Reuse OpenPAI jobs to run multiple trials (#2521)

Designed new interface to support reusable training service, currently only applies to OpenPAI, and default disabled.

Replace trial_keeper.py to trial_runner.py, trial_runner holds an environment, and receives commands from nni manager to run or stop an trial, and return events to nni manager.
Add trial dispatcher, which inherits from original trianing service interface. It uses to share as many as possible code of all training service, and isolate with training services.
Add EnvironmentService interface to manage environment, including start/stop an environment, refresh status of environments.
Add command channel on both nni manager and trial runner parts, it supports different ways to pass messages between them. Current supported channels are file, web sockets. and supported commands from nni manager are start, kill trial, send new parameters; from runner are initialized(support some channel doesn't know which runner connected), trial end, stdout ((new type), including metric like before), version check (new type), gpu info (new type).
Add storage service to wrapper a storage to standard file operations, like NFS, azure storage and so on.
Partial support run multiple trials in parallel on runner side, but not supported by trial dispatcher side.
Other minor changes,

Add log_level to TS UT, so that UT can show debug level log.
Expose platform to start info.
Add RouterTrainingService to keep origianl OpenPAI training service, and support dynamic IOC binding.
Add more GPU info for future usage, including GPU mem total/free/used, gpu type.
Make some license information consistence.
Fix async/await problems on Array.forEach, this method doesn't support async actually.
Fix IT errors on download data, which causes by my #2484 .
Accelerate some run loop pattern by reducing sleep seconds.

deployment/pypi/setup.py

@@ -64,7 +64,8 @@
         'coverage',
         'colorama',
         'scikit-learn>=0.20,<0.22',
-        'pkginfo'
+        'pkginfo',
+        'websockets'
     ],
     classifiers = [
         'Programming Language :: Python :: 3',

docs/en_US/TrainingService/PaiMode.md

@@ -1,36 +1,46 @@
 **Run an Experiment on OpenPAI**
 ===
-NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker.
+NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker.
 
 ## Setup environment
+
 Step 1. Install NNI, follow the install guide [here](../Tutorial/QuickStart.md).   
 
-Step 2. Get PAI token.   
-Click `My profile` button in the top-right side of PAI's webprotal.
-![](../../img/pai_profile.jpg)  
+Step 2. Get token.
+
+Open web portal of OpenPAI, and click `My profile` button in the top-right side.
+![](../../img/pai_profile.jpg)
+
 Click `copy` button in the page to copy a jwt token.
-![](../../img/pai_token.jpg)  
+![](../../img/pai_token.jpg)
 
 Step 3. Mount NFS storage to local machine.  
-  Click `Submit job` button in PAI's webportal.
-![](../../img/pai_job_submission_page.jpg)  
-   Find the data management region in job submission page.
+
+  Click `Submit job` button in web portal.
+![](../../img/pai_job_submission_page.jpg)
+
+  Find the data management region in job submission page.
 ![](../../img/pai_data_management_page.jpg)  
-The `Preview container paths` is the NFS host and path that PAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the PAI's NFS storage.  
+
+The `Preview container paths` is the NFS host and path that OpenPAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the OpenPAI's NFS storage.  
 For example, use the following command:
-```
+
+```bash
 sudo mount -t nfs4 gcr-openpai-infra02:/pai/data /local/mnt
 ```
+
 Then the `/data` folder in container will be mounted to `/local/mnt` folder in your local machine.  
 You could use the following configuration in your NNI's config file:
-```
+
+```yaml
 nniManagerNFSMountPath: /local/mnt
 ```    
 
-Step 4. Get PAI's storage config name and nniManagerMountPath
-The `Team share storage` field is storage configuration used to specify storage value in PAI. You can get `paiStorageConfigName` and `containerNFSMountPath` field in `Team share storage`, for example:
+Step 4. Get OpenPAI's storage config name and nniManagerMountPath
 
-```
+The `Team share storage` field is storage configuration used to specify storage value in OpenPAI. You can get `paiStorageConfigName` and `containerNFSMountPath` field in `Team share storage`, for example:
+
+```yaml
 paiStorageConfigName: confignfs-data
 containerNFSMountPath: /mnt/confignfs-data
 ```
@@ -73,44 +83,86 @@ paiConfig:
   userName: your_pai_nni_user
   token: your_pai_token
   host: 10.1.1.1
+  # optional, experimental feature.
+  reuse: true
 ```
 
-Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode. The host field in configuration file is PAI's job submission page uri, like `10.10.5.1`, the default http protocol in NNI is `http`, if your PAI's cluster enabled https, please use the uri in `https://10.10.5.1` format.
+Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode.
+
+### Trial configurations
+
+Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMode.md), `trial` configuration in pai mode have these additional keys:
 
-Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMode.md), trial configuration in pai mode have these additional keys:
 * cpuNum
-    * Optional key. Should be positive number based on your trial program's CPU  requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
+  Optional key. Should be positive number based on your trial program's CPU  requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
 * memoryMB
-    * Optional key. Should be positive number based on your trial program's memory requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
+  Optional key. Should be positive number based on your trial program's memory requirement. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
 * image
-    * Optional key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your trial will run.
-    * We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
+  Optional key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your trial will run.
+
+  We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
 * virtualCluster
-    * Optional key. Set the virtualCluster of OpenPAI. If omitted, the job will run on default virtual cluster.
+
+  Optional key. Set the virtualCluster of OpenPAI. If omitted, the job will run on default virtual cluster.
+
 * nniManagerNFSMountPath
-    * Required key. Set the mount path in your nniManager machine.
+
+  Required key. Set the mount path in your nniManager machine.
+
 * containerNFSMountPath
-    * Required key. Set the mount path in your container used in PAI.
+
+  Required key. Set the mount path in your container used in OpenPAI.
+
 * paiStorageConfigName:
-    * Optional key. Set the storage name used in PAI. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
-* command  
-    * Optional key. Set the commands used in PAI container.
+
+  Optional key. Set the storage name used in OpenPAI. If it is not set in trial configuration, it should be set in the config file specified in `paiConfigPath` field.
+
+* command
+
+  Optional key. Set the commands used in OpenPAI container.
+
 * paiConfigPath
-    * Optional key. Set the file path of pai job configuration, the file is in yaml format.
-    If users set `paiConfigPath` in NNI's configuration file, no need to specify the fields `command`, `paiStorageConfigName`, `virtualCluster`, `image`, `memoryMB`, `cpuNum`, `gpuNum` in `trial` configuration. These fields will use the values from the config file specified by  `paiConfigPath`. 
-    ```
-    Note:
-      1. The job name in PAI's configuration file will be replaced by a new job name, the new job name is created by NNI, the name format is nni_exp_${this.experimentId}_trial_${trialJobId}.
+  Optional key. Set the file path of OpenPAI job configuration, the file is in yaml format.
+
+  If users set `paiConfigPath` in NNI's configuration file, no need to specify the fields `command`, `paiStorageConfigName`, `virtualCluster`, `image`, `memoryMB`, `cpuNum`, `gpuNum` in `trial` configuration. These fields will use the values from the config file specified by  `paiConfigPath`.
+
+  Note:
+  1. The job name in OpenPAI's configuration file will be replaced by a new job name, the new job name is created by NNI, the name format is nni_exp_${this.experimentId}_trial_${trialJobId}.
 
-      2.  If users set multiple taskRoles in PAI's configuration file, NNI will wrap all of these taksRoles and start multiple tasks in one trial job, users should ensure that only one taskRole report metric to NNI, otherwise there might be some conflict error. 
+  2. If users set multiple taskRoles in OpenPAI's configuration file, NNI will wrap all of these taksRoles and start multiple tasks in one trial job, users should ensure that only one taskRole report metric to NNI, otherwise there might be some conflict error.
 
-    ```  
+### OpenPAI configurations
 
+`paiConfig` includes OpenPAI specific configurations,
+
+* userName
+
+  Required key. User name of OpenPAI platform.
+
+* token
+
+  Required key. Authentication key of OpenPAI platform.
+
+* host
+
+  Required key. The host of OpenPAI platform. It's OpenPAI's job submission page uri, like `10.10.5.1`, the default http protocol in NNI is `http`, if your OpenPAI cluster enabled https, please use the uri in `https://10.10.5.1` format.
+
+* reuse (experimental feature)
+
+  Optional key, default is false. If it's true, NNI will reuse OpenPAI jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials.
 
 Once complete to fill NNI experiment config file and save (for example, save as exp_pai.yml), then run the following command
-```
+
+```bash
 nnictl create --config exp_pai.yml
 ```
+
 to start the experiment in pai mode. NNI will create OpenPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`.
 You can see jobs created by NNI in the OpenPAI cluster's web portal, like:
 ![](../../img/nni_pai_joblist.jpg)
@@ -128,11 +180,12 @@ And you will be redirected to HDFS web portal to browse the output files of that
 You can see there're three fils in output folder: stderr, stdout, and trial.log
 
 ## data management
-Before using NNI to start your experiment, users should set the corresponding mount data path in your nniManager machine. PAI has their own storage(NFS, AzureBlob ...), and the storage will used in PAI will be mounted to the container when it start a job. Users should set the PAI storage type by `paiStorageConfigName` field to choose a storage in PAI. Then users should mount the storage to their nniManager machine, and set the `nniManagerNFSMountPath` field in configuration file, NNI will generate bash files and copy data in `codeDir` to the `nniManagerNFSMountPath` folder, then NNI will start a trial job. The data in `nniManagerNFSMountPath` will be sync to PAI storage, and will be mounted to PAI's container. The data path in container is set in `containerNFSMountPath`, NNI will enter this folder first, and then run scripts to start a trial job. 
+Before using NNI to start your experiment, users should set the corresponding mount data path in your nniManager machine. OpenPAI has their own storage(NFS, AzureBlob ...), and the storage will used in OpenPAI will be mounted to the container when it start a job. Users should set the OpenPAI storage type by `paiStorageConfigName` field to choose a storage in OpenPAI. Then users should mount the storage to their nniManager machine, and set the `nniManagerNFSMountPath` field in configuration file, NNI will generate bash files and copy data in `codeDir` to the `nniManagerNFSMountPath` folder, then NNI will start a trial job. The data in `nniManagerNFSMountPath` will be sync to OpenPAI storage, and will be mounted to OpenPAI's container. The data path in container is set in `containerNFSMountPath`, NNI will enter this folder first, and then run scripts to start a trial job. 
 
 ## version check
 NNI support version check feature in since version 0.6. It is a policy to insure the version of NNIManager is consistent with trialKeeper, and avoid errors caused by version incompatibility.
 Check policy:
+
 1. NNIManager before v0.6 could run any version of trialKeeper, trialKeeper support backward compatibility.
 2. Since version 0.6, NNIManager version should keep same with triakKeeper version. For example, if NNIManager version is 0.6, trialKeeper version should be 0.6 too.
 3. Note that the version check feature only check first two digits of version.For example, NNIManager v0.6.1 could use trialKeeper v0.6 or trialKeeper v0.6.2, but could not use trialKeeper v0.5.1 or trialKeeper v0.7.

docs/en_US/Tutorial/ExperimentConfig.md

@@ -70,6 +70,7 @@ This document describes the rules to write the config file, and provides some ex
       - [password](#password)
       - [token](#token)
       - [host](#host)
+      - [reuse](#reuse)
   * [Examples](#examples)
     + [Local mode](#local-mode)
     + [Remote mode](#remote-mode)
@@ -656,6 +657,12 @@ Required. String.
 
 The hostname of IP address of PAI.
 
+#### reuse
+
+Optional. Bool. default: `false`. It's an experimental feature.
+
+If it's true, NNI will reuse OpenPAI jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials.
+
 ## Examples
 
 ### Local mode

setup.py

@@ -42,7 +42,8 @@ def read(fname):
         'PythonWebHDFS',
         'colorama',
         'scikit-learn>=0.20,<0.22',
-        'pkginfo'
+        'pkginfo',
+        'websockets'
     ],
 
     entry_points = {

src/nni_manager/common/experimentStartupInfo.ts

@@ -17,14 +17,16 @@ class ExperimentStartupInfo {
     private logDir: string = '';
     private logLevel: string = '';
     private readonly: boolean = false;
+    private platform: string = '';
 
-    public setStartupInfo(newExperiment: boolean, experimentId: string, basePort: number, logDir?: string, logLevel?: string, readonly?: boolean): void {
+    public setStartupInfo(newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean): void {
         assert(!this.initialized);
         assert(experimentId.trim().length > 0);
         this.newExperiment = newExperiment;
         this.experimentId = experimentId;
         this.basePort = basePort;
         this.initialized = true;
+        this.platform = platform;
 
         if (logDir !== undefined && logDir.length > 0) {
             this.logDir = path.join(path.normalize(logDir), this.getExperimentId());
@@ -59,6 +61,12 @@ class ExperimentStartupInfo {
         return this.newExperiment;
     }
 
+    public getPlatform(): string {
+        assert(this.initialized);
+
+        return this.platform;
+    }
+
     public getLogDir(): string {
         assert(this.initialized);
 
@@ -90,19 +98,25 @@ function isNewExperiment(): boolean {
     return component.get<ExperimentStartupInfo>(ExperimentStartupInfo).isNewExperiment();
 }
 
+function getPlatform(): string {
+    return component.get<ExperimentStartupInfo>(ExperimentStartupInfo).getPlatform();
+}
+
 function getExperimentStartupInfo(): ExperimentStartupInfo {
     return component.get<ExperimentStartupInfo>(ExperimentStartupInfo);
 }
 
 function setExperimentStartupInfo(
-    newExperiment: boolean, experimentId: string, basePort: number, logDir?: string, logLevel?: string, readonly?: boolean): void {
+    newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean): void {
     component.get<ExperimentStartupInfo>(ExperimentStartupInfo)
-    .setStartupInfo(newExperiment, experimentId, basePort, logDir, logLevel, readonly);
+        .setStartupInfo(newExperiment, experimentId, basePort, platform, logDir, logLevel, readonly);
 }
 
 function isReadonly(): boolean {
     return component.get<ExperimentStartupInfo>(ExperimentStartupInfo).isReadonly();
 }
 
-export { ExperimentStartupInfo, getBasePort, getExperimentId, isNewExperiment, getExperimentStartupInfo,
-    setExperimentStartupInfo, isReadonly };
+export {
+    ExperimentStartupInfo, getBasePort, getExperimentId, isNewExperiment, getPlatform, getExperimentStartupInfo,
+    setExperimentStartupInfo, isReadonly
+};

src/nni_manager/common/utils.ts

@@ -19,6 +19,7 @@ import { Database, DataStore } from './datastore';
 import { ExperimentStartupInfo, getExperimentStartupInfo, setExperimentStartupInfo } from './experimentStartupInfo';
 import { ExperimentParams, Manager } from './manager';
 import { HyperParameters, TrainingService, TrialJobStatus } from './trainingService';
+import { logLevelNameMap } from './log';
 
 function getExperimentRootDir(): string {
     return getExperimentStartupInfo()
@@ -184,7 +185,12 @@ function prepareUnitTest(): void {
     Container.snapshot(TrainingService);
     Container.snapshot(Manager);
 
-    setExperimentStartupInfo(true, 'unittest', 8080);
+    const logLevel: string = parseArg(['--log_level', '-ll']);
+    if (logLevel.length > 0 && !logLevelNameMap.has(logLevel)) {
+        console.log(`FATAL: invalid log_level: ${logLevel}`);
+    }
+
+    setExperimentStartupInfo(true, 'unittest', 8080, 'unittest', undefined, logLevel);
     mkDirPSync(getLogDir());
 
     const sqliteFile: string = path.join(getDefaultDatabaseDir(), 'nni.sqlite');

src/nni_manager/core/commands.ts

@@ -12,12 +12,30 @@ const TRIAL_END = 'EN';
 const TERMINATE = 'TE';
 const PING = 'PI';
 
+const GPU_INFO = 'GI';
+const STDOUT = 'SO';
+const VERSION_CHECK = 'VC';
+
 const INITIALIZED = 'ID';
 const NEW_TRIAL_JOB = 'TR';
 const SEND_TRIAL_JOB_PARAMETER = 'SP';
 const NO_MORE_TRIAL_JOBS = 'NO';
 const KILL_TRIAL_JOB = 'KI';
 
+const TRIAL_COMMANDS: Set<string> = new Set([
+    // from ctl to node
+    NEW_TRIAL_JOB,
+    SEND_TRIAL_JOB_PARAMETER,
+    KILL_TRIAL_JOB,
+
+    // from node to ctl
+    INITIALIZED,
+    TRIAL_END,
+    GPU_INFO,
+    STDOUT,
+    VERSION_CHECK,
+]);
+
 const TUNER_COMMANDS: Set<string> = new Set([
     INITIALIZE,
     REQUEST_TRIAL_JOBS,
@@ -53,11 +71,15 @@ export {
     TRIAL_END,
     TERMINATE,
     PING,
+    GPU_INFO,
+    STDOUT,
+    VERSION_CHECK,
     INITIALIZED,
     NEW_TRIAL_JOB,
     NO_MORE_TRIAL_JOBS,
     KILL_TRIAL_JOB,
     TUNER_COMMANDS,
     ASSESSOR_COMMANDS,
+    TRIAL_COMMANDS,
     SEND_TRIAL_JOB_PARAMETER
 };

src/nni_manager/core/ipcInterface.ts

@@ -135,4 +135,4 @@ function createDispatcherInterface(process: ChildProcess): IpcInterface {
     return new IpcInterface(process, new Set([...CommandType.TUNER_COMMANDS, ...CommandType.ASSESSOR_COMMANDS]));
 }
 
-export { IpcInterface, createDispatcherInterface };
+export { IpcInterface, createDispatcherInterface, encodeCommand, decodeCommand };