Designated gpu devices for NNI trial jobs (#991)

* Refactoring local training service
* Designated GPU for local training service
* RemoteMachine designated GPU configuration

docs/en_US/ExperimentConfig.md

@@ -392,6 +392,13 @@ machineList:
 
       __image__ set the image to be used in __worker__.
 
+* __localConfig__
+
+  __localConfig__ is applicable only if __trainingServicePlatform__ is set to ```local```, otherwise there should not be __localConfig__ section in configuration file.
+  * __gpuIndices__
+  
+    __gpuIndices__ is used to specify designated GPU devices for NNI, if it is set, only the specified GPU devices are used for NNI trial jobs. Single or multiple GPU indices can be specified, multiple GPU indices are seperated by comma(,), such as ```1``` or  ```0,1,3```.
+
 * __machineList__
 
   __machineList__ should be set if __trainingServicePlatform__ is set to remote, or it should be empty.
@@ -422,6 +429,10 @@ machineList:
 
     __passphrase__ is used to protect ssh key, which could be empty if users don't have passphrase.
 
+  * __gpuIndices__
+  
+    __gpuIndices__ is used to specify designated GPU devices for NNI on this remote machine, if it is set, only the specified GPU devices are used for NNI trial jobs. Single or multiple GPU indices can be specified, multiple GPU indices are seperated by comma(,), such as ```1``` or  ```0,1,3```.
+
 * __kubeflowConfig__:
 
   * __operator__

src/nni_manager/main.ts

@@ -21,27 +21,29 @@
 
 import { Container, Scope } from 'typescript-ioc';
 
-import * as component from './common/component';
 import * as fs from 'fs';
+import * as component from './common/component';
 import { Database, DataStore } from './common/datastore';
 import { setExperimentStartupInfo } from './common/experimentStartupInfo';
 import { getLogger, Logger, logLevelNameMap } from './common/log';
 import { Manager } from './common/manager';
 import { TrainingService } from './common/trainingService';
-import { parseArg, uniqueString, mkDirP, getLogDir } from './common/utils';
+import { getLogDir, mkDirP, parseArg, uniqueString } from './common/utils';
 import { NNIDataStore } from './core/nniDataStore';
 import { NNIManager } from './core/nnimanager';
 import { SqlDB } from './core/sqlDatabase';
 import { NNIRestServer } from './rest_server/nniRestServer';
-import { LocalTrainingServiceForGPU } from './training_service/local/localTrainingServiceForGPU';
+import { FrameworkControllerTrainingService } from './training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService';
+import { KubeflowTrainingService } from './training_service/kubernetes/kubeflow/kubeflowTrainingService';
+import { LocalTrainingService } from './training_service/local/localTrainingService';
+import { PAITrainingService } from './training_service/pai/paiTrainingService';
 import {
     RemoteMachineTrainingService
 } from './training_service/remote_machine/remoteMachineTrainingService';
-import { PAITrainingService } from './training_service/pai/paiTrainingService';
-import { KubeflowTrainingService } from './training_service/kubernetes/kubeflow/kubeflowTrainingService';
-import { FrameworkControllerTrainingService } from './training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService';
 
-function initStartupInfo(startExpMode: string, resumeExperimentId: string, basePort: number, logDirectory: string, experimentLogLevel: string) {
+function initStartupInfo(
+    startExpMode: string, resumeExperimentId: string, basePort: number,
+    logDirectory: string, experimentLogLevel: string): void {
     const createNew: boolean = (startExpMode === 'new');
     const expId: string = createNew ? uniqueString(8) : resumeExperimentId;
     setExperimentStartupInfo(createNew, expId, basePort, logDirectory, experimentLogLevel);
@@ -49,29 +51,45 @@ function initStartupInfo(startExpMode: string, resumeExperimentId: string, baseP
 
 async function initContainer(platformMode: string): Promise<void> {
     if (platformMode === 'local') {
-        Container.bind(TrainingService).to(LocalTrainingServiceForGPU).scope(Scope.Singleton);
+        Container.bind(TrainingService)
+            .to(LocalTrainingService)
+            .scope(Scope.Singleton);
     } else if (platformMode === 'remote') {
-        Container.bind(TrainingService).to(RemoteMachineTrainingService).scope(Scope.Singleton);
+        Container.bind(TrainingService)
+            .to(RemoteMachineTrainingService)
+            .scope(Scope.Singleton);
     } else if (platformMode === 'pai') {
-        Container.bind(TrainingService).to(PAITrainingService).scope(Scope.Singleton);
+        Container.bind(TrainingService)
+            .to(PAITrainingService)
+            .scope(Scope.Singleton);
     } else if (platformMode === 'kubeflow') {
-        Container.bind(TrainingService).to(KubeflowTrainingService).scope(Scope.Singleton);
+        Container.bind(TrainingService)
+            .to(KubeflowTrainingService)
+            .scope(Scope.Singleton);
     } else if (platformMode === 'frameworkcontroller') {
-        Container.bind(TrainingService).to(FrameworkControllerTrainingService).scope(Scope.Singleton);
-    }
-    else {
+        Container.bind(TrainingService)
+            .to(FrameworkControllerTrainingService)
+            .scope(Scope.Singleton);
+    } else {
         throw new Error(`Error: unsupported mode: ${mode}`);
     }
-    Container.bind(Manager).to(NNIManager).scope(Scope.Singleton);
-    Container.bind(Database).to(SqlDB).scope(Scope.Singleton);
-    Container.bind(DataStore).to(NNIDataStore).scope(Scope.Singleton);
+    Container.bind(Manager)
+        .to(NNIManager)
+        .scope(Scope.Singleton);
+    Container.bind(Database)
+        .to(SqlDB)
+        .scope(Scope.Singleton);
+    Container.bind(DataStore)
+        .to(NNIDataStore)
+        .scope(Scope.Singleton);
     const ds: DataStore = component.get(DataStore);
 
     await ds.init();
 }
 
 function usage(): void {
-    console.info('usage: node main.js --port <port> --mode <local/remote/pai/kubeflow/frameworkcontroller> --start_mode <new/resume> --experiment_id <id>');
+    console.info('usage: node main.js --port <port> --mode \
+    <local/remote/pai/kubeflow/frameworkcontroller> --start_mode <new/resume> --experiment_id <id>');
 }
 
 const strPort: string = parseArg(['--port', '-p']);
@@ -117,7 +135,8 @@ if (logLevel.length > 0 && !logLevelNameMap.has(logLevel)) {
 
 initStartupInfo(startMode, experimentId, port, logDir, logLevel);
 
-mkDirP(getLogDir()).then(async () => {
+mkDirP(getLogDir())
+    .then(async () => {
     const log: Logger = getLogger();
     try {
         await initContainer(mode);
@@ -128,25 +147,26 @@ mkDirP(getLogDir()).then(async () => {
         log.error(`${err.stack}`);
         throw err;
     }
-}).catch((err: Error) => {
+})
+.catch((err: Error) => {
     console.error(`Failed to create log dir: ${err.stack}`);
 });
 
 process.on('SIGTERM', async () => {
     const log: Logger = getLogger();
     let hasError: boolean = false;
-    try{
+    try {
         const nniManager: Manager = component.get(Manager);
         await nniManager.stopExperiment();
         const ds: DataStore = component.get(DataStore);
         await ds.close();
         const restServer: NNIRestServer = component.get(NNIRestServer);
         await restServer.stop();
-    }catch(err){
+    } catch (err) {
         hasError = true;
         log.error(`${err.stack}`);
-    }finally{
+    } finally {
         await log.close();
-        process.exit(hasError?1:0);
+        process.exit(hasError ? 1 : 0);
     }
-})
+});
\ No newline at end of file

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -30,8 +30,12 @@ export namespace ValidationSchemas {
                 port: joi.number().min(1).max(65535).required(),
                 passwd: joi.string(),
                 sshKeyPath: joi.string(),
-                passphrase: joi.string()
+                passphrase: joi.string(),
+                gpuIndices: joi.string()
             })),
+            local_config: joi.object({
+                gpuIndices: joi.string()
+            }),
             trial_config: joi.object({
                 image: joi.string().min(1),
                 codeDir: joi.string().min(1).required(),

src/nni_manager/training_service/common/trialConfigMetadataKey.ts

@@ -24,6 +24,7 @@
  */
 export enum TrialConfigMetadataKey {
     MACHINE_LIST = 'machine_list',
+    LOCAL_CONFIG = 'local_config',
     TRIAL_CONFIG = 'trial_config',
     EXPERIMENT_ID = 'experimentId',
     MULTI_PHASE = 'multiPhase',

src/nni_manager/training_service/local/gpuScheduler.ts

@@ -19,19 +19,18 @@
 
 'use strict';
 
-import { delay } from '../../common/utils';
-import { GPUInfo, GPUSummary } from '../common/gpuData';
-import { getLogger, Logger } from '../../common/log';
-import * as cp from 'child_process';
 import * as cpp from 'child-process-promise';
-import * as path from 'path';
-import * as os from 'os';
+import * as cp from 'child_process';
 import * as fs from 'fs';
+import * as os from 'os';
+import * as path from 'path';
 import { String } from 'typescript-string-operations';
-import { GPU_INFO_COLLECTOR_FORMAT } from '../common/gpuData'
+import { getLogger, Logger } from '../../common/log';
+import { delay } from '../../common/utils';
+import { GPU_INFO_COLLECTOR_FORMAT, GPUInfo, GPUSummary } from '../common/gpuData';
 
 /**
- * GPUScheduler
+ * GPUScheduler for local training service
  */
 class GPUScheduler {
 
@@ -58,45 +57,55 @@ class GPUScheduler {
         }
     }
 
-    /**
-     * Generate gpu metric collector shell script in local machine, 
-     * used to run in remote machine, and will be deleted after uploaded from local. 
-     */
-    private async runGpuMetricsCollectorScript(): Promise<void> {
-        await cpp.exec(`mkdir -p ${this.gpuMetricCollectorScriptFolder}`);
-        //generate gpu_metrics_collector.sh
-        let gpuMetricsCollectorScriptPath: string = path.join(this.gpuMetricCollectorScriptFolder, 'gpu_metrics_collector.sh');
-        const gpuMetricsCollectorScriptContent: string = String.Format(
-            GPU_INFO_COLLECTOR_FORMAT,
-            this.gpuMetricCollectorScriptFolder,
-            path.join(this.gpuMetricCollectorScriptFolder, 'pid'),
-        );
-        await fs.promises.writeFile(gpuMetricsCollectorScriptPath, gpuMetricsCollectorScriptContent, { encoding: 'utf8' });
-        cp.exec(`bash ${gpuMetricsCollectorScriptPath}`);
-    }
-
     public getAvailableGPUIndices(): number[] {
         if (this.gpuSummary !== undefined) {
-            return this.gpuSummary.gpuInfos.filter((info: GPUInfo) => info.activeProcessNum === 0).map((info: GPUInfo) => info.index);
+            return this.gpuSummary.gpuInfos.filter((info: GPUInfo) => info.activeProcessNum === 0)
+                .map((info: GPUInfo) => info.index);
         }
 
         return [];
     }
 
-    public async stop() {
+    public getSystemGpuCount(): number {
+        if (this.gpuSummary !== undefined) {
+            return this.gpuSummary.gpuCount;
+        }
+
+        return 0;
+    }
+
+    public async stop(): Promise<void> {
         this.stopping = true;
         try {
             const pid: string = await fs.promises.readFile(path.join(this.gpuMetricCollectorScriptFolder, 'pid'), 'utf8');
             await cpp.exec(`pkill -P ${pid}`);
             await cpp.exec(`rm -rf ${this.gpuMetricCollectorScriptFolder}`);
-        } catch (error){
+        } catch (error) {
             this.log.error(`GPU scheduler error: ${error}`);
         }
     }
 
-    private async updateGPUSummary() {
-        const cmdresult = await cpp.exec(`tail -n 1 ${path.join(this.gpuMetricCollectorScriptFolder, 'gpu_metrics')}`);
-        if(cmdresult && cmdresult.stdout) {
+    /**
+     * Generate gpu metric collector shell script in local machine,
+     * used to run in remote machine, and will be deleted after uploaded from local.
+     */
+    private async runGpuMetricsCollectorScript(): Promise<void> {
+        await cpp.exec(`mkdir -p ${this.gpuMetricCollectorScriptFolder}`);
+        //generate gpu_metrics_collector.sh
+        const gpuMetricsCollectorScriptPath: string = path.join(this.gpuMetricCollectorScriptFolder, 'gpu_metrics_collector.sh');
+        const gpuMetricsCollectorScriptContent: string = String.Format(
+            GPU_INFO_COLLECTOR_FORMAT,
+            this.gpuMetricCollectorScriptFolder,
+            path.join(this.gpuMetricCollectorScriptFolder, 'pid')
+        );
+        await fs.promises.writeFile(gpuMetricsCollectorScriptPath, gpuMetricsCollectorScriptContent, { encoding: 'utf8' });
+        cp.exec(`bash ${gpuMetricsCollectorScriptPath}`);
+    }
+
+    private async updateGPUSummary(): Promise<void> {
+        const cmdresult: cpp.childProcessPromise.Result =
+            await cpp.exec(`tail -n 1 ${path.join(this.gpuMetricCollectorScriptFolder, 'gpu_metrics')}`);
+        if (cmdresult && cmdresult.stdout) {
             this.gpuSummary = <GPUSummary>JSON.parse(cmdresult.stdout);
         } else {
             this.log.error('Could not get gpu metrics information!');

src/nni_manager/training_service/local/localTrainingService.ts

@@ -19,7 +19,6 @@
 
 'use strict';
 
-import * as assert from 'assert';
 import * as cpp from 'child-process-promise';
 import * as cp from 'child_process';
 import { EventEmitter } from 'events';
@@ -27,15 +26,16 @@ import * as fs from 'fs';
 import * as path from 'path';
 import * as ts from 'tail-stream';
 import { NNIError, NNIErrorNames } from '../../common/errors';
-import { getLogger, Logger } from '../../common/log';
-import { TrialConfig } from '../common/trialConfig';
-import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
 import { getInitTrialSequenceId } from '../../common/experimentStartupInfo';
+import { getLogger, Logger } from '../../common/log';
 import {
-    HostJobApplicationForm, JobApplicationForm, HyperParameters, TrainingService, TrialJobApplicationForm,
+    HostJobApplicationForm, HyperParameters, JobApplicationForm, TrainingService, TrialJobApplicationForm,
     TrialJobDetail, TrialJobMetric, TrialJobStatus
 } from '../../common/trainingService';
-import { delay, generateParamFileName, getExperimentRootDir, uniqueString, getJobCancelStatus } from '../../common/utils';
+import { delay, generateParamFileName, getExperimentRootDir, getJobCancelStatus, uniqueString } from '../../common/utils';
+import { TrialConfig } from '../common/trialConfig';
+import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
+import { GPUScheduler } from './gpuScheduler';
 
 const tkill = require('tree-kill');
 
@@ -46,6 +46,7 @@ const tkill = require('tree-kill');
  *          success: true if the buffer contains at least one complete command; otherwise false
  *          remain: remaining data after the first command
  */
+// tslint:disable-next-line:informative-docs
 function decodeCommand(data: Buffer): [boolean, string, string, Buffer] {
     if (data.length < 8) {
         return [false, '', '', data];
@@ -76,8 +77,10 @@ class LocalTrialJobDetail implements TrialJobDetail {
     public form: JobApplicationForm;
     public sequenceId: number;
     public pid?: number;
+    public gpuIndices?: number[];
 
-    constructor(id: string, status: TrialJobStatus, submitTime: number,
+    constructor(
+        id: string, status: TrialJobStatus, submitTime: number,
         workingDirectory: string, form: JobApplicationForm, sequenceId: number) {
         this.id = id;
         this.status = status;
@@ -86,6 +89,19 @@ class LocalTrialJobDetail implements TrialJobDetail {
         this.form = form;
         this.url = `file://localhost:${workingDirectory}`;
         this.sequenceId = sequenceId;
+        this.gpuIndices = [];
+    }
+}
+
+/**
+ * Local training service config
+ */
+class LocalConfig {
+    public gpuIndices?: string;
+    constructor(gpuIndices?: string) {
+        if (gpuIndices !== undefined) {
+            this.gpuIndices = gpuIndices;
+        }
     }
 }
 
@@ -100,10 +116,14 @@ class LocalTrainingService implements TrainingService {
     private stopping: boolean;
     private rootDir!: string;
     private trialSequenceId: number;
-    protected log: Logger;
-    protected localTrailConfig?: TrialConfig;
+    private gpuScheduler!: GPUScheduler;
+    private occupiedGpuIndices: Set<number>;
+    private designatedGpuIndices!: Set<number>;
+    private log: Logger;
+    private localTrailConfig?: TrialConfig;
+    private localConfig?: LocalConfig;
     private isMultiPhase: boolean = false;
-    protected jobStreamMap: Map<string, ts.Stream>;
+    private jobStreamMap: Map<string, ts.Stream>;
 
     constructor() {
         this.eventEmitter = new EventEmitter();
@@ -115,26 +135,16 @@ class LocalTrainingService implements TrainingService {
         this.trialSequenceId = -1;
         this.jobStreamMap = new Map<string, ts.Stream>();
         this.log.info('Construct local machine training service.');
+        this.occupiedGpuIndices = new Set<number>();
     }
 
     public async run(): Promise<void> {
         this.log.info('Run local machine training service.');
-        while (!this.stopping) {
-            while (this.jobQueue.length !== 0) {
-                const trialJobId: string = this.jobQueue[0];
-                const trialJobDeatil = this.jobMap.get(trialJobId)
-                if (trialJobDeatil !== undefined && trialJobDeatil.status === 'WAITING'){
-                    const [success, resource] = this.tryGetAvailableResource();
-                    if (!success) {
-                        break;
-                    }
-                    this.occupyResource(resource);
-                    await this.runTrialJob(trialJobId, resource);
-                }
-                this.jobQueue.shift();
-            }
-            await delay(5000);
+        const longRunningTasks: Promise<void>[] = [this.runJobLoop()];
+        if (this.gpuScheduler !== undefined) {
+            longRunningTasks.push(this.gpuScheduler.run());
         }
+        await Promise.all(longRunningTasks);
         this.log.info('Local machine training service exit.');
     }
 
@@ -172,7 +182,8 @@ class LocalTrainingService implements TrainingService {
                 this.setTrialJobStatus(trialJob, 'FAILED');
                 try {
                     const state: string = await fs.promises.readFile(path.join(trialJob.workingDirectory, '.nni', 'state'), 'utf8');
-                    const match: RegExpMatchArray | null = state.trim().match(/^(\d+)\s+(\d+)/);
+                    const match: RegExpMatchArray | null = state.trim()
+                        .match(/^(\d+)\s+(\d+)/);
                     if (match !== null) {
                         const { 1: code, 2: timestamp } = match;
                         if (parseInt(code, 10) === 0) {
@@ -253,8 +264,9 @@ class LocalTrainingService implements TrainingService {
         if (trialJob === undefined) {
             throw new NNIError(NNIErrorNames.NOT_FOUND, 'Trial job not found');
         }
-        if (trialJob.pid === undefined){
+        if (trialJob.pid === undefined) {
             this.setTrialJobStatus(trialJob, 'USER_CANCELED');
+
             return Promise.resolve();
         }
         if (trialJob.form.jobType === 'TRIAL') {
@@ -265,6 +277,7 @@ class LocalTrainingService implements TrainingService {
             throw new Error(`Job type not supported: ${trialJob.form.jobType}`);
         }
         this.setTrialJobStatus(trialJob, getJobCancelStatus(isEarlyStopped));
+
         return Promise.resolve();
     }
 
@@ -281,6 +294,21 @@ class LocalTrainingService implements TrainingService {
                 if (!this.localTrailConfig) {
                     throw new Error('trial config parsed failed');
                 }
+                this.log.info(`required GPU number is ${this.localTrailConfig.gpuNum}`);
+                if (this.gpuScheduler === undefined && this.localTrailConfig.gpuNum > 0) {
+                    this.gpuScheduler = new GPUScheduler();
+                }
+                break;
+            case TrialConfigMetadataKey.LOCAL_CONFIG:
+                this.localConfig = <LocalConfig>JSON.parse(value);
+                this.log.info(`Specified GPU indices: ${this.localConfig.gpuIndices}`);
+                if (this.localConfig.gpuIndices !== undefined) {
+                    this.designatedGpuIndices = new Set(this.localConfig.gpuIndices.split(',')
+                            .map((x: string) => parseInt(x, 10)));
+                    if (this.designatedGpuIndices.size === 0) {
+                        throw new Error('gpuIndices can not be empty if specified.');
+                    }
+                }
                 break;
             case TrialConfigMetadataKey.MULTI_PHASE:
                 this.isMultiPhase = (value === 'true' || value === 'True');
@@ -298,37 +326,51 @@ class LocalTrainingService implements TrainingService {
                 } else {
                     getResult = Promise.resolve(!this.localTrailConfig ? '' : JSON.stringify(this.localTrailConfig));
                 }
+
                 return getResult;
             default:
                 return Promise.reject(new NNIError(NNIErrorNames.NOT_FOUND, 'Key not found'));
         }
     }
 
-    public cleanUp(): Promise<void> {
+    public async cleanUp(): Promise<void> {
         this.log.info('Stopping local machine training service...');
         this.stopping = true;
         for (const stream of this.jobStreamMap.values()) {
             stream.destroy();
         }
+        if (this.gpuScheduler !== undefined) {
+            await this.gpuScheduler.stop();
+        }
+
         return Promise.resolve();
     }
 
-    protected onTrialJobStatusChanged(trialJob: TrialJobDetail, oldStatus: TrialJobStatus): void {
+    private onTrialJobStatusChanged(trialJob: LocalTrialJobDetail, oldStatus: TrialJobStatus): void {
         //if job is not running, destory job stream
-        if(['SUCCEEDED', 'FAILED', 'USER_CANCELED', 'SYS_CANCELED', 'EARLY_STOPPED'].includes(trialJob.status)) {
-            if(this.jobStreamMap.has(trialJob.id)) {
-                const stream = this.jobStreamMap.get(trialJob.id);
-                if(!stream) {
+        if (['SUCCEEDED', 'FAILED', 'USER_CANCELED', 'SYS_CANCELED', 'EARLY_STOPPED'].includes(trialJob.status)) {
+            if (this.jobStreamMap.has(trialJob.id)) {
+                const stream: ts.Stream | undefined = this.jobStreamMap.get(trialJob.id);
+                if (!stream) {
                     throw new Error(`Could not find stream in trial ${trialJob.id}`);
                 }
                 stream.destroy();
                 this.jobStreamMap.delete(trialJob.id);
             }
         }
+        if (trialJob.gpuIndices !== undefined && trialJob.gpuIndices.length > 0 && this.gpuScheduler !== undefined) {
+            if (oldStatus === 'RUNNING' && trialJob.status !== 'RUNNING') {
+                for (const index of trialJob.gpuIndices) {
+                    this.occupiedGpuIndices.delete(index);
+                }
+            }
+        }
     }
 
-    protected getEnvironmentVariables(trialJobDetail: TrialJobDetail, _: {}): { key: string; value: string }[] {
-        return [
+    private getEnvironmentVariables(
+        trialJobDetail: TrialJobDetail,
+        resource?: { gpuIndices: number[] }): { key: string; value: string }[] {
+        const envVariables: { key: string; value: string }[] = [
             { key: 'NNI_PLATFORM', value: 'local' },
             { key: 'NNI_SYS_DIR', value: trialJobDetail.workingDirectory },
             { key: 'NNI_TRIAL_JOB_ID', value: trialJobDetail.id },
@@ -336,18 +378,85 @@ class LocalTrainingService implements TrainingService {
             { key: 'NNI_TRIAL_SEQ_ID', value: trialJobDetail.sequenceId.toString() },
             { key: 'MULTI_PHASE', value: this.isMultiPhase.toString() }
         ];
+
+        if (resource !== undefined && resource.gpuIndices.length > 0) {
+            envVariables.push({
+                    key: 'CUDA_VISIBLE_DEVICES',
+                    value: this.gpuScheduler === undefined ? '' : resource.gpuIndices.join(',')
+            });
+        }
+
+        return envVariables;
+    }
+
+    private setExtraProperties(trialJobDetail: LocalTrialJobDetail, resource: { gpuIndices: number[] }): void {
+        trialJobDetail.gpuIndices = resource.gpuIndices;
+    }
+
+    private tryGetAvailableResource(): [boolean, { gpuIndices: number[]}] {
+        if (this.localTrailConfig === undefined) {
+            throw new Error('localTrailConfig is not initialized!');
+        }
+
+        const resource: { gpuIndices: number[] } = { gpuIndices: [] };
+        if (this.gpuScheduler === undefined) {
+            return [true, resource];
+        }
+
+        let selectedGPUIndices: number[] = this.gpuScheduler.getAvailableGPUIndices()
+            .filter((index: number) => !this.occupiedGpuIndices.has(index));
+
+        if (this.designatedGpuIndices !== undefined) {
+            this.checkSpecifiedGpuIndices();
+            selectedGPUIndices = selectedGPUIndices.filter((index: number) => this.designatedGpuIndices.has(index));
+        }
+
+        if (selectedGPUIndices.length < this.localTrailConfig.gpuNum) {
+            return [false, resource];
+        }
+
+        selectedGPUIndices.splice(this.localTrailConfig.gpuNum);
+        Object.assign(resource, { gpuIndices: selectedGPUIndices });
+
+        return [true, resource];
     }
 
-    protected setExtraProperties(trialJobDetail: TrialJobDetail, resource: {}): void {
-        //abstract
+    private checkSpecifiedGpuIndices(): void {
+        const gpuCount: number = this.gpuScheduler.getSystemGpuCount();
+        if (this.designatedGpuIndices !== undefined) {
+            for (const index of this.designatedGpuIndices) {
+                if (index >= gpuCount) {
+                    throw new Error(`Specified GPU index not found: ${index}`);
+                }
+            }
+        }
     }
 
-    protected tryGetAvailableResource(): [boolean, {}] {
-        return [true, {}];
+    private occupyResource(resource: {gpuIndices: number[]}): void {
+        if (this.gpuScheduler !== undefined) {
+            for (const index of resource.gpuIndices) {
+                this.occupiedGpuIndices.add(index);
+            }
+        }
     }
 
-    protected occupyResource(_: {}): void {
-        //abstract
+    private async runJobLoop(): Promise<void> {
+        while (!this.stopping) {
+            while (!this.stopping && this.jobQueue.length !== 0) {
+                const trialJobId: string = this.jobQueue[0];
+                const trialJobDeatil: LocalTrialJobDetail | undefined = this.jobMap.get(trialJobId);
+                if (trialJobDeatil !== undefined && trialJobDeatil.status === 'WAITING') {
+                    const [success, resource] = this.tryGetAvailableResource();
+                    if (!success) {
+                        break;
+                    }
+                    this.occupyResource(resource);
+                    await this.runTrialJob(trialJobId, resource);
+                }
+                this.jobQueue.shift();
+            }
+            await delay(5000);
+        }
     }
 
     private setTrialJobStatus(trialJob: LocalTrialJobDetail, newStatus: TrialJobStatus): void {
@@ -358,7 +467,7 @@ class LocalTrainingService implements TrainingService {
         }
     }
 
-    private async runTrialJob(trialJobId: string, resource: {}): Promise<void> {
+    private async runTrialJob(trialJobId: string, resource: {gpuIndices: number[]}): Promise<void> {
         const trialJobDetail: LocalTrialJobDetail = <LocalTrialJobDetail>this.jobMap.get(trialJobId);
         const variables: { key: string; value: string }[] = this.getEnvironmentVariables(trialJobDetail, resource);
 
@@ -380,7 +489,8 @@ class LocalTrainingService implements TrainingService {
         await cpp.exec(`mkdir -p ${trialJobDetail.workingDirectory}`);
         await cpp.exec(`mkdir -p ${path.join(trialJobDetail.workingDirectory, '.nni')}`);
         await cpp.exec(`touch ${path.join(trialJobDetail.workingDirectory, '.nni', 'metrics')}`);
-        await fs.promises.writeFile(path.join(trialJobDetail.workingDirectory, 'run.sh'), runScriptLines.join('\n'), { encoding: 'utf8', mode: 0o777 });
+        await fs.promises.writeFile(
+            path.join(trialJobDetail.workingDirectory, 'run.sh'), runScriptLines.join('\n'), { encoding: 'utf8', mode: 0o777 });
         await this.writeParameterFile(trialJobDetail.workingDirectory, (<TrialJobApplicationForm>trialJobDetail.form).hyperParameters);
         const process: cp.ChildProcess = cp.exec(`bash ${path.join(trialJobDetail.workingDirectory, 'run.sh')}`);
 
@@ -406,7 +516,7 @@ class LocalTrainingService implements TrainingService {
                 buffer = remain;
             }
         });
-        
+
         this.jobStreamMap.set(trialJobDetail.id, stream);
     }
 

src/nni_manager/training_service/local/localTrainingServiceForGPU.ts

-/**
- * Copyright (c) Microsoft Corporation
- * All rights reserved.
- *
- * MIT License
- *
- * 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.
- */
-
-'use strict';
-
-import { TrialJobDetail, TrialJobStatus } from '../../common/trainingService';
-import { GPUScheduler } from './gpuScheduler';
-import { LocalTrainingService } from './localTrainingService';
-import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
-
-type LocalTrialJobDetailForGPU = TrialJobDetail & { gpuIndices: number[] };
-
-/**
- * Local training service for GPU
- */
-class LocalTrainingServiceForGPU extends LocalTrainingService {
-    private requiredGPUNum!: number;
-    private gpuScheduler!: GPUScheduler;
-    private availableGPUIndices: boolean[];
-
-    constructor() {
-        super();
-        this.availableGPUIndices = Array(16).fill(false); // Assume the maximum gpu number is 16
-    }
-
-    public async run(): Promise<void> {
-        if (this.gpuScheduler !== undefined) {
-            await Promise.all([
-                this.gpuScheduler.run(),
-                super.run()
-            ]);
-        } else {
-            await super.run();
-        }
-    }
-
-    public async setClusterMetadata(key: string, value: string): Promise<void> {
-        await super.setClusterMetadata(key, value);
-        switch (key) {
-            case TrialConfigMetadataKey.TRIAL_CONFIG:
-                if(this.localTrailConfig !== undefined) {
-                    this.requiredGPUNum = this.localTrailConfig.gpuNum;
-                } else {
-                    // If no valid trial config is initialized, set requiredGPUNum to 0 as fallback value.
-                    this.requiredGPUNum = 0;
-                }
-                this.log.info('required GPU number is ' + this.requiredGPUNum);
-                if (this.gpuScheduler === undefined && this.requiredGPUNum > 0) {
-                    this.gpuScheduler = new GPUScheduler();
-                }
-                break;
-            default:
-        }
-    }
-
-    public async cleanUp(): Promise<void> {
-        if (this.gpuScheduler !== undefined) {
-            await this.gpuScheduler.stop();
-        }
-
-        return super.cleanUp();
-    }
-
-    protected onTrialJobStatusChanged(trialJob: LocalTrialJobDetailForGPU, oldStatus: TrialJobStatus): void {
-        super.onTrialJobStatusChanged(trialJob, oldStatus);
-        if (trialJob.gpuIndices !== undefined && trialJob.gpuIndices.length !== 0 && this.gpuScheduler !== undefined) {
-            if (oldStatus === 'RUNNING' && trialJob.status !== 'RUNNING') {
-                for (const index of trialJob.gpuIndices) {
-                    this.availableGPUIndices[index] = false;
-                }
-            }
-        }
-    }
-
-    protected getEnvironmentVariables(
-        trialJobDetail: TrialJobDetail,
-        resource: { gpuIndices: number[] }): { key: string; value: string }[] {
-        const variables: { key: string; value: string }[] = super.getEnvironmentVariables(trialJobDetail, resource);
-        variables.push({
-            key: 'CUDA_VISIBLE_DEVICES',
-            value: this.gpuScheduler === undefined ? '' : resource.gpuIndices.join(',')
-        });
-
-        return variables;
-    }
-
-    protected setExtraProperties(trialJobDetail: LocalTrialJobDetailForGPU, resource: { gpuIndices: number[] }): void {
-        super.setExtraProperties(trialJobDetail, resource);
-        trialJobDetail.gpuIndices = resource.gpuIndices;
-    }
-
-    protected tryGetAvailableResource(): [boolean, {}] {
-        const [success, resource] = super.tryGetAvailableResource();
-        if (!success || this.gpuScheduler === undefined) {
-            return [success, resource];
-        }
-
-        const availableGPUIndices: number[] = this.gpuScheduler.getAvailableGPUIndices();
-        const selectedGPUIndices: number[] = availableGPUIndices.filter((index: number) => this.availableGPUIndices[index] === false);
-
-        if (selectedGPUIndices.length < this.requiredGPUNum) {
-            return [false, resource];
-        }
-
-        selectedGPUIndices.splice(this.requiredGPUNum);
-        Object.assign(resource, { gpuIndices: selectedGPUIndices });
-
-        return [true, resource];
-    }
-
-    protected occupyResource(resource: { gpuIndices: number[] }): void {
-        super.occupyResource(resource);
-        if (this.gpuScheduler !== undefined) {
-            for (const index of resource.gpuIndices) {
-                this.availableGPUIndices[index] = true;
-            }
-        }
-    }
-}
-
-export { LocalTrainingServiceForGPU };

src/nni_manager/training_service/remote_machine/gpuScheduler.ts

@@ -20,11 +20,10 @@
 'use strict';
 
 import * as assert from 'assert';
-import { Client } from 'ssh2';
 import { getLogger, Logger } from '../../common/log';
 import { randomSelect } from '../../common/utils';
 import { GPUInfo } from '../common/gpuData';
-import { RemoteMachineMeta, RemoteMachineScheduleResult, ScheduleResultType, SSHClientManager } from './remoteMachineData';
+import { parseGpuIndices, RemoteMachineMeta, RemoteMachineScheduleResult, ScheduleResultType, SSHClientManager } from './remoteMachineData';
 
 /**
  * A simple GPU scheduler implementation
@@ -85,6 +84,20 @@ export class GPUScheduler {
         };
     }
 
+    /**
+     * remove the job's gpu reversion
+     */
+    public removeGpuReservation(trialJobId: string, rmMeta?: RemoteMachineMeta): void {
+        // If remote machine has no GPU, gpuReservcation is not initialized, so check if it's undefined
+        if (rmMeta !== undefined && rmMeta.gpuReservation !== undefined) {
+            rmMeta.gpuReservation.forEach((reserveTrialJobId : string, gpuIndex : number) => {
+                if (reserveTrialJobId === trialJobId) {
+                    rmMeta.gpuReservation.delete(gpuIndex);
+                }
+            });
+        }
+    }
+
     private scheduleGPUHost(requiredGPUNum: number, trialJobId: string): RemoteMachineScheduleResult | undefined {
         const totalResourceMap: Map<RemoteMachineMeta, GPUInfo[]> = this.gpuResourceDetection();
         const qualifiedRMs: RemoteMachineMeta[] = [];
@@ -120,11 +133,14 @@ export class GPUScheduler {
                 if (rmMeta.gpuReservation === undefined) {
                     rmMeta.gpuReservation = new Map<number, string>();
                 }
-
+                const designatedGpuIndices: Set<number> | undefined = parseGpuIndices(rmMeta.gpuIndices);
+                this.log.debug(`designated gpu indices: ${designatedGpuIndices}`);
                 rmMeta.gpuSummary.gpuInfos.forEach((gpuInfo: GPUInfo) => {
                     // if the GPU has active process, OR be reserved by a job,
+                    // or index not in gpuIndices configuration in machineList,
                     // We should NOT allocate this GPU
-                    if (gpuInfo.activeProcessNum === 0 && !rmMeta.gpuReservation.has(gpuInfo.index)) {
+                    if (gpuInfo.activeProcessNum === 0 && !rmMeta.gpuReservation.has(gpuInfo.index)
+                        && (designatedGpuIndices === undefined || designatedGpuIndices.has(gpuInfo.index))) {
                         availableGPUs.push(gpuInfo);
                     }
                 });
@@ -163,20 +179,5 @@ export class GPUScheduler {
             }
         };
     }
-    
-    /**
-     * remove the job's gpu reversion
-     * @param trialJobId 
-     * @param rmMeta 
-     */
-    public removeGpuReservation(trialJobId: string, rmMeta?: RemoteMachineMeta): void{
-        // If remote machine has no GPU, gpuReservcation is not initialized, so check if it's undefined
-        if(rmMeta !== undefined && rmMeta.gpuReservation !== undefined) {
-            rmMeta.gpuReservation.forEach((reserveTrialJobId : string, gpuIndex : number) => {
-                if(reserveTrialJobId == trialJobId) {
-                    rmMeta.gpuReservation.delete(gpuIndex);
-                }
-            });
-        }
-    }
+
 }

src/nni_manager/training_service/remote_machine/remoteMachineData.ts

@@ -19,12 +19,11 @@
 
 'use strict';
 
-import { JobApplicationForm, TrialJobDetail, TrialJobStatus  } from '../../common/trainingService';
-import { GPUSummary } from '../common/gpuData';
+import * as fs from 'fs';
 import { Client, ConnectConfig } from 'ssh2';
 import { Deferred } from 'ts-deferred';
-import * as fs from 'fs';
-
+import { JobApplicationForm, TrialJobDetail, TrialJobStatus  } from '../../common/trainingService';
+import { GPUSummary } from '../common/gpuData';
 
 /**
  * Metadata of remote machine for configuration and statuc query
@@ -37,11 +36,12 @@ export class RemoteMachineMeta {
     public readonly sshKeyPath?: string;
     public readonly passphrase?: string;
     public gpuSummary : GPUSummary | undefined;
-    /* GPU Reservation info, the key is GPU index, the value is the job id which reserves this GPU*/
+    // GPU Reservation info, the key is GPU index, the value is the job id which reserves this GPU
     public gpuReservation : Map<number, string>;
+    public readonly gpuIndices?: string;
 
-    constructor(ip : string, port : number, username : string, passwd : string, 
-        sshKeyPath : string, passphrase : string) {
+    constructor(ip : string, port : number, username : string, passwd : string,
+                sshKeyPath: string, passphrase : string, gpuIndices?: string) {
         this.ip = ip;
         this.port = port;
         this.username = username;
@@ -49,6 +49,19 @@ export class RemoteMachineMeta {
         this.sshKeyPath = sshKeyPath;
         this.passphrase = passphrase;
         this.gpuReservation = new Map<number, string>();
+        this.gpuIndices = gpuIndices;
+    }
+}
+
+export function parseGpuIndices(gpuIndices?: string): Set<number> | undefined {
+    if (gpuIndices !== undefined) {
+        const indices: number[] = gpuIndices.split(',')
+            .map((x: string) => parseInt(x, 10));
+        if (indices.length > 0) {
+            return new Set(indices);
+        } else {
+            throw new Error('gpuIndices can not be empty if specified.');
+        }
     }
 }
 

src/nni_manager/training_service/test/localTrainingService.test.ts

@@ -28,7 +28,7 @@ import * as component from '../../common/component';
 import { TrialJobApplicationForm, TrialJobDetail, TrainingService } from '../../common/trainingService';
 import { cleanupUnitTest, delay, prepareUnitTest } from '../../common/utils';
 import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
-import { LocalTrainingServiceForGPU } from '../local/localTrainingServiceForGPU';
+import { LocalTrainingService } from '../local/localTrainingService';
 
 // TODO: copy mockedTrail.py to local folder
 const localCodeDir: string = tmp.dirSync().name
@@ -38,7 +38,7 @@ fs.copyFileSync(mockedTrialPath, localCodeDir + '/mockedTrial.py')
 describe('Unit Test for LocalTrainingService', () => {
     let trialConfig: any = `{"command":"sleep 1h && echo hello","codeDir":"${localCodeDir}","gpuNum":1}`
 
-    let localTrainingService: LocalTrainingServiceForGPU;
+    let localTrainingService: LocalTrainingService;
 
     before(() => {
         chai.should();
@@ -51,7 +51,7 @@ describe('Unit Test for LocalTrainingService', () => {
     });
 
     beforeEach(() => {
-        localTrainingService = component.get(LocalTrainingServiceForGPU);
+        localTrainingService = component.get(LocalTrainingService);
         localTrainingService.run();
     });
 

tools/nni_cmd/config_schema.py

@@ -135,6 +135,9 @@ Optional('assessor'): Or({
     Optional('classArgs'): dict,
     Optional('gpuNum'): And(int, lambda x: 0 <= x <= 99999),
 }),
+Optional('localConfig'): {
+    Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0))
+}
 }
 
 common_trial_schema = {
@@ -269,13 +272,15 @@ Optional('machineList'):[Or({
     'ip': str,
     Optional('port'): And(int, lambda x: 0 < x < 65535),
     'username': str,
-    'passwd': str
+    'passwd': str,
+    Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0))
     },{
     'ip': str,
     Optional('port'): And(int, lambda x: 0 < x < 65535),
     'username': str,
     'sshKeyPath': os.path.exists,
-    Optional('passphrase'): str
+    Optional('passphrase'): str,
+    Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0))
 })]
 }
 

tools/nni_cmd/launcher.py

@@ -152,6 +152,23 @@ def set_trial_config(experiment_config, port, config_file_name):
 
 def set_local_config(experiment_config, port, config_file_name):
     '''set local configuration'''
+    #set machine_list
+    request_data = dict()
+    if experiment_config.get('localConfig'):
+        request_data['local_config'] = experiment_config['localConfig']
+        if request_data['local_config'] and request_data['local_config'].get('gpuIndices') \
+            and isinstance(request_data['local_config'].get('gpuIndices'), int):
+            request_data['local_config']['gpuIndices'] = str(request_data['local_config'].get('gpuIndices'))
+        response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
+        err_message = ''
+        if not response or not check_response(response):
+            if response is not None:
+                err_message = response.text
+                _, stderr_full_path = get_log_path(config_file_name)
+                with open(stderr_full_path, 'a+') as fout:
+                    fout.write(json.dumps(json.loads(err_message), indent=4, sort_keys=True, separators=(',', ':')))
+            return False, err_message
+
     return set_trial_config(experiment_config, port, config_file_name)
 
 def set_remote_config(experiment_config, port, config_file_name):
@@ -159,6 +176,10 @@ def set_remote_config(experiment_config, port, config_file_name):
     #set machine_list
     request_data = dict()
     request_data['machine_list'] = experiment_config['machineList']
+    if request_data['machine_list']:
+        for i in range(len(request_data['machine_list'])):
+            if isinstance(request_data['machine_list'][i].get('gpuIndices'), int):
+                request_data['machine_list'][i]['gpuIndices'] = str(request_data['machine_list'][i].get('gpuIndices'))
     response = rest_put(cluster_metadata_url(port), json.dumps(request_data), REST_TIME_OUT)
     err_message = ''
     if not response or not check_response(response):