Full Code of LGE-ARC-AdvancedAI/auptimizer for AI

gitextract_g8wyt_2k/

├── .coveragerc
├── .dockerignore
├── .gitignore
├── .travis.yml
├── CODE_OF_CONDUCT.md
├── Examples/
│   ├── .gitignore
│   ├── 2dfunc_diff_mult_res/
│   │   ├── README.md
│   │   ├── cpu.ini
│   │   ├── exp_cpu.json
│   │   └── rosenbrock_hpo.py
│   ├── 2dfunc_diff_opt/
│   │   ├── .gitignore
│   │   ├── History.ipynb
│   │   ├── README.md
│   │   ├── experiment_auto.json
│   │   ├── experiment_bohb.json
│   │   ├── experiment_hyperband.json
│   │   ├── experiment_hyperopt.json
│   │   ├── experiment_random.json
│   │   ├── experiment_sequence.json
│   │   ├── experiment_spearmint.json
│   │   ├── rosenbrock_hpo.py
│   │   └── rosenbrock_origin.py
│   ├── 2dfunc_diff_res/
│   │   ├── README.md
│   │   ├── aws.txt
│   │   ├── cpu.ini
│   │   ├── env_local_template.ini
│   │   ├── env_user_template.ini
│   │   ├── exp_aws.json
│   │   ├── exp_cpu.json
│   │   ├── exp_gpu.json
│   │   ├── exp_node.json
│   │   ├── exp_node_async.json
│   │   ├── exp_passive.json
│   │   ├── gpu.txt
│   │   ├── node.txt
│   │   ├── plainGPU.txt
│   │   ├── rosenbrock_hpo.py
│   │   └── singleGPU.txt
│   ├── cai_eas/
│   │   ├── .gitignore
│   │   ├── README.md
│   │   ├── eas/
│   │   │   ├── client.py
│   │   │   ├── data_providers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── base_provider.py
│   │   │   │   ├── cifar.py
│   │   │   │   ├── downloader.py
│   │   │   │   ├── svhn.py
│   │   │   │   └── utils.py
│   │   │   ├── expdir_monitor/
│   │   │   │   ├── __init__.py
│   │   │   │   └── expdir_monitor.py
│   │   │   ├── experiment.json
│   │   │   └── models/
│   │   │       ├── __init__.py
│   │   │       ├── basic_model.py
│   │   │       ├── convnet.py
│   │   │       ├── dense_net.py
│   │   │       ├── layer_cascade.py
│   │   │       ├── layer_multi_branch.py
│   │   │       ├── layers.py
│   │   │       └── utils.py
│   │   └── start_nets/
│   │       └── start_net_convnet_small_C10+/
│   │           ├── init
│   │           └── net.config
│   ├── compression/
│   │   ├── mnist_pytorch/
│   │   │   ├── README.md
│   │   │   ├── exp_activation_apoz_rank.json
│   │   │   ├── exp_activation_apoz_rank_dependency_aware.json
│   │   │   ├── exp_activation_mean_rank.json
│   │   │   ├── exp_activation_mean_rank_dependency_aware.json
│   │   │   ├── exp_admm.json
│   │   │   ├── exp_agp.json
│   │   │   ├── exp_amc.json
│   │   │   ├── exp_auto_activation_apoz_rank.json
│   │   │   ├── exp_auto_activation_mean_rank.json
│   │   │   ├── exp_auto_admm.json
│   │   │   ├── exp_auto_agp.json
│   │   │   ├── exp_auto_fpgm_aup_args.json
│   │   │   ├── exp_auto_fpgm_bohb.json
│   │   │   ├── exp_auto_fpgm_hyperband.json
│   │   │   ├── exp_auto_fpgm_hyperopt.json
│   │   │   ├── exp_auto_fpgm_no_expand.json
│   │   │   ├── exp_auto_fpgm_random.json
│   │   │   ├── exp_auto_fpgm_random_no_expand.json
│   │   │   ├── exp_auto_fpgm_sequence.json
│   │   │   ├── exp_auto_fpgm_spearmint.json
│   │   │   ├── exp_auto_l1filter.json
│   │   │   ├── exp_auto_l2filter.json
│   │   │   ├── exp_auto_level.json
│   │   │   ├── exp_auto_lottery_ticket.json
│   │   │   ├── exp_auto_taylor_fo.json
│   │   │   ├── exp_autocompress.json
│   │   │   ├── exp_fpgm.json
│   │   │   ├── exp_fpgm_aup_args.json
│   │   │   ├── exp_fpgm_dependency_aware.json
│   │   │   ├── exp_l1filter.json
│   │   │   ├── exp_l1filter_dependency_aware.json
│   │   │   ├── exp_l2filter.json
│   │   │   ├── exp_l2filter_dependency_aware.json
│   │   │   ├── exp_level.json
│   │   │   ├── exp_lottery_ticket.json
│   │   │   ├── exp_net_adapt.json
│   │   │   ├── exp_quantization_bnn.json
│   │   │   ├── exp_quantization_dorefa.json
│   │   │   ├── exp_quantization_naive.json
│   │   │   ├── exp_quantization_qat.json
│   │   │   ├── exp_sensitivity.json
│   │   │   ├── exp_simulated_annealing.json
│   │   │   ├── exp_taylor_fo.json
│   │   │   ├── exp_taylor_fo_dependency_aware.json
│   │   │   ├── mnist.py
│   │   │   ├── mnist_admm.py
│   │   │   ├── mnist_agp.py
│   │   │   ├── mnist_amc.py
│   │   │   ├── mnist_aup_args.py
│   │   │   ├── mnist_autocompress.py
│   │   │   ├── mnist_dependency_aware.py
│   │   │   ├── mnist_lottery_ticket.py
│   │   │   ├── mnist_net_adapt.py
│   │   │   ├── mnist_no_speedup.py
│   │   │   ├── mnist_pretrained.pth
│   │   │   ├── mnist_pretrained.py
│   │   │   ├── mnist_sensitivity.py
│   │   │   └── mnist_simulated_annealing.py
│   │   ├── mnist_tensorflow/
│   │   │   ├── README.md
│   │   │   ├── exp_auto_level.json
│   │   │   ├── exp_level.json
│   │   │   ├── mnist.py
│   │   │   └── mnist_pretrained.py
│   │   └── utility_functions/
│   │       ├── README.md
│   │       └── mnist.py
│   ├── converter_examples/
│   │   ├── Convert_Benchmark/
│   │   │   ├── .gitignore
│   │   │   ├── Benchmark.ipynb
│   │   │   ├── README.md
│   │   │   ├── repdata.py
│   │   │   └── script_mobilenet.py
│   │   ├── Convert_Profiler/
│   │   │   ├── .gitignore
│   │   │   ├── README.md
│   │   │   ├── convert_onnx.json
│   │   │   ├── convert_tflite.json
│   │   │   ├── download_test_models.py
│   │   │   ├── env_onnx.template
│   │   │   ├── env_tflite.template
│   │   │   ├── model_names_tflite.txt
│   │   │   ├── test_perf_onnx.py
│   │   │   ├── test_perf_tflite.py
│   │   │   └── tflite_output.txt
│   │   ├── README.md
│   │   ├── Tested_Models/
│   │   │   ├── .gitignore
│   │   │   ├── README.md
│   │   │   ├── conversion_jsons/
│   │   │   │   ├── convert_checkpoint_to_onnx.json
│   │   │   │   ├── convert_checkpoint_to_tflite.json
│   │   │   │   ├── convert_keras_to_onnx.json
│   │   │   │   ├── convert_keras_to_tflite.json
│   │   │   │   ├── convert_protobuf_to_onnx.json
│   │   │   │   ├── convert_protobuf_to_tflite.json
│   │   │   │   ├── convert_pytorch_to_onnx.json
│   │   │   │   ├── convert_pytorch_to_tflite.json
│   │   │   │   ├── convert_savedmodel_to_onnx.json
│   │   │   │   └── convert_savedmodel_to_tflite.json
│   │   │   ├── convert_pb_to_tflite.json
│   │   │   ├── create_test_models.py
│   │   │   ├── download_models.py
│   │   │   └── download_urls.json
│   │   └── dlconvert_requirements.txt
│   ├── decorator_example/
│   │   ├── origin.py
│   │   └── use_decorator.py
│   ├── demo/
│   │   ├── .gitignore
│   │   ├── demo.yml
│   │   ├── experiment_wrapper.json
│   │   ├── hpo_wrapper.py
│   │   └── origin.py
│   ├── early_stopping/
│   │   ├── README.md
│   │   ├── mnist_keras/
│   │   │   ├── cpu.ini
│   │   │   ├── exp_BOHB_bandit.json
│   │   │   ├── exp_BOHB_curve_fitting.json
│   │   │   ├── exp_BOHB_median.json
│   │   │   ├── exp_BOHB_trunc.json
│   │   │   ├── exp_random_bandit.json
│   │   │   ├── exp_random_curve_fitting.json
│   │   │   ├── exp_random_median.json
│   │   │   ├── exp_random_trunc.json
│   │   │   ├── exp_spearmint_bandit.json
│   │   │   ├── exp_spearmint_curve_fitting.json
│   │   │   ├── exp_spearmint_median.json
│   │   │   ├── exp_spearmint_trunc.json
│   │   │   └── mnist.py
│   │   └── quad_equation_min/
│   │       ├── cpu.ini
│   │       ├── quad_min.py
│   │       ├── quad_min_BOHB_bandit.json
│   │       ├── quad_min_BOHB_median.json
│   │       ├── quad_min_BOHB_trunc.json
│   │       ├── quad_min_random_bandit.json
│   │       ├── quad_min_random_median.json
│   │       ├── quad_min_random_trunc.json
│   │       ├── quad_min_spearmint_bandit.json
│   │       ├── quad_min_spearmint_median.json
│   │       └── quad_min_spearmint_trunc.json
│   ├── hpo_mnist/
│   │   ├── .gitignore
│   │   ├── MNIST Hyperparameter Optimization Demo.ipynb
│   │   ├── README.md
│   │   ├── demo.json
│   │   ├── exp_aws_async.json
│   │   ├── exp_aws_demo.json
│   │   ├── exp_aws_retry_job.json
│   │   ├── exp_bohb.json
│   │   ├── exp_hyperband.json
│   │   ├── exp_hyperopt.json
│   │   ├── exp_random.json
│   │   ├── exp_random_async.json
│   │   ├── exp_sequence.json
│   │   ├── exp_spearmint.json
│   │   ├── job_retries_random.json
│   │   ├── mnist_hpo_demo.py
│   │   └── mnist_hpo_fail.py
│   ├── intermediate_results/
│   │   ├── README.md
│   │   └── quad_equation_min/
│   │       ├── quad_min.py
│   │       ├── quad_min_BOHB.json
│   │       ├── quad_min_random.json
│   │       └── quad_min_spearmint.json
│   ├── job_failure_control/
│   │   ├── README.md
│   │   ├── experiment_extra_argument.json
│   │   ├── experiment_job_ignore_fail.json
│   │   ├── experiment_job_retries.json
│   │   ├── experiment_job_retries_ignore_fail.json
│   │   ├── experiment_no_nsample.json
│   │   ├── experiment_no_param_config.json
│   │   ├── experiment_no_proposer.json
│   │   ├── experiment_no_resource.json
│   │   ├── experiment_no_script.json
│   │   ├── experiment_test.json
│   │   ├── rosenbrock_hpo.py
│   │   └── test.py
│   ├── mnist_keras_save_model/
│   │   ├── README.md
│   │   ├── cpu.ini
│   │   ├── exp_random_cpu.json
│   │   ├── exp_random_node.json
│   │   ├── mnist.py
│   │   ├── mnist_wo_decorator.py
│   │   └── node.txt
│   ├── profiler_examples/
│   │   ├── README.md
│   │   ├── bench/
│   │   │   ├── download.sh
│   │   │   └── test_perf.py
│   │   ├── env_benchmark.template
│   │   ├── env_mnist.template
│   │   ├── experiments/
│   │   │   └── Readme.md
│   │   ├── internal/
│   │   │   └── ImageNet Experiments.ipynb
│   │   ├── issues.md
│   │   ├── mnist/
│   │   │   └── mnist.py
│   │   └── model_names.txt
│   ├── quad_equation_min/
│   │   ├── QUAD_MIN_Demo.ipynb
│   │   ├── cpu.ini
│   │   ├── quad_min.py
│   │   ├── quad_min_BOHB.json
│   │   ├── quad_min_random.json
│   │   └── quad_min_spearmint.json
│   ├── tf_flags/
│   │   ├── README.md
│   │   ├── experiment.json
│   │   ├── rosenbrock_hpo.py
│   │   └── rosenbrock_tf.py
│   └── tf_iris_diff_opt/
│       ├── History.ipynb
│       ├── README.md
│       ├── experiment_demo.json
│       ├── experiment_hpo.json
│       ├── experiment_hyperband.json
│       ├── experiment_random.json
│       ├── experiment_sequence.json
│       ├── experiment_spearmint.json
│       ├── iris_data.py
│       ├── premade_estimator.py
│       ├── premade_estimator_hpo.py
│       ├── premade_estimator_hyper.py
│       └── premade_estimator_wrapper.py
├── LICENSE
├── MANIFEST.in
├── R-src/
│   ├── README.md
│   ├── Rpackage/
│   │   ├── DESCRIPTION
│   │   ├── NAMESPACE
│   │   ├── R/
│   │   │   └── auptimizer.R
│   │   ├── man/
│   │   │   ├── get_config.Rd
│   │   │   └── print_result.Rd
│   │   ├── tests/
│   │   │   ├── testthat/
│   │   │   │   ├── test_IO.R
│   │   │   │   └── test_io.json
│   │   │   └── testthat.R
│   │   └── vignettes/
│   │       ├── .gitignore
│   │       └── auptimizer.Rmd
│   └── examples/
│       ├── exp_ridge.R
│       ├── exp_rosenbrock.R
│       ├── ridge.json
│       ├── ridgeRegression.R
│       ├── rosenbrock.R
│       └── rosenbrock.json
├── README.md
├── docs/
│   ├── Database/
│   │   ├── schema.dot
│   │   ├── schema.sql
│   │   └── sql_graphviz.py
│   ├── Dockerfile
│   ├── LICENSE
│   ├── Makefile
│   ├── README.rst
│   ├── _static/
│   │   └── .gitkeep
│   ├── algorithm.rst
│   ├── archive/
│   │   ├── Auptimizer-1.0-py2-none-any.whl
│   │   ├── Auptimizer-1.0-py3-none-any.whl
│   │   ├── Auptimizer-1.1-py2-none-any.whl
│   │   ├── Auptimizer-1.1-py3-none-any.whl
│   │   ├── Auptimizer-1.2-py2-none-any.whl
│   │   ├── Auptimizer-1.2-py3-none-any.whl
│   │   ├── Auptimizer-1.3-py2-none-any.whl
│   │   ├── Auptimizer-1.3-py3-none-any.whl
│   │   ├── Auptimizer-1.4-py2.py3-none-any.whl
│   │   ├── Auptimizer-2.0-py2.py3-none-any.whl
│   │   └── aup.py
│   ├── aup.EE.Experiment.rst
│   ├── aup.EE.Job.rst
│   ├── aup.EE.Resource.rst
│   ├── aup.EE.rst
│   ├── aup.ET.Connector.rst
│   ├── aup.ET.rst
│   ├── aup.Proposer.BOHBProposer.rst
│   ├── aup.Proposer.EASProposer.rst
│   ├── aup.Proposer.HyperbandProposer.rst
│   ├── aup.Proposer.HyperoptProposer.rst
│   ├── aup.Proposer.RandomProposer.rst
│   ├── aup.Proposer.SequenceProposer.rst
│   ├── aup.Proposer.SpearmintProposer.rst
│   ├── aup.Proposer.rst
│   ├── aup.__main__.rst
│   ├── aup.compression.rst
│   ├── aup.convert.rst
│   ├── aup.dlconvert.rst
│   ├── aup.dlconvert_API.rst
│   ├── aup.init.rst
│   ├── aup.profiler.rst
│   ├── aup.rst
│   ├── aup.setup.rst
│   ├── aup.setupdb.rst
│   ├── aup.setupdb.sqlite.rst
│   ├── aup.setupdb_API.rst
│   ├── aup.utils.rst
│   ├── aup.visualize.rst
│   ├── compression.rst
│   ├── compression_main.rst
│   ├── compression_utilities.rst
│   ├── compressors.rst
│   ├── conf.py
│   ├── dashboard.rst
│   ├── demo.rst
│   ├── developer.rst
│   ├── developer_guide.rst
│   ├── dlconvert.rst
│   ├── dlconvert_example.rst
│   ├── dlconvert_readme.rst
│   ├── early_stop.rst
│   ├── edge.rst
│   ├── environment.rst
│   ├── errors.rst
│   ├── experiment.rst
│   ├── hpo.rst
│   ├── index.rst
│   ├── install.rst
│   ├── prepare.sh
│   ├── prof_example.rst
│   ├── prof_readme.rst
│   ├── profiler.rst
│   ├── r_user.rst
│   ├── requirements.txt
│   ├── setup.rst
│   └── version.rst
├── oss-package.info
├── publish.sh
├── requirements.txt
├── setup.py
├── src/
│   └── aup/
│       ├── EE/
│       │   ├── Experiment.py
│       │   ├── Job.py
│       │   ├── Resource/
│       │   │   ├── AWSResourceManager.py
│       │   │   ├── AbstractResourceManager.py
│       │   │   ├── CPUResourceManager.py
│       │   │   ├── GPUResourceManager.py
│       │   │   ├── PassiveResourceManager.py
│       │   │   ├── SSHResourceManager.py
│       │   │   ├── __init__.py
│       │   │   └── utils/
│       │   │       ├── ResourceThreadPoolExecutor.py
│       │   │       ├── __init__.py
│       │   │       └── curve_fitting.py
│       │   └── __init__.py
│       ├── ET/
│       │   ├── Connector/
│       │   │   ├── AbstractConnector.py
│       │   │   ├── SQLiteConnector.py
│       │   │   └── __init__.py
│       │   └── __init__.py
│       ├── Proposer/
│       │   ├── AbstractProposer.py
│       │   ├── BOHBProposer.py
│       │   ├── EASProposer.py
│       │   ├── HyperbandProposer.py
│       │   ├── HyperoptProposer.py
│       │   ├── Hyperopt_LICENSE
│       │   ├── RandomProposer.py
│       │   ├── SequenceProposer.py
│       │   ├── SpearmintProposer.py
│       │   ├── Spearmint_LICENSE
│       │   ├── __init__.py
│       │   ├── eas/
│       │   │   ├── LICENSE
│       │   │   ├── __init__.py
│       │   │   ├── arch_search/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── arch_search_convnet_net2net.py
│       │   │   │   └── arch_search_densenet_net2net.py
│       │   │   ├── arch_search.py
│       │   │   ├── client.py
│       │   │   ├── data_providers/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── base_provider.py
│       │   │   │   ├── cifar.py
│       │   │   │   ├── downloader.py
│       │   │   │   ├── svhn.py
│       │   │   │   └── utils.py
│       │   │   ├── expdir_monitor/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── arch_manager.py
│       │   │   │   ├── distributed.py
│       │   │   │   └── expdir_monitor.py
│       │   │   ├── main.py
│       │   │   ├── meta_controller/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── base_controller.py
│       │   │   │   └── rl_controller.py
│       │   │   ├── models/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── basic_model.py
│       │   │   │   ├── convnet.py
│       │   │   │   ├── dense_net.py
│       │   │   │   ├── layer_cascade.py
│       │   │   │   ├── layer_multi_branch.py
│       │   │   │   ├── layers.py
│       │   │   │   └── utils.py
│       │   │   ├── run_dense_net.py
│       │   │   └── run_simple_convnet.py
│       │   ├── hpbandster/
│       │   │   ├── __init__.py
│       │   │   ├── core/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── base_config_generator.py
│       │   │   │   ├── base_iteration.py
│       │   │   │   ├── dispatcher.py
│       │   │   │   ├── master.py
│       │   │   │   ├── nameserver.py
│       │   │   │   ├── result.py
│       │   │   │   └── worker.py
│       │   │   ├── examples/
│       │   │   │   ├── README.txt
│       │   │   │   ├── __init__.py
│       │   │   │   ├── commons.py
│       │   │   │   ├── example_1_local_sequential.py
│       │   │   │   ├── example_2_local_parallel_threads.py
│       │   │   │   ├── example_3_local_parallel_processes.py
│       │   │   │   ├── example_4_cluster.py
│       │   │   │   ├── example_5_keras_worker.py
│       │   │   │   ├── example_5_mnist.py
│       │   │   │   ├── example_5_pytorch_worker.py
│       │   │   │   ├── example_5_run/
│       │   │   │   │   ├── configs.json
│       │   │   │   │   └── results.json
│       │   │   │   ├── example_8_mnist_continued.py
│       │   │   │   ├── plot_example_6_analysis.py
│       │   │   │   └── plot_example_7_interactive_plot.py
│       │   │   ├── optimizers/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── bohb.py
│       │   │   │   ├── config_generators/
│       │   │   │   │   ├── __init__.py
│       │   │   │   │   ├── bohb.py
│       │   │   │   │   ├── h2bo.py
│       │   │   │   │   ├── kde.py
│       │   │   │   │   ├── lcnet.py
│       │   │   │   │   └── random_sampling.py
│       │   │   │   ├── h2bo.py
│       │   │   │   ├── hyperband.py
│       │   │   │   ├── iterations/
│       │   │   │   │   ├── __init__.py
│       │   │   │   │   ├── successivehalving.py
│       │   │   │   │   └── successiveresampling.py
│       │   │   │   ├── kde/
│       │   │   │   │   ├── __init__.py
│       │   │   │   │   ├── kernels.py
│       │   │   │   │   └── mvkde.py
│       │   │   │   ├── lcnet.py
│       │   │   │   ├── learning_curve_models/
│       │   │   │   │   ├── __init__.py
│       │   │   │   │   ├── arif.py
│       │   │   │   │   ├── base.py
│       │   │   │   │   └── lcnet.py
│       │   │   │   └── randomsearch.py
│       │   │   ├── utils.py
│       │   │   ├── visualization.py
│       │   │   └── workers/
│       │   │       ├── __init__.py
│       │   │       └── hpolibbenchmark.py
│       │   ├── hpbandster_LICENSE
│       │   ├── hyperband_LICENSE
│       │   ├── hyperopt/
│       │   │   ├── LICENSE.txt
│       │   │   ├── __init__.py
│       │   │   ├── algobase.py
│       │   │   ├── anneal.py
│       │   │   ├── base.py
│       │   │   ├── criteria.py
│       │   │   ├── exceptions.py
│       │   │   ├── fmin.py
│       │   │   ├── graphviz.py
│       │   │   ├── hp.py
│       │   │   ├── ipy.py
│       │   │   ├── main.py
│       │   │   ├── mix.py
│       │   │   ├── mongoexp.py
│       │   │   ├── plotting.py
│       │   │   ├── pyll/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── base.py
│       │   │   │   └── stochastic.py
│       │   │   ├── pyll_utils.py
│       │   │   ├── rand.py
│       │   │   ├── rdists.py
│       │   │   ├── tpe.py
│       │   │   ├── utils.py
│       │   │   └── vectorize.py
│       │   └── spearmint/
│       │       ├── ExperimentGrid.py
│       │       ├── Locker.py
│       │       ├── __init__.py
│       │       ├── chooser/
│       │       │   ├── CMAChooser.py
│       │       │   ├── GPConstrainedEIChooser.py
│       │       │   ├── GPEIChooser.py
│       │       │   ├── GPEIOptChooser.py
│       │       │   ├── GPEIperSecChooser.py
│       │       │   ├── RandomChooser.py
│       │       │   ├── RandomForestEIChooser.py
│       │       │   ├── SequentialChooser.py
│       │       │   ├── __init__.py
│       │       │   └── cma.py
│       │       ├── gp.py
│       │       ├── helpers.py
│       │       ├── runner.py
│       │       ├── sobol_lib.py
│       │       ├── spearmint_pb2.py
│       │       └── util.py
│       ├── RestAPI/
│       │   ├── __init__.py
│       │   ├── server.py
│       │   └── templates/
│       │       └── home.html
│       ├── __init__.py
│       ├── __main__.py
│       ├── aup.py
│       ├── compression/
│       │   ├── Compressor.py
│       │   ├── NNI-LICENSE
│       │   ├── __init__.py
│       │   ├── __main__.py
│       │   ├── tensorflow/
│       │   │   ├── __init__.py
│       │   │   ├── compressor.py
│       │   │   ├── default_layers.py
│       │   │   └── pruning/
│       │   │       ├── __init__.py
│       │   │       └── one_shot.py
│       │   ├── torch/
│       │   │   ├── __init__.py
│       │   │   ├── _graph_utils.py
│       │   │   ├── compressor.py
│       │   │   ├── default_layers.py
│       │   │   ├── parameter_expressions.py
│       │   │   ├── pruning/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── admm_pruner.py
│       │   │   │   ├── agp.py
│       │   │   │   ├── amc/
│       │   │   │   │   ├── __init__.py
│       │   │   │   │   ├── amc_pruner.py
│       │   │   │   │   ├── channel_pruning_env.py
│       │   │   │   │   └── lib/
│       │   │   │   │       ├── __init__.py
│       │   │   │   │       ├── agent.py
│       │   │   │   │       ├── memory.py
│       │   │   │   │       ├── net_measure.py
│       │   │   │   │       └── utils.py
│       │   │   │   ├── apply_compression.py
│       │   │   │   ├── auto_compress_pruner.py
│       │   │   │   ├── constants.py
│       │   │   │   ├── constants_pruner.py
│       │   │   │   ├── finegrained_pruning.py
│       │   │   │   ├── lottery_ticket.py
│       │   │   │   ├── net_adapt_pruner.py
│       │   │   │   ├── one_shot.py
│       │   │   │   ├── sensitivity_pruner.py
│       │   │   │   ├── simulated_annealing_pruner.py
│       │   │   │   ├── structured_pruning.py
│       │   │   │   └── weight_masker.py
│       │   │   ├── quantization/
│       │   │   │   ├── __init__.py
│       │   │   │   └── quantizers.py
│       │   │   ├── speedup/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── compress_modules.py
│       │   │   │   ├── compressor.py
│       │   │   │   └── infer_shape.py
│       │   │   ├── torch_utils.py
│       │   │   └── utils/
│       │   │       ├── __init__.py
│       │   │       ├── config_validation.py
│       │   │       ├── counter.py
│       │   │       ├── mask_conflict.py
│       │   │       ├── num_param_counter.py
│       │   │       ├── sensitivity_analysis.py
│       │   │       ├── shape_dependency.py
│       │   │       └── utils.py
│       │   └── utils.py
│       ├── convert.py
│       ├── dashboard/
│       │   ├── README.md
│       │   ├── __init__.py
│       │   ├── dashboard.py
│       │   └── frontend/
│       │       ├── .browserslistrc
│       │       ├── .editorconfig
│       │       ├── .eslintrc.json
│       │       ├── .gitignore
│       │       ├── .prettierignore
│       │       ├── .prettierrc
│       │       ├── Angular-LICENSE
│       │       ├── README.md
│       │       ├── angular.json
│       │       ├── e2e/
│       │       │   ├── protractor.conf.js
│       │       │   ├── src/
│       │       │   │   ├── app.e2e-spec.ts
│       │       │   │   └── app.po.ts
│       │       │   └── tsconfig.json
│       │       ├── febuild/
│       │       │   └── auptimizer-dashboard/
│       │       │       ├── 3rdpartylicenses.txt
│       │       │       ├── 4.3bf463bd37e16d085b6c.js
│       │       │       ├── 5.0e291298e9c49cb93c71.js
│       │       │       ├── _redirects
│       │       │       ├── index.html
│       │       │       ├── main.09d22743789a55973001.js
│       │       │       ├── polyfills.9cfb3f513e777138fb2c.js
│       │       │       ├── runtime.29a333e72cc7398676d0.js
│       │       │       └── styles.5a1d2b21684fc92c4f99.css
│       │       ├── karma.conf.js
│       │       ├── package.json
│       │       ├── src/
│       │       │   ├── _redirects
│       │       │   ├── app/
│       │       │   │   ├── @core/
│       │       │   │   │   ├── app-load.service.ts
│       │       │   │   │   └── core.module.ts
│       │       │   │   ├── app-routing.module.ts
│       │       │   │   ├── app.component.html
│       │       │   │   ├── app.component.scss
│       │       │   │   ├── app.component.spec.ts
│       │       │   │   ├── app.component.ts
│       │       │   │   ├── app.module.ts
│       │       │   │   ├── app.service.ts
│       │       │   │   ├── appStore/
│       │       │   │   │   ├── app-state.model.ts
│       │       │   │   │   ├── app.actions.ts
│       │       │   │   │   └── app.store.ts
│       │       │   │   ├── guards/
│       │       │   │   │   └── db.guard.ts
│       │       │   │   ├── main/
│       │       │   │   │   ├── containers/
│       │       │   │   │   │   ├── index.ts
│       │       │   │   │   │   └── main/
│       │       │   │   │   │       ├── main.component.html
│       │       │   │   │   │       ├── main.component.scss
│       │       │   │   │   │       ├── main.component.spec.ts
│       │       │   │   │   │       └── main.component.ts
│       │       │   │   │   ├── experiment/
│       │       │   │   │   │   ├── components/
│       │       │   │   │   │   │   ├── experiment-dropdown/
│       │       │   │   │   │   │   │   ├── experiment-dropdown.component.html
│       │       │   │   │   │   │   │   ├── experiment-dropdown.component.scss
│       │       │   │   │   │   │   │   ├── experiment-dropdown.component.spec.ts
│       │       │   │   │   │   │   │   └── experiment-dropdown.component.ts
│       │       │   │   │   │   │   ├── header/
│       │       │   │   │   │   │   │   ├── header.component.html
│       │       │   │   │   │   │   │   ├── header.component.scss
│       │       │   │   │   │   │   │   ├── header.component.spec.ts
│       │       │   │   │   │   │   │   └── header.component.ts
│       │       │   │   │   │   │   ├── index.ts
│       │       │   │   │   │   │   ├── sidenav/
│       │       │   │   │   │   │   │   ├── sidenav.component.html
│       │       │   │   │   │   │   │   ├── sidenav.component.scss
│       │       │   │   │   │   │   │   ├── sidenav.component.spec.ts
│       │       │   │   │   │   │   │   └── sidenav.component.ts
│       │       │   │   │   │   │   └── sidenav-element/
│       │       │   │   │   │   │       ├── sidenav-element.component.html
│       │       │   │   │   │   │       ├── sidenav-element.component.scss
│       │       │   │   │   │   │       ├── sidenav-element.component.spec.ts
│       │       │   │   │   │   │       └── sidenav-element.component.ts
│       │       │   │   │   │   ├── containers/
│       │       │   │   │   │   │   ├── create-experiment/
│       │       │   │   │   │   │   │   ├── create-experiment.component.html
│       │       │   │   │   │   │   │   ├── create-experiment.component.scss
│       │       │   │   │   │   │   │   ├── create-experiment.component.spec.ts
│       │       │   │   │   │   │   │   ├── create-experiment.component.ts
│       │       │   │   │   │   │   │   └── exampleJSON.ts
│       │       │   │   │   │   │   ├── experiment/
│       │       │   │   │   │   │   │   ├── experiment.component.html
│       │       │   │   │   │   │   │   ├── experiment.component.scss
│       │       │   │   │   │   │   │   ├── experiment.component.spec.ts
│       │       │   │   │   │   │   │   └── experiment.component.ts
│       │       │   │   │   │   │   ├── index.ts
│       │       │   │   │   │   │   ├── initialize/
│       │       │   │   │   │   │   │   ├── initialize.component.html
│       │       │   │   │   │   │   │   ├── initialize.component.scss
│       │       │   │   │   │   │   │   ├── initialize.component.spec.ts
│       │       │   │   │   │   │   │   └── initialize.component.ts
│       │       │   │   │   │   │   ├── interm-results/
│       │       │   │   │   │   │   │   ├── interm-results.component.html
│       │       │   │   │   │   │   │   ├── interm-results.component.scss
│       │       │   │   │   │   │   │   ├── interm-results.component.spec.ts
│       │       │   │   │   │   │   │   └── interm-results.component.ts
│       │       │   │   │   │   │   ├── job-status/
│       │       │   │   │   │   │   │   ├── job-status.component.html
│       │       │   │   │   │   │   │   ├── job-status.component.scss
│       │       │   │   │   │   │   │   ├── job-status.component.spec.ts
│       │       │   │   │   │   │   │   └── job-status.component.ts
│       │       │   │   │   │   │   ├── list/
│       │       │   │   │   │   │   │   ├── list.component.html
│       │       │   │   │   │   │   │   ├── list.component.scss
│       │       │   │   │   │   │   │   ├── list.component.spec.ts
│       │       │   │   │   │   │   │   └── list.component.ts
│       │       │   │   │   │   │   ├── main/
│       │       │   │   │   │   │   │   ├── main.component.html
│       │       │   │   │   │   │   │   ├── main.component.scss
│       │       │   │   │   │   │   │   ├── main.component.spec.ts
│       │       │   │   │   │   │   │   └── main.component.ts
│       │       │   │   │   │   │   ├── multi-exp-comp/
│       │       │   │   │   │   │   │   ├── multi-exp-comp.component.html
│       │       │   │   │   │   │   │   ├── multi-exp-comp.component.scss
│       │       │   │   │   │   │   │   ├── multi-exp-comp.component.spec.ts
│       │       │   │   │   │   │   │   └── multi-exp-comp.component.ts
│       │       │   │   │   │   │   ├── notification/
│       │       │   │   │   │   │   │   ├── notification.component.html
│       │       │   │   │   │   │   │   ├── notification.component.scss
│       │       │   │   │   │   │   │   ├── notification.component.spec.ts
│       │       │   │   │   │   │   │   └── notification.component.ts
│       │       │   │   │   │   │   ├── overview/
│       │       │   │   │   │   │   │   ├── overview.component.html
│       │       │   │   │   │   │   │   ├── overview.component.scss
│       │       │   │   │   │   │   │   ├── overview.component.spec.ts
│       │       │   │   │   │   │   │   └── overview.component.ts
│       │       │   │   │   │   │   └── pcg/
│       │       │   │   │   │   │       ├── pcg.component.html
│       │       │   │   │   │   │       ├── pcg.component.scss
│       │       │   │   │   │   │       ├── pcg.component.spec.ts
│       │       │   │   │   │   │       └── pcg.component.ts
│       │       │   │   │   │   ├── experiment-routing.module.ts
│       │       │   │   │   │   ├── experiment.module.ts
│       │       │   │   │   │   ├── services/
│       │       │   │   │   │   │   └── experiment.service.ts
│       │       │   │   │   │   └── store/
│       │       │   │   │   │       ├── experiment-state.model.ts
│       │       │   │   │   │       ├── experiment.actions.ts
│       │       │   │   │   │       └── experiment.store.ts
│       │       │   │   │   ├── main-routing.module.ts
│       │       │   │   │   └── main.module.ts
│       │       │   │   ├── material/
│       │       │   │   │   └── material.module.ts
│       │       │   │   ├── models/
│       │       │   │   │   ├── data/
│       │       │   │   │   │   ├── custom-icons.data.ts
│       │       │   │   │   │   ├── graph-colors.data.ts
│       │       │   │   │   │   ├── nav-items.ts
│       │       │   │   │   │   └── plotly-hidden-displays.ts
│       │       │   │   │   ├── enum/
│       │       │   │   │   │   ├── experiment-status.enum.ts
│       │       │   │   │   │   ├── experiment-view-type.enum.ts
│       │       │   │   │   │   ├── notification-type.enum.ts
│       │       │   │   │   │   └── theme-option.enum.ts
│       │       │   │   │   ├── experiment.model.ts
│       │       │   │   │   ├── nav-items.model.ts
│       │       │   │   │   ├── progress-bar.model.ts
│       │       │   │   │   └── resource.model.ts
│       │       │   │   ├── page-not-found/
│       │       │   │   │   ├── page-not-found.component.html
│       │       │   │   │   ├── page-not-found.component.scss
│       │       │   │   │   ├── page-not-found.component.spec.ts
│       │       │   │   │   └── page-not-found.component.ts
│       │       │   │   └── shared/
│       │       │   │       ├── dialogs/
│       │       │   │       │   ├── confirm/
│       │       │   │       │   │   ├── confirm-dialog.component.html
│       │       │   │       │   │   ├── confirm-dialog.component.scss
│       │       │   │       │   │   └── confirm-dialog.component.ts
│       │       │   │       │   └── index.ts
│       │       │   │       ├── pipes/
│       │       │   │       │   ├── experiment-status.pipe.ts
│       │       │   │       │   ├── first-letter-uppercase/
│       │       │   │       │   │   ├── first-letter-uppercase.pipe.spec.ts
│       │       │   │       │   │   └── first-letter-uppercase.pipe.ts
│       │       │   │       │   ├── index.ts
│       │       │   │       │   ├── min-to-hour/
│       │       │   │       │   │   ├── min-to-hour.pipe.spec.ts
│       │       │   │       │   │   └── min-to-hour.pipe.ts
│       │       │   │       │   ├── notification-icon.pipe.ts
│       │       │   │       │   ├── progressBarWidth.pipe.ts
│       │       │   │       │   ├── roudNumber.pipe.ts
│       │       │   │       │   ├── sec-to-min/
│       │       │   │       │   │   ├── sec-to-min.pipe.spec.ts
│       │       │   │       │   │   └── sec-to-min.pipe.ts
│       │       │   │       │   └── truncate.pipe.ts
│       │       │   │       ├── services/
│       │       │   │       │   ├── api.service.ts
│       │       │   │       │   ├── color-scheme.service.ts
│       │       │   │       │   ├── helper.service.ts
│       │       │   │       │   ├── index.ts
│       │       │   │       │   ├── snackbar.service.ts
│       │       │   │       │   └── utils.service.ts
│       │       │   │       ├── shared.module.ts
│       │       │   │       └── validators/
│       │       │   │           ├── at-least.validator.ts
│       │       │   │           └── index.ts
│       │       │   ├── assets/
│       │       │   │   └── .gitkeep
│       │       │   ├── environments/
│       │       │   │   ├── environment.prod.ts
│       │       │   │   └── environment.ts
│       │       │   ├── index.html
│       │       │   ├── main.ts
│       │       │   ├── polyfills.ts
│       │       │   ├── scss/
│       │       │   │   ├── _colors.scss
│       │       │   │   ├── _helpers.scss
│       │       │   │   ├── _material-overrides.scss
│       │       │   │   ├── jsoneditor-dark.scss
│       │       │   │   ├── jsoneditor.scss
│       │       │   │   ├── main.scss
│       │       │   │   └── theme.scss
│       │       │   ├── styles.scss
│       │       │   └── test.ts
│       │       ├── tailwind.config.js
│       │       ├── tsconfig.app.json
│       │       ├── tsconfig.json
│       │       ├── tsconfig.spec.json
│       │       └── webpack.config.js
│       ├── dlconvert/
│       │   ├── .gitignore
│       │   ├── README.md
│       │   ├── __init__.py
│       │   ├── __main__.py
│       │   ├── __version__.py
│       │   ├── checkpoint_to_onnx.py
│       │   ├── checkpoint_to_pb.py
│       │   ├── checkpoint_to_tflite.py
│       │   ├── keras_to_onnx.py
│       │   ├── keras_to_pb.py
│       │   ├── keras_to_tflite.py
│       │   ├── pb_to_onnx.py
│       │   ├── pb_to_tflite.py
│       │   ├── pytorch_to_keras.py
│       │   ├── pytorch_to_onnx.py
│       │   ├── pytorch_to_tflite.py
│       │   ├── savedmodel_to_onnx.py
│       │   ├── savedmodel_to_tflite.py
│       │   ├── spec_utils/
│       │   │   ├── __init__.py
│       │   │   └── pb.py
│       │   ├── to_frozen_pb.py
│       │   ├── to_onnx.py
│       │   ├── to_tflite.py
│       │   └── utils.py
│       ├── init.py
│       ├── profiler/
│       │   ├── README.md
│       │   ├── __init__.py
│       │   ├── __main__.py
│       │   ├── calculate.py
│       │   ├── compile_stats.py
│       │   ├── issues.md
│       │   ├── profiler.sh
│       │   └── statscript.sh
│       ├── setup.py
│       ├── setupdb/
│       │   ├── __init__.py
│       │   ├── __main__.py
│       │   ├── reset.py
│       │   └── sqlite.py
│       ├── utils.py
│       └── visualize.py
└── tests/
    ├── EE/
    │   ├── Resource/
    │   │   ├── .gitignore
    │   │   ├── __init__.py
    │   │   ├── test_AbstractResourceManager.py
    │   │   ├── test_CPUResourceManager.py
    │   │   ├── test_GPUResourceManager.py
    │   │   ├── test_PassiveResourceManager.py
    │   │   └── test_SSHResourceManager.py
    │   ├── __init__.py
    │   ├── test_Experiment.py
    │   └── test_Job.py
    ├── ET/
    │   ├── Connector/
    │   │   ├── __init__.py
    │   │   └── test_SQLiteConnector.py
    │   └── __init__.py
    ├── Proposer/
    │   ├── __init__.py
    │   ├── test_AbstractProposer.py
    │   ├── test_BOHBProposer.py
    │   ├── test_HyperbandProposer.py
    │   ├── test_HyperoptProposer.py
    │   ├── test_RandomProposer.py
    │   ├── test_SequenceProposer.py
    │   └── test_SpearmintProposer.py
    ├── __init__.py
    ├── command/
    │   ├── __init__.py
    │   ├── test_convert.py
    │   └── test_setup.py
    ├── compression/
    │   ├── __init__.py
    │   ├── test_aup_compressor.py
    │   ├── test_compression_utils.py
    │   ├── test_compressor_tf.py
    │   ├── test_compressor_torch.py
    │   └── test_model_speedup.py
    ├── data/
    │   ├── config/
    │   │   ├── test_read.json
    │   │   └── test_wrong.json
    │   ├── exp4.json
    │   ├── exp4_no_name.json
    │   ├── exp4_no_param.json
    │   ├── exp4_no_script.json
    │   ├── exp5.json
    │   ├── exp6.json
    │   ├── exp7.json
    │   ├── gpus.txt
    │   ├── nodes.txt
    │   ├── plain_env.ini
    │   ├── target_gpu_env.ini
    │   ├── target_plain_env.ini
    │   ├── task1.py
    │   ├── task2.py
    │   ├── task3.py
    │   ├── task4.py
    │   ├── task5.py
    │   ├── task6.py
    │   ├── task7.py
    │   ├── task8.py
    │   ├── test_script.py
    │   ├── wrapper1.json
    │   └── wrapper2.json
    ├── dlconvert/
    │   ├── .gitignore
    │   ├── Dockerfile_tf1
    │   ├── Dockerfile_tf2
    │   ├── README.md
    │   ├── __init__.py
    │   ├── data/
    │   │   ├── .gitignore
    │   │   ├── create_test_model.py
    │   │   ├── evaluate_pred.py
    │   │   ├── flag_names.json
    │   │   ├── prepare.sh
    │   │   ├── prepare_docker.sh
    │   │   ├── pytorch_model.py
    │   │   └── repdata.py
    │   ├── dlconvert_requirements.txt
    │   ├── pytest.ini
    │   ├── test_checkpoint_to_onnx.py
    │   ├── test_checkpoint_to_pb.py
    │   ├── test_checkpoint_to_tflite.py
    │   ├── test_keras_to_onnx.py
    │   ├── test_keras_to_pb.py
    │   ├── test_keras_to_tflite.py
    │   ├── test_pb_to_onnx.py
    │   ├── test_pb_to_tflite.py
    │   ├── test_pytorch_to_keras.py
    │   ├── test_pytorch_to_onnx.py
    │   ├── test_pytorch_to_tflite.py
    │   ├── test_savedmodel_to_onnx.py
    │   ├── test_savedmodel_to_tflite.py
    │   ├── unittest_tf1.sh
    │   └── unittest_tf2.sh
    ├── setupdb/
    │   ├── __init__.py
    │   └── test_sqlite.py
    ├── test_BasicConfig.py
    ├── test_utils.py
    └── test_wrapper.py

SYMBOL INDEX (13556 symbols across 368 files)

FILE: Examples/2dfunc_diff_mult_res/rosenbrock_hpo.py
  function rosenbrock (line 34) | def rosenbrock(x, y, a=1, b=100):

FILE: Examples/2dfunc_diff_opt/rosenbrock_hpo.py
  function rosenbrock (line 34) | def rosenbrock(x, y, a=1, b=100):

FILE: Examples/2dfunc_diff_opt/rosenbrock_origin.py
  function rosenbrock (line 12) | def rosenbrock(x, y, a=1, b=100):

FILE: Examples/2dfunc_diff_res/rosenbrock_hpo.py
  function rosenbrock (line 34) | def rosenbrock(x, y, a=1, b=100):

FILE: Examples/cai_eas/eas/client.py
  function run (line 16) | def run(expdir):
  function main (line 26) | def main():

FILE: Examples/cai_eas/eas/data_providers/base_provider.py
  class DataSet (line 4) | class DataSet:
    method num_examples (line 8) | def num_examples(self):
    method next_batch (line 12) | def next_batch(self, batch_size):
  class ImagesDataSet (line 17) | class ImagesDataSet(DataSet):
    method measure_mean_and_std (line 21) | def measure_mean_and_std(images):
    method shuffle_images_and_labels (line 32) | def shuffle_images_and_labels(images, labels):
    method normalize_images (line 39) | def normalize_images(images, normalization_type, meanstd=None):
  class DataProvider (line 65) | class DataProvider:
    method data_shape (line 69) | def data_shape(self):
    method n_classes (line 74) | def n_classes(self):
    method labels_to_one_hot (line 78) | def labels_to_one_hot(self, labels):
    method labels_from_one_hot (line 89) | def labels_from_one_hot(labels):

FILE: Examples/cai_eas/eas/data_providers/cifar.py
  function augment_image (line 12) | def augment_image(image, pad):
  function augment_all_images (line 34) | def augment_all_images(initial_images, pad=4):
  class CifarDataSet (line 41) | class CifarDataSet(ImagesDataSet):
    method __init__ (line 42) | def __init__(self, images, labels, n_classes, shuffle, normalization, ...
    method start_new_epoch (line 80) | def start_new_epoch(self):
    method num_examples (line 93) | def num_examples(self):
    method next_batch (line 96) | def next_batch(self, batch_size):
  class CifarDataProvider (line 109) | class CifarDataProvider(DataProvider):
    method __init__ (line 111) | def __init__(self, save_path=None, validation_size=None, shuffle=None,...
    method save_path (line 175) | def save_path(self):
    method data_url (line 182) | def data_url(self):
    method data_shape (line 189) | def data_shape(self):
    method n_classes (line 193) | def n_classes(self):
    method get_filenames (line 196) | def get_filenames(self, save_path):
    method read_cifar (line 200) | def read_cifar(self, filenames):
  class Cifar10DataProvider (line 223) | class Cifar10DataProvider(CifarDataProvider):
    method get_filenames (line 227) | def get_filenames(self, save_path):
  class Cifar100DataProvider (line 237) | class Cifar100DataProvider(CifarDataProvider):
    method get_filenames (line 241) | def get_filenames(self, save_path):
  class Cifar10AugmentedDataProvider (line 248) | class Cifar10AugmentedDataProvider(Cifar10DataProvider):
  class Cifar100AugmentedDataProvider (line 253) | class Cifar100AugmentedDataProvider(Cifar100DataProvider):

FILE: Examples/cai_eas/eas/data_providers/downloader.py
  function report_download_progress (line 8) | def report_download_progress(count, block_size, total_size):
  function download_data_url (line 15) | def download_data_url(url, download_dir):

FILE: Examples/cai_eas/eas/data_providers/svhn.py
  class SVHNDataSet (line 11) | class SVHNDataSet(ImagesDataSet):
    method __init__ (line 14) | def __init__(self, images, labels, shuffle, normalization):
    method start_new_epoch (line 35) | def start_new_epoch(self):
    method num_examples (line 44) | def num_examples(self):
    method next_batch (line 47) | def next_batch(self, batch_size):
  class SVHNDataProvider (line 64) | class SVHNDataProvider(DataProvider):
    method __init__ (line 65) | def __init__(self, save_path=None, validation_size=None, shuffle=False...
    method get_images_and_labels (line 119) | def get_images_and_labels(self, name_part, one_hot=False):
    method n_classes (line 132) | def n_classes(self):
    method save_path (line 136) | def save_path(self):
    method data_url (line 142) | def data_url(self):
    method data_shape (line 146) | def data_shape(self):

FILE: Examples/cai_eas/eas/data_providers/utils.py
  function get_data_provider_by_name (line 6) | def get_data_provider_by_name(name, train_params):

FILE: Examples/cai_eas/eas/expdir_monitor/expdir_monitor.py
  class ExpdirMonitor (line 9) | class ExpdirMonitor:
    method __init__ (line 10) | def __init__(self, expdir):
    method logs (line 15) | def logs(self): return '%s/logs' % self.expdir
    method checkpoint (line 18) | def checkpoint(self): return '%s/checkpoint' % self.expdir
    method snapshot (line 21) | def snapshot(self): return '%s/snapshot' % self.expdir
    method output (line 24) | def output(self): return '%s/output' % self.expdir
    method init (line 27) | def init(self): return '%s/init' % self.expdir
    method run_config_path (line 30) | def run_config_path(self): return '%s/run.config' % self.expdir
    method net_config_path (line 33) | def net_config_path(self): return '%s/net.config' % self.expdir
    method load_run_config (line 35) | def load_run_config(self, print_info=False, dataset='C10+'):
    method load_init (line 47) | def load_init(self):
    method load_net_config (line 54) | def load_net_config(self, init, print_info=False):
    method run (line 62) | def run(self, pure=True, restore=False, test=False, valid=False, valid...

FILE: Examples/cai_eas/eas/models/basic_model.py
  class BasicModel (line 11) | class BasicModel:
    method __init__ (line 12) | def __init__(self, path, data_provider, run_config, net_config, pure=F...
    method save_path (line 38) | def save_path(self):
    method logs_path (line 47) | def logs_path(self):
    method _build_graph (line 56) | def _build_graph(self, only_forward=False):
    method _define_inputs (line 59) | def _define_inputs(self):
    method _initialize_session (line 76) | def _initialize_session(self, set_logs=True):
    method train_all_epochs (line 88) | def train_all_epochs(self, start_epoch=1):
    method train_one_epoch (line 131) | def train_one_epoch(self, data, batch_size, learning_rate):
    method test (line 159) | def test(self, data, batch_size):
    method save_config (line 191) | def save_config(self, save_path, print_info=True):
    method save_init (line 200) | def save_init(self, save_path, print_info=True):
    method pure_train (line 208) | def pure_train(self):
    method save_model (line 230) | def save_model(self, global_step=None):
    method load_model (line 233) | def load_model(self):
    method log_loss_accuracy (line 241) | def log_loss_accuracy(self, loss, accuracy, epoch, prefix, should_prin...
    method _count_trainable_params (line 256) | def _count_trainable_params():
    method dropout (line 267) | def dropout(_input, keep_prob, is_training):
    method weight_variable (line 279) | def weight_variable(shape, name, initializer):
    method avg_pool (line 287) | def avg_pool(_input, k=2, s=2):
    method max_pool (line 297) | def max_pool(_input, k=2, s=2):
    method conv2d (line 307) | def conv2d(_input, out_features, kernel_size, strides=1, padding='SAME...
    method fc_layer (line 321) | def fc_layer(_input, out_units, use_bias=False, param_initializer=None):
    method batch_norm (line 338) | def batch_norm(_input, is_training, epsilon=1e-3, decay=0.999, param_i...
    method activation (line 345) | def activation(_input, activation='relu'):
    method build_optimizer (line 360) | def build_optimizer(learning_rate, opt_name, opt_param):
    method flatten (line 369) | def flatten(_input):

FILE: Examples/cai_eas/eas/models/convnet.py
  class SimpleConvnetConfig (line 9) | class SimpleConvnetConfig:
    method __init__ (line 10) | def __init__(self):
    method weight_decay (line 20) | def weight_decay(self): return self.net_config['weight_decay']
    method bn_epsilon (line 23) | def bn_epsilon(self): return self.net_config['bn_epsilon']
    method bn_decay (line 26) | def bn_decay(self): return self.net_config['bn_decay']
    method drop_scheme (line 29) | def drop_scheme(self): return self.net_config['drop_scheme']
    method depth (line 32) | def depth(self): return self.layer_cascade.depth
    method get_config (line 34) | def get_config(self):
    method copy (line 41) | def copy(self):
    method renew_init (line 46) | def renew_init(self, convnet):
    method set_standard_convnet (line 51) | def set_standard_convnet(self, data_provider: DataProvider, conv_block...
    method set_net_from_config (line 99) | def set_net_from_config(self, net_config_json, init=None, print_info=T...
    method widen (line 108) | def widen(self, layer_idx, new_width, widen_type='output_dim', noise=N...
    method deepen (line 113) | def deepen(self, layer_idx, new_layer_config):
    method set_identity4deepen (line 116) | def set_identity4deepen(self, to_set_layers, data_provider, batch_size...
  class SimpleConvnet (line 159) | class SimpleConvnet(BasicModel):
    method _build_graph (line 160) | def _build_graph(self, only_forward=False):

FILE: Examples/cai_eas/eas/models/dense_net.py
  function get_block_by_name (line 10) | def get_block_by_name(name):
  class TransitionBlock (line 19) | class TransitionBlock(LayerCascade):
    method get_config (line 20) | def get_config(self):
    method set_from_config (line 27) | def set_from_config(config_json, init=None, return_class=True):
    method prev_widen (line 31) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 35) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 38) | def deepen(self, loc, new_layer_config, input_dim):
  class DenseBlock (line 42) | class DenseBlock:
    method __init__ (line 43) | def __init__(self, _id, miniblocks):
    method id (line 50) | def id(self):
    method id (line 54) | def id(self, value):
    method depth (line 58) | def depth(self):
    method out_features_dim (line 64) | def out_features_dim(self, in_features_dim):
    method build (line 70) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 80) | def get_config(self):
    method renew_init (line 87) | def renew_init(self, densenet):
    method set_from_config (line 94) | def set_from_config(config_json, init=None):
    method insert_miniblock (line 106) | def insert_miniblock(self, idx, miniblock_config, input_dim, noise=Non...
    method prev_widen (line 173) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 190) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 224) | def deepen(self, loc, new_layer_config, input_dim):
  class DenseNetConfig (line 231) | class DenseNetConfig:
    method __init__ (line 232) | def __init__(self):
    method model_type (line 246) | def model_type(self): return self.net_config['model_type']
    method weight_decay (line 249) | def weight_decay(self): return self.net_config['weight_decay']
    method first_ratio (line 252) | def first_ratio(self): return self.net_config['first_ratio']
    method reduction (line 255) | def reduction(self): return self.net_config['reduction']
    method bc_ratio (line 258) | def bc_ratio(self): return self.net_config['bc_ratio']
    method bn_epsilon (line 261) | def bn_epsilon(self): return self.net_config['bn_epsilon']
    method bn_decay (line 264) | def bn_decay(self): return self.net_config['bn_decay']
    method depth (line 267) | def depth(self):
    method average_growth_rate (line 274) | def average_growth_rate(self):
    method copy (line 283) | def copy(self):
    method get_config (line 288) | def get_config(self):
    method renew_init (line 295) | def renew_init(self, densenet):
    method set_standard_dense_net (line 300) | def set_standard_dense_net(self, data_provider: DataProvider, growth_r...
    method set_net_from_config (line 389) | def set_net_from_config(self, net_config_json, init=None, print_info=T...
    method widen (line 405) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 423) | def deepen(self, loc, new_layer_config, image_channel=3):
    method set_identity4deepen (line 435) | def set_identity4deepen(self, to_set_layers, data_provider, batch_size...
    method insert_miniblock (line 477) | def insert_miniblock(self, loc, miniblock_config, image_channel=3, noi...
  class DenseNet (line 492) | class DenseNet(BasicModel):
    method _build_graph (line 493) | def _build_graph(self, only_forward=False):

FILE: Examples/cai_eas/eas/models/layer_cascade.py
  class LayerCascade (line 5) | class LayerCascade:
    method __init__ (line 6) | def __init__(self, _id, layers):
    method id (line 13) | def id(self):
    method id (line 17) | def id(self, value):
    method out_features_dim (line 21) | def out_features_dim(self):
    method depth (line 30) | def depth(self):
    method get_str (line 37) | def get_str(self):
    method build (line 41) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 50) | def get_config(self):
    method renew_init (line 56) | def renew_init(self, densenet):
    method copy (line 62) | def copy(self):
    method set_from_config (line 66) | def set_from_config(config_json, init=None, return_class=True):
    method prev_widen (line 82) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 90) | def widen(self, idx, new_width, widen_type='output_dim', noise=None):
    method deepen (line 113) | def deepen(self, idx, new_layer_config, input_dim):

FILE: Examples/cai_eas/eas/models/layer_multi_branch.py
  class LayerMultiBranch (line 6) | class LayerMultiBranch:
    method __init__ (line 7) | def __init__(self, _id, branches, merge=None, in_bottle=None, out_bott...
    method id (line 24) | def id(self):
    method id (line 28) | def id(self, value):
    method out_features_dim (line 32) | def out_features_dim(self):
    method depth (line 46) | def depth(self):
    method get_str (line 58) | def get_str(self):
    method build (line 65) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 90) | def get_config(self):
    method renew_init (line 99) | def renew_init(self, densenet):
    method set_from_config (line 108) | def set_from_config(config_json, init=None):
    method prev_widen (line 130) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 137) | def widen(self, loc, new_width, widen_type='output_dim', noise=None):
    method deepen (line 184) | def deepen(self, loc, new_layer_config, input_dim):
    method remapped_branches (line 199) | def remapped_branches(self, noise=None):

FILE: Examples/cai_eas/eas/models/layers.py
  function apply_noise (line 7) | def apply_noise(weights, noise_config):
  function get_layer_by_name (line 25) | def get_layer_by_name(name):
  function get_magnifier (line 36) | def get_magnifier(old_size, indices):
  function get_random_remapping (line 44) | def get_random_remapping(old_size, new_size):
  class BaseLayer (line 52) | class BaseLayer:
    method __init__ (line 56) | def __init__(self, _id, use_bn=True, activation='relu', keep_prob=1.0,...
    method id (line 69) | def id(self): return self._id
    method id (line 72) | def id(self, value): self._id = value
    method init (line 75) | def init(self):
    method param_initializer (line 79) | def param_initializer(self):
    method renew_init (line 90) | def renew_init(self, net: BasicModel):
    method copy (line 102) | def copy(self):
    method get_config (line 105) | def get_config(self):
    method variable_list (line 115) | def variable_list(self):
    method set_from_config (line 132) | def set_from_config(layer_config, layer_init):
    method build (line 135) | def build(self, _input, net, store_output_op):
    method prev_widen (line 138) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 141) | def set_identity_layer(self, strict, param, noise):
    method widen_bn (line 144) | def widen_bn(self, indices, magnifier, noise=None):
    method set_bn_identity (line 151) | def set_bn_identity(self, strict=True, param=None, noise=None):
  class ConvLayer (line 164) | class ConvLayer(BaseLayer):
    method __init__ (line 165) | def __init__(self, _id, filter_num, kernel_size=3, strides=1,
    method layer_str (line 173) | def layer_str(self):
    method variable_list (line 177) | def variable_list(self):
    method get_config (line 182) | def get_config(self):
    method set_from_config (line 192) | def set_from_config(layer_config, layer_init=None):
    method build (line 197) | def build(self, _input, net: BasicModel, store_output_op=False):
    method widen_filters (line 230) | def widen_filters(self, new_filter_num, noise=None):
    method prev_widen (line 249) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 256) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method remap (line 265) | def remap(self, indices, noise=None):
  class FCLayer (line 274) | class FCLayer(BaseLayer):
    method __init__ (line 275) | def __init__(self, _id, units, use_bn=True, use_bias=False, activation...
    method layer_str (line 282) | def layer_str(self):
    method variable_list (line 286) | def variable_list(self):
    method get_config (line 293) | def get_config(self):
    method set_from_config (line 302) | def set_from_config(layer_config, layer_init=None):
    method build (line 307) | def build(self, _input, net: BasicModel, store_output_op=False):
    method widen_units (line 340) | def widen_units(self, new_units_num, noise=None):
    method prev_widen (line 364) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 371) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method remap (line 380) | def remap(self, indices, noise=None):
  class PoolLayer (line 391) | class PoolLayer(BaseLayer):
    method __init__ (line 392) | def __init__(self, _id, _type, kernel_size=2, strides=2, use_bn=False,...
    method layer_str (line 401) | def layer_str(self):
    method get_config (line 404) | def get_config(self):
    method set_from_config (line 414) | def set_from_config(layer_config, layer_init=None):
    method build (line 419) | def build(self, _input, net: BasicModel, store_output_op=False):
    method set_identity_layer (line 460) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method prev_widen (line 463) | def prev_widen(self, indices, magnifier, noise=None):

FILE: Examples/cai_eas/eas/models/utils.py
  function get_model_config_by_name (line 6) | def get_model_config_by_name(name):
  function get_model_by_name (line 15) | def get_model_by_name(name):
  class RunConfig (line 24) | class RunConfig:
    method __init__ (line 25) | def __init__(self, batch_size, n_epochs, init_lr, reduce_lr_epochs, re...
    method get_config (line 46) | def get_config(self):
    method update (line 49) | def update(self, new_config):
    method copy (line 52) | def copy(self):
    method learning_rate (line 55) | def learning_rate(self, epoch):
    method get_default_run_config (line 71) | def get_default_run_config(dataset='C10+'):

FILE: Examples/compression/mnist_pytorch/mnist.py
  class Net (line 29) | class Net(nn.Module):
    method __init__ (line 30) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function train (line 55) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 72) | def test(model, device, test_loader):
  function main (line 93) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_admm.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_agp.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_amc.py
  function train (line 21) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 38) | def test(model, device, test_loader):
  function main (line 59) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_aup_args.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 84) | def main(compression_type, compression_framework, compressor, config_list):

FILE: Examples/compression/mnist_pytorch/mnist_autocompress.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_dependency_aware.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_lottery_ticket.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_net_adapt.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_no_speedup.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_pretrained.py
  class Net (line 11) | class Net(nn.Module):
    method __init__ (line 12) | def __init__(self):
    method forward (line 21) | def forward(self, x):
  function train (line 37) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 54) | def test(model, device, test_loader):
  function main (line 73) | def main():

FILE: Examples/compression/mnist_pytorch/mnist_sensitivity.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_pytorch/mnist_simulated_annealing.py
  class Net (line 19) | class Net(nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  function train (line 45) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 62) | def test(model, device, test_loader):
  function main (line 83) | def main(config):

FILE: Examples/compression/mnist_tensorflow/mnist.py
  function get_model (line 33) | def get_model(**kwargs):
  function test_model (line 50) | def test_model(model, test_dataset):
  function main (line 58) | def main(config):

FILE: Examples/compression/mnist_tensorflow/mnist_pretrained.py
  function get_model (line 21) | def get_model(**kwargs):
  function test_model (line 38) | def test_model(model, test_dataset):
  function main (line 46) | def main():

FILE: Examples/compression/utility_functions/mnist.py
  class Net (line 33) | class Net(nn.Module):
    method __init__ (line 34) | def __init__(self):
    method forward (line 43) | def forward(self, x):
  function train (line 59) | def train(args, model, device, train_loader, optimizer, epoch):
  function test (line 76) | def test(model, device, test_loader):
  function main (line 97) | def main():

FILE: Examples/converter_examples/Convert_Benchmark/repdata.py
  function parse_arg (line 12) | def parse_arg():
  function get_dataset (line 20) | def get_dataset():

FILE: Examples/converter_examples/Convert_Benchmark/script_mobilenet.py
  function preprocess (line 27) | def preprocess(name):
  function push_to_device (line 42) | def push_to_device(name):
  function run_benchmark (line 52) | def run_benchmark(name):
  function compile_results (line 69) | def compile_results(infer, memo, df, of, op, alpha, size):
  function convert (line 100) | def convert(name, size, op):

FILE: Examples/converter_examples/Convert_Profiler/download_test_models.py
  function download_all_url (line 16) | def download_all_url(all_urls, model_folder):

FILE: Examples/converter_examples/Convert_Profiler/test_perf_onnx.py
  function compute (line 16) | def compute(model_path):

FILE: Examples/converter_examples/Convert_Profiler/test_perf_tflite.py
  function compute (line 18) | def compute(model_path):

FILE: Examples/converter_examples/Tested_Models/create_test_models.py
  class Test_Model_Classification (line 33) | class Test_Model_Classification(object):
    method __init__ (line 34) | def __init__(self, model_name):
    method create_model (line 37) | def create_model(self):
  class Test_Model_Segmentation (line 41) | class Test_Model_Segmentation(object):
    method __init__ (line 42) | def __init__(self, model_name):
    method create_model (line 45) | def create_model(self):
  class Test_Model_Video (line 49) | class Test_Model_Video(object):
    method __init__ (line 50) | def __init__(self, model_name):
    method create_model (line 53) | def create_model(self):
  function create_torch_cnn_models (line 57) | def create_torch_cnn_models():
  function create_keras_cnn_models (line 92) | def create_keras_cnn_models():
  function create_tf_lstm_model (line 100) | def create_tf_lstm_model():
  function create_tf_gru_model (line 127) | def create_tf_gru_model():

FILE: Examples/converter_examples/Tested_Models/download_models.py
  function download_all_url (line 11) | def download_all_url(url_file, model_folder):

FILE: Examples/decorator_example/origin.py
  function func (line 2) | def func(hyperparameter):
  function main (line 17) | def main(unused):

FILE: Examples/decorator_example/use_decorator.py
  function func (line 7) | def func(hyperparameter):
  function main (line 21) | def main():

FILE: Examples/demo/hpo_wrapper.py
  function demo_func (line 8) | def demo_func(x, a=1, b=0):

FILE: Examples/demo/origin.py
  function demo_func (line 5) | def demo_func(x, a=1, b=0):

FILE: Examples/early_stopping/mnist_keras/mnist.py
  function get_flops (line 44) | def get_flops():
  function get_model (line 54) | def get_model(**kwargs):
  class CustomCallback (line 74) | class CustomCallback(keras.callbacks.Callback):
    method on_train_batch_end (line 77) | def on_train_batch_end(self, batch, logs=None):
    method on_train_begin (line 82) | def on_train_begin(self, logs=None):
  function init (line 85) | def init(**kwargs):
  function do_train (line 90) | def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64):

FILE: Examples/early_stopping/quad_equation_min/quad_min.py
  function rosenbrock (line 17) | def rosenbrock(x, a=2, b=4, c=5):

FILE: Examples/hpo_mnist/mnist_hpo_demo.py
  function conv_net (line 65) | def conv_net(x_dict, n_classes, reuse, is_training):
  function model_fn (line 101) | def model_fn(features, labels, mode):
  function train (line 139) | def train():
  function get_flop (line 165) | def get_flop():
  function main (line 177) | def main(_):

FILE: Examples/hpo_mnist/mnist_hpo_fail.py
  function conv_net (line 65) | def conv_net(x_dict, n_classes, reuse, is_training):
  function model_fn (line 101) | def model_fn(features, labels, mode):
  function train (line 139) | def train():
  function get_flop (line 165) | def get_flop():
  function main (line 177) | def main(_):

FILE: Examples/intermediate_results/quad_equation_min/quad_min.py
  function rosenbrock (line 17) | def rosenbrock(x, a=2, b=4, c=5):

FILE: Examples/job_failure_control/rosenbrock_hpo.py
  function rosenbrock (line 32) | def rosenbrock(x, y, a=1, b=100):

FILE: Examples/mnist_keras_save_model/mnist.py
  function get_flops (line 44) | def get_flops():
  function get_model (line 54) | def get_model(**kwargs):
  function do_train (line 75) | def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64):
  function save_model (line 102) | def save_model(model):

FILE: Examples/mnist_keras_save_model/mnist_wo_decorator.py
  function get_flops (line 45) | def get_flops():
  function get_model (line 55) | def get_model(**kwargs):
  function do_train (line 75) | def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64,
  function save_model_fun (line 119) | def save_model_fun(model):

FILE: Examples/profiler_examples/bench/test_perf.py
  function compute (line 18) | def compute(model_path):

FILE: Examples/profiler_examples/mnist/mnist.py
  function conv_net (line 30) | def conv_net(x_dict, n_classes, dropout, reuse, is_training):
  function model_fn (line 66) | def model_fn(features, labels, mode):

FILE: Examples/quad_equation_min/quad_min.py
  function rosenbrock (line 35) | def rosenbrock(x, a=2, b=4, c=5):

FILE: Examples/tf_flags/rosenbrock_hpo.py
  function rosenbrock (line 49) | def rosenbrock(x, y, a=1, b=100):
  function main (line 54) | def main(unused):

FILE: Examples/tf_flags/rosenbrock_tf.py
  function rosenbrock (line 22) | def rosenbrock(x, y, a=1, b=100):
  function main (line 26) | def main(unused):

FILE: Examples/tf_iris_diff_opt/iris_data.py
  function maybe_download (line 13) | def maybe_download():
  function load_data (line 19) | def load_data(y_name='Species'):
  function train_input_fn (line 32) | def train_input_fn(features, labels, batch_size):
  function eval_input_fn (line 44) | def eval_input_fn(features, labels, batch_size):
  function _parse_line (line 71) | def _parse_line(line):
  function csv_input_fn (line 84) | def csv_input_fn(csv_path, batch_size):

FILE: Examples/tf_iris_diff_opt/premade_estimator.py
  function main (line 34) | def main(argv):

FILE: Examples/tf_iris_diff_opt/premade_estimator_hpo.py
  function main (line 46) | def main(conf):

FILE: Examples/tf_iris_diff_opt/premade_estimator_hyper.py
  function main (line 38) | def main(layer1, layer2):

FILE: Examples/tf_iris_diff_opt/premade_estimator_wrapper.py
  function main (line 39) | def main(layer1, layer2):
  function aup_wrapper (line 99) | def aup_wrapper(config):

FILE: docs/Database/schema.sql
  type user (line 1) | CREATE TABLE user
  type resource (line 3) | CREATE TABLE resource
  type experiment (line 5) | CREATE TABLE experiment
  type job (line 8) | CREATE TABLE job
  type job_attempt (line 13) | CREATE TABLE job_attempt
  type intermediate_result (line 17) | CREATE TABLE intermediate_result
  type multiple_result (line 20) | CREATE TABLE multiple_result

FILE: docs/Database/sql_graphviz.py
  function field_act (line 9) | def field_act(s, loc, tok):
  function field_list_act (line 13) | def field_list_act(s, loc, tok):
  function create_table_act (line 17) | def create_table_act(s, loc, tok):
  function add_fkey_act (line 29) | def add_fkey_act(s, loc, tok):
  function other_statement_act (line 33) | def other_statement_act(s, loc, tok):
  function grammar (line 37) | def grammar():
  function graphviz (line 64) | def graphviz(filename):

FILE: docs/archive/aup.py
  function print_result (line 38) | def print_result(result):
  class BasicConfig (line 52) | class BasicConfig(dict):
    method load (line 63) | def load(self, filename):
    method save (line 80) | def save(self, filename):
    method _get_format (line 93) | def _get_format(filename):
    method _load_json (line 100) | def _load_json(filename):
    method _load_pkl (line 105) | def _load_pkl(filename):
    method _save_json (line 109) | def _save_json(self, filename):
    method _save_pkl (line 113) | def _save_pkl(self, filename):
    method save_flags (line 118) | def save_flags(filename):
    method to_flags (line 129) | def to_flags(self, FLAGS):
    method __setattr__ (line 142) | def __setattr__(self, key, value):
    method __getattr__ (line 145) | def __getattr__(self, key):
    method __delattr__ (line 148) | def __delattr__(self, key):
    method __hash__ (line 151) | def __hash__(self):
  function aup_args (line 155) | def aup_args(func):
  function aup_flags (line 229) | def aup_flags(flags):
  function aup_save_model (line 254) | def aup_save_model(callback_fn, *args, **kwargs):

FILE: setup.py
  function find_version (line 10) | def find_version():

FILE: src/aup/EE/Experiment.py
  function _verify_config (line 37) | def _verify_config(config):
  class Experiment (line 50) | class Experiment:
    method __init__ (line 66) | def __init__(self,
    method add_suspend_signal (line 141) | def add_suspend_signal(self):
    method add_refresh_signal (line 144) | def add_refresh_signal(self):
    method finish (line 147) | def finish(self):
    method resume (line 182) | def resume(self, filename):
    method start (line 192) | def start(self):
    method submit_job (line 212) | def submit_job(self, job=None, rid_blacklist=None):
    method update (line 257) | def update(self, score, jid):
    method _suspend (line 306) | def _suspend(self, sig, frame):
    method _check_status (line 328) | def _check_status(self):
    method _force_refresh (line 354) | def _force_refresh(self, sig, frame):

FILE: src/aup/EE/Job.py
  class Job (line 29) | class Job:
    method __init__ (line 32) | def __init__(self, script, config, path=".", retries=3):
    method verify_local (line 63) | def verify_local(self):
    method verify_remote (line 90) | def verify_remote(self, remote, overwrite=False):  # pragma: no cover

FILE: src/aup/EE/Resource/AWSResourceManager.py
  function _is_instance_id (line 32) | def _is_instance_id(hostname):
  class AWSRemote (line 36) | class AWSRemote(Remote):  # pragma: no cover
    method change_status (line 41) | def change_status(cls, hostnames, target='start'):
    method verify (line 78) | def verify(cls, hostname):
    method __init__ (line 92) | def __init__(self, instance):
    method exec_command (line 126) | def exec_command(self, command, *args, **kwargs):
    method __enter__ (line 129) | def __enter__(self):
  class AWSResourceManager (line 147) | class AWSResourceManager(SSHResourceManager):  # pragma: no cover
    method __init__ (line 148) | def __init__(self, *args, **kwargs):
    method __del__ (line 156) | def __del__(self):
    method run (line 178) | def run(self, job, rid, *args, **kwargs):

FILE: src/aup/EE/Resource/AbstractResourceManager.py
  function get_resource_manager (line 44) | def get_resource_manager(resource, connector, n_parallel, auppath=DEFAUL...
  class AbstractResourceManager (line 68) | class AbstractResourceManager(ABC):
    method __init__ (line 76) | def __init__(self, connector, n_parallel, *args, **kwargs):
    method finish (line 123) | def finish(self, status="FINISHED"):
    method finish_job (line 138) | def finish_job(self, jid, score, status=None):
    method get_available (line 153) | def get_available(self, username, rtype, rid_blacklist=None):
    method run_job (line 170) | def run_job(self, job, rid, exp_config, call_back_func, **kwargs):
    method append_interm_res (line 199) | def append_interm_res(self, jid, interm_res):
    method append_multiple_results (line 214) | def append_multiple_results(self, jid, irid, eid, scores):
    method set_last_multiple_results (line 224) | def set_last_multiple_results(self, eid, jid):
    method stop_job (line 231) | def stop_job(self, jid):
    method is_job_stopped (line 243) | def is_job_stopped(self, jid):
    method run (line 256) | def run(self, job, rid, exp_config, call_back_func, **kwargs):
    method suspend (line 272) | def suspend(self):
    method run_curve_fitting (line 280) | def run_curve_fitting(self, interm_res, c_jid, step, comp_fn, curve_fi...
    method early_stop_daemon_fun (line 292) | def early_stop_daemon_fun(self):
    method refresh (line 378) | def refresh(self):
    method log_error_message (line 384) | def log_error_message(self, msg):

FILE: src/aup/EE/Resource/CPUResourceManager.py
  class CPUResourceManager (line 34) | class CPUResourceManager(AbstractResourceManager):
    method __init__ (line 35) | def __init__(self, connector, n_parallel, *args, **kwargs):
    method finish (line 47) | def finish(self, maximize=True, status="FINISHED"):
    method run (line 88) | def run(self, job, rid, exp_config, call_back_func, **kwargs):

FILE: src/aup/EE/Resource/GPUResourceManager.py
  function _load_gpu_mapping (line 29) | def _load_gpu_mapping(auppath=DEFAULT_AUPTIMIZER_PATH):
  class GPUResourceManager (line 40) | class GPUResourceManager(CPUResourceManager):
    method __init__ (line 41) | def __init__(self, connector, n_parallel, auppath=DEFAULT_AUPTIMIZER_P...
    method run (line 45) | def run(self, job, rid, exp_config, call_back_func, **kwargs):

FILE: src/aup/EE/Resource/PassiveResourceManager.py
  class PassiveResourceManager (line 27) | class PassiveResourceManager(AbstractResourceManager):
    method __init__ (line 28) | def __init__(self, connector, *args, **kwargs):
    method get_available (line 32) | def get_available(self, username, rtype):
    method run (line 43) | def run(self, job, rid, exp_config, call_back_func, **kwargs):

FILE: src/aup/EE/Resource/SSHResourceManager.py
  function parse_hostname (line 31) | def parse_hostname(host):
  class Remote (line 66) | class Remote(object):  # pragma: no cover
    method __init__ (line 67) | def __init__(self, host):
    method exec_command (line 80) | def exec_command(self, command, command_async=None, *args, **kwargs):
    method __enter__ (line 83) | def __enter__(self):
    method __exit__ (line 88) | def __exit__(self, exc_type, exc_val, exc_tb):
  class SSHResourceManager (line 92) | class SSHResourceManager(CPUResourceManager):  # pragma: no cover
    method __init__ (line 93) | def __init__(self, connector, n_parallel, key="node_mapping", auppath=...
    method load_node_mapping (line 124) | def load_node_mapping(key="node_mapping", auppath=DEFAULT_AUPTIMIZER_P...
    method run (line 134) | def run(self, job, rid, exp_config, call_back_func, **kwargs):
    method refresh (line 497) | def refresh(self):

FILE: src/aup/EE/Resource/utils/ResourceThreadPoolExecutor.py
  class ResourceThreadPoolExecutor (line 20) | class ResourceThreadPoolExecutor(ThreadPoolExecutor):
    method __init__ (line 21) | def __init__(self, *args, **kwargs):
    method submit (line 24) | def submit(self, fn, *args, **kwargs):

FILE: src/aup/EE/Resource/utils/curve_fitting.py
  function vap (line 11) | def vap(x, a, b, c):
  function pow3 (line 14) | def pow3(x, c, a, alpha):
  function linear (line 17) | def linear(x, a, b):
  function logx_linear (line 20) | def logx_linear(x, a, b):
  function dr_hill_zero_background (line 24) | def dr_hill_zero_background(x, theta, eta, kappa):
  function log_power (line 27) | def log_power(x, a, b, c):
  function pow4 (line 30) | def pow4(x, alpha, a, b, c):
  function mmf (line 33) | def mmf(x, alpha, beta, kappa, delta):
  function exp4 (line 36) | def exp4(x, c, a, b, alpha):
  function ilog2 (line 39) | def ilog2(x, c, a):
  function weibull (line 42) | def weibull(x, alpha, beta, kappa, delta):
  function janoschek (line 45) | def janoschek(x, a, beta, k, delta):
  class CurveModel (line 64) | class CurveModel:
    method __init__ (line 69) | def __init__(self, iterations):
    method fit_theta (line 72) | def fit_theta(self):
    method filter_curve (line 89) | def filter_curve(self, func_params):
    method normalize_weights (line 113) | def normalize_weights(self, samples, num_funcs):
    method f_comb (line 122) | def f_comb(self, pos, sample, funcs, func_params):
    method sigma_sq (line 129) | def sigma_sq(self, sample, funcs, func_params):
    method normal_distribution (line 136) | def normal_distribution(self, pos, sample, funcs, func_params):
    method likelihood (line 141) | def likelihood(self, samples, funcs, func_params):
    method prior (line 148) | def prior(self, samples, funcs, func_params):
    method target_distribution (line 158) | def target_distribution(self, samples, funcs, func_params):
    method mcmc_sampling (line 166) | def mcmc_sampling(self, filtered_funcs, func_params, timeout):
    method predict (line 183) | def predict(self, interm_res, timeout):

FILE: src/aup/ET/Connector/AbstractConnector.py
  class AbstractConnector (line 23) | class AbstractConnector(ABC):
    method close (line 26) | def close(self):
    method is_closed (line 33) | def is_closed(self):
    method free_used_resource (line 41) | def free_used_resource(self, rid):
    method get_resource_type (line 51) | def get_resource_type(self):
    method get_available_resource (line 60) | def get_available_resource(self, username, rtype):
    method take_available_resource (line 75) | def take_available_resource(self, rid):
    method end_experiment (line 89) | def end_experiment(self, eid):
    method get_best_result (line 99) | def get_best_result(self, eid, maximize=True):
    method start_experiment (line 113) | def start_experiment(self, username, name, exp_config_blob):
    method end_job (line 130) | def end_job(self, jid, score, status):
    method get_all_experiment (line 144) | def get_all_experiment(self, username=None):
    method get_all_history (line 156) | def get_all_history(self, eid):
    method get_running_job (line 167) | def get_running_job(self, eid):
    method start_job (line 179) | def start_job(self, eid, rid, job_config):
    method start_job_attempt (line 195) | def start_job_attempt(self, rid, jid):
    method end_job_attempt (line 209) | def end_job_attempt(self, jid):
    method update_job_status (line 218) | def update_job_status(self, jid, status):
    method job_finished (line 230) | def job_finished(self, rid, jid, score=None, status=None):
    method job_started (line 248) | def job_started(self, eid, rid, job_config):
    method job_failed (line 264) | def job_failed(self, rid, jid):
    method job_retry (line 276) | def job_retry(self, rid, jid):
    method save_intermediate_result (line 288) | def save_intermediate_result(self, jid, score):
    method get_intermediate_results_job (line 301) | def get_intermediate_results_job(self, jid):
    method get_intermediate_results_jobs (line 312) | def get_intermediate_results_jobs(self, jids):
    method get_intermediate_results_experiment (line 323) | def get_intermediate_results_experiment(self, eid, status):
    method create_experiment (line 337) | def create_experiment(self, username, name, exp_config_blob):
    method delete_experiment (line 343) | def delete_experiment(self, eid):
    method get_experiment_status (line 349) | def get_experiment_status(self, eid):
    method start_experiment_by_eid (line 355) | def start_experiment_by_eid(self, eid):
    method log_error_message (line 361) | def log_error_message(self, eid, msg):
    method save_multiple_results (line 367) | def save_multiple_results(self, jid, irid, eid, labels, scores):
    method set_last_multiple_results (line 373) | def set_last_multiple_results(self, eid, jid, num_labels):

FILE: src/aup/ET/Connector/SQLiteConnector.py
  function _delayed (line 31) | def _delayed(func):
  class SQLiteConnector (line 51) | class SQLiteConnector(AbstractConnector):
    method __init__ (line 52) | def __init__(self, filename):
    method _fix_name (line 59) | def _fix_name(self, name):
    method _mark_resource (line 74) | def _mark_resource(self, rid, status):
    method free_all_resources (line 82) | def free_all_resources(self):
    method close (line 87) | def close(self):
    method is_closed (line 93) | def is_closed(self):
    method end_experiment (line 97) | def end_experiment(self, eid, status="FINISHED"):
    method change_experiment_status (line 102) | def change_experiment_status(self, eid, status="FINISHED"):
    method end_job (line 107) | def end_job(self, jid, score=None, status=None):
    method end_job_attempt (line 112) | def end_job_attempt(self, jid):
    method free_used_resource (line 118) | def free_used_resource(self, rid):
    method get_all_experiment (line 123) | def get_all_experiment(self, username=None):
    method get_available_resource (line 136) | def get_available_resource(self, username, rtype, rid_blacklist=None):
    method get_all_history (line 153) | def get_all_history(self, eid):
    method get_best_result (line 158) | def get_best_result(self, eid, maximize=True):
    method get_best_result_config (line 176) | def get_best_result_config(self, eid, maximize=True):
    method get_running_job (line 188) | def get_running_job(self, eid):
    method get_resource_type (line 195) | def get_resource_type(self):
    method start_experiment (line 200) | def start_experiment(self, username, name, exp_config_blob):
    method start_experiment_by_eid (line 215) | def start_experiment_by_eid(self, eid):
    method start_job (line 229) | def start_job(self, eid, rid, job_config):
    method start_job_attempt (line 240) | def start_job_attempt(self, rid, jid):
    method update_job_status (line 248) | def update_job_status(self, jid, status):
    method take_available_resource (line 253) | def take_available_resource(self, rid):
    method save_intermediate_result (line 258) | def save_intermediate_result(self, jid, score):
    method get_intermediate_results_job (line 270) | def get_intermediate_results_job(self, jid):
    method get_intermediate_results_jobs (line 280) | def get_intermediate_results_jobs(self, jids):
    method get_intermediate_results_experiment (line 295) | def get_intermediate_results_experiment(self, eid, status):
    method create_experiment (line 310) | def create_experiment(self, username, name, exp_config_blob):
    method delete_experiment (line 324) | def delete_experiment(self, eid):
    method get_experiment_status (line 340) | def get_experiment_status(self, eid):
    method maybe_get_experiment_status (line 350) | def maybe_get_experiment_status(self, eid):
    method log_error_message (line 364) | def log_error_message(self, eid, msg):
    method save_multiple_results (line 369) | def save_multiple_results(self, jid, irid, eid, labels, scores):
    method set_last_multiple_results (line 390) | def set_last_multiple_results(self, eid, jid, num_labels):

FILE: src/aup/Proposer/AbstractProposer.py
  function create_param_config (line 29) | def create_param_config(name, vrange, vtype):
  class AbstractProposer (line 40) | class AbstractProposer(ABC):
    method __init__ (line 48) | def __init__(self, config):
    method set_status (line 56) | def set_status(self, status):
    method get_status (line 60) | def get_status(self):
    method increment_job_counter (line 64) | def increment_job_counter(self):
    method check_termination (line 68) | def check_termination(self):
    method get_remaining_jobs (line 73) | def get_remaining_jobs(self):
    method setup_config (line 78) | def setup_config():  # pragma: no cover
    method parse_param_config (line 106) | def parse_param_config(config):
    method get (line 123) | def get(self, **kwargs):
    method get_param (line 144) | def get_param(self, **kwargs):
    method reload (line 150) | def reload(self, path):
    method reset (line 164) | def reset(self):
    method save (line 172) | def save(self, path):
    method update (line 184) | def update(self, score, job):
    method failed (line 195) | def failed(self, job):
    method verify_config (line 204) | def verify_config(self, config):
    method __getstate__ (line 219) | def __getstate__(self):
    method __setstate__ (line 224) | def __setstate__(self, state):

FILE: src/aup/Proposer/BOHBProposer.py
  class BOHBProposer (line 72) | class BOHBProposer(AbstractProposer):
    method __init__ (line 73) | def __init__(self, config):
    method get_param (line 105) | def get_param(self):
    method update (line 139) | def update(self, score, job):
    method failed (line 153) | def failed(self, job):
    method get_next_iteration (line 163) | def get_next_iteration(self, iteration, iteration_kwargs={}):
    method _get_nSample (line 172) | def _get_nSample(self):
    method create_configspace (line 183) | def create_configspace(parameter_config):
    method setup_config (line 205) | def setup_config():  # pragma: no cover
    method active_iterations (line 219) | def active_iterations(self):  # pragma: no cover
    method save (line 225) | def save(self, path):
    method reload (line 230) | def reload(self, path):

FILE: src/aup/Proposer/EASProposer.py
  class EASProposer (line 47) | class EASProposer(AbstractProposer):
    method get_net_str (line 49) | def get_net_str(self, net_configs):
    method get_net_seq (line 70) | def get_net_seq(net_configs, vocabulary, num_steps):
    method get_block_layer_num (line 83) | def get_block_layer_num(net_configs):
    method apply_wider_decision (line 102) | def apply_wider_decision(wider_decision, net_configs, filter_num_list,...
    method apply_deeper_decision (line 149) | def apply_deeper_decision(deeper_decision, net_configs, kernel_size_li...
    method __init__ (line 203) | def __init__(self, config):
    method setup (line 306) | def setup(self):
    method get_param (line 443) | def get_param(self, **kwargs):
    method update (line 493) | def update(self, score, job):
    method failed (line 502) | def failed(self, job):
    method save (line 506) | def save(self, path):
    method reload (line 511) | def reload(self, path):

FILE: src/aup/Proposer/HyperbandProposer.py
  class HyperbandProposer (line 62) | class HyperbandProposer(AbstractProposer):
    method __init__ (line 63) | def __init__(self, config):
    method setup (line 95) | def setup(self, s):
    method verify_config (line 111) | def verify_config(self, config):
    method get_param (line 122) | def get_param(self):
    method update (line 150) | def update(self, score, job):
    method failed (line 153) | def failed(self, job):
    method save (line 157) | def save(self, path):
    method reload (line 162) | def reload(self, path):
    method setup_config (line 168) | def setup_config():  # pragma: no cover

FILE: src/aup/Proposer/HyperoptProposer.py
  function _get_algo (line 54) | def _get_algo(engine):
  function _convert (line 59) | def _convert(param_config):
  class HyperoptProposer (line 79) | class HyperoptProposer(AbstractProposer):
    method __init__ (line 81) | def __init__(self, config, random_seed=0):
    method setup_config (line 98) | def setup_config():  # pragma: no cover
    method get_param (line 106) | def get_param(self, **kwargs):
    method reload (line 130) | def reload(self, path):
    method save (line 134) | def save(self, path):
    method update (line 138) | def update(self, score, job):
    method failed (line 164) | def failed(self, job):
    method verify_config (line 174) | def verify_config(self, config):

FILE: src/aup/Proposer/RandomProposer.py
  function _random_int (line 50) | def _random_int(x):
  function _random_float (line 58) | def _random_float(x):
  function _random_choice (line 66) | def _random_choice(x):
  class RandomProposer (line 81) | class RandomProposer(AbstractProposer):
    method __init__ (line 89) | def __init__(self, config, random_seed=0):
    method setup_config (line 102) | def setup_config():  # pragma: no cover
    method get_param (line 113) | def get_param(self, **kwargs):
    method reload (line 126) | def reload(self, path):
    method save (line 132) | def save(self, path):
    method verify_config (line 138) | def verify_config(self, config):

FILE: src/aup/Proposer/SequenceProposer.py
  class _AbstractGen (line 57) | class _AbstractGen(object):
    method __init__ (line 58) | def __init__(self, conf):
    method get (line 64) | def get(self, next_flag=True):  # pragma: no cover
    method get_gen (line 69) | def get_gen(cls, conf):
  class _IntGen (line 82) | class _IntGen(_AbstractGen):
    method __init__ (line 83) | def __init__(self, conf):
    method get (line 96) | def get(self, next_flag=True):
  class _FloatGen (line 109) | class _FloatGen(_AbstractGen):
    method __init__ (line 110) | def __init__(self, conf):
    method get (line 124) | def get(self, next_flag=True):
  class _ChoiceGen (line 137) | class _ChoiceGen(_AbstractGen):
    method __init__ (line 138) | def __init__(self, conf):
    method get (line 144) | def get(self, next_flag=True):
  class SequenceProposer (line 156) | class SequenceProposer(AbstractProposer):
    method __init__ (line 157) | def __init__(self, config):
    method setup_config (line 169) | def setup_config():  # pragma: no cover
    method get_param (line 199) | def get_param(self, **kwargs):

FILE: src/aup/Proposer/SpearmintProposer.py
  class SpearmintProposer (line 64) | class SpearmintProposer(AbstractProposer):
    method __init__ (line 65) | def __init__(self, config):
    method setup_config (line 107) | def setup_config():  # pragma: no cover
    method get_param (line 120) | def get_param(self, **kwargs):
    method save (line 145) | def save(self, path):
    method reload (line 150) | def reload(self, path):
    method update (line 155) | def update(self, score, job):
    method failed (line 158) | def failed(self, job):
    method verify_config (line 162) | def verify_config(self, config):

FILE: src/aup/Proposer/__init__.py
  function get_proposer (line 27) | def get_proposer(proposer, disable_proposer_logging=False):

FILE: src/aup/Proposer/eas/arch_search/arch_search_convnet_net2net.py
  function get_net_str (line 11) | def get_net_str(net_configs):
  function get_net_seq (line 33) | def get_net_seq(net_configs, vocabulary, num_steps):
  function get_block_layer_num (line 47) | def get_block_layer_num(net_configs):
  function apply_wider_decision (line 67) | def apply_wider_decision(wider_decision, net_configs, filter_num_list, u...
  function apply_deeper_decision (line 114) | def apply_deeper_decision(deeper_decision, net_configs, kernel_size_list...
  function arch_search_convnet (line 168) | def arch_search_convnet(start_net_path, arch_search_folder, net_pool_fol...

FILE: src/aup/Proposer/eas/arch_search/arch_search_densenet_net2net.py
  function get_net_str (line 11) | def get_net_str(net_configs):
  function get_net_seq (line 32) | def get_net_seq(net_configs, vocabulary, num_steps):
  function get_block_layer_num (line 46) | def get_block_layer_num(net_configs):
  function apply_wider_decision (line 61) | def apply_wider_decision(wider_decision, net_configs, growth_rate_list, ...
  function apply_deeper_decision (line 108) | def apply_deeper_decision(deeper_decision, net_configs, noise):
  function widen_transition (line 137) | def widen_transition(net_configs, noise):
  function arch_search_densenet (line 162) | def arch_search_densenet(start_net_path, arch_search_folder, net_pool_fo...

FILE: src/aup/Proposer/eas/client.py
  function run (line 11) | def run(expdir):
  function main (line 21) | def main():

FILE: src/aup/Proposer/eas/data_providers/base_provider.py
  class DataSet (line 4) | class DataSet:
    method num_examples (line 8) | def num_examples(self):
    method next_batch (line 12) | def next_batch(self, batch_size):
  class ImagesDataSet (line 17) | class ImagesDataSet(DataSet):
    method measure_mean_and_std (line 21) | def measure_mean_and_std(images):
    method shuffle_images_and_labels (line 32) | def shuffle_images_and_labels(images, labels):
    method normalize_images (line 39) | def normalize_images(images, normalization_type, meanstd=None):
  class DataProvider (line 65) | class DataProvider:
    method data_shape (line 69) | def data_shape(self):
    method n_classes (line 74) | def n_classes(self):
    method labels_to_one_hot (line 78) | def labels_to_one_hot(self, labels):
    method labels_from_one_hot (line 89) | def labels_from_one_hot(labels):

FILE: src/aup/Proposer/eas/data_providers/cifar.py
  function augment_image (line 12) | def augment_image(image, pad):
  function augment_all_images (line 34) | def augment_all_images(initial_images, pad=4):
  class CifarDataSet (line 41) | class CifarDataSet(ImagesDataSet):
    method __init__ (line 42) | def __init__(self, images, labels, n_classes, shuffle, normalization, ...
    method start_new_epoch (line 80) | def start_new_epoch(self):
    method num_examples (line 93) | def num_examples(self):
    method next_batch (line 96) | def next_batch(self, batch_size):
  class CifarDataProvider (line 109) | class CifarDataProvider(DataProvider):
    method __init__ (line 111) | def __init__(self, save_path=None, validation_size=None, shuffle=None,...
    method save_path (line 175) | def save_path(self):
    method data_url (line 182) | def data_url(self):
    method data_shape (line 189) | def data_shape(self):
    method n_classes (line 193) | def n_classes(self):
    method get_filenames (line 196) | def get_filenames(self, save_path):
    method read_cifar (line 200) | def read_cifar(self, filenames):
  class Cifar10DataProvider (line 223) | class Cifar10DataProvider(CifarDataProvider):
    method get_filenames (line 227) | def get_filenames(self, save_path):
  class Cifar100DataProvider (line 237) | class Cifar100DataProvider(CifarDataProvider):
    method get_filenames (line 241) | def get_filenames(self, save_path):
  class Cifar10AugmentedDataProvider (line 248) | class Cifar10AugmentedDataProvider(Cifar10DataProvider):
  class Cifar100AugmentedDataProvider (line 253) | class Cifar100AugmentedDataProvider(Cifar100DataProvider):

FILE: src/aup/Proposer/eas/data_providers/downloader.py
  function report_download_progress (line 8) | def report_download_progress(count, block_size, total_size):
  function download_data_url (line 15) | def download_data_url(url, download_dir):

FILE: src/aup/Proposer/eas/data_providers/svhn.py
  class SVHNDataSet (line 11) | class SVHNDataSet(ImagesDataSet):
    method __init__ (line 14) | def __init__(self, images, labels, shuffle, normalization):
    method start_new_epoch (line 35) | def start_new_epoch(self):
    method num_examples (line 44) | def num_examples(self):
    method next_batch (line 47) | def next_batch(self, batch_size):
  class SVHNDataProvider (line 64) | class SVHNDataProvider(DataProvider):
    method __init__ (line 65) | def __init__(self, save_path=None, validation_size=None, shuffle=False...
    method get_images_and_labels (line 119) | def get_images_and_labels(self, name_part, one_hot=False):
    method n_classes (line 132) | def n_classes(self):
    method save_path (line 136) | def save_path(self):
    method data_url (line 142) | def data_url(self):
    method data_shape (line 146) | def data_shape(self):

FILE: src/aup/Proposer/eas/data_providers/utils.py
  function get_data_provider_by_name (line 6) | def get_data_provider_by_name(name, train_params):

FILE: src/aup/Proposer/eas/expdir_monitor/arch_manager.py
  class NetPool (line 14) | class NetPool:
    method __init__ (line 15) | def __init__(self, path):
    method str2id_path (line 26) | def str2id_path(self):
    method id2val_path (line 30) | def id2val_path(self):
    method on_load (line 33) | def on_load(self):
    method add_net (line 63) | def add_net(self, net_str, net_val):
    method get_net_val (line 72) | def get_net_val(self, net_str):
    method on_running_finished (line 89) | def on_running_finished(self, net_str, net_folder, net_val):
    method save (line 95) | def save(self):
    method get_net_folder (line 100) | def get_net_folder(net_id):
  class ArchManager (line 104) | class ArchManager:
    method __init__ (line 105) | def __init__(self, start_net_path, arch_path, net_pool_path):
    method meta_controller_path (line 122) | def meta_controller_path(self):
    method val_logs_path (line 126) | def val_logs_path(self):
    method net_logs_path (line 130) | def net_logs_path(self):
    method on_load (line 133) | def on_load(self):
    method get_start_net (line 143) | def get_start_net(self, copy=False):
    method prepare_folder_for_valid (line 164) | def prepare_folder_for_valid(net_str, net_config, run_config, exp_dir):
    method get_net_tasks (line 172) | def get_net_tasks(self, net_str_list, net_configs, run_configs):
    method val2reward (line 201) | def val2reward(self, net_val_list, func=None):
    method reward (line 213) | def reward(self, net_val_list, reward_config):
    method log_nets (line 226) | def log_nets(self, net_str_list, running_time, print_info=True):

FILE: src/aup/Proposer/eas/expdir_monitor/distributed.py
  class GpuChecker (line 20) | class GpuChecker:
    method __init__ (line 21) | def __init__(self, nvidia_getter, gpuid):
    method state_parser (line 25) | def state_parser(self, state_str):
    method instance_available (line 38) | def instance_available(self, state_str):
    method check (line 45) | def check(self):
    method is_on (line 56) | def is_on(self):
  class RemoteController (line 67) | class RemoteController:
    method __init__ (line 68) | def __init__(self, remote, gpuid, executive):
    method occupied (line 81) | def occupied(self):
    method occupied (line 87) | def occupied(self, val):
    method run (line 93) | def run(self, cmd, stdin=None):
    method gpu_state (line 102) | def gpu_state(self):
    method exe_cmd (line 106) | def exe_cmd(self):
    method check_on (line 112) | def check_on(self, queue):
    method remote_executer (line 123) | def remote_executer(self, idx, expdir, queue):
    method execute (line 155) | def execute(self, idx, expdir, queue):
  class ClusterController (line 169) | class ClusterController:
    method __init__ (line 170) | def __init__(self, config_list):
    method choice (line 175) | def choice(self, queue):
    method get_available (line 189) | def get_available(self, queue):
    method execute (line 204) | def execute(self, idx, expdir, queue):
  function run_tasks (line 209) | def run_tasks(config_list, expdir_list):
  function run (line 237) | def run(task_list):

FILE: src/aup/Proposer/eas/expdir_monitor/expdir_monitor.py
  class ExpdirMonitor (line 9) | class ExpdirMonitor:
    method __init__ (line 10) | def __init__(self, expdir):
    method logs (line 15) | def logs(self): return '%s/logs' % self.expdir
    method checkpoint (line 18) | def checkpoint(self): return '%s/checkpoint' % self.expdir
    method snapshot (line 21) | def snapshot(self): return '%s/snapshot' % self.expdir
    method output (line 24) | def output(self): return '%s/output' % self.expdir
    method init (line 27) | def init(self): return '%s/init' % self.expdir
    method run_config_path (line 30) | def run_config_path(self): return '%s/run.config' % self.expdir
    method net_config_path (line 33) | def net_config_path(self): return '%s/net.config' % self.expdir
    method load_run_config (line 35) | def load_run_config(self, print_info=False, dataset='C10+'):
    method load_init (line 47) | def load_init(self):
    method load_net_config (line 54) | def load_net_config(self, init, print_info=False):
    method run (line 62) | def run(self, pure=True, restore=False, test=False, valid=False, valid...

FILE: src/aup/Proposer/eas/meta_controller/base_controller.py
  class BaseController (line 9) | class BaseController:
    method __init__ (line 10) | def __init__(self, path):
    method load (line 14) | def load(self):
    method save (line 17) | def save(self, global_step=None):
    method save_path (line 21) | def save_path(self):
    method logs_path (line 25) | def logs_path(self):
  class Vocabulary (line 29) | class Vocabulary:
    method __init__ (line 30) | def __init__(self, token_list):
    method size (line 38) | def size(self):
    method get_code (line 41) | def get_code(self, token_list):
    method get_token (line 44) | def get_token(self, code_list):
    method pad_code (line 48) | def pad_code(self):
  function embedding (line 52) | def embedding(_input, vocab_size, embedding_dim, name='embedding'):
  function build_cell (line 68) | def build_cell(units, cell_type='lstm', num_layers=1):
  function seq_len (line 83) | def seq_len(sequence):
  class EncoderNet (line 95) | class EncoderNet:
    method __init__ (line 96) | def __init__(self, num_steps, vocab, embedding_dim, rnn_units, rnn_typ...
    method bidirectional (line 111) | def bidirectional(self):
    method cell_type (line 115) | def cell_type(self):
    method _define_input (line 118) | def _define_input(self):
    method build (line 131) | def build(self):
  class WiderActorNet (line 173) | class WiderActorNet:
    method __init__ (line 174) | def __init__(self, out_dim, num_steps, net_type='simple', net_config=N...
    method build_forward (line 183) | def build_forward(self, _input):
  class DeeperActorNet (line 209) | class DeeperActorNet:
    method __init__ (line 210) | def __init__(self, decision_num, out_dims, embedding_dim,
    method _define_input (line 225) | def _define_input(self):
    method build_decoder_cell (line 231) | def build_decoder_cell(self, encoder_state):
    method build_forward (line 240) | def build_forward(self, encoder_output, encoder_state, is_training, de...

FILE: src/aup/Proposer/eas/meta_controller/rl_controller.py
  class RLNet2NetController (line 10) | class RLNet2NetController(BaseController):
    method save (line 11) | def save(self, global_step=None):
    method load (line 14) | def load(self):
    method __init__ (line 25) | def __init__(self, path, entropy_penalty,
    method _define_input (line 45) | def _define_input(self):
    method update_controller (line 87) | def update_controller(self, learning_rate, wider_seg_deeper, wider_dec...
    method build_forward (line 106) | def build_forward(self):
    method build_training_process (line 131) | def build_training_process(self):
    method sample_wider_decision (line 134) | def sample_wider_decision(self, input_seq, seq_len):
    method sample_deeper_decision (line 146) | def sample_deeper_decision(self, input_seq, seq_len, block_layer_num):
    method _initialize_session (line 160) | def _initialize_session(self):
    method get_wider_entropy (line 170) | def get_wider_entropy(self):
    method get_deeper_entropy (line 178) | def get_deeper_entropy(self):
  class ReinforceNet2NetController (line 189) | class ReinforceNet2NetController(RLNet2NetController):
    method build_training_process (line 190) | def build_training_process(self):
    method get_wider_side_obj (line 210) | def get_wider_side_obj(self):
    method get_deeper_side_obj (line 223) | def get_deeper_side_obj(self):

FILE: src/aup/Proposer/eas/models/basic_model.py
  class BasicModel (line 11) | class BasicModel:
    method __init__ (line 12) | def __init__(self, path, data_provider, run_config, net_config, pure=F...
    method save_path (line 38) | def save_path(self):
    method logs_path (line 47) | def logs_path(self):
    method _build_graph (line 56) | def _build_graph(self, only_forward=False):
    method _define_inputs (line 59) | def _define_inputs(self):
    method _initialize_session (line 76) | def _initialize_session(self, set_logs=True):
    method train_all_epochs (line 88) | def train_all_epochs(self, start_epoch=1):
    method train_one_epoch (line 131) | def train_one_epoch(self, data, batch_size, learning_rate):
    method test (line 159) | def test(self, data, batch_size):
    method save_config (line 191) | def save_config(self, save_path, print_info=True):
    method save_init (line 200) | def save_init(self, save_path, print_info=True):
    method pure_train (line 208) | def pure_train(self):
    method save_model (line 230) | def save_model(self, global_step=None):
    method load_model (line 233) | def load_model(self):
    method log_loss_accuracy (line 241) | def log_loss_accuracy(self, loss, accuracy, epoch, prefix, should_prin...
    method _count_trainable_params (line 256) | def _count_trainable_params():
    method dropout (line 267) | def dropout(_input, keep_prob, is_training):
    method weight_variable (line 279) | def weight_variable(shape, name, initializer):
    method avg_pool (line 287) | def avg_pool(_input, k=2, s=2):
    method max_pool (line 297) | def max_pool(_input, k=2, s=2):
    method conv2d (line 307) | def conv2d(_input, out_features, kernel_size, strides=1, padding='SAME...
    method fc_layer (line 321) | def fc_layer(_input, out_units, use_bias=False, param_initializer=None):
    method batch_norm (line 338) | def batch_norm(_input, is_training, epsilon=1e-3, decay=0.999, param_i...
    method activation (line 345) | def activation(_input, activation='relu'):
    method build_optimizer (line 360) | def build_optimizer(learning_rate, opt_name, opt_param):
    method flatten (line 369) | def flatten(_input):

FILE: src/aup/Proposer/eas/models/convnet.py
  class SimpleConvnetConfig (line 9) | class SimpleConvnetConfig:
    method __init__ (line 10) | def __init__(self):
    method weight_decay (line 20) | def weight_decay(self): return self.net_config['weight_decay']
    method bn_epsilon (line 23) | def bn_epsilon(self): return self.net_config['bn_epsilon']
    method bn_decay (line 26) | def bn_decay(self): return self.net_config['bn_decay']
    method drop_scheme (line 29) | def drop_scheme(self): return self.net_config['drop_scheme']
    method depth (line 32) | def depth(self): return self.layer_cascade.depth
    method get_config (line 34) | def get_config(self):
    method copy (line 41) | def copy(self):
    method renew_init (line 46) | def renew_init(self, convnet):
    method set_standard_convnet (line 51) | def set_standard_convnet(self, data_provider: DataProvider, conv_block...
    method set_net_from_config (line 99) | def set_net_from_config(self, net_config_json, init=None, print_info=T...
    method widen (line 108) | def widen(self, layer_idx, new_width, widen_type='output_dim', noise=N...
    method deepen (line 113) | def deepen(self, layer_idx, new_layer_config):
    method set_identity4deepen (line 116) | def set_identity4deepen(self, to_set_layers, data_provider, batch_size...
  class SimpleConvnet (line 159) | class SimpleConvnet(BasicModel):
    method _build_graph (line 160) | def _build_graph(self, only_forward=False):

FILE: src/aup/Proposer/eas/models/dense_net.py
  function get_block_by_name (line 10) | def get_block_by_name(name):
  class TransitionBlock (line 19) | class TransitionBlock(LayerCascade):
    method get_config (line 20) | def get_config(self):
    method set_from_config (line 27) | def set_from_config(config_json, init=None, return_class=True):
    method prev_widen (line 31) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 35) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 38) | def deepen(self, loc, new_layer_config, input_dim):
  class DenseBlock (line 42) | class DenseBlock:
    method __init__ (line 43) | def __init__(self, _id, miniblocks):
    method id (line 50) | def id(self):
    method id (line 54) | def id(self, value):
    method depth (line 58) | def depth(self):
    method out_features_dim (line 64) | def out_features_dim(self, in_features_dim):
    method build (line 70) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 80) | def get_config(self):
    method renew_init (line 87) | def renew_init(self, densenet):
    method set_from_config (line 94) | def set_from_config(config_json, init=None):
    method insert_miniblock (line 106) | def insert_miniblock(self, idx, miniblock_config, input_dim, noise=Non...
    method prev_widen (line 173) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 190) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 224) | def deepen(self, loc, new_layer_config, input_dim):
  class DenseNetConfig (line 231) | class DenseNetConfig:
    method __init__ (line 232) | def __init__(self):
    method model_type (line 246) | def model_type(self): return self.net_config['model_type']
    method weight_decay (line 249) | def weight_decay(self): return self.net_config['weight_decay']
    method first_ratio (line 252) | def first_ratio(self): return self.net_config['first_ratio']
    method reduction (line 255) | def reduction(self): return self.net_config['reduction']
    method bc_ratio (line 258) | def bc_ratio(self): return self.net_config['bc_ratio']
    method bn_epsilon (line 261) | def bn_epsilon(self): return self.net_config['bn_epsilon']
    method bn_decay (line 264) | def bn_decay(self): return self.net_config['bn_decay']
    method depth (line 267) | def depth(self):
    method average_growth_rate (line 274) | def average_growth_rate(self):
    method copy (line 283) | def copy(self):
    method get_config (line 288) | def get_config(self):
    method renew_init (line 295) | def renew_init(self, densenet):
    method set_standard_dense_net (line 300) | def set_standard_dense_net(self, data_provider: DataProvider, growth_r...
    method set_net_from_config (line 389) | def set_net_from_config(self, net_config_json, init=None, print_info=T...
    method widen (line 405) | def widen(self, loc, new_width, widen_type='output_dim', noise=None, i...
    method deepen (line 423) | def deepen(self, loc, new_layer_config, image_channel=3):
    method set_identity4deepen (line 435) | def set_identity4deepen(self, to_set_layers, data_provider, batch_size...
    method insert_miniblock (line 477) | def insert_miniblock(self, loc, miniblock_config, image_channel=3, noi...
  class DenseNet (line 492) | class DenseNet(BasicModel):
    method _build_graph (line 493) | def _build_graph(self, only_forward=False):

FILE: src/aup/Proposer/eas/models/layer_cascade.py
  class LayerCascade (line 5) | class LayerCascade:
    method __init__ (line 6) | def __init__(self, _id, layers):
    method id (line 13) | def id(self):
    method id (line 17) | def id(self, value):
    method out_features_dim (line 21) | def out_features_dim(self):
    method depth (line 30) | def depth(self):
    method get_str (line 37) | def get_str(self):
    method build (line 41) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 50) | def get_config(self):
    method renew_init (line 56) | def renew_init(self, densenet):
    method copy (line 62) | def copy(self):
    method set_from_config (line 66) | def set_from_config(config_json, init=None, return_class=True):
    method prev_widen (line 82) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 90) | def widen(self, idx, new_width, widen_type='output_dim', noise=None):
    method deepen (line 113) | def deepen(self, idx, new_layer_config, input_dim):

FILE: src/aup/Proposer/eas/models/layer_multi_branch.py
  class LayerMultiBranch (line 6) | class LayerMultiBranch:
    method __init__ (line 7) | def __init__(self, _id, branches, merge=None, in_bottle=None, out_bott...
    method id (line 24) | def id(self):
    method id (line 28) | def id(self, value):
    method out_features_dim (line 32) | def out_features_dim(self):
    method depth (line 46) | def depth(self):
    method get_str (line 58) | def get_str(self):
    method build (line 65) | def build(self, _input, densenet, store_output_op=False):
    method get_config (line 90) | def get_config(self):
    method renew_init (line 99) | def renew_init(self, densenet):
    method set_from_config (line 108) | def set_from_config(config_json, init=None):
    method prev_widen (line 130) | def prev_widen(self, indices, magnifier, noise=None):
    method widen (line 137) | def widen(self, loc, new_width, widen_type='output_dim', noise=None):
    method deepen (line 184) | def deepen(self, loc, new_layer_config, input_dim):
    method remapped_branches (line 199) | def remapped_branches(self, noise=None):

FILE: src/aup/Proposer/eas/models/layers.py
  function apply_noise (line 7) | def apply_noise(weights, noise_config):
  function get_layer_by_name (line 25) | def get_layer_by_name(name):
  function get_magnifier (line 36) | def get_magnifier(old_size, indices):
  function get_random_remapping (line 44) | def get_random_remapping(old_size, new_size):
  class BaseLayer (line 52) | class BaseLayer:
    method __init__ (line 56) | def __init__(self, _id, use_bn=True, activation='relu', keep_prob=1.0,...
    method id (line 69) | def id(self): return self._id
    method id (line 72) | def id(self, value): self._id = value
    method init (line 75) | def init(self):
    method param_initializer (line 79) | def param_initializer(self):
    method renew_init (line 90) | def renew_init(self, net: BasicModel):
    method copy (line 102) | def copy(self):
    method get_config (line 105) | def get_config(self):
    method variable_list (line 115) | def variable_list(self):
    method set_from_config (line 132) | def set_from_config(layer_config, layer_init):
    method build (line 135) | def build(self, _input, net, store_output_op):
    method prev_widen (line 138) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 141) | def set_identity_layer(self, strict, param, noise):
    method widen_bn (line 144) | def widen_bn(self, indices, magnifier, noise=None):
    method set_bn_identity (line 151) | def set_bn_identity(self, strict=True, param=None, noise=None):
  class ConvLayer (line 164) | class ConvLayer(BaseLayer):
    method __init__ (line 165) | def __init__(self, _id, filter_num, kernel_size=3, strides=1,
    method layer_str (line 173) | def layer_str(self):
    method variable_list (line 177) | def variable_list(self):
    method get_config (line 182) | def get_config(self):
    method set_from_config (line 192) | def set_from_config(layer_config, layer_init=None):
    method build (line 197) | def build(self, _input, net: BasicModel, store_output_op=False):
    method widen_filters (line 230) | def widen_filters(self, new_filter_num, noise=None):
    method prev_widen (line 249) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 256) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method remap (line 265) | def remap(self, indices, noise=None):
  class FCLayer (line 274) | class FCLayer(BaseLayer):
    method __init__ (line 275) | def __init__(self, _id, units, use_bn=True, use_bias=False, activation...
    method layer_str (line 282) | def layer_str(self):
    method variable_list (line 286) | def variable_list(self):
    method get_config (line 293) | def get_config(self):
    method set_from_config (line 302) | def set_from_config(layer_config, layer_init=None):
    method build (line 307) | def build(self, _input, net: BasicModel, store_output_op=False):
    method widen_units (line 340) | def widen_units(self, new_units_num, noise=None):
    method prev_widen (line 364) | def prev_widen(self, indices, magnifier, noise=None):
    method set_identity_layer (line 371) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method remap (line 380) | def remap(self, indices, noise=None):
  class PoolLayer (line 391) | class PoolLayer(BaseLayer):
    method __init__ (line 392) | def __init__(self, _id, _type, kernel_size=2, strides=2, use_bn=False,...
    method layer_str (line 401) | def layer_str(self):
    method get_config (line 404) | def get_config(self):
    method set_from_config (line 414) | def set_from_config(layer_config, layer_init=None):
    method build (line 419) | def build(self, _input, net: BasicModel, store_output_op=False):
    method set_identity_layer (line 460) | def set_identity_layer(self, strict=True, param=None, noise=None):
    method prev_widen (line 463) | def prev_widen(self, indices, magnifier, noise=None):

FILE: src/aup/Proposer/eas/models/utils.py
  function get_model_config_by_name (line 6) | def get_model_config_by_name(name):
  function get_model_by_name (line 15) | def get_model_by_name(name):
  class RunConfig (line 24) | class RunConfig:
    method __init__ (line 25) | def __init__(self, batch_size, n_epochs, init_lr, reduce_lr_epochs, re...
    method get_config (line 46) | def get_config(self):
    method update (line 49) | def update(self, new_config):
    method copy (line 52) | def copy(self):
    method learning_rate (line 55) | def learning_rate(self, epoch):
    method get_default_run_config (line 71) | def get_default_run_config(dataset='C10+'):

FILE: src/aup/Proposer/hpbandster/core/base_config_generator.py
  class base_config_generator (line 4) | class base_config_generator(object):
    method __init__ (line 10) | def __init__(self, logger=None):
    method get_config (line 31) | def get_config(self, budget):
    method new_result (line 47) | def new_result(self, job, update_model=True):

FILE: src/aup/Proposer/hpbandster/core/base_iteration.py
  class Datum (line 11) | class Datum(object):
    method __init__ (line 12) | def __init__(self, config, config_info, results=None, time_stamps=None...
    method __repr__ (line 21) | def __repr__(self):
  class BaseIteration (line 32) | class BaseIteration(object):
    method __init__ (line 38) | def __init__(self, HPB_iter, num_configs, budgets, config_sampler, log...
    method add_configuration (line 72) | def add_configuration(self, config = None, config_info={}):
    method register_result (line 105) | def register_result(self, job, skip_sanity_checks=False):
    method get_next_run (line 141) | def get_next_run(self):
    method _advance_to_next_stage (line 178) | def _advance_to_next_stage(self, config_ids, losses):
    method process_results (line 201) | def process_results(self):
    method finish_up (line 244) | def finish_up(self):
  class WarmStartIteration (line 252) | class WarmStartIteration(BaseIteration):
    method __init__ (line 257) | def __init__(self, Result, config_generator):
    method fix_timestamps (line 290) | def fix_timestamps(self, time_ref):

FILE: src/aup/Proposer/hpbandster/core/dispatcher.py
  class Job (line 9) | class Job(object):
    method __init__ (line 10) | def __init__(self, id, **kwargs):
    method time_it (line 22) | def time_it(self, which_time):
    method __repr__ (line 25) | def __repr__(self):
    method recreate_from_run (line 32) | def recreate_from_run(self, run):
  class Worker (line 44) | class Worker(object):
    method __init__ (line 45) | def __init__(self, name, uri):
    method is_alive (line 50) | def is_alive(self):
    method shutdown (line 59) | def shutdown(self):
    method is_busy (line 62) | def is_busy(self):
    method __repr__ (line 65) | def __repr__(self):
  class Dispatcher (line 69) | class Dispatcher(object):
    method __init__ (line 74) | def __init__(self, new_result_callback, run_id='0',
    method run (line 129) | def run(self):
    method shutdown_all_workers (line 169) | def shutdown_all_workers(self, rediscover=False):
    method shutdown (line 177) | def shutdown(self, shutdown_workers=False):
    method trigger_discover_worker (line 186) | def trigger_discover_worker(self):
    method discover_workers (line 193) | def discover_workers(self):
    method number_of_workers (line 268) | def number_of_workers(self):
    method job_runner (line 272) | def job_runner(self):
    method submit_job (line 304) | def submit_job(self, id, **kwargs):
    method register_result (line 316) | def register_result(self, id=None, result=None):

FILE: src/aup/Proposer/hpbandster/core/master.py
  class Master (line 19) | class Master(object):
    method __init__ (line 20) | def __init__(self,
    method shutdown (line 129) | def shutdown(self, shutdown_workers=False):
    method wait_for_workers (line 135) | def wait_for_workers(self, min_n_workers=1):
    method get_next_iteration (line 155) | def get_next_iteration(self, iteration, iteration_kwargs):
    method run (line 176) | def run(self, n_iterations=1, min_n_workers=1, iteration_kwargs = {},):
    method adjust_queue_size (line 236) | def adjust_queue_size(self, number_of_workers=None):
    method job_callback (line 248) | def job_callback(self, job):
    method _queue_wait (line 271) | def _queue_wait(self):
    method _submit_job (line 281) | def _submit_job(self, config_id, config, budget):
    method active_iterations (line 297) | def active_iterations(self):
    method __del__ (line 309) | def __del__(self):

FILE: src/aup/Proposer/hpbandster/core/nameserver.py
  function nic_name_to_host (line 9) | def nic_name_to_host(nic_name):
  class NameServer (line 16) | class NameServer(object):
    method __init__ (line 22) | def __init__(self, run_id, working_directory=None, host=None, port=0, ...
    method start (line 48) | def start(self):
    method shutdown (line 82) | def shutdown(self):
    method __del__ (line 95) | def __del__(self):

FILE: src/aup/Proposer/hpbandster/core/result.py
  class Run (line 7) | class Run(object):
    method __init__ (line 12) | def __init__(self, config_id, budget, loss, info, time_stamps, error_l...
    method __repr__ (line 20) | def __repr__(self):
    method __getitem__ (line 28) | def __getitem__ (self, k):
  function extract_HBS_learning_curves (line 35) | def extract_HBS_learning_curves(runs):
  class json_result_logger (line 64) | class json_result_logger(object):
    method __init__ (line 65) | def __init__(self, directory, overwrite=False):
    method new_config (line 120) | def new_config(self, config_id, config, config_info):
    method __call__ (line 127) | def __call__(self, job):
  function logged_results_to_HBS_result (line 139) | def logged_results_to_HBS_result(directory):
  class Result (line 203) | class Result(object):
    method __init__ (line 210) | def __init__ (self, HB_iteration_data, HB_config):
    method __getitem__ (line 215) | def __getitem__(self, k):
    method get_incumbent_id (line 219) | def get_incumbent_id(self):
    method get_incumbent_trajectory (line 245) | def get_incumbent_trajectory(self, all_budgets=True, bigger_is_better=...
    method get_runs_by_id (line 319) | def get_runs_by_id(self, config_id):
    method get_learning_curves (line 344) | def get_learning_curves(self, lc_extractor=extract_HBS_learning_curves...
    method get_all_runs (line 374) | def get_all_runs(self, only_largest_budget=False):
    method get_id2config_mapping (line 400) | def get_id2config_mapping(self):
    method _merge_results (line 415) | def _merge_results(self):
    method num_iterations (line 431) | def num_iterations(self):
    method get_fANOVA_data (line 435) | def get_fANOVA_data(self, config_space, budgets=None, loss_fn=lambda r...
    method get_pandas_dataframe (line 485) | def get_pandas_dataframe(self, budgets=None, loss_fn=lambda r: r.loss):

FILE: src/aup/Proposer/hpbandster/core/worker.py
  class Worker (line 13) | class Worker(object):
    method __init__ (line 22) | def __init__(self, run_id, nameserver=None, nameserver_port=None, logg...
    method load_nameserver_credentials (line 70) | def load_nameserver_credentials(self, working_directory, num_tries=60,...
    method run (line 98) | def run(self, background=False):
    method _run (line 118) | def _run(self):
    method compute (line 165) | def compute(self, config_id, config, budget, working_directory):
    method start_computation (line 194) | def start_computation(self, callback, id, *args, **kwargs):
    method is_busy (line 225) | def is_busy(self):
    method shutdown (line 230) | def shutdown(self):

FILE: src/aup/Proposer/hpbandster/examples/commons.py
  class MyWorker (line 15) | class MyWorker(Worker):
    method __init__ (line 17) | def __init__(self, *args, sleep_interval=0, **kwargs):
    method compute (line 22) | def compute(self, config, budget, **kwargs):
    method get_configspace (line 49) | def get_configspace():

FILE: src/aup/Proposer/hpbandster/examples/example_5_keras_worker.py
  class KerasWorker (line 80) | class KerasWorker(Worker):
    method __init__ (line 81) | def __init__(self, N_train=8192, N_valid=1024, **kwargs):
    method compute (line 124) | def compute(self, config, budget, working_directory, *args, **kwargs):
    method get_configspace (line 189) | def get_configspace():

FILE: src/aup/Proposer/hpbandster/examples/example_5_pytorch_worker.py
  class PyTorchWorker (line 79) | class PyTorchWorker(Worker):
    method __init__ (line 80) | def __init__(self, N_train = 8192, N_valid = 1024, **kwargs):
    method compute (line 99) | def compute(self, config, budget, working_directory, *args, **kwargs):
    method evaluate_accuracy (line 146) | def evaluate_accuracy(self, model, data_loader):
    method get_configspace (line 161) | def get_configspace():
  class MNISTConvNet (line 213) | class MNISTConvNet(torch.nn.Module):
    method __init__ (line 214) | def __init__(self, num_conv_layers, num_filters_1, num_filters_2, num_...
    method forward (line 243) | def forward(self, x):
    method number_of_parameters (line 265) | def number_of_parameters(self):

FILE: src/aup/Proposer/hpbandster/examples/plot_example_7_interactive_plot.py
  function realtime_learning_curves (line 39) | def realtime_learning_curves(runs):

FILE: src/aup/Proposer/hpbandster/optimizers/bohb.py
  class BOHB (line 16) | class BOHB(Master):
    method __init__ (line 17) | def __init__(self, configspace = None,
    method get_next_iteration (line 119) | def get_next_iteration(self, iteration, iteration_kwargs={}):

FILE: src/aup/Proposer/hpbandster/optimizers/config_generators/bohb.py
  class BOHB (line 17) | class BOHB(base_config_generator):
    method __init__ (line 18) | def __init__(self, configspace, min_points_in_model = None,
    method largest_budget_with_model (line 94) | def largest_budget_with_model(self):
    method get_config (line 99) | def get_config(self, budget):
    method impute_conditional_data (line 238) | def impute_conditional_data(self, array):
    method new_result (line 267) | def new_result(self, job, update_model=True):

FILE: src/aup/Proposer/hpbandster/optimizers/config_generators/h2bo.py
  class H2BO (line 15) | class H2BO(base_config_generator):
    method __init__ (line 16) | def __init__(self, configspace, min_points_in_model = None,
    method largest_budget_with_model (line 72) | def largest_budget_with_model(self):
    method get_config (line 77) | def get_config(self, budget):
    method impute_conditional_data (line 138) | def impute_conditional_data(self, array):
    method new_result (line 167) | def new_result(self, job, update_model=True):

FILE: src/aup/Proposer/hpbandster/optimizers/config_generators/kde.py
  class KernelDensityEstimator (line 9) | class KernelDensityEstimator(base_config_generator):
    method __init__ (line 11) | def __init__(self, configspace, top_n_percent=10, update_after_n_point...
    method get_config (line 50) | def get_config(self, budget):
    method new_result (line 84) | def new_result(self, job):

FILE: src/aup/Proposer/hpbandster/optimizers/config_generators/lcnet.py
  function smoothing (line 10) | def smoothing(lc):
  class LCNetWrapper (line 20) | class LCNetWrapper(base_config_generator):
    method __init__ (line 21) | def __init__(self,
    method get_config (line 63) | def get_config(self, budget):
    method new_result (line 105) | def new_result(self, job):

FILE: src/aup/Proposer/hpbandster/optimizers/config_generators/random_sampling.py
  class RandomSampling (line 6) | class RandomSampling(base_config_generator):
    method __init__ (line 11) | def __init__(self, configspace, **kwargs):
    method get_config (line 28) | def get_config(self, budget):

FILE: src/aup/Proposer/hpbandster/optimizers/h2bo.py
  class H2BO (line 16) | class H2BO(Master):
    method __init__ (line 17) | def __init__(self,
    method get_next_iteration (line 115) | def get_next_iteration(self, iteration, iteration_kwargs={}):

FILE: src/aup/Proposer/hpbandster/optimizers/hyperband.py
  class HyperBand (line 17) | class HyperBand(Master):
    method __init__ (line 18) | def __init__(self, configspace = None,
    method get_next_iteration (line 73) | def get_next_iteration(self, iteration, iteration_kwargs={}):

FILE: src/aup/Proposer/hpbandster/optimizers/iterations/successivehalving.py
  class SuccessiveHalving (line 5) | class SuccessiveHalving(BaseIteration):
    method _advance_to_next_stage (line 7) | def _advance_to_next_stage(self, config_ids, losses):

FILE: src/aup/Proposer/hpbandster/optimizers/iterations/successiveresampling.py
  class SuccessiveResampling (line 8) | class SuccessiveResampling(BaseIteration):
    method __init__ (line 10) | def __init__(self, *args, resampling_rate = 0.5, min_samples_advance =...
    method _advance_to_next_stage (line 28) | def _advance_to_next_stage(self, config_ids, losses):

FILE: src/aup/Proposer/hpbandster/optimizers/kde/kernels.py
  class BaseKernel (line 11) | class BaseKernel(object):
    method __init__ (line 12) | def __init__(self, data=None, bandwidth=None, fix_boundary=False, num_...
    method set_bandwidth (line 25) | def set_bandwidth(self, bandwidth):
    method _compute_weights (line 29) | def _compute_weights(self):
    method __call__ (line 32) | def __call__(self, x_test):
    method sample (line 35) | def sample(self, sample_indices=None, num_samples=1):
  class Gaussian (line 39) | class Gaussian(BaseKernel):
    method _compute_weights (line 41) | def _compute_weights(self):
    method __call__ (line 51) | def __call__(self, x_test):
    method sample (line 60) | def sample(self, sample_indices=None, num_samples=1):
  class AitchisonAitken (line 90) | class AitchisonAitken(BaseKernel):
    method __call__ (line 91) | def __call__(self, x_test):
    method sample (line 101) | def sample(self, sample_indices=None, num_samples=1):
  class WangRyzinOrdinal (line 132) | class WangRyzinOrdinal(BaseKernel):
    method _compute_weights (line 134) | def _compute_weights(self):
    method __call__ (line 144) | def __call__(self, x_test):
    method sample (line 157) | def sample(self, sample_indices=None, num_samples=1):
  class WangRyzinInteger (line 192) | class WangRyzinInteger(BaseKernel):
    method _compute_weights (line 193) | def _compute_weights(self):
    method __call__ (line 203) | def __call__(self, x_test):
    method sample (line 216) | def sample(self, sample_indices=None, num_samples=1):

FILE: src/aup/Proposer/hpbandster/optimizers/kde/mvkde.py
  class MultivariateKDE (line 9) | class MultivariateKDE(object):
    method __init__ (line 10) | def __init__(self, configspace, fully_dimensional=True, min_bandwidth=...
    method fit (line 60) | def fit(self, data, weights=None, bw_estimator='scott', efficient_bw_e...
    method _set_kernel_bandwidths (line 117) | def _set_kernel_bandwidths(self):
    method set_bandwidths (line 122) | def set_bandwidths(self, bandwidths):
    method _normalize_weights (line 126) | def _normalize_weights(self, weights):
    method _individual_pdfs (line 134) | def _individual_pdfs(self, x_test):
    method loo_negloglikelihood (line 144) | def loo_negloglikelihood(self):
    method pdf (line 172) | def pdf(self, x_test):
    method sample (line 192) | def sample(self, num_samples=1):
    method _get_types (line 210) | def _get_types(self):

FILE: src/aup/Proposer/hpbandster/optimizers/lcnet.py
  class LCNet (line 17) | class LCNet(Master):
    method __init__ (line 18) | def __init__(self, configspace=None,
    method get_next_iteration (line 98) | def get_next_iteration(self, iteration, iteration_kwargs={}):

FILE: src/aup/Proposer/hpbandster/optimizers/learning_curve_models/arif.py
  class ARIF (line 11) | class ARIF(lcm_base):
    method __init__ (line 15) | def __init__(self, order=2, diff_order=0):
    method apply_differencing (line 29) | def apply_differencing(self, series, order=None):
    method invert_differencing (line 38) | def invert_differencing(self, initial_part, differenced_rest, order=No...
    method fit (line 56) | def fit(self, losses, configs=None):
    method extend_partial (line 80) | def extend_partial(self, obs_losses, num_steps, config=None):

FILE: src/aup/Proposer/hpbandster/optimizers/learning_curve_models/base.py
  class LCModel (line 1) | class LCModel():
    method fit (line 6) | def fit(self, times, losses, configs=None):
    method predict_unseen (line 25) | def predict_unseen(self, times, config):
    method extend_partial (line 45) | def extend_partial(self, times, obs_times, obs_losses, config=None):

FILE: src/aup/Proposer/hpbandster/optimizers/learning_curve_models/lcnet.py
  class LCNetWrapper (line 8) | class LCNetWrapper(LCModel):
    method __init__ (line 13) | def __init__(self, max_num_epochs):
    method fit (line 24) | def fit(self, times, losses, configs=None):
    method predict_unseen (line 65) | def predict_unseen(self, times, config):
    method extend_partial (line 95) | def extend_partial(self, times, obs_times, obs_losses, config=None):

FILE: src/aup/Proposer/hpbandster/optimizers/randomsearch.py
  class RandomSearch (line 16) | class RandomSearch(Master):
    method __init__ (line 17) | def __init__(self, configspace = None,
    method get_next_iteration (line 67) | def get_next_iteration(self, iteration, iteration_kwargs={}):

FILE: src/aup/Proposer/hpbandster/utils.py
  function nic_name_to_host (line 14) | def nic_name_to_host(nic_name):
  function start_local_nameserver (line 22) | def start_local_nameserver(host=None, port=0, nic_name=None):

FILE: src/aup/Proposer/hpbandster/visualization.py
  function default_tool_tips (line 10) | def default_tool_tips(result_object, learning_curves, include_run_info=F...
  function concurrent_runs_over_time (line 48) | def concurrent_runs_over_time(runs, num_points = 512, show=False):
  function finished_runs_over_time (line 64) | def finished_runs_over_time(runs, show=False):
  function performance_histogram_model_vs_random (line 92) | def performance_histogram_model_vs_random(runs, id2conf, show=False):
  function correlation_across_budgets (line 137) | def correlation_across_budgets(results_object, show=False):
  function losses_over_time (line 203) | def losses_over_time(runs, get_loss_from_run_fn = lambda r: r.loss, cmap...
  function interactive_HBS_plot (line 243) | def interactive_HBS_plot(learning_curves, tool_tip_strings=None,log_y=Fa...

FILE: src/aup/Proposer/hpbandster/workers/hpolibbenchmark.py
  class HPOlib2Worker (line 5) | class HPOlib2Worker(Worker):
    method __init__ (line 6) | def __init__(self,benchmark, configspace=None, budget_name='budget', b...
    method compute (line 27) | def compute(self, config, budget, **kwargs):

FILE: src/aup/Proposer/hyperopt/algobase.py
  class ExprEvaluator (line 18) | class ExprEvaluator(object):
    method __init__ (line 19) | def __init__(self, expr,
    method eval_nodes (line 63) | def eval_nodes(self, memo=None):
    method set_in_memo (line 133) | def set_in_memo(self, memo, k, v):
    method on_switch (line 154) | def on_switch(self, memo, node):
    method on_node (line 191) | def on_node(self, memo, node):
  class SuggestAlgo (line 218) | class SuggestAlgo(ExprEvaluator):
    method __init__ (line 225) | def __init__(self, domain, trials, seed):
    method __call__ (line 234) | def __call__(self, new_id):
    method on_node (line 252) | def on_node(self, memo, node):
    method batch (line 259) | def batch(self, new_ids):

FILE: src/aup/Proposer/hyperopt/anneal.py
  class AnnealingAlgo (line 42) | class AnnealingAlgo(SuggestAlgo):
    method __init__ (line 100) | def __init__(self, domain, trials, seed,
    method shrinking (line 139) | def shrinking(self, label):
    method choose_ltv (line 150) | def choose_ltv(self, label, size):
    method on_node_hyperparameter (line 183) | def on_node_hyperparameter(self, memo, node, label):
    method hp_uniform (line 229) | def hp_uniform(self, memo, node, label, tid, val,
    method hp_quniform (line 290) | def hp_quniform(self, *args, **kwargs):
    method hp_loguniform (line 297) | def hp_loguniform(self, *args, **kwargs):
    method hp_qloguniform (line 305) | def hp_qloguniform(self, *args, **kwargs):
    method hp_randint (line 313) | def hp_randint(self, memo, node, label, tid, val):
    method hp_categorical (line 330) | def hp_categorical(self, memo, node, label, tid, val):
    method hp_normal (line 364) | def hp_normal(self, memo, node, label, tid, val):
    method hp_lognormal (line 374) | def hp_lognormal(self, memo, node, label, tid, val):
    method hp_qlognormal (line 384) | def hp_qlognormal(self, memo, node, label, tid, val):
    method hp_qnormal (line 396) | def hp_qnormal(self, memo, node, label, tid, val):
  function suggest (line 408) | def suggest(new_ids, domain, trials, seed, *args, **kwargs):
  function suggest_batch (line 413) | def suggest_batch(new_ids, domain, trials, seed, *args, **kwargs):

FILE: src/aup/Proposer/hyperopt/base.py
  function _all_same (line 114) | def _all_same(*args):
  function SONify (line 118) | def SONify(arg, memo=None):
  function miscs_update_idxs_vals (line 161) | def miscs_update_idxs_vals(miscs, idxs, vals,
  function miscs_to_idxs_vals (line 192) | def miscs_to_idxs_vals(miscs, keys=None):
  function spec_from_misc (line 210) | def spec_from_misc(misc):
  class Trials (line 222) | class Trials(object):
    method __init__ (line 268) | def __init__(self, exp_key=None, refresh=True):
    method view (line 276) | def view(self, exp_key=None, refresh=True):
    method aname (line 286) | def aname(self, trial, name):
    method trial_attachments (line 289) | def trial_attachments(self, trial):
    method __iter__ (line 314) | def __iter__(self):
    method __len__ (line 321) | def __len__(self):
    method __getitem__ (line 328) | def __getitem__(self, item):
    method refresh (line 333) | def refresh(self):
    method trials (line 347) | def trials(self):
    method tids (line 351) | def tids(self):
    method specs (line 355) | def specs(self):
    method results (line 359) | def results(self):
    method miscs (line 363) | def miscs(self):
    method idxs_vals (line 367) | def idxs_vals(self):
    method idxs (line 371) | def idxs(self):
    method vals (line 375) | def vals(self):
    method assert_valid_trial (line 378) | def assert_valid_trial(self, trial):
    method _insert_trial_docs (line 408) | def _insert_trial_docs(self, docs):
    method insert_trial_doc (line 415) | def insert_trial_doc(self, doc):
    method insert_trial_docs (line 426) | def insert_trial_docs(self, docs):
    method new_trial_ids (line 433) | def new_trial_ids(self, N):
    method new_trial_docs (line 439) | def new_trial_docs(self, tids, specs, results, miscs):
    method source_trial_docs (line 457) | def source_trial_docs(self, tids, specs, results, miscs, sources):
    method delete_all (line 482) | def delete_all(self):
    method count_by_state_synced (line 487) | def count_by_state_synced(self, arg, trials=None):
    method count_by_state_unsynced (line 504) | def count_by_state_unsynced(self, arg):
    method losses (line 517) | def losses(self, bandit=None):
    method statuses (line 523) | def statuses(self, bandit=None):
    method average_best_error (line 529) | def average_best_error(self, bandit=None):
    method best_trial (line 578) | def best_trial(self):
    method argmin (line 589) | def argmin(self):
    method fmin (line 600) | def fmin(self, fn, space, algo, max_evals,
  function trials_from_docs (line 638) | def trials_from_docs(docs, validate=True, **kwargs):
  class Ctrl (line 650) | class Ctrl(object):
    method __init__ (line 658) | def __init__(self, trials, current_trial=None):
    method checkpoint (line 670) | def checkpoint(self, r=None):
    method attachments (line 676) | def attachments(self):
    method inject_results (line 683) | def inject_results(self, specs, results, miscs, new_tids=None):
  class Domain (line 708) | class Domain(object):
    method __init__ (line 720) | def __init__(self, fn, expr,
    method memo_from_config (line 815) | def memo_from_config(self, config):
    method evaluate (line 827) | def evaluate(self, config, ctrl, attach_attachments=True):
    method evaluate_async (line 867) | def evaluate_async(self, config, ctrl, attach_attachments=True,):
    method evaluate_async2 (line 888) | def evaluate_async2(self, rval, ctrl, attach_attachments=True):
    method short_str (line 917) | def short_str(self):
    method loss (line 920) | def loss(self, result, config=None):
    method loss_variance (line 925) | def loss_variance(self, result, config=None):
    method true_loss (line 929) | def true_loss(self, result, config=None):
    method true_loss_variance (line 937) | def true_loss_variance(self, config=None):
    method status (line 943) | def status(self, result, config=None):
    method new_result (line 948) | def new_result(self):

FILE: src/aup/Proposer/hyperopt/criteria.py
  function EI_empirical (line 9) | def EI_empirical(samples, thresh):
  function EI_gaussian_empirical (line 18) | def EI_gaussian_empirical(mean, var, thresh, rng, N):
  function EI_gaussian (line 26) | def EI_gaussian(mean, var, thresh):
  function logEI_gaussian (line 37) | def logEI_gaussian(mean, var, thresh):
  function UCB (line 88) | def UCB(mean, var, zscore):

FILE: src/aup/Proposer/hyperopt/exceptions.py
  class BadSearchSpace (line 6) | class BadSearchSpace(Exception):
  class DuplicateLabel (line 10) | class DuplicateLabel(BadSearchSpace):
  class InvalidTrial (line 14) | class InvalidTrial(ValueError):
    method __init__ (line 16) | def __init__(self, msg, obj):
  class InvalidResultStatus (line 21) | class InvalidResultStatus(ValueError):
    method __init__ (line 23) | def __init__(self, result):
  class InvalidLoss (line 28) | class InvalidLoss(ValueError):
    method __init__ (line 30) | def __init__(self, result):

FILE: src/aup/Proposer/hyperopt/fmin.py
  function generate_trial (line 30) | def generate_trial(tid, space):
  function generate_trials_to_calculate (line 52) | def generate_trials_to_calculate(points):
  function fmin_pass_expr_memo_ctrl (line 70) | def fmin_pass_expr_memo_ctrl(f):
  function partial (line 85) | def partial(fn, **kwargs):
  class FMinIter (line 95) | class FMinIter(object):
    method __init__ (line 101) | def __init__(self, algo, domain, trials, rstate, asynchronous=None,
    method serial_evaluate (line 127) | def serial_evaluate(self, N=-1):
    method block_until_done (line 158) | def block_until_done(self):
    method run (line 177) | def run(self, N, block_until_done=True):
    method __iter__ (line 233) | def __iter__(self):
    method __next__ (line 236) | def __next__(self):
    method exhaust (line 242) | def exhaust(self):
  function fmin (line 249) | def fmin(fn, space, algo, max_evals, trials=None, rstate=None,
  function space_eval (line 383) | def space_eval(space, hp_assignment):

FILE: src/aup/Proposer/hyperopt/graphviz.py
  function dot_hyperparameters (line 16) | def dot_hyperparameters(expr):

FILE: src/aup/Proposer/hyperopt/ipy.py
  class LostEngineError (line 31) | class LostEngineError(RuntimeError):
  class IPythonTrials (line 35) | class IPythonTrials(Trials):
    method __init__ (line 37) | def __init__(self, client,
    method _insert_trial_docs (line 48) | def _insert_trial_docs(self, docs):
    method refresh (line 53) | def refresh(self):
    method fmin (line 95) | def fmin(self, fn, space, **kw):
    method wait (line 178) | def wait(self, verbose=False, verbose_print_interval=1.0):
    method __getstate__ (line 200) | def __getstate__(self):
    method __setstate__ (line 208) | def __setstate__(self, dct):
  class IPYAsync (line 216) | class IPYAsync(object):
    method __init__ (line 218) | def __init__(self, asynchronous, domain, rv, eid, tid, ctrl):
    method ready (line 227) | def ready(self):
    method get (line 230) | def get(self):
  function call_domain (line 241) | def call_domain(domain, spec, ctrl, trial, view, eid, tid):

FILE: src/aup/Proposer/hyperopt/main.py
  function main_search (line 29) | def main_search():
  function main (line 96) | def main(cmd, fn_pos=1):

FILE: src/aup/Proposer/hyperopt/mix.py
  function suggest (line 5) | def suggest(new_ids, domain, trials, seed, p_suggest):

FILE: src/aup/Proposer/hyperopt/mongoexp.py
  class OperationFailure (line 145) | class OperationFailure(Exception):
  class Shutdown (line 151) | class Shutdown(Exception):
  class WaitQuit (line 157) | class WaitQuit(Exception):
  class InvalidMongoTrial (line 163) | class InvalidMongoTrial(InvalidTrial):
  class DomainSwapError (line 167) | class DomainSwapError(Exception):
  class ReserveTimeout (line 173) | class ReserveTimeout(Exception):
  function read_pw (line 178) | def read_pw():
  function parse_url (line 182) | def parse_url(url, pwfile=None):
  function connection_with_tunnel (line 237) | def connection_with_tunnel(dbname, host='localhost',
  function connection_from_string (line 272) | def connection_from_string(s):
  class MongoJobs (line 292) | class MongoJobs(object):
    method __init__ (line 306) | def __init__(self, db, jobs, gfs, conn, tunnel, config_name):
    method alloc (line 344) | def alloc(cls, dbname, host='localhost',
    method new_from_connection_str (line 354) | def new_from_connection_str(cls, conn_str, gfs_coll='fs', config_name=...
    method __iter__ (line 359) | def __iter__(self):
    method __len__ (line 362) | def __len__(self):
    method create_jobs_indexes (line 368) | def create_jobs_indexes(self):
    method create_drivers_indexes (line 373) | def create_drivers_indexes(self):
    method create_indexes (line 377) | def create_indexes(self):
    method jobs_complete (line 381) | def jobs_complete(self, cursor=False):
    method jobs_error (line 385) | def jobs_error(self, cursor=False):
    method jobs_running (line 389) | def jobs_running(self, cursor=False):
    method jobs_dead (line 397) | def jobs_dead(self, cursor=False):
    method jobs_queued (line 405) | def jobs_queued(self, cursor=False):
    method insert (line 409) | def insert(self, job):
    method delete (line 425) | def delete(self, job):
    method delete_all (line 434) | def delete_all(self, cond=None):
    method delete_all_error_jobs (line 453) | def delete_all_error_jobs(self):
    method reserve (line 456) | def reserve(self, host_id, cond=None, exp_key=None):
    method refresh (line 491) | def refresh(self, doc):
    method update (line 494) | def update(self, doc, dct, collection=None, do_sanity_checks=True):
    method attachment_names (line 564) | def attachment_names(self, doc):
    method set_attachment (line 570) | def set_attachment(self, doc, blob, name, collection=None):
    method get_attachment (line 614) | def get_attachment(self, doc, name):
    method delete_attachment (line 629) | def delete_attachment(self, doc, name, collection=None):
  class MongoTrials (line 643) | class MongoTrials(Trials):
    method __init__ (line 664) | def __init__(self, arg, exp_key=None, cmd=None, workdir=None,
    method view (line 678) | def view(self, exp_key=None, cmd=None, workdir=None, refresh=True):
    method refresh_tids (line 686) | def refresh_tids(self, tids):
    method refresh (line 805) | def refresh(self):
    method _insert_trial_docs (line 808) | def _insert_trial_docs(self, docs):
    method count_by_state_unsynced (line 814) | def count_by_state_unsynced(self, arg):
    method delete_all (line 831) | def delete_all(self, cond=None):
    method new_trial_ids (line 850) | def new_trial_ids(self, N):
    method trial_attachments (line 878) | def trial_attachments(self, trial):
    method attachments (line 927) | def attachments(self):
  class MongoWorker (line 976) | class MongoWorker(object):
    method __init__ (line 980) | def __init__(self, mj,
    method make_log_handler (line 998) | def make_log_handler(self):
    method run_one (line 1005) | def run_one(self,
  class MongoCtrl (line 1111) | class MongoCtrl(Ctrl):
    method __init__ (line 1121) | def __init__(self, trials, current_trial, read_only):
    method debug (line 1126) | def debug(self, *args, **kwargs):
    method info (line 1130) | def info(self, *args, **kwargs):
    method warn (line 1134) | def warn(self, *args, **kwargs):
    method error (line 1138) | def error(self, *args, **kwargs):
    method checkpoint (line 1142) | def checkpoint(self, result=None):
    method attachments (line 1150) | def attachments(self):
    method set_attachment (line 1158) | def set_attachment(self):
  function exec_import (line 1164) | def exec_import(cmd_module, cmd):
  function as_mongo_str (line 1170) | def as_mongo_str(s):
  function main_worker_helper (line 1177) | def main_worker_helper(options, args):
  function main_worker (line 1265) | def main_worker():

FILE: src/aup/Proposer/hyperopt/plotting.py
  function algo_as_str (line 37) | def algo_as_str(algo):
  function main_plot_history (line 43) | def main_plot_history(trials, bandit=None, algo=None, do_show=True,
  function main_plot_histogram (line 81) | def main_plot_histogram(trials, bandit=None, algo=None, do_show=True):
  function main_plot_vars (line 104) | def main_plot_vars(trials, bandit=None, do_show=True, fontsize=10,
  function erf (line 185) | def erf(x):
  function mixed_max_erf (line 214) | def mixed_max_erf(scores, n_valid):
  function mixed_max_sampled (line 243) | def mixed_max_sampled(scores, n_valid, n_samples=100, rng=None):
  function rexp_plot_acc (line 289) | def rexp_plot_acc(scores, n_valid, n_test, pbest_n_samples=100, rng=None):
  function rexp_pairs_raw (line 384) | def rexp_pairs_raw(x, y, vscores):
  class CoordType (line 407) | class CoordType(object):
  class RealCoord (line 410) | class RealCoord(CoordType):
    method preimage (line 413) | def preimage(x):
  class LogCoord (line 416) | class LogCoord(CoordType):
    method preimage (line 419) | def preimage(x):
  class Log0Coord (line 422) | class Log0Coord(CoordType):
    method preimage (line 425) | def preimage(x):
  class CategoryCoord (line 431) | class CategoryCoord(CoordType):
    method __init__ (line 433) | def __init__(self, categories=None):
    method preimage (line 436) | def preimage(self, x):
  function rexp_pairs (line 442) | def rexp_pairs(x, y, vscores, xtype, ytype):
  class MultiHistory (line 445) | class MultiHistory(object):
    method __init__ (line 450) | def __init__(self):
    method add_experiment (line 453) | def add_experiment(self, mj, y_fn, start=0, stop=sys.maxint,
    method add_scatters (line 470) | def add_scatters(self):
    method main_show (line 480) | def main_show(self, title=None):
  function main_plot_histories (line 489) | def main_plot_histories(cls):
  class ScatterByConf (line 529) | class ScatterByConf(object):
    method __init__ (line 532) | def __init__(self, conf_template, confs, status, y):
    method trial_color (line 555) | def trial_color(self, t):
    method scatter_one (line 558) | def scatter_one(self, column):
    method main_show_one (line 568) | def main_show_one(self, column):
    method main_show_all (line 573) | def main_show_all(self, columns=None):
  function main_plot_scatter (line 600) | def main_plot_scatter(self, argv):

FILE: src/aup/Proposer/hyperopt/pyll/base.py
  class PyllImportError (line 32) | class PyllImportError(ImportError):
  class MissingArgument (line 36) | class MissingArgument(object):
  class SymbolTable (line 41) | class SymbolTable(object):
    method __init__ (line 51) | def __init__(self):
    method _new_apply (line 66) | def _new_apply(self, name, args, kwargs, o_len, pure):
    method dict (line 76) | def dict(self, *args, **kwargs):
    method int (line 81) | def int(self, arg):
    method float (line 85) | def float(self, arg):
    method len (line 89) | def len(self, obj):
    method list (line 93) | def list(self, init):
    method map (line 97) | def map(self, fn, seq, pure=False):
    method range (line 104) | def range(self, *args):
    method max (line 107) | def max(self, *args):
    method min (line 112) | def min(self, *args):
    method getattr (line 117) | def getattr(self, obj, attr, *args):
    method _define (line 125) | def _define(self, f, o_len, pure):
    method define (line 132) | def define(self, f, o_len=None, pure=False):
    method define_if_new (line 140) | def define_if_new(self, f, o_len=None, pure=False):
    method undefine (line 148) | def undefine(self, f):
    method define_pure (line 156) | def define_pure(self, f):
    method define_info (line 159) | def define_info(self, o_len=None, pure=False):
    method inject (line 164) | def inject(self, *args, **kwargs):
    method import_ (line 183) | def import_(self, _globals, *args, **kwargs):
  class SymbolTableEntry (line 187) | class SymbolTableEntry(object):
    method __init__ (line 190) | def __init__(self, symbol_table, apply_name, o_len, pure):
    method __call__ (line 196) | def __call__(self, *args, **kwargs):
  function as_apply (line 207) | def as_apply(obj):
  class Apply (line 234) | class Apply(object):
    method __init__ (line 243) | def __init__(self, name, pos_args, named_args,
    method __setstate__ (line 260) | def __setstate__(self, state):
    method eval (line 266) | def eval(self, memo=None):
    method inputs (line 297) | def inputs(self):
    method arg (line 307) | def arg(self):
    method set_kwarg (line 399) | def set_kwarg(self, name, value):
    method clone_from_inputs (line 411) | def clone_from_inputs(self, inputs, o_len='same'):
    method replace_input (line 423) | def replace_input(self, old_node, new_node):
    method pprint (line 435) | def pprint(self, ofile, lineno=None, indent=0, memo=None):
    method __str__ (line 455) | def __str__(self):
    method __add__ (line 460) | def __add__(self, other):
    method __radd__ (line 463) | def __radd__(self, other):
    method __sub__ (line 466) | def __sub__(self, other):
    method __rsub__ (line 469) | def __rsub__(self, other):
    method __neg__ (line 472) | def __neg__(self):
    method __mul__ (line 475) | def __mul__(self, other):
    method __rmul__ (line 478) | def __rmul__(self, other):
    method __div__ (line 481) | def __div__(self, other):
    method __rdiv__ (line 484) | def __rdiv__(self, other):
    method __truediv__ (line 487) | def __truediv__(self, other):
    method __rtruediv__ (line 490) | def __rtruediv__(self, other):
    method __floordiv__ (line 493) | def __floordiv__(self, other):
    method __rfloordiv__ (line 496) | def __rfloordiv__(self, other):
    method __pow__ (line 499) | def __pow__(self, other):
    method __rpow__ (line 502) | def __rpow__(self, other):
    method __gt__ (line 505) | def __gt__(self, other):
    method __ge__ (line 508) | def __ge__(self, other):
    method __lt__ (line 511) | def __lt__(self, other):
    method __le__ (line 514) | def __le__(self, other):
    method __getitem__ (line 517) | def __getitem__(self, idx):
    method __len__ (line 525) | def __len__(self):
    method __call__ (line 530) | def __call__(self, *args, **kwargs):
  function apply (line 534) | def apply(name, *args, **kwargs):
  class Literal (line 544) | class Literal(Apply):
    method __init__ (line 545) | def __init__(self, obj=None):
    method eval (line 553) | def eval(self, memo=None):
    method obj (line 559) | def obj(self):
    method arg (line 563) | def arg(self):
    method pprint (line 566) | def pprint(self, ofile, lineno=None, indent=0, memo=None):
    method replace_input (line 584) | def replace_input(self, old_node, new_node):
    method clone_from_inputs (line 587) | def clone_from_inputs(self, inputs, o_len='same'):
  class Lambda (line 591) | class Lambda(object):
    method __init__ (line 597) | def __init__(self, name, params, expr):
    method __call__ (line 602) | def __call__(self, *args, **kwargs):
  class UndefinedValue (line 617) | class UndefinedValue(object):
  function call (line 630) | def call(fn, args=(), kwargs={}):
  function callpipe1 (line 639) | def callpipe1(fn_list, arg):
  function partial (line 661) | def partial(name, *args, **kwargs):
  function dfs (line 684) | def dfs(aa, seq=None, seqset=None):
  function toposort (line 704) | def toposort(expr):
  function clone (line 719) | def clone(expr, memo=None):
  function clone_merge (line 731) | def clone_merge(expr, memo=None, merge_literals=False):
  class GarbageCollected (line 775) | class GarbageCollected(object):
  function rec_eval (line 779) | def rec_eval(expr, deepcopy_inputs=False, memo=None,
  function pos_args (line 944) | def pos_args(*args):
  function identity (line 949) | def identity(obj):
  function exp (line 976) | def exp(a):
  function log (line 981) | def log(a):
  function pow (line 986) | def pow(a, b):
  function sin (line 991) | def sin(a):
  function cos (line 996) | def cos(a):
  function tan (line 1001) | def tan(a):
  function sum (line 1006) | def sum(x, axis=None):
  function sqrt (line 1014) | def sqrt(x):
  function minimum (line 1019) | def minimum(x, y):
  function maximum (line 1024) | def maximum(x, y):
  function array_union1 (line 1029) | def array_union1(args):
  function array_union (line 1037) | def array_union(*args):
  function asarray (line 1042) | def asarray(a, dtype=None):
  function str_join (line 1050) | def str_join(s, seq):
  function _bincount_slow (line 1054) | def _bincount_slow(x, weights=None, minlength=None):
  function bincount (line 1070) | def bincount(x, weights=None, minlength=None):
  function repeat (line 1084) | def repeat(n_times, obj):
  function call_method (line 1089) | def call_method(obj, methodname, *args, **kwargs):
  function call_method_pure (line 1095) | def call_method_pure(obj, methodname, *args, **kwargs):
  function copy_call_method_pure (line 1101) | def copy_call_method_pure(obj, methodname, *args, **kwargs):
  function switch (line 1113) | def switch(pos, *args):
  function _kwswitch (line 1124) | def _kwswitch(kw, **kwargs):
  function Raise (line 1135) | def Raise(etype, *args, **kwargs):
  function curtime (line 1140) | def curtime(obj):
  function pdb_settrace (line 1145) | def pdb_settrace(obj):

FILE: src/aup/Proposer/hyperopt/pyll/stochastic.py
  function ERR (line 16) | def ERR(msg):
  function implicit_stochastic (line 23) | def implicit_stochastic(f):
  function rng_from_seed (line 29) | def rng_from_seed(seed):
  function uniform (line 37) | def uniform(low, high, rng=None, size=()):
  function loguniform (line 43) | def loguniform(low, high, rng=None, size=()):
  function quniform (line 50) | def quniform(low, high, q, rng=None, size=()):
  function qloguniform (line 57) | def qloguniform(low, high, q, rng=None, size=()):
  function normal (line 66) | def normal(mu, sigma, rng=None, size=()):
  function qnormal (line 72) | def qnormal(mu, sigma, q, rng=None, size=()):
  function lognormal (line 79) | def lognormal(mu, sigma, rng=None, size=()):
  function qlognormal (line 86) | def qlognormal(mu, sigma, q, rng=None, size=()):
  function randint (line 96) | def randint(upper, rng=None, size=()):
  function categorical (line 111) | def categorical(p, upper=None, rng=None, size=()):
  function choice (line 150) | def choice(args):
  function one_of (line 155) | def one_of(*args):
  function recursive_set_rng_kwarg (line 161) | def recursive_set_rng_kwarg(expr, rng=None):
  function sample (line 181) | def sample(expr, rng=None, **kwargs):

FILE: src/aup/Proposer/hyperopt/pyll_utils.py
  function validate_label (line 14) | def validate_label(f):
  function hyperopt_param (line 31) | def hyperopt_param(label, obj):
  function hp_pchoice (line 42) | def hp_pchoice(label, p_options):
  function hp_choice (line 57) | def hp_choice(label, options):
  function hp_randint (line 64) | def hp_randint(label, *args, **kwargs):
  function hp_uniform (line 70) | def hp_uniform(label, *args, **kwargs):
  function hp_quniform (line 77) | def hp_quniform(label, *args, **kwargs):
  function hp_loguniform (line 84) | def hp_loguniform(label, *args, **kwargs):
  function hp_qloguniform (line 91) | def hp_qloguniform(label, *args, **kwargs):
  function hp_normal (line 98) | def hp_normal(label, *args, **kwargs):
  function hp_qnormal (line 105) | def hp_qnormal(label, *args, **kwargs):
  function hp_lognormal (line 112) | def hp_lognormal(label, *args, **kwargs):
  function hp_qlognormal (line 119) | def hp_qlognormal(label, *args, **kwargs):
  class Cond (line 130) | class Cond(object):
    method __init__ (line 132) | def __init__(self, name, val, op):
    method __str__ (line 137) | def __str__(self):
    method __eq__ (line 140) | def __eq__(self, other):
    method __hash__ (line 143) | def __hash__(self):
    method __repr__ (line 146) | def __repr__(self):
  function _expr_to_config (line 152) | def _expr_to_config(expr, conditions, hps):
  function expr_to_config (line 182) | def expr_to_config(expr, conditions, hps):
  function _remove_allpaths (line 207) | def _remove_allpaths(hps, conditions):

FILE: src/aup/Proposer/hyperopt/rand.py
  function suggest (line 15) | def suggest(new_ids, domain, trials, seed):
  function suggest_batch (line 34) | def suggest_batch(new_ids, domain, trials, seed):

FILE: src/aup/Proposer/hyperopt/rdists.py
  class loguniform_gen (line 15) | class loguniform_gen(rv_continuous):
    method __init__ (line 20) | def __init__(self, low=0, high=1):
    method _rvs (line 27) | def _rvs(self):
    method _pdf (line 34) | def _pdf(self, x):
    method _logpdf (line 37) | def _logpdf(self, x):
    method _cdf (line 40) | def _cdf(self, x):
  class lognorm_gen (line 46) | class lognorm_gen(scipy_lognorm_gen):
    method __init__ (line 47) | def __init__(self, mu, sigma):
    method _parse_args (line 58) | def _parse_args(self, *args, **kwds):
  function qtable_pmf (line 67) | def qtable_pmf(x, q, qlow, xs, ps):
  function qtable_logpmf (line 81) | def qtable_logpmf(x, q, qlow, xs, ps):
  class quniform_gen (line 93) | class quniform_gen(object):
    method __init__ (line 100) | def __init__(self, low, high, q):
    method pmf (line 127) | def pmf(self, x):
    method logpmf (line 130) | def logpmf(self, x):
    method rvs (line 133) | def rvs(self, size=()):
  class qloguniform_gen (line 139) | class qloguniform_gen(quniform_gen):
    method __init__ (line 146) | def __init__(self, low, high, q):
    method pmf (line 183) | def pmf(self, x):
    method logpmf (line 186) | def logpmf(self, x):
    method rvs (line 189) | def rvs(self, size=()):
  class qnormal_gen (line 195) | class qnormal_gen(object):
    method __init__ (line 199) | def __init__(self, mu, sigma, q):
    method in_domain (line 204) | def in_domain(self, x):
    method pmf (line 207) | def pmf(self, x):
    method logpmf (line 210) | def logpmf(self, x):
    method rvs (line 234) | def rvs(self, size=()):
  class qlognormal_gen (line 240) | class qlognormal_gen(object):
    method __init__ (line 244) | def __init__(self, mu, sigma, q):
    method in_domain (line 249) | def in_domain(self, x):
    method pmf (line 253) | def pmf(self, x):
    method logpmf (line 267) | def logpmf(self, x):
    method rvs (line 277) | def rvs(self, size=()):

FILE: src/aup/Proposer/hyperopt/tpe.py
  function adaptive_parzen_sampler (line 41) | def adaptive_parzen_sampler(name):
  function categorical_lpdf (line 57) | def categorical_lpdf(sample, p, upper):
  function GMM1 (line 70) | def GMM1(weights, mus, sigmas, low=None, high=None, q=None, rng=None,
  function normal_cdf (line 103) | def normal_cdf(x, mu, sigma):
  function GMM1_lpdf (line 111) | def GMM1_lpdf(samples, weights, mus, sigmas, low=None, high=None, q=None):
  function lognormal_cdf (line 178) | def lognormal_cdf(x, mu, sigma):
  function lognormal_lpdf (line 200) | def lognormal_lpdf(x, mu, sigma):
  function qlognormal_lpdf (line 212) | def qlognormal_lpdf(x, mu, sigma, q):
  function LGMM1 (line 224) | def LGMM1(weights, mus, sigmas, low=None, high=None, q=None,
  function logsum_rows (line 259) | def logsum_rows(x):
  function LGMM1_lpdf (line 266) | def LGMM1_lpdf(samples, weights, mus, sigmas, low=None, high=None, q=None):
  function adaptive_parzen_normal_orig (line 316) | def adaptive_parzen_normal_orig(mus, prior_weight, prior_mu, prior_sigma):
  function linear_forgetting_weights (line 386) | def linear_forgetting_weights(N, LF):
  function adaptive_parzen_normal (line 405) | def adaptive_parzen_normal(mus, prior_weight, prior_mu, prior_sigma,
  function ap_uniform_sampler (line 494) | def ap_uniform_sampler(obs, prior_weight, low, high, size=(), rng=None):
  function ap_quniform_sampler (line 504) | def ap_quniform_sampler(obs, prior_weight, low, high, q, size=(), rng=No...
  function ap_loguniform_sampler (line 514) | def ap_loguniform_sampler(obs, prior_weight, low, high,
  function ap_qloguniform_sampler (line 526) | def ap_qloguniform_sampler(obs, prior_weight, low, high, q,
  function ap_normal_sampler (line 549) | def ap_normal_sampler(obs, prior_weight, mu, sigma, size=(), rng=None):
  function ap_qnormal_sampler (line 556) | def ap_qnormal_sampler(obs, prior_weight, mu, sigma, q, size=(), rng=None):
  function ap_loglognormal_sampler (line 563) | def ap_loglognormal_sampler(obs, prior_weight, mu, sigma, size=(), rng=N...
  function ap_qlognormal_sampler (line 571) | def ap_qlognormal_sampler(obs, prior_weight, mu, sigma, q, size=(), rng=...
  function ap_categorical_sampler (line 582) | def ap_categorical_sampler(obs, prior_weight, upper,
  function tpe_cat_pseudocounts (line 599) | def tpe_cat_pseudocounts(counts, upper, prior_weight, p, size):
  function ap_categorical_sampler (line 612) | def ap_categorical_sampler(obs, prior_weight, p, upper=None,
  function ap_filter_trials (line 625) | def ap_filter_trials(o_idxs, o_vals, l_idxs, l_vals, gamma,
  function build_posterior (line 651) | def build_posterior(specs, prior_idxs, prior_vals, obs_idxs, obs_vals,
  function idxs_prod (line 743) | def idxs_prod(full_idxs, idxs_by_label, llik_by_label):
  function broadcast_best (line 770) | def broadcast_best(samples, below_llik, above_llik):
  function tpe_transform (line 794) | def tpe_transform(domain, prior_weight, gamma):
  function suggest (line 823) | def suggest(new_ids, domain, trials, seed,

FILE: src/aup/Proposer/hyperopt/utils.py
  function import_tokens (line 27) | def import_tokens(tokens):
  function load_tokens (line 45) | def load_tokens(tokens):
  function json_lookup (line 54) | def json_lookup(json):
  function json_call (line 59) | def json_call(json, args=(), kwargs=None):
  function get_obj (line 84) | def get_obj(f, argfile=None, argstr=None, args=(), kwargs=None):
  function pmin_sampled (line 101) | def pmin_sampled(mean, var, n_samples=1000, rng=None):
  function fast_isin (line 121) | def fast_isin(X, Y):
  function get_most_recent_inds (line 145) | def get_most_recent_inds(obj):
  function use_obj_for_literal_in_memo (line 156) | def use_obj_for_literal_in_memo(expr, obj, lit, memo):
  function coarse_utcnow (line 177) | def coarse_utcnow():
  function working_dir (line 190) | def working_dir(dir):
  function path_split_all (line 197) | def path_split_all(path):
  function get_closest_dir (line 208) | def get_closest_dir(workdir):
  function temp_dir (line 225) | def temp_dir(dir, erase_after=False, with_sentinel=True):

FILE: src/aup/Proposer/hyperopt/vectorize.py
  function ERR (line 21) | def ERR(msg):
  function vchoice_split (line 26) | def vchoice_split(idxs, choices, n_options):
  function vchoice_merge (line 37) | def vchoice_merge(idxs, choices, *vals):
  function idxs_map (line 47) | def idxs_map(idxs, cmd, *args, **kwargs):
  function idxs_take (line 110) | def idxs_take(idxs, vals, which):
  function uniq (line 122) | def uniq(lst):
  function vectorize_stochastic (line 132) | def vectorize_stochastic(orig):
  function replace_repeat_stochastic (line 172) | def replace_repeat_stochastic(expr, return_memo=False):
  class VectorizeHelper (line 220) | class VectorizeHelper(object):
    method __init__ (line 233) | def __init__(self, expr, expr_idxs, build=True):
    method assert_integrity_idxs_take (line 257) | def assert_integrity_idxs_take(self):
    method build_idxs_vals (line 271) | def build_idxs_vals(self, node, wanted_idxs):
    method idxs_by_label (line 440) | def idxs_by_label(self):
    method vals_by_label (line 444) | def vals_by_label(self):

FILE: src/aup/Proposer/spearmint/ExperimentGrid.py
  class ExperimentGrid (line 49) | class ExperimentGrid:
    method job_running (line 52) | def job_running(expt_dir, id):
    method job_complete (line 57) | def job_complete(expt_dir, id, value, duration):
    method job_broken (line 64) | def job_broken(expt_dir, id):
    method __init__ (line 68) | def __init__(self, expt_dir, variables=None, grid_size=None, grid_seed...
    method __del__ (line 92) | def __del__(self):
    method get_grid (line 99) | def get_grid(self):
    method get_candidates (line 102) | def get_candidates(self):
    method get_pending (line 105) | def get_pending(self):
    method get_complete (line 108) | def get_complete(self):
    method get_broken (line 111) | def get_broken(self):
    method get_params (line 114) | def get_params(self, index):
    method get_best (line 117) | def get_best(self):
    method get_proc_id (line 126) | def get_proc_id(self, id):
    method add_to_grid (line 129) | def add_to_grid(self, candidate):
    method set_candidate (line 147) | def set_candidate(self, id):
    method set_submitted (line 151) | def set_submitted(self, id, proc_id):
    method set_running (line 156) | def set_running(self, id):
    method set_complete (line 160) | def set_complete(self, id, value, duration):
    method set_broken (line 166) | def set_broken(self, id):
    method _load_jobs (line 170) | def _load_jobs(self):
    method _save_jobs (line 182) | def _save_jobs(self):
    method _hypercube_grid (line 198) | def _hypercube_grid(self, dims, size):
  class GridMap (line 204) | class GridMap:
    method __init__ (line 206) | def __init__(self, variables, grid_size):
    method get_params (line 237) | def get_params(self, u):
    method card (line 268) | def card(self):
    method _index_map (line 271) | def _index_map(self, u, items):

FILE: src/aup/Proposer/spearmint/Locker.py
  function safe_delete (line 26) | def safe_delete(filename):
  class Locker (line 32) | class Locker:
    method __init__ (line 34) | def __init__(self):
    method __del__ (line 37) | def __del__(self):
    method lock (line 43) | def lock(self, filename):
    method unlock (line 54) | def unlock(self, filename):
    method lock_wait (line 68) | def lock_wait(self, filename):

FILE: src/aup/Proposer/spearmint/chooser/CMAChooser.py
  function init (line 5) | def init(expt_dir, arg_string):
  class CMAChooser (line 12) | class CMAChooser:
    method __init__ (line 14) | def __init__(self, expt_dir):
    method _real_init (line 25) | def _real_init(self, dims, values):
    method __del__ (line 38) | def __del__(self):
    method next (line 66) | def next(self, grid, values, durations, candidates, pending, complete):

FILE: src/aup/Proposer/spearmint/chooser/GPConstrainedEIChooser.py
  function optimize_pt (line 46) | def optimize_pt(c, b, comp, pend, vals, labels, model):
  function init (line 52) | def init(expt_dir, arg_string):
  class GPConstrainedEIChooser (line 65) | class GPConstrainedEIChooser:
    method __init__ (line 67) | def __init__(self, expt_dir, covar="Matern52", mcmc_iters=20,
    method dump_hypers (line 114) | def dump_hypers(self):
    method _real_init (line 155) | def _real_init(self, dims, values, durations):
    method cov (line 206) | def cov(self, amp2, ls, x1, x2=None):
    method next (line 216) | def next(self, grid, values, durations,
    method pred_constraint_voilation (line 437) | def pred_constraint_voilation(self, cand, comp, vals):
    method ei_over_hypers (line 462) | def ei_over_hypers(self,comp,pend,cand,vals,labels):
    method grad_optimize_ei_over_hypers (line 483) | def grad_optimize_ei_over_hypers(self, cand, comp, pend, vals, labels,
    method check_grad_ei (line 515) | def check_grad_ei(self, cand, comp, pend, vals, labels):
    method grad_optimize_ei (line 533) | def grad_optimize_ei(self, cand, comp, pend, vals, labels, compute_gra...
    method grad_optimize_ei_pend (line 541) | def grad_optimize_ei_pend(self, cand, comp, pend, vals, labels, comput...
    method grad_optimize_ei_nopend (line 704) | def grad_optimize_ei_nopend(self, cand, comp, vals, labels, compute_gr...
    method compute_constrained_ei (line 818) | def compute_constrained_ei(self, comp, pend, cand, vals, labels):
    method compute_ei (line 954) | def compute_ei(self, comp, pend, cand, vals, labels):
    method sample_constraint_hypers (line 1026) | def sample_constraint_hypers(self, comp, labels):
    method sample_hypers (line 1044) | def sample_hypers(self, comp, vals):
    method _sample_ls (line 1054) | def _sample_ls(self, comp, vals):
    method _sample_constraint_ls (line 1070) | def _sample_constraint_ls(self, comp, vals):
    method _sample_noisy (line 1128) | def _sample_noisy(self, comp, vals):
    method _sample_constraint_noisy (line 1163) | def _sample_constraint_noisy(self, comp, vals):
    method _sample_noiseless (line 1215) | def _sample_noiseless(self, comp, vals):
    method elliptical_slice (line 1246) | def elliptical_slice(self, xx, chol_Sigma, log_like_fn, cur_log_like=N...

FILE: src/aup/Proposer/spearmint/chooser/GPEIChooser.py
  function init (line 41) | def init(expt_dir, arg_string):
  class GPEIChooser (line 51) | class GPEIChooser:
    method __init__ (line 53) | def __init__(self, expt_dir, covar="Matern52", mcmc_iters=10,
    method __del__ (line 74) | def __del__(self):
    method _real_init (line 93) | def _real_init(self, dims, values):
    method cov (line 124) | def cov(self, x1, x2=None):
    method next (line 131) | def next(self, grid, values, durations, candidates, pending, complete):
    method compute_ei (line 184) | def compute_ei(self, comp, pend, cand, vals):
    method sample_hypers (line 274) | def sample_hypers(self, comp, vals):
    method _sample_ls (line 282) | def _sample_ls(self, comp, vals):
    method _sample_noisy (line 295) | def _sample_noisy(self, comp, vals):
    method _sample_noiseless (line 328) | def _sample_noiseless(self, comp, vals):
    method optimize_hypers (line 354) | def optimize_hypers(self, comp, vals):

FILE: src/aup/Proposer/spearmint/chooser/GPEIOptChooser.py
  function optimize_pt (line 43) | def optimize_pt(c, b, comp, pend, vals, model):
  function init (line 49) | def init(expt_dir, arg_string):
  class GPEIOptChooser (line 59) | class GPEIOptChooser:
    method __init__ (line 61) | def __init__(self, expt_dir, covar="Matern52", mcmc_iters=10,
    method dump_hypers (line 92) | def dump_hypers(self):
    method generate_stats_html (line 133) | def generate_stats_html(self):
    method _read_only (line 159) | def _read_only(self):
    method _real_init (line 176) | def _real_init(self, dims, values):
    method cov (line 215) | def cov(self, x1, x2=None):
    method next (line 225) | def next(self, grid, values, durations,
    method ei_over_hypers (line 339) | def ei_over_hypers(self,comp,pend,cand,vals):
    method check_grad_ei (line 351) | def check_grad_ei(self, cand, comp, pend, vals):
    method grad_optimize_ei_over_hypers (line 368) | def grad_optimize_ei_over_hypers(self, cand, comp, pend, vals, compute...
    method grad_optimize_ei (line 399) | def grad_optimize_ei(self, cand, comp, pend, vals, compute_grad=True):
    method compute_ei (line 535) | def compute_ei(self, comp, pend, cand, vals):
    method sample_hypers (line 629) | def sample_hypers(self, comp, vals):
    method _sample_ls (line 638) | def _sample_ls(self, comp, vals):
    method _sample_noisy (line 653) | def _sample_noisy(self, comp, vals):
    method _sample_noiseless (line 686) | def _sample_noiseless(self, comp, vals):
    method optimize_hypers (line 716) | def optimize_hypers(self, comp, vals):

FILE: src/aup/Proposer/spearmint/chooser/GPEIperSecChooser.py
  function init (line 41) | def init(expt_dir, arg_string):
  class GPEIperSecChooser (line 53) | class GPEIperSecChooser:
    method __init__ (line 55) | def __init__(self, expt_dir, covar="Matern52", mcmc_iters=10,
    method dump_hypers (line 96) | def dump_hypers(self):
    method _real_init (line 119) | def _real_init(self, dims, values, durations):
    method cov (line 159) | def cov(self, amp2, ls, x1, x2=None):
    method next (line 169) | def next(self, grid, values, durations,
    method ei_over_hypers (line 298) | def ei_over_hypers(self,comp,pend,cand,vals,durs):
    method check_grad_ei_per (line 318) | def check_grad_ei_per(self, cand, comp, vals, durs):
    method grad_optimize_ei_over_hypers (line 335) | def grad_optimize_ei_over_hypers(self, cand, comp, vals, durs, compute...
    method grad_optimize_ei (line 365) | def grad_optimize_ei(self, cand, comp, vals, durs, compute_grad=True):
    method compute_ei_per_s (line 451) | def compute_ei_per_s(self, comp, pend, cand, vals, durs):
    method sample_hypers (line 564) | def sample_hypers(self, comp, vals, durs):
    method _sample_ls (line 579) | def _sample_ls(self, comp, vals):
    method _sample_time_ls (line 592) | def _sample_time_ls(self, comp, vals):
    method _sample_noisy (line 605) | def _sample_noisy(self, comp, vals):
    method _sample_time_noisy (line 637) | def _sample_time_noisy(self, comp, vals):
    method _sample_noiseless (line 669) | def _sample_noiseless(self, comp, vals):
    method optimize_hypers (line 697) | def optimize_hypers(self, comp, vals, durs):

FILE: src/aup/Proposer/spearmint/chooser/RandomChooser.py
  function init (line 25) | def init(expt_dir, arg_string):
  class RandomChooser (line 28) | class RandomChooser:
    method __init__ (line 30) | def __init__(self):
    method next (line 33) | def next(self, grid, values, durations,

FILE: src/aup/Proposer/spearmint/chooser/RandomForestEIChooser.py
  function init (line 13) | def init(expt_dir, arg_string):
  class RandomForestRegressorWithVariance (line 17) | class RandomForestRegressorWithVariance(sklearn.ensemble.RandomForestReg...
    method predict (line 19) | def predict(self,X):
  class RandomForestEIChooser (line 31) | class RandomForestEIChooser:
    method __init__ (line 33) | def __init__(self,n_trees=50,
    method next (line 53) | def next(self, grid, values, durations,

FILE: src/aup/Proposer/spearmint/chooser/SequentialChooser.py
  function init (line 24) | def init(expt_dir, arg_string):
  class SequentialChooser (line 27) | class SequentialChooser:
    method __init__ (line 29) | def __init__(self):
    method next (line 32) | def next(self, grid, values, durations,

FILE: src/aup/Proposer/spearmint/chooser/cma.py
  function show (line 174) | def show():
  function unitdoctest (line 243) | def unitdoctest():
  class BlancClass (line 330) | class BlancClass(object):
  class DerivedDictBase (line 336) | class DerivedDictBase(collections.MutableMapping):
    method __init__ (line 340) | def __init__(self, *args, **kwargs):
    method __len__ (line 345) | def __len__(self):
    method __contains__ (line 347) | def __contains__(self, value):
    method __iter__ (line 349) | def __iter__(self):
    method __setitem__ (line 351) | def __setitem__(self, key, value):
    method __getitem__ (line 354) | def __getitem__(self, key):
    method __delitem__ (line 357) | def __delitem__(self, key):
  class SolutionDict (line 360) | class SolutionDict(DerivedDictBase):
    method __init__ (line 377) | def __init__(self, *args, **kwargs):
    method key (line 380) | def key(self, x):
    method __setitem__ (line 385) | def __setitem__(self, key, value):
    method __getitem__ (line 393) | def __getitem__(self, key):
    method __delitem__ (line 396) | def __delitem__(self, key):
    method truncate (line 406) | def truncate(self, max_len, min_iter):
  class SolutionDictOld (line 412) | class SolutionDictOld(dict):
    method __init__ (line 430) | def __init__(self):
    method key (line 436) | def key(self, x):
    method insert (line 439) | def insert(self, x, datadict):
    method _get_derived (line 446) | def _get_derived(self, x, default=None):
    method _pop_derived (line 448) | def _pop_derived(self, x):
  class BestSolution (line 457) | class BestSolution(object):
    method __init__ (line 459) | def __init__(self, x=None, f=np.inf, evals=None):
    method update (line 472) | def update(self, arx, xarchive=None, arf=None, evals=None):
    method get (line 504) | def get(self):
  class BoundPenalty (line 511) | class BoundPenalty(object):
    method __init__ (line 527) | def __init__(self, bounds=None):
    method has_bounds (line 540) | def has_bounds(self):
    method repair (line 553) | def repair(self, x, bounds=None, copy=False, copy_always=False):
    method __call__ (line 596) | def __call__(self, x, archive, gp):
    method feasible_ratio (line 622) | def feasible_ratio(self, solutions):
    method update (line 637) | def update(self, function_values, es, bounds=None):
  class GenoPhenoBase (line 729) | class GenoPhenoBase(object):
    method pheno (line 756) | def pheno(self, x):
  class GenoPheno (line 763) | class GenoPheno(object):
    method __init__ (line 786) | def __init__(self, dim, scaling=None, typical_x=None, bounds=None, fix...
    method into_bounds (line 905) | def into_bounds(self, y, bounds=None, copy_never=False, copy_always=Fa...
    method pheno (line 936) | def pheno(self, x, bounds=None, copy=True, copy_always=False):
    method geno (line 974) | def geno(self, y, bounds=None, copy=True, copy_always=False, archive=N...
  class OOOptimizer (line 1032) | class OOOptimizer(object):
    method __init__ (line 1100) | def __init__(self, xstart, **more_args):
    method initialize (line 1105) | def initialize(self):
    method ask (line 1110) | def ask(self):
    method tell (line 1114) | def tell(self, solutions, function_values):
    method stop (line 1118) | def stop(self):
    method disp (line 1124) | def disp(self, modulo=None):
    method result (line 1127) | def result(self):
    method optimize (line 1131) | def optimize(self, objectivefct, logger=None, verb_disp=20, iterations...
  class CMAEvolutionStrategy (line 1192) | class CMAEvolutionStrategy(OOOptimizer):
    method popsize (line 1432) | def popsize(self):
    method stop (line 1446) | def stop(self, check=True):
    method __init__ (line 1460) | def __init__(self, x0, sigma0, inopts = {}):
    method ask (line 1607) | def ask(self, number=None, xmean=None, sigma_fac=1):
    method ask_geno (line 1663) | def ask_geno(self, number=None, xmean=None, sigma_fac=1):
    method get_mirror (line 1763) | def get_mirror(self, x):
    method mirror_penalized (line 1791) | def mirror_penalized(self, f_values, idx):
    method mirror_idx_cov (line 1810) | def mirror_idx_cov(self, f_values, idx1):  # will most likely be removed
    method ask_and_eval (line 1840) | def ask_and_eval(self, func, args=(), number=None, xmean=None, sigma_f...
    method tell (line 1966) | def tell(self, solutions, function_values, check_points=None, copy=Fal...
    method result (line 2329) | def result(self):
    method clip_or_fit_solutions (line 2335) | def clip_or_fit_solutions(self, pop, idx):
    method repair_genotype (line 2344) | def repair_genotype(self, x):
    method updateBD (line 2378) | def updateBD(self):
    method multiplyC (line 2467) | def multiplyC(self, alpha):
    method update_exponential (line 2473) | def update_exponential(self, Z, eta, BDpair=None):
    method _updateCholesky (line 2503) | def _updateCholesky(self, A, Ainv, p, alpha, beta):
    method feedForResume (line 2530) | def feedForResume(self, X, function_values):
    method readProperties (line 2598) | def readProperties(self):
    method mahalanobisNorm (line 2605) | def mahalanobisNorm(self, dx):
    method timesCroot (line 2632) | def timesCroot(self, mat):
    method divCroot (line 2643) | def divCroot(self, mat):
    method disp_annotation (line 2655) | def disp_annotation(self):
    method disp (line 2662) | def disp(self, modulo=None):  # TODO: rather assign opt['verb_disp'] a...
  class Options (line 2692) | class Options(dict):
    method defaults (line 2740) | def defaults():
    method versatileOptions (line 2746) | def versatileOptions():
    method __init__ (line 2755) | def __init__(self, s=None, unchecked=False):
    method init (line 2783) | def init(self, dict_or_str, val=None, warn=True):
    method set (line 2815) | def set(self, dic, val=None, warn=True):
    method complement (line 2843) | def complement(self):
    method settable (line 2851) | def settable(self):
    method __call__ (line 2862) | def __call__(self, key, default=None, loc=None):
    method eval (line 2897) | def eval(self, key, default=None, loc=None):
    method evalall (line 2913) | def evalall(self, loc=None):
    method match (line 2927) | def match(self, s=''):
    method pp (line 2942) | def pp(self):
    method printme (line 2945) | def printme(self, linebreak=80):
  class CMAParameters (line 2960) | class CMAParameters(object):
    method __init__ (line 3003) | def __init__(self, N, opts, ccovfac=1, verbose=True):
    method set (line 3013) | def set(self, opts, popsize=None, ccovfac=1, verbose=True):
    method disp (line 3151) | def disp(self):
  class CMAStopDict (line 3156) | class CMAStopDict(dict):
    method __init__ (line 3167) | def __init__(self, d={}):
    method __call__ (line 3176) | def __call__(self, es):
    method _addstop (line 3180) | def _addstop(self, key, cond, val=None):
    method _update (line 3187) | def _update(self, es):
  class BaseDataLogger2 (line 3287) | class BaseDataLogger2(DerivedDictBase):
    method add (line 3289) | def add(self, optim=None, more_data=[]):
    method register (line 3294) | def register(self, optim):
    method disp (line 3298) | def disp(self):
    method plot (line 3301) | def plot(self):
    method data (line 3304) | def data(self):
  class BaseDataLogger (line 3307) | class BaseDataLogger(object):
    method add (line 3309) | def add(self, optim=None, more_data=[]):
    method register (line 3314) | def register(self, optim):
    method disp (line 3318) | def disp(self):
    method plot (line 3321) | def plot(self):
    method data (line 3324) | def data(self):
  class CMADataLogger (line 3331) | class CMADataLogger(BaseDataLogger):  # might become a dict at some point
    method __init__ (line 3377) | def __init__(self, name_prefix=default_prefix, modulo=1, append=False):
    method register (line 3395) | def register(self, es, append=None, modulo=None):
    method initialize (line 3413) | def initialize(self, modulo=None):
    method load (line 3498) | def load(self, filenameprefix=None):
    method add (line 3521) | def add(self, es=None, more_data=[], modulo=None): # TODO: find a diff...
    method closefig (line 3647) | def closefig(self):
    method save (line 3650) | def save(self, nameprefix, switch=False):
    method plot (line 3671) | def plot(self, fig=None, iabscissa=1, iteridx=None, plot_mean=True,  #...
    method plotdivers (line 3891) | def plotdivers(dat, iabscissa, foffset):
    method downsampling (line 3969) | def downsampling(self, factor=10, first=3, switch=True):
    method disp (line 4013) | def disp(self, idx=100):  # r_[0:5,1e2:1e9:1e2,-10:0]):
    method disp_header (line 4085) | def disp_header(self):
  class DEAPCMADataLogger (line 4097) | class DEAPCMADataLogger(BaseDataLogger):  # might become a dict at some ...
    method __init__ (line 4143) | def __init__(self, name_prefix=default_prefix, modulo=1, append=False):
    method register (line 4159) | def register(self, es, append=None, modulo=None):
    method initialize (line 4175) | def initialize(self, modulo=None):
    method load (line 4263) | def load(self, filenameprefix=None):
    method add (line 4288) | def add(self, fitness_values, es=None, more_data=[], modulo=None): # T...
    method closefig (line 4394) | def closefig(self):
    method save (line 4397) | def save(self, nameprefix, switch=False):
    method plot (line 4418) | def plot(self, fig=None, iabscissa=1, iteridx=None, plot_mean=True,  #...
    method plotdivers (line 4635) | def plotdivers(dat, iabscissa, foffset):
    method downsampling (line 4713) | def downsampling(self, factor=10, first=3, switch=True):
    method disp_header (line 4757) | def disp_header(self):
    method disp (line 4761) | def disp(self, idx=100):  # r_[0:5,1e2:1e9:1e2,-10:0]):
  function irg (line 4835) | def irg(ar):
  class AII (line 4837) | class AII(object):
    method __init__ (line 4849) | def __init__(self, x0, sigma0, randn=np.random.randn):
    method initialize (line 4874) | def initialize(self):
    method ask (line 4884) | def ask(self, popsize):
    method tell (line 4895) | def tell(self, X, f):
  function fmin (line 4935) | def fmin(func, x0, sigma0=None, args=()
  function plot (line 5345) | def plot(name=None, fig=None, abscissa=1, iteridx=None, plot_mean=True, ...
  function disp (line 5392) | def disp(name=None, idx=None):
  function _fileToMatrix (line 5425) | def _fileToMatrix(file_name):
  class NoiseHandler (line 5450) | class NoiseHandler(object):
    method __init__ (line 5505) | def __init__(self, N, maxevals=10, aggregate=np.median, reevals=None, ...
    method __call__ (line 5557) | def __call__(self, X, fit, func, ask=None, args=()):
    method get_evaluations (line 5591) | def get_evaluations(self):
    method treat (line 5595) | def treat(self):
    method reeval (line 5607) | def reeval(self, X, fit, func, ask, args=()):
    method update_measure (line 5634) | def update_measure(self):
    method indices (line 5660) | def indices(self, fit):
  class Sections (line 5675) | class Sections(object):
    method __init__ (line 5712) | def __init__(self, func, x, args=(), basis=None, name=None,
    method do (line 5752) | def do(self, repetitions=1, locations=np.arange(-0.5, 0.6, 0.2), plot=...
    method plot (line 5791) | def plot(self, plot_cmd=None, tf=lambda y: y):
    method flattened (line 5818) | def flattened(self):
    method save (line 5835) | def save(self, name=None):
    method load (line 5845) | def load(self, name=None):
  class _Error (line 5854) | class _Error(Exception):
  class ElapsedTime (line 5861) | class ElapsedTime(object):
    method __init__ (line 5863) | def __init__(self):
    method __call__ (line 5871) | def __call__(self):
  class TimeIt (line 5891) | class TimeIt(object):
    method __init__ (line 5892) | def __init__(self, fct, args=(), seconds=1):
  class Misc (line 5895) | class Misc(object):
    class MathHelperFunctions (line 5899) | class MathHelperFunctions(object):
      method aclamp (line 5905) | def aclamp(x, upper):
      method expms (line 5908) | def expms(A, eig=np.linalg.eigh):
      method amax (line 5915) | def amax(vec, vec_or_scalar):
      method max (line 5918) | def max(vec, vec_or_scalar):
      method amin (line 5926) | def amin(vec_or_scalar, vec_or_scalar2):
      method min (line 5929) | def min(a, b):
      method norm (line 5941) | def norm(vec, expo=2):
      method apos (line 5944) | def apos(x, lower=0):
      method prctile (line 5951) | def prctile(data, p_vals=[0, 25, 50, 75, 100], sorted_=False):
      method sround (line 5976) | def sround(nb):  # TODO: to be vectorized
      method cauchy_with_variance_one (line 5981) | def cauchy_with_variance_one():
      method standard_finite_cauchy (line 5987) | def standard_finite_cauchy(size=1):
    method likelihood (line 6005) | def likelihood(x, m=None, Cinv=None, sigma=1, detC=None):
    method loglikelihood (line 6022) | def loglikelihood(self, x, previous=False):
    method eig (line 6068) | def eig(C):
  function pprint (line 6397) | def pprint(to_be_printed):
  class Rotation (line 6407) | class Rotation(object):
    method __init__ (line 6422) | def __init__(self):
    method __call__ (line 6424) | def __call__(self, x, inverse=False): # function when calling an object
  class FitnessFunctions (line 6446) | class FitnessFunctions(object):
    method __init__ (line 6449) | def __init__(self):
    method rot (line 6451) | def rot(self, x, fun, rot=1, args=()):
    method somenan (line 6463) | def somenan(self, x, fun, p=0.1):
    method rand (line 6469) | def rand(self, x):
    method linear (line 6472) | def linear(self, x):
    method lineard (line 6474) | def lineard(self, x):
    method sphere (line 6480) | def sphere(self, x):
    method spherewithoneconstraint (line 6484) | def spherewithoneconstraint(self, x):
    method elliwithoneconstraint (line 6486) | def elliwithoneconstraint(self, x, idx=[-1]):
    method spherewithnconstraints (line 6489) | def spherewithnconstraints(self, x):
    method noisysphere (line 6492) | def noisysphere(self, x, noise=4.0, cond=1.0):
    method spherew (line 6495) | def spherew(self, x):
    method partsphere (line 6502) | def partsphere(self, x):
    method sectorsphere (line 6513) | def sectorsphere(self, x):
    method cornersphere (line 6516) | def cornersphere(self, x):
    method cornerelli (line 6522) | def cornerelli(self, x):
    method cornerellirot (line 6527) | def cornerellirot(self, x):
    method normalSkew (line 6532) | def normalSkew(self, f):
    method noiseC (line 6537) | def noiseC(self, x, func=sphere, fac=10, expon=0.8):
    method noise (line 6541) | def noise(self, x, func=sphere, fac=10, expon=1):
    method cigar (line 6551) | def cigar(self, x, rot=0, cond=1e6):
    method tablet (line 6558) | def tablet(self, x, rot=0):
    method cigtab (line 6565) | def cigtab(self, y):
    method twoaxes (line 6570) | def twoaxes(self, y):
    method ellirot (line 6576) | def ellirot(self, x):
    method hyperelli (line 6578) | def hyperelli(self, x):
    method elli (line 6581) | def elli(self, x, rot=0, xoffset=0, cond=1e6, actuator_noise=0.0, both...
    method elliconstraint (line 6604) | def elliconstraint(self, x, cfac = 1e8, tough=True, cond=1e6):
    method rosen (line 6616) | def rosen(self, x, alpha=1e2):
    method diffpow (line 6621) | def diffpow(self, x, rot=0):
    method rosenelli (line 6627) | def rosenelli(self, x):
    method ridge (line 6630) | def ridge(self, x, expo=2):
    method ridgecircle (line 6634) | def ridgecircle(self, x, expo=0.5):
    method happycat (line 6639) | def happycat(self, x, alpha=1./8):
    method flat (line 6642) | def flat(self,x):
    method branin (line 6646) | def branin(self, x):
    method goldsteinprice (line 6651) | def goldsteinprice(self, x):
    method griewank (line 6656) | def griewank(self, x):
    method rastrigin (line 6660) | def rastrigin(self, x):
    method schaffer (line 6668) | def schaffer(self, x):
    method schwefelelli (line 6674) | def schwefelelli(self, x):
    method schwefelmult (line 6681) | def schwefelmult(self, x, pen_fac = 1e4):
    method optprob (line 6688) | def optprob(self, x):
    method lincon (line 6692) | def lincon(self, x, theta=0.01):
    method rosen_nesterov (line 6697) | def rosen_nesterov(self, x, rho=100):
  function felli (line 6710) | def felli(x): # unbound function, needed to test multiprocessor
  function _test (line 6716) | def _test(module=None):  # None is fine when called from inside the module
  function process_test (line 6719) | def process_test(stream=None):
  function main (line 6761) | def main(argv=None):

FILE: src/aup/Proposer/spearmint/gp.py
  function dist2 (line 33) | def dist2(ls, x1, x2=None):
  function grad_dist2 (line 55) | def grad_dist2(ls, x1, x2=None):
  function SE (line 86) | def SE(ls, x1, x2=None, grad=False):
  function ARDSE (line 94) | def ARDSE(ls, x1, x2=None, grad=False):
  function grad_ARDSE (line 101) | def grad_ARDSE(ls, x1, x2=None):
  function Matern32 (line 106) | def Matern32(ls, x1, x2=None, grad=False):
  function grad_Matern32 (line 114) | def grad_Matern32(ls, x1, x2=None):
  function Matern52 (line 119) | def Matern52(ls, x1, x2=None, grad=False):
  function grad_Matern52 (line 128) | def grad_Matern52(ls, x1, x2=None):
  class GP (line 133) | class GP:
    method __init__ (line 134) | def __init__(self, covar="Matern52", mcmc_iters=10, noiseless=False):
    method real_init (line 146) | def real_init(self, dims, values):
    method cov (line 162) | def cov(self, x1, x2=None):
    method logprob (line 169) | def logprob(self, comp, vals):
    method optimize_hypers (line 180) | def optimize_hypers(self, comp, vals):
  function main (line 293) | def main():

FILE: src/aup/Proposer/spearmint/helpers.py
  function log (line 10) | def log(*args):
  function sh (line 17) | def sh(cmd):
  function redirect_output (line 22) | def redirect_output(path):
  function check_dir (line 29) | def check_dir(path):
  function grid_for (line 35) | def grid_for(job):
  function file_write_safe (line 40) | def file_write_safe(path, data):
  function save_experiment (line 49) | def save_experiment(filename, expt):
  function load_experiment (line 53) | def load_experiment(filename):
  function job_output_file (line 61) | def job_output_file(job):
  function job_file_for (line 65) | def job_file_for(job):
  function save_job (line 70) | def save_job(job):
  function load_job (line 75) | def load_job(filename):

FILE: src/aup/Proposer/spearmint/runner.py
  function job_runner (line 21) | def job_runner(job):
  function run_matlab_job (line 90) | def run_matlab_job(job):
  function run_python_job (line 105) | def run_python_job(job):
  function run_torch_job (line 140) | def run_torch_job(job):
  function run_shell_job (line 175) | def run_shell_job(job):
  function run_mcr_job (line 189) | def run_mcr_job(job):

FILE: src/aup/Proposer/spearmint/sobol_lib.py
  function i4_bit_hi1 (line 4) | def i4_bit_hi1 ( n ):
  function i4_bit_lo0 (line 64) | def i4_bit_lo0 ( n ):
  function i4_sobol_generate (line 126) | def i4_sobol_generate ( m, n, skip ):
  function i4_sobol (line 159) | def i4_sobol ( dim_num, seed ):
  function i4_uniform (line 13790) | def i4_uniform ( a, b, seed ):
  function prime_ge (line 13883) | def prime_ge ( n ):
  function isprime (line 13931) | def isprime(n):

FILE: src/aup/Proposer/spearmint/spearmint_pb2.py
  class Job (line 348) | class Job(_message.Message):
  class Parameter (line 354) | class Parameter(_message.Message):
  class Experiment (line 360) | class Experiment(_message.Message):
    class ParameterSpec (line 363) | class ParameterSpec(_message.Message):

FILE: src/aup/Proposer/spearmint/util.py
  function unpack_args (line 30) | def unpack_args(str):
  function slice_sample (line 38) | def slice_sample(init_x, logprob, sigma=1.0, step_out=True, max_steps_ou...

FILE: src/aup/RestAPI/server.py
  function fix_none_res (line 57) | def fix_none_res(arg, value):
  function get_display_names (line 60) | def get_display_names(params, exp_config=None, eid=None, cur=None):
  function fix_compression_job_config (line 85) | def fix_compression_job_config(jobs, params, display_names):
  function is_compression_experiment (line 109) | def is_compression_experiment(exp_config=None, eid=None, cur=None):
  function get_params_for_experiment (line 119) | def get_params_for_experiment(cur, eid):
  function query_db (line 134) | def query_db(cur, query, args=(), one=False):
  function start_experiment_daemon (line 140) | def start_experiment_daemon(json_conf, eid, cwd, event):
  function get_valid_jobs_interval (line 184) | def get_valid_jobs_interval(cursor, eid):
  function home (line 216) | def home():
  function get_resource_types (line 225) | def get_resource_types():
  function get_experiment (line 234) | def get_experiment(eid):
  function get_experiments (line 334) | def get_experiments():
  function get_job_status (line 369) | def get_job_status():
  function get_hps_space (line 408) | def get_hps_space():
  function get_experiment_history (line 419) | def get_experiment_history():
  function get_experiment_history_best (line 429) | def get_experiment_history_best(eid, label):
  function get_experiments_history_best (line 489) | def get_experiments_history_best(label):
  function get_experiment_comparison_best (line 553) | def get_experiment_comparison_best():
  function get_metrics_vs_hparams (line 566) | def get_metrics_vs_hparams():
  function get_experiment_status (line 590) | def get_experiment_status():
  function get_job_stats (line 601) | def get_job_stats(eid):
  function get_current_db (line 613) | def get_current_db():
  function perform_setup (line 618) | def perform_setup():
  function create_experiment (line 672) | def create_experiment():
  function start_experiment (line 720) | def start_experiment():
  function stop_experiment (line 770) | def stop_experiment():
  function refresh_all (line 803) | def refresh_all():
  function get_interm_res (line 826) | def get_interm_res():
  function get_interm_res_by_eid (line 850) | def get_interm_res_by_eid(eid, label):
  function delete_experiment (line 911) | def delete_experiment(eid):
  function disable_http_logs (line 928) | def disable_http_logs(disable):
  function main (line 943) | def main(path, port):

FILE: src/aup/__main__.py
  function main (line 49) | def main(experiment_file, test, user, aup_folder, resume, log, sleep, la...

FILE: src/aup/aup.py
  function print_result (line 38) | def print_result(result):
  class BasicConfig (line 53) | class BasicConfig(dict):
    method load (line 64) | def load(self, filename):
    method save (line 81) | def save(self, filename):
    method _get_format (line 94) | def _get_format(filename):
    method _load_json (line 101) | def _load_json(filename):
    method _load_pkl (line 106) | def _load_pkl(filename):
    method _save_json (line 110) | def _save_json(self, filename):
    method _save_pkl (line 114) | def _save_pkl(self, filename):
    method save_flags (line 119) | def save_flags(filename):
    method to_flags (line 130) | def to_flags(self, FLAGS):
    method __setattr__ (line 143) | def __setattr__(self, key, value):
    method __getattr__ (line 146) | def __getattr__(self, key):
    method __delattr__ (line 149) | def __delattr__(self, key):
    method __hash__ (line 152) | def __hash__(self):
  function aup_args (line 156) | def aup_args(func):
  function aup_flags (line 231) | def aup_flags(flags):
  function aup_save_model (line 256) | def aup_save_model(callback_fn, *args, **kwargs):

FILE: src/aup/compression/Compressor.py
  function create_compressor (line 68) | def create_compressor(model, config, *args, **kwargs):
  class Compressor (line 113) | class Compressor:
    method __init__ (line 114) | def __init__(self, model, nni_compressor, c_framework, c_type, c_str):
    method compress (line 122) | def compress(self, *args, **kwargs):
    method update_epoch (line 125) | def update_epoch(self, epoch):
    method step (line 128) | def step(self):
    method get_prune_iterations (line 131) | def get_prune_iterations(self):
    method prune_iteration_start (line 134) | def prune_iteration_start(self):
    method apply_speedup (line 137) | def apply_speedup(self, dummy_input, mask_path=None, *args, **kwargs):
    method count_flops_params (line 162) | def count_flops_params(self, *args, **kwargs):
    method export_model (line 173) | def export_model(self, model_path, mask_path=None, folder_name=".", sp...

FILE: src/aup/compression/__main__.py
  function main (line 57) | def main(experiment_file, automatic, user, aup_folder, resume, log, slee...

FILE: src/aup/compression/tensorflow/compressor.py
  class Compressor (line 25) | class Compressor:
    method __init__ (line 49) | def __init__(self, model, config_list, LayerWrapperClass):
    method set_wrappers_attribute (line 64) | def set_wrappers_attribute(self, name, value):
    method validate_config (line 71) | def validate_config(self, model, config_list):
    method _instrument (line 78) | def _instrument(self, layer):
    method _instrument_sequential (line 97) | def _instrument_sequential(self, seq):
    method _instrument_model (line 107) | def _instrument_model(self, model):
    method _select_config (line 120) | def _select_config(self, layer):
  class Pruner (line 139) | class Pruner(Compressor):
    method __init__ (line 159) | def __init__(self, model, config_list):
    method compress (line 163) | def compress(self):
    method calc_masks (line 177) | def calc_masks(self, wrapper, **kwargs):
    method _update_mask (line 202) | def _update_mask(self):
  class PrunerLayerWrapper (line 209) | class PrunerLayerWrapper(tf.keras.Model):
    method __init__ (line 234) | def __init__(self, layer, config, pruner):
    method call (line 242) | def call(self, *inputs):

FILE: src/aup/compression/tensorflow/pruning/one_shot.py
  class OneshotPruner (line 16) | class OneshotPruner(Pruner):
    method __init__ (line 17) | def __init__(self, model, config_list, pruning_algorithm='level', **al...
    method validate_config (line 22) | def validate_config(self, model, config_list):
    method calc_masks (line 25) | def calc_masks(self, wrapper, wrapper_idx=None):
  class LevelPruner (line 35) | class LevelPruner(OneshotPruner):
    method __init__ (line 36) | def __init__(self, model, config_list):
  class WeightMasker (line 40) | class WeightMasker:
    method __init__ (line 41) | def __init__(self, model, pruner, **kwargs):
    method calc_masks (line 45) | def calc_masks(self, sparsity, wrapper, wrapper_idx=None):
  class LevelPrunerMasker (line 49) | class LevelPrunerMasker(WeightMasker):
    method calc_masks (line 50) | def calc_masks(self, sparsity, wrapper, wrapper_idx=None):

FILE: src/aup/compression/torch/_graph_utils.py
  function build_module_graph (line 23) | def build_module_graph(model, dummy_input):
  function build_graph (line 27) | def build_graph(model, dummy_input, verbose=False):
  function parse_traced_name (line 32) | def parse_traced_name(module_name):
  class TorchGraph (line 40) | class TorchGraph:
    method __init__ (line 45) | def __init__(self, model=None, dummy_input=None, traced_model=None):
    method _trace (line 71) | def _trace(self, model, dummy_input):
  class TorchProtoGraph (line 79) | class TorchProtoGraph(TorchGraph):
    method __init__ (line 88) | def __init__(self, model, dummy_input, verbose=False):
    method parse (line 104) | def parse(self, graph, trace, args=None, omit_useless_nodes=True):
  class NodePyGroup (line 180) | class NodePyGroup(NodePy):
    method __init__ (line 187) | def __init__(self, name, unique_name, node_type, op_type, node_cpps, i...
    method add_nodes (line 226) | def add_nodes(self, node_cpps):
    method sub_node_names (line 232) | def sub_node_names(self):
    method __repr__ (line 235) | def __repr__(self):
  class TorchModuleGraph (line 242) | class TorchModuleGraph(TorchGraph):
    method __init__ (line 247) | def __init__(self, model=None, dummy_input=None, traced_model=None):
    method _expand_key_func_node (line 253) | def _expand_key_func_node(self, node, nodes, input_to_node, output_to_...
    method _expand_module_node (line 319) | def _expand_module_node(self, node, node_name, unique_name, op_type, n...
    method _extract_cat_info (line 398) | def _extract_cat_info(self, node_group, cpp_node):
    method _extract_linear_shape_info (line 450) | def _extract_linear_shape_info(self, node_group):
    method _extract_shape_info (line 480) | def _extract_shape_info(self, node):
    method _extract_leaf_modules (line 505) | def _extract_leaf_modules(self):
    method _get_module_name (line 538) | def _get_module_name(self, scope_name):
    method _build_index (line 558) | def _build_index(self, nodes_op):
    method _is_key_func (line 572) | def _is_key_func(self, node_cpp):
    method unpack_manually (line 596) | def unpack_manually(self):
    method _build_graph (line 645) | def _build_graph(self):
    method _extract_auxiliary_info (line 754) | def _extract_auxiliary_info(self):
    method find_predecessors (line 776) | def find_predecessors(self, unique_name):
    method find_successors (line 799) | def find_successors(self, unique_name):

FILE: src/aup/compression/torch/compressor.py
  class LayerInfo (line 12) | class LayerInfo:
    method __init__ (line 13) | def __init__(self, name, module):
  function _setattr (line 18) | def _setattr(model, name, module):
  class Compressor (line 25) | class Compressor:
    method __init__ (line 30) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 62) | def validate_config(self, model, config_list):
    method reset (line 68) | def reset(self, checkpoint=None):
    method _detect_modules_to_compress (line 85) | def _detect_modules_to_compress(self):
    method _wrap_model (line 101) | def _wrap_model(self):
    method _unwrap_model (line 110) | def _unwrap_model(self):
    method compress (line 119) | def compress(self):
    method set_wrappers_attribute (line 133) | def set_wrappers_attribute(self, name, value):
    method get_modules_to_compress (line 152) | def get_modules_to_compress(self):
    method get_modules_wrapper (line 164) | def get_modules_wrapper(self):
    method select_config (line 175) | def select_config(self, layer):
    method update_epoch (line 214) | def update_epoch(self, epoch):
    method _wrap_modules (line 226) | def _wrap_modules(self, layer, config):
    method add_activation_collector (line 240) | def add_activation_collector(self, collector):
    method remove_activation_collector (line 248) | def remove_activation_collector(self, fwd_hook_id):
    method patch_optimizer (line 255) | def patch_optimizer(self, *tasks):
  class PrunerModuleWrapper (line 268) | class PrunerModuleWrapper(torch.nn.Module):
    method __init__ (line 269) | def __init__(self, module, module_name, module_type, config, pruner):
    method forward (line 302) | def forward(self, *inputs):
  class Pruner (line 309) | class Pruner(Compressor):
    method __init__ (line 321) | def __init__(self, model, config_list, optimizer=None):
    method compress (line 326) | def compress(self):
    method update_mask (line 330) | def update_mask(self):
    method calc_mask (line 338) | def calc_mask(self, wrapper, **kwargs):
    method _wrap_modules (line 352) | def _wrap_modules(self, layer, config):
    method get_mask_dict (line 370) | def get_mask_dict(self):
    method export_model (line 395) | def export_model(self, model_path, mask_path=None, onnx_path=None, inp...
    method load_model_state_dict (line 434) | def load_model_state_dict(self, model_state):
  class QuantizerModuleWrapper (line 450) | class QuantizerModuleWrapper(torch.nn.Module):
    method __init__ (line 451) | def __init__(self, module, module_name, module_type, config, quantizer):
    method forward (line 489) | def forward(self, *inputs):
  class Quantizer (line 512) | class Quantizer(Compressor):
    method __init__ (line 517) | def __init__(self, model, config_list, optimizer=None):
    method quantize_weight (line 528) | def quantize_weight(self, weight, wrapper, **kwargs):
    method quantize_output (line 541) | def quantize_output(self, output, wrapper, **kwargs):
    method quantize_input (line 554) | def quantize_input(self, *inputs, wrapper, **kwargs):
    method _wrap_modules (line 568) | def _wrap_modules(self, layer, config):
    method step_with_optimizer (line 589) | def step_with_optimizer(self):
  class QuantType (line 592) | class QuantType:
  class QuantGrad (line 606) | class QuantGrad(torch.autograd.Function):
    method _quantize (line 611) | def _quantize(cls, x, scale, zero_point):
    method get_bits_length (line 629) | def get_bits_length(cls, config, quant_type):
    method quant_backward (line 649) | def quant_backward(tensor, grad_output, quant_type, scale, zero_point,...
    method forward (line 676) | def forward(ctx, tensor, quant_type, wrapper, **kwargs):
    method backward (line 698) | def backward(cls, ctx, grad_output):
  function _check_weight (line 703) | def _check_weight(module):

FILE: src/aup/compression/torch/parameter_expressions.py
  function choice (line 11) | def choice(options, random_state):
  function randint (line 19) | def randint(lower, upper, random_state):
  function uniform (line 29) | def uniform(low, high, random_state):
  function quniform (line 39) | def quniform(low, high, q, random_state):
  function loguniform (line 49) | def loguniform(low, high, random_state):
  function qloguniform (line 59) | def qloguniform(low, high, q, random_state):
  function normal (line 69) | def normal(mu, sigma, random_state):
  function qnormal (line 82) | def qnormal(mu, sigma, q, random_state):
  function lognormal (line 92) | def lognormal(mu, sigma, random_state):
  function qlognormal (line 101) | def qlognormal(mu, sigma, q, random_state):

FILE: src/aup/compression/torch/pruning/admm_pruner.py
  class ADMMPruner (line 20) | class ADMMPruner(OneshotPruner):
    method __init__ (line 65) | def __init__(self, model, config_list, trainer, num_iterations=30, tra...
    method validate_config (line 78) | def validate_config(self, model, config_list):
    method _projection (line 103) | def _projection(self, weight, sparsity, wrapper):
    method compress (line 125) | def compress(self):

FILE: src/aup/compression/torch/pruning/agp.py
  class AGPPruner (line 26) | class AGPPruner(Pruner):
    method __init__ (line 46) | def __init__(self, model, config_list, optimizer, pruning_algorithm='l...
    method validate_config (line 54) | def validate_config(self, model, config_list):
    method calc_mask (line 75) | def calc_mask(self, wrapper, wrapper_idx=None):
    method compute_target_sparsity (line 110) | def compute_target_sparsity(self, config):
    method update_epoch (line 142) | def update_epoch(self, epoch):

FILE: src/aup/compression/torch/pruning/amc/amc_pruner.py
  class AMCPruner (line 24) | class AMCPruner(Pruner):
    method __init__ (line 113) | def __init__(
    method compress (line 212) | def compress(self):
    method train (line 215) | def train(self, num_episode, agent, env, output_dir):

FILE: src/aup/compression/torch/pruning/amc/channel_pruning_env.py
  function acc_reward (line 22) | def acc_reward(net, acc, flops):
  function acc_flops_reward (line 26) | def acc_flops_reward(net, acc, flops):
  class ChannelPruningEnv (line 31) | class ChannelPruningEnv:
    method __init__ (line 110) | def __init__(self, pruner, evaluator, val_loader, checkpoint, args):
    method step (line 166) | def step(self, action):
    method reset (line 231) | def reset(self):
    method prune_kernel (line 252) | def prune_kernel(self, op_idx, preserve_ratio, preserve_idx=None):
    method _is_final_layer (line 280) | def _is_final_layer(self):
    method _action_wall (line 283) | def _action_wall(self, action):
    method _get_buffer_flops (line 325) | def _get_buffer_flops(self, idx):
    method _cur_flops (line 330) | def _cur_flops(self):
    method _cur_reduced (line 339) | def _cur_reduced(self):
    method _build_index (line 344) | def _build_index(self):
    method _extract_layer_information (line 420) | def _extract_layer_information(self):
    method _build_state_embedding (line 504) | def _build_state_embedding(self):

FILE: src/aup/compression/torch/pruning/amc/lib/agent.py
  class Actor (line 17) | class Actor(nn.Module):
    method __init__ (line 18) | def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300):
    method forward (line 26) | def forward(self, x):
  class Critic (line 36) | class Critic(nn.Module):
    method __init__ (line 37) | def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300):
    method forward (line 45) | def forward(self, xs):
  class DDPG (line 55) | class DDPG(object):
    method __init__ (line 56) | def __init__(self, nb_states, nb_actions, args):
    method update_policy (line 109) | def update_policy(self):
    method eval (line 159) | def eval(self):
    method cuda (line 165) | def cuda(self):
    method observe (line 171) | def observe(self, r_t, s_t, s_t1, a_t, done):
    method random_action (line 176) | def random_action(self):
    method select_action (line 181) | def select_action(self, s_t, episode):
    method reset (line 192) | def reset(self, obs):
    method load_weights (line 197) | def load_weights(self, output):
    method save_model (line 208) | def save_model(self, output):
    method soft_update (line 218) | def soft_update(self, target, source):
    method hard_update (line 224) | def hard_update(self, target, source):
    method sample_from_truncated_normal_distribution (line 228) | def sample_from_truncated_normal_distribution(self, lower, upper, mu, ...

FILE: src/aup/compression/torch/pruning/amc/lib/memory.py
  function sample_batch_indexes (line 18) | def sample_batch_indexes(low, high, size):
  class RingBuffer (line 38) | class RingBuffer(object):
    method __init__ (line 39) | def __init__(self, maxlen):
    method __len__ (line 45) | def __len__(self):
    method __getitem__ (line 48) | def __getitem__(self, idx):
    method append (line 53) | def append(self, v):
  function zeroed_observation (line 66) | def zeroed_observation(observation):
  class Memory (line 78) | class Memory(object):
    method __init__ (line 79) | def __init__(self, window_length, ignore_episode_boundaries=False):
    method sample (line 86) | def sample(self, batch_size, batch_idxs=None):
    method append (line 89) | def append(self, observation, action, reward, terminal, training=True):
    method get_recent_state (line 93) | def get_recent_state(self, current_observation):
    method get_config (line 111) | def get_config(self):
  class SequentialMemory (line 119) | class SequentialMemory(Memory):
    method __init__ (line 120) | def __init__(self, limit, **kwargs):
    method sample (line 132) | def sample(self, batch_size, batch_idxs=None):
    method sample_and_split (line 185) | def sample_and_split(self, batch_size, batch_idxs=None):
    method append (line 209) | def append(self, observation, action, reward, terminal, training=True):
    method nb_entries (line 221) | def nb_entries(self):
    method get_config (line 224) | def get_config(self):

FILE: src/aup/compression/torch/pruning/amc/lib/net_measure.py
  function get_num_gen (line 9) | def get_num_gen(gen):
  function is_leaf (line 13) | def is_leaf(model):
  function get_layer_info (line 17) | def get_layer_info(layer):
  function get_layer_param (line 23) | def get_layer_param(model):
  function measure_layer (line 32) | def measure_layer(layer, x):
  function measure_model (line 88) | def measure_model(model, H, W, device):

FILE: src/aup/compression/torch/pruning/amc/lib/utils.py
  class TextLogger (line 7) | class TextLogger(object):
    method __init__ (line 9) | def __init__(self, filepath):
    method close (line 14) | def close(self):
    method write (line 17) | def write(self, content):
    method write_buf (line 22) | def write_buf(self, content):
    method print_and_write (line 25) | def print_and_write(self, content):
  function to_numpy (line 29) | def to_numpy(var):
  function to_tensor (line 34) | def to_tensor(ndarray, requires_grad=False):  # return a float tensor by...
  function measure_layer_for_pruning (line 41) | def measure_layer_for_pruning(wrapper, x):
  function least_square_sklearn (line 74) | def least_square_sklearn(X, Y):
  function get_output_folder (line 81) | def get_output_folder(parent_dir, env_name):

FILE: src/aup/compression/torch/pruning/apply_compression.py
  function apply_compression_results (line 9) | def apply_compression_results(model, masks=None, masks_file=None, map_lo...

FILE: src/aup/compression/torch/pruning/auto_compress_pruner.py
  class AutoCompressPruner (line 25) | class AutoCompressPruner(Pruner):
    method __init__ (line 106) | def __init__(self, model, config_list, trainer, evaluator, dummy_input,
    method validate_config (line 141) | def validate_config(self, model, config_list):
    method calc_mask (line 166) | def calc_mask(self, wrapper, **kwargs):
    method compress (line 169) | def compress(self):
    method export_model (line 241) | def export_model(self, model_path, mask_path=None, onnx_path=None, inp...

FILE: src/aup/compression/torch/pruning/finegrained_pruning.py
  class LevelPrunerMasker (line 13) | class LevelPrunerMasker(WeightMasker):
    method calc_mask (line 18) | def calc_mask(self, sparsity, wrapper, wrapper_idx=None):

FILE: src/aup/compression/torch/pruning/lottery_ticket.py
  class LotteryTicketPruner (line 17) | class LotteryTicketPruner(Pruner):
    method __init__ (line 34) | def __init__(self, model, config_list, optimizer=None, lr_scheduler=No...
    method validate_config (line 51) | def validate_config(self, model, config_list):
    method _calc_sparsity (line 72) | def _calc_sparsity(self, sparsity):
    method _calc_mask (line 77) | def _calc_mask(self, wrapper, sparsity):
    method calc_mask (line 86) | def calc_mask(self, wrapper, **kwargs):
    method get_prune_iterations (line 104) | def get_prune_iterations(self):
    method prune_iteration_start (line 116) | def prune_iteration_start(self):

FILE: src/aup/compression/torch/pruning/net_adapt_pruner.py
  class NetAdaptPruner (line 25) | class NetAdaptPruner(Pruner):
    method __init__ (line 88) | def __init__(self, model, config_list, short_term_fine_tuner, evaluator,
    method validate_config (line 115) | def validate_config(self, model, config_list):
    method calc_mask (line 140) | def calc_mask(self, wrapper, **kwargs):
    method _update_config_list (line 143) | def _update_config_list(self, config_list, op_name, sparsity):
    method _get_op_num_weights_remained (line 164) | def _get_op_num_weights_remained(self, op_name, module):
    method _get_op_sparsity (line 182) | def _get_op_sparsity(self, op_name):
    method _calc_num_related_weights (line 188) | def _calc_num_related_weights(self, op_name):
    method compress (line 234) | def compress(self):

FILE: src/aup/compression/torch/pruning/one_shot.py
  class OneshotPruner (line 23) | class OneshotPruner(Pruner):
    method __init__ (line 28) | def __init__(self, model, config_list, pruning_algorithm='level', opti...
    method validate_config (line 49) | def validate_config(self, model, config_list):
    method calc_mask (line 66) | def calc_mask(self, wrapper, wrapper_idx=None):
  class LevelPruner (line 98) | class LevelPruner(OneshotPruner):
    method __init__ (line 112) | def __init__(self, model, config_list, optimizer=None):
  class SlimPruner (line 116) | class SlimPruner(OneshotPruner):
    method __init__ (line 130) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 133) | def validate_config(self, model, config_list):
  class _StructuredFilterPruner (line 146) | class _StructuredFilterPruner(OneshotPruner):
    method __init__ (line 157) | def __init__(self, model, config_list, pruning_algorithm, optimizer=No...
    method update_mask (line 180) | def update_mask(self):
    method validate_config (line 190) | def validate_config(self, model, config_list):
    method _dependency_calc_mask (line 203) | def _dependency_calc_mask(self, wrappers, channel_dsets, wrappers_idx=...
    method _dependency_update_mask (line 238) | def _dependency_update_mask(self):
  class L1FilterPruner (line 268) | class L1FilterPruner(_StructuredFilterPruner):
    method __init__ (line 293) | def __init__(self, model, config_list, optimizer=None, dependency_awar...
  class L2FilterPruner (line 298) | class L2FilterPruner(_StructuredFilterPruner):
    method __init__ (line 323) | def __init__(self, model, config_list, optimizer=None, dependency_awar...
  class FPGMPruner (line 328) | class FPGMPruner(_StructuredFilterPruner):
    method __init__ (line 353) | def __init__(self, model, config_list, optimizer=None, dependency_awar...
  class TaylorFOWeightFilterPruner (line 358) | class TaylorFOWeightFilterPruner(_StructuredFilterPruner):
    method __init__ (line 386) | def __init__(self, model, config_list, optimizer=None, statistics_batc...
  class ActivationAPoZRankFilterPruner (line 393) | class ActivationAPoZRankFilterPruner(_StructuredFilterPruner):
    method __init__ (line 423) | def __init__(self, model, config_list, optimizer=None, activation='relu',
  class ActivationMeanRankFilterPruner (line 430) | class ActivationMeanRankFilterPruner(_StructuredFilterPruner):
    method __init__ (line 459) | def __init__(self, model, config_list, optimizer=None, activation='relu',

FILE: src/aup/compression/torch/pruning/sensitivity_pruner.py
  class SensitivityPruner (line 26) | class SensitivityPruner(Pruner):
    method __init__ (line 92) | def __init__(self, model, config_list, evaluator,
    method validate_config (line 141) | def validate_config(self, model, config_list):
    method load_sensitivity (line 166) | def load_sensitivity(self, filepath):
    method _max_prune_ratio (line 184) | def _max_prune_ratio(self, ori_acc, threshold, sensitivities):
    method normalize (line 216) | def normalize(self, ratios, target_pruned):
    method create_cfg (line 257) | def create_cfg(self, ratios):
    method current_sparsity (line 284) | def current_sparsity(self):
    method compress (line 295) | def compress(self, eval_args=None, eval_kwargs=None,
    method calc_mask (line 416) | def calc_mask(self, wrapper, **kwargs):

FILE: src/aup/compression/torch/pruning/simulated_annealing_pruner.py
  class SimulatedAnnealingPruner (line 26) | class SimulatedAnnealingPruner(Pruner):
    method __init__ (line 76) | def __init__(self, model, config_list, evaluator, optimize_mode='maxim...
    method validate_config (line 110) | def validate_config(self, model, config_list):
    method _sparsities_2_config_list (line 135) | def _sparsities_2_config_list(self, sparsities):
    method _rescale_sparsities (line 169) | def _rescale_sparsities(self, sparsities, target_sparsity):
    method _init_sparsities (line 208) | def _init_sparsities(self):
    method _generate_perturbations (line 226) | def _generate_perturbations(self):
    method calc_mask (line 254) | def calc_mask(self, wrapper, **kwargs):
    method compress (line 257) | def compress(self, return_config_list=False):

FILE: src/aup/compression/torch/pruning/structured_pruning.py
  class StructuredWeightMasker (line 17) | class StructuredWeightMasker(WeightMasker):
    method __init__ (line 36) | def __init__(self, model, pruner, preserve_round=1, dependency_aware=F...
    method calc_mask (line 42) | def calc_mask(self, sparsity, wrapper, wrapper_idx=None, **depen_kwargs):
    method _get_current_state (line 71) | def _get_current_state(self, sparsity, wrapper, wrapper_idx=None):
    method _normal_calc_mask (line 129) | def _normal_calc_mask(self, sparsity, wrapper, wrapper_idx=None):
    method _common_channel_to_prune (line 155) | def _common_channel_to_prune(self, sparsities, wrappers, wrappers_idx,...
    method _dependency_calc_mask (line 238) | def _dependency_calc_mask(self, sparsities, wrappers, wrappers_idx, ch...
    method get_mask (line 292) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method get_channel_sum (line 320) | def get_channel_sum(self, wrapper, wrapper_idx):
  class L1FilterPrunerMasker (line 340) | class L1FilterPrunerMasker(StructuredWeightMasker):
    method get_mask (line 349) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method get_channel_sum (line 364) | def get_channel_sum(self, wrapper, wrapper_idx):
  class L2FilterPrunerMasker (line 372) | class L2FilterPrunerMasker(StructuredWeightMasker):
    method get_mask (line 378) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method get_channel_sum (line 393) | def get_channel_sum(self, wrapper, wrapper_idx):
  class FPGMPrunerMasker (line 401) | class FPGMPrunerMasker(StructuredWeightMasker):
    method get_mask (line 408) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method _get_min_gm_kernel_idx (line 417) | def _get_min_gm_kernel_idx(self, num_prune, wrapper, wrapper_idx, chan...
    method _get_distance_sum (line 426) | def _get_distance_sum(self, weight, out_idx):
    method get_channel_sum (line 452) | def get_channel_sum(self, wrapper, wrapper_idx):
  class TaylorFOWeightFilterPrunerMasker (line 462) | class TaylorFOWeightFilterPrunerMasker(StructuredWeightMasker):
    method __init__ (line 471) | def __init__(self, model, pruner, statistics_batch_num=1):
    method get_mask (line 478) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method calc_contributions (line 492) | def calc_contributions(self):
    method get_channel_sum (line 510) | def get_channel_sum(self, wrapper, wrapper_idx):
  class ActivationFilterPrunerMasker (line 518) | class ActivationFilterPrunerMasker(StructuredWeightMasker):
    method __init__ (line 519) | def __init__(self, model, pruner, statistics_batch_num=1, activation='...
    method _add_activation_collector (line 532) | def _add_activation_collector(self, pruner):
  class ActivationAPoZRankFilterPrunerMasker (line 551) | class ActivationAPoZRankFilterPrunerMasker(ActivationFilterPrunerMasker):
    method get_mask (line 560) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method _calc_apoz (line 579) | def _calc_apoz(self, activations):
    method get_channel_sum (line 599) | def get_channel_sum(self, wrapper, wrapper_idx):
  class ActivationMeanRankFilterPrunerMasker (line 608) | class ActivationMeanRankFilterPrunerMasker(ActivationFilterPrunerMasker):
    method get_mask (line 617) | def get_mask(self, base_mask, weight, num_prune, wrapper, wrapper_idx,...
    method _cal_mean_activation (line 638) | def _cal_mean_activation(self, activations):
    method get_channel_sum (line 656) | def get_channel_sum(self, wrapper, wrapper_idx):
  class SlimPrunerMasker (line 667) | class SlimPrunerMasker(WeightMasker):
    method __init__ (line 675) | def __init__(self, model, pruner, **kwargs):
    method calc_mask (line 685) | def calc_mask(self, sparsity, wrapper, wrapper_idx=None):
  function least_square_sklearn (line 707) | def least_square_sklearn(X, Y):
  class AMCWeightMasker (line 714) | class AMCWeightMasker(WeightMasker):
    method __init__ (line 735) | def __init__(self, model, pruner, preserve_round=1):
    method calc_mask (line 740) | def calc_mask(self, sparsity, wrapper, wrapper_idx=None, preserve_idx=...
    method get_mask (line 793) | def get_mask(self, base_mask, weight, num_preserve, wrapper, wrapper_i...

FILE: src/aup/compression/torch/pruning/weight_masker.py
  class WeightMasker (line 4) | class WeightMasker(object):
    method __init__ (line 5) | def __init__(self, model, pruner, **kwargs):
    method calc_mask (line 9) | def calc_mask(self, sparsity, wrapper, wrapper_idx=None):

FILE: src/aup/compression/torch/quantization/quantizers.py
  class NaiveQuantizer (line 19) | class NaiveQuantizer(Quantizer):
    method __init__ (line 23) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 27) | def validate_config(self, model, config_list):
    method quantize_weight (line 37) | def quantize_weight(self, wrapper, **kwargs):
  function update_ema (line 47) | def update_ema(biased_ema, value, decay):
  function update_quantization_param (line 68) | def update_quantization_param(bits, rmin, rmax):
  function get_bits_length (line 110) | def get_bits_length(config, quant_type):
  class QATGrad (line 117) | class QATGrad(QuantGrad):
    method quant_backward (line 119) | def quant_backward(tensor, grad_output, quant_type, scale, zero_point,...
  class QAT_Quantizer (line 126) | class QAT_Quantizer(Quantizer):
    method __init__ (line 132) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 166) | def validate_config(self, model, config_list):
    method _quantize (line 188) | def _quantize(self, bits, op, real_val):
    method _dequantize (line 214) | def _dequantize(self, op, quantized_val):
    method quantize_weight (line 234) | def quantize_weight(self, wrapper, **kwargs):
    method quantize_output (line 265) | def quantize_output(self, output, wrapper, **kwargs):
    method fold_bn (line 288) | def fold_bn(self, config, **kwargs):
    method step_with_optimizer (line 292) | def step_with_optimizer(self):
  class DoReFaQuantizer (line 299) | class DoReFaQuantizer(Quantizer):
    method __init__ (line 305) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 308) | def validate_config(self, model, config_list):
    method quantize_weight (line 328) | def quantize_weight(self, wrapper, **kwargs):
    method quantize (line 339) | def quantize(self, input_ri, q_bits):
  class ClipGrad (line 345) | class ClipGrad(QuantGrad):
    method quant_backward (line 347) | def quant_backward(tensor, grad_output, quant_type, scale, zero_point,...
  class BNNQuantizer (line 353) | class BNNQuantizer(Quantizer):
    method __init__ (line 359) | def __init__(self, model, config_list, optimizer=None):
    method validate_config (line 363) | def validate_config(self, model, config_list):
    method quantize_weight (line 384) | def quantize_weight(self, wrapper, **kwargs):
    method quantize_output (line 392) | def quantize_output(self, output, wrapper, **kwargs):

FILE: src/aup/compression/torch/speedup/compress_modules.py
  function no_replace (line 27) | def no_replace(module, mask):
  function replace_linear (line 35) | def replace_linear(linear, mask):
  function replace_batchnorm2d (line 67) | def replace_batchnorm2d(norm, mask):
  function replace_conv2d (line 102) | def replace_conv2d(conv, mask):
  function replace_convtranspose2d (line 195) | def replace_convtranspose2d(convtrans, mask):

FILE: src/aup/compression/torch/speedup/compressor.py
  class ModelSpeedup (line 17) | class ModelSpeedup:
    met

[
  {
    "path": ".coveragerc",
    "chars": 708,
    "preview": "[run]\nomit =\n     ./src/aup/Proposer/spearmint/*\n     ./src/aup/Proposer/hyperopt/*\n     ./src/aup/Proposer/eas/*\n     ."
  },
  {
    "path": ".dockerignore",
    "chars": 224,
    "preview": "docs\nExamples/2d*\nExamples/dlconvert_examples/Convert*\nExamples/dlconvert_examples/Tested*\nExamples/demo\nExamples/cai_ea"
  },
  {
    "path": ".gitignore",
    "chars": 1458,
    "preview": "Examples/demo/auto.py\ntestenv/\n.aup/\n.idea/\n*~\n*.pkl\ntests/EE/jobs/\ntests/data/jobs/\ntests/EE/Resource/data\n.vscode/\nana"
  },
  {
    "path": ".travis.yml",
    "chars": 1288,
    "preview": "git:\n  depth: false\n  quiet: true\nbranches: \n  only:\n  - master\n  - release\nlanguage: python\npython:\n- '3.7'\nenv:\n  glob"
  },
  {
    "path": "CODE_OF_CONDUCT.md",
    "chars": 3350,
    "preview": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, w"
  },
  {
    "path": "Examples/.gitignore",
    "chars": 17,
    "preview": "jobs/\nspearmint/\n"
  },
  {
    "path": "Examples/2dfunc_diff_mult_res/README.md",
    "chars": 2028,
    "preview": "# Return multiple results\n\nThis feature allows the user to save and track multiple secondary results along with the prim"
  },
  {
    "path": "Examples/2dfunc_diff_mult_res/cpu.ini",
    "chars": 179,
    "preview": "[Auptimizer]\n# Auptimizer environment folder to be created, this file will be copied over\nAuptimizer_PATH=./.aup\n# Temp "
  },
  {
    "path": "Examples/2dfunc_diff_mult_res/exp_cpu.json",
    "chars": 499,
    "preview": "{\n  \"name\": \"./2dfunc_diff_mult_res/exp_cpu.json\",\n  \"proposer\": \"random\",\n  \"n_samples\": 10,\n  \"random_seed\": 1,\n  \"scr"
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  {
    "path": "Examples/2dfunc_diff_mult_res/rosenbrock_hpo.py",
    "chars": 883,
    "preview": "#!/usr/bin/env python\n\"\"\"\nModified Rosenbrock function for HPO and aup\n============================================\n\n..\n"
  },
  {
    "path": "Examples/2dfunc_diff_opt/.gitignore",
    "chars": 30,
    "preview": "spearmint/\nrosenbrock_auto.py\n"
  },
  {
    "path": "Examples/2dfunc_diff_opt/History.ipynb",
    "chars": 187213,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n "
  },
  {
    "path": "Examples/2dfunc_diff_opt/README.md",
    "chars": 755,
    "preview": "# Rosenbrock demo\n\n## Setup\n\n    python -m aup.setup\n\nTo setup different resource configuration, see [resource example]("
  },
  {
    "path": "Examples/2dfunc_diff_opt/experiment_auto.json",
    "chars": 442,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_auto.json\",\n  \"proposer\": \"random\",\n  \"n_samples\": 10,\n  \"random_seed\": 1,\n  \""
  },
  {
    "path": "Examples/2dfunc_diff_opt/experiment_bohb.json",
    "chars": 659,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_bohb.json\",\n  \"script\": \"rosenbrock_hpo.py\",\n  \"resource\": \"cpu\",\n  \"n_paralle"
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  {
    "path": "Examples/2dfunc_diff_opt/experiment_hyperband.json",
    "chars": 400,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_hyperband.json\",\n  \"proposer\": \"hyperband\",\n  \"script\": \"rosenbrock_hpo.py\",\n "
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  {
    "path": "Examples/2dfunc_diff_opt/experiment_hyperopt.json",
    "chars": 466,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_hyperopt.json\",\n  \"proposer\": \"hyperopt\",\n  \"random_seed\": 1,\n  \"script\": \"ros"
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    "path": "Examples/2dfunc_diff_opt/experiment_random.json",
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  {
    "path": "Examples/2dfunc_diff_opt/experiment_sequence.json",
    "chars": 438,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_sequence.json\",\n  \"proposer\": \"sequence\",\n  \"script\": \"rosenbrock_hpo.py\",\n  \""
  },
  {
    "path": "Examples/2dfunc_diff_opt/experiment_spearmint.json",
    "chars": 510,
    "preview": "{\n  \"name\": \"./2dfunc_diff_opt/experiment_spearmint.json\",\n  \"proposer\": \"spearmint\",\n  \"n_samples\": 100,\n  \"random_seed"
  },
  {
    "path": "Examples/2dfunc_diff_opt/rosenbrock_hpo.py",
    "chars": 875,
    "preview": "#!/usr/bin/env python\n\"\"\"\nModified Rosenbrock function for HPO and aup\n============================================\n\n..\n"
  },
  {
    "path": "Examples/2dfunc_diff_opt/rosenbrock_origin.py",
    "chars": 264,
    "preview": "\"\"\"\nDemonstration code for Rosenbrock function\n==========================================\n\n..\n  Copyright (c) 2018 LG El"
  },
  {
    "path": "Examples/2dfunc_diff_res/README.md",
    "chars": 1582,
    "preview": "# Rosenbrock demo for different resources\n\n## CPU\n\n    python -m aup.setup cpu.ini\n\tpython -m aup exp_cpu.json\n\n## GPU\n\n"
  },
  {
    "path": "Examples/2dfunc_diff_res/aws.txt",
    "chars": 37,
    "preview": "AWS_EC2_ID SSH_KEY\nAWS_EC2_ID SSH_KEY"
  },
  {
    "path": "Examples/2dfunc_diff_res/cpu.ini",
    "chars": 179,
    "preview": "[Auptimizer]\n# Auptimizer environment folder to be created, this file will be copied over\nAuptimizer_PATH=./.aup\n# Temp "
  },
  {
    "path": "Examples/2dfunc_diff_res/env_local_template.ini",
    "chars": 179,
    "preview": "[Auptimizer]\n# Auptimizer environment folder to be created, this file will be copied over\nAuptimizer_PATH=./.aup\n# Temp "
  },
  {
    "path": "Examples/2dfunc_diff_res/env_user_template.ini",
    "chars": 177,
    "preview": "[Auptimizer]\n# Auptimizer environment folder to be created, this file will be copied over\nAuptimizer_PATH=~/.aup\n# Temp "
  },
  {
    "path": "Examples/2dfunc_diff_res/exp_aws.json",
    "chars": 604,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_aws.json\",\n    \"workingdir\": \"/home/ubuntu/\",\n    \"proposer\": \"random\",\n    \"n_sample"
  },
  {
    "path": "Examples/2dfunc_diff_res/exp_cpu.json",
    "chars": 435,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_cpu.json\",\n  \"proposer\": \"sequence\",\n  \"n_samples\": 10,\n  \"random_seed\": 1,\n  \"script"
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  {
    "path": "Examples/2dfunc_diff_res/exp_gpu.json",
    "chars": 433,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_gpu.json\",\n  \"proposer\": \"random\",\n  \"n_samples\": 10,\n  \"random_seed\": 1,\n  \"script\":"
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  {
    "path": "Examples/2dfunc_diff_res/exp_node.json",
    "chars": 696,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_node.json\",\n  \"workingdir\": \"/home/ubuntu/aup_demo\",\n  \"proposer\": \"random\",\n  \"n_sam"
  },
  {
    "path": "Examples/2dfunc_diff_res/exp_node_async.json",
    "chars": 775,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_node_async.json\",\n  \"workingdir\": \"/home/paul/aup_demo\",\n  \"proposer\": \"random\",\n  \"n"
  },
  {
    "path": "Examples/2dfunc_diff_res/exp_passive.json",
    "chars": 421,
    "preview": "{\n  \"name\": \"./2dfunc_diff_res/exp_passive.json\",\n  \"proposer\": \"random\",\n  \"n_samples\": 5,\n  \"random_seed\": 1,\n  \"scrip"
  },
  {
    "path": "Examples/2dfunc_diff_res/gpu.txt",
    "chars": 4,
    "preview": "0\n1\n"
  },
  {
    "path": "Examples/2dfunc_diff_res/node.txt",
    "chars": 34,
    "preview": "jasonliu@ubuntu1\njasonliu@ubuntu2\n"
  },
  {
    "path": "Examples/2dfunc_diff_res/plainGPU.txt",
    "chars": 1,
    "preview": "0"
  },
  {
    "path": "Examples/2dfunc_diff_res/rosenbrock_hpo.py",
    "chars": 875,
    "preview": "#!/usr/bin/env python\n\"\"\"\nModified Rosenbrock function for HPO and aup\n============================================\n\n..\n"
  },
  {
    "path": "Examples/2dfunc_diff_res/singleGPU.txt",
    "chars": 3,
    "preview": "0\n0"
  },
  {
    "path": "Examples/cai_eas/.gitignore",
    "chars": 1448,
    "preview": "net_pool\narch_search\n\n\n## Core latex/pdflatex auxiliary files:\n*.aux\n*.lof\n*.log\n*.lot\n*.fls\n*.out\n*.toc\n\n## Intermediat"
  },
  {
    "path": "Examples/cai_eas/README.md",
    "chars": 350,
    "preview": "# HPO for Deep Neural Networks\n\nDemonstration using https://github.com/han-cai/EAS\n\nThe HPO engine is integrated into th"
  },
  {
    "path": "Examples/cai_eas/eas/client.py",
    "chars": 835,
    "preview": "#!/usr/bin/env python\n\"\"\"\nModified from https://github.com/han-cai/EAS\n\nThe file to run in the client side\nTrain the net"
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/base_provider.py",
    "chars": 2496,
    "preview": "import numpy as np\n\n\nclass DataSet:\n\t\"\"\"Class to represent some dataset: train, validation, test\"\"\"\n\t\n\t@property\n\tdef nu"
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/cifar.py",
    "chars": 8055,
    "preview": "import tempfile\nimport os\nimport pickle\nimport random\n\nimport numpy as np\n\nfrom data_providers.base_provider import Imag"
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/downloader.py",
    "chars": 924,
    "preview": "import sys\nimport os\nimport urllib.request\nimport tarfile\nimport zipfile\n\n\ndef report_download_progress(count, block_siz"
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/svhn.py",
    "chars": 4628,
    "preview": "import tempfile\nimport os\nimport scipy.io\n\nimport numpy as np\n\nfrom data_providers.base_provider import ImagesDataSet, D"
  },
  {
    "path": "Examples/cai_eas/eas/data_providers/utils.py",
    "chars": 730,
    "preview": "from data_providers.cifar import Cifar10DataProvider, Cifar100DataProvider, \\\n\tCifar10AugmentedDataProvider, Cifar100Aug"
  },
  {
    "path": "Examples/cai_eas/eas/expdir_monitor/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "Examples/cai_eas/eas/expdir_monitor/expdir_monitor.py",
    "chars": 4383,
    "preview": "import json\nimport os\nimport subprocess\nfrom models.utils import RunConfig, get_model_config_by_name, get_model_by_name\n"
  },
  {
    "path": "Examples/cai_eas/eas/experiment.json",
    "chars": 179,
    "preview": "{\n  \"name\": \"./cai_eas/eas/experiment.json\",\n  \"proposer\": \"eas\",\n  \"script\": \"client.py\",\n  \"resource\": \"gpu\",\n  \"n_par"
  },
  {
    "path": "Examples/cai_eas/eas/models/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "Examples/cai_eas/eas/models/basic_model.py",
    "chars": 12413,
    "preview": "import os\nimport shutil\nimport tensorflow as tf\nimport numpy as np\nimport time\nfrom datetime import timedelta\nimport jso"
  },
  {
    "path": "Examples/cai_eas/eas/models/convnet.py",
    "chars": 6614,
    "preview": "from models.basic_model import BasicModel\nfrom data_providers.base_provider import DataProvider\nfrom models.layers impor"
  },
  {
    "path": "Examples/cai_eas/eas/models/dense_net.py",
    "chars": 18831,
    "preview": "import tensorflow as tf\nfrom models.basic_model import BasicModel\nfrom models.layers import ConvLayer, FCLayer, PoolLaye"
  },
  {
    "path": "Examples/cai_eas/eas/models/layer_cascade.py",
    "chars": 5893,
    "preview": "from models.layers import ConvLayer, FCLayer, PoolLayer, get_layer_by_name\nimport tensorflow as tf\n\n\nclass LayerCascade:"
  },
  {
    "path": "Examples/cai_eas/eas/models/layer_multi_branch.py",
    "chars": 8211,
    "preview": "import tensorflow as tf\nimport numpy as np\nfrom models.layer_cascade import LayerCascade\n\n\nclass LayerMultiBranch:\n\tdef "
  },
  {
    "path": "Examples/cai_eas/eas/models/layers.py",
    "chars": 15496,
    "preview": "from models.basic_model import BasicModel\nimport tensorflow as tf\nimport numpy as np\nimport copy\n\n\ndef apply_noise(weigh"
  },
  {
    "path": "Examples/cai_eas/eas/models/utils.py",
    "chars": 3791,
    "preview": "from models.dense_net import DenseNetConfig, DenseNet\nfrom models.convnet import SimpleConvnetConfig, SimpleConvnet\nimpo"
  },
  {
    "path": "Examples/cai_eas/start_nets/start_net_convnet_small_C10+/net.config",
    "chars": 3588,
    "preview": "{\n    \"name\": \"SimpleConvnet\",\n    \"weight_decay\": 0.0001,\n    \"bn_epsilon\": 1e-05,\n    \"bn_decay\": 0.9,\n    \"drop_schem"
  },
  {
    "path": "Examples/compression/mnist_pytorch/README.md",
    "chars": 1904,
    "preview": "# Compression examples (PyTorch)\n\n## Requirements\n\nThe base requirements for Auptimizer apply here.\n\nAdditionally, in or"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_activation_apoz_rank.json",
    "chars": 378,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation APoZ Rank Filter Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \""
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_activation_apoz_rank_dependency_aware.json",
    "chars": 414,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation APoZ Rank Filter Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_awa"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_activation_mean_rank.json",
    "chars": 378,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation Mean Rank Filter Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \""
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_activation_mean_rank_dependency_aware.json",
    "chars": 414,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation Mean Rank Filter Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_awa"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_admm.json",
    "chars": 504,
    "preview": "{\n    \"name\": \"PyTorch MNIST ADMM Pruner\",\n    \"script\": \"mnist_admm.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n  "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_agp.json",
    "chars": 546,
    "preview": "{\n    \"name\": \"PyTorch MNIST AGP Pruner\",\n    \"script\": \"mnist_agp.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n    "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_amc.json",
    "chars": 311,
    "preview": "{\n    \"name\": \"PyTorch MNIST AMC Pruner\",\n    \"script\": \"mnist_amc.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n    "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_activation_apoz_rank.json",
    "chars": 636,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation APoZ Rank Filter Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_activation_mean_rank.json",
    "chars": 636,
    "preview": "{\n    \"name\": \"PyTorch MNIST Activation Mean Rank Filter Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_admm.json",
    "chars": 599,
    "preview": "{\n    \"name\": \"PyTorch MNIST ADMM Pruner (automatic)\",\n    \"script\": \"mnist_admm.py\",\n    \"resource\": \"cpu\",\n    \"resour"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_agp.json",
    "chars": 758,
    "preview": "{\n    \"name\": \"PyTorch MNIST AGP Pruner (automatic)\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"r"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_aup_args.json",
    "chars": 611,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (@aup_args) (automatic)\",\n    \"script\": \"mnist_aup_args.py\",\n    \"resource\": \"c"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_bohb.json",
    "chars": 610,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_hyperband.json",
    "chars": 593,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_hyperopt.json",
    "chars": 592,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_no_expand.json",
    "chars": 762,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_random.json",
    "chars": 590,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_random_no_expand.json",
    "chars": 632,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_sequence.json",
    "chars": 629,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_fpgm_spearmint.json",
    "chars": 593,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resource_ar"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_l1filter.json",
    "chars": 601,
    "preview": "{\n    \"name\": \"PyTorch MNIST L1 Filter Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resour"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_l2filter.json",
    "chars": 600,
    "preview": "{\n    \"name\": \"PyTorch MNIST L2 Filter Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resour"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_level.json",
    "chars": 617,
    "preview": "{\n    \"name\": \"PyTorch MNIST Level Pruner (automatic)\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_lottery_ticket.json",
    "chars": 746,
    "preview": "{\n    \"name\": \"PyTorch MNIST Lottery Ticket Pruner (automatic)\",\n    \"script\": \"mnist_lottery_ticket.py\",\n    \"resource\""
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_auto_taylor_fo.json",
    "chars": 628,
    "preview": "{\n    \"name\": \"PyTorch MNIST Taylor FO Weight Filter Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_autocompress.json",
    "chars": 363,
    "preview": "{\n    \"name\": \"PyTorch MNIST Auto Compress Pruner\",\n    \"script\": \"mnist_autocompress.py\",\n    \"resource\": \"cpu\",\n    \"c"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_fpgm.json",
    "chars": 333,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n       "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_fpgm_aup_args.json",
    "chars": 354,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (@aup_args)\",\n    \"script\": \"mnist_aup_args.py\",\n    \"resource\": \"cpu\",\n    \"co"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_fpgm_dependency_aware.json",
    "chars": 369,
    "preview": "{\n    \"name\": \"PyTorch MNIST FPGM Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_aware.py\",\n    \"resource\":"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_l1filter.json",
    "chars": 342,
    "preview": "{\n    \"name\": \"PyTorch MNIST L1 Filter Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n  "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_l1filter_dependency_aware.json",
    "chars": 378,
    "preview": "{\n    \"name\": \"PyTorch MNIST L1 Filter Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_aware.py\",\n    \"resou"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_l2filter.json",
    "chars": 342,
    "preview": "{\n    \"name\": \"PyTorch MNIST L2 Filter Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n  "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_l2filter_dependency_aware.json",
    "chars": 378,
    "preview": "{\n    \"name\": \"PyTorch MNIST L2 Filter Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_aware.py\",\n    \"resou"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_level.json",
    "chars": 346,
    "preview": "{\n    \"name\": \"PyTorch MNIST Level Pruner\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"compression"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_lottery_ticket.json",
    "chars": 406,
    "preview": "{\n    \"name\": \"PyTorch MNIST Lottery Ticket Pruner\",\n    \"script\": \"mnist_lottery_ticket.py\",\n    \"resource\": \"cpu\",\n   "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_net_adapt.json",
    "chars": 351,
    "preview": "{\n    \"name\": \"PyTorch MNIST NetAdapt Pruner\",\n    \"script\": \"mnist_net_adapt.py\",\n    \"resource\": \"cpu\",\n    \"compressi"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_quantization_bnn.json",
    "chars": 455,
    "preview": "{\n    \"name\": \"PyTorch MNIST BNN Quantizer\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"compressio"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_quantization_dorefa.json",
    "chars": 374,
    "preview": "{\n    \"name\": \"PyTorch MNIST DoReFa Quantizer\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"compres"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_quantization_naive.json",
    "chars": 257,
    "preview": "{\n    \"name\": \"PyTorch MNIST Naive Quantizer\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"compress"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_quantization_qat.json",
    "chars": 571,
    "preview": "{\n    \"name\": \"PyTorch MNIST QAT Quantizer\",\n    \"script\": \"mnist_no_speedup.py\",\n    \"resource\": \"cpu\",\n    \"compressio"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_sensitivity.json",
    "chars": 358,
    "preview": "{\n    \"name\": \"PyTorch MNIST Sensitivity Pruner\",\n    \"script\": \"mnist_sensitivity.py\",\n    \"resource\": \"cpu\",\n    \"comp"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_simulated_annealing.json",
    "chars": 382,
    "preview": "{\n    \"name\": \"PyTorch MNIST Simulated Annealing Pruner\",\n    \"script\": \"mnist_simulated_annealing.py\",\n    \"resource\": "
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_taylor_fo.json",
    "chars": 370,
    "preview": "{\n    \"name\": \"PyTorch MNIST Taylor FO Weight Filter Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"comp"
  },
  {
    "path": "Examples/compression/mnist_pytorch/exp_taylor_fo_dependency_aware.json",
    "chars": 406,
    "preview": "{\n    \"name\": \"PyTorch MNIST Taylor FO Weight Filter Pruner (dependency aware)\",\n    \"script\": \"mnist_dependency_aware.p"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist.py",
    "chars": 6175,
    "preview": "#!/usr/bin/env python3\n\n\"\"\"\nMNIST convolutional network using pytorch\n============================================\n\n..\n "
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_admm.py",
    "chars": 6695,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_agp.py",
    "chars": 5898,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_amc.py",
    "chars": 6040,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_aup_args.py",
    "chars": 6027,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_autocompress.py",
    "chars": 6830,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_dependency_aware.py",
    "chars": 6094,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_lottery_ticket.py",
    "chars": 5990,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_net_adapt.py",
    "chars": 6032,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_no_speedup.py",
    "chars": 5859,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_pretrained.py",
    "chars": 5324,
    "preview": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_sensitivity.py",
    "chars": 6034,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_pytorch/mnist_simulated_annealing.py",
    "chars": 5799,
    "preview": "#!/usr/bin/env python3\n\nfrom __future__ import print_function\nimport argparse\nimport sys\nimport time\n\nimport numpy as np"
  },
  {
    "path": "Examples/compression/mnist_tensorflow/README.md",
    "chars": 538,
    "preview": "# Compression examples (Tensorflow)\n\n## Requirements\n\nThe basic requirements for Auptimizer apply here.\n\nAdditionally, i"
  },
  {
    "path": "Examples/compression/mnist_tensorflow/exp_auto_level.json",
    "chars": 616,
    "preview": "{\n    \"name\": \"Tensorflow MNIST Level Pruner (automatic)\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"resourc"
  },
  {
    "path": "Examples/compression/mnist_tensorflow/exp_level.json",
    "chars": 343,
    "preview": "{\n    \"name\": \"Tensorflow MNIST Level Pruner\",\n    \"script\": \"mnist.py\",\n    \"resource\": \"cpu\",\n    \"compression\": {\n   "
  },
  {
    "path": "Examples/compression/mnist_tensorflow/mnist.py",
    "chars": 2862,
    "preview": "#!/usr/bin/env python3\n\n\"\"\"\nMNIST convolutional network using pytorch\n============================================\n\n..\n "
  },
  {
    "path": "Examples/compression/mnist_tensorflow/mnist_pretrained.py",
    "chars": 2467,
    "preview": "#!/usr/bin/env python3\n\nfrom aup import print_result, aup_args\n\nimport tensorflow as tf\nimport numpy as np\nfrom tensorfl"
  },
  {
    "path": "Examples/compression/utility_functions/README.md",
    "chars": 349,
    "preview": "# Compression utility functions\n\nThis example provides an example training script ``mnist.py``, where the compression ut"
  },
  {
    "path": "Examples/compression/utility_functions/mnist.py",
    "chars": 5822,
    "preview": "#!/usr/bin/env python3\n\n\"\"\"\nMNIST convolutional network using pytorch\n============================================\n\n..\n "
  },
  {
    "path": "Examples/converter_examples/Convert_Benchmark/.gitignore",
    "chars": 48,
    "preview": ".ipynb_checkpoints/\n*.tflite\n*.h5\n*.tgz\npytorch/"
  },
  {
    "path": "Examples/converter_examples/Convert_Benchmark/Benchmark.ipynb",
    "chars": 228547,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "Examples/converter_examples/Convert_Benchmark/README.md",
    "chars": 2396,
    "preview": "# Quantization Benchmarking on Android Phone\n\nIn this example, we use Converter to quantize and convert models into Tens"
  },
  {
    "path": "Examples/converter_examples/Convert_Benchmark/repdata.py",
    "chars": 999,
    "preview": "\"\"\"\n..\n  Copyright (c) 2018 LG Electronics Inc.\n  SPDX-License-Identifier: GPL-3.0-or-later\n\nCreate a synthetic tf.datas"
  },
  {
    "path": "Examples/converter_examples/Convert_Benchmark/script_mobilenet.py",
    "chars": 5687,
    "preview": "\"\"\"\n  Copyright (c) 2018 LG Electronics Inc.\n  SPDX-License-Identifier: GPL-3.0-or-later\n\"\"\" \nimport keras\nimport subpro"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/.gitignore",
    "chars": 87,
    "preview": "mobilenet*\ninput_models\noutput_models\n*.tgz\nDockerfile_*\ndensenet*\nnasnet*\nsqueezenet*\n"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/README.md",
    "chars": 2313,
    "preview": "# Profiling TensorFlow Lite/ONNX model performance for CPU\nIn this example, we show how to use Converter to convert mode"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/convert_onnx.json",
    "chars": 661,
    "preview": "[\n    {\n        \"convert_from\":\"input_models/densenet/densenet.pb\",\n        \"convert_to\":\"output_models/densenet_convert"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/convert_tflite.json",
    "chars": 671,
    "preview": "[\n    {\n        \"convert_from\":\"input_models/squeezenet.pb\",\n        \"convert_to\":\"output_models/squeezenet_converted.tf"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/download_test_models.py",
    "chars": 1404,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n\nimport os\nimport json\nimport arg"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/env_onnx.template",
    "chars": 670,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n#User data Variables\nIMAGEREPO=te"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/env_tflite.template",
    "chars": 837,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n#User data Variables\nIMAGEREPO=te"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/model_names_tflite.txt",
    "chars": 229,
    "preview": "input_models/densenet/densenet.tflite\noutput_models/densenet_converted.tflite\ninput_models/squeezenet.tflite\noutput_mode"
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/test_perf_onnx.py",
    "chars": 1311,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n\n# Compute the overall inference "
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/test_perf_tflite.py",
    "chars": 1196,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n\n# Compute the overall inference "
  },
  {
    "path": "Examples/converter_examples/Convert_Profiler/tflite_output.txt",
    "chars": 1168,
    "preview": "Usage stats for Experiment ran on : 2020-10-08 10:26:18.302189\nNAME                     AVG CPU %      PEAK CPU  AVG MEM"
  },
  {
    "path": "Examples/converter_examples/README.md",
    "chars": 1784,
    "preview": "# Model Conversion Examples\n\nThis folder contains examples that demonstrate Converter coverage and efficacy.\n\n### - Eval"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/.gitignore",
    "chars": 64,
    "preview": "test_models/\nconverted_models/\n*.tgz\n*.gz\n*.pb\n*.h5\n*_internal*\n"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/README.md",
    "chars": 7617,
    "preview": "# Model Coverage\nIn this example, we have evaluated the capabilities and limitations of Converter conversion functions b"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_checkpoint_to_onnx.json",
    "chars": 1318,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224.ckpt.meta\",\n        \"convert_to\""
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_checkpoint_to_tflite.json",
    "chars": 1315,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/mobilenet_v1_0.25_224/mobilenet_v1_0.25_224.ckpt.meta\",\n        \"convert_to\""
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_keras_to_onnx.json",
    "chars": 1336,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/VGG16.h5\",\n        \"convert_to\":\"converted_models/VGG16_keras.onnx\",\n       "
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_keras_to_tflite.json",
    "chars": 1795,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/VGG16.h5\",\n        \"convert_to\":\"converted_models/VGG16_keras.tflite\",\n     "
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_protobuf_to_onnx.json",
    "chars": 2034,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/densenet/densenet.pb\",\n        \"convert_to\":\"converted_models/densenet_conve"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_protobuf_to_tflite.json",
    "chars": 2119,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/densenet/densenet.pb\",\n        \"convert_to\":\"converted_models/densenet_conve"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_pytorch_to_onnx.json",
    "chars": 4685,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/resnet18.pt\",\n        \"convert_to\":\"converted_models/resnet_torch.onnx\",\n   "
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_pytorch_to_tflite.json",
    "chars": 4712,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/resnet18.pt\",\n        \"convert_to\":\"converted_models/resnet_torch.tflite\",\n "
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_savedmodel_to_onnx.json",
    "chars": 1047,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/resnet_50_TF2\",\n        \"convert_to\":\"converted_models/resnet_50_TF2_convert"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/conversion_jsons/convert_savedmodel_to_tflite.json",
    "chars": 1058,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/resnet_50_TF2\",\n        \"convert_to\":\"converted_models/resnet_50_TF2_convert"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/convert_pb_to_tflite.json",
    "chars": 230,
    "preview": "[\n    {\n        \"convert_from\":\"test_models/densenet/densenet.pb\",\n        \"convert_to\":\"converted_models/densenet_conve"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/create_test_models.py",
    "chars": 4941,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n\nimport torchvision.models as mod"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/download_models.py",
    "chars": 1739,
    "preview": "#  Copyright (c) 2020 LG Electronics Inc.\n#  SPDX-License-Identifier: GPL-3.0-or-later\n\nimport os\nimport json\nimport arg"
  },
  {
    "path": "Examples/converter_examples/Tested_Models/download_urls.json",
    "chars": 1287,
    "preview": "{   \n    \"protobuf_checkpoint\":{\n      \"densenet\": \"https://storage.googleapis.com/download.tensorflow.org/models/tflite"
  },
  {
    "path": "Examples/converter_examples/dlconvert_requirements.txt",
    "chars": 63,
    "preview": "keras2onnx==1.7.0\ntf2onnx\npytest\npytest-cov\ntorch\npytorch2keras"
  },
  {
    "path": "Examples/decorator_example/origin.py",
    "chars": 388,
    "preview": "#----------------------------------\ndef func(hyperparameter):\n    return 'training results'\n\n\n\n\n\n\n\n\n#-------------------"
  },
  {
    "path": "Examples/decorator_example/use_decorator.py",
    "chars": 527,
    "preview": "#----------------------------------\n#!/usr/bin/env python\nimport sys\nfrom aup import aup_args\n\n@aup_args\ndef func(hyperp"
  },
  {
    "path": "Examples/demo/.gitignore",
    "chars": 46,
    "preview": "env_template.ini\nhpo.py\n.aup/\nexperiment.json\n"
  },
  {
    "path": "Examples/demo/demo.yml",
    "chars": 19481,
    "preview": "# The configurations that used for the recording, feel free to edit them\nconfig:\n\n  # Specify a command to be executed\n "
  },
  {
    "path": "Examples/demo/experiment_wrapper.json",
    "chars": 350,
    "preview": "{\n  \"name\": \"./demo/experiment_wrapper.json\",\n  \"script\": \"hpo_wrapper.py\",\n  \"resource\": \"cpu\",\n  \"n_parallel\": 2,\n  \"t"
  },
  {
    "path": "Examples/demo/hpo_wrapper.py",
    "chars": 272,
    "preview": "#!/usr/bin/env python\n# Demonstration code for 1D function, centered around a=1\nfrom time import sleep\nfrom aup import a"
  },
  {
    "path": "Examples/demo/origin.py",
    "chars": 150,
    "preview": "# Demonstration code for 1D function, centered around a=1\nfrom time import sleep\n\n\ndef demo_func(x, a=1, b=0):\n    sleep"
  },
  {
    "path": "Examples/early_stopping/README.md",
    "chars": 1219,
    "preview": "# Early stopping of jobs\n\n## Usage\n\nIn order to use early stopping to accelerate the HPO experiment,\nwe need to add the "
  },
  {
    "path": "Examples/early_stopping/mnist_keras/cpu.ini",
    "chars": 179,
    "preview": "[Auptimizer]\n# Auptimizer environment folder to be created, this file will be copied over\nAuptimizer_PATH=./.aup\n# Temp "
  },
  {
    "path": "Examples/early_stopping/mnist_keras/exp_BOHB_bandit.json",
    "chars": 972,
    "preview": "{\n  \"name\": \"./early_stopping/mnist_keras/exp_BOHB_bandit.json\",\n  \"proposer\": \"bohb\",\n  \"script\": \"mnist.py\",\n  \"resour"
  },
  {
    "path": "Examples/early_stopping/mnist_keras/exp_BOHB_curve_fitting.json",
    "chars": 1078,
    "preview": "{\n  \"name\": \"./early_stopping/mnist_keras/exp_BOHB_curve_fitting.json\",\n  \"proposer\": \"bohb\",\n  \"script\": \"mnist.py\",\n  "
  },
  {
    "path": "Examples/early_stopping/mnist_keras/exp_BOHB_median.json",
    "chars": 940,
    "preview": "{\n  \"name\": \"./early_stopping/mnist_keras/exp_BOHB_median.json\",\n  \"proposer\": \"bohb\",\n  \"script\": \"mnist.py\",\n  \"resour"
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]

// ... and 692 more files (download for full content)