Full Code of msr-fiddle/philly-traces for AI

Repository: msr-fiddle/philly-traces
Branch: master
Commit: 29a1b87fa2d9
Files: 5
Total size: 650.8 KB

Directory structure:
gitextract_hqlw59m8/

├── .gitattributes
├── LICENSE
├── README.md
└── analysis/
    ├── LICENSE-CODE
    └── Philly Trace Analysis.ipynb

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitattributes
================================================
trace-data.tar.gz filter=lfs diff=lfs merge=lfs -text


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
## Overview

This repository contains a representative subset of the first-party DNN training workloads on Microsoft's internal Philly clusters. The trace is a sanitized subset of the workload described in ["Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads"](https://arxiv.org/abs/1901.05758) in ATC’19.  This work was done as part of Microsoft Research's [Project Fiddle](https://aka.ms/msr-fiddle).

We include in this repository a [jupyter notebook](https://github.com/msr-fiddle/philly-traces/blob/master/analysis/Philly%20Trace%20Analysis.ipynb) that highlights the main characteristics of the traces and shows how to parse them (a huge thank you to [Keshav Santhanam](https://github.com/santhnm2) for putting this together).

We provide the trace as is.  If you do use this trace in your research, please make sure to cite our ATC’19 paper (mentioned above).

## Trace Details

### Main characteristics:
*	**Dataset size**: 6.6 GB
*	**Compressed dataset size**: 0.98 GB
*	**Number of files**: 5 files
*	**Duration**: Jobs submitted between 2017-08-07 - 2017-12-22
*	**Total number of jobs**: 117325

### Schema:
#### `cluster_job_log`

**Description**: Contains information about each job, including each individual
successful scheduling attempt.

**Format**: JSON

**Example entry**:

```
{
    "status": "Pass",
    "vc": "ee9e8c",
    "jobid": "application_1506638472019_14199",
    "attempts": [
        {
            "start_time": "2017-10-07 01:12:09",
            "end_time": "2017-10-07 01:13:23",
            "detail": [
                {
                    "ip": "m47",
                    "gpus": [
                        "gpu0",
                        "gpu1",
                        "gpu2",
                        "gpu3",
                        "gpu4",
                        "gpu5",
                        "gpu6",
                        "gpu7"
                    ]
                }
            ]
        },
        {
            "start_time": "2017-10-07 01:13:30",
            "end_time": "2017-10-09 06:53:12",
            "detail": [
                {
                    "ip": "m412",
                    "gpus": [
                        "gpu0",
                        "gpu1",
                        "gpu2",
                        "gpu3",
                        "gpu4",
                        "gpu5",
                        "gpu6",
                        "gpu7"
                    ]
                }
            ]
        }
    ],
    "submitted_time": "2017-10-07 01:11:39",
    "user": "ce2f4c"
}
```

**List of keys**:
* `status`: The job's status upon completion. One of `Pass`, `Killed`, or `Failed`.
* `vc`: The hash of the virtual cluster the job was run in.
* `jobid`: The id of the job.
* `attempts`: A list of `dict`s where each `dict` has the following keys:
    * `start_time`: The start time of the attempt.
    * `end_time`: The end time of the attempt.
    * `detail`: A list of `dict`s where each `dict` has the following keys:
        * `ip`: The id of the server the attempt was scheduled on.
        * `gpus`: A list of GPUs used by the attempt.
* `submitted_time`: The time the job was submitted to the scheduler.
* `user`: A hash of the user id.

**Notes**:
* A job may have no recorded scheduling attempts.
* A scheduling attempt may have no recorded `start_time` and/or `end_time` -
  this could be the result of a logging error.
* If a job has a `None` value for its last attempt's `end_time`, the job was
  still running at the time the snapshot was taken.

---

#### `cluster_gpu_util`

**Description**: Provides a per-minute record of each GPU's utilization as
reported by `nvidia-smi`.

**Format**: CSV

**Columns**:

| time | machineId | gpu0_util | gpu1_util | gpu2_util | gpu3_util | gpu4_util | gpu5_util | gpu6_util | gpu7_util |
|------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|

**Example entry**:

```
2017-10-03 00:08:00 PDT,m29,60.8,99.366666667,100.0,63.333333333,100.0,100.0,100.0,100.0,
```

**Notes**: 
* Some `gpu*_util` values may be `"NA"`, indicating the GPU was offline at the
  time of measurement.

---

#### `cluster_cpu_util`

**Description**: Provides a per-minute record of each server's CPU utilization.

**Format**: CSV

**Columns**:

| time | machine_id | cpu_util |
|------|------------|----------|

**Example entry**:

```
2017-11-27 00:04:00 PST,m29,31.845
```

**Notes**:
* Some `cpu_util` values may be `"NA"`, indicating the server was
offline at the time of measurement.

---

#### `cluster_mem_util`

**Description**: Provides a per-minute record of each server's memory
utilization.

**Format**: CSV

**Columns**:

| time | machine_id | mem_total | mem_free |
|------|------------|-----------|----------|

**Example entry**:

```
2017-10-03 00:06:00 PDT,m29,528272672.0,2030730.6667
```

**Notes**:
* Some `mem_total` and `mem_free` values may be `"NA"`, indicating the server
was offline at the time of measurement.

---

#### `cluster_machine_list`

**Description**: Lists the number of GPUs and per-GPU memory available on each
server in the cluster.

**Format**: CSV

**Columns**:

| machineId | number of GPUs | single GPU mem |
|-----------|----------------|----------------|

**Example entry**:
```
m31,8, 24GB
```


================================================
FILE: analysis/LICENSE-CODE
================================================
The MIT License (MIT)
Copyright (c) Microsoft Corporation

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and 
associated documentation files (the "Software"), to deal in the Software without restriction, 
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial 
portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT 
NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

================================================
FILE: analysis/Philly Trace Analysis.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import datetime\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOGDIR = '../trace-data'\n",
    "DATE_FORMAT_STR = '%Y-%m-%d %H:%M:%S'\n",
    "MINUTES_PER_DAY = (24 * 60)\n",
    "MICROSECONDS_PER_MINUTE = (60 * 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_date(date_str):\n",
    "    \"\"\"Parses a date string and returns a datetime object if possible.\n",
    "    \n",
    "       Args:\n",
    "           date_str: A string representing a date.\n",
    "        \n",
    "       Returns:\n",
    "           A datetime object if the input string could be successfully\n",
    "           parsed, None otherwise.\n",
    "    \"\"\"\n",
    "    if date_str is None or date_str == '' or date_str == 'None':\n",
    "        return None\n",
    "    return datetime.datetime.strptime(date_str, DATE_FORMAT_STR)\n",
    "\n",
    "def timedelta_to_minutes(timedelta):\n",
    "    \"\"\"Converts a datetime timedelta object to minutes.\n",
    "    \n",
    "       Args:\n",
    "           timedelta: The timedelta to convert.\n",
    "           \n",
    "       Returns:\n",
    "           The number of minutes captured in the timedelta.\n",
    "    \"\"\"\n",
    "    minutes = 0.0\n",
    "    minutes += timedelta.days * MINUTES_PER_DAY\n",
    "    minutes += timedelta.seconds / 60.0\n",
    "    minutes += timedelta.microseconds / MICROSECONDS_PER_MINUTE\n",
    "    return minutes\n",
    "\n",
    "def round_to_nearest_minute(t):\n",
    "    \"\"\"Rounds a datetime object down to the nearest minute.\n",
    "    \n",
    "       Args:\n",
    "           t: A datetime object.\n",
    "           \n",
    "        Returns:\n",
    "            A new rounded down datetime object.\n",
    "    \"\"\"\n",
    "    return t - datetime.timedelta(seconds=t.second, microseconds=t.microsecond)\n",
    "\n",
    "def add_minute(t):\n",
    "    \"\"\"Adds a single minute to a datetime object.\n",
    "    \n",
    "       Args:\n",
    "           t: A datetime object.\n",
    "           \n",
    "        Returns:\n",
    "            A new datetime object with an additional minute.\n",
    "    \"\"\"\n",
    "    return t + datetime.timedelta(seconds=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cdf(data):\n",
    "    \"\"\"Returns the CDF of the given data.\n",
    "    \n",
    "       Args:\n",
    "           data: A list of numerical values.\n",
    "           \n",
    "       Returns:\n",
    "           An pair of lists (x, y) for plotting the CDF.\n",
    "    \"\"\"\n",
    "    sorted_data = sorted(data)\n",
    "    p = 100. * np.arange(len(sorted_data)) / (len(sorted_data) - 1)\n",
    "    return sorted_data, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Job:\n",
    "    \"\"\"Encapsulates a job.\"\"\"\n",
    "    \n",
    "    def __init__(self, status, vc, jobid, attempts, submitted_time, user):\n",
    "        \"\"\"Records job parameters and computes key metrics.\n",
    "        \n",
    "           Stores the passed in arguments as well as the number of GPUs\n",
    "           requested by the job. In addition, computes the queueing delay\n",
    "           as defined as the delta between the submission time and the start\n",
    "           time of the first attempt. Finally, computes run time as defined\n",
    "           as the delta between the initial attempt's start time and the last\n",
    "           attempt's finish time.\n",
    "           \n",
    "           NOTE: Some jobs do not have any recorded attempts, and some attempts\n",
    "           have missing start and/or end times. A job's latest attempt having no\n",
    "           end time indicates that the job was still running when the log data\n",
    "           was collected.\n",
    "   \n",
    "           Args:\n",
    "               status: One of 'Pass', 'Killed', 'Failed'.\n",
    "               vc: The hash of the virtual cluster id the job was run in.\n",
    "               jobid: The hash of the job id.\n",
    "               attempts: A list of dicts, where each dict contains the following keys:\n",
    "                   'start_time': The start time of the attempt.\n",
    "                   'end_time': The end time of the attempt.\n",
    "                   'detail': A list of nested dicts where each dict contains \n",
    "                             the following keys:\n",
    "                        'ip': The server id.\n",
    "                        'gpus': A list of the GPU ids allotted for this attempt.\n",
    "                submitted_time: The time the job was submitted to the queue.\n",
    "                user: The user's id.            \n",
    "        \"\"\"\n",
    "        self._status = status\n",
    "        self._vc = vc\n",
    "        self._jobid = jobid\n",
    "        for attempt in attempts:\n",
    "            attempt['start_time'] = parse_date(attempt['start_time'])\n",
    "            attempt['end_time'] = parse_date(attempt['end_time'])\n",
    "        self._attempts = attempts\n",
    "        self._submitted_time = parse_date(submitted_time)\n",
    "        self._user = user\n",
    "        \n",
    "        if len(self._attempts) == 0:\n",
    "            self._num_gpus = None\n",
    "            self._run_time = None\n",
    "            self._queueing_delay = None\n",
    "        else:\n",
    "            self._num_gpus = sum([len(detail['gpus']) for detail in self._attempts[0]['detail']])\n",
    "            if self._attempts[0]['start_time'] is None:\n",
    "                self._run_time = None\n",
    "                self._queueing_delay = None\n",
    "            else:\n",
    "                if self._attempts[-1]['end_time'] is None:\n",
    "                    self._run_time = None\n",
    "                else:\n",
    "                    self._run_time = \\\n",
    "                        timedelta_to_minutes(self._attempts[-1]['end_time'] -\n",
    "                                             self._attempts[0]['start_time'])\n",
    "                self._queueing_delay = \\\n",
    "                    timedelta_to_minutes(self._attempts[0]['start_time'] -\n",
    "                                         self._submitted_time)\n",
    "    \n",
    "    @property\n",
    "    def status(self):\n",
    "        return self._status\n",
    "    \n",
    "    @property\n",
    "    def vc(self):\n",
    "        return self._vc\n",
    "    \n",
    "    @property\n",
    "    def jobid(self):\n",
    "        return self._jobid\n",
    "    \n",
    "    @property\n",
    "    def attempts(self):\n",
    "        return self._attempts\n",
    "    \n",
    "    @property\n",
    "    def submitted_time(self):\n",
    "        return self._submitted_time\n",
    "    \n",
    "    @property\n",
    "    def user(self):\n",
    "        return self._user\n",
    "    \n",
    "    @property\n",
    "    def num_gpus(self):\n",
    "        return self._num_gpus\n",
    "    \n",
    "    @property\n",
    "    def queueing_delay(self):\n",
    "        return self._queueing_delay\n",
    "    \n",
    "    @property\n",
    "    def run_time(self):\n",
    "        return self._run_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bucket_from_num_gpus(num_gpus):\n",
    "    \"\"\"Maps GPU count to a bucket for plotting purposes.\"\"\"\n",
    "    if num_gpus is None:\n",
    "        return None\n",
    "    if num_gpus == 1:\n",
    "        return 0\n",
    "    elif num_gpus >= 2 and num_gpus <= 4:\n",
    "        return 1\n",
    "    elif num_gpus >= 5 and num_gpus <= 8:\n",
    "        return 2\n",
    "    elif num_gpus > 8:\n",
    "        return 3\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "def get_plot_config_from_bucket(bucket):\n",
    "    \"\"\"Returns plotting configuration information.\"\"\"\n",
    "    if bucket == 0:\n",
    "        return ('1', 'green', '-')\n",
    "    elif bucket == 1:\n",
    "        return ('2-4', 'blue', '-.')\n",
    "    elif bucket == 2:\n",
    "        return ('5-8', 'red', '--')\n",
    "    elif bucket == 3:\n",
    "        return ('>8', 'purple', ':')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the cluster log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_job_log_path = os.path.join(LOGDIR, 'cluster_job_log')\n",
    "with open(cluster_job_log_path, 'r') as f:\n",
    "    cluster_job_log = json.load(f)\n",
    "jobs = [Job(**job) for job in cluster_job_log]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Job Runtimes (Figure 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_times = {}\n",
    "for job in jobs:\n",
    "    num_gpus = job.num_gpus\n",
    "    bucket = get_bucket_from_num_gpus(num_gpus)\n",
    "    if bucket is None:\n",
    "        continue\n",
    "    if bucket not in run_times:\n",
    "        run_times[bucket] = []\n",
    "    run_time = job.run_time\n",
    "    if run_time is not None:\n",
    "        run_times[bucket].append(run_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hybElTOkwpdg6DXNdF77lfOnzQx+ajmuatc1ggGnT5MrZV16Rwd355vZtcHKCfv2s4jTMgUM7DkfuLZkbeytSY0uUFiby0iIpLYmhy4cyadskhjcczlsPv2Uly6xPUa+L8xvPc2jeIfQ6PWGDwvCr4IfBAL//Dg89JCPB+/SBkycLENy3cydUqQKrVxfJNmvj0I5DoVAUDiEEm85vouXcliw9sZSpHaeyoO8Ch11qaw2iz0ez46MdpKfI1Zw3b8oOwoABEB0NixbBsmX5nAgHuHYNhgyB8uWhXTvLGW4BHHpyXMVxmAiwcQoCe0JpYeJ+LSJ1kcw5OIcFhxdwLvoc5XzKsXbIWrrX6G4jC61HYa+LtKQ03LzdaDquKaEPheIZ5MmVK/DAA5CUBPPnw7BhUKBUWJGR0Lq1rBG+aZNMj+5AOLTjUENVJlJTU1VyvwyUFiYytbgce5kZe2bww6Ef0Ol1tK3clnfbvsvA+gPxcPGwtZlWoTDXxdm1Z1k+dDmjd44mJCyE8k3Lc/y47GWkpMh6Gg0bFtCQlBTo3h0iImDrVovVBbckDn13qUJOJpKTk1U68QyUFiYSkxKZsmcK0/dMB2BYw2FMbDWReqXr2dgy61OQ6yI9NR0Xdxcqta5E7T61cfM1pSupWVNOgn/5ZSGcBsjgjrFjITQUWthvGvrccGjHoVAocibdmM7Tm55mVfgqRjUexQftP6Cif0Vbm2X3/Dv3X/Z/tZ+RO0biEeBBv5/6cfYsDO4BixdDUJD8XSiio2UDzzxjVputjUPPhKlALxPqG7YJpYXkrb/eYlX4KqZ1nsa8R+eVeKeR3+vCM8iTck3K4exmmkNNSZEVWsPDi2DAL79A9erw999FaMQ+sMmTV9O0VzRNO6Fp2nFN037WNM1D07Sqmqbt0zQtXNO0pZqm5ZnKUmXHNaEWCphQWsDxiONM3zOdcQ+MY2LribY2xy7I67qIPh+NMArq9qvLo/MfxdXTlWPH5HsNG8KlS3LZbaE4eFCWeq1XTzoPB8fqjkPTtFDgRaCZECIMcAYGAZ8CXwghagAxwJi82lIBgCbiCxRxVLxRWsCUHVPwdvNmYhPlNDLJ7brQJ+uZVX8WS/stzdo2bx40aiQ7CiBrKBWK3bvlZHiZMvDHH3L5rYNjq7EeF8BT0zQXwAu4CXQEfst4/0egr41sUygcmp1XdvLriV95qflLBHoE2tocuybpTpKsn+HpyqPzH6XP3D6ArNQ3dix06QKPPlqEEwgBL74oE1Zt3CjnN4oBVp8cF0Jc1zTtc+AKkAxsBA4CsUKIzDzp14DQ7I7XNO0p4CmAChUqEBkZCcjxSxcXl6y0Am5ubvj6+malUnZyciIoKIi4uLisCNKAgABSU1NJTk7OasPZ2Tnrm4m7uzve3t5ER0ff00ZsbGxWSvfAwECSk5NJSUkBwMfHB03TSEhIAMDDwwMvL6+sNpydnQkMDCQmJiarxxQUFIROp8tqw9fXFyEEiYmJWW14enoSExMDyNrJAQEBREdHZ60sc3V1JSEhgdTUVAD8/PwwGAwkJSUB4Onpibu7O7GxsVn7+/v739NGcHAwCQkJpKWlAeDv7096enpWG15eXri6umZpnNlGVFQUQgg0TSM4OPgejf39/dHr9eh0Oqt9Tpk62uPnFBQURFJSksU+J51ex9hVY6nsX5lxdceh0+mIi4uzy8/J2veTTqcjMjIy63O6cuwKS1ovoeXbLen4TkdCu4WiM+q4djaZAQOCCA0VfPttDElJAmfnwn9OTo89hggMxKNGDdKTk+3ifioyQgir/gCBwBagNOAKrASGAeF37VMROJ5XW02bNhUKicFgsLUJdkNJ1mLChgmCyYhN5zcJIUq2FveTqYXRaBQGvfx766StIuJExD37PfOMrKSxe3cRTmY0CnHyZBEasCzAAVGE57gthqo6AxeFEHeEEHpgOdAaCMgYugKoAFzPqyFVyMlE5jcwRcnVYkP4Br7Y+wVPN32aztU6AyVXi+zI1OLA7AP8M+sfANpPbk/peqYCVcuWwbffwmuvQcuWRTjZyy/LGfWDB4tist1iC8dxBWihaZqXJpdFdQJOAluBARn7jABW2cA2hcIhuRJ3hWHLhxEWEsYX3b6wtTl2zZ1TdwjfEP6fzBP798s6Si1awMcfF7Lx9HT44AP46isYMULmJSmG2GKOY5+mab8B/wLpwCFgDrAW+EXTtI8yts21tm2OjIppMVHStDgffZ5HljxCmiGN3wb+hqerZ9Z7JU2L3MjUovsX3UmKSLpnOf/Fi/DII7Icxm+/yeDuAnP9OoweLSfBhw6VXZdiqr9NIseFEJOASfdtvgAUaJW0ykdkIqiYrNYwByVFi0hdJB/t+IjZB2bj4eLBmiFrqBVc6559SooW+SH9RjqxCbEEVA7IqtKXSVqaTBn12WcyE0ih2LlTBvfNng1PPw3FOM7Mod2hiuMwkbm6Q1EytNh/fT8NZzfk6/1fM7zhcE49f4q2lf9bBKgkaJFfdkzbwaJuixBG0xBVbKwsvle7NqxZA3XrFqLh06fl74EDZTGOZ54p1k4DHNxx3D9GWZJRCwVMFGctjMLIjD0zaDu/LW7ObhwYd4Af+vxAed/sg8qKsxYFpdbjtejxdQ80J/lQFwIeewzat5fOo1C8/76cBD9zRjqLKlXMZa5do8Z6FAoH4oV1LzDrwCx61+rNvEfnUcorv1WDSi6nVpwipH4I5VuWp9RdVZY0DZ57TuahKvBUhNEIH34IU6bICMFikEakIDh0j0PNcZgIDFQRwpkUVy3+vvw3sw7M4pUWr7Bq0Kp8OY3iqkV+MaYbWfPUGg7NP5SlxeXLpjQijz0m57ELxPr10KSJXD315JNyDW8JexY59H+r6nGYSE5OxsfHx9Zm2AXFUQudXserG18lxDuEqR2n5jvBZ3HUoiA4uTjxxMonCKgcQFxcMitW+PDmm5CaKqu1litXiEZv3oTkZFi4UHqdYj6fkR0O3eNQjsNEZnoFRfHTIj41nq4Lu3LgxgG+6fnNPctt86K4aVEYKrWuxJmbfjRu7MnYsVCpEuzZUwinkZkqd/RoOHVK1ostgU4DHNxxKBTFnYsxF2n0bSP2XNvDkseWMKDegLwPUgCw9rm1LOy6kGNHBX37yiW3a9bAgQNQv34BGjIa4aOP5CT4pk1yWzGNz8gvDj1UpeoumCjJwxH3U1y0iE2JpeuirsSlxLFj5A5aV2pd4DaKixaFoWzjshw56caYZhre3rB+fTotWhTwmaHXw/DhsHQpdOsmx7cUqsdRXFBFrUwUBy30Bj2P/foYl2Mvs3rw6kI5DSgeWhSUxFuJ6PUwY3tT3tnehR495MhSgbN/pKbKOYylS2WPY/16cMuzvlyJwKEdhwoANJGZdlrh+FqkG9OZ8OcEtlzcwve9v6dNpTaFbsvRtSgol3ZdZ3q56VzeHM7ly/Dmm/D771C2bCG02L0bVqyAadPgnXdK7HxGdjj0UJVCUdzYfXU3T695muMRx3nxoRcZ0XiErU1yGLZuhecGe/HRK50pXa80f/9dyGe9EPLADh1kYF+1ama31dFx6B6HSuBmwqPQdS2LH46oRWp6Ku9teY+289uSkJrA8oHLmdljZpHbdUQtCkuNGlC9WSAtX2uNfyX//ziNfGsxeDD8+qv8WzmNbHHoHodyHCa8vLxsbYLd4GhaxCTH0HWRXG47vOFwvu7xNf4e/mZp29G0KAyJiYAwsvO5pcyZ3I7yOdT0zpcW8+bJOY1GjcxrZDHDoZ+8Kg+PCVWwx4SjaTH2j7EcuXWE3wf+zk/9fjKb0wDH06KgrFsns9r26Wkg9kocNw7cyHHfXLUQAj7/XBbk6NhRVnJS5IhD9zgUCkfnwI0DLD+1nKkdp9K/bn9bm+MwXLgAL70EW9ckUb28jtenl6Z1w6H/SZeeL4SQqUMWLYLHH5cR4YUqyFFycOgeR0lcapgTKqbFhCNp8d2B7/B08eT5B5+3SPuOpEV+OHMGxo6FsDA5Gf5Gh/085b+M7t3J02nkqIWmQb16MHWqTGLl7m4By4sXDt3jKG43RVEo6cns7sZRtEhITeDn4z8zOGywWYen7sZRtMgLnQ7ee09WZHVzg8GP6njv/7wI8W3OX2/mb5ntf7Q4fRquXoUuXeCttyxgdfHFoXscKo7DRExMjK1NsBscRYsVp1eQpE9i9AOjLXYOR9EiN4SQz/YZM2DkSPj7p4tUX/0l5Uun4RXsRZ/v++SrnSwtjEb44gtZXHzMGJlXXVEgHLrHoQo5mVBO1ISjaLH81HIq+lWkVcVWFjuHo2iRHSkpchTJ3V12CNzcoGtXuHPShyrtqyAMBbv/DQYDxMXB66/DnDnQowf8739QgpYsmwuH7nEoFI5KfGo8G8I30K9OPzVXlw1JSXLaYcYM+bpXL2jVMJGEGwmUrleaIWuH4O5XsLkILSJCJiqcMwfGj4e1a1WcRiFxaMehCjmZCAoKsrUJdoMjaLH5wmZSDak8Vu8xi57HEbS4m7g4+dvbG3r3lqNJAEaDkdkNZrN9yvZCtx1Yp44covr7b/j6a5VCpAg4tONQ9ThM6HQ6W5tgNziCFlsubsHL1YsWFVpY9DyOoAXIIL5XX5U1MjZvlttmzoT27eVwlJOzE50+6cQDowuaqRA4fx42bpRa9O8PbQqf+0shceiv7MpxmEhJSSnRKbTvxt61EEKwPnw97Sq3w83ZstlW7V2L1FT47ju5EjYiQmYwr1XL9P72D7aTeCuRnv/rSZMxTQp+guhoOZcRG0vKP//YtRaOhEP3OBQKR+R4xHHOx5ynd63etjbFZiQmyuwetWrJQL769WVVvp9+gooVTfsZ0gwYUg04uRTiUXX2rBzrunRJ5p7y9jab/SUdh+5xqDgOE76+hYiYLabYuxY/H/8ZZ83Z4vMbYH9aXLggM3ssWCDLdj/4IMydC506maYcru65ytXdV2n1ais6fdypcKsn//kHuneXlfr++gvatsU3NdWs/0tJRvU4iglqabIJe9ZCCMGyk8voWLUjId4hVjmfPbFmjXQUgwfLyO99+6Bz53vnqfd8vocDsw+QniJz0RVq1dnOneDrC3v3Qtu2gP1p4cg4tONw5DXq5iYxMdHWJtgN9qzFXxf+Ijw6nMFhg61yPltrkZoKzzwjM3kADBoEFy9K59G+ffYLmwb+PpDnTz2Pi0cBB0QMBjhyRP49frwsLl69etbbttaiOOHQjkOhcCSMwsj7296ngl8FBjewjuOwFZnrVtzc4OhROUQFEBIC2WU9T7iZwJJHlnD76G0AnF0LMAyt18OyZTIVeps2cpbd1RVKlSrif6HICYd2HKoeh4mSVLAnL+xVi4VHFrL32l4+bP8hHi7WsdHaWhgMMp9Uq1awfLnsUWzcCG+/nftxbt5u3D52m5gLBUiRIoQ8yUMPwcCBMqHVvHlQunS2u9vrdeGIOPSTVzkOE56enrY2wW6wRy12X93N+PXjaVGhhVXLwVpTi/375eqol16S0wtNm8rtOa2ATUtMY+WIlSRHJ+Pu584LZ1+gTt86+T/htWsyDXpkpCy+dO6cfJ3DnIg9XheOikM/eVUhJxPFIZmdubA3LcKjw+m5uCdlfcry+8DfcdKsd9tZS4udO+Hhh+VKqV9+kb2MypVzP0YXpePIT0eIPB0JkL85DaMRtmyRvY2KFWHDBrncduBAyGOVpb1dF46MQzsOhcLeEUIw7o9xaJrGpuGbKO+bfVlTR2bXLujWTT7HDx6EJ57IOZtHanwq/8z+ByEEAZUDGL1rNBVbVcx+5/tJTIQ+feTa3e0ZqUe6dMnTYSjMj0M7DpUczoTK22XCnrRYe24t2y5t4+OOH1MloIrVz28NLZ56Sk56//133vPRkWciWffcOk6vOA2Qf6exdSvUrg3r18t8U4VIG2JP14Wj49BKqgBAEwEBAbY2wW6wJy3+b+f/USWgCmObjLXJ+S2txalTcPIkTJki80zlRZmGZRj19ygqts6nwxBCzlv8/rv0Ttu3FzrXlD1dF46OTXocmqYFaJr2m6ZppzVNO6VpWktN04I0Tdukadq5jN95li5TcxwmoqOjbW2C3WAvWhyPOM7uq7t54aEXcHW2TVB2GBQAACAASURBVA1rS2tx4oT83alTzvsIIVg3fh17ZuwBoFKbSnmPFggB6elyzGvaNPjoIwgPL1KCQnu5LooDthqqmglsEELUARoBp4A3gc1CiJrA5ozXinyiEj6asBct/rf/f7g5uzG84XCb2WBpLdq2lRPhDRrkspMAN183bhy4kb/4jPPn5aTJgAHSeVSrBu+8I5dqFQF7uS6KA1Z3HJqm+QNtgbkAQog0IUQs8CjwY8ZuPwJ9rW2bQmEuLsVeYt6heYxqPIrS3tnHFTgyu3fDZ5/JkIkuXf675NagN7D5nc2cWnEKzUmjw4cd6L+oP5pTLj2NxER44w2Z+XDHDpnISqUJsUts0eOoCtwB5muadkjTtB80TfMGygghbmbscwsok1dDarLLhKMV7LEk9qDFjD2ydN27bd+1qR2W0mLLFll19erV7N93dnXm9IrTXN11Net1rk4jIUF6oGnT5LKsCxdkL8PVfEN89nBdFBds8eR1AZoALwgh9mmaNpP7hqWEEELTtGy/amia9hTwFECFChWIjJRrwL29vXFxcSEuo4SYm5sbvr6+REVFATJYMCgoiLi4OPR6PSAny1JTU0lOTs5qw9nZmfj4eADc3d3x9vbOGhvNbCM2NjZrfiUwMJDk5GRSMgre+/j4oGkaCQkJgIxW9fLyymrD2dmZwMBAYmJisnJtBQUFodPpstrw9fVFCJGVW8fDwwNPT8+sdeguLi4EBAQQHR2d1f12dXXFycmJ1IwMoH5+fhgMBpKSkgAZ/OTu7k5sbGzW/v7+/ve0ERwcTEJCAmlpaQD4+/uTnp6e1YaXlxeurq5ZGme2ERUVhRACTdMIDg6+R2N/f3/0en1WQSFrfE4pKSlUqFDBZp/TgYsHmHNwDo/XfpwKfhXu0TgoKIikpCSrfU46nQ5/f/8ifU5CwM2b7ixY4EOtWokMGJDKsGFOvPJKEOnpcURGmj6nE7+foNSDpXD3c+fJXU/i7u2edY/mej8lJuKvaTj/+CO6/v2lxpGRZv2cIiIi8PLyytf9ZO3Pydr3U5ERQlj1BygLXLrr9cPAWuAMUC5jWzngTF5tNWrUSCgkd+7csbUJdoOttej7S1/h+7GvuJlw06Z2CFE4LZKShFiyRIinnxaiSRMhvL2FACGcnYV4++2cj0tPSxefBn0qVo9bnb8T7dwpxIMPCnHrVkYD6QW2tSDY+rqwJ4ADogjPcav3OIQQtzRNu6ppWm0hxBmgE3Ay42cE8EnG71XWtk2hKCr7r+9n5emVTGo3ibI+ZW1tToH44QdYu1ZOdut04O8v00CNHSuTzPbuDVWq5Hy8s6szw/4chru/e94nW7kSnnwSPDzg+nUoU0YF8jkQtpokeAFYrGmaG3ABGIWcb/lV07QxwGVgYF6NqDgOE35+frY2wW6wlRaxKbGMWDmCcj7leKXFKzax4X5y0+LgQZni6f/+Tz6zt22TmWxHjJChE+3ayTpIeWFMN3Lyt5OEDQqjfLM8IuOFkB7q6aehSRNZma9atYL9U4VE3SPmwyaOQwhxGGiWzVu5rAbPth3zGFQMULVJTNhCC4PRwNDlQwmPDmfjsI34e/hb3YbsuF+LjGkh/PxkWMTXX8Po0VCnDixcmHOqkNy4uOUivw/+ndL1S1OmQR5rWpYskaHmnTvD6tVgxcSD6h4xHw6dckStyzaROeGmsL4WRmHkqT+eYt25dXzV/Ss6VO1g1fPnRqYWp07J2kahoXI1FMBjj0FUlHQaUDinAVCtczW6Tu9KSP1cKhpmPrQHDIDZs2VyQitnq1X3iPlwaMehUNgDU3dMZd7hebzX9j2effBZW5tzDwcPutCjB9SrB99/L51Fz57yPRcX8PIq+jk0J42WE1pmv9zWYJCOolEjiIkBd3dZElANMzs0Du04VD0OE6rWgAlranH41mEmb5/MoLBBfND+A6udNyeEkMkGR46EsDDo3j2A/ftlxo5r12DBAmjc2Hzniz4fzfYp24m7GvdfQxYtkh7ruedknqmMpa22Qt0j5sOhn7wqO64Jd/d8rGQpIVhLCyEEL214iSDPIGb1nGUX1+Nbb8k0IEuXypGgL780cOmSjKXLoTBekYg+F82297eRcD3BtNFggOefh+HDZZfm999h82Yoa9tVZuoeMR8O7TjUZJeJzEAkhfW02HxxMzsu72Byu8kEeuaZk9MiHD0qpw3275evBw+G+fNlUbx//oGhQ2OKmuIpV6p3q8576e8R+lDovW/cuAETJ8qlW/37F34CxYyoe8R8qJwdCkUhmbFnBiHeIYxpMsZmNpQtK4egLl+WMReNGskfa6FpGppzhlO4fVsmJQwNhWXLzJouRGFfOLTjsIehAXvBVd2kWVhDiwsxF1gfvp7J7Sbj4eJh8fNlEh4OmzbJekaZJSr27Mn5C72ltbh99DYnfj1B877l8B74iPRku3bZpdNQ94j5cGjHoQIATfj720fcgD1gDS0WHF6AhsboB0Zb/FwREfDdd7KW98mTcltYmCy1XbNm7qNAltYi6mwUO/9vJ4027MI7Kgp+/tkuhqWyQ90j5sOh5zhUIScTqkiNCUtroTfomX94Pl2qd6Gifz4r2RUQIeT8xZtvQo0a8P77MivHtGlw+rR8r2bNvNuxlBZpSWmkJaVRb0A93p1djqCDm+DLL6F5c4uczxyoe8R85Nrj0DRtgRBiZMbfI4QQP+a2v8J2qGBIE5bWYuHRhVyLv8bsR2ZbpP3ERDlPceGC/PLep49MC1K3bsHbsoQWukgdC9ovoFyTcvSb3wenL2ZAgzC5BtiOUfeI+chrqOruabaXMBVaUihKJDcSbvD25rdpVr4Zj9R8xGztvvOOXAW1caMsitSrlxyO6tNH9jTsCXd/d9q+1xbPQE8Zet68uYwstNMhKoX5yctx2HUyKFXIyURwcLCtTbAbLKVFfGo8fX7uQ0JaAvMfnV+kxRl37sjJ7ccek/EVISH3Dj3NnGkGgzGvFqdXnUYYBHX71yXsiTDTGwsWmO0clkTdI+YjrydvBU3TvgK0u/7OQgjxosUsywcqjsNEQkKCyv6ZgSW0SDOk0X9pfw7fOsyKJ1YQFhKW90HZcPWqdArffy8TDvr5wZAh8NJLZjU3i6JqoYvS4RUs85Ic/O4gxnQjdftnjJldvy7/kUmTHKK3oe4R85GX45h4198HLGlIYVDZcU1kVhlTmF8LIQSvbHiFzRc3s+DRBfSu3btAxxuNMinsZ5/JSW0nJxm0N3EiNG1qVlP/Q1G0OLv2LEv7LWXALwOo278uXT/vSmD1jEDHS5dkiHpMjIw6rF3bPAZbEHWPmI9cHYeaDFcoYOa+mcw6MIsJLSYwovGIAh174gSMGiXnL6pVgxdflL0LK5WgKDAXt14kJTaFuv3qUq1zNVq/3poqHaoAULpeRs6Sc+egY0eIi4O//nIIp6EwL3kux9U0bYSmaf9qmpaU8XNA07QnrWFcXqg4DhNqjboJc2qx+cJmXtv4Gn3r9OWzrp8V6Nhff5UJBS9cgMWLZfDezJnWdRr50eLEshPok2U96v1f72fL21sAcHF3oeNHHeUkeCa3bkHXrpCSAjt22PXy2/tR94j5yNVxaJo2AngZeBUoD4QCrwMvaZo23PLm5Y4aqjKhYlpMmEuLmOQYRq8eTc3gmvzU9yectNy/Z8XHw+TJsHOnfF21qiySdPy4nMewxTRAXlqc33Se3wb+xukVpwHo8XUPxuzNJYXK2bOQnAzr1pk3za4VUPeI+cirx/Es0E8IsVUIESeEiBVCbAEeA563vHm5o9Zlm1BFakyYQwuD0cDwFcO5Hn+d+Y/Ox9c950yBmd9fnJxkkaR9++TrBx+UEd+2TAqblxbVu1Rn0OpB1O4jh5v8Qv3w8L8vhYoQ8M038nfbtrIL9eCDljLZYqh7xHzk5Tj8hBCX7t+YsU0tT1AUW17b+Bprz63lqx5f0aJCi2z30enkStSOHUGvl/EX58/Dq69a19bCcGb1GY4tOQZA7d61cfNxy37H/fuhWzdZPvBAxvoYc1R/Ujg0eTmO5EK+ZxVUIScTXupmzqKoWsz6ZxZf7vuSFx96kecefO4/7yclycJIFSvKie/ERMjMZmFvw+g5aXHq91Ps+2ofxvQceu3JydCvn5zD2L1bTs40a2ZBSy2PukfMR17LcetqmnY0m+0aYPN1ISo7rgmV+dNEUbRYc3YN49eNp1etXkzvNv2e94SAWbOk07h1S0Z3jxwpn6/2+h0mU4vk6GQ2vb6JSm0q0XhkY9q+3xY3HzecXHIw/IUXYOVK+PhjWcHP3jxiIVD3iPnIT8qRMsDV+7ZXBG5ZxKICoAIATcTFxVGqVClbm2EXFFaLvdf2Mvj3wTQp14SlA5bi4mS6PWJjYcwYWL4c2rWT5SbatDGn1eZHGAWHfj7EQ08+hEeAB/HX4km4ISv1BVUPyuYAIVdLeXrKoal27WQVv2KCukfMR17fk74A4oQQl+/+AeIy3lMoigWn7pyi5+KehHiHsGrQKrxcTcMa0dHQsyesWiWD+LZutX+nAXDy95NsGL0BvU6P5qQxbMMwHn774ex3PnQImjSBp56Srxs3LlZOQ2Fe8nIcZYQQx+7fmLGtikUsKgBqqMqE6oabKKgWiWmJ9F3aFxcnFzYN30So371lUM+dg2PHZD2M116z3+waQggu/32Zq3vkAEGlNpVo8nwTXDxzGVjQ6WDCBDl/ceUKdOpkWiZWzFD3iPnIa6gqIJf3PHN5zyqoAEATKrjJREG0EELw7NpnCY8OZ9PwTVQLNE3dGY1y7qJ5cxm8Z29Zau/HkGZg02ubSIlN4bmTz+FbzpdeM3vlfMDhw7IrdfMmPPMMTJkCxXgoR90j5iOvHscBTdPG3b9R07SxwEHLmJR/VECPiaioKFubYDcURIv5h+ez6OgiJrWbRMeqHbO2JydD+/Ywd658bc9OIyU2hZgLMbi4uzB0/VDG7h+Lk7O8tXPVomZNmbt9/XqYPbtYOw1Q94g5yavH8TKwQtO0oZgcRTPADehnScMUBUNF0ZvIrxaxKbG8vul1Wldszbtt373nvdRUCAiQP/aMEIJ5refhHeLNiK0j8Azy/M/796DXw3vvwdtvy9S8Gzda0Vrbou4R85FXksPbQCtN0zoAmXmk12ZEjyvsCDXfYyK/Wny++3OikqP4usfXWelETpyAoCAoVw5Wr7aklUXDmG4kNSEVz0BPOk/rjF9o9vG492hx5owMPNmzR85pDBhgJWvtA3WPmI98VUISQmwFtlrYlgKjCjmZUEVqTORHix2Xd/DJzk8Y0mAID5R7AJA5pVq3lj/r1lnayqLxx7g/cPZwptfsXtR6pFaO+wUHB0N6OnzwgSxY7uYGP/1U4pwGqHvEnNhp2FL+UHEcJuLi4mxtgt2QlxaXYi/x+LLHqRpYlf/1+B8ACQkykM/DQ+abskeMBiNGg4z0rtCqAs6ueS8OiYuLg2+/lVGLffvCyZMldpmtukfMh0N/ZVdjlib0er2tTbAbctMiUhdJn5/7kJKewrYR2wj0lIWJxo+XeaY2b7bPWhmGNAOzG8ymwdAGtHu/HU3GNEFzymXo5fp1iIxEHxoqC5f7+sKIgtUSKW6oe8R8OHSPQ6EoCFfjrtJ+QXvORp1l2ePLqFtalkDduVOO3rz2GnToYGMj7yIlLoWTv58EwNnNmRo9alCmkVzelaPTMBph0SKoU0emDQGoVKnEOw2FeXFox6HiOEyoNeomstPiStwV2sxvw9X4q6wbuo6u1bsCsobG6NEy9fnbb1vb0tzZ/dlufhv4G/HX4gHo/mV36jxaJ/ud09Ol96tdWw5F1akDc+eq6+IulBbmw6EdhxqqMqG64Sbu1+Ja/DXaLWhHXEocW57ckhWvkZ4uv4ifOydrgtt66W1aYhpb39/KtX3XAGjxSgvG/TMOvwr5qGCwerX8Z7y9Yf582L4datZU18VdKC3Mh0PPcahCTiZ0Op1KG53B3Vpcj79OuwXtiNJFsfnJzTQt3zRrv9dekwlgP/nEPoaonFydOPvHWeIux1G+WXm8gr3wCs7lMzUY5MRMrVpyZn/TJlkc5K5Uveq6MKG0MB8263FomuasadohTdPWZLyuqmnaPk3TwjVNW6ppWg6VZRSK/HEn6Q7dF3cnIimCjcM38mDovVXrHnoIXn4Z3njDRgYCUWej+G3QbyTcSMDF3YWx+8fS98e+WZHfOXLuHLRsKSO/d++WCbQ6d7bf/O6KYoUtr7KXgFN3vf4U+EIIUQOIAXIpfCxRhZxMeHt729oEu8Hb25u4lDh6LunJmcgzrHhiRVYVPyHgn3/kfkOGwIwZtrFRr5PDJi4eLpxdc5bocFkJKs8ltnq9HIp66CFZ/3vuXOlAckBdFyaUFubDJk9eTdMqAI8AP2S81oCOwG8Zu/wI9M1HO5Yy0eFQwZAmDBjot7Qf/978l2WPL6Nztc5Z733/PbRqJcMZwDaZbo//cpzFPRZjSDPgX8mf8WfGU7lt5bwPNBplxsXRo+Xk97//yonwXP4JdV2YUFqYD1sp+SXwOuCb8ToYiBVCZGYtvAaEZnegpmlPAU8BhIaGEhkZCchvEy4uLllBPm5ubvj6+mYlNnNyciIoKIi4uLisSbKAgABSU1NJTk7OasPZ2Zn4eLmKxd3dHW9vb6Iz6oJmthEbG5uVYDEwMJDk5GRSUlIA8PHxQdM0EhJkwRwPDw+8vLyy2nB2diYwMJCYmJisAMagoCB0Ol1WG76+vgghSExMzGrD09OTmJgYQN4AAQEBREdHZ83zCCHw8PAgNTUVAD8/PwwGA0lJSQB4enri7u5ObGwsIFNM+/v739NGcHAwCQkJpKWlAXIVSnp6elYbXl5euLq6Zmmc2UZUVBRCCDRNIzg4+B6N/f390ev16HQ6q3xOeoOeUetGsfXKVr7p8g2tS7UmMjIy63Pq1CmVSZPcqVrVhdRU639OQUFBuJZ2JSkmiTsRdwgKCcI1yDXrOs72c7pzh+jAQIxC4DF0KN4ffEDCww+Tlp4OkZG5fk6ZxYvs7XOyxf10/fp1/P398/05JSUlFdv7qcgIIaz6A/QCZmX83R5YA5QCwu/apyJwPK+2GjVqJBSSO3fu2NoEm2M0GsUTy54QTEZ8tuuzrO1nzwoxaJAQt2/bzrYru66Iv97+K+t1akJq/g6cNUsINzchvv22UOdV14UJpYUJ4IAownPcFkNVrYE+mqZdAn5BDlHNBAI0TcvsAVUArufVkBqqMuHmptYSTN8znaUnlvJuq3d5rdVrgMw51aSJXHB06lQeDViQyzsuc3jeYZKj5bdxN598fF7vvCPrfbdqBf37F+q86rowobQwI0XxOkX9IaPHkfH3MmBQxt/fAs/ldXzTpk3N54IdHKPRaGsTbMquK7uEy4cuot8v/YTBYBAGgxAzZgjh5CRE48ZCXLliXXvSU9PFn6/+KY4uPir0yXqRpksTaUlp+Ts4IUGIiROFACF69RJCry+0HSX9urgbpYUJHLDHkRNvABM0TQtHznnMzesAVcjJREkuUpOUlsTwFcMp71ueH/r8wLFjsXTqJCui9ukjU4pUrGhdm5zdnLl99DbX9l7D2c0ZV09XXL3yWbo0Ohq++UYG9P32GxRhUrckXxf3o7QwHzZdZiCE2AZsy/j7AvCQLe1ROB5CCMavH8+FmAv8NXwza5YF8dRTgtRUmDMHxo617sqphBsJ3Dpyi5o9ajJ0/dC84zEySU+HqVPh3Xdlbqnz52UeFIXCDlHr04oJJTGmRQjBhD8nsODwAt5s9TZTx3Zk61Zo1szAjz+6UK+e9W3a9Pombh68SfUu1XFyyedn8uef8OSTEBEBbdpAp05mcxol8brICaWF+XBoJdW6bBNBQUG2NsGqCCGYvG0yX+6czfhmLzK18xR69JDxcPv2WddpbHpjE7cO3wKg86ed6T6ze/6chl4PEydCjx7g4wNLl0qnYUZK2nWRG0oL8+HQT15VyMlEXFxcicn+eT3+OqNWjWLT7lv4rrpIWMWyOGkaEyfK962pRUpsCofnH0YYBGUbl8Uv1C/HMq7/4cMP4fPPYdw4GcLu42N2+0rSdZEXSgvz4dCOQ6jsuFmUhMyfQggWHl3Ii6vfIs0phhnDphFTuizVqt07iWENLc78cYbAqoGEhIXwytVXMKbnM+FmYiKkpECpUrJ6VNmy8PzzFrOzJFwX+UVpYT4c2nEoSgbJ+mT+OPsHX+z5kr2/tsZ51yn+3BFJp6bVZFSQlYk8E8nSfktpOaElXaZ1wcXdBdzzceDWrTByJFSpIvNNVatmUaehUFgKh3YcqpCTCbOkEbAzYlNi+WTnJ3yzaQ2Jp1rieug7uNaA2vUENUvnPBxkaS1K1S7Fm3FvYkjL51BpXBy8/z58/TVUry7/tlJ92uJ4XRQWpYX5cGjHoYaqTKSmphabxQJX4q4wfcc3fDu9DGn/vAq6TwCoWkvw2hwYNUrLNbTBUlpc3nGZnZ/sZMiaIbh5u0F+kq3u3Qs9e0JMjAwqWbJEFluyEsXpuigqSgvz4dAqqkJOJpKTk4tF2ujFRxcz8uM/SN8wDeIqUbu+jl7dZSxcWJiWr5gMS2lxdfdV0hLTSIlNwTPIM+cdhYAtW+QKqQcfhFdfhe7doWnTnI+xEMXlujAHSgvz4dCOQ1F8OHEmmQ93TOLXG59Rzed9StUPYfK70KOH7Su2RYdHE1AlgDZvtqHNm21y3/nff2Xx8m3b4PZt8PeXOacUimKEQzsOFdBjwpG/SZ29FE/jpoL02jWZ+MlEJrd/Ay9Xj0K3Z04tUuNTmdN0DpXbVubxZY/j4pHDLWMwwKxZ8NJLMkXIu++Cr2/2+1oRR74uzI3Swnw4tONQ2XFNOMpCAYMB/v5bLjC6fh2++J+Op7b1wdA+jCkjO/Nul3FFPoc5tXD3c6f/4v6EPhSas9MA6NYNNm+G9u3h55/tJl2Io1wX1kBpYT4c+iu7CgA0kVksxx7R62H7dujaFcqUgQ4dZOzblh06HpgyjO2Xt/PNpPq8OyDPoo/5whxaJNxMYO/MvSTcSKBWr1p4h9z3bTU6GqZNg4yiRTz+OPzyi3QeduI0wL6vC2ujtDAfDt3jUNgvCQmwfr1cRPTHH7LqqZsb1K0Lb310i93uk1h59QdCvENY12cdPWr2sLXJ93D+z/NsfnMzVTtWxbf8fUNOW7fC0KFw8yY0awYdO8LTT9vGUIXCBji041BzHCbc3fMTgWZZhJB5+sqUkfPCTzwhs2i0bw+DBsHjAw3MPPQRb+/8GICXm7/M++3ex9/DvGkgiqJFUkQS3iHeNB7ZmKodq+Jf6S7bhIBPP5WT3TVrwooVsga4HWMP14U50ev1XLt2LassbEEwGo3cuXPHAlbZLx4eHlSoUAFX13ym9M8nynEUE+xh4m/8eLmY6NgxqFFDhjA0aQLJxngmb5tM4JdfABAWEsa6Ieuo6G+ZIhmF1SL+ejwzq86kz9w+NBre6F6nAfDWW9JxPPGEzNnul8+cVDbEHq4Lc3Lt2jV8fX2pUqVKgec4RUYd75KCEIKoqCiuXbtG1apVzdq2QzsOVcjJRHR0NKVKlbL6eVNT4cIFOQT1+OOyrEQmjZqmMHL1GJYcW5K1bVK7SUxqN8miN3BBtUi6k4R3aW98y/tS//H61Ohe494dhJBFPUaMgHLl4IUXwEG+tNjqurAUKSkphXIaIJ8X5v7mbc9omkZwcLBFelkO7TgUtsNohNmz4fXXoV49WVKifXv5A7AhfAM9Fst5C2fNmTm95zCq8Si7+8Z3cM5B1j2/jqcPPU1IWAj9F99V2zs2Fr74Ao4ckcNSdevKH4VNsbdryJ6xlFbKcRQTrDlsd/QotGhhWlDUty/cXerguwPf8czaZwB4r+17fND+A6ve7AXRov7A+kSdjcKvwn3DTqdOwcCBcPy4TBWi01k1VYi5UMO5JpTDMR8OfVWpvDMmrFWkZvZsaNRIOo0hQ+RS28zA6NuJt+m5uCfPrH0GVydXzr94ng87fGj1GzY/WoRvCCfxdiIeAR50/bwrHgF3BRyePClXSt2+DRs2wKpVDuk0QBUvuhtzPS9Gjx5NSEgIYWFhue63aNEiGjZsSP369WnUqBFjx44lNjYWgPbt21O7dm0aNWpE69atOXPmDABVqlQhMjIyq41t27bRq1cvs9htThzacag4DhOZF6QluHULxoyRw/rPPSeH+w8dgsWLyUo2OHXHVMpOL8v68PVU8q9E5OuRVAu0TgbY+8lLC71Oz8oRK/nzlT+z3+Hll+XvrVtlYJ8DY8nrwtEw15zoyJEj2bBhQ677bNiwgS+++IL169dz4sQJ/v33X1q1asXt27ez9lm8eDFHjhxhxIgRTMysQuYgOPRXdpUd14S5FwoIAWfPQu3a8u9582Rc2+DBMGmSTMEEkGZIY+Cygaw6swqAnx/7mUFhg8xqS0HJSwtXL1eGrB3y31VTmUybBsHBUNEyq76siVpAYsJcz4u2bdty6dKlXPeZOnUqn3/+OaGhoYCMWh89enSO7X355Zdmsc1aOLTjUFiO55+Xw1K3b8uFRBERULr0vfvsvrqbrgu7kqRPomPVjqwatAofN/OXPzUXUeeiuH30NvUeq0f5ZuX/u8OWLdJTNm5sfeMUBeblDS9z+NbhfO+fn+W4jcs25svuRX+InzhxgiZNmuRr3z/++IMGDRoU+ZzWxKGHqtQch4nAwMAiHZ+cLIejvvpK9jDGj4ePPgKvjOS0dzuN2JRY2sxrQ+t5rUnSJ/HOw++w+cnNduM0ctLi7B9nWTliJXpdNiVEP/hApkH/5hsLW2ddinpdFCdsNTl+7NgxGjduTPXq1Vm6dGnW9qFDh9K4cWN27drF559/nqON9jip79BPXlWPw0RycjI+PoV7cB84IMtGgMygMW6cXGJbr969+0UkRTBp6yS+PfgtAAEehO9iQAAAIABJREFUAewbu49awbWKYrrZuVuL0ytP4xHoQUj9EBqPbIxPOR9cve5by//NNzB5Mjz5pPxdjCjKdWHvFLRnYDAYrJbosH79+vz777906NCBBg0acPjwYcaPH09y5lJE5BxHs2bN7jkuODiYmJiYrNgbe43Dcegeh3IcJgqTgkEIWToi02m88w788w943lejKFIXSbsF7SjzeRm+Pfgtni6ezO0zl5g3YuzOaYDUQhjlePb5TedZ99y6rOJLDQbfNySwZQu8+KJccjtvnkyoVYwozHVRXLHm8+Ktt97itdde49q1a1nb7nYaOdG+fXsWLlwISEe3aNEiOnToYDE7C4tD9zgUhcNgkCtMBw2Sy2lBJiTs3v3e/aKToxmzegwrT68EwNPFk6UDltK7dm8rW5x/osOjWdJzCa0mtKLZM81o+05bvL70wtk1m2+aQsBrr8k64IsXg0q7rcgHgwcPZtu2bURGRlKhQgU++OADxowZc88+PXv25M6dO/To0QODwUBAQABhYWF0y2OV3nvvvcezzz5Lo0aNEELQvXt3hg0bZsl/p1BojrwyqWnTpuLgwYO2NsMuSElJwcMj7+JHV65A5cqm1wMGyAy2d2di+OvCX0z4cwLHIo5lbVvYbyHDGtrPBXz3ROfOT3cScyGG3t/1xphuZEmfJbR4qQU1utXIoxVkhtv4eDkpXgzJ73XhKJw6dYq6hYzeNxqNJTIgMjvNNE07KIRolsMheVLyVCym5DaB9vnn0LChTBNSsSL07y/ngq9fh2XLpNMwCiMz987EbYobXRZ24VjEMaoFVuPHvj9ifN9oN05DCMHqcav5PORzjOly6EGfpCf+SjxCCJxcnHh8xeO5O42EBHjjDdn1Kleu2DoNsM+JVYXj49BDVSoA0ERCQkJWCu1//oHp0+Ww/ZAhUKWKTOSanCwDoH//3XRcUloSEzdNZPaB2VnbHqn5CHN6z6G8bzZLVm3E+Y3ncfVypVKbSnT4oANuPm7odXrc/dxp+25bnN1Mw0x3a/Efrl6Vs/9//inH6h54wEr/gW3IVYsShsFgKJE9Dkvg0I5DYSIiQuOrr2DKFNM2Z2fpOAYMkD93E6mL5KUNL92Tufa9tu/xXtv3cHW2jwyiBr0BZ1dn9Ml6Nr66kdDmoVRqUwnf8r50/8I0IXO308iRlBT47DP45BPZ05gyRcVrKBSFxKEdR0n/9nDqlCxv/fPPEB4enLW9Xz/4+GOoU+e/xyTrkxm2YhjLTy0HQEPjqx5f8fyDz9vVsMaVXVdYPnQ5o3eOxq+CH72/702pOvlblpjtmP4bb8gglRYt5OqpEpLltjjNbxSVkv68MCfKcTgo0dFy3iIzo0SLFoK33tLo3VvmksqOX0/8yhO/PZH1elG/RQxtONQK1hYc/0r+BFQOIDU+FYAKLSrk+1gvLy+4dAmWLoUKFWSZ1/ffh969oXNnC1lsn3hlRnAqSvTzwtw4tJIlMQ9P/fqwdq3MFTV3LuzcKUde/vgjij59cnYa41aPy3IaE1pMwPi+0S6dxumVp7m27xr+Ff0ZuX0kpeuVzvugu0lJQf/oo3KJ7Ztvwr59cntwcIlzGiADyBSSkvi8sBQO7ThKAunpsGCB/C0EeHjA8uUyU+2TT0Lr1nkXo/tk5yf8cOgHAE4+d5Lp3abb1bBUJrooHZsmbuLCpguFayAjBN59zRqZ1fbsWTk8pVCYiatXr9KhQwfq1atH/fr1mTlzZq77GwwGHnjggVxTo8+YMYM6derQoEEDGjVqxIQJE9BnBFhVqVKFBg0a0LBhQ7p27cqtW7cA/pMNYMGCBYwfP76I/13+sbrj0DStoqZpWzVNO6lp2glN017K2B6kadomTdPOZfzOM8mOPT78zIXBILN7u7rCqFEyFYgQsH+/7Gnc/6/nlErhXNQ53tr8FgCXXrpE3dL2NbYfczGGjRM3kp6ajlewF91ndqflqy0L1kjm6jovL0hJIfH772HdOqhZ0/wGOxjWSrHhCJjjeeHi4sL06dM5efIke/fu5ZtvvuHkyZM57j9z5sxc406+/fZbNm7cyN69ezl27Bj//PMPISEh90SZb926laNHj9KsWTM+/vjjIv8P5sAWPY504FUhRD2gBfC8pmn1gDeBzUKImsDmjNe5Ulxvig0bZJ2LzC8zr74qHYaTU87BzTklsxu2QsZf/ND7ByoHVM52H1ty8+BNDsw6wI1/bgBQs2dNXD0LsKpr0SJ49FHpVevVgzNn8Bk71kLWOh4qyaEJcyRFLVeuXFbWW19fX+rWrcv169ez3ffatWusXbuWsblcj1OnTmX27NkEBAQA4Obmxptvvomfn99/9m3bti3h4eFF/h/MgdUnx4UQN4GbGX8naJp2CggFHgXaZ+z2I7ANeCO3topbHMfly9C2rYzuBjmn+8MPcngqL2JiYv7zkLgQc4H91/dTxrsMY5qMyeFI63P8l+MkRSTR/MXm1BtQjxo9auDmXcAcUbGxMrnWrFnQvLkM6vPzAyenbLUoqRR3LTJr3OdGr14ys0x6ejqdO7swciSMHAmRkf9dpr5tW/7PfenSJQ4dOkTz5s2zff/ll19m2rRpJCQkZPt+fHw8iYmJVK1aNV/nW7Nmjd2kX7fpHIemaVWAB4B9QJkMpwJwCyiT1/GOnC4lO/r2lU6jfHk4ckR+mc7vasrsnOg7W2RN1/9v78zjm6rSPv49XWhLS0spi2yFKjKyCKEUirIMDAgoqFOgMCAiiqyDGy7zojPv6IwOio4C7iJYBH1RGZRFFJBBkE02C1aWglCQRSht7ZKWtknO+8dJkxbS0rRJmrTn+/nk09zcc5978svtfe7ZnmfOwDmurKbTFOYUcuq7U5iLVR1T16SSsjzFFojQKachJSxdqlZ7v/OOiv++ZYtyGlZq2wNFddBa2HHl/SIvL4+RI0cyb948h62DtWvX0rRpU7p3715pm+vXr8dgMNC2bVt27Nhh+3zAgAEYDAZycnKYPXt2ucd7suu+xqbjCiHCgP8Aj0opc0p/aSmlFEI4/JWFEFOAKQAtW7a05ecNDQ0lICCA7OxsQDX5GjRoQEZGBqCm4jVq1Ijs7GzbwFPDhg0pLCy09SeGhobi7+9PTk4OAEFBQYSGhtpmppTY+O2332wzNCIjIykoKLBFIQ0LC0MIYXvKCA4Opn79+jYb/v7+REZGkpWVhdlsxmSCDRsaMWJEPpMmSY4d82fuXH+klFy6lGezERISQlZWFqCa3A0bNiQzM9MW8VNKSW5uLoWFavpqWIMwlqcsB2B4q+EYjUaCgoJsqUQDAwOJiIgoYyMqKorc3FyKiooAiIiIwGQyYTQaATW1MzAw0KZxiY2MjAxb7KioqCjSz6ZzctNJmsc357rrr+PHFT/y5aQvGbttLK1jWzN4wWAKzAVkZGY4/zsVF9Nozhxo04bsjz/G3LUrQUVFhAYG2jTOzc2lcePGLv2dQOXvzs/Pt9lo0KABUkry8ir/OzVq1Aij0Wj7ncLDwzGbzTaNQ0JCXPo7ZWdnl/s7ldY4IiKC4uJi8vPzvfr/yWKxIKW0lf/mG0FAQAAmk8nmGAICArBYLDa9/P39sViUE924UZ1XSj8iIkxs3KhuuCU2iovLtwHKRnFxMSNGjGDcuHHceeedFBcXc+bMGRISEpBSMmXKFE6dOsXq1atZt24dly9fJicnh3vuuYelS5farqfQ0FDCwsJITU0lJiaGgQMHkpyczLBhw8jPz7d9x40bNxIVFWWrh9lsJiQkBKPRSIg1lHV6ejqRkZG21fElx5bcV6/8naqNlNLjLyAQWA/MKvXZUaC59X1z4Oi17HTv3l36Ol9+KSVImZJSPTtms7nM9jc/fyN5FvnAFw9Uz3AVOLf/nHyWZ+WBpQeklFLmns+VqetSZWFuYdUMWixSvv++lJmZavvsWSmv+L6luVKLukxt0+LQoUNVPtZisVT7/BaLRd57773ykUceqfQxmzdvlsOGDXO4780335RDhgyRWVlZNvsDBw6UmzdvllJK2aZNG5menn7VcSNHjpSLFi2SUkqZn58v4+Pj5ZYtWxyew5FmwF5ZjXt4TcyqEsAi4LCU8tVSu1YD91nf3wesupYtX87HsWQJrFih+mgXLKj+BKCSJ8USXt2lpH04/uHqGa4kJzadYO+7e5FScl3X65i4dSIdE1UmqLDrwrjx9hupF1aFXBeZmfDII/Dgg2o8A1RfXgVzkK/Uoi6jtbDjivvF9u3bWbp0Kf/9738xGAwYDAbWrVtXZXvTp09n4MCBxMfH06VLF3r37k23bt3odo0YavPnz2flypUYDAZ69epFYmIi/fr1q3I9nKY6XqcqL6APIIGDQLL1dQcQhZpNdQz4Bmh0LVtdu3Z16GG9GZNJysRE1cro0sV1dks/lZgtZhnyfIgM+EeANFvc88R5IeWCfP13r0vjJaOUUsqt/9oq58XMkzlnc1xzgowMKWfMkDIwUIk1eXKFrYzSOHpCq6vUNi2q0+IoKipyYU18B3e0OGpiVtU2oLxRnIGerIunOXECYmPB2m3M8uXuOc/+8/spMBWQcFMCfsI1jUqL2cKXM77khttuoOOojvgF+JF9Opv0Q+m06dsGw30GeszoQXBENWIjFRWplY7166sm2Vtvqam2f/ubEq4Wr9vRaHwJn1457kvrODZvVlEwsrNVfL0LF1wbZ69Bgwa29x/88AEAM3rMqJbN09tPs+etPQD4+fvxy/ZfyDqhBn6j2kfxtPFp2vRVa0MatGhQdadhNitH0bq1Wt0IMH487NwJX3wB3bs75TRKa1HX0VrY8aX7hbfj00EOfYXUVJU8CdTs0fnzrx0mxFlkqamG35z8BoDerXtX2V5xQTF7395L2uY0uk/pjl+AH5N3Tyawvlqc55KpfyaTCkQ4dy4cPKjWY5RMX2zSRL2qQGkt6jpaC4078GnH4Qtz1KVUsaaCg+GFF+Dpp91znry8PIKDg8m+nE1qRirtGrUjJDDEaTuXjlzi9PbTxE6KJWFpAqYCE34BysuVOA2Xcf/9arFKx44qf+2YMS7xqCVaaLQWpdGJnFyHVtGN5OWpHpjnnoNvvlHBWt3NqqNqMtqj8Y9Wqnx+Rj6739jNr8kqeNred/ayafYmCnMKEUK41lmsWQO33qqCEQJMnw6ffw4pKTB2rOubYRqNxi349H+qtz89nD0Ls2er8Y1Ondx7Xyx5qkxKTgIgsVNiuWXP/3CeXfN2AWrsYv2s9Rxfr2Lg3DLrFiZumUhQuIvSjRYWqrgp7dqpXLbHj6tBcFBO5I9/dPmgt37CtqO1sOPt9wtfwqeV9NYLYccO+OgjFRXj7ruV03A3JStI953fR4BfAE1Dm5bZn59hn8//w+If2D53OwDBDYN5/Nzj9H5KjYdEREfQpEPVxhauIj9fxX2fPBlCQmDOHBWQ69ZbXWO/HEq00GgtSuOq+0VJqHODwUBcXFy55V577TU6depE586dGTt2rG01/JXosOoexhsTsxQUqAV9s2ap7XfegZYt3X/erKwstp/eTk5hDjN7lL2ANj2zibc6vsW6mWqhUr9n+vHnQ3+27a/fuL7r4twcOaLStJ4+rabVzpypctsePKj66jxwIysJ+aHRWpTGlfeLzZs3k5yczN6SbtcrOHv2LAsWLGDv3r2kpKRgNptZ7mD+va+GVffpwXFvpG9f2LdP9cx4mpJuqhEd1BSuX5N/JSgiiIEvDKTDiA40aK6mZoZdF1aeiarz+efw2mvw3XdqOz4eoqNVGFKNpg5iMpkoKCggMDCQ/Px8WrRocVWZF154ga1bt14VVt0R/fr1Y4GXJCbzacfhTYmcLBZYvVp14d92G7z3nmfPHxAQYJuG271Fd6SUfND3A3rM7MGgOYNo0f3qi9ZlDBoEmzap96NHw/PP12gSJVfkXagt1HotHMVVHz0aZsxQXaV33GH7OEBKNZ5WzbjqQggGDx6MEIKpU6cyZcqUq8q0bNmSJ554gujoaEJCQhg8eDCDBw8uU0aHVa8hvGlBz549kJCgFvj94Q/Qtq1nz/9z/s+czjjNw1sfJm1VGkIIBs4ZSOyDsa4/WW6u8o7W/lbuuQeefVZ9+U8+qfHMey6J/llL0FrYcdWD5rZt29i/fz9fffUVb775Jlu3br2qTFZWFqtWreLkyZOcO3cOo9HIsmXLKrSrw6p7CG8a4zhvzSSydi0MG+bZc1vMFl7f8joWfwvNTzS3re7uObOna0906hQ89JCaVgtq7OKxx9R6DC8iMzOTRo0a1XQ1vIJar0VFLYT69cvsN5lMZVtgjRs7l7nJSkvroGXTpk1JSEhg9+7dxMTEcOeddwIwbdo0oqKiiImJoYl1EeuIESPYsWMH48ePt9kJDw8nLCyMkydPEhMTw5AhQxgyZAjDhw+3hcwHNcbRuHHjMnUICQmhqKiIevVU4NDMzMyryrgTn25xeBMlDwLWsPkeoyCrgA8HfUjB8wVEZkYy48gM2wypalNcbJ86++KLqhm1Zo1qUaxYoZyGF+LLUZNdjdbCjitW0RuNRltuEKPRyIYNG+jcuTOtW7cmOTmZ5ORkpk2bRnR0NLt27SI/Px8pJZs2bXKYe3z27NlMnz7dln9FSlnu7KvS/P73v7e1YAoKCvj0008ZMGBAtb9fZfHpFoc3MXcuBAVBTxc/5JeHxWzh/L7ztOjRgnp31SNtYxr9evUjPOjqbGSVIjNTjVOkpcGZM7BtG+zfD59+ComJKqft7bcrD9m3r0u/i0bjK1y4cIGEhARAtWDGjRvH0KFDryoXHx/PqFGjiI2NJSAggG7dujkcC5k+fTpGo5H4+HiCgoIICwuzhVaviPnz5zN16lQWLFiAlJIJEyZ4NKy68OVYNnFxcbK86XCe4tgxtcBv8mTPBm9N25LGijErmLx7MsM3DWfb6W18/+D39GxZSc9VEifKz0+t2v7lFzULCiA0FJo1g/bt4fHH1eC3D2GxWLx2jY+nqW1aHD582OGTe2WQ1uyHdQ1Hmgkh9kkpy1+Ecg18usXhDc3w9HS1VGHPHli40HPnje4TTcO2Dfnlt1/YdnobzUKbVc5pSAn/+IcazAYVRGvMGBWZ9vvv1arFiAi31t3dGI1GHRXWitbCjsVi8aoJNb6MdhxVJDdX9eS0aqWianjqQebcvnMcXHaQoa8NZdLOSYz6TE0nfKx7JcYbLl2C665TYcwBnnxSdT2VPJF6qp/NzRQWFuqbpRWthR3tOFxH7WnDepgHHlBTyJcv92wXVX56Pt/P+54iYxFncs6w8vBKACZ0nFD+QSaTmjrboIEapxg1SkVgnDsXIiM9VHONRlNb8GnHUZNPD9nZKgaVpxZGSynJOJZBu6HtmLhlIvVC63H/KjUNdu6guURFOpjOZbGoWO6BgSoMiMmkVil+9pkax6ilhIdXcYJALURrYUe3NlyHTzuOmhjYP3cOpk2DAwcgPNwzcagANj65kQ/6fIAx3Uibfm1Yf3w9m06q1dqP9Hrk6twkixaBvz/89a9qOzJSzWuvA4ODvpCnxVNoLTTuQI9xOMlXX8G776rcQ6NHe+68nUZ3IiQqhKAGQVw0XmToR2oK4Oo/raaefz0uGS8R4uenHENurj1j1KRJ8MYbahC8jmA0GnVUWCtaCzs6kZPr0Co6SUkjZ/16eLRyuZKqxXdzvmPjXzbSsmdL+s7uS5F/Ec1eaQbA9Ljp3Nm0DyQlEf6nPynnsH69WoW4aBGcPKlyYdQhp6HR1DSbNm0iNjYWg8FAnz59OH78uMNyX3/9NT179uSmm27CYDAwZswYTp8+DcDEiROJiYnBYDAQGxvLzp07Aejfv3+ZiLxpaWl07tzZ/V/qCny6xeHpp4fcXHu4dE+dOu98Hhd/vAiARVpoM68NAB2bdOSNb+vDcBVOoh6otRclnm34cM9U0AvRT9h2tBZ2XHW/yMrKIrKCSSXTp09n1apVdOjQgbfeeovnn3+epKSkMmVSUlJ46KGHWL16tW2NxerVq0lLSyPaup7q5ZdfZtSoUWzYsIGpU6dy8OBBl9TfFfi04/D0Yp6wMJgwQa2La97cM+e8fcHtAJgtZnot6kX9c5eYejGct/83BfHRR6oys2ZhSkwkoDbHJHKCoCAXZS+sBWgt7LjKccTFxdGrVy8mTZrEgAEDrroPCSHIyckBIDs722E49Zdeeomnn366zMK8u+66y+H5+vXrV26rpabw6a4qTw78rVgBu3bBv/8NDz/s/jHm9bPW8+8W/+bIF0cAmLlsHA++u5dT8+Cdj3MQJhOMHw9Hj8LUqfzmBYshvYWSuD+a2q9FUv8kkpOSATAXm0nqn8TBZerJvDi/mKT+SaR8kgJAXkYeSf2TOLzyMAD5l/JJ6p/E0TVH1f5f8yp1ztTUVMaOHcsbb7xBx44d+de//sW5c+ds+99//33uuOMOWrVqxdKlSx3m1/jpp5+Ija1c5Oo1a9Z4TTj1EnzacXiSNWtg6FCVyM4TxE2Lo+8zfYnuE813Qzrw9n2fMnWfdefrr6spthqNxuP4+/szfPhwVq5cydatWzlx4gTR0dHs3r0bUClj161bx5kzZ7j//vuZVdK/XQ4ZGRkYDAbat2/PK6+8Yvv8ySefxGAw8N5777Fo0SLAcS9LTYRR0V1VFXD8uHqgb9NGjTUvWeLW0wFw7KtjHPn8CLfPHUBs8AES1/+TeuFHaNUQQl96laaTH3XY3AnUjsSG1sJObddi4rcTbe/9A/3LbAfWDyyzHdIwpMx2/cb1y2w7kxkzOzub5cuXk5SURL169Vi8eDFdunQhPT2dAwcOEB8fD8CYMWMcBkHs1KkT+/fvp2vXrkRFRZGcnMwrr7xCXp691VMyxlGaqKioMumAPR1OvQSfdhzuXNBTXAw33wyXL6uH+yVLVCxAd5N1IouzGw+TEfkgzbjI0RlwuDO8+N4JmkaWnykswsfjS7kSrYUdrYUdV2VDHD9+PDt37iQxMZEPP/yQG0slLgsICCA7O5vU1FTat2/Pxo0bHQZlfOqpp0hISKBXr162/fn5+dc8d//+/Vm2bBmDBg1CCMGSJUs8Gk69BJ92HO5I5CSlWlz988/KaTz0kJp2W8nsjlUi41gG3/79Wzr3aUiPmYOIQ+CH5N3uENW1F0WTthLoX/GTY61P2OMEWgs7Wgs7VyVyqiKjR48mKSnJoa2AgAAWLlzIyJEj8fPzIzIyksWLF19V7uabb2b+/PlMmDCBnJwcGjduTHR0NM8991yF554yZQpHjhyha9euCCGIi4tjzpw51f5OzuLTYdUNBoNMTk52qc1Dh1QoEVBTbj/7DEaMcOkpbJiLzRSfOsfFdz/l4/1GLuTuIvH8V9Qzw58TQ3jjqS30aNmjUrYuXbpUI01Wb0RrYae2aVGdsOrFxcW1vuvOETqsuptJSVE57gGWLlXRxt1ynf30E8Y357HsbX9acpbhrGVvIvynHxxo3IN/3Pkq30f3ccOJNRqNpvr4tONwVZ8lqEjjFosKGjt0KAwY4FqnIfPyOLBrMyvH7eH6hguZeOxXetOZbQ1ieHxEc3rd/zCLe82ocga/KE/nrPVitBZ2tBZ2XHm/qOv4tJKuWscxaxYkJansqfv3u8QkAEVFBRz6y4P8nHSJEb9tILCRPzmWmaxo2ZxDia3pPmYWD3YeRYBf9X+G3NxcHQnVitbCTm3UoqqZ/OpiPg53DUX4tOOoqihvvw2ff65aFytXwuDB4IpuYFlczOENH7MwZy9Z63bwwbL9dMafVfyNiPCzZA0N5O7n7+b3bV/FT7h2CU1RUZFL7fkyWgs7tU2L4OBgMjIyiIqKctp51DXHIaUkIyODYDfEqvNpx+EMKSnwww/KWfzznyprX2ysSlExdKh6VYriYooPHOJ8YSSE5xAyaQznDvixt6gH4/mYjhTya89buGn3XUybls/YkA70a3ELceNm0aCFzsSm0VSHVq1acebMGdLT050+1mw21ynHAcrRtmrVyuV2fdpxVOYiWLAANm+GL74o+/kTT8DLL1dwoJRIQFy+TN7cFzh5+AJB32+hfdoxDjZpydr0yaxMWMHGnw6RH9KCM0VteP+2aNq0i2Zan/toHBLL725/hoBgz0is5+vb0VrYqW1aBAYGElPFufF1dVaVO/AqxyGEGArMB/yB96WUL1ZUPjtbdTldvqxeo3ufJaQgk+f+10xokInHPriZs6f8MR0+ybMx27kpuoBb4k1EXTxAXlBj1r3Vj6J2RXRc/QmF61LZn3kzvUO+pd2vp/isUw8O/TSMz/7yOikvZXCKYezjHgaEJ3H8ujMcnXiE7h17sP7vT3Fr61t5OiKaQP+/e0QnR5hMJv1PYUVrYUdrYUdr4Tq8xnEIIfyBN4HbgDPAHiHEainlofKOyfv5Ip+N+D9+pRkbGMxCptCITJ7jEwCe3DKL36JzGNViJWM2G3np5Gyit6wnmp1c9q/PHnMI625fxyO/7Ob636JIy76L0PCvSW0HPzTNI8c/nfYRvZk3P5Tg62+lX5NO9O7xV/r5+fOAR1SpPDphjx2thR2thR2thevwmgWAQohbgGellEOs27MBpJTlLotsFNRERvfuhWxxlBkp6RRn3MjJFiYud7mAX716pKbfQHiLSJrdkEfT3yxkXwilSZe2BLYOoyAokJjC6wluEkyzps2IDIokKiiKZhHNaiRoWHWpbQu9qoPWwo7Wwo7Wwk5tWgDYEvil1PYZIP7KQkKIKcAU62Zh1ua1KQDTANijjtpdUjrN2TpEANkuLF/Rfkf7KvNZ6e3S7xsDl65RX2fQWlRcl+qUd7UWFemitdBaONr3u2tVtkKklF7xAkahxjVKtu8F3rjGMXtdXIf3XFm+ov2O9lXms9LbV7zXWtRRLa6hi9ZCa+FyLbwpH8dZoHWp7VbWzzzJGheXr2i/o32V+WxNBftcidai6rY9rUVFurgarUVbRxQNAAAFcklEQVTVbdcaLbxpjCMASAUGohzGHmCclPKnCo7ZK6vRT1eb0FrY0VrY0VrY0VrYqa4WXjPGIaU0CSFmAutR03EXV+Q0rLzn/pr5DFoLO1oLO1oLO1oLO9XSwmtaHBqNRqPxDbxpjEOj0Wg0PoB2HBqNRqNxCu04NBqNRuMUtdZxCCGuF0IsEkKsqOm61ARCiFAhxBIhxEIhxD01XZ+apK5fC6URQvzRek18IoQYXNP1qUmEEB2EEO8IIVYIIabXdH1qEuv9Yq8QYnhlynul4xBCLBZCXBRCpFzx+VAhxFEhxHEhxP9UZENKeUJKOcm9NfUsTuoyAlghpZwM3OXxyroZZ7SojddCaZzU4gvrNTENGFMT9XUnTmpxWEo5DRgN9K6J+rqLKtxD/wJ8Wln7Xuk4gCSgTIaMUkEQbwc6AmOFEB2FEDcLIdZe8Wrq+Sp7hCQqqQtqAWVJCBfXpEr0LpKovBa1nSSc1+Kv1v21jSSc0EIIcRfwJbDOs9V0O0lU/h56G3AIuFhZ416zjqM0UsqtQoi2V3zcEzgupTwBIIRYDtwtVRDESjWvfB1ndEFF7WoFJOO9DwhVxkktyo2wXBtwRgshxGHgReArKaULEyV7B85eF1LK1cBqIcSXwMeerKs7cVKHMCAU5UwKhBDrpJSWiuz70g3FURDEluUVFkJECSHeAbqVRNqtpZSny0pgpBDibdwbdsGbcKhFHboWSlPedfEQMAgYJYSYVhMVqwHKuy76CyEWCCHepfa1OBzhUAcp5TNSykdRjnPhtZwGeGmLwxVIKTMoCZpbB5FSGoH7a7oe3kBdvxZKI6VcACyo6Xp4A1LKb4Fva7gaXoOUMqmyZX2pxeENQRC9Ea2LHa2FHa2FHa2FwmU6+JLj2APcKISIEULUA/4ErK7hOnkDWhc7Wgs7Wgs7WguFy3TwSschhPg/YCfwOyHEGSHEJCmlCSgJgngY+LQSQRBrFVoXO1oLO1oLO1oLhbt10EEONRqNRuMUXtni0Gg0Go33oh2HRqPRaJxCOw6NRqPROIV2HBqNRqNxCu04NBqNRuMU2nFoNBqNxim049BoNBqNU2jHoakzWIMdJltfvwohzpba3uGmc3YTQixy8pj3rxUOXggxUwjxQPVqp9FUDb0AUFMnEUI8C+RJKV9x83k+A56XUh5wsd36wHYpZTdX2tVoKoNucWg0gBAiz/q3vxBiixBilRDihBDiRSHEPUKI3UKIH4UQN1jLNRFC/EcIscf6uiqDnBCiAdClxGkIIZ4VKp3vd0KIU0KIEUKIuVa7XwshAq3lvhVCxJXUSwjxghDigBBilxCiGYCUMh9IE0L09IxCGo0d7Tg0mqvpigrD3gG4F2gvpewJvI/KZwEwH3hNStkDGGnddyVxQMoVn90A/AGVzncZsFlKeTNQAAxzYCMU2CWl7ApsBSaX2rcX6Ov0t9Noqkmtzceh0VSDPVLK8wBCiJ+BDdbPfwQGWN8PAjoKIUqOCRdChEkp80rZaQ6kX2H7KyllsRDiR8Af+LqU7bYO6lIErLW+3wfcVmrfReAmJ76XRuMStOPQaK6msNR7S6ltC/b/GT+gl5TycgV2CoBgR7allBYhRLG0DzKWtl2a0mXMV5QJtp5Do/EouqtKo6kaG7B3WyGEMDgocxho58Y6tOfqrjCNxu1ox6HRVI2HgTghxEEhxCEcpKaVUh4BIqyD5O6gN7DRTbY1mnLR03E1GjcihHgMyJVSOho8r47dbsAsKeW9rrSr0VQG3eLQaNzL25QdM3EVjYG/ucGuRnNNdItDo9FoNE6hWxwajUajcQrtODQajUbjFNpxaDQajcYptOPQaDQajVNox6HRaDQap/h/lcBKJKH3q2sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buckets = sorted([bucket for bucket in run_times])\n",
    "for bucket in buckets:\n",
    "    num_gpus, color, linestyle = get_plot_config_from_bucket(bucket)\n",
    "    x, y = get_cdf(run_times[bucket])\n",
    "    plt.plot(x, y, label='%s GPU' % (num_gpus), color=color, linestyle=linestyle)\n",
    "plt.legend(loc='lower right')\n",
    "plt.xscale('log')\n",
    "plt.xlim(10 ** -1, 10 ** 4)\n",
    "plt.ylim(0, 100)\n",
    "plt.xlabel('Time (min)')\n",
    "plt.ylabel('CDF')\n",
    "plt.grid(alpha=.3, linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Queueing Delay (Figure 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "queueing_delays = {}\n",
    "for job in jobs:\n",
    "    vc = job.vc\n",
    "    if vc not in queueing_delays:\n",
    "        queueing_delays[vc] = {}\n",
    "    bucket = get_bucket_from_num_gpus(job.num_gpus)\n",
    "    if bucket is None:\n",
    "        continue\n",
    "    if bucket not in queueing_delays[vc]:\n",
    "        queueing_delays[vc][bucket] = []\n",
    "    # NOTE: Each period between the job being placed on the queue\n",
    "    # and being scheduled on a machine is recorded as an individual\n",
    "    # queueing delay.\n",
    "    queueing_delay = 0.0\n",
    "    queue_time = job.submitted_time\n",
    "    for attempt in job.attempts:\n",
    "        start_time = attempt['start_time']\n",
    "        if queue_time is not None and start_time is not None:\n",
    "            queueing_delay = timedelta_to_minutes(start_time - queue_time)\n",
    "            queue_time = attempt['end_time']\n",
    "        queueing_delays[vc][bucket].append(queueing_delay)\n",
    "for vc in queueing_delays:\n",
    "    for bucket in queueing_delays[vc]:\n",
    "        queueing_delays[vc][bucket] = filter(None, queueing_delays[vc][bucket])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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