Create a hyperparameter tuning job

Creates a hyperparameter tuning job using the create_hyperparameter_tuning_job method.

Code sample

Java

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Java API reference documentation.

import com.google.cloud.aiplatform.v1.AcceleratorType;
import com.google.cloud.aiplatform.v1.ContainerSpec;
import com.google.cloud.aiplatform.v1.CustomJobSpec;
import com.google.cloud.aiplatform.v1.HyperparameterTuningJob;
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.StudySpec;
import com.google.cloud.aiplatform.v1.WorkerPoolSpec;
import java.io.IOException;

public class CreateHyperparameterTuningJobSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String displayName = "DISPLAY_NAME";
    String containerImageUri = "CONTAINER_IMAGE_URI";
    createHyperparameterTuningJobSample(project, displayName, containerImageUri);
  }

  static void createHyperparameterTuningJobSample(
      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. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
      StudySpec.MetricSpec metric0 =
          StudySpec.MetricSpec.newBuilder()
              .setMetricId("accuracy")
              .setGoal(StudySpec.MetricSpec.GoalType.MAXIMIZE)
              .build();
      StudySpec.ParameterSpec.DoubleValueSpec doubleValueSpec =
          StudySpec.ParameterSpec.DoubleValueSpec.newBuilder()
              .setMinValue(0.001)
              .setMaxValue(0.1)
              .build();
      StudySpec.ParameterSpec parameter0 =
          StudySpec.ParameterSpec.newBuilder()
              // Learning rate.
              .setParameterId("lr")
              .setDoubleValueSpec(doubleValueSpec)
              .build();
      StudySpec studySpec =
          StudySpec.newBuilder().addMetrics(metric0).addParameters(parameter0).build();
      MachineSpec machineSpec =
          MachineSpec.newBuilder()
              .setMachineType("n1-standard-4")
              .setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80)
              .setAcceleratorCount(1)
              .build();
      ContainerSpec containerSpec =
          ContainerSpec.newBuilder().setImageUri(containerImageUri).build();
      WorkerPoolSpec workerPoolSpec0 =
          WorkerPoolSpec.newBuilder()
              .setMachineSpec(machineSpec)
              .setReplicaCount(1)
              .setContainerSpec(containerSpec)
              .build();
      CustomJobSpec trialJobSpec =
          CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec0).build();
      HyperparameterTuningJob hyperparameterTuningJob =
          HyperparameterTuningJob.newBuilder()
              .setDisplayName(displayName)
              .setMaxTrialCount(2)
              .setParallelTrialCount(1)
              .setMaxFailedTrialCount(1)
              .setStudySpec(studySpec)
              .setTrialJobSpec(trialJobSpec)
              .build();
      LocationName parent = LocationName.of(project, location);
      HyperparameterTuningJob response =
          client.createHyperparameterTuningJob(parent, hyperparameterTuningJob);
      System.out.format("response: %s\n", response);
      System.out.format("Name: %s\n", response.getName());
    }
  }
}

Node.js

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Node.js API reference documentation.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 * (Not necessary if passing values as arguments)
 */
/*
const displayName = 'YOUR HYPERPARAMETER TUNING JOB;
const containerImageUri = 'TUNING JOB CONTAINER URI;
const project = 'YOUR PROJECT ID';
const location = 'us-central1';
  */
// Imports the Google Cloud Pipeline 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 createHyperParameterTuningJob() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  // Create the hyperparameter tuning job configuration
  const hyperparameterTuningJob = {
    displayName,
    maxTrialCount: 2,
    parallelTrialCount: 1,
    maxFailedTrialCount: 1,
    studySpec: {
      metrics: [
        {
          metricId: 'accuracy',
          goal: 'MAXIMIZE',
        },
      ],
      parameters: [
        {
          parameterId: 'lr',
          doubleValueSpec: {
            minValue: 0.001,
            maxValue: 0.1,
          },
        },
      ],
    },
    trialJobSpec: {
      workerPoolSpecs: [
        {
          machineSpec: {
            machineType: 'n1-standard-4',
            acceleratorType: 'NVIDIA_TESLA_K80',
            acceleratorCount: 1,
          },
          replicaCount: 1,
          containerSpec: {
            imageUri: containerImageUri,
            command: [],
            args: [],
          },
        },
      ],
    },
  };

  const [response] = await jobServiceClient.createHyperparameterTuningJob({
    parent,
    hyperparameterTuningJob,
  });

  console.log('Create hyperparameter tuning job response:');
  console.log(`\tDisplay name: ${response.displayName}`);
  console.log(`\tTuning job resource name: ${response.name}`);
  console.log(`\tJob status: ${response.state}`);
}

createHyperParameterTuningJob();

Python

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

from google.cloud import aiplatform


def create_hyperparameter_tuning_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)
    hyperparameter_tuning_job = {
        "display_name": display_name,
        "max_trial_count": 2,
        "parallel_trial_count": 1,
        "max_failed_trial_count": 1,
        "study_spec": {
            "metrics": [
                {
                    "metric_id": "accuracy",
                    "goal": aiplatform.gapic.StudySpec.MetricSpec.GoalType.MAXIMIZE,
                }
            ],
            "parameters": [
                {
                    # Learning rate.
                    "parameter_id": "lr",
                    "double_value_spec": {"min_value": 0.001, "max_value": 0.1},
                },
            ],
        },
        "trial_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_hyperparameter_tuning_job(
        parent=parent, hyperparameter_tuning_job=hyperparameter_tuning_job
    )
    print("response:", response)

What's next

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