Supprimer un point de terminaison

Supprime un point de terminaison à l'aide de la méthode delete_endpoint.

Exemple de code

Java

Avant d'essayer cet exemple, suivez les instructions de configuration pour Java décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Java.

Pour vous authentifier auprès de Vertex AI, configurez le service Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.EndpointServiceClient;
import com.google.cloud.aiplatform.v1.EndpointServiceSettings;
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 DeleteEndpointSample {

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

  static void deleteEndpointSample(String project, String endpointId)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    EndpointServiceSettings endpointServiceSettings =
        EndpointServiceSettings.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 (EndpointServiceClient endpointServiceClient =
        EndpointServiceClient.create(endpointServiceSettings)) {
      String location = "us-central1";
      EndpointName endpointName = EndpointName.of(project, location, endpointId);

      // NOTE: Be sure to undeploy any models deployed to the endpoint
      // before attempting to delete the endpoint.
      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          endpointServiceClient.deleteEndpointAsync(endpointName);
      System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      Empty deleteResponse = operationFuture.get(300, TimeUnit.SECONDS);

      System.out.format("Delete Endpoint Response: %s\n", deleteResponse);
    }
  }
}

Node.js

Avant d'essayer cet exemple, suivez les instructions de configuration pour Node.js décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Node.js.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const endpointId = 'YOUR_ENDPOINT_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Endpoint Service Client library
const {EndpointServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const endpointServiceClient = new EndpointServiceClient(clientOptions);

async function deleteEndpoint() {
  // Configure the parent resource
  const endpoint = {
    name: `projects/${project}/locations/${location}/endpoints/${endpointId}`,
  };

  // NOTE: Be sure to undeploy any models deployed to the endpoint before
  // attempting to delete the endpoint.

  // Delete endpoint request
  const [response] = await endpointServiceClient.deleteEndpoint(endpoint);
  console.log(`Long running operation : ${response.name}`);

  // Wait for operation to complete
  await response.promise();
  const result = response.result;

  console.log('Delete endpoint response:\n', result);
}
deleteEndpoint();

Python

Avant d'essayer cet exemple, suivez les instructions de configuration pour Python décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Python.

from google.cloud import aiplatform


def delete_endpoint_sample(
    project: str,
    endpoint_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.EndpointServiceClient(client_options=client_options)
    name = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.delete_endpoint(name=name)
    print("Long running operation:", response.operation.name)
    delete_endpoint_response = response.result(timeout=timeout)
    print("delete_endpoint_response:", delete_endpoint_response)

Étapes suivantes

Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'explorateur d'exemples Google Cloud.