为自定义训练配置容器设置

 import com.google.cloud.aiplatform.v1.AcceleratorType; import com.google.cloud.aiplatform.v1.ContainerSpec; import com.google.cloud.aiplatform.v1.CustomJob; import com.google.cloud.aiplatform.v1.CustomJobSpec; import com.google.cloud.aiplatform.v1.JobServiceClient; import com.google.cloud.aiplatform.v1.JobServiceSettings; import com.google.cloud.aiplatform.v1.LocationName; import com.google.cloud.aiplatform.v1.MachineSpec; import com.google.cloud.aiplatform.v1.WorkerPoolSpec; import java.io.IOException;  // Create a custom job to run machine learning training code in Vertex AI public class CreateCustomJobSample {    public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String project = "PROJECT";     String displayName = "DISPLAY_NAME";      // Vertex AI runs your training application in a Docker container image. A Docker container     // image is a self-contained software package that includes code and all dependencies. Learn     // more about preparing your training application at     // https://cloud.google.com/vertex-ai/docs/training/overview#prepare_your_training_application     String containerImageUri = "CONTAINER_IMAGE_URI";     createCustomJobSample(project, displayName, containerImageUri);   }    static void createCustomJobSample(String project, String displayName, String containerImageUri)       throws IOException {     JobServiceSettings settings =         JobServiceSettings.newBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();     String location = "us-central1";      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests.     try (JobServiceClient client = JobServiceClient.create(settings)) {       MachineSpec machineSpec =           MachineSpec.newBuilder()               .setMachineType("n1-standard-4")               .setAcceleratorType(AcceleratorType.NVIDIA_TESLA_T4)               .setAcceleratorCount(1)               .build();        ContainerSpec containerSpec =           ContainerSpec.newBuilder().setImageUri(containerImageUri).build();        WorkerPoolSpec workerPoolSpec =           WorkerPoolSpec.newBuilder()               .setMachineSpec(machineSpec)               .setReplicaCount(1)               .setContainerSpec(containerSpec)               .build();        CustomJobSpec customJobSpecJobSpec =           CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec).build();        CustomJob customJob =           CustomJob.newBuilder()               .setDisplayName(displayName)               .setJobSpec(customJobSpecJobSpec)               .build();       LocationName parent = LocationName.of(project, location);       CustomJob response = client.createCustomJob(parent, customJob);       System.out.format("response: %s\n", response);       System.out.format("Name: %s\n", response.getName());     }   } }

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const customJobDisplayName = 'YOUR_CUSTOM_JOB_DISPLAY_NAME'; // const containerImageUri = 'YOUR_CONTAINER_IMAGE_URI'; // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION';  // Imports the Google Cloud Job Service Client library const {JobServiceClient} = require('@google-cloud/aiplatform');  // Specifies the location of the api endpoint const clientOptions = {   apiEndpoint: 'us-central1-aiplatform.googleapis.com', };  // Instantiates a client const jobServiceClient = new JobServiceClient(clientOptions);  async function createCustomJob() {   // Configure the parent resource   const parent = `projects/${project}/locations/${location}`;   const customJob = {     displayName: customJobDisplayName,     jobSpec: {       workerPoolSpecs: [         {           machineSpec: {             machineType: 'n1-standard-4',             acceleratorType: 'NVIDIA_TESLA_T4',             acceleratorCount: 1,           },           replicaCount: 1,           containerSpec: {             imageUri: containerImageUri,             command: [],             args: [],           },         },       ],     },   };   const request = {parent, customJob};    // Create custom job request   const [response] = await jobServiceClient.createCustomJob(request);    console.log('Create custom job response:\n', JSON.stringify(response)); } createCustomJob();

from google.cloud import aiplatform   def create_custom_job_sample(     project: str,     display_name: str,     container_image_uri: str,     location: str = "us-central1",     api_endpoint: str = "us-central1-aiplatform.googleapis.com", ):     # The AI Platform services require regional API endpoints.     client_options = {"api_endpoint": api_endpoint}     # Initialize client that will be used to create and send requests.     # This client only needs to be created once, and can be reused for multiple requests.     client = aiplatform.gapic.JobServiceClient(client_options=client_options)     custom_job = {         "display_name": display_name,         "job_spec": {             "worker_pool_specs": [                 {                     "machine_spec": {                         "machine_type": "n1-standard-4",                         "accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,                         "accelerator_count": 1,                     },                     "replica_count": 1,                     "container_spec": {                         "image_uri": container_image_uri,                         "command": [],                         "args": [],                     },                 }             ]         },     }     parent = f"projects/{project}/locations/{location}"     response = client.create_custom_job(parent=parent, custom_job=custom_job)     print("response:", response)