\n" }, { "path": "linalg-benchmark/tensorflow_svd_crash.py", "content": "# matrix\n# https://storage.cloud.google.com/tensorflow-community-wheels/svd_in\n# Mathematica sanity check\n# https://www.wolframcloud.com/objects/f16d71a7-cc47-4a3d-b686-da440670eed3\n\nimport tensorflow as tf\nimport numpy as np\nimport os\n\n\nif not os.path.exists(\"svd_in\"):\n import urllib.request\n url=\"https://storage.googleapis.com/tensorflow-community-wheels/svd_in\"\n response = urllib.request.urlopen(url)\n body = response.read()\n print(\"Read %d bytes\"%(len(body),))\n assert len(body) == 15366400\n open(\"svd_in\", \"wb\").write(body)\n\n # import requests\n # r = requests.get(url, auth=('usrname', 'password'), verify=False,stream=True)\n # r.raw.decode_content = True\n # with open(\"svd_in\", 'wb') as f:\n # shutil.copyfileobj(r.raw, f)\n\ndtype = np.float32\nmatrix0 = np.genfromtxt('svd_in',\n delimiter= \",\").astype(dtype)\nprint(matrix0.shape)\nassert matrix0.shape == (784, 784)\nmatrix = tf.placeholder(dtype)\nsess = tf.InteractiveSession()\ns0,u0,v0 = sess.run(tf.svd(matrix), feed_dict={matrix: matrix0})\nprint(\"u any NaNs: %s\"% (np.isnan(u0).any(),))\nprint(\"u all NaNs: %s\"% (np.isnan(u0).all(),))\nprint(\"matrix0 any NaNs: %s\"% (np.isnan(matrix0).any(),))\n\n# segfault bt\n# #0 0x00007fffe320e121 in Eigen::BDCSVD >::perturbCol0(Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Matrix const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> >) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #1 0x00007fffe320fa81 in Eigen::BDCSVD >::computeSVDofM(long, long, Eigen::Matrix&, Eigen::Matrix&, Eigen::Matrix&) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #2 0x00007fffe321e21c in Eigen::BDCSVD >::divide(long, long, long, long, long) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #3 0x00007fffe321dbb8 in Eigen::BDCSVD >::divide(long, long, long, long, long) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #4 0x00007fffe32220bd in Eigen::BDCSVD >::compute(Eigen::Matrix const&, unsigned int) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #5 0x00007fffe32227a1 in tensorflow::SvdOp::ComputeMatrix(tensorflow::OpKernelContext*, tensorflow::gtl::InlinedVector const, 0, Eigen::Stride<0, 0> >, 4> const&, tensorflow::gtl::InlinedVector, 0, Eigen::Stride<0, 0> >, 4>*) () from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so #6 0x00007fffe3228c75 in tensorflow::LinearAlgebraOp::ComputeTensorSlice(tensorflow::OpKernelContext*, long long, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n \n\n# upload notes\n\n# export fullname=svd_in\n# export bucket=tensorflow-community-wheels\n# cd ~/git0/whitening/mnist_autoencoder/data\n# gsutil cp svd_in gs://$bucket\n# gsutil acl set public-read gs://$bucket/$fullname\n# echo https://storage.googleapis.com/tensorflow-community-wheels/$fullname\n" }, { "path": "line_search_example/data/step_lengths_ada.csv", "content": 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n" }, { "path": "line_search_example/line_search_example.py", "content": "\"\"\"Example of doing backtracking line-search on MNIST autoencoder.\nNeeds:\ntensorflow\nnumpy\nkeras\n\n\"\"\"\n\nimport numpy as np\nimport math\nimport time\n\nimport os, sys\n\nimport tensorflow as tf\nimport util as u\nfrom util import t # transpose\n\nfrom util import t # transpose\nfrom util import c2v\nfrom util import v2c\nfrom util import v2c_np\nfrom util import v2r\nfrom util import kr # kronecker\nfrom util import Kmat # commutation matrix\n\ndef W_uniform(s1, s2):\n # sample two s1,s2 matrices \n r = np.sqrt(6) / np.sqrt(s1 + s2 + 1)\n return np.random.random(2*s2*s1)*2*r-r\n\n\nif __name__=='__main__':\n np.random.seed(0)\n tf.set_random_seed(0)\n dtype = np.float32\n \n from keras.datasets import mnist\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\n X_train = X_train.astype(np.float32)\n X_train = X_train.reshape((X_train.shape[0], -1))\n X_test = X_test.astype(np.float32)\n X_test = X_test.reshape((X_test.shape[0], -1))\n X_train /= 255\n X_test /= 255\n\n \n\n dsize = 100\n #patches = train_images[:,:dsize];\n patches = X_train[:dsize,:].T\n \n fs = [dsize, 28*28, 196, 28*28]\n\n fs=fs\n X0=patches\n lambda_=3e-3\n rho=0.1\n beta=3\n W0f=None\n \n if not W0f:\n W0f = W_uniform(fs[2],fs[3])\n rho = tf.constant(rho, dtype=dtype)\n\n def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]\n dsize = f(-1)\n n = len(fs) - 2\n\n init_dict = {}\n def init_var(val, name, trainable=False):\n val = np.array(val)\n holder = tf.placeholder(dtype, shape=val.shape, name=name+\"_holder\")\n var = tf.Variable(holder, name=name+\"_var\", trainable=trainable)\n init_dict[holder] = val\n return var\n\n lr = init_var(0.01, \"lr\")\n Wf = init_var(W0f, \"Wf\", True)\n Wf_copy = init_var(W0f, \"Wf_copy\")\n W = u.unflatten(Wf, fs[1:])\n X = init_var(X0, \"X\")\n W.insert(0, X)\n\n def sigmoid(x):\n return tf.sigmoid(x)\n def d_sigmoid(y):\n return y*(1-y)\n def kl(x, y):\n return x * tf.log(x / y) + (1 - x) * tf.log((1 - x) / (1 - y))\n def d_kl(x, y):\n return (1-x)/(1-y) - x/y\n \n # A[i] = activations needed to compute gradient of W[i]\n # A[n+1] = network output\n A = [None]*(n+2)\n A[0] = u.Identity(dsize, dtype=dtype)\n A[1] = W[0]\n for i in range(1, n+1):\n A[i+1] = sigmoid(W[i] @ A[i])\n \n\n # reconstruction error and sparsity error\n err = (A[3] - A[1])\n rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize\n\n # B[i] = backprops needed to compute gradient of W[i]\n B = [None]*(n+1)\n B[n] = err*d_sigmoid(A[n+1])\n for i in range(n-1, -1, -1):\n backprop = t(W[i+1]) @ B[i+1]\n if i == 1:\n backprop += beta*d_kl(rho, rho_hat)\n B[i] = backprop*d_sigmoid(A[i+1])\n\n # dW[i] = gradient of W[i]\n dW = [None]*(n+1)\n for i in range(n+1):\n dW[i] = (B[i] @ t(A[i]))/dsize\n\n # Cost function\n reconstruction = u.L2(err) / (2 * dsize)\n sparsity = beta * tf.reduce_sum(kl(rho, rho_hat))\n L2 = (lambda_ / 2) * (u.L2(W[1]) + u.L2(W[1]))\n cost = reconstruction + sparsity + L2\n\n grad = u.flatten(dW[1:])\n copy_op = Wf_copy.assign(Wf-lr*grad)\n with tf.control_dependencies([copy_op]):\n train_op = tf.group(Wf.assign(Wf_copy)) # to make it an op\n\n sess = tf.InteractiveSession()\n\n # step_len = init_var(tf.constant(0.1), \"step_len\", False)\n # step_len_assign = step_len.assign(step_len0)\n step_len0 = tf.placeholder(dtype, shape=())\n \n Wf2 = init_var(W0f, \"Wf2\")\n Wf_save_op = Wf2.assign(Wf)\n Wf_restore_op = Wf.assign(Wf2)\n grad2 = init_var(W0f, \"grad2\")\n grad_save_op = grad2.assign(grad)\n grad2_norm_op = tf.reduce_sum(tf.square(grad2))\n Wf_step_op = Wf.assign(Wf2 - step_len0*grad2)\n lr_p = tf.placeholder(lr.dtype, lr.shape)\n lr_set = lr.assign(lr_p)\n\n def save_wf(): sess.run(Wf_save_op)\n def restore_wf(): sess.run(Wf_restore_op)\n def save_grad(): sess.run(grad_save_op)\n def step_wf(step):\n # sess.run(step_len_assign, feed_dict={step_len0: step})\n sess.run(Wf_step_op, feed_dict={step_len0: step}) \n \n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n\n \n print(\"Running training.\")\n do_images = True\n u.reset_time()\n old_cost = sess.run(cost)\n old_i = 0\n frame_count = 0\n\n step_lengths = []\n costs = []\n ratios = []\n do_bt = False # do backtracking line-search\n alpha=0.3\n beta=0.8\n growth_rate = 1.05\n for i in range(10000):\n # save Wf and grad into Wf2 and grad2\n save_wf()\n save_grad()\n cost0 = cost.eval()\n train_op.run()\n lr0 = lr.eval()\n cost1 = cost.eval()\n # cost1, _ = sess.run([cost, train_op])\n target_delta = -alpha*lr0*grad2_norm_op.eval()\n expected_delta = -lr0*grad2_norm_op.eval()\n actual_delta = cost1 - cost0\n actual_slope = actual_delta/lr0\n expected_slope = -grad2_norm_op.eval()\n\n # ratio of best possible slope to actual slope\n # don't divide by actual slope because that can be 0\n slope_ratio = abs(actual_slope)/abs(expected_slope)\n costs.append(cost0)\n step_lengths.append(lr0)\n ratios.append(slope_ratio)\n\n if i%10 == 0:\n print(\"Learning rate: %f\"% (lr0,))\n print(\"Cost %.2f, expected decrease %.2f, actual decrease, %.2f ratio %.2f\"%(cost0, expected_delta, actual_delta, slope_ratio))\n\n # don't shrink learning rate once results are very close to minimum\n if slope_ratio < alpha and abs(target_delta)>1e-6:\n print(\"%.2f %.2f %.2f\"%(cost0, cost1, slope_ratio))\n print(\"Slope optimality %.2f, shrinking learning rate to %.2f\"%(slope_ratio, lr0*beta,))\n sess.run(lr_set, feed_dict={lr_p: lr0*beta})\n else:\n # see if our learning rate got too conservative, and increase it\n if i>0 and i%10 == 0 and slope_ratio>0.99:\n print(\"%.2f %.2f %.2f\"%(cost0, cost1, slope_ratio))\n print(\"Growing learning rate to %.2f\"%(lr0*growth_rate))\n sess.run(lr_set, feed_dict={lr_p: lr0*growth_rate})\n\n u.record_time()\n\n u.dump(step_lengths, \"step_lengths_ada.csv\")\n# u.dump(costs, \"costs_ada.csv\")\n# u.dump(ratios, \"ratios_ada.csv\")\n" }, { "path": "line_search_example/util.py", "content": "import socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=True # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n\n\nfrom scipy import linalg\n\n# TODO: speed-up tests by reusing session\n\nargs = None\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__ == 'Tensor' or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n logger = u.get_last_logger()\n logger('time/'+self.tag, interval_ms)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n \n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n\n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger():\n global global_last_logger\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n self.summary.value.add(tag='time/step',\n simple_value=(new_timestamp-self.last_timestamp))\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n global sess\n assert sess\n return sess\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n \nif __name__=='__main__':\n run_all_tests(sys.modules[__name__])\n" }, { "path": "linearize/linearize.py", "content": "\"\"\"Library to force a memory-efficient execution order on TensorFlow graph.\n\"\"\"\n\nimport tensorflow as tf\nimport tensorflow.contrib.graph_editor as ge\n\nfrom collections import OrderedDict\n\nDEBUG = False\n\ndef run_after(a, b):\n \"\"\"Force operation a to run after b. Do not add control dependencies\n to ops that already run after. Returns 0 if no dependencies were added,\n 1 otherwise.\"\"\"\n\n already_after = (b in a.control_inputs) or (b in [i.op for i in a.inputs])\n\n if already_after:\n return 0\n ge.reroute.add_control_inputs(a, [b])\n return 1\n\n\ncontrols = None\ncontrols_graph = None\ndef initialize_control_outputs(g):\n global controls\n global controls_graph\n controls_graph = g\n controls = tf.contrib.graph_editor.ControlOutputs(g)\n\n\n################################################################################\n# Graph ops\n################################################################################\n# Computation flows from parents to children.\n#\n# Two sets of dependencies, regular (parents/children) and regular + control\n# parents_with_controls, children_with_controls.\n#\n# The former is more relevant for estimating memory usage because a control\n# dependency on node doesn't imply that its outputs have to be kept in memory,\n# whereas input dependency does.\n\n# TODO(y): rename for consistency with memsorted\ndef nodesort(ops):\n \"\"\"sort list by op.name.\"\"\"\n return sorted(ops, key=lambda op: op.name)\n\n\ndef parents_with_controls(op):\n result = set(input.op for input in op.inputs)\n result.update(op.control_inputs)\n return nodesort(result)\n\n\ndef parents(op):\n return nodesort(set(input.op for input in op.inputs))\n\n\ndef children(op):\n return nodesort(set(op_ for out in op.outputs for op_ in out.consumers()))\n\n\ndef children_with_controls(op):\n \"\"\"Returns children, counting control outputs as children. Assumes\n initialize_control_input has been called.\"\"\"\n\n if not controls:\n initialize_control_outputs(op.graph)\n assert controls_graph == op.graph, (\"graph changed, rerun \"\n \"initialize_control_outputs\")\n result = set(op for out in op.outputs for op in out.consumers())\n result.update(controls.get(op))\n return nodesort(result)\n\n\ndef get_graph(g=None, as_hashes=False, exclude_controls=False):\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph (control edges included).\n Args:\n g: tf.Graph to use or None, in which case take default graph\n as_hashes: whether to replace nodes with their hashes (needed for\n nx.DiGraph)\n exclude_controls: if True, don't count control deps as edges\n \"\"\"\n\n if not g:\n g = tf.get_default_graph()\n\n result = OrderedDict()\n for op in nodesort(g.get_operations()):\n if as_hashes:\n key = hash(op)\n else:\n key = op\n if exclude_controls:\n result[key] = children(op)\n else:\n result[key] = children_with_controls(op)\n return result\n\ndef print_tf_graph(graph):\n \"\"\"Prints tensorflow graph in dictionary form.\"\"\"\n\n for node in graph:\n for child in graph[node]:\n print(\"%s -> %s\" % (node.name, child.name))\n\n\ndef memsorted(nodes):\n \"\"\"Sort nodes by estimated memory usage.\"\"\"\n\n def node_memory(unused_node, default_memory=1):\n return default_memory\n def node_name(node):\n return node.name\n\n def subtree_memory(node):\n return (node_memory(node) + sum(node_memory(parent) for\n parent in parents(node)))\n\n # sort by estimated memory, break ties in reverse alphabetical order\n # this gives regular alphabetic order when used with linearize\n nodes = sorted(nodes, key=node_name, reverse=True)\n nodes = sorted(nodes, key=subtree_memory)\n return nodes\n\n\ndef is_iterable(o):\n try:\n _ = iter(o)\n except Exception:\n return False\n return True\n\n\n# TODO(y): prevent deadlocks by avoiding linearizing anything with downstream\n# Assign dependencies\ndef linearize(targets=None, modify_graph=True):\n \"\"\"Obtain a single valid execution order which approximately minimizes\n peak memory usage.\n Args:\n targets: specifies list of computation Tensor or op targets. Nones are\n skipped. If not specified, all terminal nodes that are not Assign\n /AssignAdd/NoOp nodes are considered targets. The reason for excluding\n these ops is because they are usually targets for variable modifying\n session.run calls, whereas linearization assumes graph is purely\n functional.\n modify_graph: if True, will add control dependencies to force this order\n Returns:\n Number of control dependencies that were added if modify_graph=True,\n otherwise returns list of ops in this order.\n \"\"\"\n\n g = tf.get_default_graph()\n initialize_control_outputs(g) # needed for children_with_controls\n graph = get_graph(g)\n\n # The algorithm works by keeping an \"active\" set nodes that have no\n # unscheduled children, hence are ready for execution. At each iteration,\n # schedule all nodes from this set for execution with least memory-hungry\n # nodes first and repeat to convergence.\n\n def to_op(tensor_or_op):\n if hasattr(tensor_or_op, \"op\"):\n return tensor_or_op.op\n return tensor_or_op\n \n if is_iterable(targets):\n active = [to_op(target) for target in targets if target is not None]\n elif targets is not None:\n active = [to_op(targets)]\n else:\n active = []\n for node in graph:\n if not graph[node]: # no children\n if (node.type != \"Assign\" and node.type != \"AssignAdd\" and\n node.type != \"NoOp\"):\n active.append(node)\n\n last_node = None\n\n # count of unscheduled children for each node\n unscheduled = OrderedDict()\n for node in graph:\n unscheduled[node] = len(graph[node])\n control_edges_added = 0\n order = []\n while active:\n new_active = []\n for node in memsorted(active):\n assert unscheduled[node] == 0\n\n order.append(node)\n if DEBUG:\n print(\"Executing \", node.name)\n\n if last_node:\n if modify_graph:\n control_edges_added += run_after(last_node, node)\n last_node = node\n \n\n # this node is scheduled, so update unscheduled counts of parents\n # TODO(y) do we need this second memsorted?\n for parent in memsorted(parents_with_controls(node)):\n assert unscheduled[parent] > 0\n unscheduled[parent] -= 1\n if unscheduled[parent] == 0:\n new_active.append(parent)\n\n active = new_active\n\n if modify_graph:\n return control_edges_added\n else:\n return reversed(order)\n \n" }, { "path": "linearize/linearize_test.py", "content": "import linearize\n\nimport os, sys, time\nimport inspect\nimport numpy as np\nimport tensorflow as tf\nimport pdb\nimport math\nimport toposort\n\nfrom tensorflow.python.ops import gen_random_ops\n\ndef create_session():\n config = tf.ConfigProto(log_device_placement=False, graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\n return tf.InteractiveSession(config=config)\n\ndef setup_env():\n \"\"\"Sets up test enviornment.\"\"\"\n \n # download memory_util if needed\n memory_util_url = \"https://raw.githubusercontent.com/yaroslavvb/memory_util/master/memory_util.py\"\n if os.path.exists('memory_util.py'):\n size = len(open('memory_util.py').read())\n else:\n size = 0\n \n if size != 13636:\n print(\"Size changed or 0, redownloading memory_util.py\")\n import urllib.request\n response = urllib.request.urlopen(memory_util_url)\n open(\"memory_util.py\", \"wb\").write(response.read())\n\n \ndef make_caterpillar_graph(length=5, node_mbs=1):\n \"\"\"Length is number of concats.\"\"\"\n \n n = node_mbs * 250000\n n2 = int(math.sqrt(n))\n dtype = tf.float32\n \n def make_leaf(i):\n name = \"leaf\"+str(i)\n val = gen_random_ops._random_uniform((n2, n2), dtype, name=name)\n return val\n \n def make_merge(a, b, i):\n name = \"merge\"+str(i)\n merge_node = tf.matmul(a, b, name=name)\n # nonlinear_node = tf.tanh(merge_node, name=\"tanh\"+str(i))\n #nonlinear_node = tf.identity(merge_node, name=\"tanh\"+str(i))\n return merge_node\n\n leaf0 = make_leaf(0)\n node0 = tf.identity(leaf0, name=\"merge0\")\n node = node0\n nodes = [node]\n \n for i in range(1, length+1):\n leaf = make_leaf(i)\n node = make_merge(node, leaf, i)\n nodes.append(node)\n return nodes\n\ndef test_print():\n \"\"\"Should print:\n leaf1 -> merge1\n leaf0 -> merge0\n merge1 -> merge2\n merge0 -> merge1\n leaf2 -> merge2\n leaf0/shape -> leaf0\n leaf1/shape -> leaf1\n leaf2/shape -> leaf2\n \"\"\"\n \n nodes = make_caterpillar_graph(length=2)\n linearize.print_tf_graph(linearize.get_graph())\n \n\ndef test_toposort():\n nodes = make_caterpillar_graph(length=2)\n graph = linearize.get_graph()\n print(list(toposort.toposort(graph)))\n\n\ndef test_linearize():\n nodes = make_caterpillar_graph(5)\n linearize.linearize()\n\n sess = create_session()\n\n import memory_util\n memory_util.vlog(1)\n with memory_util.capture_stderr() as stderr:\n sess.run(nodes[-1].op)\n memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)\n\nif __name__=='__main__':\n setup_env()\n import memory_util\n memory_util.vlog(1)\n \n # sess = create_session()\n #nodes = make_caterpillar_graph()\n # test_print()\n # linearize.print_tf_graph(linearize.get_graph())\n # print(tf.get_default_graph().as_graph_def())\n # test_toposort()\n test_linearize()\n sys.exit()\n # with memory_util.capture_stderr() as stderr:\n # print(sess.run(nodes[-1][0,0]))\n print(len(stderr.getvalue()))\n memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)\n" }, { "path": "linearize/memory_util.py", "content": "import os\nimport re\nimport sys\nimport tempfile\nimport tensorflow as tf\n\ndebug_messages = False\n\ndef vlog(level):\n os.environ['TF_CPP_MIN_VLOG_LEVEL'] = str(level)\n\n# this helper is here in case we later want to capture huge stderr that doesn't fit in RAM\nclass TemporaryFileHelper:\n \"\"\"Provides a way to fetch contents of temporary file.\"\"\" \n def __init__(self, temporary_file):\n self.temporary_file = temporary_file\n def getvalue(self):\n return open(self.temporary_file.name).read() \n\n\nSTDOUT=1\nSTDERR=2\nclass capture_stderr:\n \"\"\"Utility to capture output, use as follows\n with util.capture_stderr() as stderr:\n sess = tf.Session()\n\n print(\"Captured:\", stderr.getvalue()).\n \"\"\"\n\n def __init__(self, fd=STDERR):\n self.fd = fd\n self.prevfd = None\n\n def __enter__(self):\n t = tempfile.NamedTemporaryFile()\n self.prevfd = os.dup(self.fd)\n os.dup2(t.fileno(), self.fd)\n return TemporaryFileHelper(t)\n\n def __exit__(self, exc_type, exc_value, traceback):\n os.dup2(self.prevfd, self.fd)\n\n\n################################################################################\n# LOG_MEMORY_PARSING\n################################################################################\n# Until https://github.com/tensorflow/tensorflow/issues/6716 is resolved, the\n# reliable way to get access to tensor deallocation information is to parse\n# __LOG_MEMORY__ from VLOG print statements. This is sensitive to print order\n# run unbuffered to prevent interleaving:\n# python -u script.py\n\n# Regex'es to parse __LOG_MEMORY__ statements\n# Each regex is preceded by an example of line it's meant to pass\n\n# I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorAllocation { step_id: -6 kernel_name: \"Unknown (from Proto)\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \"cpu\" allocation_id: 3 has_single_reference: true ptr: 29496256 } } }\ntensor_allocation_regex = re.compile(\"\"\"MemoryLogTensorAllocation.*?step_id: (?P[-0123456789]+).*kernel_name: \\\"(?P[^\"]+)\\\".*?allocated_bytes: (?P\\d+).*allocator_name: \\\"(?P[^\"]+)\\\".*allocation_id: (?P\\d+).*\"\"\")\n\n# I 6795349363.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogRawAllocation { step_id: -3 operation: \"TF_AllocateTensor\" num_bytes: 1000000 ptr: 80910752 allocation_id: 99 allocator_name: \"cpu\" }\nraw_allocation_regex = re.compile(\"\"\"MemoryLogRawAllocation.*?step_id: (?P[-0123456789]+).*operation: \\\"(?P[^\"]+)\\\".*?num_bytes: (?P\\d+).*allocation_id: (?P\\d+).*allocator_name: \"(?P[^\"]+)\".*\"\"\")\n\n# I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 1 kernel_name: \"Const\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \"cpu\" allocation_id: 3 ptr: 29496256 } } }\n# 2017-01-26 10:13:30: I tensorflow/core/framework/log_memory.cc:35] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 2 kernel_name: \"a0\" tensor { dtype: DT_FLOAT shape { dim { size: 250000 } } allocation_description { requested_bytes: 1000000 allocated_bytes: 1000192 allocator_name: \"gpu_bfc\" allocation_id: 3 ptr: 30076651520 } } }\n#tensor_output_regex = re.compile(\"\"\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\"(?P[^\"]+).*allocated_bytes: (?P\\d+).*allocation_id: (?P\\d+).*\"\"\") \ntensor_output_regex = re.compile(\"\"\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\"(?P[^\"]+).*allocated_bytes: (?P\\d+).*allocator_name: \\\"(?P[^\"]+)\\\".*allocation_id: (?P\\d+).*\"\"\")\n\n# some Shape lines are missing bytes info so have separate regex for them\n# I 5162643141.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 5 kernel_name: \"gradients/Shape\" tensor { dtype: DT_INT32 shape { dim { } } } }\ntensor_output_regex_no_bytes = re.compile(\"\"\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\"(?P[^\"]+).*\"\"\")\n\n\n# 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorDeallocation { allocation_id: 2 allocator_name: \"cpu\" }\ntensor_deallocation_regex = re.compile(\"\"\"allocation_id: (?P\\d+).*allocator_name: \\\"(?P[^\"]+)\\\".*\"\"\")\n\n# I 6796000229.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogRawDeallocation { step_id: -3 operation: \"TensorFlow C Api\" allocation_id: 177 allocator_name: \"cpu\" }\nraw_deallocation_regex = re.compile(\"\"\"allocation_id: (?P\\d+).*allocator_name: \\\"(?P[^\"]+)\\\".*\"\"\")\n\n# I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogStep { step_id: 1 handle: \"->Print:0//0/;0\" }\ntensor_logstep_regex = re.compile(\"\"\"MemoryLogStep.*?step_id: (?P[-0123456789]+).*\"\"\")\n\n\ndef _parse_logline(l):\n if 'MemoryLogTensorOutput' in l:\n m = tensor_output_regex.search(l)\n if not m:\n m = tensor_output_regex_no_bytes.search(l)\n\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogTensorOutput\"\n \n elif 'MemoryLogTensorAllocation' in l:\n m = tensor_allocation_regex.search(l)\n\n # Broadcast args give weird allocation messages without size, ignore\n # I tensorflow/core/framework/log_memory.cc:35] __LOG_MEMORY__ MemoryLogTensorAllocation { step_id: 2 kernel_name: \"gradients/node_5_grad/BroadcastGradientArgs\" tensor { dtype: DT_INT32 shape { dim { } } } }\n if not m:\n return {\"type\": \"MemoryLogTensorAllocation\", \"line\": l,\n \"allocation_id\": \"-1\"}\n\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogTensorAllocation\"\n if debug_messages:\n print(\"Got allocation for %s, %s\"%(d[\"allocation_id\"], d[\"kernel_name\"]))\n elif 'MemoryLogTensorDeallocation' in l:\n m = tensor_deallocation_regex.search(l)\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogTensorDeallocation\"\n if debug_messages:\n print(\"Got deallocation for %s\"%(d[\"allocation_id\"]))\n elif 'MemoryLogStep' in l:\n m = tensor_logstep_regex.search(l)\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogStep\"\n elif 'MemoryLogRawAllocation' in l:\n m = raw_allocation_regex.search(l)\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogRawAllocation\"\n elif 'MemoryLogRawDeallocation' in l:\n m = raw_deallocation_regex.search(l)\n assert m, l\n d = m.groupdict()\n d[\"type\"] = \"MemoryLogRawDeallocation\"\n else:\n assert False, \"Unknown log line: \"+l\n \n if not \"allocation_id\" in d:\n d[\"allocation_id\"] = \"-1\"\n\n d[\"line\"] = l\n return d\n\ndef memory_timeline(log):\n if hasattr(log, 'getvalue'):\n log = log.getvalue()\n \n def unique_alloc_id(line):\n if line[\"allocation_id\"] == \"-1\":\n return \"-1\"\n return line[\"allocation_id\"]+\"-\"+line[\"allocator_name\"]\n \n def get_alloc_names(line):\n alloc_id = unique_alloc_id(line)\n for entry in reversed(allocation_map.get(alloc_id, [])):\n kernel_name = entry.get(\"kernel_name\", \"unknown\")\n if not \"unknown\" in kernel_name:\n return kernel_name+\"(\"+unique_alloc_id(line)+\")\"\n # couldn't find an allocation message with name of kernel\n return \"(\"+alloc_id+\")\"\n\n def get_alloc_bytes(line):\n for entry in allocation_map.get(unique_alloc_id(line), []):\n if \"allocated_bytes\" in entry:\n return entry[\"allocated_bytes\"]\n return \"0\"\n\n def get_alloc_type(line):\n for entry in allocation_map.get(unique_alloc_id(line), []):\n if \"allocator_name\" in entry:\n return entry[\"allocator_name\"]\n return \"0\"\n\n parsed_lines = []\n for l in log.split(\"\\n\"):\n if 'LOG_MEMORY' in l: # and not 'step_id: -6' in l:\n parsed_lines.append(_parse_logline(l))\n\n allocation_map = {} # map of -->parsed_logline of allocation\n for line in parsed_lines:\n if (line[\"type\"] == \"MemoryLogTensorAllocation\" or line[\"type\"] == \"MemoryLogRawAllocation\" or\n line[\"type\"] == \"MemoryLogTensorOutput\"):\n allocation_map.setdefault(unique_alloc_id(line), []).append(line)\n if debug_messages:\n print(allocation_map)\n result = []\n for i, line in enumerate(parsed_lines):\n # skip lines without allocation_id, ie lines like\n # I tensorflow/core/framework/log_memory.cc:35] __LOG_MEMORY__ MemoryLogStep { step_id: 2 handle: \"->/gradients/a1_grad/TanhGrad/0/;1\" }\n\n if int(line[\"allocation_id\"]) == -1:\n continue\n alloc_names = get_alloc_names(line)\n # if line doesn't specify bytes, look in history if there was corresponding TensorOutput or TensorAllocation msg\n if int(line.get('allocated_bytes', -1)) < 0:\n alloc_bytes = get_alloc_bytes(line)\n else:\n alloc_bytes = line.get('allocated_bytes', -1)\n alloc_type = get_alloc_type(line)\n if line[\"type\"] == \"MemoryLogTensorOutput\":\n continue\n if line[\"type\"] == \"MemoryLogTensorDeallocation\" or line[\"type\"]==\"MemoryLogRawDeallocation\":\n alloc_bytes = \"-\" + alloc_bytes\n result.append((i, alloc_names, alloc_bytes, alloc_type))\n return result\n\ndef peak_memory(log, gpu_only=False):\n \"\"\"Peak memory used across all devices.\"\"\"\n peak_memory = -123456789 # to catch bugs\n total_memory = 0\n for record in memory_timeline(log):\n i, kernel_name, allocated_bytes, allocator_type = record\n allocated_bytes = int(allocated_bytes)\n if gpu_only:\n if not allocator_type.startswith(\"gpu\"):\n continue\n total_memory += allocated_bytes\n peak_memory = max(total_memory, peak_memory)\n return peak_memory\n \ndef print_memory_timeline(log, gpu_only=False, ignore_less_than_bytes=0):\n \n total_memory = 0\n for record in memory_timeline(log):\n i, kernel_name, allocated_bytes, allocator_type = record\n allocated_bytes = int(allocated_bytes)\n if gpu_only:\n if not allocator_type.startswith(\"gpu\"):\n continue\n if abs(allocated_bytes)/read, so add control dependency to trigger safe_initializer\n # on read access\n for v in variables:\n var_name = v.op.name\n var_cache = g.get_operation_by_name(var_name+\"/read\")\n ge.reroute.add_control_inputs(var_cache, [safe_initializers[var_name]])\n\n sess.run(tf.group(*safe_initializers.values()))\n \n # remove initializer dependencies to avoid slowing down future variable reads\n for v in variables:\n var_name = v.op.name\n var_cache = g.get_operation_by_name(var_name+\"/read\")\n ge.reroute.remove_control_inputs(var_cache, [safe_initializers[var_name]])\n\n" }, { "path": "matmul_benchmark.py", "content": "# On Titan X (Pascal)\n# 8192 x 8192 matmul took: 0.10 sec, 11304.59 G ops/sec\n# http://stackoverflow.com/questions/41804380/testing-gpu-with-tensorflow-matrix-multiplication\n\nimport os\nimport sys\nimport tensorflow as tf\nimport time\n\nn = 8192\ndtype = tf.float32\nwith tf.device(\"/gpu:0\"):\n matrix1 = tf.Variable(tf.ones((n, n), dtype=dtype))\n matrix2 = tf.Variable(tf.ones((n, n), dtype=dtype))\n product = tf.matmul(matrix1, matrix2)\n\n\n# avoid optimizing away redundant nodes\nconfig = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\nsess = tf.Session(config=config)\n\nsess.run(tf.global_variables_initializer())\niters = 10\n\n# pre-warming\nsess.run(product.op)\n\nstart = time.time()\nfor i in range(iters):\n sess.run(product.op)\nend = time.time()\nops = n**3 + (n-1)*n**2 # n^2*(n-1) additions, n^3 multiplications\nelapsed = (end - start)\nrate = iters*ops/elapsed/10**9\nprint('\\n %d x %d matmul took: %.2f sec, %.2f G ops/sec' % (n, n,\n elapsed/iters,\n rate,))\n" }, { "path": "matmul_benchmark_seq.py", "content": "# On Titan X (Pascal)\n# 8192 x 8192 matmul took: 0.10 sec, 11304.59 G ops/sec\n# http://stackoverflow.com/questions/41804380/testing-gpu-with-tensorflow-matrix-multiplication\n#\n# On V100/fp16 peak 85k for 8192x8192\n# timing under matmul_times subdirectory\n#\n# TODO: figure out the deal with spikes, is it CUDA autotune?\n\nfrom __future__ import print_function\n\nimport ctypes\nimport errno\nfrom ctypes.util import find_library\nfrom functools import partial\n\nCLOCK_PROCESS_CPUTIME_ID = 2 # time.h\nCLOCK_MONOTONIC_RAW = 4\nclockid_t = ctypes.c_int\ntime_t = ctypes.c_long\nclass timespec(ctypes.Structure):\n _fields_ = [\n ('tv_sec', time_t), # seconds\n ('tv_nsec', ctypes.c_long) # nanoseconds\n ]\n_clock_gettime = ctypes.CDLL(find_library('rt'), use_errno=True).clock_gettime\n_clock_gettime.argtypes = [clockid_t, ctypes.POINTER(timespec)]\n\n\ndef clock_gettime(clk_id):\n tp = timespec()\n if _clock_gettime(clk_id, ctypes.byref(tp)) < 0:\n err = ctypes.get_errno()\n msg = errno.errorcode[err]\n if err == errno.EINVAL:\n msg += (\" The clk_id specified is not supported on this system\"\n \" clk_id=%r\") % (clk_id,)\n raise OSError(err, msg)\n return tp.tv_sec + tp.tv_nsec * 1e-9\n\ntry:\n from time import perf_counter, process_time\nexcept ImportError: # Python <3.3\n perf_counter = partial(clock_gettime, CLOCK_MONOTONIC_RAW)\n perf_counter.__name__ = 'perf_counter'\n process_time = partial(clock_gettime, CLOCK_PROCESS_CPUTIME_ID)\n process_time.__name__ = 'process_time'\n\n\nimport math\nimport os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport time\n\nimport argparse\nparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n\nparser.add_argument('--dtype', type=str, default='float32',\n help='dtype, float32 or float16')\nparser.add_argument('--agg', type=str, default='min',\n help='min, mean or median')\nargs = parser.parse_args()\n\ndef bench(n):\n if args.dtype == 'float32':\n dtype = tf.float32\n elif args.dtype == 'float16':\n dtype = tf.float16\n else:\n assert False, 'unknown dtype '+args.dtype\n with tf.device(\"/gpu:0\"):\n matrix1 = tf.Variable(tf.ones((n, n), dtype=dtype))\n matrix2 = tf.Variable(tf.ones((n, n), dtype=dtype))\n product = tf.matmul(matrix1, matrix2)\n\n\n config = tf.ConfigProto()\n sess = tf.Session(config=config)\n \n sess.run(tf.global_variables_initializer())\n iters = 11\n\n # pre-warming\n sess.run(product.op)\n\n times = []\n for i in range(iters):\n start = perf_counter()\n sess.run(product.op)\n times.append(perf_counter()-start)\n\n ops = n**3 + (n-1)*n**2 # n^2*(n-1) additions, n^3 multiplications\n\n times_ms = 1000*np.array(times) # get seconds, convert to ms\n if len(times_ms)>0:\n min = np.min(times_ms)\n median = np.median(times_ms)\n formatted = [\"%.2f\"%(d,) for d in times_ms[:10]]\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\n # np.mean(times_ms)))\n\n if args.agg == 'min':\n elapsed_ms = np.min(times_ms)\n elif args.agg == 'mean':\n elapsed_ms = np.mean(times_ms)\n elif args.agg == 'median':\n elapsed_ms = np.median(times_ms)\n else:\n assert False, 'unknown aggregation method: ' + args.agg\n \n rate = ops/elapsed_ms/10**9\n # print('\\n %d x %d matmul took: %.4f ms, %.2f G ops/sec' % (n, n,\n # elapsed_ms,\n # rate,))\n return rate\n\ndef main():\n steps = 8 # number of steps between n doubling\n\n np.set_printoptions(suppress=True)\n with open(\"times.csv\", \"w\") as myfile:\n myfile.write(\"\\n\")\n \n for i in range(20*steps):\n n = int(math.pow(2, float(i)/steps))\n rate = bench(n)\n print(\"%d,%.10f\" %(n, rate))\n with open(\"times.csv\", \"a\") as myfile:\n myfile.write(\"%d,%.10f\\n\"%(n, rate))\n \nif __name__=='__main__':\n main()\n" }, { "path": "matmul_times/1080-float16.csv", "content": 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}, { "path": "matmul_times/1080-float32.csv", "content": 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}, { "path": "mavelin/machine1.py", "content": "import sys\nimport tensorflow as tf\n\ncluster_spec = tf.train.ClusterSpec({\n \"a\": { 0: \"localhost:8000\" },\n \"b\": { 0: \"localhost:8001\" },\n})\n\njobname = \"a\"\ntaskid = 0\nserver = tf.train.Server(cluster_spec, jobname, taskid)\n\nwith tf.device(\"/job:a/task:0/cpu:0\"):\n queue = tf.FIFOQueue(\n capacity=100, dtypes=[tf.int64],\n shapes=[[]], shared_name=\"a_queue\", name=\"a_queue\")\n\nif jobname == \"a\" and taskid == 0:\n enqueue_op = queue.enqueue(10)\n sess = tf.Session(server.target)\n while True:\n sess.run(enqueue_op)\nelse:\n dequeue_op = queue.dequeue()\n sess = tf.Session(server.target)\n while True:\n print(sess.run(dequeue_op))\n" }, { "path": "mavelin/machine3.py", "content": "import sys\nimport tensorflow as tf\n\ncluster_spec = tf.train.ClusterSpec({\n \"a\": { 0: \"localhost:8000\" },\n \"b\": { 1: \"localhost:8001\" },\n})\n\nDOFAIL=True\n\njobname = \"b\"\ntaskid = 1\nserver = tf.train.Server(cluster_spec, jobname, taskid)\n\nwith tf.device(\"/job:a/task:0/cpu:0\"):\n queue = tf.FIFOQueue(\n capacity=100, dtypes=[tf.int64],\n shapes=[[]], shared_name=\"a_queue\", name=\"a_queue\")\n\nif jobname == \"a\" and taskid == 0:\n enqueue_op = queue.enqueue(10)\n sess = tf.Session(server.target)\n while True:\n sess.run(enqueue_op)\nelse:\n with tf.device(\"/job:b/task:1\"):\n out = queue.dequeue()\n queue_b = tf.FIFOQueue(capacity=100, dtypes=[tf.int64], shapes=[[]], name=\"b_queue\")\n if DOFAIL:\n out = tf.cond(tf.equal(out, 10), lambda: queue_b.enqueue(out), lambda: tf.no_op())\n g = tf.get_default_graph()\n from tensorflow.core.framework import attr_value_pb2\n op = g.get_operation_by_name('cond/b_queue_enqueue/Switch_1')\n op.node_def.attr[\"_class\"].CopyFrom(attr_value_pb2.AttrValue(\n list=attr_value_pb2.AttrValue.ListValue(s=[])))\n\n op = g.get_operation_by_name('cond/b_queue_enqueue/Switch')\n op.node_def.attr[\"_class\"].CopyFrom(attr_value_pb2.AttrValue(\n list=attr_value_pb2.AttrValue.ListValue(s=[])))\n \n with open('fail.pbtxt', 'w') as outf:\n outf.write(str(tf.get_default_graph().as_graph_def()))\n else:\n enq = queue_b.enqueue(out)\n no_op = tf.no_op()\n out = tf.cond(tf.equal(out, 10), lambda: enq, lambda: no_op)\n with open('pass.pbtxt', 'w') as outf:\n outf.write(str(tf.get_default_graph().as_graph_def()))\n \n\n sess = tf.Session(server.target)\n while True:\n print(sess.run(out))\n" }, { "path": "memory tracking.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Memory tracking\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import sys, os, math, random\\n\",\n \"sys.path.append(os.environ['HOME']+'/git0/pixel-cnn-private/pixel_cnn_pp/tests')\\n\",\n \"sys.path.append(os.environ['HOME']+'/git0/pixel-cnn-private/pixel_cnn_pp')\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES']='1'\\n\",\n \"\\n\",\n \"import memory_util\\n\",\n \"memory_util.vlog(1) # vlog=2 on GPU machine will hang\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import tensorflow.contrib # takes 15 seconds\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def create_session():\\n\",\n \" config = tf.ConfigProto(operation_timeout_in_ms=50000, log_device_placement=False,graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" return tf.InteractiveSession(config=config)\\n\",\n \" \\n\",\n \"import importlib\\n\",\n \"importlib.reload(memory_util)\\n\",\n \"\\n\",\n \"#from pixel_cnn_pp.memory_saving_gradients import gradients as smart_gradients\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import os, sys\\n\",\n \"import numpy as np\\n\",\n \"import time\\n\",\n \"import tensorflow as tf\\n\",\n \"import traceback\\n\",\n \"#import resnet_model\\n\",\n \"import networkx as nx\\n\",\n \"\\n\",\n \"from collections import Counter\\n\",\n \"\\n\",\n \"# from https://github.com/yaroslavvb/notebooks/blob/master/simple_rewiring.ipynb\\n\",\n \"# make things wide\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):\\n\",\n \" if not graph_def:\\n\",\n \" graph_def = tf.get_default_graph().as_graph_def()\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" data = str(strip_def)\\n\",\n \" if ungroup_gradients:\\n\",\n \" data = data.replace('\\\"gradients/', '\\\"b_')\\n\",\n \" #print(data)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(data), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(width, height, code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 6 gradients/Const(27-GPU_0_bfc) 256 256 GPU_0_bfc\\n\",\n \" 17 gradients/Fill(28-GPU_0_bfc) 1000192 1000448 GPU_0_bfc\\n\",\n \" 19 gradients/a1_grad/TanhGrad(29-GPU_0_bfc) 1000192 2000640 GPU_0_bfc\\n\",\n \" 21 gradients/a2_grad/TanhGrad(30-GPU_0_bfc) 1000192 3000832 GPU_0_bfc\\n\",\n \" 23 gradients/a3_grad/TanhGrad(31-GPU_0_bfc) 1000192 4001024 GPU_0_bfc\\n\",\n \" 25 gradients/a4_grad/TanhGrad(32-GPU_0_bfc) 1000192 5001216 GPU_0_bfc\\n\",\n \" 28 gradients/Fill(28-GPU_0_bfc) -1000192 4001024 GPU_0_bfc\\n\",\n \" 30 gradients/a4_grad/TanhGrad(32-GPU_0_bfc) -1000192 3000832 GPU_0_bfc\\n\",\n \" 32 gradients/a3_grad/TanhGrad(31-GPU_0_bfc) -1000192 2000640 GPU_0_bfc\\n\",\n \" 34 gradients/a2_grad/TanhGrad(30-GPU_0_bfc) -1000192 1000448 GPU_0_bfc\\n\",\n \" 35 gradients/a1_grad/TanhGrad(29-GPU_0_bfc) -1000192 256 GPU_0_bfc\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"node_mbs = 1\\n\",\n \"length = 5\\n\",\n \"\\n\",\n \"dtype = np.float32\\n\",\n \"n = node_mbs * 250000\\n\",\n \"a0_ = tf.ones((n,), dtype=dtype)\\n\",\n \"a0 = tf.Variable(a0_, name=\\\"a0\\\")\\n\",\n \"a = a0\\n\",\n \"for i in range(1, length):\\n\",\n \" name = \\\"a\\\"+str(i)\\n\",\n \" a = tf.tanh(a, name=name)\\n\",\n \"\\n\",\n \"grad = tf.gradients([a], [a0])[0]\\n\",\n \"sess = create_session()\\n\",\n \"\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" sess.run(grad.op)\\n\",\n \"memory_util.print_memory_timeline(stderr.getvalue())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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DntZwNLsVpnRis9M/leu3xF+/5Sqx8GLhwZt7nVTlbfvEB9JXNIkgay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\\nSepmmEiSuhkmkqRuhokkqZthIknq9v8AabYrUFKbGRwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"memory_util.plot_memory_timeline(stderr.getvalue())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_resnet_with_memstats(length=100, name_prefix=\\\"a\\\", node_mbs=1):\\n\",\n \" \\\"\\\"\\\"Creates resnet-like chain of length length. First node is constant,\\n\",\n \" rest are tanh. Returns list of nodes. Has length - 2 articulation points (ie \\n\",\n \" for length=3 there is 1 articulation point.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" dtype = np.float32\\n\",\n \" n = node_mbs * 250000\\n\",\n \" a0 = tf.ones((n,), dtype=dtype, name=name_prefix+\\\"00\\\")\\n\",\n \" a = a0\\n\",\n \" nodes = [a]\\n\",\n \" memstat_nodes = []\\n\",\n \" for i in range(1, length):\\n\",\n \" with tf.control_dependencies([nodes[-1]]):\\n\",\n \" memstat_node = tf.contrib.memory_stats.MaxBytesInUse()\\n\",\n \" memstat_nodes.append(memstat_node)\\n\",\n \" name = \\\"%s%02d\\\"%(name_prefix, i)\\n\",\n \" with tf.control_dependencies([memstat_node]):\\n\",\n \" a_nonlin = tf.tanh(a, name=name+\\\"_tanh\\\")\\n\",\n \" a = tf.add(a, a_nonlin, name=name+\\\"_add\\\")\\n\",\n \" nodes.append(a)\\n\",\n \" \\n\",\n \" return nodes, memstat_nodes\\n\",\n \"\\n\",\n \"\\n\",\n \"def make_resnet_dual_gpu(length=10, name_prefix=\\\"a\\\", node_mbs=1):\\n\",\n \" \\\"\\\"\\\"Creates resnet-like chain of length length. First node is constant,\\n\",\n \" rest are tanh. Returns list of nodes. Has length - 2 articulation points (ie \\n\",\n \" for length=3 there is 1 articulation point.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" dtype = np.float32\\n\",\n \" n = node_mbs * 250000\\n\",\n \" a0 = tf.ones((n,), dtype=dtype, name=name_prefix+\\\"00\\\")\\n\",\n \" a = a0\\n\",\n \" nodes = [a]\\n\",\n \" for i in range(1, length):\\n\",\n \" with tf.device(\\\"/gpu:\\\"+str(i%2)):\\n\",\n \" name = \\\"%s%02d\\\"%(name_prefix, i)\\n\",\n \" a_nonlin = tf.tanh(a, name=name+\\\"_tanh\\\")\\n\",\n \" a = tf.add(a, a_nonlin, name=name+\\\"_add\\\")\\n\",\n \" nodes.append(a)\\n\",\n \" \\n\",\n \" return nodes\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": true,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"sess = create_session()\\n\",\n \"n = 10\\n\",\n \"nodes, memstat_nodes = make_resnet_with_memstats(n)\\n\",\n \"a0 = nodes[0]\\n\",\n \"a = nodes[-1]\\n\",\n \"with tf.control_dependencies([a]):\\n\",\n \" grad = tf.gradients([a], [a0])[0]\\n\",\n \"with tf.control_dependencies([grad]):\\n\",\n \" memstat_nodes.append(tf.contrib.memory_stats.MaxBytesInUse())\\n\",\n \" \\n\",\n \"run_metadata = tf.RunMetadata()\\n\",\n \"run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\\n\",\n \"\\n\",\n \"with memory_util.capture_stderr() as stderr:\\n\",\n \" _, memstat_nodes0 = sess.run([grad.op, memstat_nodes], options=run_options, run_metadata=run_metadata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 3 a00(11-GPU_0_bfc) 1000192 1000192 GPU_0_bfc\\n\",\n \" 11 gradients/Const(12-GPU_0_bfc) 256 1000448 GPU_0_bfc\\n\",\n \" 71 MaxBytesInUse_9/_19(1-cpu_pool) 8 1000456 cpu_pool\\n\",\n \" 74 gradients/a01_tanh_grad/TanhGrad(13-GPU_0_bfc) 1000192 2000648 GPU_0_bfc\\n\",\n \" 76 a09_add(14-GPU_0_bfc) 1000192 3000840 GPU_0_bfc\\n\",\n \" 78 MaxBytesInUse_9/_19(1-cpu_pool) 8 3000848 cpu_pool\\n\",\n \" 81 gradients/a02_tanh_grad/TanhGrad(15-GPU_0_bfc) 1000192 4001040 GPU_0_bfc\\n\",\n \" 84 MaxBytesInUse_9/_19(1-cpu_pool) 8 4001048 cpu_pool\\n\",\n \" 87 gradients/a03_tanh_grad/TanhGrad(16-GPU_0_bfc) 1000192 5001240 GPU_0_bfc\\n\",\n \" 90 MaxBytesInUse_9/_19(1-cpu_pool) 8 5001248 cpu_pool\\n\",\n \" 93 gradients/a04_tanh_grad/TanhGrad(17-GPU_0_bfc) 1000192 6001440 GPU_0_bfc\\n\",\n \" 96 MaxBytesInUse_9/_19(1-cpu_pool) 8 6001448 cpu_pool\\n\",\n \" 99 gradients/a05_tanh_grad/TanhGrad(18-GPU_0_bfc) 1000192 7001640 GPU_0_bfc\\n\",\n \" 102 MaxBytesInUse_9/_19(1-cpu_pool) 8 7001648 cpu_pool\\n\",\n \" 105 gradients/a06_tanh_grad/TanhGrad(19-GPU_0_bfc) 1000192 8001840 GPU_0_bfc\\n\",\n \" 108 MaxBytesInUse_9/_19(1-cpu_pool) 8 8001848 cpu_pool\\n\",\n \" 111 gradients/a07_tanh_grad/TanhGrad(20-GPU_0_bfc) 1000192 9002040 GPU_0_bfc\\n\",\n \" 114 MaxBytesInUse_9/_19(1-cpu_pool) 8 9002048 cpu_pool\\n\",\n \" 117 gradients/a08_tanh_grad/TanhGrad(21-GPU_0_bfc) 1000192 10002240 GPU_0_bfc\\n\",\n \" 120 MaxBytesInUse_9/_19(1-cpu_pool) 8 10002248 cpu_pool\\n\",\n \" 123 gradients/a09_tanh_grad/TanhGrad(22-GPU_0_bfc) 1000192 11002440 GPU_0_bfc\\n\",\n \" 126 a09_add(14-GPU_0_bfc) -1000192 10002248 GPU_0_bfc\\n\",\n \" 187 gradients/AddN_8(23-GPU_0_bfc) 1000192 11002440 GPU_0_bfc\\n\",\n \" 231 gradients/a09_tanh_grad/TanhGrad(22-GPU_0_bfc) -1000192 10002248 GPU_0_bfc\\n\",\n \" 238 gradients/a08_tanh_grad/TanhGrad(21-GPU_0_bfc) -1000192 9002056 GPU_0_bfc\\n\",\n \" 245 gradients/a07_tanh_grad/TanhGrad(20-GPU_0_bfc) -1000192 8001864 GPU_0_bfc\\n\",\n \" 252 gradients/a06_tanh_grad/TanhGrad(19-GPU_0_bfc) -1000192 7001672 GPU_0_bfc\\n\",\n \" 259 gradients/a05_tanh_grad/TanhGrad(18-GPU_0_bfc) -1000192 6001480 GPU_0_bfc\\n\",\n \" 266 gradients/a04_tanh_grad/TanhGrad(17-GPU_0_bfc) -1000192 5001288 GPU_0_bfc\\n\",\n \" 273 gradients/a03_tanh_grad/TanhGrad(16-GPU_0_bfc) -1000192 4001096 GPU_0_bfc\\n\",\n \" 280 gradients/a02_tanh_grad/TanhGrad(15-GPU_0_bfc) -1000192 3000904 GPU_0_bfc\\n\",\n \" 287 gradients/a01_tanh_grad/TanhGrad(13-GPU_0_bfc) -1000192 2000712 GPU_0_bfc\\n\",\n \" 288 gradients/AddN_8(23-GPU_0_bfc) -1000192 1000520 GPU_0_bfc\\n\",\n \" 289 MaxBytesInUse_9/_19(1-cpu_pool) 8 1000528 cpu_pool\\n\",\n \" 292 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000520 cpu_pool\\n\",\n \" 293 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000512 cpu_pool\\n\",\n \" 294 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000504 cpu_pool\\n\",\n \" 295 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000496 cpu_pool\\n\",\n \" 296 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000488 cpu_pool\\n\",\n \" 297 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000480 cpu_pool\\n\",\n \" 298 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000472 cpu_pool\\n\",\n \" 299 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000464 cpu_pool\\n\",\n \" 300 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000456 cpu_pool\\n\",\n \" 301 MaxBytesInUse_9/_19(1-cpu_pool) -8 1000448 cpu_pool\\n\"\n ]\n }\n ],\n \"source\": [\n \"memory_util.print_memory_timeline(stderr.getvalue())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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be2oaq6k14vgUGDjdvfB/zSlhY1Bc5yXbatqjpVVf/Sf/3vwIP0ekA3\\nd6/MWriv16x754RqmSYF/EOSe/tNyNXzjKo61X/9b8AzJlnMlLkmyf39ZZuZX4IYRZK9wCXA3TR4\\nr8xauGt9L6yq59Jbrro6yYsmXdC06bd99HO/Pe8GngXsB04Bb59sOVsvyZOAvwJ+p6oeHTzWyr0y\\na+HepVn3tlNVJ/v/ngb+ht7yleDLSS4C6P97esL1TIWq+nJVfauqvg38BdvsfknyBHrB/pdV9df9\\n3c3dK7MW7l2adW8rSb43yZNXXwO/ADxw7ndtG4ON218NfHSCtUyN1RDr+2W20f2SJPR6Pj9YVe8Y\\nONTcvTJz31Dtf2zrT/lOs+4/mnBJE5Xkh+nN1qHXE/fm7XhNktwCXE7v0a1fBt4CfAS4DdhD7/HS\\nr6iqbfXHxbNcl8vpLckU8AXgdQPrzU1L8kLgE8BngG/3d/8evXX3pu6VmQt3SdJws7YsI0nqwHCX\\npAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfo/HjuR13YJKWsAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"memory_util.plot_memory_timeline(stderr.getvalue())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"dev_stat = run_metadata.step_stats.dev_stats[1]\\n\",\n \"node_stat = dev_stat.node_stats[7]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"node_name: \\\"a02_add\\\"\\n\",\n \"all_start_micros: 1505768360742529\\n\",\n \"op_start_rel_micros: 32\\n\",\n \"op_end_rel_micros: 80\\n\",\n \"all_end_rel_micros: 137\\n\",\n \"memory {\\n\",\n \" allocator_name: \\\"GPU_0_bfc\\\"\\n\",\n \" allocator_bytes_in_use: 171016448\\n\",\n \"}\\n\",\n \"output {\\n\",\n \" tensor_description {\\n\",\n \" dtype: DT_FLOAT\\n\",\n \" shape {\\n\",\n \" dim {\\n\",\n \" size: 250000\\n\",\n \" }\\n\",\n \" }\\n\",\n \" allocation_description {\\n\",\n \" requested_bytes: 1000000\\n\",\n \" allocated_bytes: 1000192\\n\",\n \" allocator_name: \\\"GPU_0_bfc\\\"\\n\",\n \" allocation_id: 3735\\n\",\n \" ptr: 1108455317760\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"timeline_label: \\\"a02_add = Add(a01_add, a02_tanh)\\\"\\n\",\n \"scheduled_micros: 1505768360742492\\n\",\n \"memory_stats {\\n\",\n \"}\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"node_stat\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def memory_timeline_dict(run_metadata):\\n\",\n \"# timestamp, kernel_name, allocated_bytes, allocator_type, total = record\\n\",\n \"\\n\",\n \" result = {}\\n\",\n \" for dev_stat in run_metadata.step_stats.dev_stats:\\n\",\n \" result[dev_stat.device] = memory_timeline_from_devstats(dev_stat)\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def memory_timeline_from_devstats(dev_stat):\\n\",\n \" \\\"\\\"\\\"Takes DeviceStepStats, produces memory timeline.\\\"\\\"\\\"\\n\",\n \" # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/step_stats.proto#L65\\n\",\n \" result = []\\n\",\n \" for node_stat in dev_stat.node_stats:\\n\",\n \" memory = -12345\\n\",\n \" for memory_stat in node_stat.memory:\\n\",\n \" # sometimes get multiple allocators involved, ignore CPU allocators\\n\",\n \" # gradients/a09_add_grad/BroadcastGradientArgs [allocator_name: \\\"GPU_0_bfc\\\"\\n\",\n \" #allocator_bytes_in_use: 4007936\\n\",\n \" #, allocator_name: \\\"cpu_pool\\\"\\n\",\n \" #]\\n\",\n \" if not memory_stat.allocator_bytes_in_use:\\n\",\n \" continue\\n\",\n \" else:\\n\",\n \" memory = memory_stat.allocator_bytes_in_use\\n\",\n \" result.append([node_stat.all_start_micros, node_stat.node_name, memory])\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'/device:GPU:0/stream:13': [[1505766531585786, 'a01_tanh:Tanh', -12345],\\n\",\n \" [1505766531586105, 'a01_add:Add', -12345],\\n\",\n \" [1505766531586837, 'a02_tanh:Tanh', -12345],\\n\",\n \" [1505766531587070, 'a02_add:Add', -12345],\\n\",\n \" [1505766531587759, 'a03_tanh:Tanh', -12345],\\n\",\n \" [1505766531587971, 'a03_add:Add', -12345],\\n\",\n \" [1505766531588680, 'a04_tanh:Tanh', -12345],\\n\",\n \" [1505766531588922, 'a04_add:Add', -12345],\\n\",\n \" [1505766531589609, 'a05_tanh:Tanh', -12345],\\n\",\n \" [1505766531589839, 'a05_add:Add', -12345],\\n\",\n \" [1505766531590537, 'a06_tanh:Tanh', -12345],\\n\",\n \" [1505766531590778, 'a06_add:Add', -12345],\\n\",\n \" [1505766531591488, 'a07_tanh:Tanh', -12345],\\n\",\n \" [1505766531591723, 'a07_add:Add', -12345],\\n\",\n \" [1505766531592475, 'a08_tanh:Tanh', -12345],\\n\",\n \" [1505766531592714, 'a08_add:Add', -12345],\\n\",\n \" [1505766531593429, 'a09_tanh:Tanh', -12345],\\n\",\n \" [1505766531593630, 'a09_add:Add', -12345],\\n\",\n \" [1505766531597779, 'gradients/Fill:Fill', -12345],\\n\",\n \" [1505766531600450, 'gradients/a09_tanh_grad/TanhGrad:TanhGrad', -12345],\\n\",\n \" [1505766531600654, 'gradients/AddN:AddN', -12345],\\n\",\n \" [1505766531601547, 'gradients/a08_tanh_grad/TanhGrad:TanhGrad', -12345],\\n\",\n \" [1505766531601727, 'gradients/AddN_1:AddN', -12345],\\n\",\n \" [1505766531602601, 'gradients/a07_tanh_grad/TanhGrad:TanhGrad', -12345],\\n\",\n \" [1505766531602781, 'gradients/AddN_2:AddN', -12345],\\n\",\n \" [1505766531603638, 'gradients/a06_tanh_grad/TanhGrad:TanhGrad', -12345],\\n\",\n \" [1505766531603816, 'gradients/AddN_3:AddN', -12345],\\n\",\n \" [1505766531604722, 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14008832],\\n\",\n \" [1505766531602285, 'gradients/a07_add_grad/Reshape', 14008832],\\n\",\n \" [1505766531602405, 'gradients/a07_add_grad/Reshape_1', 14008832],\\n\",\n \" [1505766531602518, 'gradients/a07_tanh_grad/TanhGrad', 14008832],\\n\",\n \" [1505766531602683, 'gradients/AddN_2', 14008832],\\n\",\n \" [1505766531602914, 'gradients/a06_add_grad/Sum', 13008640],\\n\",\n \" [1505766531603107, 'gradients/a06_add_grad/Sum_1', 13008640],\\n\",\n \" [1505766531603321, 'gradients/a06_add_grad/Reshape', 13008640],\\n\",\n \" [1505766531603459, 'gradients/a06_add_grad/Reshape_1', 13008640],\\n\",\n \" [1505766531603557, 'gradients/a06_tanh_grad/TanhGrad', 13008640],\\n\",\n \" [1505766531603743, 'gradients/AddN_3', 13008640],\\n\",\n \" [1505766531603946, 'gradients/a05_add_grad/Sum', 12008448],\\n\",\n \" [1505766531604157, 'gradients/a05_add_grad/Sum_1', 12008448],\\n\",\n \" [1505766531604387, 'gradients/a05_add_grad/Reshape', 12008448],\\n\",\n \" [1505766531604518, 'gradients/a05_add_grad/Reshape_1', 12008448],\\n\",\n \" [1505766531604647, 'gradients/a05_tanh_grad/TanhGrad', 12008448],\\n\",\n \" [1505766531604819, 'gradients/AddN_4', 12008448],\\n\",\n \" [1505766531605023, 'gradients/a04_add_grad/Sum', 11008256],\\n\",\n \" [1505766531605238, 'gradients/a04_add_grad/Sum_1', 11008256],\\n\",\n \" [1505766531605458, 'gradients/a04_add_grad/Reshape', 11008256],\\n\",\n \" [1505766531605580, 'gradients/a04_add_grad/Reshape_1', 11008256],\\n\",\n \" [1505766531605703, 'gradients/a04_tanh_grad/TanhGrad', 11008256],\\n\",\n \" [1505766531605907, 'gradients/AddN_5', 11008256],\\n\",\n \" [1505766531606130, 'gradients/a03_add_grad/Sum', 10008064],\\n\",\n \" [1505766531606328, 'gradients/a03_add_grad/Sum_1', 10008064],\\n\",\n \" [1505766531606550, 'gradients/a03_add_grad/Reshape', 10008064],\\n\",\n \" [1505766531606681, 'gradients/a03_add_grad/Reshape_1', 10008064],\\n\",\n \" [1505766531606806, 'gradients/a03_tanh_grad/TanhGrad', 10008064],\\n\",\n \" [1505766531606984, 'gradients/AddN_6', 10008064],\\n\",\n \" [1505766531607234, 'gradients/a02_add_grad/Sum', 9007872],\\n\",\n \" [1505766531607435, 'gradients/a02_add_grad/Sum_1', 9007872],\\n\",\n \" [1505766531607646, 'gradients/a02_add_grad/Reshape', 9007872],\\n\",\n \" [1505766531607782, 'gradients/a02_add_grad/Reshape_1', 9007872],\\n\",\n \" [1505766531607903, 'gradients/a02_tanh_grad/TanhGrad', 9007872],\\n\",\n \" [1505766531608069, 'gradients/AddN_7', 9007872],\\n\",\n \" [1505766531608280, 'gradients/a01_add_grad/Sum', 8007680],\\n\",\n \" [1505766531608525, 'gradients/a01_add_grad/Sum_1', 8007680],\\n\",\n \" [1505766531608731, 'gradients/a01_add_grad/Reshape', 8007680],\\n\",\n \" [1505766531608885, 'gradients/a01_add_grad/Reshape_1', 8007680],\\n\",\n \" [1505766531609014, 'gradients/a01_tanh_grad/TanhGrad', 8007680],\\n\",\n \" [1505766531609189, 'gradients/AddN_8', 8007680],\\n\",\n \" [1505766531609425, 'MaxBytesInUse_9', 6007296]]}\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"memory_timeline_dict(run_metadata)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Plot memory timeline for resnet\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Preloading cifar10 data\\n\",\n \"Loaded 256 examples\\n\",\n \"WARNING:tensorflow:From /home/yaroslav/git0/pixel-cnn-private/pixel_cnn_pp/tests/resnet_model.py:64: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Please switch to tf.train.get_or_create_global_step\\n\",\n \"WARNING:tensorflow:From /home/yaroslav/git0/pixel-cnn-private/pixel_cnn_pp/tests/resnet_model.py:294: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.\\n\",\n \"INFO:tensorflow:Summary name learning rate is illegal; using learning_rate instead.\\n\"\n ]\n }\n ],\n \"source\": [\n \" import resnet_model\\n\",\n \" tf.reset_default_graph()\\n\",\n \" num_classes=10\\n\",\n \" batch_size = 128\\n\",\n \" mode = \\\"train\\\"\\n\",\n \" sess = create_session()\\n\",\n \" iters = 1\\n\",\n \" warm_up_iters = 1\\n\",\n \" num_batches = iters+warm_up_iters\\n\",\n \" sess = create_session()\\n\",\n \"\\n\",\n \" images, labels = resnet_model.build_random_cifar10_input(batch_size, num_batches=num_batches, sess=sess)\\n\",\n \" \\n\",\n \" hps = resnet_model.HParams(batch_size=batch_size,\\n\",\n \" num_classes=num_classes,\\n\",\n \" min_lrn_rate=0.0001,\\n\",\n \" lrn_rate=0.1,\\n\",\n \" num_residual_units=5,\\n\",\n \" use_bottleneck=False,\\n\",\n \" weight_decay_rate=0.0002,\\n\",\n \" relu_leakiness=0.1,\\n\",\n \" optimizer='mom')\\n\",\n \" model = resnet_model.ResNet(hps, images, labels, mode)\\n\",\n \" model.build_graph()\\n\",\n \"\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" sess.run(tf.local_variables_initializer())\\n\",\n \"\\n\",\n \" run_metadata = tf.RunMetadata()\\n\",\n \" run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\\n\",\n \"\\n\",\n \" sess.run(model.train_op, options=run_options, run_metadata=run_metadata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"timeline_dict = memory_timeline_dict(run_metadata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"dict_keys(['/device:GPU:0/stream:7', '/device:GPU:0/stream:14', '/job:localhost/replica:0/task:0/device:GPU:0', '/device:GPU:0/memcpy', '/device:GPU:0/stream:18', '/device:GPU:0/stream:all', '/device:GPU:0/stream:15', '/job:localhost/replica:0/task:0/cpu:0', '/device:GPU:0/stream:19', '/device:GPU:0/stream:13'])\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"timeline_dict.keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_memory_timeline(timeline):\\n\",\n \"# timestamp, nodename, memory\\n\",\n \" timestamps = []\\n\",\n \" plt.plot([record[0] for record in timeline], [record[2] for record in timeline])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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OlcuY8FzjKzxcAjwElm9seENsuBgQBm1hboBhTHeCKREnHlH6fz3OwV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\\nlNxFRIqQkruISBH6/6KSKkQk3zxvAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_memory_timeline(timeline_dict['/job:localhost/replica:0/task:0/device:GPU:0'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Analyze resnet\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1+2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"node_name: \\\"group_deps\\\"\\n\",\n \"all_start_micros: 1505766588234096\\n\",\n \"op_start_rel_micros: 63\\n\",\n \"op_end_rel_micros: 79\\n\",\n \"all_end_rel_micros: 270\\n\",\n \"memory {\\n\",\n \" allocator_name: \\\"GPU_0_bfc\\\"\\n\",\n \" allocator_bytes_in_use: 167014144\\n\",\n \"}\\n\",\n \"timeline_label: \\\"group_deps = NoOp(^train_step/_1041, ^unit_1_0/shared_activation/init_bn/AssignMovingAvg, ^unit_1_0/shared_activation/init_bn/AssignMovingAvg_1, ^unit_1_0/sub2/bn2/AssignMovingAvg, ^unit_1_0/sub2/bn2/AssignMovingAvg_1, ^unit_1_1/residual_only_activation/init_bn/AssignMovingAvg, ^unit_1_1/residual_only_activation/init_bn/AssignMovingAvg_1, ^unit_1_1/sub2/bn2/AssignMovingAvg, ^unit_1_1/sub2/bn2/AssignMovingAvg_1, ^unit_1_2/residual_only_activation/init_bn/AssignMovingAvg, ^unit_1_2/residual_only_activation/init_bn/AssignMovingAvg_1, ^unit_1_2/sub2/bn2/AssignMovingAvg, ^unit_1_2/sub2/bn2/AssignMovingAvg_1, ^unit_1_3/residual_only_activation/init_bn/AssignMovingAvg, ^unit_1_3/residual_only_activation/init_bn/AssignMovingAvg_1, ^unit_1_3/sub2/bn2/AssignMovingAvg, ^unit_1_3/sub2/bn2/AssignMovingAvg_1, ^unit_1_4/residual_only_activation/init_bn/AssignMovingAvg, 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\"unit_3_4/sub2/bn2/batchnorm/mul_1 515463168\\n\",\n \"unit_3_4/sub2/bn2/batchnorm/mul_2 515463424\\n\",\n \"unit_3_4/sub2/bn2/AssignMovingAvg_1/unit_3_4/sub2/bn2/moving_variance/sub_1 515463680\\n\",\n \"unit_3_4/sub2/bn2/AssignMovingAvg/unit_3_4/sub2/bn2/moving_mean/truediv 515463936\\n\",\n \"unit_3_4/sub2/Less 515987712\\n\",\n \"unit_3_4/sub2/mul 518084864\\n\",\n \"unit_last/final_bn/moments/SquaredDifference 520181760\\n\",\n \"unit_last/final_bn/AssignMovingAvg/unit_last/final_bn/moving_mean/sub 520182016\\n\",\n \"unit_last/final_bn/moments/variance 520182272\\n\",\n \"unit_last/final_bn/batchnorm/mul_1 520182528\\n\",\n \"unit_last/final_bn/batchnorm/mul_2 520182784\\n\",\n \"unit_last/final_bn/AssignMovingAvg_1/unit_last/final_bn/moving_variance/sub_1 520183040\\n\",\n \"unit_last/final_bn/AssignMovingAvg/unit_last/final_bn/moving_mean/truediv 520183296\\n\",\n \"unit_last/Less 521228800\\n\",\n \"unit_last/mul 523325952\\n\",\n \"gradients/unit_last/final_bn/moments/variance_grad/Tile 524434176\\n\"\n ]\n }\n ],\n \"source\": [\n \"# find critical path for memory allocations\\n\",\n \"peak_memory = 0\\n\",\n \"\\n\",\n \"dev_stat = None\\n\",\n \"for dev_stat0 in run_metadata.step_stats.dev_stats:\\n\",\n \" if '/job:localhost/replica:0/task:0/device:GPU' in dev_stat0.device:\\n\",\n \" assert not dev_stat, \\\"multiple GPU stats?\\\"\\n\",\n \" dev_stat = dev_stat0\\n\",\n \" break\\n\",\n \"assert dev_stat, \\\"No GPU device stats found\\\"\\n\",\n \"\\n\",\n \"for node_stat in dev_stat.node_stats:\\n\",\n \" for node_stat in dev_stat.node_stats:\\n\",\n \" memory = -12345\\n\",\n \" for memory_stat in node_stat.memory:\\n\",\n \" if not memory_stat.allocator_bytes_in_use:\\n\",\n \" continue\\n\",\n \" else:\\n\",\n \" memory = memory_stat.allocator_bytes_in_use\\n\",\n \" if memory > peak_memory:\\n\",\n \" print(node_stat.node_name, memory)\\n\",\n \" peak_memory = memory\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Caterpillar graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"result = tf.random_uniform((size, size))\\n\",\n \"for i in range(n):\\n\",\n \" result = result @ tf.random_uniform((size, size))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.4\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "memory-probe-examples.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"os.environ[\\\"CUDA_DEVICE_ORDER\\\"] = \\\"PCI_BUS_ID\\\"\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"0,1,2\\\"\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Download and load custom op extension\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import urllib.request\\n\",\n \"response = urllib.request.urlopen(\\\"https://raw.githubusercontent.com/yaroslavvb/memory_probe_ops/master/linux.memory_probe_ops.so\\\")\\n\",\n \"open(\\\"memory_probe_ops.so\\\", \\\"wb\\\").write(response.read())\\n\",\n \"memory_probe_ops = tf.load_op_library(\\\"./memory_probe_ops.so\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Check memory before and after initializing variable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Before init 1280 out of 12051264308 bytes\\n\",\n \"After init 67110656 out of 12051264308 bytes\\n\"\n ]\n }\n ],\n \"source\": [\n \"sess = tf.InteractiveSession()\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" probe_op = memory_probe_ops.bytes_in_use()\\n\",\n \" max_op = memory_probe_ops.bytes_limit()\\n\",\n \" mbs = 42\\n\",\n \" n = mbs*250000\\n\",\n \" inputs = tf.random_uniform((n,))\\n\",\n \"\\n\",\n \" var = tf.Variable(inputs)\\n\",\n \" probe_op = memory_probe_ops.bytes_in_use()\\n\",\n \" max_op = memory_probe_ops.bytes_limit()\\n\",\n \" print(\\\"Before init %10d out of %10d bytes\\\" % tuple(sess.run([probe_op, max_op])))\\n\",\n \" sess.run(var.initializer)\\n\",\n \" print(\\\"After init %10d out of %10d bytes\\\" % tuple(sess.run([probe_op, max_op])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Probe memory on multiple GPUs \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check that gpu:1 and gpu:2 memory are unaffected by variable initialized gpu:0 in previous cell\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"gpu:1 bytes 1280, gpu:2 bytes 1280\\n\"\n ]\n }\n ],\n \"source\": [\n \"with tf.device(\\\"/gpu:1\\\"):\\n\",\n \" probe_op_gpu1 = memory_probe_ops.bytes_in_use()\\n\",\n \"with tf.device(\\\"/gpu:2\\\"):\\n\",\n \" probe_op_gpu2 = memory_probe_ops.bytes_in_use()\\n\",\n \"print(\\\"gpu:1 bytes %d, gpu:2 bytes %d\\\" % tuple(sess.run([probe_op_gpu1, probe_op_gpu2])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Probe memory within single step\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can probe memory during step execution by using control dependencies to control when probe gets executed. Here's an example of tracing a computation that needs up to 100MB for the first half of computation, and then 50MB for the rest\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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4NelyJy0hTunVBlTR2zFxaQ1MWYf/VE0lL83+mxPQLZGdQHHaUHjnld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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# makes chain of ops\\n\",\n \"def make_chain(length, mbs_per_node):\\n\",\n \" n = mbs_per_node*250000\\n\",\n \" node0 = tf.random_uniform((n,))\\n\",\n \" node = node0\\n\",\n \" leaf = node0\\n\",\n \" probes = []\\n\",\n \" nodes = [node]\\n\",\n \" for i in range(length):\\n\",\n \" with tf.control_dependencies([node, leaf]):\\n\",\n \" probe = memory_probe_ops.bytes_in_use()\\n\",\n \" probes.append(probe)\\n\",\n \" with tf.control_dependencies([probe]):\\n\",\n \" leaf = tf.random_uniform((n,))\\n\",\n \" if i < length//2:\\n\",\n \" node = tf.add_n(nodes+[leaf])\\n\",\n \" else:\\n\",\n \" node = tf.add_n(nodes[-length//4:]+[leaf])\\n\",\n \" nodes.append(node)\\n\",\n \" return nodes, probes\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"sess = tf.InteractiveSession()\\n\",\n \"with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" nodes, probes = make_chain(20, 10)\\n\",\n \"initial_memory = sess.run(memory_probe_ops.bytes_in_use())\\n\",\n \"_, probes0 = sess.run([nodes[-1], probes])\\n\",\n \"plt.plot(np.array(probes0)-initial_memory)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:tim-jan17]\",\n \"language\": \"python\",\n \"name\": \"conda-env-tim-jan17-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"84px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "memory-release-check.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Checking whether memory gets released properly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.12.0-rc1'\"\n ]\n },\n \"execution_count\": 80,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# instuctions for VLOG-enabled Tensorflow:\\n\",\n \"# https://github.com/openai/tf_dist/blob/master/grpc/parse_vlog_memory.py\\n\",\n \"import os\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]=\\\"\\\"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import re, os, sys\\n\",\n \"tf.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Common utils\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Setup utilities to parse __LOG_MEMORY__ statements in command-line\\n\",\n \"tensor_allocation_regex = re.compile(r\\\"allocated_bytes: (?P\\\\d+).*.*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\")\\n\",\n \"tensor_output_regex = re.compile(\\\"\\\"\\\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\\\\\"(?P[^\\\"]+).*allocated_bytes: (?P\\\\d+).*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\"\\\"\\\") \\n\",\n \"tensor_deallocation_regex = re.compile(r\\\"allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\")\\n\",\n \"\\n\",\n \"def _parse_logline(l):\\n\",\n \" # There are 4 kinds of lines with __LOG_MEMORY__ tag\\n\",\n \" # MemoryLogTensorDeallocation, MemoryLogTensorAllocation, MemoryLogStep,\\n\",\n \" #\\n\",\n \" # Example lines for debugging:\\n\",\n \" # MemoryLogTensorDeallocation\\n\",\n \" # 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorDeallocation { allocation_id: 2 allocator_name: \\\"cpu\\\" } timestamp 5143420588459765\\n\",\n \"\\n\",\n \" # MemoryLogTensorAllocation\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 has_single_reference: true ptr: 29496256 } } } timestamp 5143420588526034\\n\",\n \"\\n\",\n \" # MemoryLogStep\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogStep { step_id: 1 handle: \\\"->Print:0//0/;0\\\" } timestamp 5143420588718348\\n\",\n \"\\n\",\n \" # MemoryLogTensorOutput\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 1 kernel_name: \\\"Const\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 ptr: 29496256 } } } timestamp 5143420588932740\\n\",\n \" \\n\",\n \" # some weird initialization step \\n\",\n \" # if \\\"step_id: -6\\\" in l:\\n\",\n \" # return {}\\n\",\n \" \\n\",\n \" if 'MemoryLogTensorOutput' in l:\\n\",\n \" m = tensor_output_regex.search(l)\\n\",\n \" # assert m, l\\n\",\n \" if not m: # some Shape lines are missing bytes info, ie\\n\",\n \" # I 5162643141.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"gradients/Shape\\\" tensor { dtype: DT_INT32 shape { dim { } } } } timestamp 5162643141310124\\n\",\n \" return {}\\n\",\n \" d = {f: m.group(f) for f in [\\\"timestamp\\\", \\\"allocation_id\\\",\\n\",\n \" \\\"allocated_bytes\\\", \\\"kernel_name\\\"]}\\n\",\n \" d[\\\"timestamp\\\"] = int(d[\\\"timestamp\\\"])\\n\",\n \" d[\\\"allocation_id\\\"] = int(d[\\\"allocation_id\\\"])\\n\",\n \" d[\\\"allocated_bytes\\\"] = int(d[\\\"allocated_bytes\\\"])\\n\",\n \" elif 'MemoryLogTensorAllocation' in l:\\n\",\n \" m = tensor_allocation_regex.search(l)\\n\",\n \" d = {f: int(m.group(f)) for f in [\\\"timestamp\\\", \\\"allocation_id\\\",\\n\",\n \" \\\"allocated_bytes\\\"]}\\n\",\n \" d[\\\"kernel_name\\\"] = \\\"\\\"\\n\",\n \" elif 'MemoryLogTensorDeallocation' in l:\\n\",\n \" m = tensor_deallocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = {f: int(m.group(f)) for f in [\\\"timestamp\\\", \\\"allocation_id\\\"]}\\n\",\n \" else:\\n\",\n \" d = {}\\n\",\n \" return d\\n\",\n \"\\n\",\n \"def memory_timeline(output):\\n\",\n \" \\\"\\\"\\\"Returns array of tuples representing memory actions\\n\",\n \" (timestamp_nanoseconds, kernel_name, memory_bytes)\\n\",\n \" memory is positive for allocations and negative for deallocations.\\n\",\n \" timestamps are relative to the first memory allocation line found\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" parsed_lines = [_parse_logline(l) for l in output.split(\\\"\\\\n\\\")]\\n\",\n \" parsed_lines = [l for l in parsed_lines if l] # remove Nones\\n\",\n \" parsed_lines.sort(key=lambda l: l[\\\"timestamp\\\"])\\n\",\n \" first_timestamp = parsed_lines[0][\\\"timestamp\\\"]\\n\",\n \" \\n\",\n \" # Tensors that are output have two records (allocation and output)\\n\",\n \" # since output comes after, overwrite allocation_map with output\\n\",\n \" # info (it has kernel_name)\\n\",\n \" allocation_map = {} # map of allocation_id->parsed_logline of allocation\\n\",\n \" double_records = {} # map of ids which have both allocation and output\\n\",\n \" for line in parsed_lines:\\n\",\n \" if \\\"kernel_name\\\" in line:\\n\",\n \" if line[\\\"allocation_id\\\"] in allocation_map:\\n\",\n \" double_records[line[\\\"allocation_id\\\"]] = True\\n\",\n \" line[\\\"allocation_id\\\"]\\n\",\n \" allocation_map[line[\\\"allocation_id\\\"]] = line\\n\",\n \" \\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for line in parsed_lines:\\n\",\n \" if \\\"kernel_name\\\" in line:\\n\",\n \" # ignore tensorallocation lines which will later have tensoroutput\\n\",\n \" if (line[\\\"allocation_id\\\"] in double_records and\\n\",\n \" line[\\\"kernel_name\\\"] == \\\"\\\"):\\n\",\n \" continue\\n\",\n \" kernel_name = line[\\\"kernel_name\\\"]\\n\",\n \" allocated_bytes = line[\\\"allocated_bytes\\\"]\\n\",\n \" else: # deallocation\\n\",\n \" allocation_line = allocation_map[line[\\\"allocation_id\\\"]]\\n\",\n \" kernel_name = allocation_line[\\\"kernel_name\\\"]\\n\",\n \" allocated_bytes = -allocation_line[\\\"allocated_bytes\\\"]\\n\",\n \" result.append((line[\\\"timestamp\\\"]-first_timestamp, kernel_name,\\n\",\n \" allocated_bytes))\\n\",\n \" return result\\n\",\n \"\\n\",\n \"\\n\",\n \"def print_memory_timeline(stderr):\\n\",\n \" total_memory = 0\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %40s %11d %11d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\\n\",\n \"\\n\",\n \"def plot_memory_timeline(stderr):\\n\",\n \" total_memory = 0\\n\",\n \" timestamps = []\\n\",\n \" data = []\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamps.append(record[0]-1)\\n\",\n \" data.append(total_memory)\\n\",\n \" total_memory += record[-1]\\n\",\n \" timestamps.append(record[0])\\n\",\n \" data.append(total_memory)\\n\",\n \" plt.plot(timestamps, data)\\n\",\n \" \\n\",\n \" \\n\",\n \"run_metadata = tf.RunMetadata()\\n\",\n \"def summarize(run_metadata=run_metadata):\\n\",\n \" ss = tf.contrib.stat_summarizer.NewStatSummarizer(tf.get_default_graph().as_graph_def().SerializeToString())\\n\",\n \" ss.ProcessStepStatsStr(run_metadata.step_stats.SerializeToString())\\n\",\n \" print(ss.GetOutputString())\\n\",\n \" \\n\",\n \"def create_session():\\n\",\n \" config = tf.ConfigProto(graph_options=tf.GraphOptions(\\n\",\n \" optimizer_options=tf.OptimizerOptions(\\n\",\n \" opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" return tf.Session(config=config)\\n\",\n \"\\n\",\n \"def printops(ops):\\n\",\n \" print([op.name for op in ops])\\n\",\n \" \\n\",\n \"run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\\n\",\n \"def sessrun(*args, **kwargs):\\n\",\n \" sess = create_session()\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" kwargs[\\\"run_metadata\\\"] = run_metadata\\n\",\n \" kwargs[\\\"options\\\"] = run_options\\n\",\n \" return sess.run(*args, **kwargs)\\n\",\n \"\\n\",\n \"import tensorflow.contrib.graph_editor as ge\\n\",\n \"def run_after(a_tensor, b_tensor):\\n\",\n \" \\\"\\\"\\\"Force a to run after b\\\"\\\"\\\"\\n\",\n \" ge.reroute.add_control_inputs(a_tensor.op, [b_tensor.op])\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Graph visualizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 335,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# make things wide\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):\\n\",\n \" if not graph_def:\\n\",\n \" graph_def = tf.get_default_graph().as_graph_def()\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" data = str(strip_def)\\n\",\n \" if ungroup_gradients:\\n\",\n \" data = data.replace('\\\"gradients/', '\\\"b_')\\n\",\n \" #print(data)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(data), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(width, height, code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Redirector\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"STDOUT = 1\\n\",\n \"STDERR = 2\\n\",\n \"\\n\",\n \"# this hangs when pipe buffer is full\\n\",\n \"\\n\",\n \"class FDRedirector(object):\\n\",\n \" \\\"\\\"\\\" Class to redirect output (stdout or stderr) at the OS level using\\n\",\n \" file descriptors.\\n\",\n \" \\\"\\\"\\\" \\n\",\n \"\\n\",\n \" def __init__(self, fd=STDOUT):\\n\",\n \" \\\"\\\"\\\" fd is the file descriptor of the outpout you want to capture.\\n\",\n \" It can be STDOUT or STERR.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.fd = fd\\n\",\n \" self.started = False\\n\",\n \" self.piper = None\\n\",\n \" self.pipew = None\\n\",\n \"\\n\",\n \" def start(self):\\n\",\n \" \\\"\\\"\\\" Setup the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if not self.started:\\n\",\n \" self.oldhandle = os.dup(self.fd)\\n\",\n \" self.piper, self.pipew = os.pipe()\\n\",\n \" os.dup2(self.pipew, self.fd)\\n\",\n \" os.close(self.pipew)\\n\",\n \"\\n\",\n \" self.started = True\\n\",\n \"\\n\",\n \" def flush(self):\\n\",\n \" \\\"\\\"\\\" Flush the captured output, similar to the flush method of any\\n\",\n \" stream.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.fd == STDOUT:\\n\",\n \" sys.stdout.flush()\\n\",\n \" elif self.fd == STDERR:\\n\",\n \" sys.stderr.flush()\\n\",\n \"\\n\",\n \" def stop(self):\\n\",\n \" \\\"\\\"\\\" Unset the redirection and return the captured output. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.started:\\n\",\n \" self.flush()\\n\",\n \" os.dup2(self.oldhandle, self.fd)\\n\",\n \" os.close(self.oldhandle)\\n\",\n \" f = os.fdopen(self.piper, 'r')\\n\",\n \" output = f.read()\\n\",\n \" f.close()\\n\",\n \"\\n\",\n \" self.started = False\\n\",\n \" return output\\n\",\n \" else:\\n\",\n \" return ''\\n\",\n \"\\n\",\n \" def getvalue(self):\\n\",\n \" \\\"\\\"\\\" Return the output captured since the last getvalue, or the\\n\",\n \" start of the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" output = self.stop()\\n\",\n \" self.start()\\n\",\n \" return output\\n\",\n \"\\n\",\n \"redirect=FDRedirector(STDERR)\\n\",\n \"stderr = \\\"\\\"\\n\",\n \"\\n\",\n \"from contextlib import contextmanager\\n\",\n \"@contextmanager\\n\",\n \"def grab_stderr():\\n\",\n \" global stderr\\n\",\n \" redirect.start();\\n\",\n \" yield\\n\",\n \" stderr = redirect.stop()\\n\",\n \" \\n\",\n \"def open_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def close_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def grab_output(tag, filename=\\\"/tmp/jupyter.txt\\\"):\\n\",\n \" s = open(filename).read()\\n\",\n \" opening_tag = tag+\\\"\\\"\\n\",\n \" ending_tag = tag+\\\"\\\"\\n\",\n \" starting_pos = s.rfind(opening_tag)\\n\",\n \" ending_pos = s.rfind(ending_tag)\\n\",\n \" return s[starting_pos:ending_pos]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Threading redirector\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import time\\n\",\n \"import threading\\n\",\n \"\\n\",\n \"STDOUT = 1\\n\",\n \"STDERR = 2\\n\",\n \"\\n\",\n \"# this hangs when pipe buffer is full\\n\",\n \"\\n\",\n \"class FDRedirector(object):\\n\",\n \" \\\"\\\"\\\" Class to redirect output (stdout or stderr) at the OS level using\\n\",\n \" file descriptors.\\n\",\n \" \\\"\\\"\\\" \\n\",\n \"\\n\",\n \" def __init__(self, fd=STDOUT):\\n\",\n \" \\\"\\\"\\\" fd is the file descriptor of the outpout you want to capture.\\n\",\n \" It can be STDOUT or STERR.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.fd = fd\\n\",\n \" self.started = False\\n\",\n \" self.piper = None\\n\",\n \" self.pipew = None\\n\",\n \" self.output = \\\"\\\"\\n\",\n \" self.should_stop = False\\n\",\n \"\\n\",\n \" def start(self):\\n\",\n \" \\\"\\\"\\\" Setup the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if not self.started:\\n\",\n \" self.oldhandle = os.dup(self.fd)\\n\",\n \" self.piper, self.pipew = os.pipe()\\n\",\n \" os.dup2(self.pipew, self.fd)\\n\",\n \" os.close(self.pipew)\\n\",\n \"\\n\",\n \" self.started = True\\n\",\n \"\\n\",\n \" def flush(self):\\n\",\n \" \\\"\\\"\\\" Flush the captured output, similar to the flush method of any\\n\",\n \" stream.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.fd == STDOUT:\\n\",\n \" sys.stdout.flush()\\n\",\n \" elif self.fd == STDERR:\\n\",\n \" sys.stderr.flush()\\n\",\n \"\\n\",\n \" def stop(self):\\n\",\n \" \\\"\\\"\\\" Unset the redirection and return the captured output. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.started:\\n\",\n \" self.flush()\\n\",\n \" os.dup2(self.oldhandle, self.fd)\\n\",\n \" os.close(self.oldhandle)\\n\",\n \" f = os.fdopen(self.piper, 'r')\\n\",\n \" output = f.read()\\n\",\n \" f.close()\\n\",\n \"\\n\",\n \" self.started = False\\n\",\n \" return output\\n\",\n \" else:\\n\",\n \" return ''\\n\",\n \"\\n\",\n \" def getvalue(self):\\n\",\n \" \\\"\\\"\\\" Return the output captured since the last getvalue, or the\\n\",\n \" start of the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" output = self.stop()\\n\",\n \" self.start()\\n\",\n \" return output\\n\",\n \" \\n\",\n \" def update_values(self):\\n\",\n \" while not self.should_stop:\\n\",\n \" time.sleep(0.01)\\n\",\n \" self.output+=self.getvalue()\\n\",\n \" \\n\",\n \" def real_start(self):\\n\",\n \" self.should_stop = False\\n\",\n \" self.start()\\n\",\n \" self.start_runner()\\n\",\n \" \\n\",\n \" def real_stop(self):\\n\",\n \" self.should_stop = True\\n\",\n \" time.sleep(10)\\n\",\n \" if self.runner.is_alive():\\n\",\n \" raise RuntimeError(\\\"Reading thread won't stop\\\")\\n\",\n \" self.output+=self.getvalue()\\n\",\n \" \\n\",\n \" return self.output\\n\",\n \" \\n\",\n \" def start_runner(self):\\n\",\n \" self.runner = threading.Thread(target=self.update_values, args=())\\n\",\n \" self.runner.start()\\n\",\n \"\\n\",\n \"#redirect=FDRedirector(STDERR)\\n\",\n \"\\n\",\n \"stderr = \\\"\\\"\\n\",\n \"\\n\",\n \"from contextlib import contextmanager\\n\",\n \"@contextmanager\\n\",\n \"def grab_stderr():\\n\",\n \" global stderr\\n\",\n \" redirect = FDRedirector(STDERR)\\n\",\n \" redirect.real_start()\\n\",\n \" yield\\n\",\n \" stderr = redirect.real_stop()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total time (us): curr=1147 count=11 runs, avg 1.147 ms, 9 nodes defined 7 nodes observed\\n\",\n \"0.008 avg KB per run.\\n\",\n \"\\n\",\n \"============ By run order (ms) =================\\n\",\n \" [start] [first] [avg]\\t [%] \\t [cdf%] \\t [Op]\\t[Name]\\n\",\n \" 0.000 0.042 0.042\\t 3.662%\\t 3.662%\\t \\t_SOURCE\\n\",\n \" 0.071 0.157 0.157\\t 13.688%\\t 17.350%\\t Const\\tb/y\\n\",\n \" 0.109 0.540 0.540\\t 47.079%\\t 64.429%\\t Variable\\ta\\n\",\n \" 0.244 0.087 0.087\\t 7.585%\\t 72.014%\\t Const\\tc/y\\n\",\n \" 0.667 0.055 0.055\\t 4.795%\\t 76.809%\\t Identity\\ta/read\\n\",\n \" 0.735 0.104 0.104\\t 9.067%\\t 85.876%\\t Pow\\tb\\n\",\n \" 0.853 0.162 0.162\\t 14.124%\\t100.000%\\t Pow\\tc\\n\",\n \"1 runs, avg 1.147 ms, 9 nodes defined 7 nodes observed\\n\",\n \"0.008 avg KB per run.\\n\",\n \"\\n\",\n \"============ Top by duration in ms =================\\n\",\n \" [start] [first] [avg]\\t [%] \\t [cdf%] \\t [Op]\\t[Name]\\n\",\n \" 0.109 0.540 0.540\\t 47.079%\\t 47.079%\\t Variable\\ta\\n\",\n \" 0.853 0.162 0.162\\t 14.124%\\t 61.203%\\t Pow\\tc\\n\",\n \" 0.071 0.157 0.157\\t 13.688%\\t 74.891%\\t Const\\tb/y\\n\",\n \" 0.735 0.104 0.104\\t 9.067%\\t 83.958%\\t Pow\\tb\\n\",\n \" 0.244 0.087 0.087\\t 7.585%\\t 91.543%\\t Const\\tc/y\\n\",\n \" 0.667 0.055 0.055\\t 4.795%\\t 96.338%\\t Identity\\ta/read\\n\",\n \" 0.000 0.042 0.042\\t 3.662%\\t100.000%\\t \\t_SOURCE\\n\",\n \"Total Memory (bytes): curr=8 count=11 runs, avg 1.147 ms, 9 nodes defined 7 nodes observed\\n\",\n \"0.008 avg KB per run.\\n\",\n \"\\n\",\n \"============ By run order (KB) =================\\n\",\n \" [start] [first] [avg]\\t [%] \\t [cdf%] \\t [Op]\\t[Name]\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t 0.000%\\t Identity\\ta/read\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t 0.000%\\t Const\\tc/y\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t 0.000%\\t \\t_SOURCE\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t 0.000%\\t Variable\\ta\\n\",\n \" 0.000 0.004 0.004\\t 50.000%\\t 50.000%\\t Pow\\tb\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t 50.000%\\t Const\\tb/y\\n\",\n \" 0.004 0.004 0.004\\t 50.000%\\t100.000%\\t Pow\\tc\\n\",\n \"1 runs, avg 1.147 ms, 9 nodes defined 7 nodes observed\\n\",\n \"0.008 avg KB per run.\\n\",\n \"\\n\",\n \"============ Top by memory usage in KB =================\\n\",\n \" [start] [first] [avg]\\t [%] \\t [cdf%] \\t [Op]\\t[Name]\\n\",\n \" 0.004 0.004 0.004\\t 50.000%\\t 50.000%\\t Pow\\tc\\n\",\n \" 0.000 0.004 0.004\\t 50.000%\\t100.000%\\t Pow\\tb\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t100.000%\\t \\t_SOURCE\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t100.000%\\t Const\\tb/y\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t100.000%\\t Const\\tc/y\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t100.000%\\t Variable\\ta\\n\",\n \" 0.000 0.000 0.000\\t 0.000%\\t100.000%\\t Identity\\ta/read\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"a = tf.Variable(2., name=\\\"a\\\")\\n\",\n \"b = tf.pow(a, 2, name=\\\"b\\\")\\n\",\n \"c = tf.pow(b, 3, name=\\\"c\\\")\\n\",\n \"sessrun(c)\\n\",\n \"summarize()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Medium graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"a1 = tf.random_uniform(shape=(n,), name=\\\"a1\\\")\\n\",\n \"a2 = tf.mul(a1, 2., name=\\\"a2\\\")\\n\",\n \"a3 = tf.mul(a2, 2., name=\\\"a3\\\")\\n\",\n \"a4 = tf.mul(a3, 2., name=\\\"a4\\\")\\n\",\n \"c = tf.mul(a4, 3., name=\\\"cost\\\")\\n\",\n \"b4 = tf.add(c, a4, name=\\\"b4\\\")\\n\",\n \"b3 = tf.add(b4, a3, name=\\\"b3\\\")\\n\",\n \"b2 = tf.add(b3, a2, name=\\\"b2\\\")\\n\",\n \"b1 = tf.add(b2, a1, name=\\\"b1\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sess = create_session()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 39752762 a1/RandomUniform 100000000 100000000\\n\",\n \" 51521085 a1/mul 100000000 200000000\\n\",\n \" 51624706 a1/RandomUniform -100000000 100000000\\n\",\n \" 62873624 a1 100000000 200000000\\n\",\n \" 62971457 a1/mul -100000000 100000000\\n\",\n \" 71539763 a2 100000000 200000000\\n\",\n \" 79764903 a3 100000000 300000000\\n\",\n \" 86546363 a4 100000000 400000000\\n\",\n \" 94408888 cost 100000000 500000000\\n\",\n \"105218710 b4 100000000 600000000\\n\",\n \"105289690 cost -100000000 500000000\\n\",\n \"105641953 a4 -100000000 400000000\\n\",\n \"117250163 b3 100000000 500000000\\n\",\n \"117318474 b4 -100000000 400000000\\n\",\n \"117684893 a3 -100000000 300000000\\n\",\n \"131092040 b2 100000000 400000000\\n\",\n \"131156038 b3 -100000000 300000000\\n\",\n \"131741197 a2 -100000000 200000000\\n\",\n \"142940432 b1 100000000 300000000\\n\",\n \"142992671 b2 -100000000 200000000\\n\",\n \"143395155 a1 -100000000 100000000\\n\",\n \"143818049 b1 -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"total_memory = 0\\n\",\n \"for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %20s %10d %10d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Memory plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_memory(stderr):\\n\",\n \" total_memory = 0\\n\",\n \" timestamps = []\\n\",\n \" data = []\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamps.append(record[0]-1)\\n\",\n \" data.append(total_memory)\\n\",\n \" total_memory += record[-1]\\n\",\n \" timestamps.append(record[0])\\n\",\n \" data.append(total_memory)\\n\",\n \" plt.plot(timestamps, data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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CtwFTNHc9nepb/eerwAWSilXLr6R5FnA\\nV5I8opTyia6LMgBIkjRYn6LZAnB6KeUjhxnnZGD5vv7FsDCQrfXuApAkqU9JTklyXpKH9Aad1Xt9\\nRillN3AdcG2Si5OcmeQRSV6a5Im98d8DPDzJy5I8MMkUze6APTQBovuaPQhQkqT+JHks8EF+8hoA\\n15RSnpfkBOAq4NnAzwK30xzkd3Up5XO9eVwCvBhYD3y39/5LSim7BlKzAUCSpPq4C0CSpAoZACRJ\\nqpABQJKkChkAJEmqkAFAkqQKGQAkSaqQAUCSpAoZACRJqpABQJKkChkAJEmqkAFAkqQK/T/tm/ya\\nzNJ5gwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"total_memory = 0\\n\",\n \"timestamps = []\\n\",\n \"data = []\\n\",\n \"for record in memory_timeline(stderr):\\n\",\n \" timestamps.append(record[0]-1)\\n\",\n \" data.append(total_memory)\\n\",\n \" total_memory += record[-1]\\n\",\n \" timestamps.append(record[0])\\n\",\n \" data.append(total_memory)\\n\",\n \"plt.plot(timestamps, data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Rewrite the graph for efficiency manually\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"a1 = tf.random_uniform(shape=(n,), name=\\\"a1\\\")\\n\",\n \"a2 = tf.mul(a1, 2., name=\\\"a2\\\")\\n\",\n \"A2 = tf.mul(a1, 2., name=\\\"A2\\\")\\n\",\n \"a3 = tf.mul(a2, 2., name=\\\"a3\\\")\\n\",\n \"a4 = tf.mul(a3, 2., name=\\\"a4\\\")\\n\",\n \"A4 = tf.mul(a4, 2., name=\\\"A4\\\")\\n\",\n \"c = tf.mul(a4, 3., name=\\\"cost\\\")\\n\",\n \"b4 = tf.add(c, A4, name=\\\"b4\\\")\\n\",\n \"b3 = tf.add(b4, a3, name=\\\"b3\\\")\\n\",\n \"b2 = tf.add(b3, A2, name=\\\"b2\\\")\\n\",\n \"b1 = tf.add(b2, a1, name=\\\"b1\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(b1);\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 22691426 a1/RandomUniform 100000000 100000000\\n\",\n \" 31121561 a1/mul 100000000 200000000\\n\",\n \" 31178772 a1/RandomUniform -100000000 100000000\\n\",\n \" 38841463 a1 100000000 200000000\\n\",\n \" 38929271 a1/mul -100000000 100000000\\n\",\n \" 52169264 A2 100000000 200000000\\n\",\n \" 52809745 a2 100000000 300000000\\n\",\n \" 60650414 a3 100000000 400000000\\n\",\n \" 60717234 a2 -100000000 300000000\\n\",\n \" 69346731 a4 100000000 400000000\\n\",\n \" 82773996 A4 100000000 500000000\\n\",\n \" 83162878 cost 100000000 600000000\\n\",\n \" 83204317 a4 -100000000 500000000\\n\",\n \" 94739261 b4 100000000 600000000\\n\",\n \" 94792320 cost -100000000 500000000\\n\",\n \" 95130126 A4 -100000000 400000000\\n\",\n \"105870922 b3 100000000 500000000\\n\",\n \"105920962 b4 -100000000 400000000\\n\",\n \"106262555 a3 -100000000 300000000\\n\",\n \"117870355 b2 100000000 400000000\\n\",\n \"117918200 b3 -100000000 300000000\\n\",\n \"118261776 A2 -100000000 200000000\\n\",\n \"130896511 b1 100000000 300000000\\n\",\n \"130977688 b2 -100000000 200000000\\n\",\n \"131314935 a1 -100000000 100000000\\n\",\n \"167539303 b1 -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"total_memory = 0\\n\",\n \"for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %20s %10d %10d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"run_after(A2, b3)\\n\",\n \"run_after(A4, c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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CSpQAYASZIKZACQJKlABgBJkgpkAJAkqUAGAEmSCmQAkCSpQAYASZIK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\\nQwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(b1);\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 30855829 a1/RandomUniform 100000000 100000000\\n\",\n \" 41000144 a1/mul 100000000 200000000\\n\",\n \" 41086614 a1/RandomUniform -100000000 100000000\\n\",\n \" 50624457 a1 100000000 200000000\\n\",\n \" 50728597 a1/mul -100000000 100000000\\n\",\n \" 59861251 a2 100000000 200000000\\n\",\n \" 68449844 a3 100000000 300000000\\n\",\n \" 68543889 a2 -100000000 200000000\\n\",\n \" 77135029 a4 100000000 300000000\\n\",\n \" 85546359 cost 100000000 400000000\\n\",\n \" 93934819 A4 100000000 500000000\\n\",\n \" 94010573 a4 -100000000 400000000\\n\",\n \"104751984 b4 100000000 500000000\\n\",\n \"104821862 cost -100000000 400000000\\n\",\n \"105425300 A4 -100000000 300000000\\n\",\n \"116983044 b3 100000000 400000000\\n\",\n \"117066767 b4 -100000000 300000000\\n\",\n \"117460057 a3 -100000000 200000000\\n\",\n \"125666337 A2 100000000 300000000\\n\",\n \"136094659 b2 100000000 400000000\\n\",\n \"136161082 b3 -100000000 300000000\\n\",\n \"136703476 A2 -100000000 200000000\\n\",\n \"147492790 b1 100000000 300000000\\n\",\n \"147572071 b2 -100000000 200000000\\n\",\n \"147914309 a1 -100000000 100000000\\n\",\n \"182929109 b1 -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"total_memory = 0\\n\",\n \"for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %20s %10d %10d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Plot gradients allocation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From :12 in .: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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/BlqkvUxoBLgLOAc7vbvCRJWqm6I/MjgBuAXwXmgf8LnJuZX+11YZIk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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"#a1 = tf.random_uniform(shape=(n,), name=\\\"a1\\\")\\n\",\n \"a1 = tf.Variable(tf.random_uniform(shape=(n,)), name=\\\"a1\\\")\\n\",\n \"a2 = tf.tanh(a1, name=\\\"a2\\\")\\n\",\n \"a3 = tf.tanh(a2, name=\\\"a3\\\")\\n\",\n \"a4 = tf.tanh(a3, name=\\\"a4\\\")\\n\",\n \"cost = tf.reduce_sum(a4, name=\\\"cost\\\")\\n\",\n \"grad = tf.gradients(cost, [a1])[0]\\n\",\n \"\\n\",\n \"sess = create_session()\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grad.op);\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory(stderr)\\n\",\n \"\\n\",\n \"#c = tf.mul(a4, 3., name=\\\"cost\\\")\\n\",\n \"#b4 = tf.add(c, A4, name=\\\"b4\\\")\\n\",\n \"#b3 = tf.add(b4, a3, name=\\\"b3\\\")\\n\",\n \"#b2 = tf.add(b3, A2, name=\\\"b2\\\")\\n\",\n \"#b1 = tf.add(b2, a1, name=\\\"b1\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 524107 a1 100000000 100000000\\n\",\n \" 581954 a1/read 100000000 200000000\\n\",\n \" 31013633 a2 100000000 300000000\\n\",\n \" 43400896 a3 100000000 400000000\\n\",\n \" 44004364 gradients/cost_grad/Tile 100000000 500000000\\n\",\n \" 52270796 a4 100000000 600000000\\n\",\n \" 64082445 gradients/a4_grad/TanhGrad 100000000 700000000\\n\",\n \" 64163679 a4 -100000000 600000000\\n\",\n \" 64523500 gradients/cost_grad/Tile -100000000 500000000\\n\",\n \" 75939734 gradients/a3_grad/TanhGrad 100000000 600000000\\n\",\n \" 76010501 a3 -100000000 500000000\\n\",\n \" 76708326 gradients/a4_grad/TanhGrad -100000000 400000000\\n\",\n \" 88194983 gradients/a2_grad/TanhGrad 100000000 500000000\\n\",\n \" 88292636 a2 -100000000 400000000\\n\",\n \" 88813138 gradients/a3_grad/TanhGrad -100000000 300000000\\n\",\n \" 89188661 gradients/a2_grad/TanhGrad -100000000 200000000\\n\"\n ]\n }\n ],\n \"source\": [\n \"total_memory = 0\\n\",\n \"for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %30s %10d %10d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Rewire the graph manually for better memory\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 181,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"#a1 = tf.random_uniform(shape=(n,), name=\\\"a1\\\")\\n\",\n \"a0 = tf.Variable(tf.random_uniform(shape=(n,)), name=\\\"a0\\\")\\n\",\n \"a1 = tf.identity(a0, name=\\\"a1\\\")\\n\",\n \"a2 = tf.tanh(a1, name=\\\"a2\\\")\\n\",\n \"a3 = tf.tanh(a2, name=\\\"a3\\\")\\n\",\n \"a4 = tf.tanh(a3, name=\\\"a4\\\")\\n\",\n \"cost = tf.reduce_sum(a4, name=\\\"cost\\\")\\n\",\n \"grad = tf.gradients(cost, [a1])[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 182,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 183,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"xs = [a0]\\n\",\n \"ys = [cost]\\n\",\n \"fwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs], backward_seed_ops=[y.op for y in ys])\\n\",\n \"copied_fwd_svg, info = ge.copy_with_input_replacements(ge.sgv(fwd_ops), {})\\n\",\n \"xs_copied = [info._transformed_ts[x] if isinstance(x, tf.Tensor) else info._transformed_ts[x._variable] for x in xs ]\\n\",\n \"ys_copied = [info._transformed_ts[y] for y in ys]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 184,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 185,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"grads = tf.gradients(ys_copied, xs_copied)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 186,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 187,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ys_copied = [info._transformed_ts[y] for y in ys]\\n\",\n \"xs_copied = [info._transformed_ts[x] if isinstance(x,tf.Tensor) else info._transformed_ts[x._variable] for x in xs]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 188,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"bwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs_copied],\\n\",\n \" backward_seed_ops=grads)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 189,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a0', 'a0/read', 'a1', 'a2', 'a3', 'a4', 'cost']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(fwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 190,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a0_1', 'a0/read_1', 'a1_1', 'a2_1', 'gradients_1/a2_1_grad/TanhGrad', 'a3_1', 'a4_1', 'gradients_1/a3_1_grad/TanhGrad', 'gradients_1/a4_1_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 191,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# this takes ops and returns Tensors\\n\",\n \"remember = ge.filter_ts_from_regex(fwd_ops, 'a3')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 192,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a1:0', 'a3:0']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(remember)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 193,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# this takes Tesnors\\n\",\n \"replaced_ts = [info._transformed_ts[t] for t in remember]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 194,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a1_1:0', 'a3_1:0']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(replaced_ts)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 206,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a0_1', 'a0/read_1', 'a1_1', 'a2_1', 'gradients_1/a2_1_grad/TanhGrad', 'a3_1', 'a4_1', 'gradients_1/a3_1_grad/TanhGrad', 'gradients_1/a4_1_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 195,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 195,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ge.reroute_a2b_ts(remember, replaced_ts, can_modify=bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 204,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(width=2000, height=4000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 212,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"#a1 = tf.random_uniform(shape=(n,), name=\\\"a1\\\")\\n\",\n \"a0 = tf.Variable(tf.random_uniform(shape=(n,)), name=\\\"a0\\\")\\n\",\n \"a1 = tf.identity(a0, name=\\\"a1\\\")\\n\",\n \"a2 = tf.tanh(a1, name=\\\"a2\\\")\\n\",\n \"a3 = tf.tanh(a2, name=\\\"a3\\\")\\n\",\n \"a4 = tf.tanh(a3, name=\\\"a4\\\")\\n\",\n \"cost = tf.reduce_sum(a4, name=\\\"cost\\\")\\n\",\n \"#grad = tf.gradients(cost, [a1])[0]\\n\",\n \"xs = [a0]\\n\",\n \"ys = [cost]\\n\",\n \"fwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs], backward_seed_ops=[y.op for y in ys])\\n\",\n \"copied_fwd_svg, info = ge.copy_with_input_replacements(ge.sgv(fwd_ops), {})\\n\",\n \"xs_copied = [info._transformed_ts[x] if isinstance(x, tf.Tensor) else info._transformed_ts[x._variable] for x in xs ]\\n\",\n \"ys_copied = [info._transformed_ts[y] for y in ys]\\n\",\n \"\\n\",\n \"\\n\",\n \"grads = tf.gradients(ys_copied, xs_copied)\\n\",\n \"\\n\",\n \"bwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs_copied],\\n\",\n \" backward_seed_ops=grads)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 213,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a0_1', 'a0/read_1', 'a1_1', 'a2_1', 'gradients/a2_1_grad/TanhGrad', 'a3_1', 'a4_1', 'gradients/a3_1_grad/TanhGrad', 'gradients/a4_1_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 214,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"bwd_ops=[bwd_ops[i] for i in [4, 7, 8]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 215,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['gradients/a2_1_grad/TanhGrad', 'gradients/a3_1_grad/TanhGrad', 'gradients/a4_1_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 216,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"remember = ge.filter_ts_from_regex(fwd_ops, 'a3')\\n\",\n \"replaced_ts = [info._transformed_ts[t] for t in remember]\\n\",\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 217,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ge.reroute_a2b_ts(remember, replaced_ts, can_modify=bwd_ops)\\n\",\n \"show_graph(width=2000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"iterate over all remembered nodes, for each remembered node child, add dependency on previous remembered node child or cost\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"for i in range(len(remember)):\\n\",\n \" if i == 0:\\n\",\n \" run_after(child(remember[i]), cost)\\n\",\n \" else:\\n\",\n \" run_after(child(remember[i]), child(remember[i-1]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 101,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"replaced_ts[]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From :2 in .: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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KlwhrkkSYUzzCVJKpxhLklS4QxzSZIKZ5hLklQ4w1ySpMIZ5pIkFc4w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\\nSVLhDHNJkgr3/wAT8qWptUdzewAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grad.op);\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 91,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"replaced_ts[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"run_after(replaced_ts[0], cost)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From :2 in .: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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LjXb/2SJGltaX0WwEqllJ/ualmSJGm0vBeAJEkVMgBIklQhA4AkSRUy\\nAEiSVCEDgCRJFTIASJJUIQOAJEkVMgBIklQhA4AkSRUyAEiSVCEDgCRJFTIASJJUIQOAJEkVMgBI\\nklQhA4AkSRUyAEiSVCEDgCRJFTIASJJUIQOAJEkVMgBIklQhA4AkSRUyAEiSVCEDgCRJFTIASJJU\\nodYBIMkLk3wyydLg8aEkP9NlcZIkaTSG6QG4EXgJMDt4vA/4xyTTXRQmSZJGZ0PbGUsp71gx6KIk\\nLwIeB2wbqipJkjRSrQPAckkOAc4DDgeu7WKZkiRpdIYKAEkeQbPBvy+wAzi3lHJdF4UdDBYXYceO\\nSVexNm2zj0iSJmrYHoDrgBOBo4CnAZclOW1fIWBubo6pqandhvV6PXq93pClrC2Li7B586SrWPs2\\nbpx0BZK0tvT7ffr9/m7DlpaWOl9PSindLSy5Gvi3UsqL9jBuBpifn59nZmams3WuVQsLMDsLW7fC\\ntIdF7tHGjbBp06SrkKTvuesuuO46eNjD4PDDJ13N9ywsLDA7OwswW0pZ6GKZnRwDsMwhwGEdL3Nd\\nm56GCvKOJB0UDj+8ns/s1gEgye8D76Q5HXAj8CzgdODMbkqTJEmjMkwPwNHAZcCDgCXgU8CZpZT3\\ndVGYJEkanWGuA/D8LguRJEnj470AJEmqkAFAkqQKGQAkSaqQAUCSpAoZACRJqpABQJKkChkAJEmq\\nkAFAkqQKGQAkSaqQAUCSpAoZACRJqpABQJKkChkAJEmqkAFAkqQKGQAkSaqQAUCSpAoZACRJqpAB\\nQJKkChkAJEmqkAFAkqQKGQAkSaqQAUCSpAoZACRJqlCrAJDkpUk+muSOJLcm+Yckm7suTpIkjUbb\\nHoBTgb8AHgs8AbgP8J4kP9hVYZIkaXQ2tJmplPKzy58n+WXga8As8IHhy5IkSaPU1TEARwEF+EZH\\ny5MkSSM0dABIEuDPgQ+UUj43fEmSJGnUWu0CWOG1wMOBxx/IxHNzc0xNTe02rNfr0ev1OihFkqT1\\nrd/v0+/3dxu2tLTU+XpSSmk/c/KXwJOBU0spX97PtDPA/Pz8PDMzM63XuV4sLMDsLMzPQwXNlSSN\\n0MLCArOzswCzpZSFLpbZugdgsPE/Bzh9fxt/SZK0trQKAEleC/SAs4E7kxw9GLVUSvlWV8VJkqTR\\naHsQ4AuB+wH/DHxl2eO8bsqSJEmj1PY6AF5CWJKkdcwNuSRJFTIASJJUIQOAJEkVMgBIklQhA4Ak\\nSRUyAEiSVCEDgCRJFTIASJJUIQOAJEkVMgBIklQhA4AkSRUyAEiSVCEDgCRJFTIASJJUIQOAJEkV\\nMgBIklQhA4AkSRUyAEiSVCEDgCRJFTIASJJUIQOAJEkVMgBIklQhA4AkSRUyAEiSVKHWASDJqUmu\\nTHJzkp1Jzu6yMEmSNDrD9AAcAXwCuAAo3ZQjSZLGYUPbGUsp7wLeBZAknVUkSZJGzmMAJEmqkAFA\\nkqQKtd4F0Nbc3BxTU1O7Dev1evR6vXGXIknSmtPv9+n3+7sNW1pa6nw9Yw8AW7ZsYWZmZtyrlSRp\\nXdjTl+KFhQVmZ2c7XY+7ACRJqlDrHoAkRwAPBXadAXBckhOBb5RSbuyiOEmSNBrD7AI4GXg/zTUA\\nCvCng+GXAs8bsi5JkjRCw1wH4F9wF4IkSeuSG3BJkipkAJAkqUIGAEmSKmQAkCSpQgYASZIqZACQ\\nJKlCBgBJkipkAJAkqUIGAEmSKmQAkCSpQgYASZIqZACQJKlCBgBJkipkAJAkqUIGAEmSKmQAkCSp\\nQgYASZIqZACQJKlCBgBJkipkAJAkqUIGAEmSKmQAkCSpQgaAEej3+5MuYSxs58HFdh58amlrLe3s\\n2lABIMkFSa5PcneSDyd5dFeFrWe1/DHazoOL7Tz41NLWWtrZtdYBIMnTgT8FLgYeBXwSeHeSB3RU\\nmyRJGpFhegDmgNeVUi4rpVwHvBC4C3heJ5VJkqSRaRUAktwHmAX+adewUkoB3guc0k1pkiRpVDa0\\nnO8BwKHArSuG3wqcsJd57guwbdu2lqtcH+6+G266aYmrrloA4GBu7tLSEgsLC5MuY+Rs58GllnZC\\nPW2toZ3Ltp337WqZab64r3Km5EHAzcAppZSPLBv+R8BPllJ+Yg/zPBN40xC1SpJUu2eVUi7vYkFt\\newC+DtwLHL1i+I/w/b0Cu7wbeBZwA/CtluuVJKlG9wWOpdmWdqJVDwBAkg8DHyml/NrgeYAvA68u\\npfxxVwVKkqTute0BAPgz4NIk88BHac4KOBx4Qwd1SZKkEWodAEopVwzO+f9dml0BnwDOKqX8e1fF\\nSZKk0Wi9C0CSJK1f3gtAkqQKGQAkSarQyAJAkh9K8qYkS0luT/L6JEfsZ/pXJ7kuyZ1JvpTkVUnu\\nN6oa21rtTZCS/GKSbYPpP5nkSeOqdRiraWeS5ye5Jsk3Bo+r18vNodre1CrJM5LsTPL3o66xCy3+\\nbqeSvCbJVwbzXJfkZ8ZVb1st2vnrg7bdleTLSf4syWHjqreNJKcmuTLJzYO/wbMPYJ6fSjKf5FtJ\\ntif5pXHUOozVtjPJuUnek+Rrg23Ph5KcOa5622rzfi6b9/FJ/jPJqq+ENMoegMuBaeAM4OeA04DX\\n7WP6BwMPAn4DeATwS8DPAK8fYY2rttqbICU5hea1+GvgJOBtwNuSPHw8FbfT4mZPp9O086eAxwE3\\nAu8ZXDRqzWp7U6skDwH+GLhm5EV2oMXf7X1oLu3948BTaa7w+QKaC4CtWS3a+UzglYPpH0ZzL5On\\nA78/loLbO4LmwOsLgP0eyJXkWOD/0Vy+/UTgVcDrkzxxdCV2YlXtpNnOvAd4EjADvB94e5ITR1Zh\\nN1bbTgAGX5AvpflfXb1SSucPmn+kncCjlg07C7gHeOAqlvMLwN3AIaOos2XbPgy8atnzADcBL97L\\n9G8Grlwx7FrgtZNuS5ft3MP8hwBLwPmTbkvX7Ry07V+B5wKXAH8/6XZ03U6am3stAodOuvYRt/Mv\\ngKtXDPsT4JpJt2UVbd4JnL2faf4Q+NSKYX3gqknX32U79zLfZ4CLJl3/KNo5eA//J02AXVjtukbV\\nA3AKcHsp5ePLhr2XJtk8dhXLOQq4o5Sys8vi2mp5E6RT+P509u59TD9xHd3s6QjgPsA3Oi+wI0O0\\n82Lga6WUS0ZbYTdatvPJDIJqkluSfDrJS5Os2eOGWrbzQ8Dsrt0ESY4DfhZ4x2irHbvHsc4+h7qQ\\nJMBG1vDnUFtJngscRxMAWhnmQkD78kDga8sHlFLuTfKNwbj9GnTZXcS+dxuMW5ubID1wL9Mf0Osw\\nIW3audIf0nQXt+uaGo9VtzPJ42m++a/1LsXl2ryfxwE/DWyl6U7dBLx2sJz/NZoyh7bqdpZS+oPP\\nmg8MNhaHAn9VSvnDkVY6fnv7HLpfksNKKd+eQE3j8Fs0X0aumHQhXUqyCXgFzb13djZ/uqu3qjSf\\n5JWDAxT29rg3yeZ9LYID21+1kSaBf4Yh0s0YHVC7hph+rTjQ9+9C4DzgKaWU74y8qu7tsZ1JjgTe\\nCLyglHL72Kvq3r7ez0NoNhD/rZTy8VLKFTT7xV80ruI6tNd2Jvkp4H/Q7PJ4FM3xDj+f5KKxVTc5\\nu7Ya6/GzaL8Gx3e8HPjFUsrXJ11PVwa9cG8CLi6lfGHX4DbLWm0PwJ/Q7PPcly8Ct9DcGOi7khwK\\n/BB7v1nQrumOpOma+g/gqaWUe1dZ4yi1uQnSLaucfi1o004Akvwm8GLgjFLKZ0dTXmdW287jgYfQ\\nHFS06x/uEIAk3wFOKKVcP6Jah9Hm/fwq8J1BF/ou24AHJtlQSrmn+zKH1qadvwtctmx3zmcHn0Gv\\nY+32dLSxt8+hO9ZpSN+nJM8A/g/wC6WU90+6no5tBE4GTkrymsGwQ2j2eHwHOLOU8s8HsqBV9QCU\\nUm4rpWzfz+Memn2HRyV51LLZz6BJKR/Z48L57jf/99Ac+Hf2WvvDLKX8JzBP0xbgu/uYzqDZl7gn\\n1y6ffuCJg+FrUst2kuS3gJfRXBL643ubbq1o0c5twCNpzuY4cfC4Enjf4PcbR1xyKy3fzw8CD10x\\n7ATgq2t049+2nYfTHHS13M7BrO36VdemPX0Oncka/hxqK0kP+BugV0p516TrGYE7aM6UW/459FfA\\ndYPf97qN/T4jPJLxKuBjwKOBxwOfB964bPyDaT5QTx48P5LmCN5PAP+FJq3ueqylswDOowkoz6E5\\n2+F1wG3ADw/GXwa8Ytn0pwDfoTm98QTgd2huh/zwSbel43a+eNCuc1e8d0dMui1dtnMP86+XswBW\\n+37+GM1ZHK+i2f//czTfIi+cdFs6bufFNL2NT6e51eoTac5+uHzSbdlPO4+g+bA/iSaw/Prg+TGD\\n8a8ELl02/bHAN2mOzTkB+JXB59ITJt2WjtvZG7TrhSs+h+436bZ02c49zN/qLIBRNugomgOIloDb\\nac6DP3zZ+IfQdNedNnh++uD58sfOwc8fn/QbtKJtvwLcMPiguZZBiBmMex/wtyumfxpNOrsb+BTN\\nN+SJt6PLdgLX7+H9uxf47Um3o+v3c8W86yIAtGknzRk7HwLuotkovoTB/UPW8mOVf7eH0Own3g7c\\nOZjv1etgg3H6ss/H5Y+/HYy/BHjfHuaZH7wui8CzJ92OrttJc97/nj6H9vo/vBYebd7PFfO3CgDe\\nDEiSpAqt2XN6JUnS6BgAJEmqkAFAkqQKGQAkSaqQAUCSpAoZACRJqpABQJKkChkAJEkaUpJTk1yZ\\n5ObBzfHObrGMs5Jcm+SOJF9L8tYkDxlFvWAAkCSpC0fQXMr+AlrcYTHJscDbaG6hfiLNvRoeAPxd\\nZxWuXKdXApQkqTtJdtLcDv3KZcN+AHgF8AyaS+V/mubeGv8yGP80mntQHLZsnp+nCQWHlRHcGdce\\nAEmSRu81NPfXOI/mrqJvAd6Z5PjB+HlgZ5LnJjkkyRTwbODqUWz8wR4ASZI6tbIHIMkxwBdp7u53\\ny7LprgY+Ukq5aPD8NOAK4P7AoTQ3s3pSKeWOUdRpD4AkSaP1SJoN+vYkO3Y9gNOA4wGSHE1z19xL\\ngJMH477NCI8B2DCqBUuSJACOBO4BZmhu+7vcNwc/LwCWSikv3TUiybOBG5M8ppTy0a6LMgBIkjRa\\nH6fpATi6lPLBvUxzOLByX/+usDCS3np3AUiSNKQkRyQ5MclJg0HHDZ4fU0pZBC4HLktybpJjkzwm\\nyYVJnjSY/h3Ao5O8PMlDk8zQ7A64niZAdF+zBwFKkjScJKcD7+f7rwFwaSnleUkOBS4CngP8KHAb\\nzUF+F5dSPjtYxnnAi4HNwF2D8S8ppWwfSc0GAEmS6uMuAEmSKmQAkCSpQgYASZIqZACQJKlCBgBJ\\nkipkAJAkqUIGAEmSKmQAkCSpQgYASZIqZACQJKlCBgBJkir0/wHv/JRT+di9XwAAAABJRU5ErkJg\\ngg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grads);\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 751373 a0 100000000 100000000\\n\",\n \" 788769 a0/read 100000000 200000000\\n\",\n \" 812320 a1 100000000 300000000\\n\",\n \" 25505485 a2_1 100000000 400000000\\n\",\n \" 32218122 a2 100000000 500000000\\n\",\n \" 46739649 gradients_2/cost_1_grad/Tile 100000000 600000000\\n\",\n \" 48151215 a3 100000000 700000000\\n\",\n \" 48197908 a2 -100000000 600000000\\n\",\n \" 57227563 a4_1 100000000 700000000\\n\",\n \" 67988836 gradients_2/a4_1_grad/TanhGrad 100000000 800000000\\n\",\n \" 68061666 a4_1 -100000000 700000000\\n\",\n \" 68456245 gradients_2/cost_1_grad/Tile -100000000 600000000\\n\",\n \" 78576044 gradients_2/a3_1_grad/TanhGrad 100000000 700000000\\n\",\n \" 78628205 a3 -100000000 600000000\\n\",\n \" 79095393 gradients_2/a4_1_grad/TanhGrad -100000000 500000000\\n\",\n \" 89322348 gradients_2/a2_1_grad/TanhGrad 100000000 600000000\\n\",\n \" 89371834 a2_1 -100000000 500000000\\n\",\n \" 89845874 gradients_2/a3_1_grad/TanhGrad -100000000 400000000\\n\",\n \"127103916 gradients_2/a2_1_grad/TanhGrad -100000000 300000000\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simpler graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 422,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"a1 = tf.placeholder(tf.float32, name=\\\"a1\\\", shape=(n,))\\n\",\n \"a2 = tf.tanh(a1, name=\\\"a2\\\")\\n\",\n \"a3 = tf.tanh(a2, name=\\\"a3\\\")\\n\",\n \"cost = tf.reduce_sum(a3, name=\\\"cost\\\")\\n\",\n \"xs = [a1]\\n\",\n \"ys = [cost]\\n\",\n \"fwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs], backward_seed_ops=[y.op for y in ys])\\n\",\n \"grads = tf.gradients(ys, xs)\\n\",\n \"#copied_fwd_svg, info = ge.copy_with_input_replacements(ge.sgv(fwd_ops), {})\\n\",\n \"#xs_copied = [info._transformed_ts[x] if isinstance(x, tf.Tensor) else info._transformed_ts[x._variable] for x in xs ]\\n\",\n \"#ys_copied = [info._transformed_ts[y] for y in ys]\\n\",\n \"#grads = tf.gradients(ys_copied, xs_copied)\\n\",\n \"#bwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs_copied],\\n\",\n \"# backward_seed_ops=grads)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 423,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a1', 'a2', 'a3', 'cost']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(fwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 424,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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8mOJJ9NcsQ81nt1kq1JHk5yS5KX9V6yJEnqt64jBMcCHwVeBLwEeALw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\\nXP5sZxTTj1AAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grads[0].op, feed_dict={a1:np.zeros((n,))})\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 425,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 31622726 a2 100000000 100000000\\n\",\n \" 49554548 a3 100000000 200000000\\n\",\n \" 49663968 gradients/cost_grad/Tile 100000000 300000000\\n\",\n \" 59604658 gradients/a3_grad/TanhGrad 100000000 400000000\\n\",\n \" 59670548 a3 -100000000 300000000\\n\",\n \" 60210197 gradients/cost_grad/Tile -100000000 200000000\\n\",\n \" 70434778 gradients/a2_grad/TanhGrad 100000000 300000000\\n\",\n \" 70506752 a2 -100000000 200000000\\n\",\n \" 71158042 gradients/a3_grad/TanhGrad -100000000 100000000\\n\",\n \" 71528833 gradients/a2_grad/TanhGrad -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 309,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(ungroup_gradients=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 426,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a1:0', 'a3:0']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# save everything between a2 and cost\\n\",\n \"remember_ts = ge.filter_ts_from_regex(fwd_ops, \\\"a1|a3\\\")\\n\",\n \"printops(remember_ts)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 427,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a2']\\n\"\n ]\n }\n ],\n \"source\": [\n \"intermediate_ops = ge.get_walks_intersection_ops(forward_seed_ops=[remember_ts[0].op], backward_seed_ops=[remember_ts[1].op])\\n\",\n \"intermediate_ops = intermediate_ops[1:-1] # remove starting/endpoints\\n\",\n \"printops(intermediate_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 428,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"copied_sgv, info = ge.copy_with_input_replacements(ge.sgv(intermediate_ops), {})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 429,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(ungroup_gradients=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 430,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"bwd_ops = ge.filter_ops_from_regex(tf.get_default_graph(), \\\"gradients/\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 431,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['gradients/Shape', 'gradients/Const', 'gradients/Fill', 'gradients/cost_grad/Reshape/shape', 'gradients/cost_grad/Reshape', 'gradients/cost_grad/Tile/multiples', 'gradients/cost_grad/Tile', 'gradients/a3_grad/TanhGrad', 'gradients/a2_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 432,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a2']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(info._transformed_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 433,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"intermediate_copied_ops = [info._transformed_ops[y] for y in intermediate_ops]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 434,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a2_1']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(intermediate_copied_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 435,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"intermediate_copied_ts = [op.outputs[0] for op in intermediate_copied_ops]\\n\",\n \"intermediate_ts = [op.outputs[0] for op in intermediate_ops]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 436,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 436,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# b left dangling\\n\",\n \"ge.reroute_a2b_ts(intermediate_copied_ts, intermediate_ts, can_modify=bwd_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 410,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 447,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def run_op_after(a_op, b_op):\\n\",\n \" \\\"\\\"\\\"Force a to run after b\\\"\\\"\\\"\\n\",\n \" print(\\\"Adding %s to run after %s\\\"%(a_op.name, b_op.name))\\n\",\n \" ge.reroute.add_control_inputs(a_op, [b_op])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 453,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"cost_grad = ge.filter_ops_from_regex(tf.get_default_graph(), \\\"gradients/a3_grad/TanhGrad\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 454,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['gradients/a3_grad/TanhGrad']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(cost_grad)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 449,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a2_1']\\n\"\n ]\n }\n ],\n \"source\": [\n \"printops(intermediate_copied_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 455,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Adding a2_1 to run after gradients/a3_grad/TanhGrad\\n\"\n ]\n }\n ],\n \"source\": [\n \"# force copied intermediate ops to run after cost grad evaluation\\n\",\n \"for cost_op in cost_grad:\\n\",\n \" for intermediate_op in intermediate_copied_ops:\\n\",\n \" run_op_after(intermediate_op, cost_op)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 456,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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omI/xERr4qI9wH/Ebh6yeklSVJTNLLL4CTgmxT7/xO4qhx/HXARxUGE\\n62cmzszJiPg1igLwX4CHgP+UmXN/eSBJy2Z0dHUvf66xMdiwoeoUqlIj5yG4nQW2LGTmhfPMU/8O\\nDbVEV9eev1/72upySFWY+fc/OFhtjhmzX49VGRmB/v5i2Dd3B69Wjap+ZaAKdXcXw9/7Pbj44kqj\\nSMuuuxvGx2F6utoctRoMDe15PVZpcrIYTkxUGkMVsxCsYuecU3UCqRrt8CEstRuvdihJkiwEkiTJ\\nQiBJkrAQSJIkLASSJAkLgSRJwkIgSZKwEEiSJCwEkiQJC4EkScJCIEmSsBBIkiQsBJIkCQuBJEnC\\nQiBJkrAQSJIkYE3VAbR8xsbgwANhdLTqJJJmtMPrsR0yzBgbgw0bqk6xOlkIVomREejv33tcV1c1\\nWSTtef0NDlabY7aq3xNm3qdGRqCvr9osq5GFYJWYnNzz97ZtcPLJ0N1dWRxp1evuhvFxmJ6uOgnU\\najA0VP17wsz71MREpTFWLQvBKrRxY/UvfEm+DtVePKhQkiRZCCRJkoVAkiRhIZAkSVgIJEkSFgJJ\\nkoSFQJIkYSGQJElYCCRJEhYCSZKEhUCSJGEhkCRJWAgkSRIWAkmShIVAkiRhIZAkScCaqgNIktrD\\n6OjqXv58xsZgw4aqU7SehWCVuOeePX93dVWXQ1L7mXlPGBysNseMdnqPGhmB/v5i2NdXdZrWshCs\\nEgcdVAxPPx26u6vNIqm9dHfD+DhMT1ebo1aDoaH2eo+anCyGExOVxlgWFoJVpp1eaJLah+8N8qBC\\nSZJkIZAkSRYCSZKEhUCSJGEhkCRJWAgkSRIWAkmShIVAkiRhIZAkSTRYCCLikoiYiIinI+LuiHjd\\nAtNeEBG7I+K5crg7Ip5qPLIkSWq2ugtBRPwmcBVwJfArwP3AbRFx+AKzTQHrZt1eWX9USZLUKo1s\\nIdgC/FlmXp+ZY8DFwFPARQvMk5n5o8x8tLz9qJGwkiSpNeoqBBFxENALbJ8Zl5kJjACnLDDrCyNi\\nMiIeiIibIuL4htJKkqSWqHcLweHAgcCuOeN3UewK2JfvUWw9OBc4v1zm30XEK+pctiRJapFmXf44\\ngNzXA5l5N3D3zyeMuAsYBX6b4jiEeW3ZsoW1a9fuNW5gYICBgYGl5pUkacUbHh5meHh4r3FTU1MN\\nPVe9heDHwHPAEXPGv5Rf3GqwT5n5bER8Bzh2f9Nu3bqVnp6eOiNKkrQ67OtLcq1Wo7e3t+7nqqsQ\\nZObPImIH0AfcAhARUd7/1GKeIyIOAF4NfLW+qGrUzp0wOlp1CklanHZ6v2qnLGNjsGFD656/kV0G\\nVwPXlcXgXopfHbwA+DxARFwPPJSZHyrvX0Gxy+D7wGHAByh+dvi5pYbX/u3cCccdt+f+a19bXRZJ\\nWkhXVzEcHKw2x77MZKvKyAj09xfDvr7WLKPuQpCZN5bnHPgDil0H9wFnzvop4ZHAs7Nm+SXgzykO\\nOvwJsAM4pfzJolpseroYbtsGL3oRnHNOtXkkaT7d3TA+vud9qx3UajA0VGSr0uRkMZyYaN0yGjqo\\nMDOvAa6Z57G3zLl/GXBZI8tR82zcCB6OIandVf3Bu5p5LQNJkmQhkCRJFgJJkoSFQJIkYSGQJElY\\nCCRJEhYCSZKEhUCSJGEhkCRJWAgkSRIWAkmShIVAkiRhIZAkSVgIJEkSFgJJkoSFQJIkAWuqDqDW\\neuihqhNI0so3Otr5y7cQdLCdO2Hz5uLvrq5qs0jSSjTz3jk4WG2OGa18L7cQdLDp6WJ4883Q3V1t\\nFklaibq7YXx8z/tpVWo1GBpq7Xu5hWAVOPLIqhNI0sq1Wr5QeVChJEmyEEiSJAuBJEnCQiBJkrAQ\\nSJIkLASSJAkLgSRJwkIgSZKwEEiSJCwEkiQJC4EkScJCIEmSsBBIkiQsBJIkCQuBJEnCQtDRHnqo\\n6gSSpGYaHYWdO1vz3BaCDrVzJ2zeXPzd1VVtFknS0sy8jw8OwnHHtaYUWAg61PR0Mbz5ZujurjaL\\nJGlpurthfBy2bSvuz7zHN9Oa5j+l2smRR1adQJLUDN3drSkCM9xCIEmSLASSJMlCIEmSsBBIkiQs\\nBJIkCQuBJEnCQiBJkrAQSJIkLASSJAkLgSRJwkIgSZKwEFRueHi46gjLwvXsLK5nZ3E9BQ0Wgoi4\\nJCImIuLpiLg7Il63n+l/IyJGy+nvj4izGovbeVbLP1DXs7O4np3F9RQ0UAgi4jeBq4ArgV8B7gdu\\ni4jD55n+FOAG4FrgROAm4KaIOL7R0JIkqbka2UKwBfizzLw+M8eAi4GngIvmmf5S4NbMvDozv5eZ\\nVwI14P0NJZYkSU1XVyGIiIOAXmD7zLjMTGAEOGWe2U4pH5/ttgWmlyRJy2xNndMfDhwI7Jozfhfw\\nqnnmWTfP9OsWWM7zAUZHR+uMt7I8/TQ89NAUX/hCrenPPTFRDNvlP+HU1BS1WvPXs924np3F9ews\\nnbCeM+/pX/1q8fdRR8Ehh8yd5udv/M+v57mj+IK/yIkjXgY8DJySmffMGv8x4E2Z+av7mOenwHsy\\n83/NGvc+4MOZ+fJ5lvMu4AuLDiZJkuY6PzNvWOzE9W4h+DHwHHDEnPEv5Re3Asx4pM7podilcD4w\\nCTxTZ0ZJklaz5wNHUXyWLlpdWwgAIuJu4J7MvLS8H8ADwKcy8+P7mP6vgEMyc/OscXcC92fm++pa\\nuCRJaol6txAAXA1cFxE7gHspfnXwAuDzABFxPfBQZn6onP6TwO0RcRnwFWCA4sDEoaVFlyRJzVJ3\\nIcjMG8tzDvwBxa6A+4AzM/NH5SRHAs/Omv6uiBgA/rC87QQ2Z+Y/LzW8JElqjrp3GUiSpM7jtQwk\\nSZKFQJIkrYBCEBEfiog7I+LJiHi86jzNUu8FolaiiNgUEbdExMMRsTsizq06UytExOURcW9EPBER\\nuyLiSxFxXNW5mi0iLi4vTjZV3v4uIv5D1blaqfx/uzsirq46S7NFxJXlus2+deSxXRHx8oj4nxHx\\n44h4qvx33FN1rmYqP0/m/v/cHRGfXuxztH0hAA4CbgT+pOogzVLvBaJWsEMpDjq9BOjkg1U2AZ8G\\nXg+cTvFv9usRcciCc608DwK/S/EroV7gb4GbI2JjpalapCzpQxSvz071jxQHh68rb2+qNk7zRcRh\\nwJ3AT4EzgY3AfwV+UmWuFjiJPf8f1wH9FO+7Ny72CVbMQYURcQGwNTNfXHWWpZrnXA4PUpzL4WOV\\nhmuRiNgNvDUzb6k6S6uVxe5R4NTMvKPqPK0UEY8B/y0z/7LqLM0UES8EdgC/A1wBfCczL6s2VXNF\\nxJUUv/jqqG/Kc0XERynOrnta1VmWU0R8Ajg7Mxe9tXIlbCHoKA1eIEory2EUzbxjdnHNFREHRMQ7\\nKc5BclfVeVrgs8CXM/Nvqw7SYt3lLr3/ExHbImJ91YFa4Bzg7yPixnKXXi0ifqvqUK1Ufs6cD/xF\\nPfNZCJbfQheIWuiCT1oByq09nwDu6MRzbUTEqyNimmLz6zXA28rLoHeMsuicCFxedZYWuxt4L8Vm\\n9IuBfwd8OyIOrTJUCxxNsaXne8AZwJ8Cn4qIwUpTtdbbgLXAdfXM1MiZCpcsIj5CsS9yPglszMzx\\nZYrUDoLO3s++WlwDHA+8seogLTIGnECxFeQdwPURcWqnlIKIOJKi0PVn5s+qztNKmTn7PPf/GBH3\\nAv8XOA/opF1ABwD3ZuYV5f37I+KXKUrCtupitdRFwK2Z+Ug9M1VSCIA/Zv//4H6wHEEq0MgForQC\\nRMRngLOBTZn5w6rztEJmPsue12YtIk4GLqV4c+0EvcC/AXaUW3ug2KJ3akS8Hzg4V8qBV3XKzKmI\\nGAeOrTpLk/0QmHsh+FHg7RVkabmI+LcUBze/td55KykEmfkY8FgVy65aZv6svA5EH3AL/Hwzcx/w\\nqSqzqXFlGdgMnJaZD1SdZxkdABxcdYgmGgFeM2fc5yk+QD7aqWUAfn4g5THA9VVnabI7gVfNGfcq\\niq0hnegiii+XX613xqq2ECxaeZDLi4FXAgdGxAnlQ9/PzCerS7YkC14gqlOU+yKPpdgdAnB0+f/v\\n8cx8sLpkzRUR11BctOtc4MmImNn6M5WZHXP57oj4Q+BWil/EdFEctHQaxX7ZjlC+p+x17EdEPAk8\\nlplzv2WuaBHxceDLFB+MrwB+n+I6NMNV5mqBrcCdEXE5xU/wXg/8Fh14gb3yy+V7gc9n5u5652/7\\nQkBxEaX3zLpfK4dvBr69/HGWbhEXiOoUJwHfpDg2IinOvQDFgS4XVRWqBS6mWL9vzRl/IZ31besI\\nivV5GTAFfBc4YxUcid+pWwWOBG4AXgL8CLgDeEO5BbdjZObfR8TbgI9S/IR0Arg0M/+q2mQtcTqw\\nngaPAVkx5yGQJEmt488OJUmShUCSJFkIJEkSFgJJkoSFQJIkYSGQJElYCCRJEhYCSZKWVURsiohb\\nyktP746Icxt4jjMj4q6IeCIiHo2Iv4mIVy4ll4VAkqTldSjFGWovoYEzYUbEUcBNFNfeOIHi9OGH\\nA19cSijPVChJUkUiYjfw1sy8Zda45wF/BLyT4lLj/wB8MDNvLx9/B3BDZh48a55fpygJB2fmc41k\\ncQuBJEnt5bMUF2E6j+Lqm38N3BoRx5SP7wB2R8SFEXFARKwF3g18o9EyAG4hkCSpMnO3EJRX+P0B\\nsD4zH5k13TeAezLzw+X9Uymu3vgS4EDgLuCszHyi0SxuIZAkqX28huIDfjwipmduwKnAMQDlJdav\\npbiq4UnlYz9liccQrITLH0uStFq8EHgW6AF2z3nsX8vhJcBUZl4+80BEvBt4MCJOzsx7G1mwhUCS\\npPbxHYotBEdk5p3zTPMCYO6xAjPloeEt/+4ykCRpGUXEoRFxQkScWI46ury/PjN3AjcA10fE2yLi\\nqIg4OSI+GBFnldN/BXhdRFwREcdGRA/F7oMJikLRWC4PKpQkaflExGnAN/nFcxBcl5kXRcSBwIeB\\n9wCvAB6jOGjwysz8p/I5zgM+ABwHPFU+/ruZOd5wLguBJElyl4EkSbIQSJIkC4EkScJCIEmSsBBI\\nkiQsBJIkCQuBJEnCQiBJkrAQSJIkLASSJAkLgSRJAv4/2e7p06ym+ngAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n=2000\\n\",\n \"a = tf.random_uniform(shape=(n,n))\\n\",\n \"for i in range(10):\\n\",\n \" a = tf.matmul(a, tf.random_uniform(shape=(n,n)))\\n\",\n \"\\n\",\n \"sess = tf.Session()\\n\",\n \"sess.run(a)\\n\",\n \"plot_memory_timeline()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 388,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"n=2000\\n\",\n \"a = tf.random_uniform(shape=(n,n), name=\\\"matmul00\\\")\\n\",\n \"for i in range(10):\\n\",\n \" if i>1:\\n\",\n \" with tf.control_dependencies([a]):\\n\",\n \" leaf = tf.random_uniform(shape=(n,n), name=\\\"leaf\\\"+str(i))\\n\",\n \" a = tf.matmul(a, leaf, name=\\\"matmul\\\"+str(i))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 389,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"config = tf.ConfigProto(inter_op_parallelism_threads=10, intra_op_parallelism_threads=1, graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \"sess = tf.Session(config=config)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 390,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 174 ptr: 62190240 } } } timestamp 5353673270823439\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add0\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 192 has_single_reference: true ptr: 140284302203840 } } } timestamp 5353673270834112\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add1\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 194 has_single_reference: true ptr: 140284302208352 } } } timestamp 5353673270883100\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 193 has_single_reference: true ptr: 140284235096736 } } } timestamp 5353673270876541\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add1\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 194 has_single_reference: true ptr: 140284302208352 } } } timestamp 5353673270898151\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 192 allocator_name: \\\"cpu\\\" } timestamp 5353673270908927\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 193 allocator_name: \\\"cpu\\\" } timestamp 5353673270939969\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones2\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 176 ptr: 61328704 } } } timestamp 5353673270977023\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add2\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 196 has_single_reference: true ptr: 140284302212896 } } } timestamp 5353673271028898\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add2\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 196 has_single_reference: true ptr: 140284302212896 } } } timestamp 5353673271047609\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 195 has_single_reference: true ptr: 140284235091168 } } } timestamp 5353673271034276\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 194 allocator_name: \\\"cpu\\\" } timestamp 5353673271058440\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 195 allocator_name: \\\"cpu\\\" } timestamp 5353673271097072\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones3\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 178 ptr: 60812960 } } } timestamp 5353673271133072\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 197 has_single_reference: true ptr: 140284235091168 } } } timestamp 5353673271188351\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add3\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 198 has_single_reference: true ptr: 140284637744128 } } } timestamp 5353673271222738\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 197 allocator_name: \\\"cpu\\\" } timestamp 5353673271251438\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add3\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 198 has_single_reference: true ptr: 140284637744128 } } } timestamp 5353673271264559\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 196 allocator_name: \\\"cpu\\\" } timestamp 5353673271289064\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones4\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 180 ptr: 60384256 } } } timestamp 5353673271288497\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add4\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 199 has_single_reference: true ptr: 140284302203840 } } } timestamp 5353673271337261\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add4\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 199 has_single_reference: true ptr: 140284302203840 } } } timestamp 5353673271352207\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 198 allocator_name: \\\"cpu\\\" } timestamp 5353673271362189\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 200 has_single_reference: true ptr: 140284235095808 } } } timestamp 5353673271356447\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 200 allocator_name: \\\"cpu\\\" } timestamp 5353673271421848\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones5\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 182 ptr: 59161248 } } } timestamp 5353673271457815\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add5\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 202 has_single_reference: true ptr: 140284637744096 } } } timestamp 5353673271514103\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 201 has_single_reference: true ptr: 140284235091712 } } } timestamp 5353673271512385\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add5\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 202 has_single_reference: true ptr: 140284637744096 } } } timestamp 5353673271540026\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 199 allocator_name: \\\"cpu\\\" } timestamp 5353673271559214\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 201 allocator_name: \\\"cpu\\\" } timestamp 5353673271573179\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones6\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 184 ptr: 62616576 } } } timestamp 5353673271608672\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add6\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 204 has_single_reference: true ptr: 140284302203840 } } } timestamp 5353673271659142\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add6\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 204 has_single_reference: true ptr: 140284302203840 } } } timestamp 5353673271675501\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 203 has_single_reference: true ptr: 140284235091712 } } } timestamp 5353673271660256\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 202 allocator_name: \\\"cpu\\\" } timestamp 5353673271696892\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 203 allocator_name: \\\"cpu\\\" } timestamp 5353673271744795\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones7\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 186 ptr: 60599424 } } } timestamp 5353673271782749\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add7\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 206 has_single_reference: true ptr: 140284637744096 } } } timestamp 5353673271841980\\n\",\n \" MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 205 has_single_reference: true ptr: 140284235091552 } } } timestamp 5353673271843300\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add7\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 206 has_single_reference: true ptr: 140284637744096 } } } timestamp 5353673271864867\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 204 allocator_name: \\\"cpu\\\" } timestamp 5353673271882466\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 205 allocator_name: \\\"cpu\\\" } timestamp 5353673271904790\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"ones8\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 188 ptr: 62641472 } } } timestamp 5353673271941837\\n\",\n \" MemoryLogTensorAllocation { step_id: 5 kernel_name: \\\"add8\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 207 has_single_reference: true ptr: 140284235090592 } } } timestamp 5353673272024669\\n\",\n \" MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"add8\\\" tensor { dtype: DT_FLOAT shape { dim { size: 1111 } } allocation_description { requested_bytes: 4444 allocated_bytes: 4444 allocator_name: \\\"cpu\\\" allocation_id: 207 has_single_reference: true ptr: 140284235090592 } } } timestamp 5353673272070280\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 206 allocator_name: \\\"cpu\\\" } timestamp 5353673272098227\\n\",\n \" MemoryLogTensorDeallocation { allocation_id: 207 allocator_name: \\\"cpu\\\" } timestamp 5353673272277847\\n\"\n ]\n }\n ],\n \"source\": [\n \"for line in stderr.split('\\\\n'):\\n\",\n \" if 'LOG_MEMORY' in line:\\n\",\n \" print(line.strip()[len('I 5353225381.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__'):])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 239,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From :6 in .: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([16, 16, 16], dtype=int32)\"\n ]\n },\n \"execution_count\": 239,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"sess = tf.Session()\\n\",\n \"a = tf.Variable([2, 2, 2])\\n\",\n \"b = tf.square(a)\\n\",\n \"c = tf.square(b)\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"sess.run(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 240,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"b0 = sess.run(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 242,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 4, 4], dtype=int32)\"\n ]\n },\n \"execution_count\": 242,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"b0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 243,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"b0[1] = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 244,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([16, 0, 16], dtype=int32)\"\n ]\n },\n \"execution_count\": 244,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sess.run(c, feed_dict={b:b0})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Memory allocation bug\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 457,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"a = tf.ones(())\\n\",\n \"a0 = a\\n\",\n \"for i in range(100):\\n\",\n \" a = tf.tanh(a)\\n\",\n \"grad = tf.gradients(a, a0) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 460,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"204\"\n ]\n },\n \"execution_count\": 460,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(tf.get_default_graph().as_graph_def().node)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grads[0].op, feed_dict={a1:np.zeros((n,))})\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory_timeline(stderr)\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "natural_gradient_multilayer.py", "content": "#!/bin/env python -u\n#\n# Apply natural gradient (using empirical Fisher) to a multilayer problem\n# Accompanying notebooks:\n#\n# Overview of experiments\n# https://docs.google.com/document/d/1S0q8LuwgUCWT7n3Z2-YoM7LBL1yFCQt0NEtpUDrWX3I/edit?usp=sharing\n#\n# natural_gradient_multilayer.nb\n# https://www.wolframcloud.com/objects/a273119a-6eb0-4521-b79d-30795f155dc4\n# \n# relus.nb\n# https://www.wolframcloud.com/objects/b05dd44c-c9da-4187-831b-32eebb7a5d02\n\n\nimport numpy as np\nimport sys\nimport tensorflow as tf\nimport traceback\n\n\nfrom util import *\n\ndtype = np.float64\n\n# convention, X0 is numpy, X is Tensor\ndef gd_test():\n \"\"\"Test gradient descent, using tf.gradients for backprop.\"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n W = [] # list of \"W\" matrices. W[0] is input matrix (X), W[n] is last matrix\n Wi_holders = []\n A = [Identity(dsize)] # activation matrices\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n Wi_name = \"W\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_holder = tf.placeholder(dtype, shape=Wi_shape, name=Wi_name+\"_h\")\n Wi_holders.append(Wi_holder) # TODO: delete\n Wi = tf.Variable(Wi_holder, name=Wi_name, trainable=(i>0))\n Ai_name = \"A\"+str(i+1)\n Ai = tf.matmul(Wi, A[-1], name=Ai_name)\n A.append(Ai)\n W.append(Wi)\n \n init_dict[Wi_holder] = W0s[i]\n\n assert len(A) == n+2\n \n assert W[0].shape == (2, 10)\n assert W[1].shape == (2, 2)\n assert W[2].shape == (2, 2)\n assert W[3].shape == (1, 2)\n\n assert X0.shape == (2, 10)\n assert W0s[1].shape == (2, 2)\n assert W0s[2].shape == (2, 2)\n assert W0s[3].shape == (1, 2)\n \n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = 0.5\n \n opt = tf.train.GradientDescentOptimizer(learning_rate=lr)\n grads_and_vars = opt.compute_gradients(loss, tf.trainable_variables())\n train_op = opt.apply_gradients(grads_and_vars)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_gd.csv\")\n \n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op)\n\n check_equal(observed_losses, expected_losses)\n\n# convention, X0 is numpy, X is Tensor\ndef gd_manual_test():\n \"\"\"Train network, without using tf.gradients\"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n Wi_name = \"W\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_holder = tf.placeholder(dtype, shape=Wi_shape, name=Wi_name+\"_h\")\n W[i] = tf.Variable(Wi_holder, name=Wi_name, trainable=(i>0))\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n init_dict[Wi_holder] = W0s[i]\n\n assert len(A) == n+2\n \n assert W[0].shape == (2, 10)\n assert W[1].shape == (2, 2)\n assert W[2].shape == (2, 2)\n assert W[3].shape == (1, 2)\n\n assert X0.shape == (2, 10)\n assert W0s[1].shape == (2, 2)\n assert W0s[2].shape == (2, 2)\n assert W0s[3].shape == (1, 2)\n \n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.5, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B[n] = -err/dsize\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n\n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n updates1[i] = Wcopy[i].assign(W[i]-lr*dW[i])\n updates2[i] = W[i].assign(Wcopy[i])\n\n del updates1[0] # don't update input matrices\n del updates2[0]\n \n train_op1 = tf.group(*updates1)\n train_op2 = tf.group(*updates2)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_gd.csv\")\n \n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n check_equal(observed_losses, expected_losses)\n\n# convention, X0 is numpy, X is Tensor\ndef gd_manual_vectorized_test():\n \"\"\"Train network, with manual backprop, in vectorized form\"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n\n dims = [(fs[i+1],fs[i]) for i in range(len(fs)-1)]\n sizes = [s[0]*s[1] for s in dims]\n Wf_size = np.sum(sizes[1:])\n Wf_holder = tf.placeholder(dtype, shape=W0f.shape)\n Wf = tf.Variable(Wf_holder, name=\"Wf\")\n assert Wf.shape == W0f.shape\n init_dict[Wf_holder] = W0f\n \n W = unflatten(Wf, fs[1:])\n W.insert(0, tf.constant(X0))\n assert W[0].shape == [2, 10]\n assert W[1].shape == [2, 2]\n assert W[2].shape == [2, 2]\n assert W[3].shape == [1, 2]\n \n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n \n assert len(A) == n+2\n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.5, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B[n] = -err/dsize\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n\n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n\n\n del dW[0] # get rid of W[0] update\n \n # construct flattened gradient update vector\n dWf = tf.concat([vec(grad) for grad in dW], axis=0)\n\n Wf_copy = tf.Variable(tf.zeros(dtype=dtype, shape=Wf.shape,\n name=\"Wf_copy_init\"),\n name=\"Wf_copy\")\n train_op1 = Wf_copy.assign(Wf - lr*dWf)\n train_op2 = Wf.assign(Wf_copy)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_gd.csv\")\n observed_losses = []\n \n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n check_equal(observed_losses, expected_losses)\n\n \n# convention, X0 is numpy, X is Tensor\ndef fisher_test():\n \"\"\"Test computation of empirical Fisher matrix.\"\"\"\n \n tf.reset_default_graph()\n\n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n Wi_name = \"W\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_holder = tf.placeholder(dtype, shape=Wi_shape, name=Wi_name+\"_h\")\n W[i] = tf.Variable(Wi_holder, name=Wi_name, trainable=(i>0))\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n init_dict[Wi_holder] = W0s[i]\n\n assert len(A) == n+2\n \n assert W[0].shape == (2, 10)\n assert W[1].shape == (2, 2)\n assert W[2].shape == (2, 2)\n assert W[3].shape == (1, 2)\n\n assert X0.shape == (2, 10)\n assert W0s[1].shape == (2, 2)\n assert W0s[2].shape == (2, 2)\n assert W0s[3].shape == (1, 2)\n \n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.5, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B[n] = -err/dsize\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_regular.csv\")\n\n expected_fisher = np.loadtxt(\"data/natural_gradient_multilayer_fisher0.csv\",\n delimiter= \",\")\n\n block11 = khatri_rao(A[1], B[1]) @ tf.transpose(khatri_rao(A[1], B[1]))/dsize\n expected_block11 = expected_fisher[:4, :4]\n check_equal(sess.run(block11), expected_block11)\n\n # construct fisher matrix out of individual mats\n grads = tf.concat([khatri_rao(A[i], B[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / dsize\n check_equal(sess.run(fisher), expected_fisher)\n\n # create vectorized parameter vector\n \n \n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n updates1[i] = Wcopy[i].assign(W[i]-lr*dW[i])\n updates2[i] = W[i].assign(Wcopy[i])\n\n del updates1[0] # don't update input matrices\n del updates2[0]\n \n train_op1 = tf.group(*updates1)\n train_op2 = tf.group(*updates2)\n\n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n check_equal(observed_losses, expected_losses)\n\n# convention, X0 is numpy, X is Tensor\ndef natural_gradient_test():\n \"\"\"Train network using empirical natural gradient.\"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n # should be 10,2,2,2\n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n\n dims = [(fs[i+1],fs[i]) for i in range(len(fs)-1)]\n sizes = [s[0]*s[1] for s in dims]\n Wf_size = np.sum(sizes[1:])\n Wf_holder = tf.placeholder(dtype, shape=W0f.shape)\n Wf = tf.Variable(Wf_holder, name=\"Wf\")\n assert Wf.shape == W0f.shape\n init_dict[Wf_holder] = W0f\n \n W = unflatten(Wf, fs[1:])\n W.insert(0, tf.constant(X0))\n assert W[0].shape == [2, 10]\n assert W[1].shape == [2, 2]\n assert W[2].shape == [2, 2]\n assert W[3].shape == [1, 2]\n \n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n \n assert len(A) == n+2\n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.001, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B[n] = -err/dsize\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n\n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n\n\n del dW[0] # get rid of W[0] update\n \n # construct flattened gradient update vector\n dWf = tf.concat([vec(grad) for grad in dW], axis=0)\n\n\n # inverse fisher preconditioner\n grads = tf.concat([khatri_rao(A[i], B[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / dsize\n ifisher = pseudo_inverse(fisher)\n\n Wf_copy = tf.Variable(tf.zeros(dtype=dtype, shape=Wf.shape,\n name=\"Wf_copy_init\"),\n name=\"Wf_copy\")\n new_val_matrix = Wf - lr*(ifisher @ dWf)\n train_op1 = Wf_copy.assign(new_val_matrix)\n train_op2 = Wf.assign(Wf_copy)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n\n # from notebook {0.347015, 0.301344, 0.260196, 0.224903, 0.193672...\n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_fisher.csv\")\n \n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n check_equal(observed_losses, expected_losses)\n\n\ndef newton_test():\n \"\"\"Test Newton-Rhapson implementation \"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n def f(i): return fs[i+1]\n \n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n\n dims = [(fs[i+1],fs[i]) for i in range(len(fs)-1)]\n sizes = [s[0]*s[1] for s in dims]\n Wf_size = np.sum(sizes[1:])\n Wf_holder = tf.placeholder(dtype, shape=W0f.shape)\n Wf = tf.Variable(Wf_holder, name=\"Wf\")\n assert Wf.shape == W0f.shape\n init_dict[Wf_holder] = W0f\n \n W = unflatten(Wf, fs[1:])\n W.insert(0, tf.constant(X0))\n assert W[0].shape == [2, 10]\n assert W[1].shape == [2, 2]\n assert W[2].shape == [2, 2]\n assert W[3].shape == [1, 2]\n \n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n \n assert len(A) == n+2\n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1]\n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.001, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n b = [0]*(n+1) # like backprop matrix but no error\n B[n] = -err/dsize\n b[n] = Identity(fs[-1])\n for i in range(n-1, -1, -1):\n B[i] = t(W[i+1]) @ B[i+1]\n b[i] = t(W[i+1]) @ b[i+1]\n\n\n # off-diagonal products\n U = [list(range(n+1)) for _ in range(n+1)]\n for bottom in range(n+1):\n for top in range(n+1):\n prod = Identity(fs[top+1])\n for i in range(top, bottom-1, -1):\n prod = prod @ W[i]\n U[bottom][top] = prod\n\n # block i, j gives hessian block between layer i and layer j\n block = [list(range(n+1)) for _ in range(n+1)]\n for i in range(1, n+1):\n for j in range(1, n+1):\n if i == j:\n block[i][j] = kronecker(A[i]@t(A[i]), b[i]@t(b[i]))/dsize\n elif i < j:\n block[i][j] = (kr(A[i]@t(A[j]), b[i]@t(b[j])) -\n kr((A[i]@t(B[j])), U[i+1][j-1]) @ Kmat(f(j),f(j-1)))\n else:\n block[i][j] = (kr(A[i]@t(A[j]), b[i]@t(b[j])) -\n kr(t(U[j+1][i-1]), B[i]@t(A[j])) @ Kmat(f(j),f(j-1)))\n \n # remove leftmost blocks (those are with respect to W[0] which is input)\n del block[0]\n for row in block:\n del row[0]\n \n hess = concat_blocks(block)\n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n\n\n del dW[0] # get rid of W[0] update\n \n # construct flattened gradient update vector\n dWf = tf.concat([vec(grad) for grad in dW], axis=0)\n\n\n # inverse fisher preconditioner\n grads = tf.concat([khatri_rao(A[i], B[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / dsize\n # outer_sum[i] for i in range(1, n+1)\n ifisher = pseudo_inverse(fisher)\n\n Wf_copy = tf.Variable(tf.zeros(dtype=dtype, shape=Wf.shape,\n name=\"Wf_copy_init\"),\n name=\"Wf_copy\")\n new_val_matrix = Wf - lr*(ifisher @ dWf)\n train_op1 = Wf_copy.assign(new_val_matrix)\n train_op2 = Wf.assign(Wf_copy)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/natural_gradient_multilayer_losses_newton.csv\")\n\n expected_hess = np.loadtxt(\"data/natural_gradient_multilayer_hess0.csv\",\n delimiter= \",\")\n hess0 = sess.run(hess)\n np.savetxt(\"data/natural_gradient_multilayer_hess0_tf.csv\", hess0,\n fmt=\"%.5f\", delimiter=',')\n check_equal(expected_hess, sess.run(hess))\n\n \n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n check_equal(observed_losses, expected_losses)\n\n\ndef relu_manual_vectorized_test():\n \"\"\"Train network, with manual backprop, in vectorized form\"\"\"\n \n tf.reset_default_graph()\n\n # load data into TF\n XY0 = np.genfromtxt('data/natural_gradient_multilayer_XY0.csv',\n delimiter= \",\")\n \n fs = np.genfromtxt('data/natural_gradient_multilayer_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n \n X0 = XY0[:-1,:] # 2 x d\n Y0 = XY0[-1:,:] # 1 x d\n dsize = X0.shape[1]\n Y_ = tf.placeholder(dtype, shape=Y0.shape, name=\"Y_holder\")\n Y = tf.Variable(Y_, trainable=False)\n init_dict={Y_: Y0}\n\n W0f = v2c_np(np.genfromtxt('data/natural_gradient_multilayer_W0f.csv',\n delimiter= \",\"))\n W0s = unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n\n \n # initialize data + layers\n # W[0] is input matrix (X), W[n] is last matrix\n # A[1] has activations for W[1], equal to W[0]=X\n # A[n+1] has predictions\n W = [0]*(n+1) # list of \"W\" matrices. \n A = [0]*(n+2)\n A[0] = Identity(dsize)\n\n dims = [(fs[i+1],fs[i]) for i in range(len(fs)-1)]\n sizes = [s[0]*s[1] for s in dims]\n Wf_size = np.sum(sizes[1:])\n Wf_holder = tf.placeholder(dtype, shape=W0f.shape)\n Wf = tf.Variable(Wf_holder, name=\"Wf\")\n assert Wf.shape == W0f.shape\n init_dict[Wf_holder] = W0f\n \n W = unflatten(Wf, fs[1:])\n W.insert(0, tf.constant(X0))\n assert W[0].shape == [2, 10]\n assert W[1].shape == [2, 2]\n assert W[2].shape == [2, 2]\n assert W[3].shape == [1, 2]\n \n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n if i == 0:\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n else:\n A[i+1] = tf.nn.relu(tf.matmul(W[i], A[i], name=\"A\"+str(i+1)))\n \n assert len(A) == n+2\n assert A[0].shape == (10, 10)\n assert A[1].shape == (2, 10)\n assert A[2].shape == (2, 10)\n assert A[3].shape == (2, 10)\n assert A[4].shape == (1, 10)\n\n \n # input dimensions match\n assert W[0].get_shape() == X0.shape\n # output dimensions match\n assert W[-1].get_shape()[0], W[0].get_shape()[1] == Y0.shape\n assert A[n+1].get_shape() == Y0.shape\n\n err = Y - A[n+1] \n # loss = (1./(2*dsize))*(err @ tf.transpose(err))\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n lr = tf.Variable(0.5, dtype=dtype)\n \n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B[n] = (-err/dsize)*relu_mask(A[n+1])\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n if i == 0:\n pass\n else:\n B[i] = B[i]*relu_mask(A[i+1])\n\n # Create gradient update. Make copy of variables and split update into\n # two run calls. Using single set of variables will gives updates that \n # occasionally produce wrong results/NaN's because of data race\n \n dW = [0]*(n+1)\n updates1 = [0]*(n+1) # compute updated value into Wcopy\n updates2 = [0]*(n+1) # copy value back into W\n Wcopy = [0]*(n+1)\n for i in range(n+1):\n Wi_name = \"Wcopy\"+str(i)\n Wi_shape = (fs[i+1], fs[i])\n Wi_init = tf.zeros(dtype=dtype, shape=Wi_shape, name=Wi_name+\"_init\")\n Wcopy[i] = tf.Variable(Wi_init, name=Wi_name, trainable=False)\n \n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n\n\n del dW[0] # get rid of W[0] update\n \n # construct flattened gradient update vector\n dWf = tf.concat([vec(grad) for grad in dW], axis=0)\n\n Wf_copy = tf.Variable(tf.zeros(dtype=dtype, shape=Wf.shape,\n name=\"Wf_copy_init\"),\n name=\"Wf_copy\")\n train_op1 = Wf_copy.assign(Wf - lr*dWf)\n train_op2 = Wf.assign(Wf_copy)\n\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n \n expected_losses = np.loadtxt(\"data/relu_losses_regular.csv\")\n # from relus.nb\n # {0.539407, 0.256027, 0.25684, 0.248212, 0.247842, 0.244276, 0.243793...\n\n \n observed_losses = []\n for i in range(20):\n observed_losses.append(sess.run([loss])[0])\n sess.run(train_op1)\n sess.run(train_op2)\n\n np.testing.assert_allclose(observed_losses, expected_losses)\n check_equal(observed_losses, expected_losses)\n\n\nif __name__ == '__main__':\n fisher_test()\n gd_test()\n gd_manual_test()\n gd_manual_vectorized_test()\n natural_gradient_test()\n relu_manual_vectorized_test()\n print(\"%s tests passed\" %(sys.argv[0]))\n # newton_test()\n" }, { "path": "node-merge.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Example of merging gradient nodes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Init\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import sys, os, math, random\\n\",\n \"#sys.path.append('/Users/yaroslav/openai.git/pixel-cnn-private')\\n\",\n \"\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]=\\\"\\\"\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def create_session():\\n\",\n \" config = tf.ConfigProto(log_device_placement=True,graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" return tf.InteractiveSession(config=config)\\n\",\n \" \\n\",\n \"import tensorflow.contrib.graph_editor as ge\\n\",\n \"from toposort import toposort\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Graph visualizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"# from https://github.com/yaroslavvb/notebooks/blob/master/simple_rewiring.ipynb\\n\",\n \"# make things wide\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):\\n\",\n \" if not graph_def:\\n\",\n \" graph_def = tf.get_default_graph().as_graph_def()\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" data = str(strip_def)\\n\",\n \" if ungroup_gradients:\\n\",\n \" data = data.replace('\\\"gradients/', '\\\"b_')\\n\",\n \" #print(data)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(data), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(width, height, code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create linear graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"node_mbs = 1\\n\",\n \"length = 4\\n\",\n \"\\n\",\n \"dtype = np.float32\\n\",\n \"n = node_mbs * 250000\\n\",\n \"a0_ = tf.ones((n,), dtype=dtype)\\n\",\n \"a0 = tf.Variable(a0_, name=\\\"a0\\\")\\n\",\n \"a = a0\\n\",\n \"for i in range(1, length):\\n\",\n \" name = \\\"a\\\"+str(i)\\n\",\n \" a = tf.tanh(a, name=name)\\n\",\n \"\\n\",\n \"grad = tf.gradients([a], [a0])[0]\\n\",\n \"sess = create_session()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(ungroup_gradients=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Common functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"g = tf.get_default_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"g.get_operations()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each operation has inputs (tensors flowing in) and outputs (tensors flowing out). All outputs of an operation are computed/allocated together, so operation is the core unit of the graph. Various tools (like toposort, networkx) expect graph in dictionary form {node: children}, you can convert to dictionary form using get_graph() utility.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# computation flows from parents to children\\n\",\n \"\\n\",\n \"def parents(op):\\n\",\n \" return set(input.op for input in op.inputs)\\n\",\n \" \\n\",\n \"def children(op):\\n\",\n \" return set(op for out in op.outputs for op in out.consumers())\\n\",\n \"\\n\",\n \"def get_graph():\\n\",\n \" \\\"\\\"\\\"Creates dictionary {node: {child1, child2, ..},..} for current\\n\",\n \" TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ops = tf.get_default_graph().get_operations()\\n\",\n \" return {op: children(op) for op in ops}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{,\\n\",\n \" },\\n\",\n \" {,\\n\",\n \" },\\n\",\n \" {},\\n\",\n \" {, },\\n\",\n \" {,\\n\",\n \" ,\\n\",\n \" },\\n\",\n \" {},\\n\",\n \" {},\\n\",\n \" {}]\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(toposort(get_graph()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Graph editor has some utilities to select ops using regular expression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ge.select_ops(\\\"Tanh\\\", graph=g)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ge.select_ops(\\\"a1\\\", graph=g)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select backprop nodes for a2, a3 and fuse them together\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ops_to_fuse=ge.select_ops(\\\"a3_grad|a2_grad\\\", graph=g)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ops_to_fuse\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create part of graph that'll rewired in place of those ops\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.python.ops import gen_math_ops\\n\",\n \"from tensorflow.python.framework import function\\n\",\n \"from tensorflow.python.ops import functional_ops\\n\",\n \"\\n\",\n \"@function.Defun(tf.float32, tf.float32, func_name=\\\"tanh_grad2\\\")\\n\",\n \"def tanh_grad2(val1, bp):\\n\",\n \" val2 = tf.tanh(val1)\\n\",\n \" return gen_math_ops._tanh_grad(val1, gen_math_ops._tanh_grad(val2, bp))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(ops_to_fuse[0].inputs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(ops_to_fuse[1].inputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create new node with correct inputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"new_node = tanh_grad2(ops_to_fuse[0].inputs[0], ops_to_fuse[0].inputs[1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use reroute to connect outputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ops_to_fuse[1].outputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ge.reroute_a2b_ts(new_node, ops_to_fuse[1].outputs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(ungroup_gradients=True)\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"102px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": true,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebook_util.py", "content": "import subprocess, re, os, sys\n\n# GPU picking\n# http://stackoverflow.com/a/41638727/419116\n# Nvidia-smi GPU memory parsing.\n# Tested on nvidia-smi 370.23\n\ndef run_command(cmd):\n \"\"\"Run command, return output as string.\"\"\"\n \n output = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True).communicate()[0]\n return output.decode(\"ascii\")\n\ndef list_available_gpus():\n \"\"\"Returns list of available GPU ids.\"\"\"\n \n output = run_command(\"nvidia-smi -L\")\n # lines of the form GPU 0: TITAN X\n gpu_regex = re.compile(r\"GPU (?P\\d+):\")\n result = []\n for line in output.strip().split(\"\\n\"):\n m = gpu_regex.match(line)\n assert m, \"Couldnt parse \"+line\n result.append(int(m.group(\"gpu_id\")))\n return result\n\ndef gpu_memory_map():\n \"\"\"Returns map of GPU id to memory allocated on that GPU.\"\"\"\n\n output = run_command(\"nvidia-smi\")\n gpu_output = output[output.find(\"GPU Memory\"):]\n # lines of the form\n # | 0 8734 C python 11705MiB |\n memory_regex = re.compile(r\"[|]\\s+?(?P\\d+)\\D+?(?P\\d+).+[ ](?P\\d+)MiB\")\n rows = gpu_output.split(\"\\n\")\n result = {gpu_id: 0 for gpu_id in list_available_gpus()}\n for row in gpu_output.split(\"\\n\"):\n m = memory_regex.search(row)\n if not m:\n continue\n gpu_id = int(m.group(\"gpu_id\"))\n gpu_memory = int(m.group(\"gpu_memory\"))\n result[gpu_id] += gpu_memory\n return result\n\ndef pick_gpu_lowest_memory():\n \"\"\"Returns GPU with the least allocated memory\"\"\"\n\n memory_gpu_map = [(memory, gpu_id) for (gpu_id, memory) in gpu_memory_map().items()]\n best_memory, best_gpu = sorted(memory_gpu_map)[0]\n return best_gpu\n\ndef setup_one_gpu():\n assert not 'tensorflow' in sys.modules, \"GPU setup must happen before importing TensorFlow\"\n gpu_id = pick_gpu_lowest_memory()\n print(\"Picking GPU \"+str(gpu_id))\n os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpu_id)\n\ndef setup_no_gpu():\n if 'tensorflow' in sys.modules:\n print(\"Warning, GPU setup must happen before importing TensorFlow\")\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = ''\n" }, { "path": "numpy_initializers/kfac_cifar.py", "content": "#!/usr/bin/env python\nGLOBAL_PROFILE = False\n\nfrom tensorflow.python.client import timeline\nimport argparse\nimport json\nimport os\nimport sys\nimport time\nimport util as u\nimport util\nfrom util import t # transpose\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m', '--mode', type=str, default='run', help='record to record test data, test to perform test, run to run training for longer')\nparser.add_argument('-s', '--seed', type=int, default=1, help='Random seed to use')\nparser.add_argument('--method', type=str, default=\"kfac\", help='turn on KFAC')\nparser.add_argument('--fixed_labels', type=int, default=0,\n help='if true, fix synthetic labels to all 1s')\nparser.add_argument('--lr', type=float, default=0.001,\n help='learning rate to use')\nparser.add_argument('--validate_every_n', type=int, default=10,\n help='set to positive number to measure validation')\n# lambda tuning graphs: https://wolfr.am/lojcyhYz\nparser.add_argument('-L', '--Lambda', type=float, default=0.01,\n help='lambda value')\nparser.add_argument('-r', '--run', type=str, default='default',\n help='name of experiment run')\nparser.add_argument('-n', '--num_steps', type=int, default=1000000,\n help='number of steps')\nparser.add_argument('--dataset', type=str, default=\"cifar\",\n help='which dataset to use')\n# todo: split between optimizer batch size and stats batch size\nparser.add_argument('-b', '--batch_size', type=int, default=10000,\n help='batch size')\nparser.add_argument('--kfac_batch_size', type=int, default=10000,\n help='batch size to use for KFAC stats')\nparser.add_argument('--dataset_size', type=int, default=1000000000,\n help='truncate dataset at this value')\nparser.add_argument('--advance_batch', type=int, default=0,\n help='whether to advance batch')\nparser.add_argument('--extra_kfac_batch_advance', type=int, default=0,\n help='make kfac batches out of sync')\nparser.add_argument('--kfac_polyak_factor', type=float, default=1.0,\n help='polyak averaging factor to use')\nparser.add_argument('--kfac_async', type=int, default=0,\n help='do covariance and inverses asynchronously')\n\nargs = parser.parse_args()\nu.set_global_args(args)\nprint('input args:\\n', json.dumps(vars(args), indent=4, separators=(',',':')))\n\nuse_tikhonov=False\n\n# Test generation releases\nrelease_name='kfac_cifar' # release name fixes a specific test set\nrelease_name='kfac_mnist' # release name fixes a specific test set\nrelease_test_fn = release_name+'_losses_test.csv'\n\nif args.mode == 'test' or args.mode == 'record':\n if release_name == 'kfac_cifar':\n args.num_steps = 10\n args.dataset = 'cifar'\n args.Lambda=1e-1\n args.fixed_labels = True\n args.seed = 1\n args.batch_size = 100\n\n elif release_name == 'kfac_mnist':\n args.num_steps = 5\n args.dataset = 'mnist'\n args.fixed_labels = True\n args.seed = 1\n args.batch_size = 100\n args.kfac_batch_size = 100\n args.dataset_size = args.batch_size\n \nimport load_MNIST\n\nimport kfac as kfac_lib\nfrom kfac import Model\nfrom kfac import Kfac\nfrom kfac import IndexedGrad\nimport kfac\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\n\n\n# TODO: get rid of this\npurely_linear = False # convert sigmoids into linear nonlinearities\npurely_relu = True # convert sigmoids into ReLUs\n\nregularized_svd = True # kfac_lib.regularized_svd # TODO: delete this\n\n\nrundir = u.setup_experiment_run_directory(args.run)\nwith open(rundir+'/args.txt', 'w') as f:\n f.write(json.dumps(vars(args), indent=4, separators=(',',':')))\n f.write('\\n')\n\n# TODO: get rid\ndef W_uniform(s1, s2): # uniform weight init from Ng UFLDL\n r = np.sqrt(6) / np.sqrt(s1 + s2 + 1)\n result = np.random.random(2*s2*s1)*2*r-r\n return result\n\n\ndef ng_init(rows, cols):\n # creates uniform initializer using Ng's formula\n # TODO: turn into TF\n r = np.sqrt(6) / np.sqrt(rows + cols + 1)\n result = np.random.random(rows*cols)*2*r-r\n return result.reshape((rows, cols))\n\n\nif args.dataset == 'cifar':\n # load data globally once\n from keras.datasets import cifar10\n (X_train, y_train), (X_test, y_test) = cifar10.load_data()\n X_train = X_train.astype(np.float32)\n X_train = X_train.reshape((X_train.shape[0], -1))\n X_test = X_test.astype(np.float32)\n X_test = X_test.reshape((X_test.shape[0], -1))\n X_train /= 255\n X_test /= 255\nelif args.dataset == 'mnist':\n train_images = load_MNIST.load_MNIST_images('data/train-images-idx3-ubyte').astype(np.float32)\n # todo: load test images from separate file like with cifar\n # todo: remove this extra truncation since it happens later\n # test_patches = train_images[:,-args.dataset_size:]\n test_images = load_MNIST.load_MNIST_images('data/t10k-images-idx3-ubyte').astype(np.float32)\n\n X_train = train_images[:,:args.dataset_size].T # batch last\n X_test = test_images.T # test_patches.T\n \n \n# todo: rename to better names\ntrain_images = X_train.T # batch first\ntest_images = X_test.T\n\nfull_trace_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE,\n output_partition_graphs=True)\ndef sessrun(*args, **kwargs):\n sess = u.get_default_session()\n if not GLOBAL_PROFILE:\n return sess.run(*args, **kwargs)\n \n run_metadata = tf.RunMetadata()\n\n kwargs['options'] = full_trace_options\n kwargs['run_metadata'] = run_metadata\n result = sess.run(*args, **kwargs)\n first_entry = args[0]\n if isinstance(first_entry, list):\n if len(first_entry) == 0 and len(args) == 1:\n return None\n first_entry = first_entry[0]\n name = first_entry.name\n name = name.replace('/', '-')\n\n tl = timeline.Timeline(run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format()\n with open('timelines/%s.json'%(name,), 'w') as f:\n f.write(ctf)\n with open('timelines/%s.pbtxt'%(name,), 'w') as f:\n f.write(str(run_metadata))\n return result\n\n\ndef model_creator(batch_size, name=\"default\", dtype=np.float32):\n \"\"\"Create MNIST autoencoder model. Dataset is part of model.\"\"\"\n\n model = Model(name)\n\n def get_batch_size(data):\n if isinstance(data, IndexedGrad):\n return int(data.live[0].shape[1])\n else:\n return int(data.shape[1])\n\n init_dict = {}\n global_vars = []\n local_vars = []\n \n # TODO: factor out to reuse between scripts\n # TODO: change feed_dict logic to reuse value provided to VarStruct\n # current situation makes reinitialization of global variable change\n # it's value, counterinituitive\n def init_var(val, name, is_global=False):\n \"\"\"Helper to create variables with numpy or TF initial values.\"\"\"\n if isinstance(val, tf.Tensor):\n var = u.get_variable(name=name, initializer=val, reuse=is_global)\n else:\n val = np.array(val)\n assert u.is_numeric(val), \"Non-numeric type.\"\n \n var_struct = u.get_var(name=name, initializer=val, reuse=is_global)\n holder = var_struct.val_\n init_dict[holder] = val\n var = var_struct.var\n\n if is_global:\n global_vars.append(var)\n else:\n local_vars.append(var)\n \n return var\n\n # TODO: get rid of purely_relu\n def nonlin(x):\n if purely_relu:\n return tf.nn.relu(x)\n elif purely_linear:\n return tf.identity(x)\n else:\n return tf.sigmoid(x)\n\n # TODO: rename into \"nonlin_d\"\n def d_nonlin(y):\n if purely_relu:\n return u.relu_mask(y)\n elif purely_linear:\n return 1\n else: \n return y*(1-y)\n\n patches = train_images[:,:args.batch_size];\n test_patches = test_images[:,:args.batch_size];\n\n if args.dataset == 'cifar':\n input_dim = 3*32*32\n elif args.dataset == 'mnist':\n input_dim = 28*28\n else:\n assert False\n fs = [args.batch_size, input_dim, 1024, 1024, 1024, 196, 1024, 1024, 1024,\n input_dim]\n \n def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]\n n = len(fs) - 2\n\n # Full dataset from which new batches are sampled\n X_full = init_var(train_images, \"X_full\", is_global=True)\n\n X = init_var(patches, \"X\", is_global=False) # stores local batch per model\n W = [None]*n\n W.insert(0, X)\n A = [None]*(n+2)\n A[1] = W[0]\n for i in range(1, n+1):\n init_val = ng_init(f(i), f(i-1)).astype(dtype)\n W[i] = init_var(init_val, \"W_%d\"%(i,), is_global=True)\n A[i+1] = nonlin(kfac_lib.matmul(W[i], A[i]))\n err = A[n+1] - A[1]\n model.loss = u.L2(err) / (2 * get_batch_size(err))\n\n # create test error eval\n layer0 = init_var(test_patches, \"X_test\", is_global=True)\n layer = layer0\n for i in range(1, n+1):\n layer = nonlin(W[i] @ layer)\n verr = (layer - layer0)\n model.vloss = u.L2(verr) / (2 * get_batch_size(verr))\n\n # manually compute backprop to use for sanity checking\n B = [None]*(n+1)\n B2 = [None]*(n+1)\n B[n] = err*d_nonlin(A[n+1])\n _sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)\n if args.fixed_labels:\n _sampled_labels_live = tf.ones(shape=(f(n), f(-1)), dtype=dtype)\n \n _sampled_labels = init_var(_sampled_labels_live, \"to_be_deleted\",\n is_global=False)\n\n B2[n] = _sampled_labels*d_nonlin(A[n+1])\n for i in range(n-1, -1, -1):\n backprop = t(W[i+1]) @ B[i+1]\n B[i] = backprop*d_nonlin(A[i+1])\n backprop2 = t(W[i+1]) @ B2[i+1]\n B2[i] = backprop2*d_nonlin(A[i+1])\n\n cov_A = [None]*(n+1) # covariance of activations[i]\n cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]\n vars_svd_A = [None]*(n+1)\n vars_svd_B2 = [None]*(n+1)\n dW = [None]*(n+1)\n dW2 = [None]*(n+1)\n pre_dW = [None]*(n+1) # preconditioned dW\n # todo: decouple initial value from covariance update\n # maybe need start with identity and do running average\n for i in range(1,n+1):\n if regularized_svd:\n cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size+args.Lambda*u.Identity(f(i-1)), \"cov_A%d\"%(i,))\n cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size+args.Lambda*u.Identity(f(i)), \"cov_B2%d\"%(i,))\n else:\n cov_A[i] = init_var(A[i]@t(A[i])/args.batch_size, \"cov_A%d\"%(i,))\n cov_B2[i] = init_var(B2[i]@t(B2[i])/args.batch_size, \"cov_B2%d\"%(i,))\n vars_svd_A[i] = u.SvdWrapper(cov_A[i],\"svd_A_%d\"%(i,))\n vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],\"svd_B2_%d\"%(i,))\n if use_tikhonov:\n whitened_A = u.regularized_inverse3(vars_svd_A[i],L=args.Lambda) @ A[i]\n whitened_B2 = u.regularized_inverse3(vars_svd_B2[i],L=args.Lambda) @ B[i]\n else:\n whitened_A = u.pseudo_inverse2(vars_svd_A[i]) @ A[i]\n whitened_B2 = u.pseudo_inverse2(vars_svd_B2[i]) @ B[i]\n \n dW[i] = (B[i] @ t(A[i]))/args.batch_size\n dW2[i] = B[i] @ t(A[i])\n pre_dW[i] = (whitened_B2 @ t(whitened_A))/args.batch_size\n\n \n sampled_labels_live = A[n+1] + tf.random_normal((f(n), f(-1)),\n dtype=dtype, seed=0)\n if args.fixed_labels:\n sampled_labels_live = A[n+1]+tf.ones(shape=(f(n), f(-1)), dtype=dtype)\n sampled_labels = init_var(sampled_labels_live, \"sampled_labels\", is_global=False)\n err2 = A[n+1] - sampled_labels\n model.loss2 = u.L2(err2) / (2 * args.batch_size)\n model.global_vars = global_vars\n model.local_vars = local_vars\n model.trainable_vars = W[1:]\n\n # todo, we have 3 places where model step is tracked, reduce\n model.step = init_var(u.as_int32(0), \"step\", is_global=False)\n advance_step_op = model.step.assign_add(1)\n assert get_batch_size(X_full) % args.batch_size == 0\n batches_per_dataset = (get_batch_size(X_full) // args.batch_size)\n batch_idx = tf.mod(model.step, batches_per_dataset)\n start_idx = batch_idx * args.batch_size\n advance_batch_op = X.assign(X_full[:,start_idx:start_idx + args.batch_size])\n \n def advance_batch():\n print(\"Step for model(%s) is %s\"%(model.name, u.eval(model.step)))\n sess = u.get_default_session()\n # TODO: get rid of _sampled_labels\n sessrun([sampled_labels.initializer, _sampled_labels.initializer])\n if args.advance_batch:\n with u.timeit(\"advance_batch\"):\n sessrun(advance_batch_op)\n sessrun(advance_step_op)\n \n model.advance_batch = advance_batch\n\n # TODO: refactor this to take initial values out of Var struct\n #global_init_op = tf.group(*[v.initializer for v in global_vars])\n global_init_ops = [v.initializer for v in global_vars]\n global_init_op = tf.group(*[v.initializer for v in global_vars])\n global_init_query_ops = [tf.logical_not(tf.is_variable_initialized(v))\n for v in global_vars]\n \n def initialize_global_vars(verbose=False, reinitialize=False):\n \"\"\"If reinitialize is false, will not reinitialize variables already\n initialized.\"\"\"\n \n sess = u.get_default_session()\n if not reinitialize:\n uninited = sessrun(global_init_query_ops)\n # use numpy boolean indexing to select list of initializers to run\n to_initialize = list(np.asarray(global_init_ops)[uninited])\n else:\n to_initialize = global_init_ops\n \n if verbose:\n print(\"Initializing following:\")\n for v in to_initialize:\n print(\" \" + v.name)\n\n sessrun(to_initialize, feed_dict=init_dict)\n model.initialize_global_vars = initialize_global_vars\n\n # didn't quite work (can't initialize var in same run call as deps likely)\n # enforce that batch is initialized before everything\n # except fake labels opa\n # for v in local_vars:\n # if v != X and v != sampled_labels and v != _sampled_labels:\n # print(\"Adding dep %s on %s\"%(v.initializer.name, X.initializer.name))\n # u.add_dep(v.initializer, on_op=X.initializer)\n \n local_init_op = tf.group(*[v.initializer for v in local_vars],\n name=\"%s_localinit\"%(model.name))\n print(\"Local vars:\")\n for v in local_vars:\n print(v.name)\n \n def initialize_local_vars():\n sess = u.get_default_session()\n sessrun(_sampled_labels.initializer, feed_dict=init_dict)\n sessrun(X.initializer, feed_dict=init_dict)\n sessrun(local_init_op, feed_dict=init_dict)\n model.initialize_local_vars = initialize_local_vars\n\n return model\n\n#@profile\ndef main():\n np.random.seed(args.seed)\n tf.set_random_seed(args.seed)\n\n logger = u.TensorboardLogger(args.run)\n \n with u.timeit(\"init/session\"):\n gpu_options = tf.GPUOptions(allow_growth=False)\n sess = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=gpu_options))\n u.register_default_session(sess) # since default session is Thread-local\n\n with u.timeit(\"init/model_init\"):\n model = model_creator(args.batch_size, name=\"main\")\n model.initialize_global_vars(verbose=True)\n model.initialize_local_vars()\n \n with u.timeit(\"init/kfac_init\"):\n kfac = Kfac(model_creator, args.kfac_batch_size) \n kfac.model.initialize_global_vars(verbose=False)\n kfac.model.initialize_local_vars()\n kfac.Lambda.set(args.Lambda)\n kfac.reset() # resets optimization variables (not model variables)\n\n if args.mode != 'run':\n opt = tf.train.AdamOptimizer(0.001)\n else:\n opt = tf.train.AdamOptimizer(args.lr)\n grads_and_vars = opt.compute_gradients(model.loss,\n var_list=model.trainable_vars)\n \n grad = IndexedGrad.from_grads_and_vars(grads_and_vars)\n grad_new = kfac.correct(grad)\n with u.capture_vars() as adam_vars:\n train_op = opt.apply_gradients(grad_new.to_grads_and_vars())\n with u.timeit(\"init/adam\"):\n sessrun([v.initializer for v in adam_vars])\n \n losses = []\n u.record_time()\n\n start_time = time.time()\n vloss0 = 0\n\n # todo, unify the two data outputs\n outfn = 'data/%s_%f_%f.csv'%(args.run, args.lr, args.Lambda)\n writer = u.BufferedWriter(outfn, 60) # get rid?\n\n start_time = time.time()\n if args.extra_kfac_batch_advance:\n kfac.model.advance_batch() # advance kfac batch\n\n if args.kfac_async:\n kfac.start_stats_runners()\n \n for step in range(args.num_steps):\n \n if args.validate_every_n and step%args.validate_every_n == 0:\n loss0, vloss0 = sessrun([model.loss, model.vloss])\n else:\n loss0, = sessrun([model.loss])\n losses.append(loss0) # TODO: remove this\n\n logger('loss/loss', loss0, 'loss/vloss', vloss0)\n \n elapsed = time.time()-start_time\n print(\"%d sec, step %d, loss %.2f, vloss %.2f\" %(elapsed, step, loss0,\n vloss0))\n writer.write('%d, %f, %f, %f\\n'%(step, elapsed, loss0, vloss0))\n\n if args.method=='kfac' and not args.kfac_async:\n kfac.model.advance_batch()\n kfac.update_stats()\n\n with u.timeit(\"train\"):\n model.advance_batch()\n grad.update()\n with kfac.read_lock():\n grad_new.update()\n train_op.run()\n u.record_time()\n\n logger.next_step()\n\n # TODO: use u.global_runs_dir\n # TODO: get rid of u.timeit?\n \n with open('timelines/graphdef.txt', 'w') as f:\n f.write(str(u.get_default_graph().as_graph_def()))\n\n u.summarize_time()\n \n if args.mode == 'record':\n u.dump_with_prompt(losses, release_test_fn)\n\n elif args.mode == 'test':\n targets = np.loadtxt('data/'+release_test_fn, delimiter=\",\")\n u.check_equal(losses, targets, rtol=1e-2)\n u.summarize_difference(losses, targets)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "numpy_initializers/util.py", "content": "#!/usr/bin/env python\nimport socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=True # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n\n\nfrom scipy import linalg\n\n# TODO: speed-up tests by reusing session\n\nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__ == 'Tensor' or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n logger = u.get_last_logger(skip_existence_check=True)\n if logger:\n logger('time/'+self.tag, interval_ms)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n \n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n\n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger(skip_existence_check=False):\n \"\"\"Returns last logger, if skip_existence_check is set, doesn't\n throw error if logger doesn't exist.\"\"\"\n global global_last_logger\n if not skip_existence_check:\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n interval_ms = 1000*(new_timestamp - self.last_timestamp)\n self.summary.value.add(tag='time/step',\n simple_value=interval_ms)\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n # hack, remove\n return tf.get_default_session()\n global sess\n assert sess\n return sess\n\ndef get_default_graph():\n global sess\n assert sess\n return sess.graph\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n \nif __name__=='__main__':\n run_all_tests(sys.modules[__name__])\n" }, { "path": "parallel_dequeue_test.py", "content": "# http://stackoverflow.com/questions/41830206/how-to-share-a-queue-containing-variable-length-sequences-batches-between-multip\n#\n# 0.12.1\n# v0.12.0-10-g4d924e7-dirty\n# [array([1, 2, 3, 4, 0], dtype=int32), True]\n# [array([8, 6, 7, 9, 5], dtype=int32), True]\n# [array([11, 12, 13, 14, 10], dtype=int32), True]\n# [array([16, 17, 18, 19, 15], dtype=int32), True]\n#\n# In HEAD (from Jan 17)\n# 0.12.head\n# 0.12.1-1878-g76d5960-dirty\n# [array([0, 0, 0, 0, 0], dtype=int32), False]\n# [array([1, 1, 1, 1, 1], dtype=int32), False]\n# [array([2, 2, 2, 2, 2], dtype=int32), False]\n# [array([3, 3, 3, 3, 3], dtype=int32), False]\n\nimport os, sys\nimport numpy as np\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\nimport tensorflow as tf\n\ndef create_session():\n # config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\n config = tf.ConfigProto()\n sess = tf.InteractiveSession(\"\", config=config)\n return sess\n\nimport time\nimport threading\nimport os\nos.environ['PYTHONUNBUFFERED'] = 'True'\n\nn = 100\nnum_parallel = 5\ndtype = tf.int32\nqueue = tf.FIFOQueue(capacity=n, dtypes=[dtype], shapes=[()])\nenqueue_op = queue.enqueue_many(tf.range(n))\nsize_op = queue.size()\n\ndequeue_ops = []\nfor i in range(num_parallel):\n dequeue_ops.append(queue.dequeue())\n\nif hasattr(tf, \"stack\"):\n batch = tf.stack(dequeue_ops)\nelse:\n batch = tf.pack(dequeue_ops)\nall_unique = tf.equal(tf.size(tf.unique(batch)[0]), num_parallel)\nsess = create_session()\nsess.run(enqueue_op)\nprint(tf.__version__)\nprint(tf.__git_version__)\nfor i in range(n//num_parallel):\n print(sess.run([batch, all_unique, size_op]))\nprint(tf.get_default_graph().as_graph_def())\n\n# node {\n# name: \"fifo_queue\"\n# op: \"FIFOQueueV2\"\n# attr {\n# key: \"capacity\"\n# value {\n# i: 100\n# }\n# }\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"container\"\n# value {\n# s: \"\"\n# }\n# }\n# attr {\n# key: \"shapes\"\n# value {\n# list {\n# shape {\n# }\n# }\n# }\n# }\n# attr {\n# key: \"shared_name\"\n# value {\n# s: \"\"\n# }\n# }\n# }\n# node {\n# name: \"range/start\"\n# op: \"Const\"\n# attr {\n# key: \"dtype\"\n# value {\n# type: DT_INT32\n# }\n# }\n# attr {\n# key: \"value\"\n# value {\n# tensor {\n# dtype: DT_INT32\n# tensor_shape {\n# }\n# int_val: 0\n# }\n# }\n# }\n# }\n# node {\n# name: \"range/limit\"\n# op: \"Const\"\n# attr {\n# key: \"dtype\"\n# value {\n# type: DT_INT32\n# }\n# }\n# attr {\n# key: \"value\"\n# value {\n# tensor {\n# dtype: DT_INT32\n# tensor_shape {\n# }\n# int_val: 100\n# }\n# }\n# }\n# }\n# node {\n# name: \"range/delta\"\n# op: \"Const\"\n# attr {\n# key: \"dtype\"\n# value {\n# type: DT_INT32\n# }\n# }\n# attr {\n# key: \"value\"\n# value {\n# tensor {\n# dtype: DT_INT32\n# tensor_shape {\n# }\n# int_val: 1\n# }\n# }\n# }\n# }\n# node {\n# name: \"range\"\n# op: \"Range\"\n# input: \"range/start\"\n# input: \"range/limit\"\n# input: \"range/delta\"\n# attr {\n# key: \"Tidx\"\n# value {\n# type: DT_INT32\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_EnqueueMany\"\n# op: \"QueueEnqueueManyV2\"\n# input: \"fifo_queue\"\n# input: \"range\"\n# attr {\n# key: \"Tcomponents\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_Size\"\n# op: \"QueueSizeV2\"\n# input: \"fifo_queue\"\n# }\n# node {\n# name: \"fifo_queue_Dequeue\"\n# op: \"QueueDequeueV2\"\n# input: \"fifo_queue\"\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_Dequeue_1\"\n# op: \"QueueDequeueV2\"\n# input: \"fifo_queue\"\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_Dequeue_2\"\n# op: \"QueueDequeueV2\"\n# input: \"fifo_queue\"\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_Dequeue_3\"\n# op: \"QueueDequeueV2\"\n# input: \"fifo_queue\"\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"fifo_queue_Dequeue_4\"\n# op: \"QueueDequeueV2\"\n# input: \"fifo_queue\"\n# attr {\n# key: \"component_types\"\n# value {\n# list {\n# type: DT_INT32\n# }\n# }\n# }\n# attr {\n# key: \"timeout_ms\"\n# value {\n# i: -1\n# }\n# }\n# }\n# node {\n# name: \"stack\"\n# op: \"Pack\"\n# input: \"fifo_queue_Dequeue\"\n# input: \"fifo_queue_Dequeue_1\"\n# input: \"fifo_queue_Dequeue_2\"\n# input: \"fifo_queue_Dequeue_3\"\n# input: \"fifo_queue_Dequeue_4\"\n# attr {\n# key: \"N\"\n# value {\n# i: 5\n# }\n" }, { "path": "phantomjs-tryout.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Trying recipe from http://stackoverflow.com/questions/10628262/inserting-image-into-ipython-notebook-markdown\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shell-init: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shell-init: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory\\r\\n\",\n \"pwd: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory\\r\\n\",\n \"pwd: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pwd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"helloworld\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"helloworld\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Jupyter Version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Script used\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"// address of the notebook\\n\",\n \"var address = \\\"http://localhost:8888/notebooks/phantomjs-tryout.ipynb\\\"\\n\",\n \"// auth token from Jupyter console\\n\",\n \"var authToken = \\\"b05a65d622136665a1292b9f3a29192364eaccb4bfbc6887\\\"\\n\",\n \"// cell number with a widget output\\n\",\n \"var cellNumber = 1;\\n\",\n \"\\n\",\n \"// this function is used to verify that a page is fully loaded\\n\",\n \"// source: https://github.com/ariya/phantomjs/blob/master/examples/waitfor.js\\n\",\n \"function waitFor(testFx, onReady, timeOutMillis) {\\n\",\n \" var maxtimeOutMillis = timeOutMillis ? timeOutMillis : 3000,\\n\",\n \" start = new Date().getTime(),\\n\",\n \" condition = false,\\n\",\n \" interval = setInterval(function() {\\n\",\n \" if ( (new Date().getTime() - start < maxtimeOutMillis) && !condition ) {\\n\",\n \" // If not time-out yet and condition not yet fulfilled\\n\",\n \" condition = (typeof(testFx) === \\\"string\\\" ? eval(testFx) : testFx());\\n\",\n \" } else {\\n\",\n \" if(!condition) {\\n\",\n \" // If condition still not fulfilled (timeout but condition is 'false')\\n\",\n \" console.log(\\\"'waitFor()' timeout\\\");\\n\",\n \" phantom.exit(1);\\n\",\n \" } else {\\n\",\n \" // Condition fulfilled (timeout and/or condition is 'true')\\n\",\n \" console.log(\\\"'waitFor()' finished in \\\" + (new Date().getTime() - start) + \\\"ms.\\\");\\n\",\n \" typeof(onReady) === \\\"string\\\" ? eval(onReady) : onReady();\\n\",\n \" clearInterval(interval); //< Stop this interval\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }, 250); //< repeat check every 250ms\\n\",\n \"};\\n\",\n \"\\n\",\n \"// log in to a notebook using a token\\n\",\n \"function logIn() {\\n\",\n \" console.log(\\\"Logging in\\\");\\n\",\n \" page.evaluate(function(token) {\\n\",\n \" document.forms[0].password.value = token;\\n\",\n \" document.forms[0].submit();\\n\",\n \" }, authToken);\\n\",\n \"}\\n\",\n \"\\n\",\n \"// wait for a notebook to fully load, find the\\n\",\n \"// needed output cell and save it as a PNG file\\n\",\n \"function saveAsPNG() {\\n\",\n \" console.log(\\\"Saving PNG\\\")\\n\",\n \" // Wait for 'notebook-container' to be visible\\n\",\n \" waitFor(function() {\\n\",\n \" // Check in the page if a specific element is now visible\\n\",\n \" return page.evaluate(function() {\\n\",\n \" return $(\\\"#notebook-container\\\").is(\\\":visible\\\");\\n\",\n \" });\\n\",\n \" }, function() {\\n\",\n \" console.log(\\\"The notebook-container element should be visible now.\\\");\\n\",\n \" var clipRect = page.evaluate(function(cell){\\n\",\n \" // we are selecting only the output cell\\n\",\n \" var searchStr = 'div.output_area:contains(\\\"Out[' + cell + ']\\\")'\\n\",\n \" console.log('DEBUG: Search string: ' + searchStr);\\n\",\n \" var outputArea = $(searchStr)[0];\\n\",\n \" console.log('DEBUG: output_area div: ' + outputArea);\\n\",\n \" // get only the data div\\n\",\n \" var outputResult = $(outputArea).children('div.output_result')[0];\\n\",\n \" console.log('DEBUG: output_result div: ' + outputResult);\\n\",\n \" if (outputResult === undefined) {\\n\",\n \" return -1\\n\",\n \" }\\n\",\n \" // get the coordinates of the data div\\n\",\n \" return outputResult.getBoundingClientRect()\\n\",\n \" }, cellNumber);\\n\",\n \"\\n\",\n \" console.log('DEBUG: clipRect: ' + clipRect);\\n\",\n \"\\n\",\n \" if (clipRect === -1) {\\n\",\n \" phantom.exit(clipRect);\\n\",\n \" }\\n\",\n \"\\n\",\n \" page.clipRect = {\\n\",\n \" top: clipRect.top,\\n\",\n \" left: clipRect.left,\\n\",\n \" width: clipRect.width,\\n\",\n \" height: clipRect.height\\n\",\n \" };\\n\",\n \" page.render('example.png');\\n\",\n \" phantom.exit();\\n\",\n \" });\\n\",\n \"}\\n\",\n \"\\n\",\n \"var page = require('webpage').create();\\n\",\n \"// it seems, viewportSize should fully cover the\\n\",\n \"// the rendered div position, or nothing will be saved.\\n\",\n \"page.viewportSize = { width: 5000, height: 5000 };\\n\",\n \"\\n\",\n \"page.onConsoleMessage = function(msg, lineNum, sourceId) {\\n\",\n \" console.log('CONSOLE: ' + msg);\\n\",\n \"}\\n\",\n \"\\n\",\n \"page.open(address, function (status) {\\n\",\n \" // Check for page load success\\n\",\n \" if (status !== \\\"success\\\") {\\n\",\n \" console.log(\\\"Unable to open a page\\\");\\n\",\n \" } else {\\n\",\n \" console.log('DEBUG: NB address: ' + address)\\n\",\n \" console.log('DEBUG: Auth token: ' + authToken)\\n\",\n \" console.log('DEBUG: Cell number: ' + cellNumber)\\n\",\n \" // Wait for 'password_input' to be visible\\n\",\n \" waitFor(function() {\\n\",\n \" // Check in the page if a specific element is now visible\\n\",\n \" return page.evaluate(function() {\\n\",\n \" return $(\\\"#password_input\\\").is(\\\":visible\\\");\\n\",\n \" });\\n\",\n \" }, function() {\\n\",\n \" console.log(\\\"The password_input element should be visible now.\\\");\\n\",\n \" logIn();\\n\",\n \" saveAsPNG();\\n\",\n \" });\\n\",\n \" }\\n\",\n \"});\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Error\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"DEBUG: NB address: http://localhost:8888/notebooks/test.ipynb\\n\",\n \"DEBUG: Auth token: b05a65d622136665a1292b9f3a29192364eaccb4bfbc6887\\n\",\n \"DEBUG: Cell number: 1\\n\",\n \"'waitFor()' finished in 500ms.\\n\",\n \"The password_input element should be visible now.\\n\",\n \"Logging in\\n\",\n \"Saving PNG\\n\",\n \"CONSOLE: loaded custom.js\\n\",\n \"CONSOLE: Default extension for cell metadata editing loaded.\\n\",\n \"CONSOLE: Raw Cell Format toolbar preset loaded.\\n\",\n \"CONSOLE: Slideshow extension for metadata editing loaded.\\n\",\n \"CONSOLE: actions jupyter-notebook:find-and-replace does not exist, still binding it in case it will be defined later...\\n\",\n \"CONSOLE: load_extensions [object Arguments]\\n\",\n \"CONSOLE: Loading extension: contrib_nbextensions_help_item/main\\n\",\n \"Failed to load extension: nbextensions/nbextensions_configurator/config_menu/main Error: Script error for \\\"nbextensions/nbextensions_configurator/config_menu/main\\\"\\n\",\n \"http://requirejs.org/docs/errors.html#scripterror\\n\",\n \"\\n\",\n \" http://localhost:8888/static/notebook/js/main.min.js?v=edbc43d9bb99b1be2585a121ed720314:12233\\n\",\n \"CONSOLE: Session: kernel_created (ef710647-5137-46c7-bd1e-cdc1778b0790)\\n\",\n \"CONSOLE: Starting WebSockets: ws://localhost:8888/api/kernels/b9b038d9-1801-4258-a62b-dc0e9b91d499\\n\",\n \"CONSOLE: Loading extension: toc2/main\\n\",\n \"CONSOLE: [toc2] Notebook fully loaded -- toc2 initialized \\n\",\n \"CONSOLE: Widgets are not available. Please install widgetsnbextension or ipywidgets 4.0\\n\",\n \"CONSOLE: ******Restoring toc display\\n\",\n \"'waitFor()' finished in 563ms.\\n\",\n \"The notebook-container element should be visible now.\\n\",\n \"CONSOLE: DEBUG: Search string: div.output_area:contains(\\\"Out[1]\\\")\\n\",\n \"CONSOLE: DEBUG: output_area div: undefined\\n\",\n \"CONSOLE: DEBUG: output_result div: undefined\\n\",\n \"DEBUG: clipRect: -1\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Try with visualizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# from https://github.com/yaroslavvb/notebooks/blob/master/simple_rewiring.ipynb\\n\",\n \"# make things wide\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):\\n\",\n \" if not graph_def:\\n\",\n \" graph_def = tf.get_default_graph().as_graph_def()\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" data = str(strip_def)\\n\",\n \" if ungroup_gradients:\\n\",\n \" data = data.replace('\\\"gradients/', '\\\"b_')\\n\",\n \" #print(data)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(data), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(width, height, code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"a = tf.ones(())\\n\",\n \"b = tf.ones(())\\n\",\n \"c = a + b \\n\",\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"12px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": true,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "phantomjs-tryout.js", "content": "// address of the notebook\nvar address = \"http://localhost:8888/notebooks/phantomjs-tryout.ipynb\"\n// auth token from Jupyter console\nvar authToken = \"b05a65d622136665a1292b9f3a29192364eaccb4bfbc6887\"\n// cell number with a widget output\nvar cellNumber = 8;\n\n// this function is used to verify that a page is fully loaded\n// source: https://github.com/ariya/phantomjs/blob/master/examples/waitfor.js\nfunction waitFor(testFx, onReady, timeOutMillis) {\n var maxtimeOutMillis = timeOutMillis ? timeOutMillis : 3000,\n start = new Date().getTime(),\n condition = false,\n interval = setInterval(function() {\n if ( (new Date().getTime() - start < maxtimeOutMillis) && !condition ) {\n // If not time-out yet and condition not yet fulfilled\n condition = (typeof(testFx) === \"string\" ? eval(testFx) : testFx());\n } else {\n if(!condition) {\n // If condition still not fulfilled (timeout but condition is 'false')\n console.log(\"'waitFor()' timeout\");\n phantom.exit(1);\n } else {\n // Condition fulfilled (timeout and/or condition is 'true')\n console.log(\"'waitFor()' finished in \" + (new Date().getTime() - start) + \"ms.\");\n typeof(onReady) === \"string\" ? eval(onReady) : onReady();\n clearInterval(interval); //< Stop this interval\n }\n }\n }, 250); //< repeat check every 250ms\n};\n\n// log in to a notebook using a token\nfunction logIn() {\n console.log(\"Logging in\");\n page.evaluate(function(token) {\n document.forms[0].password.value = token;\n document.forms[0].submit();\n }, authToken);\n}\n\n// wait for a notebook to fully load, find the\n// needed output cell and save it as a PNG file\nfunction saveAsPNG() {\n console.log(\"Saving PNG\")\n // Wait for 'notebook-container' to be visible\n waitFor(function() {\n // Check in the page if a specific element is now visible\n return page.evaluate(function() {\n return $(\"#notebook-container\").is(\":visible\");\n });\n }, function() {\n console.log(\"The notebook-container element should be visible now.\");\n var clipRect = page.evaluate(function(cell){\n // we are selecting only the output cell\n var searchStr = 'div.input_prompt:contains(\"[' + cell + ']:\")';\n var parentCell = $(searchStr).parents('div.cell')[0];\n // get only the data div\n var outputSubarea = $(parentCell).find('div.output_subarea')[0];\n // get the coordinates of the data div\n return outputSubarea.getBoundingClientRect()\n }, cellNumber);\n\n page.clipRect = {\n top: clipRect.top,\n left: clipRect.left,\n width: clipRect.width,\n height: clipRect.height\n };\n page.render('example.png');\n phantom.exit();\n });\n}\n\nvar page = require('webpage').create();\n// it seems, viewportSize should fully cover the\n// the rendered div position, or nothing will be saved.\npage.viewportSize = { width: 5000, height: 5000 };\n\npage.open(address, function (status) {\n // Check for page load success\n if (status !== \"success\") {\n console.log(\"Unable to open a page\");\n } else {\n // Wait for 'password_input' to be visible\n waitFor(function() {\n // Check in the page if a specific element is now visible\n return page.evaluate(function() {\n return $(\"#password_input\").is(\":visible\");\n });\n }, function() {\n console.log(\"The password_input element should be visible now.\");\n logIn();\n saveAsPNG();\n });\n }\n});\n\n" }, { "path": "pytorch-hessian.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def jacobian(y, x, create_graph=False): \\n\",\n \" jac = [] \\n\",\n \" flat_y = y.reshape(-1) \\n\",\n \" grad_y = torch.zeros_like(flat_y) \\n\",\n \" for i in range(len(flat_y)): \\n\",\n \" grad_y[i] = 1. \\n\",\n \" grad_x, = torch.autograd.grad(flat_y, x, grad_y, retain_graph=True, create_graph=create_graph)\\n\",\n \" jac.append(grad_x.reshape(x.shape)) \\n\",\n \" grad_y[i] = 0. \\n\",\n \" return torch.stack(jac).reshape(y.shape + x.shape) \\n\",\n \" \\n\",\n \"def hessian(y, x): \\n\",\n \" return jacobian(jacobian(y, x, create_graph=True), x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"loss 0.6931471824645996\\n\",\n \"hessnorm 0.5\\n\",\n \"loss 0.4740769863128662\\n\",\n \"hessnorm 0.47000741958618164\\n\",\n \"loss 0.34769776463508606\\n\",\n \"hessnorm 0.4148704409599304\\n\",\n \"loss 0.27001631259918213\\n\",\n \"hessnorm 0.36127567291259766\\n\",\n \"loss 0.21886718273162842\\n\",\n \"hessnorm 0.31586238741874695\\n\",\n \"loss 0.18315738439559937\\n\",\n \"hessnorm 0.2787052094936371\\n\",\n \"loss 0.15702718496322632\\n\",\n \"hessnorm 0.24840296804904938\\n\",\n \"loss 0.137176513671875\\n\",\n \"hessnorm 0.2235051691532135\\n\",\n \"loss 0.121634840965271\\n\",\n \"hessnorm 0.20282311737537384\\n\",\n \"loss 0.10916388034820557\\n\",\n \"hessnorm 0.18544301390647888\\n\",\n \"loss 0.09895133972167969\\n\",\n \"hessnorm 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\"hessnorm 0.05269015580415726\\n\",\n \"loss 0.026728034019470215\\n\",\n \"hessnorm 0.05135682225227356\\n\",\n \"loss 0.026041388511657715\\n\",\n \"hessnorm 0.050088830292224884\\n\",\n \"loss 0.025388717651367188\\n\",\n \"hessnorm 0.048881400376558304\\n\",\n \"loss 0.02476799488067627\\n\",\n \"hessnorm 0.04773049056529999\\n\",\n \"loss 0.024176597595214844\\n\",\n \"hessnorm 0.04663211852312088\\n\",\n \"loss 0.023612618446350098\\n\",\n \"hessnorm 0.04558280110359192\\n\",\n \"loss 0.02307415008544922\\n\",\n \"hessnorm 0.04457937180995941\\n\",\n \"loss 0.022559642791748047\\n\",\n \"hessnorm 0.043618906289339066\\n\",\n \"loss 0.02206730842590332\\n\",\n \"hessnorm 0.04269862920045853\\n\",\n \"loss 0.021596074104309082\\n\",\n \"hessnorm 0.04181622341275215\\n\",\n \"loss 0.021144390106201172\\n\",\n \"hessnorm 0.04096932336688042\\n\",\n \"loss 0.020711064338684082\\n\",\n \"hessnorm 0.0401558019220829\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/yaroslavvb/anaconda3/envs/main/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\\n\",\n \" # Remove the CWD from sys.path while we load stuff.\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"import torch.nn as nn\\n\",\n \"import torch.nn.functional as F\\n\",\n \"import numpy as np\\n\",\n \"import torch.optim as optim\\n\",\n \"\\n\",\n \"x = torch.tensor([[0, 0.]], requires_grad=True) \\n\",\n \"target = torch.tensor([0])\\n\",\n \"for i in range(50):\\n\",\n \" loss = F.nll_loss(F.log_softmax(x), target)\\n\",\n \" flat_x = x.reshape(-1)\\n\",\n \" grad = torch.autograd.grad(loss, x, retain_graph=True)[0]\\n\",\n \" hess = hessian(loss, x)\\n\",\n \" x = x - .5 * grad\\n\",\n \"\\n\",\n \" print(\\\"loss\\\", loss.item())\\n\",\n \" print(\\\"hessnorm\\\", torch.norm(hess).item())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "queue_mismatch.py", "content": "# from http://stackoverflow.com/questions/41920371/tensorflow-multi-threaded-queuerunner?noredirect=1#comment71036438_41920371\n\nimport tensorflow as tf\nimport numpy as np\nimport time\n\nbatch_size = 4\niters = 100\na = tf.train.range_input_producer(10, shuffle=False, name=\"a\", capacity=batch_size*iters).dequeue()\nb = tf.train.range_input_producer(10, shuffle=False, name=\"b\", capacity=batch_size*iters).dequeue()\nc1, c2 = tf.train.batch([a,b], num_threads=batch_size, batch_size=batch_size, capacity=iters)\nconfig = tf.ConfigProto()\nconfig.operation_timeout_in_ms=5000 # terminate on long hangs\n#import pdb; pdb.set_trace()\nsess = tf.InteractiveSession(config=config)\nsess.run([tf.initialize_all_variables()])\n\ncoord = tf.train.Coordinator()\nthreads = tf.train.start_queue_runners(sess, coord)\n\n\ntime.sleep(1)\ncoord.request_stop()\ncoord.join(threads)\n#print(\"Queue runners: \")\n#for qr in tf.get_default_graph().get_collection(tf.GraphKeys.QUEUE_RUNNERS):\n# print(\"name: %s\" %(qr.name))\n# print(\"queue_name: %s\" %(qr.queue.name))\n# print(\"number of enqueue ops: %d\"%(len(qr.enqueue_ops),))\n\nresults = []\nfor i in range(iters):\n d1,list1,list2 = sess.run([tf.reduce_all(tf.equal(c1, c2)), c1, c2])\n if not d1:\n print(list1)\n print(list2)\n results.append(d1)\nprint(\"mismatches: %d/%d\"%(iters-sum(results), iters))\n\n\ncoord.request_stop()\n" }, { "path": "queues_talk/queues.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow queues and input pipelines\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Initialization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"import threading\\n\",\n \"import time\\n\",\n \"\\n\",\n \"import os\\n\",\n \"from cStringIO import StringIO\\n\",\n \"import numpy as np\\n\",\n \"from functools import partial\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \" \\n\",\n \"def rename_nodes(graph_def, rename_func):\\n\",\n \" res_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = res_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" n.name = rename_func(n.name)\\n\",\n \" for i, s in enumerate(n.input):\\n\",\n \" n.input[i] = rename_func(s) if s[0]!='^' else '^'+rename_func(s[1:])\\n\",\n \" return res_def\\n\",\n \"def show_graph(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\\n\",\n \" \\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\\n\",\n \" \\n\",\n \"def create_session():\\n\",\n \" sess = tf.InteractiveSession(config=tf.ConfigProto(operation_timeout_in_ms=3000))\\n\",\n \" return sess\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"a = tf.constant(1)\\n\",\n \"b = tf.constant(2)\\n\",\n \"c = a+b\\n\",\n \"d = c*a\\n\",\n \"e = d-c\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(tf.get_default_graph().as_graph_def())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple queue\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Queue size is now: 1\\n\",\n \"Queue size is now: 2\\n\",\n \"Queue size is now: 3\\n\",\n \"Queue size is now: 4\\n\",\n \"Queue size is now: 5\\n\",\n \"Queue size is now: 6\\n\",\n \"Queue size is now: 7\\n\",\n \"Queue size is now: 8\\n\",\n \"Queue size is now: 9\\n\",\n \"Queue size is now: 10\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"q = tf.FIFOQueue(capacity=20, dtypes=[tf.int32])\\n\",\n \"enqueue_placeholder = tf.placeholder(tf.int32)\\n\",\n \"enqueue_op = q.enqueue(enqueue_placeholder)\\n\",\n \"sess = create_session()\\n\",\n \"for i in range(10):\\n\",\n \" sess.run(enqueue_op, feed_dict={enqueue_placeholder:i})\\n\",\n \" print \\\"Queue size is now: \\\"+str(sess.run(q.size()))\\n\",\n \"sess.run(q.close())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"Done\\n\"\n ]\n }\n ],\n \"source\": [\n \"try:\\n\",\n \" for i in range(20):\\n\",\n \" print sess.run(q.dequeue())\\n\",\n \"except tf.errors.OutOfRangeError:\\n\",\n \" print \\\"Done\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple queue with multiple enqueues in parallel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"4\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"random_number = tf.random_uniform(shape=())\\n\",\n \"q = tf.FIFOQueue(capacity=20, dtypes=[tf.float32])\\n\",\n \"enqueue_op = q.enqueue(random_number)\\n\",\n \"\\n\",\n \"sess = create_session()\\n\",\n \"print sess.run(q.size())\\n\",\n \"def run():\\n\",\n \" for i in range(5):\\n\",\n \" sess.run(enqueue_op)\\n\",\n \"\\n\",\n \"threads = [threading.Thread(target=run, args=()) for i in range(2)]\\n\",\n \"[t.start() for t in threads]\\n\",\n \"print sess.run(q.size())\\n\",\n \"time.sleep(0.5)\\n\",\n \"print sess.run(q.size())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Setting up range_input_producer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"inn = tf.train.range_input_producer(limit=10, num_epochs=1, shuffle=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(tf.get_default_graph().as_graph_def())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Lookup name of size operation in GraphDef\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you don't want to create new Node with \\\"op.size\\\", you can find name of exiting node by looking for with op=\\\"QueueSize\\\" in the graph definition, and fetching it directly. In this case, proper run call will look like this -- sess.run(\\\"input_producer/fraction_of_32_full/fraction_of_32_full_Size:0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"node {\\n\",\n \" name: \\\"input_producer/range/start\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_INT32\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" int_val: 0\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/range/limit\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_INT32\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" int_val: 10\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/range/delta\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_INT32\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" int_val: 1\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/range\\\"\\n\",\n \" op: \\\"Range\\\"\\n\",\n \" input: \\\"input_producer/range/start\\\"\\n\",\n \" input: \\\"input_producer/range/limit\\\"\\n\",\n \" input: \\\"input_producer/range/delta\\\"\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs/Const\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT64\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_INT64\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" int64_val: 0\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" op: \\\"Variable\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"container\\\"\\n\",\n \" value {\\n\",\n \" s: \\\"\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT64\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"shape\\\"\\n\",\n \" value {\\n\",\n \" shape {\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"shared_name\\\"\\n\",\n \" value {\\n\",\n \" s: \\\"\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs/Assign\\\"\\n\",\n \" op: \\\"Assign\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/limit_epochs/Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT64\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"use_locking\\\"\\n\",\n \" value {\\n\",\n \" b: true\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"validate_shape\\\"\\n\",\n \" value {\\n\",\n \" b: true\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs/read\\\"\\n\",\n \" op: \\\"Identity\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT64\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs/CountUpTo\\\"\\n\",\n \" op: \\\"CountUpTo\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT64\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/limit_epochs/epochs\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"limit\\\"\\n\",\n \" value {\\n\",\n \" i: 1\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/limit_epochs\\\"\\n\",\n \" op: \\\"Identity\\\"\\n\",\n \" input: \\\"input_producer/range\\\"\\n\",\n \" input: \\\"^input_producer/fraction_of_32_full/limit_epochs/CountUpTo\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" op: \\\"FIFOQueue\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"capacity\\\"\\n\",\n \" value {\\n\",\n \" i: 32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"component_types\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"container\\\"\\n\",\n \" value {\\n\",\n \" s: \\\"\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"shapes\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" shape {\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"shared_name\\\"\\n\",\n \" value {\\n\",\n \" s: \\\"\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/fraction_of_32_full_EnqueueMany\\\"\\n\",\n \" op: \\\"QueueEnqueueMany\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/limit_epochs\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"Tcomponents\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"timeout_ms\\\"\\n\",\n \" value {\\n\",\n \" i: -1\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/fraction_of_32_full_Close\\\"\\n\",\n \" op: \\\"QueueClose\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"cancel_pending_enqueues\\\"\\n\",\n \" value {\\n\",\n \" b: false\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/fraction_of_32_full_Close_1\\\"\\n\",\n \" op: \\\"QueueClose\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"cancel_pending_enqueues\\\"\\n\",\n \" value {\\n\",\n \" b: true\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/fraction_of_32_full_Size\\\"\\n\",\n \" op: \\\"QueueSize\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"_class\\\"\\n\",\n \" value {\\n\",\n \" list {\\n\",\n \" s: \\\"loc:@input_producer/fraction_of_32_full/fraction_of_32_full\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/Cast\\\"\\n\",\n \" op: \\\"Cast\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/fraction_of_32_full_Size\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"DstT\\\"\\n\",\n \" value {\\n\",\n \" type: DT_FLOAT\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"SrcT\\\"\\n\",\n \" value {\\n\",\n \" type: DT_INT32\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/mul/y\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_FLOAT\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_FLOAT\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" float_val: 0.03125\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/mul\\\"\\n\",\n \" op: \\\"Mul\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/Cast\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/mul/y\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_FLOAT\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/ScalarSummary/tags\\\"\\n\",\n \" op: \\\"Const\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"dtype\\\"\\n\",\n \" value {\\n\",\n \" type: DT_STRING\\n\",\n \" }\\n\",\n \" }\\n\",\n \" attr {\\n\",\n \" key: \\\"value\\\"\\n\",\n \" value {\\n\",\n \" tensor {\\n\",\n \" dtype: DT_STRING\\n\",\n \" tensor_shape {\\n\",\n \" }\\n\",\n \" string_val: \\\"queue/input_producer/fraction_of_32_full/fraction_of_32_full/input_producer/\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"node {\\n\",\n \" name: \\\"input_producer/fraction_of_32_full/ScalarSummary\\\"\\n\",\n \" op: \\\"ScalarSummary\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/ScalarSummary/tags\\\"\\n\",\n \" input: \\\"input_producer/fraction_of_32_full/mul\\\"\\n\",\n \" attr {\\n\",\n \" key: \\\"T\\\"\\n\",\n \" value {\\n\",\n \" type: DT_FLOAT\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"versions {\\n\",\n \" producer: 9\\n\",\n \"}\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.get_default_graph().as_graph_def()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"threads = tf.train.start_queue_runners()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"23\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sess.graph.version\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sess.run(inn.size())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"24\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sess.graph.version\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"input_producer/fraction_of_32_full/fraction_of_32_full_Size:0\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Alternatives to \\\"wait forever\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Set session timeout\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"DeadlineExceededError\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"queue = tf.FIFOQueue(capacity=5, dtypes=[tf.int32])\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.operation_timeout_in_ms=2000\\n\",\n \"sess = tf.InteractiveSession(\\\"\\\", config=config)\\n\",\n \"try:\\n\",\n \" sess.run(queue.dequeue())\\n\",\n \"except tf.errors.DeadlineExceededError:\\n\",\n \" print \\\"DeadlineExceededError\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Close the queue before reading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"OutOfRangeError\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"queue = tf.FIFOQueue(capacity=5, dtypes=[tf.int32])\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.operation_timeout_in_ms=2000\\n\",\n \"sess = tf.InteractiveSession(\\\"\\\", config=config)\\n\",\n \"sess.run(queue.close())\\n\",\n \"try: \\n\",\n \" sess.run(queue.dequeue())\\n\",\n \"except tf.errors.OutOfRangeError as e:\\n\",\n \" print \\\"OutOfRangeError\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Close the queue before writing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AbortedError\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"queue = tf.FIFOQueue(capacity=5, dtypes=[tf.int32])\\n\",\n \"config = tf.ConfigProto()\\n\",\n \"config.operation_timeout_in_ms=2000\\n\",\n \"sess = tf.InteractiveSession(\\\"\\\", config=config)\\n\",\n \"sess.run(queue.close())\\n\",\n \"try:\\n\",\n \" sess.run(queue.enqueue(5))\\n\",\n \"except tf.errors.AbortedError:\\n\",\n \" print \\\"AbortedError\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple batching example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 73,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"ranges = tf.train.range_input_producer(limit=10, num_epochs=1, shuffle=False)\\n\",\n \"number = ranges.dequeue()\\n\",\n \"numeric_batch = tf.train.batch_join([[number]]*3, batch_size=2)\\n\",\n \"batch_number = numeric_batch\\n\",\n \"\\n\",\n \"sess = tf.InteractiveSession(config=tf.ConfigProto(operation_timeout_in_ms=6000))\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"tf.train.start_queue_runners()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[,\\n\",\n \" ]\"\n ]\n },\n \"execution_count\": 74,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Python/TensorFlow gotcha\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 0, 0]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"ranges = tf.train.range_input_producer(limit=10, num_epochs=1, shuffle=False)\\n\",\n \"number = ranges.dequeue()\\n\",\n \"sess = tf.InteractiveSession(config=tf.ConfigProto(operation_timeout_in_ms=6000))\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"tf.train.start_queue_runners()\\n\",\n \"\\n\",\n \"print sess.run([number]*3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solution 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 0, 0]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"ranges = tf.train.range_input_producer(limit=10, num_epochs=1, shuffle=False)\\n\",\n \"number = ranges.dequeue()\\n\",\n \"sess = tf.InteractiveSession(config=tf.ConfigProto(operation_timeout_in_ms=6000))\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"tf.train.start_queue_runners()\\n\",\n \"\\n\",\n \"print sess.run([ranges.dequeue()]*3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solution 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 2, 0]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception AssertionError: AssertionError() in > ignored\\n\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"ranges = tf.train.range_input_producer(limit=10, num_epochs=1, shuffle=False)\\n\",\n \"number = ranges.dequeue()\\n\",\n \"sess = tf.InteractiveSession(config=tf.ConfigProto(operation_timeout_in_ms=6000))\\n\",\n \"sess.run(tf.initialize_all_variables())\\n\",\n \"tf.train.start_queue_runners()\\n\",\n \"\\n\",\n \"print sess.run([ranges.dequeue(), ranges.dequeue(), ranges.dequeue()])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "resnet_8_simple.pbtxt", "content": "node {\n name: \"filenames\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n 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int64_val: 20384\n }\n }\n }\n }\n node_def {\n name: \"ShuffleDataset/seed\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n type: DT_INT64\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_INT64\n tensor_shape {\n }\n int64_val: 0\n }\n }\n }\n }\n node_def {\n name: \"ShuffleDataset/seed2\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n type: DT_INT64\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_INT64\n tensor_shape {\n }\n int64_val: 0\n }\n }\n }\n }\n node_def {\n name: \"ShuffleDataset\"\n op: \"ShuffleDataset\"\n input: \"PrefetchDataset_1:handle:0\"\n input: \"ShuffleDataset/buffer_size_1:output:0\"\n input: \"ShuffleDataset/seed:output:0\"\n input: \"ShuffleDataset/seed2:output:0\"\n attr {\n key: \"output_shapes\"\n value {\n list {\n shape {\n dim {\n size: 32\n }\n dim {\n size: 32\n }\n dim {\n size: 3\n }\n }\n shape {\n dim {\n size: 10\n }\n }\n }\n }\n }\n attr {\n key: \"output_types\"\n value {\n list {\n type: DT_FLOAT\n type: DT_FLOAT\n }\n }\n }\n }\n node_def {\n name: \"ParallelMapDataset_2/num_parallel_calls_2\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n type: DT_INT32\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_INT32\n tensor_shape {\n }\n int_val: 1\n }\n }\n }\n }\n node_def {\n name: \"ParallelMapDataset_2\"\n op: \"ParallelMapDataset\"\n input: \"ShuffleDataset:handle:0\"\n input: \"ParallelMapDataset_2/num_parallel_calls_2:output:0\"\n attr {\n key: \"Targuments\"\n value {\n list {\n }\n }\n }\n attr {\n key: \"f\"\n value {\n func {\n name: \"tf_map_func_c5fa6fa0\"\n }\n }\n }\n attr {\n key: \"output_shapes\"\n value {\n list {\n shape {\n dim {\n size: 32\n }\n dim {\n size: 32\n }\n dim {\n size: 3\n }\n }\n shape {\n dim {\n size: 10\n }\n }\n }\n }\n }\n attr {\n key: \"output_types\"\n value {\n list {\n type: DT_FLOAT\n type: DT_FLOAT\n }\n }\n }\n }\n node_def {\n name: \"PrefetchDataset_2/Const_2\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n type: DT_INT64\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_INT64\n tensor_shape {\n }\n int64_val: 256\n }\n }\n }\n }\n node_def {\n name: \"PrefetchDataset_2\"\n op: \"PrefetchDataset\"\n input: \"ParallelMapDataset_2:handle:0\"\n input: \"PrefetchDataset_2/Const_2:output:0\"\n attr {\n key: \"output_shapes\"\n value {\n list {\n shape {\n dim {\n size: 32\n }\n dim {\n size: 32\n }\n dim {\n size: 3\n }\n }\n shape {\n dim {\n size: 10\n }\n }\n }\n }\n }\n attr {\n key: \"output_types\"\n value {\n list {\n type: DT_FLOAT\n type: DT_FLOAT\n }\n }\n }\n }\n node_def {\n name: \"BatchDataset/batch_size\"\n op: \"Const\"\n attr {\n key: \"dtype\"\n value {\n type: DT_INT64\n }\n }\n attr {\n key: \"value\"\n value {\n tensor {\n dtype: DT_INT64\n tensor_shape {\n }\n int64_val: 128\n }\n }\n }\n }\n node_def {\n name: \"BatchDataset\"\n op: \"BatchDataset\"\n input: \"PrefetchDataset_2:handle:0\"\n input: \"BatchDataset/batch_size:output:0\"\n attr {\n key: \"output_shapes\"\n value {\n list {\n shape {\n dim {\n size: -1\n }\n dim {\n size: 32\n }\n dim {\n size: 32\n }\n dim {\n size: 3\n }\n }\n shape {\n dim {\n size: -1\n }\n dim {\n size: 10\n }\n }\n }\n }\n }\n attr {\n key: \"output_types\"\n value {\n list {\n type: DT_FLOAT\n type: DT_FLOAT\n }\n }\n }\n }\n ret {\n key: \"BatchDataset\"\n value: \"BatchDataset:handle:0\"\n }\n }\n}\nversions {\n producer: 24\n min_consumer: 12\n}\n" }, { "path": "resnet_leak_report.py", "content": "# test whether memory gets cleared on creating new sessions\nimport sys, os, math, random\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n\n\nimport tensorflow as tf\nimport numpy as np\n\nif __name__=='__main__':\n for i in range(10):\n tf.reset_default_graph()\n sess = tf.InteractiveSession()\n\n size = 12000\n example_queue = tf.FIFOQueue(1, dtypes=[tf.float32], shapes=[[size]])\n from tensorflow.python.ops import gen_random_ops\n image = tf.random_uniform([size])\n example_enqueue_op = example_queue.enqueue([image])\n sess.run(example_enqueue_op)\n sess.run(example_queue.close())\n\n images = example_queue.dequeue_many(1)\n images = tf.concat([images]*size, axis=0)\n var = tf.Variable(tf.ones_like(images))\n\n sess.run(tf.global_variables_initializer())\n sess.run(tf.local_variables_initializer())\n def relu(x):\n return tf.where(tf.less(x, 0.0), x, x, name='leaky_relu')\n cost = tf.reduce_sum(relu(images+var))\n\n grads = tf.gradients(cost, var)\n _, memuse = sess.run([grads, tf.contrib.memory_stats.MaxBytesInUse()])\n print(\"Run %d, GBs in use %.1f\"%(i, memuse/10**9))\n\n sess.close()\n del sess\n" }, { "path": "resnet_leak_report2.py", "content": "# test whether memory gets cleared on creating new sessions\nimport sys, os, math, random\n\n\nimport tensorflow as tf\nimport numpy as np\n\nif __name__=='__main__':\n try:\n\n from tensorflow.core.protobuf import rewriter_config_pb2\n rewrite_options = rewriter_config_pb2.RewriterConfig(\n disable_model_pruning=True,\n constant_folding=rewriter_config_pb2.RewriterConfig.OFF,\n memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL)\n optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)\n graph_options=tf.GraphOptions(optimizer_options=optimizer_options,\n rewrite_options=rewrite_options)\n config = tf.ConfigProto(graph_options=graph_options)\n sess = tf.Session(config=config)\n \n size = 12000\n num_runs = 10\n\n images = tf.random_uniform([size, size])\n var = tf.Variable(tf.ones_like(images))\n sess.run(var.initializer)\n for i in range(10):\n def relu(x):\n return tf.where(tf.less(x, 0.0), x, x, name='leaky_relu')\n cost = tf.reduce_sum(relu(images+var))\n\n grads = tf.gradients(cost, var)\n _, memuse, memuse2 = sess.run([grads, tf.contrib.memory_stats.MaxBytesInUse(), tf.contrib.memory_stats.BytesInUse()])\n print(\"Run %d, GBs in use %.2f, %.2f\"%(i, memuse/10**9,memuse2/10**9))\n except:\n pass\n finally:\n [memuse] = sess.run([tf.contrib.memory_stats.MaxBytesInUse()])\n print(\"Memory GBs in use %.2f\"%(memuse/10**9,))\n \n\n# 576000000\n# 2017-09-21 14:53:23.483412: I tensorflow/core/framework/log_memory.cc:35] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 2 kernel_name: \"gradients/leaky_relu_grad/zeros_like\" tensor { dtype: DT_FLOAT shape { dim { size: 144000000 } } allocation_description { requested_bytes: 576000000 allocated_bytes: 576000000 allocator_name: \"GPU_0_bfc\" allocation_id: 6 ptr: 1109438113536 } } }\n" }, { "path": "resource_variable_test.py", "content": "import tensorflow as tf\nfrom tensorflow.python.ops import resource_variable_ops\nimport portpicker\n\nport = portpicker.pick_unused_port()\nhost = \"127.0.0.1\"\njob_name = \"worker\"\ncluster = {job_name: [host+\":\"+str(port)]}\ncluster_spec = tf.train.ClusterSpec(cluster).as_cluster_def()\n\nserver = tf.train.Server(cluster_spec, job_name=job_name)\nsess = tf.Session(server.target)\n\nx = tf.get_variable(\"x\", shape=[], dtype=tf.float32,\n initializer=tf.constant_initializer(2), use_resource=True)\nsess.run(tf.global_variables_initializer())\nprint(sess.run(x))\n" }, { "path": "rotations_comparison.py", "content": "import numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport util as u\nfrom util import t # transpose\nfrom util import c2v\nfrom util import v2c\nfrom util import v2c_np\nfrom util import v2r\nfrom util import kr # kronecker\nfrom util import Kmat # commutation matrix\n\ndtype = np.float64\n\ndef kr(A, B):\n return u.kronecker(A, B, do_shape_inference=False)\n\n\ndef gradient(lr0):\n init_dict[lr_holder] = lr0\n\n # gradient update rule\n train_op = grad_update(Wf - lr * dWf)\n\n return do_run(train_op)\n\ndef newton(lr0):\n init_dict[lr_holder] = lr0\n\n # todo, get rid of B's\n # Create B's\n B = [0]*(n+1)\n B[n] = -err/dsize\n Bn = [0]*(n+1) # Newton-modified backprop\n Bn[n] = u.Identity(f(n))\n for i in range(n-1, -1, -1):\n B[i] = t(W[i+1]) @ B[i+1]\n Bn[i] = t(W[i+1]) @ Bn[i+1]\n \n # Create U's\n U = [list(range(n+1)) for _ in range(n+1)]\n for bottom in range(n+1):\n for top in range(n+1):\n if bottom > top:\n prod = u.Identity(f(top))\n else:\n prod = u.Identity(f(bottom-1))\n for i in range(bottom, top+1):\n prod = prod@t(W[i])\n U[bottom][top] = prod\n\n # Block i, j gives hessian block between layer i and layer j\n blocks = [list(range(n+1)) for _ in range(n+1)]\n for i in range(1, n+1):\n for j in range(1, n+1):\n term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;\n if i == j:\n term2 = tf.zeros((f(i)*f(i-1), f(i)*f(i-1)), dtype=dtype)\n elif i < j:\n term2 = kr(A[i] @ t(B[j]), U[i+1][j-1])\n else:\n term2 = kr(t(U[j+1][i-1]), B[i] @ t(A[j]))\n \n blocks[i][j]=term1 + term2 @ Kmat(f(j), f(j-1))\n\n \n # remove leftmost blocks (those are with respect to W[0] which is input)\n del blocks[0]\n for row in blocks:\n del row[0]\n \n hess = u.concat_blocks(blocks)\n ihess = u.pseudo_inverse(hess)\n train_op = grad_update(Wf - lr * ihess @ dWf)\n return do_run(train_op)\n\n\ndef newton_bd(lr0):\n init_dict[lr_holder] = lr0\n\n # Create B's\n B = [0]*(n+1)\n B[n] = -err/dsize\n Bn = [0]*(n+1) # Newton-modified backprop\n Bn[n] = u.Identity(f(n))\n for i in range(n-1, -1, -1):\n B[i] = t(W[i+1]) @ B[i+1]\n Bn[i] = t(W[i+1]) @ Bn[i+1]\n\n # Create U's\n U = [list(range(n+1)) for _ in range(n+1)]\n for bottom in range(n+1):\n for top in range(n+1):\n if bottom > top:\n prod = u.Identity(f(top))\n else:\n prod = u.Identity(f(bottom-1))\n for i in range(bottom, top+1):\n prod = prod@t(W[i])\n U[bottom][top] = prod\n\n # Block i, j gives hessian block between layer i and layer j\n blocks = [list(range(n+1)) for _ in range(n+1)]\n for i in range(1, n+1):\n for j in range(1, n+1):\n term1 = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;\n if i == j:\n term2 = tf.zeros((f(i)*f(i-1), f(i)*f(i-1)), dtype=dtype)\n elif i < j:\n term2 = kr(A[i] @ t(B[j]), U[i+1][j-1])\n else:\n term2 = kr(t(U[j+1][i-1]), B[i] @ t(A[j]))\n \n blocks[i][j]=term1 + term2 @ Kmat(f(j), f(j-1))\n\n \n # remove leftmost blocks (those are with respect to W[0] which is input)\n del blocks[0]\n for row in blocks:\n del row[0]\n\n # todo -- figure out why this is not the same as block inversion\n # grads = tf.concat([u.khatri_rao(A[i], Bn[i]) for i in range(1, n+1)], axis=0)\n # hess = grads @ tf.transpose(grads) / dsize\n # blocks = u.partition_matrix_evenly(hess, 10)\n ihess = u.concat_blocks(u.block_diagonal_inverse(blocks))\n train_op = grad_update(Wf - lr * ihess @ dWf)\n return do_run(train_op)\n\n\ndef newton_kfac(lr0):\n init_dict[lr_holder] = lr0\n\n # Create B's\n B = [0]*(n+1)\n B[n] = -err/dsize\n Bn = [0]*(n+1) # Newton-modified backprop\n Bn[n] = u.Identity(f(n))\n for i in range(n-1, -1, -1):\n B[i] = t(W[i+1]) @ B[i+1]\n Bn[i] = t(W[i+1]) @ Bn[i+1]\n \n # inverse Hessian blocks\n iblocks = u.empty_grid(n+1, n+1)\n for i in range(1, n+1):\n for j in range(1, n+1):\n # reuse Hess tensor calculation in order to get off-diag block sizes\n dummy_term = kr(A[i] @ t(A[j]), Bn[i] @ t(Bn[j])) / dsize;\n if i == j:\n acov = A[i] @ t(A[j])\n bcov = (Bn[i] @ t(Bn[j]))/dsize\n term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))\n else:\n term = tf.zeros(shape=dummy_term.get_shape(), dtype=dtype)\n iblocks[i][j]=term\n \n # remove leftmost blocks (those are with respect to W[0] which is input)\n del iblocks[0]\n for row in iblocks:\n del row[0]\n \n ihess = u.concat_blocks(iblocks)\n \n train_op = grad_update(Wf - lr * ihess @ dWf)\n return do_run(train_op)\n\n\ndef natural_empirical(lr0):\n init_dict[lr_holder] = lr0\n\n grads = tf.concat([u.khatri_rao(A[i], B[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / dsize\n ifisher = u.pseudo_inverse(fisher)\n \n train_op = grad_update(Wf - lr * ifisher @ dWf)\n return do_run(train_op)\n\ndef natural_sampled(lr0, num_samples=1):\n def kr(A, B):\n return u.kronecker(A, B, do_shape_inference=False)\n init_dict[lr_holder] = lr0\n np.random.seed(0)\n tf.set_random_seed(0)\n\n A = [0]*(n+2)\n A2 = [0]*(n+2) # augmented forward props for natural gradient\n A[0] = u.Identity(dsize)\n A2[0] = u.Identity(dsize*num_samples)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n if i == 0:\n A2[i+1] = tf.concat([W[0]]*num_samples, axis=1)\n else:\n A2[i+1] = tf.matmul(W[i], A2[i], name=\"A2\"+str(i+1))\n\n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B2 = [0]*(n+1)\n B[n] = -err/dsize\n B2[n] = tf.random_normal((f(n), dsize*num_samples), 0, 1, dtype=dtype)\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n B2[i] = tf.matmul(tf.transpose(W[i+1]), B2[i+1], name=\"B2\"+str(i))\n\n grads = tf.concat([u.khatri_rao(A2[i], B2[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / (dsize*num_samples)\n ifisher = u.pseudo_inverse(fisher)\n train_op = grad_update(Wf - lr * ifisher @ dWf)\n return do_run(train_op)\n\n\ndef natural_bd(lr0, num_samples=1):\n init_dict[lr_holder] = lr0\n np.random.seed(0)\n tf.set_random_seed(0)\n\n A = [0]*(n+2)\n A2 = [0]*(n+2) # augmented forward props for natural gradient\n A[0] = u.Identity(dsize)\n A2[0] = u.Identity(dsize*num_samples)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n if i == 0:\n A2[i+1] = tf.concat([W[0]]*num_samples, axis=1)\n else:\n A2[i+1] = tf.matmul(W[i], A2[i], name=\"A2\"+str(i+1))\n\n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B2 = [0]*(n+1)\n B[n] = -err/dsize\n B2[n] = tf.random_normal((f(n), dsize*num_samples), 0, 1, seed=0,\n dtype=dtype)\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n B2[i] = tf.matmul(tf.transpose(W[i+1]), B2[i+1], name=\"B2\"+str(i))\n\n grads = tf.concat([u.khatri_rao(A2[i], B2[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / (dsize*num_samples)\n blocks = u.partition_matrix_evenly(fisher, 10)\n # ifisher = u.pseudo_inverse(fisher)\n ifisher = u.concat_blocks(u.block_diagonal_inverse(blocks))\n train_op = grad_update(Wf - lr * ifisher @ dWf)\n return do_run(train_op)\n\ndef natural_bd_sqrt(lr0, num_samples=1):\n init_dict[lr_holder] = lr0\n np.random.seed(0)\n tf.set_random_seed(0)\n\n A = [0]*(n+2)\n A2 = [0]*(n+2) # augmented forward props for natural gradient\n A[0] = u.Identity(dsize)\n A2[0] = u.Identity(dsize*num_samples)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n if i == 0:\n A2[i+1] = tf.concat([W[0]]*num_samples, axis=1)\n else:\n A2[i+1] = tf.matmul(W[i], A2[i], name=\"A2\"+str(i+1))\n\n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B2 = [0]*(n+1)\n B[n] = -err/dsize\n B2[n] = tf.random_normal((f(n), dsize*num_samples), 0, 1, seed=0,\n dtype=dtype)\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n B2[i] = tf.matmul(tf.transpose(W[i+1]), B2[i+1], name=\"B2\"+str(i))\n\n grads = tf.concat([u.khatri_rao(A2[i], B2[i]) for i in range(1, n+1)], axis=0)\n fisher = grads @ tf.transpose(grads) / (dsize*num_samples)\n blocks = u.partition_matrix_evenly(fisher, 10)\n # ifisher = u.pseudo_inverse(fisher)\n ifisher = u.concat_blocks(u.block_diagonal_inverse_sqrt(blocks))\n train_op = grad_update(Wf - lr * ifisher @ dWf)\n return do_run(train_op)\n\n\ndef natural_kfac(lr0, num_samples=1):\n init_dict[lr_holder] = lr0\n np.random.seed(0)\n tf.set_random_seed(0)\n\n A = [0]*(n+2)\n A2 = [0]*(n+2) # augmented forward props for natural gradient\n A[0] = u.Identity(dsize)\n A2[0] = u.Identity(dsize*num_samples)\n for i in range(n+1):\n # fs is off by 2 from common notation, ie W[0] has shape f[0],f[-1]\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n if i == 0:\n A2[i+1] = tf.concat([W[0]]*num_samples, axis=1)\n else:\n A2[i+1] = tf.matmul(W[i], A2[i], name=\"A2\"+str(i+1))\n\n # create backprop matrices\n # B[i] has backprop for matrix i\n B = [0]*(n+1)\n B2 = [0]*(n+1)\n B[n] = -err/dsize\n B2[n] = tf.random_normal((f(n), dsize*num_samples), 0, 1, seed=0,\n dtype=dtype)\n for i in range(n-1, -1, -1):\n B[i] = tf.matmul(tf.transpose(W[i+1]), B[i+1], name=\"B\"+str(i))\n B2[i] = tf.matmul(tf.transpose(W[i+1]), B2[i+1], name=\"B2\"+str(i))\n\n # Kronecker factored covariance blocks\n iblocks = u.empty_grid(n+1, n+1)\n for i in range(1, n+1):\n for j in range(1, n+1):\n if i == j:\n acov = A2[i] @ t(A2[j]) / (dsize*num_samples)\n bcov = B2[i] @ t(B2[j]) / (dsize*num_samples);\n term = kr(u.pseudo_inverse(acov), u.pseudo_inverse(bcov))\n else:\n term = tf.zeros(shape=(f(i)*f(i-1), f(j)*f(j-1)), dtype=dtype)\n iblocks[i][j]=term\n \n # remove leftmost blocks (those are with respect to W[0] which is input)\n del iblocks[0]\n for row in iblocks:\n del row[0]\n\n ifisher = u.concat_blocks(iblocks)\n train_op = grad_update(Wf - lr * ifisher @ dWf)\n return do_run(train_op)\n\n\ndo_run_iters = 100\ndef do_run(train_op):\n sess = setup_session()\n observed_losses = []\n u.reset_time()\n for i in range(do_run_iters):\n loss0 = sess.run(loss)\n print(loss0)\n observed_losses.append(loss0)\n sess.run(train_op)\n u.record_time()\n u.summarize_time()\n return observed_losses\n\n \ndef setup_session():\n sess = tf.Session()\n sess.run(tf.global_variables_initializer(), feed_dict=init_dict)\n return sess\n\ndef grad_update(new_val):\n copy_op = Wf_copy.assign(new_val)\n with tf.control_dependencies([copy_op]):\n train_op = Wf.assign(Wf_copy)\n return train_op\n\n\nif __name__ == '__main__':\n # Compare a set of algorithms on rotations problem\n\n X0 = np.genfromtxt('data/large_rotations2_X0.csv',\n delimiter= \",\")\n Y0 = np.genfromtxt('data/large_rotations2_Y0.csv',\n delimiter= \",\")\n W0f = v2c_np(np.genfromtxt('data/large_rotations2_W0f.csv',\n delimiter= \",\"))\n fs = np.genfromtxt('data/large_rotations2_fs.csv',\n delimiter= \",\").astype(np.int32)\n n = len(fs)-2 # number of layers\n\n def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]\n\n dsize = X0.shape[1]\n assert f(-1) == dsize\n\n # load W0f and do shape checks (can remove)\n W0s = u.unflatten_np(W0f, fs[1:]) # Wf doesn't have first layer (data matrix)\n W0s.insert(0, X0)\n Wf_holder = tf.placeholder(dtype, shape=W0f.shape)\n Wf = tf.Variable(Wf_holder, name=\"Wf\")\n Wf_copy = tf.Variable(Wf_holder, name=\"Wf_copy\")\n init_dict = {Wf_holder: W0f}\n\n # Create W's\n W = u.unflatten(Wf, fs[1:])\n X = tf.constant(X0)\n Y = tf.constant(Y0)\n W.insert(0, X)\n for (numpy_W, tf_W) in zip(W0s, W):\n u.check_equal(numpy_W.shape, u.fix_shape(tf_W.shape))\n\n # Create A's\n # A[1] == X\n A = [0]*(n+2)\n A[0] = u.Identity(dsize)\n for i in range(n+1):\n A[i+1] = tf.matmul(W[i], A[i], name=\"A\"+str(i+1))\n\n assert W[0].get_shape() == X0.shape\n assert A[n+1].get_shape() == X0.shape\n assert A[1].get_shape() == X0.shape\n\n err = Y - A[n+1]\n loss = tf.reduce_sum(tf.square(err))/(2*dsize)\n\n # Create B's\n B = [0]*(n+1)\n B[n] = -err/dsize\n for i in range(n-1, -1, -1):\n B[i] = t(W[i+1]) @ B[i+1]\n\n # create dW's\n dW = [0]*(n+1)\n for i in range(n+1):\n dW[i] = tf.matmul(B[i], tf.transpose(A[i]), name=\"dW\"+str(i))\n del dW[0] # get rid of W[0] update\n\n dWf = tf.concat([u.vec(dWi) for dWi in dW], axis=0)\n lr_holder = tf.placeholder(dtype=dtype, shape=())\n lr = tf.Variable(lr_holder, dtype=dtype)\n\n # run tests\n do_run_iters = 5\n result = newton(1.0)\n expected_result = [8.9023744225439743e-05, 0.060120791316053412, 0.0059295249954177918, 1.9856240803246437e-05, 2.7125563957575423e-10]\n u.check_equal(result, expected_result)\n \n # 720 ms per step\n # result = newton(1.0)\n # np.savetxt(\"data/newton.csv\", result, delimiter=',')\n # sys.exit()\n # natural_empirical(0.000000002)\n\n # 620 ms per step\n # result = natural_sampled(lr0=0.1, num_samples=5)\n # np.savetxt(\"data/natural_sampled.csv\", result, delimiter=',')\n # sys.exit()\n\n # 620 per step\n # result = natural_sampled(lr0=0.1, num_samples=1)\n # np.savetxt(\"data/natural_sampled1.csv\", result, delimiter=',')\n # sys.exit()\n\n # runs = []\n # runs.append(gradient(0.01)) # 1.84 ms\n # runs.append(natural_bd(lr0=0.01, num_samples=5)) # 13.92 ms\n # runs.append(natural_kfac(lr0=0.01, num_samples=5)) # 7.96 ms\n # # # runs.append(natural_kfac(lr0=0.01, num_samples=1)) # 7.70 ms # diverges\n # runs.append(newton_bd(0.1)) # 17.18 ms\n # runs.append(newton_kfac(0.1)) # 7.69 ms\n # np.savetxt(\"data/rotations_comparison_fast.csv\", runs, delimiter=',')\n\n \n # runs = []\n # runs.append(natural_bd(lr0=0.01, num_samples=5))\n # runs.append(natural_kfac(lr0=0.01, num_samples=5))\n # np.savetxt(\"data/rotations_comparison_fast.csv\", runs,\n # fmt=\"%.20f\", delimiter=',')\n\n # runs = []\n # runs.append(natural_bd(lr0=0.005*2, num_samples=5)) # 13.92 ms\n # runs.append(natural_bd(lr0=0.005*2, num_samples=50)) # 42 ms\n # runs.append(natural_kfac(lr0=0.005*2, num_samples=5)) # 7.70 ms # diverges\n # runs.append(natural_kfac(lr0=0.005*2, num_samples=50)) # 9 ms \n # np.savetxt(\"data/sampled_comparison.csv\", runs, delimiter=',')\n \n # newton_bd(0.001)\n # np.savetxt(\"data/rotations_comparison_bd.csv\", runs, delimiter=',')\n\n # try with badly conditioned data\n # runs = []\n # runs.append(gradient(0.01)) # 1.84 ms\n # runs.append(natural_bd(lr0=0.01, num_samples=5)) # 13.92 ms\n # runs.append(natural_kfac(lr0=0.01, num_samples=5)) # 7.96 ms\n # # # runs.append(natural_kfac(lr0=0.01, num_samples=1)) # 7.70 ms # diverges\n # runs.append(newton_bd(0.1)) # 17.18 ms\n # runs.append(newton_kfac(0.1)) # 7.69 ms\n # np.savetxt(\"data/rotations_comparison_fast_bad.csv\", runs, delimiter=',')\n\n\n # result = natural_bd_sqrt(lr0=0.05, num_samples=5)\n # np.savetxt(\"data/natural_bd_sqrt.csv\", result, delimiter=',')\n \n" }, { "path": "saving memory by using functions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Init\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]=\\\"\\\"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import re, os, sys\\n\",\n \"tf.__version__\\n\",\n \"\\n\",\n \"def create_session():\\n\",\n \" config = tf.ConfigProto(log_device_placement=True, graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" return tf.InteractiveSession(config=config)\\n\",\n \"\\n\",\n \"# Setup utilities to parse __LOG_MEMORY__ statements in command-line\\n\",\n \"#tensor_allocation_regex = re.compile(r\\\"allocated_bytes: (?P\\\\d+).*.*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\")\\n\",\n \"# allocated_bytes: 2400000 allocator_name: \\\"cpu\\\"\\n\",\n \"tensor_allocation_regex = re.compile(\\\"\\\"\\\"MemoryLogTensorAllocation.*?step_id: (?P[-0123456789]+).*kernel_name: \\\\\\\"(?P[^\\\"]+)\\\\\\\".*?allocated_bytes: (?P\\\\d+).*allocator_name: \\\\\\\"(?P[^\\\"]+)\\\\\\\".*.*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\"\\\"\\\")\\n\",\n \"raw_allocation_regex = re.compile(\\\"\\\"\\\"MemoryLogRawAllocation.*?step_id: (?P[-0123456789]+).*operation: \\\\\\\"(?P[^\\\"]+)\\\\\\\".*?num_bytes: (?P\\\\d+).*allocation_id: (?P\\\\d+).*allocator_name: \\\"(?P[^\\\"]+)\\\".*timestamp (?P\\\\d+)\\\"\\\"\\\")\\n\",\n \"tensor_output_regex = re.compile(\\\"\\\"\\\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\\\\\"(?P[^\\\"]+).*allocated_bytes: (?P\\\\d+).*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\"\\\"\\\") \\n\",\n \"tensor_output_regex_no_bytes = re.compile(\\\"\\\"\\\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\\\\\"(?P[^\\\"]+).*timestamp (?P\\\\d+)\\\"\\\"\\\") \\n\",\n \"tensor_deallocation_regex = re.compile(\\\"\\\"\\\"allocation_id: (?P\\\\d+).*allocator_name: \\\\\\\"(?P[^\\\"]+)\\\\\\\".*timestamp (?P\\\\d+)\\\"\\\"\\\")\\n\",\n \"raw_deallocation_regex = re.compile(\\\"\\\"\\\"allocation_id: (?P\\\\d+).*allocator_name: \\\\\\\"(?P[^\\\"]+)\\\\\\\".*timestamp (?P\\\\d+)\\\"\\\"\\\")\\n\",\n \"\\n\",\n \"#\\\"MemoryLogStep { step_id: 2 handle: \\\"->/group_deps/0/;1\\\" } timestamp 5673393661785237'\\\"\\n\",\n \"tensor_logstep_regex = re.compile(\\\"\\\"\\\"MemoryLogStep.*?step_id: (?P[-0123456789]+).*?.*timestamp (?P\\\\d+)\\\"\\\"\\\")\\n\",\n \"\\n\",\n \"def _parse_logline(l):\\n\",\n \" # There are 4 kinds of lines with __LOG_MEMORY__ tag\\n\",\n \" # MemoryLogTensorDeallocation, MemoryLogTensorAllocation, MemoryLogStep,\\n\",\n \" #\\n\",\n \" # Example lines for debugging:\\n\",\n \" # MemoryLogTensorDeallocation\\n\",\n \" # 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorDeallocation { allocation_id: 2 allocator_name: \\\"cpu\\\" } timestamp 5143420588459765\\n\",\n \" \\n\",\n \" # MemoryLogTensorAllocation\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 has_single_reference: true ptr: 29496256 } } } timestamp 5143420588526034\\n\",\n \"\\n\",\n \" # MemoryLogStep\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogStep { step_id: 1 handle: \\\"->Print:0//0/;0\\\" } timestamp 5143420588718348\\n\",\n \"\\n\",\n \" # MemoryLogTensorOutput\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 1 kernel_name: \\\"Const\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 ptr: 29496256 } } } timestamp 5143420588932740\\n\",\n \" #\\n\",\n \" # MemoryLogRawAllocation\\n\",\n \" # I 6795349363.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogRawAllocation { step_id: -3 operation: \\\"TF_AllocateTensor\\\" num_bytes: 1000000 ptr: 80910752 allocation_id: 99 allocator_name: \\\"cpu\\\" } timestamp 6795349363944733\\n\",\n \" #\\n\",\n \" # MemoryLogRawDeallcation\\n\",\n \" # I 6796000229.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogRawDeallocation { step_id: -3 operation: \\\"TensorFlow C Api\\\" allocation_id: 177 allocator_name: \\\"cpu\\\" } timestamp 6796000229369431\\n\",\n \" \\n\",\n \" # some weird initialization step \\n\",\n \" # if \\\"step_id: -6\\\" in l:\\n\",\n \" # return {}\\n\",\n \" \\n\",\n \" if 'MemoryLogTensorOutput' in l:\\n\",\n \" m = tensor_output_regex.search(l)\\n\",\n \" # some Shape lines are missing bytes info, ie # I 5162643141.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"gradients/Shape\\\" tensor { dtype: DT_INT32 shape { dim { } } } } timestamp 5162643141310124\\n\",\n \" if not m:\\n\",\n \" m = tensor_output_regex_no_bytes.search(l)\\n\",\n \"\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogTensorOutput\\\"\\n\",\n \" \\n\",\n \" elif 'MemoryLogTensorAllocation' in l:\\n\",\n \" m = tensor_allocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogTensorAllocation\\\"\\n\",\n \" elif 'MemoryLogTensorDeallocation' in l:\\n\",\n \" m = tensor_deallocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogTensorDeallocation\\\"\\n\",\n \" elif 'MemoryLogStep' in l:\\n\",\n \" m = tensor_logstep_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogStep\\\"\\n\",\n \" elif 'MemoryLogRawAllocation' in l:\\n\",\n \" m = raw_allocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogRawAllocation\\\"\\n\",\n \" elif 'MemoryLogRawDeallocation' in l:\\n\",\n \" m = raw_deallocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = m.groupdict()\\n\",\n \" d[\\\"type\\\"] = \\\"MemoryLogRawDeallocation\\\"\\n\",\n \" else:\\n\",\n \" assert False, \\\"Unknown log line: \\\"+l\\n\",\n \" \\n\",\n \" if not \\\"allocation_id\\\" in d:\\n\",\n \" d[\\\"allocation_id\\\"] = \\\"-1\\\"\\n\",\n \"\\n\",\n \" d[\\\"line\\\"] = l\\n\",\n \" d[\\\"timestamp\\\"] = int(d[\\\"timestamp\\\"])\\n\",\n \" return d\\n\",\n \"\\n\",\n \"def memory_timeline(output):\\n\",\n \" def get_alloc_names(allocation_id):\\n\",\n \" for entry in allocation_map[allocation_id]:\\n\",\n \" kernel_name = entry.get(\\\"kernel_name\\\", \\\"unknown\\\")\\n\",\n \" if not \\\"unknown\\\" in kernel_name.lower():\\n\",\n \" return kernel_name+\\\"(\\\"+allocation_id+\\\")\\\"\\n\",\n \" return \\\"(\\\"+allocation_id+\\\")\\\"\\n\",\n \"\\n\",\n \" def get_alloc_bytes(allocation_id):\\n\",\n \" for entry in allocation_map[allocation_id]:\\n\",\n \" if \\\"allocated_bytes\\\" in entry:\\n\",\n \" return entry[\\\"allocated_bytes\\\"]\\n\",\n \" return \\\"0\\\"\\n\",\n \"\\n\",\n \" def get_alloc_type(allocation_id):\\n\",\n \" for entry in allocation_map[allocation_id]:\\n\",\n \" if \\\"allocator_name\\\" in entry:\\n\",\n \" return entry[\\\"allocator_name\\\"]\\n\",\n \" return \\\"0\\\"\\n\",\n \"\\n\",\n \" parsed_lines = [_parse_logline(l) for l in output.split(\\\"\\\\n\\\") if '__LOG_MEMORY__' in l]\\n\",\n \" parsed_lines.sort(key=lambda l: l[\\\"timestamp\\\"])\\n\",\n \" first_timestamp = parsed_lines[0][\\\"timestamp\\\"]\\n\",\n \"\\n\",\n \" allocation_map = {} # map of allocation_id->parsed_logline of allocation\\n\",\n \" for line in parsed_lines:\\n\",\n \" if (line[\\\"type\\\"] == \\\"MemoryLogTensorAllocation\\\" or line[\\\"type\\\"] == \\\"MemoryLogRawAllocation\\\" or\\n\",\n \" line[\\\"type\\\"] == \\\"MemoryLogTensorOutput\\\"):\\n\",\n \" allocation_map.setdefault(line[\\\"allocation_id\\\"], []).append(line)\\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for line in parsed_lines:\\n\",\n \" assert \\\"allocation_id\\\" in line\\n\",\n \" alloc_names = get_alloc_names(line[\\\"allocation_id\\\"])\\n\",\n \" alloc_bytes = get_alloc_bytes(line[\\\"allocation_id\\\"])\\n\",\n \" alloc_type = get_alloc_type(line[\\\"allocation_id\\\"])\\n\",\n \" if line[\\\"type\\\"] == \\\"MemoryLogTensorOutput\\\":\\n\",\n \" continue\\n\",\n \" if line[\\\"type\\\"] == \\\"MemoryLogTensorDeallocation\\\" or line[\\\"type\\\"]==\\\"MemoryLogRawDeallocation\\\":\\n\",\n \" alloc_bytes = \\\"-\\\" + alloc_bytes\\n\",\n \" result.append((line[\\\"timestamp\\\"]-first_timestamp, alloc_names, alloc_bytes, alloc_type))\\n\",\n \" return result\\n\",\n \"\\n\",\n \"\\n\",\n \"def print_memory_timeline(stderr, gpu_only=False):\\n\",\n \" total_memory = 0\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes, allocator_type = record\\n\",\n \" allocated_bytes = int(allocated_bytes)\\n\",\n \" if gpu_only:\\n\",\n \" if not allocator_type.startswith(\\\"gpu\\\"):\\n\",\n \" continue\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %40s %11d %11d %s\\\"%(timestamp/1000, kernel_name, allocated_bytes, total_memory, allocator_type))\\n\",\n \"\\n\",\n \"def plot_memory_timeline(stderr, gpu_only=False):\\n\",\n \" total_memory = 0\\n\",\n \" timestamps = []\\n\",\n \" data = []\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes, allocator_type = record\\n\",\n \" allocated_bytes = int(allocated_bytes)\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" if gpu_only:\\n\",\n \" if not record[3].startswith(\\\"gpu\\\"):\\n\",\n \" continue\\n\",\n \" timestamps.append(record[0]/1000-1)\\n\",\n \" data.append(total_memory)\\n\",\n \" total_memory += int(record[2])\\n\",\n \" timestamps.append(record[0]/1000)\\n\",\n \" data.append(total_memory)\\n\",\n \" plt.plot(timestamps, data)\\n\",\n \" \\n\",\n \" \\n\",\n \"run_metadata = tf.RunMetadata()\\n\",\n \"def summarize(run_metadata=run_metadata):\\n\",\n \" ss = tf.contrib.stat_summarizer.NewStatSummarizer(tf.get_default_graph().as_graph_def().SerializeToString())\\n\",\n \" ss.ProcessStepStatsStr(run_metadata.step_stats.SerializeToString())\\n\",\n \" print(ss.GetOutputString())\\n\",\n \" \\n\",\n \"def printops(ops):\\n\",\n \" print([op.name for op in ops])\\n\",\n \" \\n\",\n \"run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\\n\",\n \"def sessrun(*args, **kwargs):\\n\",\n \" sess = create_session()\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" kwargs[\\\"run_metadata\\\"] = run_metadata\\n\",\n \" kwargs[\\\"options\\\"] = run_options\\n\",\n \" return sess.run(*args, **kwargs)\\n\",\n \"\\n\",\n \"import tensorflow.contrib.graph_editor as ge\\n\",\n \"def run_after(a_tensor, b_tensor):\\n\",\n \" \\\"\\\"\\\"Force a to run after b\\\"\\\"\\\"\\n\",\n \" ge.reroute.add_control_inputs(a_tensor.op, [b_tensor.op])\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"STDOUT = 1\\n\",\n \"STDERR = 2\\n\",\n \"\\n\",\n \"# this hangs when pipe buffer is full\\n\",\n \"\\n\",\n \"class FDRedirector(object):\\n\",\n \" \\\"\\\"\\\" Class to redirect output (stdout or stderr) at the OS level using\\n\",\n \" file descriptors.\\n\",\n \" \\\"\\\"\\\" \\n\",\n \"\\n\",\n \" def __init__(self, fd=STDOUT):\\n\",\n \" \\\"\\\"\\\" fd is the file descriptor of the outpout you want to capture.\\n\",\n \" It can be STDOUT or STERR.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.fd = fd\\n\",\n \" self.started = False\\n\",\n \" self.piper = None\\n\",\n \" self.pipew = None\\n\",\n \"\\n\",\n \" def start(self):\\n\",\n \" \\\"\\\"\\\" Setup the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if not self.started:\\n\",\n \" self.oldhandle = os.dup(self.fd)\\n\",\n \" self.piper, self.pipew = os.pipe()\\n\",\n \" os.dup2(self.pipew, self.fd)\\n\",\n \" os.close(self.pipew)\\n\",\n \"\\n\",\n \" self.started = True\\n\",\n \"\\n\",\n \" def flush(self):\\n\",\n \" \\\"\\\"\\\" Flush the captured output, similar to the flush method of any\\n\",\n \" stream.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.fd == STDOUT:\\n\",\n \" sys.stdout.flush()\\n\",\n \" elif self.fd == STDERR:\\n\",\n \" sys.stderr.flush()\\n\",\n \"\\n\",\n \" def stop(self):\\n\",\n \" \\\"\\\"\\\" Unset the redirection and return the captured output. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.started:\\n\",\n \" self.flush()\\n\",\n \" os.dup2(self.oldhandle, self.fd)\\n\",\n \" os.close(self.oldhandle)\\n\",\n \" f = os.fdopen(self.piper, 'r')\\n\",\n \" output = f.read()\\n\",\n \" f.close()\\n\",\n \"\\n\",\n \" self.started = False\\n\",\n \" return output\\n\",\n \" else:\\n\",\n \" return ''\\n\",\n \"\\n\",\n \" def getvalue(self):\\n\",\n \" \\\"\\\"\\\" Return the output captured since the last getvalue, or the\\n\",\n \" start of the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" output = self.stop()\\n\",\n \" self.start()\\n\",\n \" return output\\n\",\n \"\\n\",\n \"redirect=FDRedirector(STDERR)\\n\",\n \"stderr = \\\"\\\"\\n\",\n \"\\n\",\n \"from contextlib import contextmanager\\n\",\n \"@contextmanager\\n\",\n \"def grab_stderr():\\n\",\n \" global stderr\\n\",\n \" redirect.start();\\n\",\n \" yield\\n\",\n \" stderr = redirect.stop()\\n\",\n \" \\n\",\n \"def open_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def close_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def grab_output(tag, filename=\\\"/tmp/jupyter.txt\\\"):\\n\",\n \" s = open(filename).read()\\n\",\n \" opening_tag = tag+\\\"\\\"\\n\",\n \" ending_tag = tag+\\\"\\\"\\n\",\n \" starting_pos = s.rfind(opening_tag)\\n\",\n \" ending_pos = s.rfind(ending_tag)\\n\",\n \" return s[starting_pos:ending_pos]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 300 tf.tanh calls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"node_mbs = 1\\n\",\n \"length = 300\\n\",\n \"\\n\",\n \"dtype = np.float32\\n\",\n \"n = node_mbs * 250000\\n\",\n \"a0 = tf.placeholder(dtype, name=\\\"a0\\\", shape=(n,))\\n\",\n \"a = a0\\n\",\n \"for i in range(1, length):\\n\",\n \" name = \\\"a\\\"+str(i)\\n\",\n \" a = tf.tanh(a, name=name)\\n\",\n \"\\n\",\n \"grad = tf.gradients([a], [a0])[0]\\n\",\n \"a0_ = np.ones((n,), dtype=dtype)\\n\",\n \"sess = create_session()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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MnzeUBRUVEReek2h7VImrvmGrjiCj+XydixodOk3qef+jM3AD16wLRp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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess.run(grad.op, feed_dict={a0: a0_})\\n\",\n \"\\n\",\n \"open_tag('mem')\\n\",\n \"sess.run(grad.op, feed_dict={a0: a0_})\\n\",\n \"close_tag('mem')\\n\",\n \"logs=grab_output('mem')\\n\",\n \"plot_memory_timeline(logs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.Varial\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 100 tf.tanh(tf.tanh(tf.tanh calls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@function.Defun(tf.float32, func_name=\\\"tanh3\\\")\\n\",\n \"def tanh3(a):\\n\",\n \" return tf.tanh(tf.tanh(tf.tanh(a)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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iWXlC5WEREpnBKNGOy7b1jeeOP44ihHv/41/P73vvzhh347ZAjceGNh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\\n/w8HKaC2k8sc/QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"node_mbs = 1\\n\",\n \"length = 100\\n\",\n \"\\n\",\n \"dtype = np.float32\\n\",\n \"n = node_mbs * 250000\\n\",\n \"b0 = tf.placeholder(dtype, name=\\\"a0\\\", shape=(n,))\\n\",\n \"b = b0\\n\",\n \"for i in range(1, length):\\n\",\n \" name = \\\"b\\\"+str(i)\\n\",\n \" b = tanh3(b, name=name)\\n\",\n \"\\n\",\n \"grad = tf.gradients([b], [b0])[0]\\n\",\n \"b0_ = np.ones((n,), dtype=dtype)\\n\",\n \"sess = create_session()\\n\",\n \"\\n\",\n \"sess.run(grad.op, feed_dict={b0: b0_})\\n\",\n \"open_tag('mem')\\n\",\n \"sess.run(grad.op, feed_dict={b0: b0_})\\n\",\n \"close_tag('mem')\\n\",\n \"logs=grab_output('mem')\\n\",\n \"plot_memory_timeline(logs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1. 0. 0.]\\n\",\n \" [ 0. 1. 0.]\\n\",\n \" [ 0. 0. 1.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = tf.Variable(tf.eye(3))\\n\",\n \"sess.run(a.initializer)\\n\",\n \"print(sess.run(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"66px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "simple_rewiring.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Rewire gradients for less memory\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Utilities and imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'1.1.0'\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# for instructions of how to enable Log memory usage see\\n\",\n \"# https://github.com/yaroslavvb/notebooks/blob/master/mnist-memory.ipynb\\n\",\n \"import os\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]=\\\"\\\"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import re, os, sys\\n\",\n \"tf.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Graph visualizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# make things wide\\n\",\n \"from IPython.core.display import display, HTML\\n\",\n \"display(HTML(\\\"\\\"))\\n\",\n \"\\n\",\n \"from IPython.display import clear_output, Image, display, HTML\\n\",\n \"\\n\",\n \"def strip_consts(graph_def, max_const_size=32):\\n\",\n \" \\\"\\\"\\\"Strip large constant values from graph_def.\\\"\\\"\\\"\\n\",\n \" strip_def = tf.GraphDef()\\n\",\n \" for n0 in graph_def.node:\\n\",\n \" n = strip_def.node.add() \\n\",\n \" n.MergeFrom(n0)\\n\",\n \" if n.op == 'Const':\\n\",\n \" tensor = n.attr['value'].tensor\\n\",\n \" size = len(tensor.tensor_content)\\n\",\n \" if size > max_const_size:\\n\",\n \" tensor.tensor_content = \\\"\\\"%size\\n\",\n \" return strip_def\\n\",\n \"\\n\",\n \"def show_graph(graph_def=None, width=1200, height=800, max_const_size=32, ungroup_gradients=False):\\n\",\n \" if not graph_def:\\n\",\n \" graph_def = tf.get_default_graph().as_graph_def()\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"Visualize TensorFlow graph.\\\"\\\"\\\"\\n\",\n \" if hasattr(graph_def, 'as_graph_def'):\\n\",\n \" graph_def = graph_def.as_graph_def()\\n\",\n \" strip_def = strip_consts(graph_def, max_const_size=max_const_size)\\n\",\n \" data = str(strip_def)\\n\",\n \" if ungroup_gradients:\\n\",\n \" data = data.replace('\\\"gradients/', '\\\"b_')\\n\",\n \" #print(data)\\n\",\n \" code = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"\\\"\\\".format(data=repr(data), id='graph'+str(np.random.rand()))\\n\",\n \"\\n\",\n \" iframe = \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\\"\\\"\\\".format(width, height, code.replace('\\\"', '"'))\\n\",\n \" display(HTML(iframe))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input redirector\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Better solution here: http://stackoverflow.com/questions/41216215/using-pipes-to-capture-things-printed-to-stderr-into-python-variable-from-jupyte\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"STDOUT = 1\\n\",\n \"STDERR = 2\\n\",\n \"\\n\",\n \"# this hangs when pipe buffer is full\\n\",\n \"\\n\",\n \"class FDRedirector(object):\\n\",\n \" \\\"\\\"\\\" Class to redirect output (stdout or stderr) at the OS level using\\n\",\n \" file descriptors.\\n\",\n \" \\\"\\\"\\\" \\n\",\n \"\\n\",\n \" def __init__(self, fd=STDOUT):\\n\",\n \" \\\"\\\"\\\" fd is the file descriptor of the outpout you want to capture.\\n\",\n \" It can be STDOUT or STERR.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.fd = fd\\n\",\n \" self.started = False\\n\",\n \" self.piper = None\\n\",\n \" self.pipew = None\\n\",\n \"\\n\",\n \" def start(self):\\n\",\n \" \\\"\\\"\\\" Setup the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if not self.started:\\n\",\n \" self.oldhandle = os.dup(self.fd)\\n\",\n \" self.piper, self.pipew = os.pipe()\\n\",\n \" os.dup2(self.pipew, self.fd)\\n\",\n \" os.close(self.pipew)\\n\",\n \"\\n\",\n \" self.started = True\\n\",\n \"\\n\",\n \" def flush(self):\\n\",\n \" \\\"\\\"\\\" Flush the captured output, similar to the flush method of any\\n\",\n \" stream.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.fd == STDOUT:\\n\",\n \" sys.stdout.flush()\\n\",\n \" elif self.fd == STDERR:\\n\",\n \" sys.stderr.flush()\\n\",\n \"\\n\",\n \" def stop(self):\\n\",\n \" \\\"\\\"\\\" Unset the redirection and return the captured output. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if self.started:\\n\",\n \" self.flush()\\n\",\n \" os.dup2(self.oldhandle, self.fd)\\n\",\n \" os.close(self.oldhandle)\\n\",\n \" f = os.fdopen(self.piper, 'r')\\n\",\n \" output = f.read()\\n\",\n \" f.close()\\n\",\n \"\\n\",\n \" self.started = False\\n\",\n \" return output\\n\",\n \" else:\\n\",\n \" return ''\\n\",\n \"\\n\",\n \" def getvalue(self):\\n\",\n \" \\\"\\\"\\\" Return the output captured since the last getvalue, or the\\n\",\n \" start of the redirection.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" output = self.stop()\\n\",\n \" self.start()\\n\",\n \" return output\\n\",\n \"\\n\",\n \"redirect=FDRedirector(STDERR)\\n\",\n \"stderr = \\\"\\\"\\n\",\n \"\\n\",\n \"from contextlib import contextmanager\\n\",\n \"@contextmanager\\n\",\n \"def grab_stderr():\\n\",\n \" global stderr\\n\",\n \" redirect.start();\\n\",\n \" yield\\n\",\n \" stderr = redirect.stop()\\n\",\n \" \\n\",\n \"def open_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def close_tag(tag):\\n\",\n \" sess.run(tf.Print(tf.constant(1), [tf.constant(1)], tag+\\\"\\\"))\\n\",\n \" \\n\",\n \"def grab_output(tag, filename=\\\"/tmp/jupyter.txt\\\"):\\n\",\n \" s = open(filename).read()\\n\",\n \" opening_tag = tag+\\\"\\\"\\n\",\n \" ending_tag = tag+\\\"\\\"\\n\",\n \" starting_pos = s.rfind(opening_tag)\\n\",\n \" ending_pos = s.rfind(ending_tag)\\n\",\n \" return s[starting_pos:ending_pos]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Memory timeline utils\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import re\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"# Setup utilities to parse __LOG_MEMORY__ statements in command-line\\n\",\n \"tensor_allocation_regex = re.compile(r\\\"allocated_bytes: (?P\\\\d+).*.*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\")\\n\",\n \"tensor_output_regex = re.compile(\\\"\\\"\\\"MemoryLogTensorOutput.* step_id: (?P[-0123456789]+) kernel_name: \\\\\\\"(?P[^\\\"]+).*allocated_bytes: (?P\\\\d+).*allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\"\\\"\\\") \\n\",\n \"tensor_deallocation_regex = re.compile(r\\\"allocation_id: (?P\\\\d+).*timestamp (?P\\\\d+)\\\")\\n\",\n \"\\n\",\n \"def _parse_logline(l):\\n\",\n \" # There are 4 kinds of lines with __LOG_MEMORY__ tag\\n\",\n \" # MemoryLogTensorDeallocation, MemoryLogTensorAllocation, MemoryLogStep,\\n\",\n \" #\\n\",\n \" # Example lines for debugging:\\n\",\n \" # MemoryLogTensorDeallocation\\n\",\n \" # 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorDeallocation { allocation_id: 2 allocator_name: \\\"cpu\\\" } timestamp 5143420588459765\\n\",\n \"\\n\",\n \" # MemoryLogTensorAllocation\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorAllocation { step_id: -6 kernel_name: \\\"Unknown (from Proto)\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 has_single_reference: true ptr: 29496256 } } } timestamp 5143420588526034\\n\",\n \"\\n\",\n \" # MemoryLogStep\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogStep { step_id: 1 handle: \\\"->Print:0//0/;0\\\" } timestamp 5143420588718348\\n\",\n \"\\n\",\n \" # MemoryLogTensorOutput\\n\",\n \" # I 5143420588.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 1 kernel_name: \\\"Const\\\" tensor { dtype: DT_INT32 shape { dim { size: 3 } } allocation_description { requested_bytes: 12 allocated_bytes: 12 allocator_name: \\\"cpu\\\" allocation_id: 3 ptr: 29496256 } } } timestamp 5143420588932740\\n\",\n \" \\n\",\n \" # some weird initialization step \\n\",\n \" # if \\\"step_id: -6\\\" in l:\\n\",\n \" # return {}\\n\",\n \" \\n\",\n \" if 'MemoryLogTensorOutput' in l:\\n\",\n \" m = tensor_output_regex.search(l)\\n\",\n \" # assert m, l\\n\",\n \" if not m: # some Shape lines are missing bytes info, ie\\n\",\n \" # I 5162643141.000000 file tensorflow/core/framework/log_memory.cc:41] __LOG_MEMORY__ MemoryLogTensorOutput { step_id: 5 kernel_name: \\\"gradients/Shape\\\" tensor { dtype: DT_INT32 shape { dim { } } } } timestamp 5162643141310124\\n\",\n \" return {}\\n\",\n \" d = {f: m.group(f) for f in [\\\"timestamp\\\", \\\"allocation_id\\\",\\n\",\n \" \\\"allocated_bytes\\\", \\\"kernel_name\\\"]}\\n\",\n \" d[\\\"timestamp\\\"] = int(d[\\\"timestamp\\\"])\\n\",\n \" d[\\\"allocation_id\\\"] = int(d[\\\"allocation_id\\\"])\\n\",\n \" d[\\\"allocated_bytes\\\"] = int(d[\\\"allocated_bytes\\\"])\\n\",\n \" elif 'MemoryLogTensorAllocation' in l:\\n\",\n \" m = tensor_allocation_regex.search(l)\\n\",\n \" d = {f: int(m.group(f)) for f in [\\\"timestamp\\\", \\\"allocation_id\\\",\\n\",\n \" \\\"allocated_bytes\\\"]}\\n\",\n \" d[\\\"kernel_name\\\"] = \\\"\\\"\\n\",\n \" elif 'MemoryLogTensorDeallocation' in l:\\n\",\n \" m = tensor_deallocation_regex.search(l)\\n\",\n \" assert m, l\\n\",\n \" d = {f: int(m.group(f)) for f in [\\\"timestamp\\\", \\\"allocation_id\\\"]}\\n\",\n \" else:\\n\",\n \" d = {}\\n\",\n \" return d\\n\",\n \"\\n\",\n \"def memory_timeline(output):\\n\",\n \" \\\"\\\"\\\"Returns array of tuples representing memory actions\\n\",\n \" (timestamp_nanoseconds, kernel_name, memory_bytes)\\n\",\n \" memory is positive for allocations and negative for deallocations.\\n\",\n \" timestamps are relative to the first memory allocation line found\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" parsed_lines = [_parse_logline(l) for l in output.split(\\\"\\\\n\\\")]\\n\",\n \" parsed_lines = [l for l in parsed_lines if l] # remove Nones\\n\",\n \" parsed_lines.sort(key=lambda l: l[\\\"timestamp\\\"])\\n\",\n \" first_timestamp = parsed_lines[0][\\\"timestamp\\\"]\\n\",\n \" \\n\",\n \" # Tensors that are output have two records (allocation and output)\\n\",\n \" # since output comes after, overwrite allocation_map with output\\n\",\n \" # info (it has kernel_name)\\n\",\n \" allocation_map = {} # map of allocation_id->parsed_logline of allocation\\n\",\n \" double_records = {} # map of ids which have both allocation and output\\n\",\n \" for line in parsed_lines:\\n\",\n \" if \\\"kernel_name\\\" in line:\\n\",\n \" if line[\\\"allocation_id\\\"] in allocation_map:\\n\",\n \" double_records[line[\\\"allocation_id\\\"]] = True\\n\",\n \" line[\\\"allocation_id\\\"]\\n\",\n \" allocation_map[line[\\\"allocation_id\\\"]] = line\\n\",\n \" \\n\",\n \"\\n\",\n \" result = []\\n\",\n \" for line in parsed_lines:\\n\",\n \" if \\\"kernel_name\\\" in line:\\n\",\n \" # ignore tensorallocation lines which will later have tensoroutput\\n\",\n \" if (line[\\\"allocation_id\\\"] in double_records and\\n\",\n \" line[\\\"kernel_name\\\"] == \\\"\\\"):\\n\",\n \" continue\\n\",\n \" kernel_name = line[\\\"kernel_name\\\"]\\n\",\n \" allocated_bytes = line[\\\"allocated_bytes\\\"]\\n\",\n \" else: # deallocation\\n\",\n \" allocation_line = allocation_map[line[\\\"allocation_id\\\"]]\\n\",\n \" kernel_name = allocation_line[\\\"kernel_name\\\"]\\n\",\n \" allocated_bytes = -allocation_line[\\\"allocated_bytes\\\"]\\n\",\n \" result.append((line[\\\"timestamp\\\"]-first_timestamp, kernel_name,\\n\",\n \" allocated_bytes))\\n\",\n \" return result\\n\",\n \"\\n\",\n \"\\n\",\n \"def print_memory_timeline(stderr):\\n\",\n \" total_memory = 0\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamp, kernel_name, allocated_bytes = record\\n\",\n \" if abs(allocated_bytes)<1000:\\n\",\n \" continue # ignore small allocations\\n\",\n \" total_memory += allocated_bytes\\n\",\n \" print(\\\"%9d %40s %11d %11d\\\"%(timestamp, kernel_name, allocated_bytes, total_memory))\\n\",\n \"\\n\",\n \"def plot_memory_timeline(stderr):\\n\",\n \" total_memory = 0\\n\",\n \" timestamps = []\\n\",\n \" data = []\\n\",\n \" for record in memory_timeline(stderr):\\n\",\n \" timestamps.append(record[0]-1)\\n\",\n \" data.append(total_memory)\\n\",\n \" total_memory += record[-1]\\n\",\n \" timestamps.append(record[0])\\n\",\n \" data.append(total_memory)\\n\",\n \" plt.plot(timestamps, data)\\n\",\n \" \\n\",\n \" \\n\",\n \"run_metadata = tf.RunMetadata()\\n\",\n \"def summarize(run_metadata=run_metadata):\\n\",\n \" ss = tf.contrib.stat_summarizer.NewStatSummarizer(tf.get_default_graph().as_graph_def().SerializeToString())\\n\",\n \" ss.ProcessStepStatsStr(run_metadata.step_stats.SerializeToString())\\n\",\n \" print(ss.GetOutputString())\\n\",\n \" \\n\",\n \"def create_session():\\n\",\n \" config = tf.ConfigProto(graph_options=tf.GraphOptions(\\n\",\n \" optimizer_options=tf.OptimizerOptions(\\n\",\n \" opt_level=tf.OptimizerOptions.L0)))\\n\",\n \" return tf.Session(config=config)\\n\",\n \"\\n\",\n \"def printops(ops):\\n\",\n \" print([op.name for op in ops])\\n\",\n \" \\n\",\n \"run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\\n\",\n \"def sessrun(*args, **kwargs):\\n\",\n \" sess = create_session()\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" kwargs[\\\"run_metadata\\\"] = run_metadata\\n\",\n \" kwargs[\\\"options\\\"] = run_options\\n\",\n \" return sess.run(*args, **kwargs)\\n\",\n \"\\n\",\n \"import tensorflow.contrib.graph_editor as ge\\n\",\n \"def run_after(a_tensor, b_tensor):\\n\",\n \" \\\"\\\"\\\"Force a to run after b\\\"\\\"\\\"\\n\",\n \" ge.reroute.add_control_inputs(a_tensor.op, [b_tensor.op])\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simple graph\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"n=1000*1000*25\\n\",\n \"tf.reset_default_graph()\\n\",\n \"a1 = tf.placeholder(tf.float32, name=\\\"a1\\\", shape=(n,))\\n\",\n \"a2 = tf.tanh(a1, name=\\\"a2\\\")\\n\",\n \"a3 = tf.tanh(a2, name=\\\"a3\\\")\\n\",\n \"cost = tf.reduce_sum(a3, name=\\\"cost\\\")\\n\",\n \"xs = [a1]\\n\",\n \"ys = [cost]\\n\",\n \"fwd_ops = ge.get_walks_intersection_ops(forward_seed_ops=[x.op for x in xs], backward_seed_ops=[y.op for y in ys])\\n\",\n \"grads = tf.gradients(ys, xs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'redirect' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0msess\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcreate_session\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mredirect\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstart\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m;\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0msess\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrun\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mgrads\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mop\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m{\\u001b[0m\\u001b[0ma1\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mzeros\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mn\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mstderr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mredirect\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstop\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mplot_memory_timeline\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mstderr\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'redirect' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grads[0].op, feed_dict={a1:np.zeros((n,))})\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 39971463 a2 100000000 100000000\\n\",\n \" 40383258 gradients/cost_grad/Tile 100000000 200000000\\n\",\n \" 48615535 a3 100000000 300000000\\n\",\n \" 62861878 gradients/a3_grad/TanhGrad 100000000 400000000\\n\",\n \" 62953267 a3 -100000000 300000000\\n\",\n \" 63341227 gradients/cost_grad/Tile -100000000 200000000\\n\",\n \" 74271814 gradients/a2_grad/TanhGrad 100000000 300000000\\n\",\n \" 74369276 a2 -100000000 200000000\\n\",\n \" 74920551 gradients/a3_grad/TanhGrad -100000000 100000000\\n\",\n \" 75289873 gradients/a2_grad/TanhGrad -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"show_graph(ungroup_gradients=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['a2']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# save everything between a3 and a1\\n\",\n \"remember_ts = ge.filter_ts_from_regex(fwd_ops, \\\"a1|a3\\\")\\n\",\n \"intermediate_ops = ge.get_walks_intersection_ops(forward_seed_ops=[remember_ts[0].op], backward_seed_ops=[remember_ts[1].op])\\n\",\n \"intermediate_ops = intermediate_ops[1:-1] # remove starting/endpoints\\n\",\n \"printops(intermediate_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:VARIABLES collection name is deprecated, please use GLOBAL_VARIABLES instead; VARIABLES will be removed after 2017-03-02.\\n\",\n \"WARNING:tensorflow:VARIABLES collection name is deprecated, please use GLOBAL_VARIABLES instead; VARIABLES will be removed after 2017-03-02.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"copied_sgv, info = ge.copy_with_input_replacements(ge.sgv(intermediate_ops), {})\\n\",\n \"show_graph(ungroup_gradients=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"bwd_ops = ge.filter_ops_from_regex(tf.get_default_graph(), \\\"gradients/\\\")\\n\",\n \"intermediate_copied_ops = [info._transformed_ops[y] for y in intermediate_ops]\\n\",\n \"intermediate_copied_ts = [op.outputs[0] for op in intermediate_copied_ops]\\n\",\n \"intermediate_ts = [op.outputs[0] for op in intermediate_ops]\\n\",\n \"# b left dangling\\n\",\n \"ge.reroute_ts(intermediate_copied_ts, intermediate_ts, can_modify=bwd_ops)\\n\",\n \"show_graph()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def run_op_after(a_op, b_op):\\n\",\n \" \\\"\\\"\\\"Force a to run after b\\\"\\\"\\\"\\n\",\n \" print(\\\"Adding %s to run after %s\\\"%(a_op.name, b_op.name))\\n\",\n \" ge.reroute.add_control_inputs(a_op, [b_op])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Adding a2_1 to run after gradients/a3_grad/TanhGrad\\n\"\n ]\n }\n ],\n \"source\": [\n \"# force copied intermediate ops to run after earliest backprop op\\n\",\n \"cost_grad = ge.filter_ops_from_regex(tf.get_default_graph(), \\\"gradients/a3_grad/TanhGrad\\\")\\n\",\n \"for cost_op in cost_grad:\\n\",\n \" for intermediate_op in intermediate_copied_ops:\\n\",\n \" run_op_after(intermediate_op, cost_op)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gT+MclhA9i2JElapM6HIboqpWwBtvQUXZ/kAGAdcHKt7bp161i1atWT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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sess = create_session()\\n\",\n \"redirect.start();\\n\",\n \"sess.run(grads[0].op, feed_dict={a1:np.zeros((n,))})\\n\",\n \"stderr = redirect.stop()\\n\",\n \"plot_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 50385696 a2 100000000 100000000\\n\",\n \" 62436162 gradients/cost_grad/Tile 100000000 200000000\\n\",\n \" 68648386 a3 100000000 300000000\\n\",\n \" 68695205 a2 -100000000 200000000\\n\",\n \" 80062870 gradients/a3_grad/TanhGrad 100000000 300000000\\n\",\n \" 80151949 a3 -100000000 200000000\\n\",\n \" 80628014 gradients/cost_grad/Tile -100000000 100000000\\n\",\n \" 91433279 a2_1 100000000 200000000\\n\",\n \"104269881 gradients/a2_grad/TanhGrad 100000000 300000000\\n\",\n \"104355949 a2_1 -100000000 200000000\\n\",\n \"104935598 gradients/a3_grad/TanhGrad -100000000 100000000\\n\",\n \"105331258 gradients/a2_grad/TanhGrad -100000000 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_memory_timeline(stderr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"toc\": {\n \"colors\": {\n \"hover_highlight\": \"#DAA520\",\n \"running_highlight\": \"#FF0000\",\n \"selected_highlight\": \"#FFD700\"\n },\n \"moveMenuLeft\": true,\n \"nav_menu\": {\n \"height\": \"120px\",\n \"width\": \"252px\"\n },\n \"navigate_menu\": true,\n \"number_sections\": false,\n \"sideBar\": true,\n \"threshold\": 4,\n \"toc_cell\": false,\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "simple_train.py", "content": "import torch\nfrom torch.utils.data import (DataLoader, SequentialSampler,\n TensorDataset)\n\nfrom pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel\n\n\ndef main():\n # 3 examples\n train_dataset = 'small brown fox jumps over the lazy dog\\n' \\\n 'small brown fox jumps over the lazy dog\\n' \\\n 'small brown fox jumps over the lazy dog\\n'\n tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt',\n special_tokens=[])\n tokenized = [tokenizer.tokenize(t) for t in train_dataset.strip().split('\\n')]\n\n encoded=[tokenizer.convert_tokens_to_ids(t) for t in tokenized] # 3x8\n dataset = TensorDataset(torch.tensor(encoded))\n sampler = SequentialSampler(dataset)\n dataloader = DataLoader(dataset, sampler=sampler, batch_size=1)\n model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')\n\n optimizer = torch.optim.SGD(model.parameters(), lr = 0.0001, momentum=0.9)\n\n batch = next(iter(dataloader))\n batch=batch[0] # dataloader gives [batch] instead of batch...why?\n \n for i in range(20):\n loss = model(input_ids=batch, lm_labels=batch)\n print(loss.detach().numpy())\n loss.backward()\n optimizer.step()\n optimizer.zero_grad()\n\n# Should produce this\n# 6.134997\n# 5.3747735\n# 5.164842\n# 4.8581843\n# 4.346232\n# 4.158811\n# 3.7503657\n# 3.29156\n# 2.8858535\n# 2.760832\n# 2.562772\n# 2.0645103\n# 1.6837901\n# 1.6822727\n# 1.5878279\n# 1.3873199\n# 1.158909\n# 0.92595655\n# 0.8487712\n# 0.82774204\n\n\nif __name__=='__main__':\n main()\n \n" }, { "path": "svd_benchmark.py", "content": "# Fastest way to compute eigenvectors for 4k matrix?\n#\n# Xeon V3 benchmarks:\n# n=4096 eigs min: 27758.34, median: 28883.69\n# n=4096 gesdd min: 7241.70, median: 8477.95\n# n=4096 gesvd min=20487.48, median: 22057.64,\n# n=4096 inv min: 556.67, median: 579.25,\n# n=4096 linsolve: min: 534.40, median: 558.06, mean: 579.19\n#\n# Xeon V4:\n# n=4096 gesdd min: 5586.02, median: 6032.16\n#\n#\n# i7-5820K CPU @ 3.30GHz\n# n=4096 gesdd 7288.02, median: 7397.23, mean: 7478.78\n# n=4096 inv 520 msec\n#\n# after upgrading things\n# b'Intel(R) Math Kernel Library Version 2017.0.3 Product Build 20170413 for Intel(R) 64 architecture applications'\n# n=4096 inv 1427.54\n\n\nfrom scipy import linalg # for svd\nimport numpy as np\nimport time\nimport sys\n\nmethods = ['gesdd', 'gesvd', 'eigh', 'inv', 'inv2', 'linsolve']\n\nif len(sys.argv)<2:\n method = methods[0]\nelse:\n method = sys.argv[1]\n\n# from @eamartin\ndef empty_aligned(n, align):\n \"\"\"Get n bytes of memory wih alignment align.\"\"\"\n a = np.empty(n + (align - 1), dtype=np.float32)\n data_align = a.ctypes.data % align\n offset = 0 if data_align == 0 else (align - data_align)\n return a[offset : offset + n]\n\nassert method in methods\n\nn=4096\n#n=1024\nx_old = np.random.randn(n*n).reshape((n,n)).astype(dtype=np.float32)\nx = empty_aligned(n*n, 32).reshape((n, n))\nx[:] = x_old\nx = x @ x.T\n\nx0 = np.random.randn(n).reshape((n,1)).astype(dtype=np.float32)\n\nstart_time = time.time()\ntimes = []\n\nprint(\"n=%d %s \"%(n, method))\nfor i in range(9):\n if method == 'gesdd':\n result = linalg.svd(x)\n elif method == 'gesvd':\n result = linalg.svd(x, lapack_driver='gesvd')\n elif method == 'eigh':\n result = linalg.eigh(x)\n elif method == 'inv':\n result = linalg.inv(x)\n elif method == 'inv2':\n result = linalg.inv(x, overwrite_a=True)\n elif method == 'linsolve':\n result = linalg.solve(x, x0)\n else:\n assert False\n new_time = time.time()\n elapsed_time = 1000*(new_time - start_time)\n print(\"%.2f msec\" %(elapsed_time))\n start_time = new_time\n times.append(elapsed_time)\n\nprint(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(np.min(times), np.median(times), np.mean(times)))\n\n\n# Other timings: svd\n# n=1000 Times: min: 126.04, median: 132.48\n# n=2000 Times: min: 573.03, median: 621.49\n# n=4096 Times: min: 5586.02, median: 6032.16\n# Other timings: inv\n# Times: min: 17.87, median: 23.41, mean: 27.90\n" }, { "path": "svd_noconverge.py", "content": "import scipy\nimport scipy.linalg as linalg\nimport numpy as np\nimport os\nimport sys\nimport ctypes\nimport numpy as np\n\ndef mklVersion():\n ver = np.zeros(199, dtype=np.uint8)\n mkl = ctypes.cdll.LoadLibrary(\"libmkl_rt.so\")\n mkl.MKL_Get_Version_String(ver.ctypes.data_as(ctypes.c_char_p), 198)\n return ver[ver != 0].tostring()\n\n# mklVersion()\n\ndef download_if_needed(fn,target_length=0,bucket=\"yaroslavvb_stuff\"):\n import urllib.request\n url=\"https://storage.googleapis.com/%s/%s\"%(bucket, fn)\n response = urllib.request.urlopen(url)\n body = response.read()\n print(\"Read %d bytes from %s\"%(len(body), url))\n if target_length:\n assert len(body)==target_length\n \n open(fn, \"wb\").write(body)\n\nfn='badsvd0'\ndownload_if_needed(fn, 2458624)\ntarget0 = np.fromfile(fn, np.float32).reshape(784,784)\n\nsuccess = True\ntry:\n u0, s0, vt0 = linalg.svd(target0)\nexcept Exception as e:\n print(\"SVD failure\")\n print(repr(e))\n success = False\nelse:\n print(\"SVD success\")\n\nprint(\"Scipy version: \", scipy.version.full_version)\nprint(\"Numpy version: \", np.version.full_version)\nprint(\"Python version: \", sys.version)\nprint(\"Python binary: \", sys.executable)\n\nprint(\"-\"*80)\nprint(\"MKL version:\")\nprint(mklVersion())\nprint(\"-\"*80)\nprint(\"Conda version:\")\nos.system(\"conda list --explicit\")\nprint(\"-\"*80)\nprint(\"CPU version\")\nfor l in open(\"/proc/cpuinfo\").read().split('\\n'):\n if 'model name' in l:\n print(l)\n break\n\nif success:\n print(\"Success.\")\nelse:\n print(\"Failure.\")\n \n# Upload notes:\n# export fullname=badsvd0\n# export bucket=yaroslavvb_stuff\n# gsutil cp $fullname gs://$bucket\n# gsutil acl set public-read gs://$bucket/$fullname\n# echo https://storage.googleapis.com/$bucket/$fullname\n" }, { "path": "svd_test.py", "content": "# matrix\n# https://storage.cloud.google.com/tensorflow-community-wheels/svd_in\n# Mathematica sanity check\n# https://www.wolframcloud.com/objects/f16d71a7-cc47-4a3d-b686-da440670eed3\n\nimport tensorflow as tf\nimport numpy as np\nimport os\n\n\nif not os.path.exists(\"svd_in\"):\n import urllib.request\n url=\"https://storage.googleapis.com/tensorflow-community-wheels/svd_in\"\n response = urllib.request.urlopen(url)\n body = response.read()\n print(\"Read %d bytes\"%(len(body),))\n assert len(body) == 15366400\n open(\"svd_in\", \"wb\").write(body)\n\n # import requests\n # r = requests.get(url, auth=('usrname', 'password'), verify=False,stream=True)\n # r.raw.decode_content = True\n # with open(\"svd_in\", 'wb') as f:\n # shutil.copyfileobj(r.raw, f)\n\ndtype = np.float32\nmatrix0 = np.genfromtxt('svd_in',\n delimiter= \",\").astype(dtype)\nprint(matrix0.shape)\nassert matrix0.shape == (784, 784)\nmatrix = tf.placeholder(dtype)\nsess = tf.InteractiveSession()\ns0,u0,v0 = sess.run(tf.svd(matrix), feed_dict={matrix: matrix0})\nprint(\"u any NaNs: %s\"% (np.isnan(u0).any(),))\nprint(\"u all NaNs: %s\"% (np.isnan(u0).all(),))\nprint(\"matrix0 any NaNs: %s\"% (np.isnan(matrix0).any(),))\n\n# segfault bt\n# #0 0x00007fffe320e121 in Eigen::BDCSVD >::perturbCol0(Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Matrix const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> > const&, Eigen::Ref, 0, Eigen::InnerStride<1> >) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #1 0x00007fffe320fa81 in Eigen::BDCSVD >::computeSVDofM(long, long, Eigen::Matrix&, Eigen::Matrix&, Eigen::Matrix&) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #2 0x00007fffe321e21c in Eigen::BDCSVD >::divide(long, long, long, long, long) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #3 0x00007fffe321dbb8 in Eigen::BDCSVD >::divide(long, long, long, long, long) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #4 0x00007fffe32220bd in Eigen::BDCSVD >::compute(Eigen::Matrix const&, unsigned int) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n# #5 0x00007fffe32227a1 in tensorflow::SvdOp::ComputeMatrix(tensorflow::OpKernelContext*, tensorflow::gtl::InlinedVector const, 0, Eigen::Stride<0, 0> >, 4> const&, tensorflow::gtl::InlinedVector, 0, Eigen::Stride<0, 0> >, 4>*) () from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so #6 0x00007fffe3228c75 in tensorflow::LinearAlgebraOp::ComputeTensorSlice(tensorflow::OpKernelContext*, long long, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&, tensorflow::gtl::InlinedVector const&) ()\n# from /home/yaroslav/.conda/envs/whitening/lib/python3.5/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so\n \n\n# upload notes\n\n# export fullname=svd_in\n# export bucket=tensorflow-community-wheels\n# cd ~/git0/whitening/mnist_autoencoder/data\n# gsutil cp svd_in gs://$bucket\n# gsutil acl set public-read gs://$bucket/$fullname\n# echo https://storage.googleapis.com/tensorflow-community-wheels/$fullname\n\n" }, { "path": "tf_initializer_bug_report.py", "content": "# UNCOMMENT LINES BELOW TO MAKE THINGS WORK AGAIN\n# from tensorflow.python.ops import variables\n# def passthrough(obj, value): return value\n# try:\n# variables.Variable._build_initializer_expr=passthrough\n# except: # older versions of TF don't have this\n# pass\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#!/usr/bin/env python\nfrom collections import OrderedDict\nfrom scipy import linalg\nfrom tensorflow.contrib import graph_editor as ge\nimport contextlib\nimport inspect\nimport math\nimport numpy as np\nimport os\nimport socket\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=False # Tensorflow vs \n\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\n\n\n# TODO: speed-up tests by reusing session\n\nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n# work-around for graph_editor.copy_with_input_replacements scaling\n# quadratically with size of the graph\nfrom tensorflow.contrib.graph_editor import transform\noriginal_assign_renamed_collections_handler = transform.assign_renamed_collections_handler\ndef dummy_collections_handler(info, elem, elem_): pass\ndef disable_collections_handler():\n transform.assign_renamed_collections_handler = dummy_collections_handler\ndef enable_collections_handler():\n transform.assign_renamed_collections_handler = original_assign_renamed_collections_handler\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__ == 'Tensor' or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n \"\"\"Decorator to measure length of time spent in the block in millis and log\n it to TensorBoard.\"\"\"\n \n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n logger = u.get_last_logger(skip_existence_check=True)\n if logger:\n newtag = 'time/'+self.tag\n # since tensorboard doesn't allow hierarchical tags, merge init times\n if newtag.startswith('time/init'):\n newtag = newtag.replace('time/init', 'timeinit')\n logger(newtag, interval_ms)\n\n\nglobal_record_dict = OrderedDict()\ndef record(tag, stat):\n global global_record_dict\n global_record_dict.setdefault(tag, []).append(stat)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n \n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n\n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = tf.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = tf.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = tf.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = tf.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = tf.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger(skip_existence_check=False):\n \"\"\"Returns last logger, if skip_existence_check is set, doesn't\n throw error if logger doesn't exist.\"\"\"\n global global_last_logger\n if not skip_existence_check:\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n interval_ms = 1000*(new_timestamp - self.last_timestamp)\n self.summary.value.add(tag='time/step',\n simple_value=interval_ms)\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\n\nu = sys.modules[__name__]\nutil = u\n\ndrop_l2 = True # drop L2 term\ndrop_sparsity = True # drop KL term\nuse_gpu = True\ndo_line_search = False # line-search and dump values at each iter\n\nwhitening_mode = 4 # 0 for gradient, 4 for full whitening\nwhiten_every_n_steps = 1 # how often to whiten\nreport_frequency = 3 # how often to print loss\n\nnum_steps = 20000 if whitening_mode==0 else 20\nutil.USE_MKL_SVD=True # Tensorflow vs MKL SVD\n\npurely_linear = False # convert sigmoids into linear nonlinearities\nuse_tikhonov = True # use Tikhonov reg instead of Moore-Penrose pseudo-inv\nLambda = 1e-3 # magic lambda value from Jimmy Ba for Tikhonov\n\n# adaptive line search\nadaptive_step = False # adjust step length based on predicted decrease\nadaptive_step_frequency = 1 # how often to adjust\nadaptive_step_burn_in = 0 # let optimization go for a bit before adjusting\nlocal_quadratics = False # use quadratic approximation to predict loss drop\n\n\nimport tensorflow as tf\n#from util import t # transpose\nt = util.t\n\ndef W_uniform(s1, s2): # uniform weight init from Ng UFLDL\n r = np.sqrt(6) / np.sqrt(s1 + s2 + 1)\n result = np.random.random(2*s2*s1)*2*r-r\n return result\n\n\ndef main():\n np.random.seed(0)\n tf.set_random_seed(0)\n \n dtype = np.float32\n # 64-bit doesn't help much, search for 64-bit in\n # https://www.wolframcloud.com/objects/5f297f41-30f7-4b1b-972c-cac8d1f8d8e4\n u.default_dtype = dtype\n machine_epsilon = np.finfo(dtype).eps # 1e-7 or 1e-16\n dsize = 10000\n train_images = np.random.random([28*28, dsize])\n patches = train_images[:,:dsize];\n fs = [dsize, 28*28, 196, 28*28]\n\n # values from deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial\n X0=patches\n lambda_=3e-3\n rho=tf.constant(0.1, dtype=dtype)\n beta=3\n W0f = W_uniform(fs[2],fs[3])\n\n def f(i): return fs[i+1] # W[i] has shape f[i] x f[i-1]\n dsize = f(-1)\n n = len(fs) - 2\n\n # helper to create variables with numpy or TF initial value\n init_dict = {} # {var_placeholder: init_value}\n vard = {} # {var: util.VarInfo}\n def init_var(val, name, trainable=False, noinit=False):\n if isinstance(val, tf.Tensor):\n collections = [] if noinit else None\n var = tf.Variable(val, name=name, collections=collections)\n else:\n val = np.array(val)\n assert u.is_numeric, \"Unknown type\"\n holder = tf.placeholder(dtype, shape=val.shape, name=name+\"_holder\")\n var = tf.Variable(holder, name=name, trainable=trainable)\n init_dict[holder] = val\n var_p = tf.placeholder(var.dtype, var.shape)\n var_setter = var.assign(var_p)\n vard[var] = u.VarInfo(var_setter, var_p)\n return var\n\n lr = init_var(0.2, \"lr\")\n if purely_linear: # need lower LR without sigmoids\n lr = init_var(.02, \"lr\")\n \n Wf = init_var(W0f, \"Wf\", True)\n Wf_copy = init_var(W0f, \"Wf_copy\", True)\n W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes\n X = init_var(X0, \"X\")\n W.insert(0, X)\n\n def sigmoid(x):\n if not purely_linear:\n return tf.sigmoid(x)\n else:\n return tf.identity(x)\n \n def d_sigmoid(y):\n if not purely_linear:\n return y*(1-y)\n else:\n return 1\n \n def kl(x, y):\n return x * tf.log(x / y) + (1 - x) * tf.log((1 - x) / (1 - y))\n def d_kl(x, y):\n return (1-x)/(1-y) - x/y\n \n # A[i] = activations needed to compute gradient of W[i]\n # A[n+1] = network output\n A = [None]*(n+2)\n\n # A[0] is just for shape checks, assert fail on run\n # tf.assert always fails because of static assert\n # fail_node = tf.assert_equal(1, 0, message=\"too huge\")\n fail_node = tf.Print(0, [0], \"fail, this must never run\")\n with tf.control_dependencies([fail_node]):\n A[0] = u.Identity(dsize, dtype=dtype)\n A[1] = W[0]\n for i in range(1, n+1):\n A[i+1] = sigmoid(W[i] @ A[i])\n \n # reconstruction error and sparsity error\n err = (A[3] - A[1])\n rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True)/dsize\n\n # B[i] = backprops needed to compute gradient of W[i]\n # B2[i] = backprops from sampled labels needed for natural gradient\n B = [None]*(n+1)\n B2 = [None]*(n+1)\n B[n] = err*d_sigmoid(A[n+1])\n sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)\n sampled_labels = init_var(sampled_labels_live, \"sampled_labels\", noinit=True)\n B2[n] = sampled_labels*d_sigmoid(A[n+1])\n for i in range(n-1, -1, -1):\n backprop = t(W[i+1]) @ B[i+1]\n backprop2 = t(W[i+1]) @ B2[i+1]\n if i == 1 and not drop_sparsity:\n backprop += beta*d_kl(rho, rho_hat)\n backprop2 += beta*d_kl(rho, rho_hat)\n B[i] = backprop*d_sigmoid(A[i+1])\n B2[i] = backprop2*d_sigmoid(A[i+1])\n\n # dW[i] = gradient of W[i]\n dW = [None]*(n+1)\n pre_dW = [None]*(n+1) # preconditioned dW\n pre_dW_stable = [None]*(n+1) # preconditioned stable dW\n\n cov_A = [None]*(n+1) # covariance of activations[i]\n cov_B2 = [None]*(n+1) # covariance of synthetic backprops[i]\n vars_svd_A = [None]*(n+1)\n vars_svd_B2 = [None]*(n+1)\n for i in range(1,n+1):\n cov_A[i] = init_var(A[i]@t(A[i])/dsize, \"cov_A%d\"%(i,))\n cov_B2[i] = init_var(B2[i]@t(B2[i])/dsize, \"cov_B2%d\"%(i,))\n vars_svd_A[i] = u.SvdWrapper(cov_A[i],\"svd_A_%d\"%(i,))\n vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],\"svd_B2_%d\"%(i,))\n if use_tikhonov:\n whitened_A = u.regularized_inverse2(vars_svd_A[i],L=Lambda) @ A[i]\n else:\n whitened_A = u.pseudo_inverse2(vars_svd_A[i]) @ A[i]\n if use_tikhonov:\n whitened_B2 = u.regularized_inverse2(vars_svd_B2[i],L=Lambda) @ B[i]\n else:\n whitened_B2 = u.pseudo_inverse2(vars_svd_B2[i]) @ B[i]\n whitened_A_stable = u.pseudo_inverse_sqrt2(vars_svd_A[i]) @ A[i]\n whitened_B2_stable = u.pseudo_inverse_sqrt2(vars_svd_B2[i]) @ B[i]\n pre_dW[i] = (whitened_B2 @ t(whitened_A))/dsize\n pre_dW_stable[i] = (whitened_B2_stable @ t(whitened_A_stable))/dsize\n dW[i] = (B[i] @ t(A[i]))/dsize\n\n # Loss function\n reconstruction = u.L2(err) / (2 * dsize)\n sparsity = beta * tf.reduce_sum(kl(rho, rho_hat))\n L2 = (lambda_ / 2) * (u.L2(W[1]) + u.L2(W[1]))\n\n loss = reconstruction\n if not drop_l2:\n loss = loss + L2\n if not drop_sparsity:\n loss = loss + sparsity\n\n grad_live = u.flatten(dW[1:])\n pre_grad_live = u.flatten(pre_dW[1:]) # fisher preconditioned gradient\n pre_grad_stable_live = u.flatten(pre_dW_stable[1:]) # sqrt fisher preconditioned grad\n grad = init_var(grad_live, \"grad\")\n pre_grad = init_var(pre_grad_live, \"pre_grad\")\n pre_grad_stable = init_var(pre_grad_stable_live, \"pre_grad_stable\")\n\n update_params_op = Wf.assign(Wf-lr*pre_grad).op\n update_params_stable_op = Wf.assign(Wf-lr*pre_grad_stable).op\n save_params_op = Wf_copy.assign(Wf).op\n pre_grad_dot_grad = tf.reduce_sum(pre_grad*grad)\n pre_grad_stable_dot_grad = tf.reduce_sum(pre_grad*grad)\n grad_norm = tf.reduce_sum(grad*grad)\n pre_grad_norm = u.L2(pre_grad)\n pre_grad_stable_norm = u.L2(pre_grad_stable)\n\n def dump_svd_info(step):\n \"\"\"Dump singular values and gradient values in those coordinates.\"\"\"\n for i in range(1, n+1):\n svd = vars_svd_A[i]\n s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])\n util.dump(s0, \"A_%d_%d\"%(i, step))\n A0 = A[i].eval()\n At0 = v0.T @ A0\n util.dump(A0 @ A0.T, \"Acov_%d_%d\"%(i, step))\n util.dump(At0 @ At0.T, \"Atcov_%d_%d\"%(i, step))\n util.dump(s0, \"As_%d_%d\"%(i, step))\n\n for i in range(1, n+1):\n svd = vars_svd_B2[i]\n s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])\n util.dump(s0, \"B2_%d_%d\"%(i, step))\n B0 = B[i].eval()\n Bt0 = v0.T @ B0\n util.dump(B0 @ B0.T, \"Bcov_%d_%d\"%(i, step))\n util.dump(Bt0 @ Bt0.T, \"Btcov_%d_%d\"%(i, step))\n util.dump(s0, \"Bs_%d_%d\"%(i, step)) \n \n def advance_batch():\n sess.run(sampled_labels.initializer) # new labels for next call\n\n def update_covariances():\n ops_A = [cov_A[i].initializer for i in range(1, n+1)]\n ops_B2 = [cov_B2[i].initializer for i in range(1, n+1)]\n sess.run(ops_A+ops_B2)\n\n def update_svds():\n if whitening_mode>1:\n vars_svd_A[2].update()\n if whitening_mode>2:\n vars_svd_B2[2].update()\n if whitening_mode>3:\n vars_svd_B2[1].update()\n\n def init_svds():\n \"\"\"Initialize our SVD to identity matrices.\"\"\"\n ops = []\n for i in range(1, n+1):\n ops.extend(vars_svd_A[i].init_ops)\n ops.extend(vars_svd_B2[i].init_ops)\n sess = tf.get_default_session()\n sess.run(ops)\n \n init_op = tf.global_variables_initializer()\n # tf.get_default_graph().finalize()\n \n sess = tf.InteractiveSession()\n sess.run(Wf.initializer, feed_dict=init_dict)\n sess.run(X.initializer, feed_dict=init_dict)\n advance_batch()\n update_covariances()\n init_svds()\n sess.run(init_op, feed_dict=init_dict) # initialize everything else\n \n print(\"Running training.\")\n u.reset_time()\n\n step_lengths = [] # keep track of learning rates\n losses = []\n ratios = [] # actual loss decrease / expected decrease\n grad_norms = [] \n pre_grad_norms = [] # preconditioned grad norm squared\n pre_grad_stable_norms = [] # sqrt preconditioned grad norms squared\n target_delta_list = [] # predicted decrease linear approximation\n target_delta2_list = [] # predicted decrease quadratic appromation\n actual_delta_list = [] # actual decrease\n \n # adaptive line search parameters\n alpha=0.3 # acceptable fraction of predicted decrease\n beta=0.8 # how much to shrink when violation\n growth_rate=1.05 # how much to grow when too conservative\n \n def update_cov_A(i):\n sess.run(cov_A[i].initializer)\n def update_cov_B2(i):\n sess.run(cov_B2[i].initializer)\n\n # only update whitening matrix of input activations in the beginning\n if whitening_mode>0:\n vars_svd_A[1].update()\n\n # compute t(delta).H.delta/2\n def hessian_quadratic(delta):\n # update_covariances()\n W = u.unflatten(delta, fs[1:])\n W.insert(0, None)\n total = 0\n for l in range(1, n+1):\n decrement = tf.trace(t(W[l])@cov_B2[l]@W[l]@cov_A[l])\n total+=decrement\n return (total/2).eval()\n \n # compute t(delta).H^-1.delta/2\n def hessian_quadratic_inv(delta):\n # update_covariances()\n W = u.unflatten(delta, fs[1:])\n W.insert(0, None)\n total = 0\n for l in range(1, n+1):\n invB2 = u.pseudo_inverse2(vars_svd_B2[l])\n invA = u.pseudo_inverse2(vars_svd_A[l])\n decrement = tf.trace(t(W[l])@invB2@W[l]@invA)\n total+=decrement\n return (total/2).eval()\n\n # do line search, dump values as csv\n def line_search(initial_value, direction, step, num_steps):\n saved_val = tf.Variable(Wf)\n sess.run(saved_val.initializer)\n pl = tf.placeholder(dtype, shape=(), name=\"linesearch_p\")\n assign_op = Wf.assign(initial_value - direction*step*pl)\n vals = []\n for i in range(num_steps):\n sess.run(assign_op, feed_dict={pl: i})\n vals.append(loss.eval())\n sess.run(Wf.assign(saved_val)) # restore original value\n return vals\n \n for step in range(num_steps): \n update_covariances()\n if step % whiten_every_n_steps==0:\n update_svds()\n\n sess.run(grad.initializer)\n sess.run(pre_grad.initializer)\n \n lr0, loss0 = sess.run([lr, loss])\n save_params_op.run()\n\n # regular inverse becomes unstable when grad norm exceeds 1\n stabilized_mode = grad_norm.eval()<1\n\n if stabilized_mode and not use_tikhonov:\n update_params_stable_op.run()\n else:\n update_params_op.run()\n\n loss1 = loss.eval()\n advance_batch()\n\n # line search stuff\n target_slope = (-pre_grad_dot_grad.eval() if stabilized_mode else\n -pre_grad_stable_dot_grad.eval())\n target_delta = lr0*target_slope\n target_delta_list.append(target_delta)\n\n # second order prediction of target delta\n # TODO: the sign is wrong, debug this\n # https://www.wolframcloud.com/objects/8f287f2f-ceb7-42f7-a599-1c03fda18f28\n if local_quadratics:\n x0 = Wf_copy.eval()\n x_opt = x0-pre_grad.eval()\n # computes t(x)@H^-1 @(x)/2\n y_opt = loss0 - hessian_quadratic_inv(grad)\n # computes t(x)@H @(x)/2\n y_expected = hessian_quadratic(Wf-x_opt)+y_opt\n target_delta2 = y_expected - loss0\n target_delta2_list.append(target_delta2)\n \n \n actual_delta = loss1 - loss0\n actual_slope = actual_delta/lr0\n slope_ratio = actual_slope/target_slope # between 0 and 1.01\n actual_delta_list.append(actual_delta)\n\n if do_line_search:\n vals1 = line_search(Wf_copy, pre_grad, lr/100, 40)\n vals2 = line_search(Wf_copy, grad, lr/100, 40)\n u.dump(vals1, \"line1-%d\"%(i,))\n u.dump(vals2, \"line2-%d\"%(i,))\n \n losses.append(loss0)\n step_lengths.append(lr0)\n ratios.append(slope_ratio)\n grad_norms.append(grad_norm.eval())\n pre_grad_norms.append(pre_grad_norm.eval())\n pre_grad_stable_norms.append(pre_grad_stable_norm.eval())\n\n if step % report_frequency == 0:\n print(\"Step %d loss %.2f, target decrease %.3f, actual decrease, %.3f ratio %.2f grad norm: %.2f pregrad norm: %.2f\"%(step, loss0, target_delta, actual_delta, slope_ratio, grad_norm.eval(), pre_grad_norm.eval()))\n \n if adaptive_step_frequency and adaptive_step and step>adaptive_step_burn_in:\n # shrink if wrong prediction, don't shrink if prediction is tiny\n if slope_ratio < alpha and abs(target_delta)>1e-6 and adaptive_step:\n print(\"%.2f %.2f %.2f\"%(loss0, loss1, slope_ratio))\n print(\"Slope optimality %.2f, shrinking learning rate to %.2f\"%(slope_ratio, lr0*beta,))\n sess.run(vard[lr].setter, feed_dict={vard[lr].p: lr0*beta})\n \n # grow learning rate, slope_ratio .99 worked best for gradient\n elif step>0 and i%50 == 0 and slope_ratio>0.90 and adaptive_step:\n print(\"%.2f %.2f %.2f\"%(loss0, loss1, slope_ratio))\n print(\"Growing learning rate to %.2f\"%(lr0*growth_rate))\n sess.run(vard[lr].setter, feed_dict={vard[lr].p:\n lr0*growth_rate})\n\n u.record_time()\n\n print(\"Test passed\")\n\nif __name__=='__main__':\n main()\n" }, { "path": "tiny_runs/qr_test.py", "content": "# qr on 4096 x 4096\n# tf 6.89\n# np openblas 11.38\n# np mkl: 2.36\n\nimport tensorflow as tf\nimport time\nimport numpy as np\n\nnp.__config__.show()\n\ntry:\n tf.reset_default_graph()\n n = 2048*2\n mat = tf.Variable(tf.random_uniform((n,n)))\n qr = tf.qr(mat)\n sess = tf.Session(config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0))))\n sess.run(tf.initialize_all_variables())\n sess.run(qr[0].op)\n start_time = time.time()\n sess.run(qr[0].op)\n end_time = time.time()\n print(\"TF QR on %d by %d matrix in %.2f seconds\"%(n, n, end_time-start_time))\nexcept:\n print(\"No tf\")\n\na = np.random.randn(n, n)\nstart_time = time.time()\nq, r = np.linalg.qr(a)\nend_time = time.time()\nprint(\"numpy QR on %d by %d matrix in %.2f seconds\"%(n, n, end_time-start_time))\n" }, { "path": "tiny_runs/tiny_tf.py", "content": "# mac: tf -- 90 usec,\n# tf low-level with fetches -- 44 usec\n# tf low-level -- 36 usec\n# numpy -- 20 usec\n# \n# xeon: tf -- 130 usec, tf low level -- 77, tf low level+XLA -- 20 usec, numpy -- 30\n#\n# benchmark tf\n# python tiny_tf.py\n#\n# benchmark tf using low level API\n# python tiny_tf.py fast\n#\n# benchmark tf using low level API\n# python tiny_tf.py fast-nofetch\n#\n# bencharmk tf using low level API + XLA\n# python tiny_tf.py fastxla\n#\n# bencharmk tf using low level API + XLA\n# python tiny_tf.py fastxla-nofetch\n#\n# benchmark numpy\n# python tiny_tf.py np\n\n\nimport os, sys\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\n\nfrom tensorflow.python import pywrap_tensorflow\nfrom tensorflow.python import pywrap_tensorflow as tf_session\nfrom tensorflow.python.framework import errors\nimport tensorflow as tf\nimport numpy as np\nimport time\n\ntry:\n from tensorflow.contrib.compiler import jit\n jit_scope = jit.experimental_jit_scope\nexcept:\n print(\"No XLA for you\")\n pass\n\nif len(sys.argv)>1 and sys.argv[1]=='np':\n run_numpy = True\nelse:\n run_numpy = False\n\nif len(sys.argv)>1 and 'fast' in sys.argv[1]:\n run_fast = True\nelse:\n run_fast = False\n \nif len(sys.argv)>1 and 'xla' in sys.argv[1]:\n run_fastxla = True\nelse:\n run_fastxla = False\n \nn = 600\n\ndef create_graph():\n def _create_graph():\n a = tf.constant(np.random.random((n, 64)).astype(np.float32))\n b = tf.constant(np.random.random((64, 64)).astype(np.float32))\n c = tf.constant(np.random.random((64, 64)).astype(np.float32))\n x = tf.random_uniform((1, n))\n\n y = tf.matmul(x, a)\n y = tf.matmul(y, b)\n y = tf.matmul(y, c)\n return y\n\n if run_fastxla:\n with jit_scope(compile_ops=True):\n return _create_graph()\n else:\n return _create_graph()\n \nconfig = tf.ConfigProto()\n \nsess = tf.Session(config=config)\n\na_n = np.empty((n, 64))\nb_n = np.empty((64, 64))\nc_n = np.empty((64, 64))\ndtype = np.float32\ndef f_numpy(): return np.empty((1, n), dtype=dtype).dot(a_n).dot(b_n).dot(c_n)\n\n# setup low level args for TF_Run call\nsession = sess._session\noptions=None\nfeed_dict = {}\n\n# uncomment lines below if you want to fetch things\nfetch_list = [b'MatMul_2:0']\ntarget_list = []\n\nif len(sys.argv)>1 and 'nofetch' in sys.argv[1]:\n fetch_list=[]\n target_list=[b'MatMul_2']\n \nrun_metadata = None\nstatus_orig = errors.raise_exception_on_not_ok_status()\nstatus = pywrap_tensorflow.TF_NewStatus()\n\ndef fast_tf():\n return tf_session.TF_Run(session, options,\n feed_dict, fetch_list, target_list,\n status, run_metadata)\n\nnum_iters = 5000\nwarmup_iters = 2\niter_times = np.zeros((num_iters+warmup_iters,))\ny = create_graph()\nfor i in range(num_iters+warmup_iters):\n iter_start = time.time()\n if i == warmup_iters:\n start_time = time.time()\n if run_numpy:\n f_numpy()\n elif (run_fast or run_fastxla) and i>=warmup_iters:\n fast_tf()\n else:\n sess.run(y)\n\n iter_end = time.time()\n iter_times[i] = iter_end - iter_start\n\n#import pdb; pdb.set_trace() \n \nend_time = time.time()\nprint(\"Per iteration min: %d, avg: %d\"%(min(iter_times)*10**6, (time.time()-start_time)/num_iters*10**6))\n" }, { "path": "whitening_util.py", "content": "import socket\nimport contextlib\nimport inspect\nimport inspect\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport time\nimport traceback\nfrom tensorflow.contrib import graph_editor as ge\nfrom collections import OrderedDict\nfrom collections import defaultdict\n\n# shortcuts to refer to util module, this lets move external code into\n# this module unmodified\nutil = sys.modules[__name__] \nu = util\n\n\ndefault_dtype = tf.float32\nUSE_MKL_SVD=True # Tensorflow vs MKL SVD\nDUMP_BAD_SVD=False # when SVD fails, dump matrix to temp\n\nif USE_MKL_SVD:\n assert np.__config__.get_info(\"lapack_mkl_info\"), \"No MKL detected :(\"\n\n\nfrom scipy import linalg\n\n# TODO: speed-up tests by reusing session\n\nargs = None # TODO: replace with object that crashes on access\ndef set_global_args(local_args):\n global args\n assert args is None\n args = local_args\n\ndef concat_blocks(blocks, validate_dims=True):\n \"\"\"Takes 2d grid of blocks representing matrices and concatenates to single\n matrix (aka ArrayFlatten)\"\"\"\n\n if validate_dims:\n col_dims = np.array([[int(b.shape[1]) for b in row] for row in blocks])\n col_sums = col_dims.sum(1)\n assert (col_sums[0] == col_sums).all()\n row_dims = np.array([[int(b.shape[0]) for b in row] for row in blocks])\n row_sums = row_dims.sum(0)\n assert (row_sums[0] == row_sums).all()\n \n block_rows = [tf.concat(row, axis=1) for row in blocks]\n return tf.concat(block_rows, axis=0)\n\ndef concat_blocks_test():\n blocks = [[tf.constant([[1]]), tf.constant([[1,2]])],\n [tf.transpose(tf.constant([[1,2]])), tf.constant([[1,2],[3,4]])]]\n result = concat_blocks(blocks)\n sess = tf.Session()\n result0 = sess.run(result)\n check_equal(result0, [[1, 1, 2], [1, 1, 2], [2, 3, 4]])\n\n\ndef partition_matrix_evenly(mat, splits):\n \"\"\"Breaks matrix into 2d grid of equal size.\"\"\"\n assert int(mat.shape[0])%splits==0\n assert int(mat.shape[1])%splits==0\n \n row_chunks = tf.split(mat, splits, axis=0)\n col_chunks = [tf.split(chunk, splits, axis=1) for chunk in row_chunks]\n return col_chunks\n\ndef partition_matrix_evenly_test():\n a = tf.reshape([1,2,3,4], (2,2))\n blocks = partition_matrix_evenly(a, 2)\n a2 = concat_blocks(blocks)\n sess = tf.Session()\n check_equal(sess.run(a2), sess.run(a))\n\n# inverse of concat blocks\ndef partition_matrix(mat, sizes):\n pass\n\ndef partition_matrix_test():\n pass\n\n\n # TODO: add name property\ndef pseudo_inverse(mat, eps=1e-10):\n \"\"\"Computes pseudo-inverse of mat, treating eigenvalues below eps as 0.\"\"\"\n \n s, u, v = tf.svd(mat)\n eps = 1e-10 # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./s)\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef symsqrt(mat, eps=1e-7):\n \"\"\"Symmetric square root.\"\"\"\n s, u, v = tf.svd(mat)\n # sqrt is unstable around 0, just use 0 in such case\n print(\"Warning, cutting off at eps\")\n si = tf.where(tf.less(s, eps), s, tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt(mat, eps=1e-7):\n \"\"\"half pseduo-inverse\"\"\"\n s, u, v = tf.svd(mat)\n # zero threshold for eigenvalues\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse_sqrt2(svd, eps=1e-7):\n \"\"\"half pseduo-inverse, accepting existing values\"\"\"\n # zero threshold for eigenvalues\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n si = tf.where(tf.less(s, eps), s, 1./tf.sqrt(s))\n return u @ tf.diag(si) @ tf.transpose(v)\n\ndef pseudo_inverse2(svd, eps=1e-7):\n \"\"\"pseudo-inverse, accepting existing values\"\"\"\n # use float32 machine precision as cut-off (works for MKL)\n # https://www.wolframcloud.com/objects/927b2aa5-de9c-46f5-89fe-c4a58aa4c04b\n if svd.__class__.__name__=='SvdTuple':\n (s, u, v) = (svd.s, svd.u, svd.v)\n elif svd.__class__.__name__=='SvdWrapper':\n (s, u, v) = (svd.s, svd.u, svd.v)\n else:\n assert False, \"Unknown type\"\n max_eigen = tf.reduce_max(s)\n si = tf.where(s/max_eigen (2, 10)\ndef fix_shape(tf_shape):\n return tuple(int(dim) for dim in tf_shape)\n\ndef kronecker_cols(a, b):\n \"\"\"Treats rank-1 vectors a, b as columns, returns Kronecker product a x b.\"\"\"\n \n assert len(a.get_shape())==1, \"Input a must be rank-1, got shape %s\" %(a.get_shape(),)\n assert len(b.get_shape())==1, \"Input b must be rank-1, got shape %s\"%(a.get_shape(),)\n segments = []\n for i in range(a.get_shape()[0]):\n segments.append(a[i]*b)\n result_vec = tf.concat(segments, axis=0)\n result_col = tf.expand_dims(result_vec, 1)\n return result_col\n\ndef kronecker_cols_test():\n a = tf.constant([1,2])\n b = tf.constant([3,4])\n c = tf.transpose(tf.constant([[3,4,6,8]]))\n sess = tf.Session()\n assert sess.run(tf.equal(kronecker_cols(a, b), c)).all()\n\n\ndef kronecker(A, B, do_shape_inference=True):\n \"\"\"Kronecker product of A,B.\n turn_off_shape_inference: if True, makes 10x10 kron go 2.4 sec -> 0.9 sec\n \"\"\"\n\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n Crows, Ccols = Arows*Brows, Acols*Bcols\n \n temp = tf.reshape(A, [-1, 1, 1])*tf.expand_dims(B, 0)\n Bshape = tf.constant((Brows, Bcols))\n\n # turn off shape inference\n if not do_shape_inference:\n disable_shape_inference()\n\n # [1, n, m] => [n, m]\n slices = [tf.reshape(s, Bshape) for s in tf.split(temp, Crows)]\n \n # import pdb; pdb.set_trace()\n grid = list(chunks(slices, Acols))\n assert len(grid) == Arows\n result = concat_blocks(grid, validate_dims=do_shape_inference)\n\n if not do_shape_inference:\n enable_shape_inference()\n result.set_shape((Arows*Brows, Acols*Bcols))\n \n return result\n\nkr = kronecker\n\ndef kronecker_test():\n A0 = [[1,2],[3,4]]\n B0 = [[6,7],[8,9]]\n A = tf.constant(A0)\n B = tf.constant(B0)\n C = kronecker(A, B)\n sess = tf.Session()\n C0 = sess.run(C)\n Ct = [[6, 7, 12, 14], [8, 9, 16, 18], [18, 21, 24, 28], [24, 27, 32, 36]]\n Cnp = np.kron(A0, B0)\n check_equal(C0, Ct)\n check_equal(C0, Cnp)\n\n\ndef col(A,i):\n \"\"\"Extracts i'th column of matrix A\"\"\"\n assert len(A.get_shape())==2\n assert i>=0 and i < A.get_shape()[1]\n return tf.expand_dims(A[:,i], 1)\n\n\ndef khatri_rao(A, B):\n Arows, Acols = fix_shape(A.shape)\n Brows, Bcols = fix_shape(B.shape)\n assert Acols==Bcols\n return tf.reshape(tf.einsum(\"ik,jk->ijk\", A, B), (Arows*Brows, Acols))\n\n\ndef khatri_rao_test():\n A = tf.constant([[1, 2], [3, 4]])\n B = tf.constant([[5, 6], [7, 8]])\n C = tf.constant([[5,12], [7,16], [15,24], [21,32]])\n sess = tf.Session()\n assert sess.run(tf.equal(khatri_rao(A, B), C)).all()\n\n \ndef relu_mask(a, dtype=default_dtype):\n \"\"\"Produces mask of 1s for positive values and 0s for negative values.\"\"\"\n from tensorflow.python.ops import gen_nn_ops\n ones = tf.ones(a.get_shape(), dtype=dtype)\n return gen_nn_ops._relu_grad(ones, a)\n\ndef relu_mask_test():\n a = tf.constant([-1,0,1,2], dtype=default_dtype)\n sess = tf.Session()\n check_equal(sess.run(relu_mask(a)), [0,0,1,1])\n\ndef assert_rectangular(blocks):\n lengths = np.array([len(row) for row in blocks])\n assert (lengths==lengths[0]).all()\n \ndef empty_grid(rows, cols):\n \"\"\"Create empty list of lists of rows-by-cols shape.\"\"\"\n result = []\n for i in range(rows):\n result.append([None]*cols)\n return result\n\ndef block_diagonal_inverse(blocks):\n \"\"\"Invert diagonal blocks, leave remaining unchanged.\"\"\"\n \n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n \ndef block_diagonal_inverse_sqrt(blocks):\n assert_rectangular(blocks)\n num_rows = len(blocks)\n num_cols = len(blocks[0])\n\n result = empty_grid(num_rows, num_cols)\n dtype = blocks[0][0].dtype # TODO: assert same dtype\n \n for i in range(len(blocks)):\n for j in range(len(blocks[0])):\n block = blocks[i][j]\n if i == j:\n result[i][j] = pseudo_inverse_sqrt(block)\n else:\n result[i][j] = tf.zeros(shape=block.get_shape(),\n dtype=dtype)\n return result\n\n\ndef block_diagonal_inverse_test():\n sess = tf.Session()\n blocks = [[2*Identity(3), tf.ones((3, 1))],\n [tf.ones((1,3)), 2*Identity(1)]]\n new_blocks = block_diagonal_inverse(blocks)\n actual = concat_blocks(new_blocks)\n expected = 0.5*Identity(4)\n check_equal(sess.run(actual), sess.run(expected))\n\n \ndef t(x):\n return tf.transpose(x)\n\n \n# Time tracking functions\nglobal_time_list = []\nglobal_last_time = 0\ndef reset_time():\n global global_time_list, global_last_time\n global_time_list = []\n global_last_time = time.perf_counter()\n \ndef record_time():\n global global_last_time, global_time_list\n new_time = time.perf_counter()\n global_time_list.append(new_time - global_last_time)\n global_last_time = time.perf_counter()\n\ndef last_time():\n global global_last_time, global_time_list\n if global_time_list:\n return 1000*global_time_list[-1]\n else:\n return 0\n\ndef summarize_time(time_list=None):\n if time_list is None:\n time_list = global_time_list\n\n if time_list[0]>3600*10: # large first interval means no relative ts\n del time_list[0]\n \n time_list = 1000*np.array(time_list) # get seconds, convert to ms\n if len(time_list)>0:\n min = np.min(time_list)\n median = np.median(time_list)\n formatted = [\"%.2f\"%(d,) for d in time_list[:10]]\n print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median, np.mean(time_list)))\n # print(\"Times: min: %.2f, median: %.2f, mean: %.2f\"%(min, median,\",\".join(formatted)))\n else:\n print(\"Times: \")\n \ndef summarize_graph(g=None):\n if not g:\n g = tf.get_default_graph()\n print(\"Graph: %d ops, %d MBs\"%(len(g.get_operations()),\n len(str(g.as_graph_def()))/10**6))\n\nfrom tensorflow.python.framework import ops\noriginal_shape_func = ops.set_shapes_for_outputs\ndef disable_shape_inference():\n ops.set_shapes_for_outputs = lambda _: _\n \ndef enable_shape_inference():\n ops.set_shapes_for_outputs = original_shape_func\n\n# work-around for graph_editor.copy_with_input_replacements scaling\n# quadratically with size of the graph\nfrom tensorflow.contrib.graph_editor import transform\noriginal_assign_renamed_collections_handler = transform.assign_renamed_collections_handler\ndef dummy_collections_handler(info, elem, elem_): pass\ndef disable_collections_handler():\n transform.assign_renamed_collections_handler = dummy_collections_handler\ndef enable_collections_handler():\n transform.assign_renamed_collections_handler = original_assign_renamed_collections_handler\n\n\n\ndef dump_with_prompt(result, fname, no_prefix=False):\n \"\"\"Helper function to ask for confirmation before overwriting.\"\"\"\n location = os.getcwd()+\"/data/\"+fname # TODO: factor out locations logic\n if os.path.exists(location):\n answer = input(\"%s exists, overwrite? (Y/n) \"%(location,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n else:\n u.dump(result, fname, no_prefix)\n else:\n u.dump(result, fname, no_prefix)\n \n\ndef dump(result, fname, no_prefix=False):\n \"\"\"Save result to file.\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n if result.shape == (): # savetxt has problems with scalars\n result = np.expand_dims(result, 0)\n if no_prefix:\n location = os.getcwd()+\"/\"+fname\n else:\n location = os.getcwd()+\"/data/\"+fname\n # special handling for integer datatypes\n if (\n result.dtype == np.uint8 or result.dtype == np.int8 or\n result.dtype == np.uint16 or result.dtype == np.int16 or\n result.dtype == np.uint32 or result.dtype == np.int32 or\n result.dtype == np.uint64 or result.dtype == np.int64\n ):\n np.savetxt(location, result, fmt=\"%d\", delimiter=',')\n else:\n np.savetxt(location, result, delimiter=',')\n print(location)\n\ndef dump32(result, fname):\n \"\"\"Efficient dumping of float32 vals\"\"\"\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n location = os.getcwd()+\"/data/\"+fname\n assert is_numeric(result)\n# print(location)\n return result.astype('float32').tofile(location)\n\n\ndef frobenius_np(a):\n return np.sqrt(np.sum(np.square(a)))\n\ndef nan_check(result):\n result = result.eval() if hasattr(result, \"eval\") else result\n result = np.asarray(result)\n print(\"result any NaNs: %s\"% (np.isnan(result).any(),))\n\n\ndef L2(t):\n \"\"\"Squared L2 norm of t.\"\"\"\n if t.__class__.__name__=='Grads':\n t = t.f\n else:\n assert (t.__class__.__name__ == 'Tensor' or\n t.__class__.__name__.endswith('Variable'))\n return tf.reduce_sum(tf.square(t))\n\nglobal_timeit_dict = OrderedDict()\nclass timeit:\n def __init__(self, tag=\"\"):\n self.tag = tag\n \n def __enter__(self):\n self.start = time.perf_counter()\n return self\n \n def __exit__(self, *args):\n self.end = time.perf_counter()\n interval_ms = 1000*(self.end - self.start)\n global_timeit_dict.setdefault(self.tag, []).append(interval_ms)\n# print(\" %s Elapsed: %.2f ms\"%(self.tag, interval_ms))\n# logger = u.get_last_logger()\n# logger('time/'+self.tag, interval_ms)\n\n\ndef timeit_summarize():\n global global_timeit_dict\n pass\n\n# graph traversal\n# computation flows from parents to children\n# to find path from target to dependency, do\n# nx.shortest_path(gg, dependency, target)\ndef parents(op): return set(input.op for input in op.inputs)\ndef children(op): return set(op for out in op.outputs for op in out.consumers())\ndef dict_graph():\n \"\"\"Creates dictionary {node: {child1, child2, ..},..} for current\n TensorFlow graph. Result is compatible with networkx/toposort\"\"\"\n\n ops = tf.get_default_graph().get_operations()\n return {op: children(op) for op in ops}\ndef nx_graph():\n return nx.DiGraph(dict_graph())\n\ndef shortest_path(dep, target):\n if hasattr(dep, \"op\"):\n dep = dep.op\n if hasattr(target, \"op\"):\n target = target.op\n return nx.shortest_path(nx_graph(), dep, target)\n\ndef list_or_tuple(k):\n return isinstance(k, list) or isinstance(k, tuple)\n\ndef is_numeric(ndarray):\n ndarray = np.asarray(ndarray)\n return np.issubdtype(ndarray.dtype, np.number)\n\nclass VarInfo:\n \"\"\"Encapsulate variable info.\"\"\"\n def __init__(self, setter, p):\n self.setter = setter\n self.p = p\n\nclass SvdTuple:\n \"\"\"Object to store svd tuple.\n Create as SvdTuple((s,u,v)) or SvdTuple(s, u, v).\n \"\"\"\n def __init__(self, suvi, *args):\n if list_or_tuple(suvi):\n if len(suvi) == 3:\n s, u, v = suvi\n inv = Identity(s.shape[0])\n else:\n s, u, v, inv = suvi\n else:\n s = suvi\n u = args[0]\n v = args[1]\n if len(args)>2:\n inv = args[2]\n else:\n inv = Identity(s.shape[0])\n self.s = s\n self.u = u\n self.v = v\n self.inv = inv\n\n\nclass SvdWrapper:\n \"\"\"Encapsulates variables needed to perform SVD of a TensorFlow target.\n Initialize: wrapper = SvdWrapper(tensorflow_var)\n Trigger SVD: wrapper.update_tf() or wrapper.update_scipy()\n Access result as TF vars: wrapper.s, wrapper.u, wrapper.v\n \"\"\"\n \n def __init__(self, target, name, do_inverses=False):\n self.name = name\n self.target = target\n self.do_inverses = do_inverses\n self.tf_svd = SvdTuple(tf.svd(target))\n self.update_counter = 0\n\n self.init = SvdTuple(\n ones(target.shape[0], name=name+\"_s_init\"),\n Identity(target.shape[0], name=name+\"_u_init\"),\n Identity(target.shape[0], name=name+\"_v_init\"),\n Identity(target.shape[0], name=name+\"_inv_init\"),\n )\n\n assert self.tf_svd.s.shape == self.init.s.shape\n assert self.tf_svd.u.shape == self.init.u.shape\n assert self.tf_svd.v.shape == self.init.v.shape\n # assert self.tf_svd.inv.shape == self.init.inv.shape\n\n self.cached = SvdTuple(\n tf.Variable(self.init.s, name=name+\"_s\"),\n tf.Variable(self.init.u, name=name+\"_u\"),\n tf.Variable(self.init.v, name=name+\"_v\"),\n tf.Variable(self.init.inv, name=name+\"_inv\"),\n )\n\n self.s = self.cached.s\n self.u = self.cached.u\n self.v = self.cached.v\n self.inv = self.cached.inv\n \n self.holder = SvdTuple(\n tf.placeholder(default_dtype, shape=self.cached.s.shape, name=name+\"_s_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.u.shape, name=name+\"_u_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.v.shape, name=name+\"_v_holder\"),\n tf.placeholder(default_dtype, shape=self.cached.inv.shape, name=name+\"_inv_holder\")\n )\n\n self.update_tf_op = tf.group(\n self.cached.s.assign(self.tf_svd.s),\n self.cached.u.assign(self.tf_svd.u),\n self.cached.v.assign(self.tf_svd.v),\n self.cached.inv.assign(self.tf_svd.inv)\n )\n\n self.update_external_op = tf.group(\n self.cached.s.assign(self.holder.s),\n self.cached.u.assign(self.holder.u),\n self.cached.v.assign(self.holder.v),\n )\n\n self.update_externalinv_op = tf.group(\n self.cached.inv.assign(self.holder.inv),\n )\n\n\n self.init_ops = (self.s.initializer, self.u.initializer, self.v.initializer,\n self.inv.initializer)\n \n\n def update(self):\n if USE_MKL_SVD:\n self.update_scipy()\n else:\n self.update_tf()\n self.update_counter+=1\n \n def update_tf(self):\n sess = u.get_default_session()\n sess.run(self.update_tf_op)\n \n def update_scipy(self):\n if self.do_inverses:\n return self.update_scipy_inv()\n else:\n return self.update_scipy_svd()\n\n def update_scipy_inv(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n inv0 = linalg.inv(target0)\n feed_dict = {self.holder.inv: inv0}\n sess.run(self.update_externalinv_op, feed_dict=feed_dict)\n \n def update_scipy_svd(self):\n sess = u.get_default_session()\n target0 = sess.run(self.target)\n # A=u.diag(s).v', singular vectors are columns\n # TODO: catch \"ValueError: array must not contain infs or NaNs\"\n try:\n u0, s0, vt0 = linalg.svd(target0)\n v0 = vt0.T\n except Exception as e:\n print(\"Got error %s\"%(repr(e),))\n if DUMP_BAD_SVD:\n dump32(target0, \"badsvd\")\n print(\"gesdd failed, trying gesvd\")\n u0, s0, vt0 = linalg.svd(target0, lapack_driver=\"gesvd\")\n v0 = vt0.T\n \n feed_dict = {self.holder.u: u0,\n self.holder.v: v0,\n self.holder.s: s0}\n sess.run(self.update_external_op, feed_dict=feed_dict)\n\ndef extract_grad(grads_and_vars, var):\n if isinstance(var, str):\n varname = var\n else:\n varname = var.name\n vals = []\n for (grad, var) in grads_and_vars:\n if var.name == varname:\n vals.append(var)\n assert length(vals)==1\n return vals[0]\n\ndef intersept_op_creation(op_type_name_to_intercept):\n \"\"\"Drops into PDB when particular op type is added to graph.\"\"\"\n from tensorflow.python.framework import op_def_library\n old_apply_op = op_def_library.OpDefLibrary.apply_op\n def my_apply_op(obj, op_type_name, name=None, **keywords):\n print(op_type_name+\"-\"+str(name))\n if op_type_name == op_type_name_to_intercept:\n import pdb; pdb.set_trace()\n return(old_apply_op(obj, op_type_name, name=name, **keywords))\n op_def_library.OpDefLibrary.apply_op=my_apply_op\n\n\nglobal_variables = {}\ndef get_variable(name, initializer, reuse=True):\n \"\"\"Lightweight replacement for tf.get_variable() for variables shared within\n a single process. Doesn't need variable scopes.\"\"\"\n\n global global_variables\n if name in global_variables and reuse:\n v = global_variables[name]\n else:\n v = tf.Variable(name=name, initial_value=initializer)\n # print(\"Creating new variable %s into %s\" %(name, v.op.name))\n global_variables[name] = v\n return v\n\n\nclass VarStruct:\n # TODO: refactor to behave more like variable\n \"\"\"Convenience structure to keep track of variable, its assign op\n and assignment placeholder.\n\n v = Var(6)\n v.set(5) # equivalent to sess.run(v.assign_op, feed_dict={pl: 5})\n var.var # returns underlying variable\n var.val_ # placeholder to assign op\n var.setter # assign op\n var.set(6) # same as sess.run(var.setter, feed_dict={self.val_: val})\n var.initialize() # sets variable to initial value\n \"\"\"\n\n # TODO: add names to placeholder op\n def __init__(self, initial_value, name, dtype=None):\n\n initial_value = np.array(initial_value)\n assert u.is_numeric(initial_value), \"Non-numeric type.\"\n if not dtype:\n dtype = initial_value.dtype\n else:\n initial_value = initial_value.astype(dtype)\n self.initial_value = initial_value\n self.val_ = tf.placeholder(dtype=initial_value.dtype,\n shape=initial_value.shape,\n name=name+\"_holder\")\n self.var = tf.Variable(initial_value=self.val_, name=name, dtype=dtype)\n assigned_name = self.var.op.name\n if assigned_name != name:\n print(\"Warning, conflicting variable %s\"%(assigned_name,))\n self.setter = self.var.assign(self.val_)\n\n def set(self, val):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: val})\n\n def initialize(self):\n sess = u.get_default_session()\n sess.run(self.setter, feed_dict={self.val_: self.val})\n\n\nglobal_vars = {}\ndef get_var(name, initializer, reuse=True):\n \"\"\"Global get_variable replacement for variables that need to be initialized\n with a large numpy array.\n \n a = tf.get_var([1,2,3])\n a.var # => gives tf.Variable\n a.val\n \"\"\"\n\n global global_vars\n dtype = initializer.dtype\n if name in global_vars and reuse:\n vv = global_vars[name]\n if (np.max(np.abs(vv.initial_value - initializer)))>np.finfo(dtype).eps:\n print(\"Trying to reinitialize global variable %s with new\"\n \" value, ignoring new value.\"%(name,))\n else:\n vv = VarStruct(initial_value=initializer, name=name)\n global_vars[name] = vv\n return vv\n\ndef run_all_tests(module):\n all_functions = inspect.getmembers(module, inspect.isfunction)\n for name,func in all_functions:\n if name.endswith(\"_test\"):\n print(\"Testing \"+name)\n with timeit():\n func()\n print(module.__name__+\" tests passed.\")\n\n@contextlib.contextmanager\ndef capture_ops():\n \"\"\"Decorator to capture ops created in the block.\n with capture_ops() as ops:\n # create some ops\n print(ops) # => prints ops created.\n \"\"\"\n\n micros = int(time.perf_counter()*10**6)\n scope_name = str(micros)\n op_list = []\n with tf.name_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n op_list.extend(ge.select_ops(scope_name+\"/.*\", graph=g))\n\n@contextlib.contextmanager\ndef capture_vars():\n \"\"\"Decorator to capture global variables created in the block.\n \"\"\"\n \n micros = int(time.perf_counter()*10**6)\n scope_name = \"capture_vars_\"+str(micros)\n op_list = []\n with tf.variable_scope(scope_name):\n yield op_list\n\n g = tf.get_default_graph()\n for v in tf.global_variables():\n scope = v.name.split('/', 1)[0]\n if scope == scope_name:\n op_list.append(v)\n\ndef Print(op):\n return tf.Print(op, [op], op.name)\n\n\ndef get_host_prefix():\n \"ie, returns 10 when on 10.cirrascale...\"\n return socket.gethostname().split('.',1)[0]\n\ndef summarize_difference(source, target):\n source = np.asarray(source)\n machine_epsilon = np.finfo(source.dtype).eps\n # abs_diff = np.linalg.norm(np.asarray(source)-target, ord=np.inf)\n abs_diff = abs(np.asarray(source)-target)\n rel_diff = abs_diff/abs(source)/machine_epsilon\n print(\"abs diff: %f, rel diff: %.1f eps \" %(np.max(abs_diff), np.max(rel_diff)))\n\nclass BufferedWriter:\n \"\"\"Class that aggregates multiple writes and flushes periodically.\"\"\"\n \n def __init__(self, outfn, save_every_secs=60*5):\n self.outfn = outfn\n self.last_save_ts = time.perf_counter()\n self.write_buffer = []\n self.save_every_secs = save_every_secs\n\n def write(self, line):\n self.write_buffer.append(line)\n if time.perf_counter() - self.last_save_ts > self.save_every_secs:\n self.last_save_ts = time.perf_counter()\n with open(self.outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n\n def flush():\n with open(outfn, \"a\") as myfile:\n for line in self.write_buffer:\n myfile.write(line)\n self.write_buffer = []\n \ndef ossystem(line):\n print(line)\n os.system(line)\n \ndef setup_experiment_run_directory(run, safe_mode=True):\n # TODO: factor out to use GLOBAL_RUNS_DIRECTORY\n rundir = \"runs/%s\"%(run,)\n if os.path.exists(rundir):\n if safe_mode and not run=='default':\n answer = input(\"%s exists, delete? (Y/n) \"%(rundir,))\n if not answer:\n answer = \"y\"\n if answer.lower() != \"y\":\n print(\"skipping\")\n sys.exit()\n print(\"Removing %s\"%(rundir,))\n ossystem(\"rm -Rf \"+rundir)\n ossystem(\"mkdir %s\"%(rundir,))\n return rundir\n\n########################################\n# Tensorboard logging\n########################################\n\n# TODO: have global experiment_base that I can use to move logging to\n# non-current directory\nGLOBAL_RUNS_DIRECTORY='runs'\nglobal_last_logger = None\n\ndef get_last_logger():\n global global_last_logger\n assert global_last_logger\n return global_last_logger\n\nclass TensorboardLogger:\n \"\"\"Helper class to log to single tensorboard writer from multiple places.\n logger = u.TensorboardLogger(\"mnist7\")\n logger = u.get_last_logger() # gets last logger created\n logger('svd_time', 5) # records \"svd_time\" stat at 5\n logger.next_step() # advances step counter\n logger.set_step(5) # sets step counter to 5\n \"\"\"\n \n def __init__(self, run, step=0):\n # TODO: do nothing for default run\n \n global global_last_logger\n assert global_last_logger is None\n self.run = run\n # sess = tf.get_default_session()\n\n self.summary_writer = tf.summary.FileWriter(GLOBAL_RUNS_DIRECTORY+'/'+run,\n graph=tf.get_default_graph())\n self.step = step\n self.summary = tf.Summary()\n global_last_logger = self\n self.last_timestamp = time.perf_counter()\n\n def __call__(self, *args):\n assert len(args)%2 == 0\n for (tag, value) in chunks(args, 2):\n self.summary.value.add(tag=tag, simple_value=float(value))\n\n def next_step(self):\n new_timestamp = time.perf_counter()\n self.summary.value.add(tag='time/step',\n simple_value=(new_timestamp-self.last_timestamp))\n self.last_timestamp = new_timestamp\n self.summary_writer.add_summary(self.summary, self.step)\n self.step+=1\n self.summary = tf.Summary()\n\n\ndef as_int32(v):\n \"\"\"Convert to int32 dtype.\"\"\"\n return np.dtype(np.int32).type(v)\n\ndef add_dep(from_op, on_op):\n ge.reroute.add_control_inputs(from_op, [on_op])\n\n# Three functions below are replacements for tf default session/default graph\n# mechanisms that are global (native ones are thread-local because of thread\n# safety issues that have since been fixes (ie, mrry fixed Graph to be thread\n# safe for reading)\n\nsess = None\ndef register_default_session(local_sess):\n global sess\n assert sess is None\n sess = local_sess\n\ndef get_default_session():\n # hack, remove\n return tf.get_default_session()\n global sess\n assert sess\n return sess\n\ndef get_default_graph():\n global sess\n assert sess\n return sess.graph\n\ndef eval(tensor):\n \"\"\"tensor.eval() replacement since .eval() is not multi-thread-happy\"\"\"\n global sess\n assert sess\n return sess.run(tensor)\n\nfrom collections import OrderedDict\nglobal_stats = OrderedDict()\ndef record(tag, stat):\n global global_stats\n global_stats.setdefault(tag, []).append(stat)\n\nif __name__=='__main__':\n run_all_tests(sys.modules[__name__])\n" } ]