Skip to content

GitLab

Commit 05913424 authored by suiguoxin's avatar suiguoxin
Browse files

Merge branch 'master' into quniform-tuners

parents e3c8552f 1dab3118
Hide whitespace changes
Inline Side-by-side
Showing with 526 additions and 59 deletions
docs/zh_CN/Nnictl.md
View file @ 05913424
......@@ -112,11 +112,18 @@ nnictl 支持的命令：
使用此命令来停止正在运行的单个或多个 Experiment。
* 用法
```bash
nnictl stop [id]
nnictl stop [OPTIONS]
```
* 选项
| 参数及缩写 | 是否必需 | 默认值 | 说明 |
| ----------- | ----- | --- | -------------------------------- |
| id | 否 | | 要停止的 Experiment 标识 |
| --port, -p | 否 | | 要停止的 Experiment 使用的 RESTful 服务端口 |
* 详细信息及样例
1. 如果没有指定 id，并且当前有运行的 Experiment，则会停止该 Experiment，否则会输出错误信息。
......@@ -131,19 +138,24 @@ nnictl 支持的命令：
```bash
nnictl stop [experiment_id]
```
3. 可使用 'nnictl stop all' 来停止所有的 Experiment。
3. 如果指定了端口，并且有运行中的 Experiment 正在使用该端口，那么这个 Experiment 将会停止。
```bash
nnictl stop --port 8080
````
4. 可使用 'nnictl stop all' 来停止所有的 Experiment。
```bash
nnictl stop all
```
4. 如果 id 以 * 结尾，nnictl 会停止所有匹配此通配符的 Experiment。
5. 如果 id 以 * 结尾，nnictl 会停止所有匹配此通配符的 Experiment。
5. 如果 id 不存在，但匹配了某个Experiment 的 id 前缀，nnictl 会停止匹配的Experiment 。
6. 如果 id 不存在，但匹配了多个 Experiment id 的前缀，nnictl 会输出这些 id 的信息。
6. 如果 id 不存在，但匹配了某个Experiment 的 id 前缀，nnictl 会停止匹配的Experiment 。
7. 如果 id 不存在，但匹配了多个 Experiment id 的前缀，nnictl 会输出这些 id 的信息。
<a name="update"></a>
......@@ -704,4 +716,4 @@ nnictl 支持的命令：
```bash
nnictl --version
```
\ No newline at end of file
```
docs/zh_CN/PaiMode.md
View file @ 05913424
......@@ -34,8 +34,6 @@ trial:
cpuNum: 1
memoryMB: 8196
image: msranni/nni:latest
dataDir: hdfs://10.1.1.1:9000/nni
outputDir: hdfs://10.1.1.1:9000/nni
# 配置访问的 OpenPAI 集群
paiConfig:
userName: your_pai_nni_user
......@@ -54,10 +52,6 @@ paiConfig:
* image
* 必填。 在 pai 模式中，Trial 程序由 OpenPAI 在 [Docker 容器](https://www.docker.com/)中安排运行。 此键用来指定 Trial 程序的容器使用的 Docker 映像。
* [Docker Hub](https://hub.docker.com/) 上有预制的 NNI Docker 映像 [nnimsra/nni](https://hub.docker.com/r/msranni/nni/)。 它包含了用来启动 NNI Experiment 所依赖的所有 Python 包，Node 模块和 JavaScript。 生成此 Docker 映像的文件在[这里](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile)。 可以直接使用此映像，或参考它来生成自己的映像。
* dataDir
* 可选。 指定了 Trial 用于下载数据的 HDFS 数据目录。 格式应为 hdfs://{your HDFS host}:9000/{数据目录}
* outputDir
* 可选。 指定了 Trial 的 HDFS 输出目录。 Trial 在完成（成功或失败）后，Trial 的 stdout， stderr 会被 NNI 自动复制到此目录中。 格式应为 hdfs://{your HDFS host}:9000/{输出目录}
* virtualCluster
* 可选。 设置 OpenPAI 的 virtualCluster，即虚拟集群。 如果未设置此参数，将使用默认的虚拟集群。
* shmMB
......
examples/notebooks/retrieve_nni_info_with_python.ipynb 0 → 100644
View file @ 05913424
{
"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",
"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."
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"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"
]
}
],
"source": [
"! cat ../trials/mnist/config.yml"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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"
]
}
],
"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')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve nni experiment and trial job information"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'errors': [], 'status': 'RUNNING'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nc.get_experiment_status()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'trialJobNumber': 4, 'trialJobStatus': 'SUCCEEDED'},\n",
" {'trialJobNumber': 1, 'trialJobStatus': 'RUNNING'}]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nc.get_job_statistics()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"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}"
]
},
"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"
]
}
],
"source": [
"show_json(nc.get_experiment_profile())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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"
]
}
],
"source": [
"show_json(nc.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": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"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",
"\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": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Stoping experiment PlUIfDTR\n",
"INFO: Stop experiment success.\n"
]
}
],
"source": [
"nc.stop_nni()"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
examples/trials/auto-feature-engineering/README.md 0 → 100644
View file @ 05913424
**Automatic Feature Engineering in nni**
===
Now we have an [example](https://github.com/SpongebBob/tabular_automl_NNI), which could automaticlly do feature engineering in nni.
These code come from our contributors. And thanks our lovely contributors!
And welcome more and more people to join us!
examples/trials/auto-gbdt/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/cifar10_pytorch/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/ga_squad/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 32869
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/mnist-advisor/config_pai.yml
View file @ 05913424
......@@ -27,10 +27,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/mnist-annotation/config_pai.yml
View file @ 05913424
......@@ -22,10 +22,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/mnist-batch-tune-keras/config_pai.yml
View file @ 05913424
......@@ -20,10 +20,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/mnist-keras/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/mnist/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/network_morphism/FashionMNIST/config_pai.yml
View file @ 05913424
......@@ -30,10 +30,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/network_morphism/cifar10/config_pai.yml
View file @ 05913424
......@@ -30,10 +30,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
examples/trials/sklearn/classification/config_pai.yml
View file @ 05913424
......@@ -23,10 +23,6 @@ trial:
memoryMB: 8196
#The docker image to run nni job on pai
image: msranni/nni:latest
#The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
dataDir: hdfs://10.10.10.10:9000/username/nni
#The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
outputDir: hdfs://10.10.10.10:9000/username/nni
paiConfig:
#The username to login pai
userName: username
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment