Excluir um endpoint

Exclui um endpoint usando o método delete_endpoint.

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.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

Antes de testar esse exemplo, siga as instruções de configuração para Node.js 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 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 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

Antes de testar esse exemplo, siga as instruções de configuração para Python 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 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 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)

A seguir

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