{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python wrapper for nni restful APIs\n",
    "\n",
    "nni provides nnicli module as a python wrapper for its restful APIs, which can be used to retrieve nni experiment and trial job information in your python code. This notebook shows how to use nnicli module.\n",
    "\n",
    "For a full nnicli API reference, please refer to [this documentation](https://nni.readthedocs.io/en/latest/nnicli_ref.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start nni experiment using specified configuration file\n",
    "Let's use a configruation file in nni examples directory to start an experiment. Make sure you have installed nni, seaborn and pytorch in your environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "authorName: default\nexperimentName: example_mnist_pytorch\ntrialConcurrency: 1\nmaxExecDuration: 1h\nmaxTrialNum: 10\n#choice: local, remote, pai\ntrainingServicePlatform: local\nsearchSpacePath: search_space.json\n#choice: true, false\nuseAnnotation: false\ntuner:\n  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner\n  #SMAC (SMAC should be installed through nnictl)\n  builtinTunerName: TPE\n  classArgs:\n    #choice: maximize, minimize\n    optimize_mode: maximize\ntrial:\n  command: python3 mnist.py\n  codeDir: .\n  gpuNum: 0\n"
    }
   ],
   "source": [
    "! cat ../trials/mnist-pytorch/config.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "INFO:  expand searchSpacePath: search_space.json to /home/xxx/nni/examples/trials/mnist-pytorch/search_space.json\nINFO:  expand codeDir: . to /home/xxx/nni/examples/trials/mnist-pytorch/.\nINFO:  Starting restful server...\nINFO:  Successfully started Restful server!\nINFO:  Setting local config...\nINFO:  Successfully set local config!\nINFO:  Starting experiment...\nINFO:  Successfully started experiment!\n------------------------------------------------------------------------------------\nThe experiment id is OhHNEkLQ\nThe Web UI urls are: http://127.0.0.1:8080   http://xxx.xxx.xxx.xxx:8080   http://172.17.0.1:8080\n------------------------------------------------------------------------------------\n\nYou can use these commands to get more information about the experiment\n------------------------------------------------------------------------------------\ncommands                       description\n1. nnictl experiment show        show the information of experiments\n2. nnictl trial ls               list all of trial jobs\n3. nnictl top                    monitor the status of running experiments\n4. nnictl log stderr             show stderr log content\n5. nnictl log stdout             show stdout log content\n6. nnictl stop                   stop an experiment\n7. nnictl trial kill             kill a trial job by id\n8. nnictl --help                 get help information about nnictl\n------------------------------------------------------------------------------------\nCommand reference document https://nni.readthedocs.io/en/latest/Tutorial/Nnictl.html\n------------------------------------------------------------------------------------\n\n"
    }
   ],
   "source": [
    "from nnicli import Experiment\n",
    "exp = Experiment()\n",
    "exp.start_experiment(config_file='../trials/mnist-pytorch/config.yml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve nni experiment and trial job information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{'status': 'DONE', 'errors': []}"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "exp.get_experiment_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[{'trialJobStatus': 'SUCCEEDED', 'trialJobNumber': 10}]"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "exp.get_job_statistics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{'id': 'OhHNEkLQ',\n 'revision': 181,\n 'execDuration': 1680,\n 'logDir': '/home/xxx/nni-experiments/OhHNEkLQ',\n 'nextSequenceId': 11,\n 'params': {'authorName': 'default',\n  'experimentName': 'example_mnist_pytorch',\n  'trialConcurrency': 1,\n  'maxExecDuration': 3600,\n  'maxTrialNum': 10,\n  'searchSpace': '{\"batch_size\": {\"_type\": \"choice\", \"_value\": [16, 32, 64, 128]}, \"hidden_size\": {\"_type\": \"choice\", \"_value\": [128, 256, 512, 1024]}, \"lr\": {\"_type\": \"choice\", \"_value\": [0.0001, 0.001, 0.01, 0.1]}, \"momentum\": {\"_type\": \"uniform\", \"_value\": [0, 1]}}',\n  'trainingServicePlatform': 'local',\n  'tuner': {'builtinTunerName': 'TPE',\n   'classArgs': {'optimize_mode': 'maximize'},\n   'checkpointDir': '/home/xxx/nni-experiments/OhHNEkLQ/checkpoint'},\n  'versionCheck': True,\n  'clusterMetaData': [{'key': 'codeDir',\n    'value': '/home/xxx/nni/examples/trials/mnist-pytorch/.'},\n   {'key': 'command', 'value': 'python3 mnist.py'}]},\n 'startTime': 1597942817897,\n 'endTime': 1597944680966}"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "exp.get_experiment_profile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[TrialJob(trialJobId: PTWOZ status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 0 parameter_source: algorithm parameters: {'batch_size': 32, 'hidden_size': 1024, 'lr': 0.1, 'momentum': 0.1922378994556755} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/PTWOZ startTime: 1597942828086 endTime: 1597943039314 finalMetricData: [TrialMetricData(timestamp: 1597943031202 trialJobId: PTWOZ parameterId: 0 type: FINAL sequence: 0 data: 99.36)] stderrPath: None),\n TrialJob(trialJobId: InH3J status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 1 parameter_source: algorithm parameters: {'batch_size': 16, 'hidden_size': 256, 'lr': 0.1, 'momentum': 0.8122758606731078} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/InH3J startTime: 1597943053122 endTime: 1597943249425 finalMetricData: [TrialMetricData(timestamp: 1597943249044 trialJobId: InH3J parameterId: 1 type: FINAL sequence: 0 data: 10.1)] stderrPath: None),\n TrialJob(trialJobId: aj2DE status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 2 parameter_source: algorithm parameters: {'batch_size': 64, 'hidden_size': 128, 'lr': 0.0001, 'momentum': 0.4401062752065499} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/aj2DE startTime: 1597943258156 endTime: 1597943416538 finalMetricData: [TrialMetricData(timestamp: 1597943409420 trialJobId: aj2DE parameterId: 2 type: FINAL sequence: 0 data: 85.45)] stderrPath: None),\n TrialJob(trialJobId: w3wpE status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 3 parameter_source: algorithm parameters: {'batch_size': 64, 'hidden_size': 1024, 'lr': 0.1, 'momentum': 0.26330740737640446} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/w3wpE startTime: 1597943428190 endTime: 1597943586757 finalMetricData: [TrialMetricData(timestamp: 1597943580752 trialJobId: w3wpE parameterId: 3 type: FINAL sequence: 0 data: 99.33)] stderrPath: None),\n TrialJob(trialJobId: ekUrl status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 4 parameter_source: algorithm parameters: {'batch_size': 64, 'hidden_size': 1024, 'lr': 0.001, 'momentum': 0.6196562297063133} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/ekUrl startTime: 1597943598222 endTime: 1597943757003 finalMetricData: [TrialMetricData(timestamp: 1597943747959 trialJobId: ekUrl parameterId: 4 type: FINAL sequence: 0 data: 97.91)] stderrPath: None),\n TrialJob(trialJobId: CBvzn status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 5 parameter_source: algorithm parameters: {'batch_size': 16, 'hidden_size': 256, 'lr': 0.0001, 'momentum': 0.6226217880666888} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/CBvzn startTime: 1597943768253 endTime: 1597943971248 finalMetricData: [TrialMetricData(timestamp: 1597943970892 trialJobId: CBvzn parameterId: 5 type: FINAL sequence: 0 data: 96.15)] stderrPath: None),\n TrialJob(trialJobId: Thriw status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 6 parameter_source: algorithm parameters: {'batch_size': 128, 'hidden_size': 512, 'lr': 0.1, 'momentum': 0.05546862979056} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/Thriw startTime: 1597943978282 endTime: 1597944124673 finalMetricData: [TrialMetricData(timestamp: 1597944120237 trialJobId: Thriw parameterId: 6 type: FINAL sequence: 0 data: 99.25)] stderrPath: None),\n TrialJob(trialJobId: dE0HP status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 7 parameter_source: algorithm parameters: {'batch_size': 128, 'hidden_size': 1024, 'lr': 0.01, 'momentum': 0.3669870499772513} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/dE0HP startTime: 1597944138317 endTime: 1597944291430 finalMetricData: [TrialMetricData(timestamp: 1597944291080 trialJobId: dE0HP parameterId: 7 type: FINAL sequence: 0 data: 98.8)] stderrPath: None),\n TrialJob(trialJobId: swAW3 status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 8 parameter_source: algorithm parameters: {'batch_size': 16, 'hidden_size': 1024, 'lr': 0.001, 'momentum': 0.32479400764440947} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/swAW3 startTime: 1597944303349 endTime: 1597944510877 finalMetricData: [TrialMetricData(timestamp: 1597944503684 trialJobId: swAW3 parameterId: 8 type: FINAL sequence: 0 data: 98.64)] stderrPath: None),\n TrialJob(trialJobId: LcnOg status: SUCCEEDED hyperParameters: [TrialHyperParameters(parameter_id: 9 parameter_source: algorithm parameters: {'batch_size': 64, 'hidden_size': 128, 'lr': 0.001, 'momentum': 0.07252783892989623} parameter_index: 0)] logPath: file://localhost:/home/xxx/nni-experiments/OhHNEkLQ/trials/LcnOg startTime: 1597944523381 endTime: 1597944680557 finalMetricData: [TrialMetricData(timestamp: 1597944672287 trialJobId: LcnOg parameterId: 9 type: FINAL sequence: 0 data: 95.97)] stderrPath: None)]"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "exp.list_trial_jobs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing nni experiment result\n",
    "\n",
    "With the retrieved trial job information, we can do some analysis by visualizing the metric data, below is a simple example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "jobs = exp.list_trial_jobs()\n",
    "job_ids = [x.trialJobId for x in jobs]\n",
    "final_metrics = [float(x.finalMetricData[0].data) for x in jobs]\n",
    "\n",
    "data = {'job id': job_ids, 'final metrics': final_metrics}\n",
    "sns.set(rc={'figure.figsize':(15, 6)})\n",
    "\n",
    "plt.title('Trial job final results')\n",
    "ax = sns.barplot(x='job id', y='final metrics', data=data) \n",
    "\n",
    "for i,p in enumerate(ax.patches):\n",
    "    ax.annotate('{:.4f}'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                 ha='center', va='center', fontsize=11, color='black', rotation=0, xytext=(0, 5),\n",
    "                 textcoords='offset points') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stop nni experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "INFO:  Stopping experiment OhHNEkLQ\nINFO:  Stop experiment success.\n"
    }
   ],
   "source": [
    "exp.stop_experiment()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}