Membuat tugas pelabelan data untuk pembelajaran aktif

Membuat tugas pelabelan data untuk pembelajaran aktif menggunakan metode create_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.cloud.aiplatform.v1.ActiveLearningConfig;
import com.google.cloud.aiplatform.v1.DataLabelingJob;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.gson.JsonArray;
import com.google.gson.JsonObject;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;

public class CreateDataLabelingJobActiveLearningSample {

  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 dataset = "DATASET";
    String instructionUri = "INSTRUCTION_URI";
    String inputsSchemaUri = "INPUTS_SCHEMA_URI";
    String annotationSpec = "ANNOTATION_SPEC";
    createDataLabelingJobActiveLearningSample(
        project, displayName, dataset, instructionUri, inputsSchemaUri, annotationSpec);
  }

  static void createDataLabelingJobActiveLearningSample(
      String project,
      String displayName,
      String dataset,
      String instructionUri,
      String inputsSchemaUri,
      String annotationSpec)
      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)) {
      JsonArray jsonAnnotationSpecs = new JsonArray();
      jsonAnnotationSpecs.add(annotationSpec);
      JsonObject jsonInputs = new JsonObject();
      jsonInputs.add("annotation_specs", jsonAnnotationSpecs);
      Value.Builder inputsBuilder = Value.newBuilder();
      JsonFormat.parser().merge(jsonInputs.toString(), inputsBuilder);
      Value inputs = inputsBuilder.build();
      ActiveLearningConfig activeLearningConfig =
          ActiveLearningConfig.newBuilder().setMaxDataItemCount(1).build();

      String datasetName = DatasetName.of(project, location, dataset).toString();

      DataLabelingJob dataLabelingJob =
          DataLabelingJob.newBuilder()
              .setDisplayName(displayName)
              .addDatasets(datasetName)
              .setLabelerCount(1)
              .setInstructionUri(instructionUri)
              .setInputsSchemaUri(inputsSchemaUri)
              .setInputs(inputs)
              .putAnnotationLabels(
                  "aiplatform.googleapis.com/annotation_set_name",
                  "data_labeling_job_active_learning")
              .setActiveLearningConfig(activeLearningConfig)
              .build();
      LocationName parent = LocationName.of(project, location);
      DataLabelingJob response = client.createDataLabelingJob(parent, dataLabelingJob);
      System.out.format("response: %s\n", response);
      System.out.format("Name: %s\n", response.getName());
    }
  }
}

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
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value


def create_data_labeling_job_active_learning_sample(
    project: str,
    display_name: str,
    dataset: str,
    instruction_uri: str,
    inputs_schema_uri: str,
    annotation_spec: 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)
    inputs_dict = {"annotation_specs": [annotation_spec]}
    inputs = json_format.ParseDict(inputs_dict, Value())

    active_learning_config = {"max_data_item_count": 1}

    data_labeling_job = {
        "display_name": display_name,
        # Full resource name: projects/{project}/locations/{location}/datasets/{dataset_id}
        "datasets": [dataset],
        "labeler_count": 1,
        "instruction_uri": instruction_uri,
        "inputs_schema_uri": inputs_schema_uri,
        "inputs": inputs,
        "annotation_labels": {
            "aiplatform.googleapis.com/annotation_set_name": "data_labeling_job_active_learning"
        },
        "active_learning_config": active_learning_config,
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_data_labeling_job(
        parent=parent, data_labeling_job=data_labeling_job
    )
    print("response:", response)

Langkah selanjutnya

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