Eliminazione di una pipeline di addestramento

Elimina una pipeline di addestramento utilizzando il metodo delete_training_pipeline.

Esempio di codice

Java

Prima di provare questo esempio, segui le istruzioni di configurazione di Java nella guida rapida di Vertex AI per l'utilizzo delle librerie client. Per saperne di più, consulta la documentazione di riferimento dell'API Vertex AI Java.

Per autenticarti in Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configura l'autenticazione per un ambiente di sviluppo locale.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.TrainingPipelineName;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class DeleteTrainingPipelineSample {

  public static void main(String[] args)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String trainingPipelineId = "YOUR_TRAINING_PIPELINE_ID";
    String project = "YOUR_PROJECT_ID";
    deleteTrainingPipelineSample(project, trainingPipelineId);
  }

  static void deleteTrainingPipelineSample(String project, String trainingPipelineId)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // 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 (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      TrainingPipelineName trainingPipelineName =
          TrainingPipelineName.of(project, location, trainingPipelineId);

      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          pipelineServiceClient.deleteTrainingPipelineAsync(trainingPipelineName);
      System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      operationFuture.get(300, TimeUnit.SECONDS);

      System.out.format("Deleted Training Pipeline.");
    }
  }
}

Python

Prima di provare questo esempio, segui le istruzioni di configurazione di Python nella guida rapida di Vertex AI per l'utilizzo delle librerie client. Per saperne di più, consulta la documentazione di riferimento dell'API Vertex AI Python.

Per autenticarti in Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configura l'autenticazione per un ambiente di sviluppo locale.

from google.cloud import aiplatform


def delete_training_pipeline_sample(
    project: str,
    training_pipeline_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # 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.PipelineServiceClient(client_options=client_options)
    name = client.training_pipeline_path(
        project=project, location=location, training_pipeline=training_pipeline_id
    )
    response = client.delete_training_pipeline(name=name)
    print("Long running operation:", response.operation.name)
    delete_training_pipeline_response = response.result(timeout=timeout)
    print("delete_training_pipeline_response:", delete_training_pipeline_response)

Passaggi successivi

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