Menghapus tugas pelabelan data

Menghapus tugas pelabelan data menggunakan metode delete_data_labeling_job.

Contoh kode

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DataLabelingJobName;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
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 DeleteDataLabelingJobSample {
  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 dataLabelingJobId = "YOUR_DATA_LABELING_JOB_ID";
    deleteDataLabelingJob(project, dataLabelingJobId);
  }

  static void deleteDataLabelingJob(String project, String dataLabelingJobId)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    JobServiceSettings jobServiceSettings =
        JobServiceSettings.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 (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
      String location = "us-central1";

      DataLabelingJobName dataLabelingJobName =
          DataLabelingJobName.of(project, location, dataLabelingJobId);

      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          jobServiceClient.deleteDataLabelingJobAsync(dataLabelingJobName);
      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 Data Labeling Job.");
    }
  }
}

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

from google.cloud import aiplatform


def delete_data_labeling_job_sample(
    project: str,
    data_labeling_job_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.JobServiceClient(client_options=client_options)
    name = client.data_labeling_job_path(
        project=project, location=location, data_labeling_job=data_labeling_job_id
    )
    response = client.delete_data_labeling_job(name=name)
    print("Long running operation:", response.operation.name)
    delete_data_labeling_job_response = response.result(timeout=timeout)
    print("delete_data_labeling_job_response:", delete_data_labeling_job_response)

Langkah selanjutnya

Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat browser contoh Google Cloud.