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Unverified Commit a8e77fe2 authored by Junwei Sun's avatar Junwei Sun Committed by GitHub
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update nnicli notebook (#2810)

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examples/notebooks/retrieve_nni_info_with_python.ipynb
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......@@ -8,37 +8,7 @@
"\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",
"Following are the functions available in nnicli module:\n",
"\n",
"#### start_nni(config_file)\n",
"Starts nni experiment with specified configuration file\n",
"\n",
"#### stop_nni()\n",
"Stop nni experiment.\n",
"\n",
"#### set_endpoint(endpoint)\n",
"Set nni endpoint for nnicli, the endpoint is showed while nni experiment is started successfully using nnictl command or start_nni function\n",
"\n",
"#### version()\n",
"Returns nni version\n",
"\n",
"#### get_experiment_profile()\n",
"Returns experiment profile.\n",
"\n",
"#### get_experiment_status()\n",
"Returns nni experiment status.\n",
"\n",
"#### get_job_metrics(trial_job_id)\n",
"Returns specified trial job metrics, including final results and intermediate results.\n",
"\n",
"#### get_job_statistics()\n",
"Returns trial job statistics information\n",
"\n",
"#### get_trial_job(trial_job_id)\n",
"Returns information of a specified trial job.\n",
"\n",
"#### list_trial_jobs()\n",
"Returns information of all trial jobs of current experiment."
"For a full nnicli API reference, please refer to [this documentation](https://nni.readthedocs.io/en/latest/nnicli_ref.html)."
]
},
{
......@@ -46,104 +16,43 @@
"metadata": {},
"source": [
"### Start nni experiment using specified configuration file\n",
"Let's use a configruation file in nni examples directory to start an experiment."
"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": 27,
"metadata": {},
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"authorName: default\r\n",
"experimentName: example_mnist\r\n",
"trialConcurrency: 1\r\n",
"maxExecDuration: 1h\r\n",
"maxTrialNum: 10\r\n",
"#choice: local, remote, pai\r\n",
"trainingServicePlatform: local\r\n",
"searchSpacePath: search_space.json\r\n",
"#choice: true, false\r\n",
"useAnnotation: false\r\n",
"tuner:\r\n",
" #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner\r\n",
" #SMAC (SMAC should be installed through nnictl)\r\n",
" builtinTunerName: TPE\r\n",
" classArgs:\r\n",
" #choice: maximize, minimize\r\n",
" optimize_mode: maximize\r\n",
"trial:\r\n",
" command: python3 mnist.py\r\n",
" codeDir: .\r\n",
" gpuNum: 0\r\n"
]
"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/config.yml"
"! cat ../trials/mnist-pytorch/config.yml"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: expand searchSpacePath: search_space.json to /mnt/d/Repos/nni/examples/trials/mnist/search_space.json\n",
"INFO: expand codeDir: . to /mnt/d/Repos/nni/examples/trials/mnist/.\n",
"INFO: Starting restful server...\n",
"INFO: Successfully started Restful server!\n",
"INFO: Setting local config...\n",
"INFO: Successfully set local config!\n",
"INFO: Starting experiment...\n",
"INFO: Successfully started experiment!\n",
"-----------------------------------------------------------------------\n",
"The experiment id is PlUIfDTR\n",
"The Web UI urls are: http://172.18.17.1:8080 http://10.172.121.40:8080 http://10.0.75.1:8080 http://127.0.0.1:8080\n",
"-----------------------------------------------------------------------\n",
"\n",
"You can use these commands to get more information about the experiment\n",
"-----------------------------------------------------------------------\n",
"commands description\n",
"1. nnictl experiment show show the information of experiments\n",
"2. nnictl trial ls list all of trial jobs\n",
"3. nnictl top monitor the status of running experiments\n",
"4. nnictl log stderr show stderr log content\n",
"5. nnictl log stdout show stdout log content\n",
"6. nnictl stop stop an experiment\n",
"7. nnictl trial kill kill a trial job by id\n",
"8. nnictl --help get help information about nnictl\n",
"-----------------------------------------------------------------------\n",
"\n"
]
"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": [
"import nnicli as nc\n",
"nc.start_nni(config_file='../trials/mnist/config.yml')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect nnicli module to started nni experiment\n",
"Call set_endpoint to connect nnicli moduele to the rest server of started nni experiment. Local mode training serviced is used in this notebook, but nnicli module can connect to any started nni experiment. The endpoint can be found in the output of start_nni function."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"nc.set_endpoint('http://127.0.0.1:8080')"
"from nnicli import Experiment\n",
"exp = Experiment()\n",
"exp.start_experiment(config_file='../trials/mnist-pytorch/config.yml')"
]
},
{
......@@ -155,248 +64,76 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'errors': [], 'status': 'RUNNING'}"
]
"text/plain": "{'status': 'DONE', 'errors': []}"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
"execution_count": 4
}
],
"source": [
"nc.get_experiment_status()"
"exp.get_experiment_status()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'trialJobNumber': 4, 'trialJobStatus': 'SUCCEEDED'},\n",
" {'trialJobNumber': 1, 'trialJobStatus': 'RUNNING'}]"
]
"text/plain": "[{'trialJobStatus': 'SUCCEEDED', 'trialJobNumber': 10}]"
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
"execution_count": 5
}
],
"source": [
"nc.get_job_statistics()"
"exp.get_job_statistics()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'execDuration': 1117,\n",
" 'id': 'PlUIfDTR',\n",
" 'logDir': '/home/chicm/nni/experiments/PlUIfDTR',\n",
" 'maxSequenceId': 3,\n",
" 'params': {'authorName': 'default',\n",
" 'clusterMetaData': [{'key': 'codeDir',\n",
" 'value': '/mnt/d/Repos/nni/examples/trials/mnist/.'},\n",
" {'key': 'command', 'value': 'python3 mnist.py'}],\n",
" 'experimentName': 'example_mnist',\n",
" 'maxExecDuration': 3600,\n",
" 'maxTrialNum': 10,\n",
" 'searchSpace': '{\"hidden_size\": {\"_value\": [124, 512, 1024], \"_type\": \"choice\"}, \"batch_size\": {\"_value\": [1, 4, 8, 16, 32], \"_type\": \"choice\"}, \"conv_size\": {\"_value\": [2, 3, 5, 7], \"_type\": \"choice\"}, \"dropout_rate\": {\"_value\": [0.5, 0.9], \"_type\": \"uniform\"}, \"learning_rate\": {\"_value\": [0.0001, 0.001, 0.01, 0.1], \"_type\": \"choice\"}}',\n",
" 'trainingServicePlatform': 'local',\n",
" 'trialConcurrency': 1,\n",
" 'tuner': {'builtinTunerName': 'TPE',\n",
" 'checkpointDir': '/home/chicm/nni/experiments/PlUIfDTR/checkpoint',\n",
" 'classArgs': {'optimize_mode': 'maximize'},\n",
" 'className': 'TPE'},\n",
" 'versionCheck': True},\n",
" 'revision': 116,\n",
" 'startTime': 1564484985839}"
]
"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}"
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nc.get_experiment_profile()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define an utility function to format json string returned by nnicli module."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"def show_json(res):\n",
" print(json.dumps(res, indent=4))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"params\": {\n",
" \"searchSpace\": \"{\\\"hidden_size\\\": {\\\"_value\\\": [124, 512, 1024], \\\"_type\\\": \\\"choice\\\"}, \\\"batch_size\\\": {\\\"_value\\\": [1, 4, 8, 16, 32], \\\"_type\\\": \\\"choice\\\"}, \\\"conv_size\\\": {\\\"_value\\\": [2, 3, 5, 7], \\\"_type\\\": \\\"choice\\\"}, \\\"dropout_rate\\\": {\\\"_value\\\": [0.5, 0.9], \\\"_type\\\": \\\"uniform\\\"}, \\\"learning_rate\\\": {\\\"_value\\\": [0.0001, 0.001, 0.01, 0.1], \\\"_type\\\": \\\"choice\\\"}}\",\n",
" \"clusterMetaData\": [\n",
" {\n",
" \"key\": \"codeDir\",\n",
" \"value\": \"/mnt/d/Repos/nni/examples/trials/mnist/.\"\n",
" },\n",
" {\n",
" \"key\": \"command\",\n",
" \"value\": \"python3 mnist.py\"\n",
" }\n",
" ],\n",
" \"tuner\": {\n",
" \"classArgs\": {\n",
" \"optimize_mode\": \"maximize\"\n",
" },\n",
" \"builtinTunerName\": \"TPE\",\n",
" \"checkpointDir\": \"/home/chicm/nni/experiments/PlUIfDTR/checkpoint\",\n",
" \"className\": \"TPE\"\n",
" },\n",
" \"maxTrialNum\": 10,\n",
" \"maxExecDuration\": 3600,\n",
" \"experimentName\": \"example_mnist\",\n",
" \"authorName\": \"default\",\n",
" \"trialConcurrency\": 1,\n",
" \"trainingServicePlatform\": \"local\",\n",
" \"versionCheck\": true\n",
" },\n",
" \"execDuration\": 1192,\n",
" \"revision\": 124,\n",
" \"logDir\": \"/home/chicm/nni/experiments/PlUIfDTR\",\n",
" \"maxSequenceId\": 3,\n",
" \"id\": \"PlUIfDTR\",\n",
" \"startTime\": 1564484985839\n",
"}\n"
]
"execution_count": 6
}
],
"source": [
"show_json(nc.get_experiment_profile())"
"exp.get_experiment_profile()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"startTime\": 1564484995992,\n",
" \"hyperParameters\": [\n",
" \"{\\\"parameter_source\\\":\\\"algorithm\\\",\\\"parameter_id\\\":0,\\\"parameter_index\\\":0,\\\"parameters\\\":{\\\"batch_size\\\":8,\\\"conv_size\\\":3,\\\"hidden_size\\\":1024,\\\"learning_rate\\\":0.0001,\\\"dropout_rate\\\":0.8055724367106529}}\"\n",
" ],\n",
" \"id\": \"BW0NR\",\n",
" \"endTime\": 1564485259753,\n",
" \"status\": \"SUCCEEDED\",\n",
" \"sequenceId\": 0,\n",
" \"finalMetricData\": [\n",
" {\n",
" \"parameterId\": \"0\",\n",
" \"type\": \"FINAL\",\n",
" \"trialJobId\": \"BW0NR\",\n",
" \"timestamp\": 1564485258774,\n",
" \"data\": \"0.9078999757766724\",\n",
" \"sequence\": 0\n",
" }\n",
" ],\n",
" \"logPath\": \"file://localhost:/home/chicm/nni/experiments/PlUIfDTR/trials/BW0NR\"\n",
" },\n",
" {\n",
" \"startTime\": 1564485271947,\n",
" \"hyperParameters\": [\n",
" \"{\\\"parameter_source\\\":\\\"algorithm\\\",\\\"parameter_id\\\":1,\\\"parameter_index\\\":0,\\\"parameters\\\":{\\\"batch_size\\\":4,\\\"conv_size\\\":5,\\\"hidden_size\\\":512,\\\"learning_rate\\\":0.01,\\\"dropout_rate\\\":0.5547528540531742}}\"\n",
" ],\n",
" \"id\": \"x0P5w\",\n",
" \"endTime\": 1564485642784,\n",
" \"status\": \"SUCCEEDED\",\n",
" \"sequenceId\": 1,\n",
" \"finalMetricData\": [\n",
" {\n",
" \"parameterId\": \"1\",\n",
" \"type\": \"FINAL\",\n",
" \"trialJobId\": \"x0P5w\",\n",
" \"timestamp\": 1564485642072,\n",
" \"data\": \"0.10100000351667404\",\n",
" \"sequence\": 0\n",
" }\n",
" ],\n",
" \"logPath\": \"file://localhost:/home/chicm/nni/experiments/PlUIfDTR/trials/x0P5w\"\n",
" },\n",
" {\n",
" \"startTime\": 1564485652151,\n",
" \"hyperParameters\": [\n",
" \"{\\\"parameter_source\\\":\\\"algorithm\\\",\\\"parameter_id\\\":2,\\\"parameter_index\\\":0,\\\"parameters\\\":{\\\"batch_size\\\":8,\\\"conv_size\\\":3,\\\"hidden_size\\\":512,\\\"learning_rate\\\":0.0001,\\\"dropout_rate\\\":0.5584485925416655}}\"\n",
" ],\n",
" \"id\": \"V9jSG\",\n",
" \"endTime\": 1564485917057,\n",
" \"status\": \"SUCCEEDED\",\n",
" \"sequenceId\": 2,\n",
" \"finalMetricData\": [\n",
" {\n",
" \"parameterId\": \"2\",\n",
" \"type\": \"FINAL\",\n",
" \"trialJobId\": \"V9jSG\",\n",
" \"timestamp\": 1564485916403,\n",
" \"data\": \"0.928600013256073\",\n",
" \"sequence\": 0\n",
" }\n",
" ],\n",
" \"logPath\": \"file://localhost:/home/chicm/nni/experiments/PlUIfDTR/trials/V9jSG\"\n",
" },\n",
" {\n",
" \"startTime\": 1564485927295,\n",
" \"hyperParameters\": [\n",
" \"{\\\"parameter_source\\\":\\\"algorithm\\\",\\\"parameter_id\\\":3,\\\"parameter_index\\\":0,\\\"parameters\\\":{\\\"batch_size\\\":8,\\\"conv_size\\\":7,\\\"hidden_size\\\":124,\\\"learning_rate\\\":0.001,\\\"dropout_rate\\\":0.6281630602835235}}\"\n",
" ],\n",
" \"id\": \"CDlRX\",\n",
" \"status\": \"RUNNING\",\n",
" \"sequenceId\": 3,\n",
" \"logPath\": \"file://localhost:/home/chicm/nni/experiments/PlUIfDTR/trials/CDlRX\"\n",
" }\n",
"]\n"
]
"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": [
"show_json(nc.list_trial_jobs())"
"exp.list_trial_jobs()"
]
},
{
......@@ -410,20 +147,19 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
"text/plain": "<Figure size 1080x432 with 1 Axes>",
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\n"
},
"metadata": {},
"output_type": "display_data"
"metadata": {}
}
],
"source": [
......@@ -431,9 +167,9 @@
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"whitegrid\")\n",
"\n",
"jobs = nc.list_trial_jobs()\n",
"job_ids = [x['id'] for x in jobs]\n",
"final_metrics = [float(x['finalMetricData'][0]['data']) for x in jobs]\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",
......@@ -456,20 +192,19 @@
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"execution_count": 10,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Stoping experiment PlUIfDTR\n",
"INFO: Stop experiment success.\n"
]
"name": "stdout",
"text": "INFO: Stopping experiment OhHNEkLQ\nINFO: Stop experiment success.\n"
}
],
"source": [
"nc.stop_nni()"
"exp.stop_experiment()"
]
}
],
......@@ -489,9 +224,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"version": "3.8.5-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
}
\ No newline at end of file
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