Crea un trabajo de etiquetado de datos para el aprendizaje activo

Crea un trabajo de etiquetado de datos para el aprendizaje activo mediante el método create_data_labeling_job.

Muestra de código

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Java.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.

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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Python incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Python.

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)

¿Qué sigue?

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