Merge branch 'master' into quniform-tuners

examples/trials/sklearn/regression/config_pai.yml

@@ -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/tuners/random_nas_tuner/random_nas_tuner.py

@@ -7,7 +7,9 @@ def random_archi_generator(nas_ss, random_state):
     '''
     chosen_archi = {}
     print("zql: nas search space: ", nas_ss)
-    for block_name, block in nas_ss.items():
+    for block_name, block_value in nas_ss.items():
+        assert block_value['_type'] == "mutable_layer", "Random NAS Tuner only receives NAS search space whose _type is 'mutable_layer'"
+        block = block_value['_value']
         tmp_block = {}
         for layer_name, layer in block.items():
             tmp_layer = {}

setup.py

@@ -35,9 +35,10 @@ setup(
     license = 'MIT',
     url = 'https://github.com/Microsoft/nni',
 
-    packages = find_packages('src/sdk/pynni', exclude=['tests']) + find_packages('tools'),
+    packages = find_packages('src/sdk/pynni', exclude=['tests']) + find_packages('src/sdk/pycli') + find_packages('tools'),
     package_dir = {
         'nni': 'src/sdk/pynni/nni',
+        'nnicli': 'src/sdk/pycli/nnicli',
         'nni_annotation': 'tools/nni_annotation',
         'nni_cmd': 'tools/nni_cmd',
         'nni_trial_tool':'tools/nni_trial_tool',

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -51,10 +51,12 @@ export namespace ValidationSchemas {
                 command: joi.string().min(1),
                 virtualCluster: joi.string(),
                 shmMB: joi.number(),
+                authFile: joi.string(),
                 nasMode: joi.string().valid('classic_mode', 'enas_mode', 'oneshot_mode'),
                 worker: joi.object({
                     replicas: joi.number().min(1).required(),
                     image: joi.string().min(1),
+                    privateRegistryAuthPath: joi.string().min(1),
                     outputDir: joi.string(),
                     cpuNum: joi.number().min(1),
                     memoryMB: joi.number().min(100),
@@ -64,6 +66,7 @@ export namespace ValidationSchemas {
                 ps: joi.object({
                         replicas: joi.number().min(1).required(),
                         image: joi.string().min(1),
+                        privateRegistryAuthPath: joi.string().min(1),
                         outputDir: joi.string(),
                         cpuNum: joi.number().min(1),
                         memoryMB: joi.number().min(100),
@@ -73,6 +76,7 @@ export namespace ValidationSchemas {
                 master: joi.object({
                     replicas: joi.number().min(1).required(),
                     image: joi.string().min(1),
+                    privateRegistryAuthPath: joi.string().min(1),
                     outputDir: joi.string(),
                     cpuNum: joi.number().min(1),
                     memoryMB: joi.number().min(100),
@@ -83,6 +87,7 @@ export namespace ValidationSchemas {
                     name: joi.string().min(1),
                     taskNum: joi.number().min(1).required(),
                     image: joi.string().min(1),
+                    privateRegistryAuthPath: joi.string().min(1),
                     outputDir: joi.string(),
                     cpuNum: joi.number().min(1),
                     memoryMB: joi.number().min(100),

src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerConfig.ts

@@ -43,8 +43,8 @@ export class FrameworkControllerTrialConfigTemplate extends KubernetesTrialConfi
     public readonly taskNum: number;
     constructor(taskNum: number, command : string, gpuNum : number,
                 cpuNum: number, memoryMB: number, image: string,
-                frameworkAttemptCompletionPolicy: FrameworkAttemptCompletionPolicy) {
-        super(command, gpuNum, cpuNum, memoryMB, image);
+                frameworkAttemptCompletionPolicy: FrameworkAttemptCompletionPolicy, privateRegistryFilePath?: string | undefined) {
+        super(command, gpuNum, cpuNum, memoryMB, image, privateRegistryFilePath);
         this.frameworkAttemptCompletionPolicy = frameworkAttemptCompletionPolicy;
         this.name = name;
         this.taskNum = taskNum;

src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts

@@ -305,7 +305,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
         }
         // Generate frameworkcontroller job resource config object
         const frameworkcontrollerJobConfig: any =
-          this.generateFrameworkControllerJobConfig(trialJobId, trialWorkingFolder, frameworkcontrollerJobName, podResources);
+          await this.generateFrameworkControllerJobConfig(trialJobId, trialWorkingFolder, frameworkcontrollerJobName, podResources);
 
         return Promise.resolve(frameworkcontrollerJobConfig);
     }
@@ -329,8 +329,8 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
      * @param frameworkcontrollerJobName job name
      * @param podResources  pod template
      */
-    private generateFrameworkControllerJobConfig(trialJobId: string, trialWorkingFolder: string,
-                                                 frameworkcontrollerJobName : string, podResources : any) : any {
+    private async generateFrameworkControllerJobConfig(trialJobId: string, trialWorkingFolder: string,
+                                                 frameworkcontrollerJobName : string, podResources : any) : Promise<any> {
         if (this.fcClusterConfig === undefined) {
             throw new Error('frameworkcontroller Cluster config is not initialized');
         }
@@ -345,12 +345,14 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
             if (containerPort === undefined) {
                 throw new Error('Container port is not initialized');
             }
+            
             const taskRole: any = this.generateTaskRoleConfig(
                 trialWorkingFolder,
                 this.fcTrialConfig.taskRoles[index].image,
                 `run_${this.fcTrialConfig.taskRoles[index].name}.sh`,
                 podResources[index],
-                containerPort
+                containerPort,
+                await this.createRegistrySecret(this.fcTrialConfig.taskRoles[index].privateRegistryAuthPath)
             );
             taskRoles.push({
                 name: this.fcTrialConfig.taskRoles[index].name,
@@ -363,7 +365,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
             });
         }
 
-        return {
+        return Promise.resolve({
             apiVersion: `frameworkcontroller.microsoft.com/v1`,
             kind: 'Framework',
             metadata: {
@@ -379,11 +381,11 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
                 executionType: 'Start',
                 taskRoles: taskRoles
             }
-        };
+        });
     }
 
-    private generateTaskRoleConfig(trialWorkingFolder: string, replicaImage: string, runScriptFile: string,
-                                   podResources: any, containerPort: number): any {
+    private  generateTaskRoleConfig(trialWorkingFolder: string, replicaImage: string, runScriptFile: string,
+                                   podResources: any, containerPort: number, privateRegistrySecretName: string | undefined): any {
         if (this.fcClusterConfig === undefined) {
             throw new Error('frameworkcontroller Cluster config is not initialized');
         }
@@ -451,13 +453,22 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
                     mountPath: '/mnt/frameworkbarrier'
                 }]
         }];
-        const spec: any = {
-                containers: containers,
-                initContainers: initContainers,
-                restartPolicy: 'OnFailure',
-                volumes: volumeSpecMap.get('nniVolumes'),
-                hostNetwork: false
+        
+        let spec: any = {
+            containers: containers,
+            initContainers: initContainers,
+            restartPolicy: 'OnFailure',
+            volumes: volumeSpecMap.get('nniVolumes'),
+            hostNetwork: false
         };
+        if(privateRegistrySecretName) {
+            spec.imagePullSecrets = [
+                {
+                    name: privateRegistrySecretName 
+                }
+             ]
+        }
+
         if (this.fcClusterConfig.serviceAccountName !== undefined) {
             spec.serviceAccountName = this.fcClusterConfig.serviceAccountName;
         }

src/nni_manager/training_service/kubernetes/kubeflow/kubeflowConfig.ts

@@ -135,8 +135,8 @@ export class KubeflowTrialConfig extends KubernetesTrialConfig {
 export class KubeflowTrialConfigTemplate extends KubernetesTrialConfigTemplate {
     public readonly replicas: number;
     constructor(replicas: number, command : string, gpuNum : number,
-                cpuNum: number, memoryMB: number, image: string) {
-        super(command, gpuNum, cpuNum, memoryMB, image);
+                cpuNum: number, memoryMB: number, image: string, privateRegistryAuthPath?: string) {
+        super(command, gpuNum, cpuNum, memoryMB, image, privateRegistryAuthPath);
         this.replicas = replicas;
     }
 }

src/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts

@@ -347,7 +347,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         }
 
         // Generate kubeflow job resource config object
-        const kubeflowJobConfig: any = this.generateKubeflowJobConfig(trialJobId, trialWorkingFolder, kubeflowJobName, workerPodResources,
+        const kubeflowJobConfig: any = await this.generateKubeflowJobConfig(trialJobId, trialWorkingFolder, kubeflowJobName, workerPodResources,
                                                                       nonWorkerResources);
 
         return Promise.resolve(kubeflowJobConfig);
@@ -361,8 +361,8 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
      * @param workerPodResources worker pod template
      * @param nonWorkerPodResources non-worker pod template, like ps or master
      */
-    private generateKubeflowJobConfig(trialJobId: string, trialWorkingFolder: string, kubeflowJobName : string, workerPodResources : any,
-                                      nonWorkerPodResources?: any) : any {
+    private async generateKubeflowJobConfig(trialJobId: string, trialWorkingFolder: string, kubeflowJobName : string, workerPodResources : any,
+                                            nonWorkerPodResources?: any) : Promise<any> {
         if (this.kubeflowClusterConfig === undefined) {
             throw new Error('Kubeflow Cluster config is not initialized');
         }
@@ -377,29 +377,32 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
 
         const replicaSpecsObj: any = {};
         const replicaSpecsObjMap: Map<string, object> = new Map<string, object>();
-
         if (this.kubeflowTrialConfig.operatorType === 'tf-operator') {
             const tensorflowTrialConfig: KubeflowTrialConfigTensorflow = <KubeflowTrialConfigTensorflow>this.kubeflowTrialConfig;
+            let privateRegistrySecretName = await this.createRegistrySecret(tensorflowTrialConfig.worker.privateRegistryAuthPath);
             replicaSpecsObj.Worker = this.generateReplicaConfig(trialWorkingFolder, tensorflowTrialConfig.worker.replicas,
-                                                                tensorflowTrialConfig.worker.image, 'run_worker.sh', workerPodResources);
+                                                                tensorflowTrialConfig.worker.image, 'run_worker.sh', workerPodResources, privateRegistrySecretName);
             if (tensorflowTrialConfig.ps !== undefined) {
+                let privateRegistrySecretName: string | undefined = await this.createRegistrySecret(tensorflowTrialConfig.ps.privateRegistryAuthPath);
                 replicaSpecsObj.Ps = this.generateReplicaConfig(trialWorkingFolder, tensorflowTrialConfig.ps.replicas,
-                                                                tensorflowTrialConfig.ps.image, 'run_ps.sh', nonWorkerPodResources);
+                                                                tensorflowTrialConfig.ps.image, 'run_ps.sh', nonWorkerPodResources, privateRegistrySecretName);
             }
             replicaSpecsObjMap.set(this.kubernetesCRDClient.jobKind, {tfReplicaSpecs: replicaSpecsObj});
         } else if (this.kubeflowTrialConfig.operatorType === 'pytorch-operator') {
             const pytorchTrialConfig: KubeflowTrialConfigPytorch = <KubeflowTrialConfigPytorch>this.kubeflowTrialConfig;
             if (pytorchTrialConfig.worker !== undefined) {
+                let privateRegistrySecretName: string | undefined = await this.createRegistrySecret(pytorchTrialConfig.worker.privateRegistryAuthPath);
                 replicaSpecsObj.Worker = this.generateReplicaConfig(trialWorkingFolder, pytorchTrialConfig.worker.replicas,
-                                                                    pytorchTrialConfig.worker.image, 'run_worker.sh', workerPodResources);
+                                                                    pytorchTrialConfig.worker.image, 'run_worker.sh', workerPodResources, privateRegistrySecretName);
             }
+            let privateRegistrySecretName: string | undefined = await this.createRegistrySecret(pytorchTrialConfig.master.privateRegistryAuthPath);
             replicaSpecsObj.Master = this.generateReplicaConfig(trialWorkingFolder, pytorchTrialConfig.master.replicas,
-                                                                pytorchTrialConfig.master.image, 'run_master.sh', nonWorkerPodResources);
+                                                                pytorchTrialConfig.master.image, 'run_master.sh', nonWorkerPodResources, privateRegistrySecretName);
 
             replicaSpecsObjMap.set(this.kubernetesCRDClient.jobKind, {pytorchReplicaSpecs: replicaSpecsObj});
         }
 
-        return {
+        return Promise.resolve({
             apiVersion: `kubeflow.org/${this.kubernetesCRDClient.apiVersion}`,
             kind: this.kubernetesCRDClient.jobKind,
             metadata: {
@@ -412,7 +415,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
                 }
             },
             spec: replicaSpecsObjMap.get(this.kubernetesCRDClient.jobKind)
-        };
+        });
     }
 
     /**
@@ -424,7 +427,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
      * @param podResources pod resource config section
      */
     private generateReplicaConfig(trialWorkingFolder: string, replicaNumber: number, replicaImage: string, runScriptFile: string,
-                                  podResources: any): any {
+                                  podResources: any, privateRegistrySecretName: string | undefined): any {
         if (this.kubeflowClusterConfig === undefined) {
             throw new Error('Kubeflow Cluster config is not initialized');
         }
@@ -436,7 +439,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         if (this.kubernetesCRDClient === undefined) {
             throw new Error('Kubeflow operator client is not initialized');
         }
-
+        // The config spec for volume field
         const volumeSpecMap: Map<string, object> = new Map<string, object>();
         if (this.kubeflowClusterConfig.storageType === 'azureStorage') {
             volumeSpecMap.set('nniVolumes', [
@@ -459,7 +462,34 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
                 }
             }]);
         }
-
+        // The config spec for container field
+        const containersSpecMap: Map<string, object> = new Map<string, object>(); 
+        containersSpecMap.set('containers', [
+        {
+                // Kubeflow tensorflow operator requires that containers' name must be tensorflow
+                // TODO: change the name based on operator's type
+                name: this.kubernetesCRDClient.containerName,
+                image: replicaImage,
+                args: ['sh', `${path.join(trialWorkingFolder, runScriptFile)}`],
+                volumeMounts: [
+                {
+                    name: 'nni-vol',
+                    mountPath: this.CONTAINER_MOUNT_PATH
+                }],
+                resources: podResources
+            }
+        ]);
+        let spec: any = {
+            containers: containersSpecMap.get('containers'),
+            restartPolicy: 'ExitCode',
+            volumes: volumeSpecMap.get('nniVolumes')
+        }
+        if (privateRegistrySecretName) {
+            spec.imagePullSecrets = [
+                {
+                    name: privateRegistrySecretName
+                }]
+        }
         return {
             replicas: replicaNumber,
             template: {
@@ -467,26 +497,9 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
                     // tslint:disable-next-line:no-null-keyword
                     creationTimestamp: null
                 },
-                spec: {
-                    containers: [
-                    {
-                        // Kubeflow tensorflow operator requires that containers' name must be tensorflow
-                        // TODO: change the name based on operator's type
-                        name: this.kubernetesCRDClient.containerName,
-                        image: replicaImage,
-                        args: ['sh', `${path.join(trialWorkingFolder, runScriptFile)}`],
-                        volumeMounts: [
-                        {
-                            name: 'nni-vol',
-                            mountPath: this.CONTAINER_MOUNT_PATH
-                        }],
-                        resources: podResources
-                    }],
-                    restartPolicy: 'ExitCode',
-                    volumes: volumeSpecMap.get('nniVolumes')
-                }
+                spec: spec
             }
-        };
+        }
     }
 }
 // tslint:enable: no-unsafe-any no-any

src/nni_manager/training_service/kubernetes/kubernetesConfig.ts

@@ -179,6 +179,9 @@ export class KubernetesTrialConfigTemplate {
     // Docker image
     public readonly image: string;
 
+    // Private registry config file path to download docker iamge
+    public readonly privateRegistryAuthPath?: string;
+
     // Trail command
     public readonly command : string;
 
@@ -186,12 +189,13 @@ export class KubernetesTrialConfigTemplate {
     public readonly gpuNum : number;
 
     constructor(command : string, gpuNum : number,
-                cpuNum: number, memoryMB: number, image: string) {
+                cpuNum: number, memoryMB: number, image: string, privateRegistryAuthPath?: string) {
         this.command = command;
         this.gpuNum = gpuNum;
         this.cpuNum = cpuNum;
         this.memoryMB = memoryMB;
         this.image = image;
+        this.privateRegistryAuthPath = privateRegistryAuthPath;
     }
 }
 

src/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts

@@ -38,6 +38,8 @@ import { KubernetesClusterConfig } from './kubernetesConfig';
 import { kubernetesScriptFormat, KubernetesTrialJobDetail } from './kubernetesData';
 import { KubernetesJobRestServer } from './kubernetesJobRestServer';
 
+var fs = require('fs');
+
 /**
  * Training Service implementation for Kubernetes
  */
@@ -327,5 +329,34 @@ abstract class KubernetesTrainingService {
 
         return Promise.resolve();
     }
+
+    protected async createRegistrySecret(filePath: string | undefined): Promise<string | undefined> {
+        if(filePath === undefined || filePath === '') {
+            return undefined;
+        }
+        let body = fs.readFileSync(filePath).toString('base64');
+        let registrySecretName = String.Format('nni-secret-{0}', uniqueString(8)
+                                                                            .toLowerCase());
+        await this.genericK8sClient.createSecret(
+            {
+                apiVersion: 'v1',
+                kind: 'Secret',
+                metadata: {
+                    name: registrySecretName,
+                    namespace: 'default',
+                    labels: {
+                        app: this.NNI_KUBERNETES_TRIAL_LABEL,
+                        expId: getExperimentId()
+                    }
+                },
+                type: 'kubernetes.io/dockerconfigjson',
+                data: {
+                    '.dockerconfigjson': body
+                }
+            }
+        );
+        return registrySecretName;
+    }
+     
 }
 export { KubernetesTrainingService };

src/nni_manager/training_service/local/localTrainingService.ts

@@ -307,9 +307,11 @@ class LocalTrainingService implements TrainingService {
                 if (this.localTrailConfig === undefined) {
                     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();
+                if (this.localTrailConfig.gpuNum !== undefined) {
+                    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:
@@ -399,7 +401,8 @@ class LocalTrainingService implements TrainingService {
 
     private getEnvironmentVariables(
         trialJobDetail: TrialJobDetail,
-        resource: { gpuIndices: number[] }): { key: string; value: string }[] {
+        resource: { gpuIndices: number[] },
+        gpuNum: number | undefined): { key: string; value: string }[] {
         const envVariables: { key: string; value: string }[] = [
             { key: 'NNI_PLATFORM', value: 'local' },
             { key: 'NNI_EXP_ID', value: this.experimentId },
@@ -409,11 +412,12 @@ class LocalTrainingService implements TrainingService {
             { key: 'NNI_TRIAL_SEQ_ID', value: trialJobDetail.sequenceId.toString() },
             { key: 'MULTI_PHASE', value: this.isMultiPhase.toString() }
         ];
-
-        envVariables.push({
-            key: 'CUDA_VISIBLE_DEVICES',
-            value: this.gpuScheduler === undefined ? '-1' : resource.gpuIndices.join(',')
-        });
+        if (gpuNum !== undefined) {
+            envVariables.push({
+                key: 'CUDA_VISIBLE_DEVICES',
+                value: this.gpuScheduler === undefined ? '-1' : resource.gpuIndices.join(',')
+            });
+        }
 
         return envVariables;
     }
@@ -490,6 +494,7 @@ class LocalTrainingService implements TrainingService {
                     if (!success) {
                         break;
                     }
+                    
                     this.occupyResource(resource);
                     await this.runTrialJob(trialJobId, resource);
                 }
@@ -526,7 +531,10 @@ class LocalTrainingService implements TrainingService {
 
     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);
+        if (this.localTrailConfig === undefined) {
+            throw new Error(`localTrialConfig not initialized!`);
+        }
+        const variables: { key: string; value: string }[] = this.getEnvironmentVariables(trialJobDetail, resource, this.localTrailConfig.gpuNum);
 
         if (this.localTrailConfig === undefined) {
             throw new Error('trial config is not initialized');

src/nni_manager/training_service/pai/paiConfig.ts

@@ -69,12 +69,10 @@ export class PAIJobConfig {
     public readonly jobName: string;
     // URL pointing to the Docker image for all tasks in the job
     public readonly image: string;
-    // Data directory existing on HDFS
-    public readonly dataDir: string;
-    // Output directory on HDFS
-    public readonly outputDir: string;
     // Code directory on HDFS
     public readonly codeDir: string;
+    //authentication file used for private Docker registry 
+    public readonly authFile?: string;
 
     // List of taskRole, one task role at least
     public taskRoles: PAITaskRole[];
@@ -90,15 +88,14 @@ export class PAIJobConfig {
      * @param outputDir Output directory on HDFS
      * @param taskRoles List of taskRole, one task role at least
      */
-    constructor(jobName: string, image : string, dataDir : string, outputDir : string, codeDir : string,
-                taskRoles : PAITaskRole[], virtualCluster: string) {
+    constructor(jobName: string, image : string, codeDir : string,
+                taskRoles : PAITaskRole[], virtualCluster: string, authFile?: string) {
         this.jobName = jobName;
         this.image = image;
-        this.dataDir = dataDir;
-        this.outputDir = outputDir;
         this.codeDir = codeDir;
         this.taskRoles = taskRoles;
         this.virtualCluster = virtualCluster;
+        this.authFile = authFile;
     }
 }
 
@@ -130,23 +127,22 @@ export class NNIPAITrialConfig extends TrialConfig {
     public readonly cpuNum: number;
     public readonly memoryMB: number;
     public readonly image: string;
-    public readonly dataDir: string;
-    public outputDir: string;
 
     //The virtual cluster job runs on. If omitted, the job will run on default virtual cluster
     public virtualCluster?: string;
     //Shared memory for one task in the task role
     public shmMB?: number;
+    //authentication file used for private Docker registry 
+    public authFile?: string;
 
     constructor(command : string, codeDir : string, gpuNum : number, cpuNum: number, memoryMB: number,
-                image: string, dataDir: string, outputDir: string, virtualCluster?: string, shmMB?: number) {
+                image: string, virtualCluster?: string, shmMB?: number, authFile?: string) {
         super(command, codeDir, gpuNum);
         this.cpuNum = cpuNum;
         this.memoryMB = memoryMB;
         this.image = image;
-        this.dataDir = dataDir;
-        this.outputDir = outputDir;
         this.virtualCluster = virtualCluster;
         this.shmMB = shmMB;
+        this.authFile = authFile;
     }
 }

src/nni_manager/training_service/pai/paiData.ts

@@ -70,9 +70,6 @@ export const PAI_TRIAL_COMMAND_FORMAT: string =
 --pai_hdfs_output_dir '{9}' --pai_hdfs_host '{10}' --pai_user_name {11} --nni_hdfs_exp_dir '{12}' --webhdfs_path '/webhdfs/api/v1' \
 --nni_manager_version '{13}' --log_collection '{14}'`;
 
-export const PAI_OUTPUT_DIR_FORMAT: string =
-`hdfs://{0}:9000/`;
-
 // tslint:disable:no-http-string
 export const PAI_LOG_PATH_FORMAT: string =
 `http://{0}/webhdfs/explorer.html#{1}`;

src/nni_manager/training_service/pai/paiTrainingService.ts

@@ -43,7 +43,7 @@ import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
 import { execMkdir, validateCodeDir } from '../common/util';
 import { HDFSClientUtility } from './hdfsClientUtility';
 import { NNIPAITrialConfig, PAIClusterConfig, PAIJobConfig, PAITaskRole } from './paiConfig';
-import { PAI_LOG_PATH_FORMAT, PAI_OUTPUT_DIR_FORMAT, PAI_TRIAL_COMMAND_FORMAT, PAITrialJobDetail } from './paiData';
+import { PAI_LOG_PATH_FORMAT, PAI_TRIAL_COMMAND_FORMAT, PAITrialJobDetail } from './paiData';
 import { PAIJobInfoCollector } from './paiJobInfoCollector';
 import { PAIJobRestServer, ParameterFileMeta } from './paiJobRestServer';
 
@@ -70,9 +70,6 @@ class PAITrainingService implements TrainingService {
     private readonly paiTokenUpdateInterval: number;
     private readonly experimentId! : string;
     private readonly paiJobCollector : PAIJobInfoCollector;
-    private readonly hdfsDirPattern: string;
-    private hdfsBaseDir: string | undefined;
-    private hdfsOutputHost: string | undefined;
     private nextTrialSequenceId: number;
     private paiRestServerPort?: number;
     private nniManagerIpConfig?: NNIManagerIpConfig;
@@ -80,6 +77,8 @@ class PAITrainingService implements TrainingService {
     private versionCheck: boolean = true;
     private logCollection: string;
     private isMultiPhase: boolean = false;
+    private hdfsCodeDir?: string;
+    private hdfsOutputDir?: string;
 
     constructor() {
         this.log = getLogger();
@@ -90,7 +89,6 @@ class PAITrainingService implements TrainingService {
         this.expRootDir = path.join('/nni', 'experiments', getExperimentId());
         this.experimentId = getExperimentId();
         this.paiJobCollector = new PAIJobInfoCollector(this.trialJobsMap);
-        this.hdfsDirPattern = 'hdfs://(?<host>([0-9]{1,3}.){3}[0-9]{1,3})(:[0-9]{2,5})?(?<baseDir>/.*)?';
         this.nextTrialSequenceId = -1;
         this.paiTokenUpdateInterval = 7200000; //2hours
         this.logCollection = 'none';
@@ -144,10 +142,10 @@ class PAITrainingService implements TrainingService {
     }
 
     public async submitTrialJob(form: JobApplicationForm): Promise<TrialJobDetail> {
-        const deferred : Deferred<PAITrialJobDetail> = new Deferred<PAITrialJobDetail>();
-        if (this.hdfsBaseDir === undefined) {
-            throw new Error('hdfsBaseDir is not initialized');
+        if (this.paiClusterConfig === undefined) {
+            throw new Error(`paiClusterConfig not initialized!`);
         }
+        const deferred : Deferred<PAITrialJobDetail> = new Deferred<PAITrialJobDetail>();
 
         this.log.info(`submitTrialJob: form: ${JSON.stringify(form)}`);
 
@@ -156,12 +154,14 @@ class PAITrainingService implements TrainingService {
         //TODO: use HDFS working folder instead
         const trialWorkingFolder: string = path.join(this.expRootDir, 'trials', trialJobId);
         const paiJobName: string = `nni_exp_${this.experimentId}_trial_${trialJobId}`;
+        this.hdfsCodeDir = HDFSClientUtility.getHdfsTrialWorkDir(this.paiClusterConfig.userName, trialJobId);
+        this.hdfsOutputDir = unixPathJoin(this.hdfsCodeDir, 'nnioutput');
 
-        const hdfsOutputDir : string = path.join(this.hdfsBaseDir, this.experimentId, trialJobId);
         const hdfsLogPath : string = String.Format(
             PAI_LOG_PATH_FORMAT,
-            this.hdfsOutputHost,
-            hdfsOutputDir);
+            this.paiClusterConfig.host,
+            this.hdfsOutputDir
+            );
 
         const trialJobDetail: PAITrialJobDetail = new PAITrialJobDetail(
             trialJobId,
@@ -278,14 +278,6 @@ class PAITrainingService implements TrainingService {
                     break;
                 }
                 this.paiTrialConfig = <NNIPAITrialConfig>JSON.parse(value);
-                //paiTrialConfig.outputDir could be null if it is not set in nnictl
-                if (this.paiTrialConfig.outputDir === undefined || this.paiTrialConfig.outputDir === null) {
-                    this.paiTrialConfig.outputDir = String.Format(
-                        PAI_OUTPUT_DIR_FORMAT,
-                        this.paiClusterConfig.host
-                    )
-                    .replace(/\r\n|\n|\r/gm, '');
-                }
 
                 // Validate to make sure codeDir doesn't have too many files
                 try {
@@ -295,43 +287,7 @@ class PAITrainingService implements TrainingService {
                     deferred.reject(new Error(error));
                     break;
                 }
-
-                const hdfsDirContent: any = this.paiTrialConfig.outputDir.match(this.hdfsDirPattern);
-
-                if (hdfsDirContent === null) {
-                    throw new Error('Trial outputDir format Error');
-                }
-                const groups: any = hdfsDirContent.groups;
-                if (groups === undefined) {
-                    throw new Error('Trial outputDir format Error');
-                }
-                this.hdfsOutputHost = groups.host;
-                //TODO: choose to use /${username} as baseDir
-                this.hdfsBaseDir = groups.baseDir;
-                if (this.hdfsBaseDir === undefined) {
-                    this.hdfsBaseDir = '/';
-                }
-
-                let dataOutputHdfsClient: any;
-                if (this.paiClusterConfig.host === this.hdfsOutputHost && this.hdfsClient) {
-                    dataOutputHdfsClient = this.hdfsClient;
-                } else {
-                    dataOutputHdfsClient = WebHDFS.createClient({
-                        user: this.paiClusterConfig.userName,
-                        port: 50070,
-                        host: this.hdfsOutputHost
-                    });
-                }
-
-                try {
-                    const exist : boolean = await HDFSClientUtility.pathExists('/', dataOutputHdfsClient);
-                    if (!exist) {
-                        deferred.reject(new Error(`Please check hdfsOutputDir host!`));
-                    }
-                } catch (error) {
-                    deferred.reject(new Error(`HDFS encounters problem, error is ${error}. Please check hdfsOutputDir host!`));
-                }
-
+           
                 // Copy experiment files from local folder to HDFS
                 this.copyExpCodeDirPromise = HDFSClientUtility.copyDirectoryToHdfs(
                     this.paiTrialConfig.codeDir,
@@ -409,12 +365,12 @@ class PAITrainingService implements TrainingService {
             throw new Error('PAI token is not initialized');
         }
 
-        if (this.hdfsBaseDir === undefined) {
-            throw new Error('hdfsBaseDir is not initialized');
+        if (this.hdfsCodeDir === undefined) {
+            throw new Error('hdfsCodeDir is not initialized');
         }
 
-        if (this.hdfsOutputHost === undefined) {
-            throw new Error('hdfsOutputHost is not initialized');
+        if (this.hdfsOutputDir === undefined) {
+            throw new Error('hdfsOutputDir is not initialized');
         }
 
         if (this.paiRestServerPort === undefined) {
@@ -428,8 +384,6 @@ class PAITrainingService implements TrainingService {
         }
 
         // Step 1. Prepare PAI job configuration
-        const hdfsOutputDir : string = unixPathJoin(this.hdfsBaseDir, this.experimentId, trialJobId);
-        const hdfsCodeDir: string = HDFSClientUtility.getHdfsTrialWorkDir(this.paiClusterConfig.userName, trialJobId);
 
         const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials-local', trialJobId);
         //create tmp trial working folder locally.
@@ -463,8 +417,8 @@ class PAITrainingService implements TrainingService {
             this.paiTrialConfig.command,
             nniManagerIp,
             this.paiRestServerPort,
-            hdfsOutputDir,
-            this.hdfsOutputHost,
+            this.hdfsOutputDir,
+            this.paiClusterConfig.host,
             this.paiClusterConfig.userName,
             HDFSClientUtility.getHdfsExpCodeDir(this.paiClusterConfig.userName),
             version,
@@ -488,7 +442,7 @@ class PAITrainingService implements TrainingService {
                 // Task command
                 nniPaiTrialCommand,
                 // Task shared memory
-                this.paiTrialConfig.shmMB
+                this.paiTrialConfig.shmMB,
             )
         ];
 
@@ -497,23 +451,21 @@ class PAITrainingService implements TrainingService {
             trialJobDetail.paiJobName,
             // Docker image
             this.paiTrialConfig.image,
-            // dataDir
-            this.paiTrialConfig.dataDir,
-            // outputDir
-            this.paiTrialConfig.outputDir,
             // codeDir
-            `$PAI_DEFAULT_FS_URI${hdfsCodeDir}`,
+            `$PAI_DEFAULT_FS_URI${this.hdfsCodeDir}`,
             // PAI Task roles
             paiTaskRoles,
             // Add Virutal Cluster
-            this.paiTrialConfig.virtualCluster === undefined ? 'default' : this.paiTrialConfig.virtualCluster.toString()
+            this.paiTrialConfig.virtualCluster === undefined ? 'default' : this.paiTrialConfig.virtualCluster.toString(),
+            //Task auth File
+            this.paiTrialConfig.authFile
         );
 
         // Step 2. Upload code files in codeDir onto HDFS
         try {
-            await HDFSClientUtility.copyDirectoryToHdfs(trialLocalTempFolder, hdfsCodeDir, this.hdfsClient);
+            await HDFSClientUtility.copyDirectoryToHdfs(trialLocalTempFolder, this.hdfsCodeDir, this.hdfsClient);
         } catch (error) {
-            this.log.error(`PAI Training service: copy ${this.paiTrialConfig.codeDir} to HDFS ${hdfsCodeDir} failed, error is ${error}`);
+            this.log.error(`PAI Training service: copy ${this.paiTrialConfig.codeDir} to HDFS ${this.hdfsCodeDir} failed, error is ${error}`);
             trialJobDetail.status = 'FAILED';
             deferred.resolve(true);
 

src/nni_manager/training_service/remote_machine/gpuScheduler.ts

@@ -48,14 +48,17 @@ export class GPUScheduler {
      * Schedule a machine according to the constraints (requiredGPUNum)
      * @param requiredGPUNum required GPU number
      */
-    public scheduleMachine(requiredGPUNum: number, trialJobDetail : RemoteMachineTrialJobDetail) : RemoteMachineScheduleResult {
+    public scheduleMachine(requiredGPUNum: number | undefined, trialJobDetail : RemoteMachineTrialJobDetail) : RemoteMachineScheduleResult {
+        if(requiredGPUNum === undefined) {
+            requiredGPUNum = 0;
+        }
         assert(requiredGPUNum >= 0);
         const allRMs: RemoteMachineMeta[] = Array.from(this.machineSSHClientMap.keys());
         assert(allRMs.length > 0);
 
         // Step 1: Check if required GPU number not exceeds the total GPU number in all machines
         const eligibleRM: RemoteMachineMeta[] = allRMs.filter((rmMeta : RemoteMachineMeta) =>
-                 rmMeta.gpuSummary === undefined || requiredGPUNum === 0 || rmMeta.gpuSummary.gpuCount >= requiredGPUNum);
+                 rmMeta.gpuSummary === undefined || requiredGPUNum === 0 || (requiredGPUNum !== undefined && rmMeta.gpuSummary.gpuCount >= requiredGPUNum));
         if (eligibleRM.length === 0) {
             // If the required gpu number exceeds the upper limit of all machine's GPU number
             // Return REQUIRE_EXCEED_TOTAL directly

src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts

@@ -601,12 +601,16 @@ class RemoteMachineTrainingService implements TrainingService {
         let command: string;
         // Set CUDA_VISIBLE_DEVICES environment variable based on cuda_visible_device
         // If no valid cuda_visible_device is defined, set CUDA_VISIBLE_DEVICES to empty string to hide GPU device
-        if (typeof cuda_visible_device === 'string' && cuda_visible_device.length > 0) {
-            command = `CUDA_VISIBLE_DEVICES=${cuda_visible_device} ${this.trialConfig.command}`;
+        // If gpuNum is undefined, will not set CUDA_VISIBLE_DEVICES in script
+        if (this.trialConfig.gpuNum === undefined) {
+            command = this.trialConfig.command;
         } else {
-            command = `CUDA_VISIBLE_DEVICES=" " ${this.trialConfig.command}`;
+            if (typeof cuda_visible_device === 'string' && cuda_visible_device.length > 0) {
+                command = `CUDA_VISIBLE_DEVICES=${cuda_visible_device} ${this.trialConfig.command}`;
+            } else {
+                command = `CUDA_VISIBLE_DEVICES=" " ${this.trialConfig.command}`;
+            }
         }
-
         // tslint:disable-next-line: strict-boolean-expressions
         const nniManagerIp: string = this.nniManagerIpConfig ? this.nniManagerIpConfig.nniManagerIp : getIPV4Address();
         if (this.remoteRestServerPort === undefined) {

src/sdk/pycli/nnicli/__init__.py

+# 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.
+# ==================================================================================================
+
+from .nni_client import *

src/sdk/pycli/nnicli/nni_client.py

+# 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.
+# ==================================================================================================
+
+""" A python wrapper for nni rest api
+
+Example:
+
+import nnicli as nc
+
+nc.start_nni('../../../../examples/trials/mnist/config.yml')
+
+nc.set_endpoint('http://localhost:8080')
+
+print(nc.version())
+print(nc.get_experiment_status())
+
+print(nc.get_job_statistics())
+print(nc.list_trial_jobs())
+
+nc.stop_nni()
+
+"""
+
+import sys
+import os
+import subprocess
+import requests
+
+__all__ = [
+    'start_nni',
+    'stop_nni',
+    'set_endpoint',
+    'version',
+    'get_experiment_status',
+    'get_experiment_profile',
+    'get_trial_job',
+    'list_trial_jobs',
+    'get_job_statistics',
+    'get_job_metrics',
+    'export_data'
+]
+
+EXPERIMENT_PATH = 'experiment'
+VERSION_PATH = 'version'
+STATUS_PATH = 'check-status'
+JOB_STATISTICS_PATH = 'job-statistics'
+TRIAL_JOBS_PATH = 'trial-jobs'
+METRICS_PATH = 'metric-data'
+EXPORT_DATA_PATH = 'export-data'
+
+API_ROOT_PATH = 'api/v1/nni'
+
+_api_endpoint = None
+
+def set_endpoint(endpoint):
+    """set endpoint of nni rest server for nnicli, for example:
+    http://localhost:8080
+    """
+    global _api_endpoint
+    _api_endpoint = endpoint
+
+def _check_endpoint():
+    if _api_endpoint is None:
+        raise AssertionError("Please call set_endpoint to specify nni endpoint")
+
+def _nni_rest_get(api_path, response_type='json'):
+    _check_endpoint()
+    uri = '{}/{}/{}'.format(_api_endpoint, API_ROOT_PATH, api_path)
+    res = requests.get(uri)
+    if _http_succeed(res.status_code):
+        if response_type == 'json':
+            return res.json()
+        elif response_type == 'text':
+            return res.text
+        else:
+            raise AssertionError('Incorrect response_type')
+    else:
+        return None
+
+def _http_succeed(status_code):
+    return status_code // 100 == 2
+
+def _create_process(cmd):
+    if sys.platform == 'win32':
+        process = subprocess.Popen(cmd, stdout=subprocess.PIPE, creationflags=subprocess.CREATE_NEW_PROCESS_GROUP)
+    else:
+        process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
+
+    while process.poll() is None:
+        output = process.stdout.readline()
+        if output:
+            print(output.decode('utf-8').strip())
+    return process.returncode
+
+def start_nni(config_file):
+    """start nni experiment with specified configuration file"""
+    cmd = 'nnictl create --config {}'.format(config_file).split(' ')
+    if _create_process(cmd) != 0:
+        raise RuntimeError('Failed to start nni.')
+
+def stop_nni():
+    """stop nni experiment"""
+    cmd = 'nnictl stop'.split(' ')
+    if _create_process(cmd) != 0:
+        raise RuntimeError('Failed to stop nni.')
+
+def version():
+    """return version of nni"""
+    return _nni_rest_get(VERSION_PATH, 'text')
+
+def get_experiment_status():
+    """return experiment status as a dict"""
+    return _nni_rest_get(STATUS_PATH)
+
+def get_experiment_profile():
+    """return experiment profile as a dict"""
+    return _nni_rest_get(EXPERIMENT_PATH)
+
+def get_trial_job(trial_job_id):
+    """return trial job information as a dict"""
+    assert trial_job_id is not None
+    return _nni_rest_get(os.path.join(TRIAL_JOBS_PATH, trial_job_id))
+
+def list_trial_jobs():
+    """return information for all trial jobs as a list"""
+    return _nni_rest_get(TRIAL_JOBS_PATH)
+
+def get_job_statistics():
+    """return trial job statistics information as a dict"""
+    return _nni_rest_get(JOB_STATISTICS_PATH)
+
+def get_job_metrics(trial_job_id=None):
+    """return trial job metrics"""
+    api_path = METRICS_PATH if trial_job_id is None else os.path.join(METRICS_PATH, trial_job_id)
+    return _nni_rest_get(api_path)
+
+def export_data():
+    """return exported information for all trial jobs"""
+    return _nni_rest_get(EXPORT_DATA_PATH)

src/sdk/pycli/setup.py

+import setuptools
+
+setuptools.setup(
+    name = 'nnicli',
+    version = '999.0.0-developing',
+    packages = setuptools.find_packages(),
+
+    python_requires = '>=3.5',
+    install_requires = [
+        'requests'
+    ],
+
+    author = 'Microsoft NNI Team',
+    author_email = 'nni@microsoft.com',
+    description = 'nnicli for Neural Network Intelligence project',
+    license = 'MIT',
+    url = 'https://github.com/Microsoft/nni',
+)

src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py

@@ -39,16 +39,14 @@ logger = logging.getLogger('grid_search_AutoML')
 class GridSearchTuner(Tuner):
     '''
     GridSearchTuner will search all the possible configures that the user define in the searchSpace.
-    The only acceptable types of search space are 'quniform', 'qloguniform' and 'choice'
+    The only acceptable types of search space are 'choice', 'quniform', 'randint'
 
     Type 'choice' will select one of the options. Note that it can also be nested.
 
-    Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the number of values that will be sampled evenly.
-    Note that q should be at least 2.
-    It will be sampled in a way that the first sampled value is 'low', and each of the following values is (high-low)/q larger that the value in front of it.
+    Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the interval
+    It will be sampled in a way that the first sampled value is 'low', and each of the following values is 'interval' larger than the value in front of it.
 
-    Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log(low), log(high)]
-    and sample and then change the sampled value back.
+    Type 'randint' gives all possible intergers in range[low, high). Note that 'high' is not included.
     '''
 
     def __init__(self):
@@ -73,8 +71,12 @@ class GridSearchTuner(Tuner):
                             chosen_params.extend(choice)
                         else:
                             chosen_params.append(choice)
+                elif _type == 'quniform':
+                    chosen_params = self._parse_quniform(_value)
+                elif _type == 'randint':
+                    chosen_params = self._parse_randint(_value)
                 else:
-                    chosen_params = self.parse_qtype(_type, _value)
+                    raise RuntimeError("Not supported type: %s" % _type)
             else:
                 chosen_params = dict()
                 for key in ss_spec.keys():
@@ -95,21 +97,13 @@ class GridSearchTuner(Tuner):
 
     def _parse_quniform(self, param_value):
         '''parse type of quniform parameter and return a list'''
-        if param_value[2] < 2:
-            raise RuntimeError("The number of values sampled (q) should be at least 2")
-        low, high, count = param_value[0], param_value[1], param_value[2]
-        interval = (high - low) / (count - 1)
-        return [float(low + interval * i) for i in range(count)]
-
-    def parse_qtype(self, param_type, param_value):
-        '''parse type of quniform or qloguniform'''
-        if param_type == 'quniform':
-            return self._parse_quniform(param_value)
-        if param_type == 'qloguniform':
-            param_value[:2] = np.log(param_value[:2])
-            return list(np.exp(self._parse_quniform(param_value)))
-
-        raise RuntimeError("Not supported type: %s" % param_type)
+        low, high, interval = param_value[0], param_value[1], param_value[2]
+        count = int(np.floor((high - low) / interval)) + 1
+        return [low + interval * i for i in range(count)]
+
+    def _parse_randint(self, param_value):
+        '''parse type of randint parameter and return a list'''
+        return np.arange(param_value[0], param_value[1]).tolist()
 
     def expand_parameters(self, para):
         '''
@@ -133,7 +127,7 @@ class GridSearchTuner(Tuner):
 
     def update_search_space(self, search_space):
         '''
-        Check if the search space is valid and expand it: only contains 'choice' type or other types beginnning with the letter 'q'
+        Check if the search space is valid and expand it: support only 'choice', 'quniform', randint'
         '''
         self.expanded_search_space = self.json2parameter(search_space)