Criar um job de ajuste de hiperparâmetros

Cria um job de ajuste de hiperparâmetro usando o método create_ hiperparâmetro_tuning_job.

Exemplo de código

Java

Antes de testar esse exemplo, siga as instruções de configuração para Java no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Java.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

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

Antes de testar essa amostra, siga as instruções de configuração para Node.js Guia de início rápido da Vertex AI: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Node.js.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

/**
 * 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

Antes de testar essa amostra, siga as instruções de configuração para Python Guia de início rápido da Vertex AI: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Python.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

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)

A seguir

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