Receber um job de previsão em lote

Recebe um job de previsão em lote usando o método get_batch_predict_job.

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.cloud.aiplatform.v1.BatchPredictionJob;
import com.google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig;
import com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig;
import com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputInfo;
import com.google.cloud.aiplatform.v1.BatchPredictionJobName;
import com.google.cloud.aiplatform.v1.BigQueryDestination;
import com.google.cloud.aiplatform.v1.BigQuerySource;
import com.google.cloud.aiplatform.v1.CompletionStats;
import com.google.cloud.aiplatform.v1.GcsDestination;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.ResourcesConsumed;
import com.google.protobuf.Any;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.List;

public class GetBatchPredictionJobSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String batchPredictionJobId = "YOUR_BATCH_PREDICTION_JOB_ID";
    getBatchPredictionJobSample(project, batchPredictionJobId);
  }

  static void getBatchPredictionJobSample(String project, String batchPredictionJobId)
      throws IOException {
    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";
      BatchPredictionJobName batchPredictionJobName =
          BatchPredictionJobName.of(project, location, batchPredictionJobId);

      BatchPredictionJob batchPredictionJob =
          jobServiceClient.getBatchPredictionJob(batchPredictionJobName);

      System.out.println("Get Batch Prediction Job Response");
      System.out.format("\tName: %s\n", batchPredictionJob.getName());
      System.out.format("\tDisplay Name: %s\n", batchPredictionJob.getDisplayName());
      System.out.format("\tModel: %s\n", batchPredictionJob.getModel());

      System.out.format("\tModel Parameters: %s\n", batchPredictionJob.getModelParameters());
      System.out.format("\tState: %s\n", batchPredictionJob.getState());

      System.out.format("\tCreate Time: %s\n", batchPredictionJob.getCreateTime());
      System.out.format("\tStart Time: %s\n", batchPredictionJob.getStartTime());
      System.out.format("\tEnd Time: %s\n", batchPredictionJob.getEndTime());
      System.out.format("\tUpdate Time: %s\n", batchPredictionJob.getUpdateTime());
      System.out.format("\tLabels: %s\n", batchPredictionJob.getLabelsMap());

      InputConfig inputConfig = batchPredictionJob.getInputConfig();
      System.out.println("\tInput Config");
      System.out.format("\t\tInstances Format: %s\n", inputConfig.getInstancesFormat());

      GcsSource gcsSource = inputConfig.getGcsSource();
      System.out.println("\t\tGcs Source");
      System.out.format("\t\t\tUris: %s\n", gcsSource.getUrisList());

      BigQuerySource bigquerySource = inputConfig.getBigquerySource();
      System.out.println("\t\tBigquery Source");
      System.out.format("\t\t\tInput Uri: %s\n", bigquerySource.getInputUri());

      OutputConfig outputConfig = batchPredictionJob.getOutputConfig();
      System.out.println("\tOutput Config");
      System.out.format("\t\tPredictions Format: %s\n", outputConfig.getPredictionsFormat());

      GcsDestination gcsDestination = outputConfig.getGcsDestination();
      System.out.println("\t\tGcs Destination");
      System.out.format("\t\t\tOutput Uri Prefix: %s\n", gcsDestination.getOutputUriPrefix());

      BigQueryDestination bigqueryDestination = outputConfig.getBigqueryDestination();
      System.out.println("\t\tBigquery Destination");
      System.out.format("\t\t\tOutput Uri: %s\n", bigqueryDestination.getOutputUri());

      OutputInfo outputInfo = batchPredictionJob.getOutputInfo();
      System.out.println("\tOutput Info");
      System.out.format("\t\tGcs Output Directory: %s\n", outputInfo.getGcsOutputDirectory());
      System.out.format("\t\tBigquery Output Dataset: %s\n", outputInfo.getBigqueryOutputDataset());

      Status status = batchPredictionJob.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());

      List<Any> detailsList = status.getDetailsList();

      for (Status partialFailure : batchPredictionJob.getPartialFailuresList()) {
        System.out.println("\tPartial Failure");
        System.out.format("\t\tCode: %s\n", partialFailure.getCode());
        System.out.format("\t\tMessage: %s\n", partialFailure.getMessage());
        List<Any> details = partialFailure.getDetailsList();
      }

      ResourcesConsumed resourcesConsumed = batchPredictionJob.getResourcesConsumed();
      System.out.println("\tResources Consumed");
      System.out.format("\t\tReplica Hours: %s\n", resourcesConsumed.getReplicaHours());

      CompletionStats completionStats = batchPredictionJob.getCompletionStats();
      System.out.println("\tCompletion Stats");
      System.out.format("\t\tSuccessful Count: %s\n", completionStats.getSuccessfulCount());
      System.out.format("\t\tFailed Count: %s\n", completionStats.getFailedCount());
      System.out.format("\t\tIncomplete Count: %s\n", completionStats.getIncompleteCount());
    }
  }
}

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 get_batch_prediction_job_sample(
    project: str,
    batch_prediction_job_id: 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)
    name = client.batch_prediction_job_path(
        project=project, location=location, batch_prediction_job=batch_prediction_job_id
    )
    response = client.get_batch_prediction_job(name=name)
    print("response:", response)

A seguir

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