Obtenir une tâche de prédiction par lot

Restez organisé à l'aide des collections Enregistrez et classez les contenus selon vos préférences.

Récupère une tâche de prédiction par lot à l'aide de la méthode get_batch_prediction_job.

Exemple de code

Java

Pour savoir comment installer et utiliser la bibliothèque cliente pour Vertex AI, consultez Bibliothèques clientes Vertex AI. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Java.


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());
    }
  }
}

Node.js

Pour savoir comment installer et utiliser la bibliothèque cliente pour Vertex AI, consultez la page Bibliothèques clientes Vertex AI. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Node.js.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const batchPredictionJobId = 'YOUR_BATCH_PREDICTION_JOB_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Job Service Client library
const {JobServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const jobServiceClient = new JobServiceClient(clientOptions);

async function getBatchPredictionJob() {
  // Configure the parent resource
  const name = `projects/${project}/locations/${location}/batchPredictionJobs/${batchPredictionJobId}`;
  const request = {
    name,
  };

  // Get batch prediction request
  const [response] = await jobServiceClient.getBatchPredictionJob(request);

  console.log('Get batch prediction job response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tDisplayName : ${response.displayName}`);
  console.log(`\tModel : ${response.model}`);
  console.log(`\tModel parameters : ${response.modelParameters}`);
  console.log(`\tGenerate explanation : ${response.generateExplanation}`);
  console.log(`\tState : ${response.state}`);
  console.log(`\tCreate Time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tStart Time : ${JSON.stringify(response.startTime)}`);
  console.log(`\tEnd Time : ${JSON.stringify(response.endTime)}`);
  console.log(`\tUpdate Time : ${JSON.stringify(response.updateTime)}`);
  console.log(`\tLabels : ${JSON.stringify(response.labels)}`);

  const inputConfig = response.inputConfig;
  console.log('\tInput config');
  console.log(`\t\tInstances format : ${inputConfig.instancesFormat}`);

  const gcsSource = inputConfig.gcsSource;
  console.log('\t\tGcs source');
  console.log(`\t\t\tUris : ${gcsSource.uris}`);

  const bigquerySource = inputConfig.bigquerySource;
  console.log('\t\tBigQuery Source');
  if (!bigquerySource) {
    console.log('\t\t\tInput Uri : {}');
  } else {
    console.log(`\t\t\tInput Uri : ${bigquerySource.inputUri}`);
  }

  const outputConfig = response.outputConfig;
  console.log('\t\tOutput config');
  console.log(`\t\tPredictions format : ${outputConfig.predictionsFormat}`);

  const gcsDestination = outputConfig.gcsDestination;
  console.log('\t\tGcs Destination');
  console.log(`\t\t\tOutput uri prefix : ${gcsDestination.outputUriPrefix}`);

  const bigqueryDestination = outputConfig.bigqueryDestination;
  if (!bigqueryDestination) {
    console.log('\t\tBigquery Destination');
    console.log('\t\t\tOutput uri : {}');
  } else {
    console.log('\t\tBigquery Destination');
    console.log(`\t\t\tOutput uri : ${bigqueryDestination.outputUri}`);
  }

  const outputInfo = response.outputInfo;
  if (!outputInfo) {
    console.log('\tOutput info');
    console.log('\t\tGcs output directory : {}');
    console.log('\t\tBigquery_output_dataset : {}');
  } else {
    console.log('\tOutput info');
    console.log(
      `\t\tGcs output directory : ${outputInfo.gcsOutputDirectory}`
    );
    console.log(`\t\tBigquery_output_dataset :
          ${outputInfo.bigqueryOutputDataset}`);
  }

  const error = response.error;
  console.log('\tError');
  console.log(`\t\tCode : ${error.code}`);
  console.log(`\t\tMessage : ${error.message}`);

  const details = error.details;
  console.log(`\t\tDetails : ${details}`);

  const partialFailures = response.partialFailures;
  console.log('\tPartial failure');
  console.log(partialFailures);

  const resourcesConsumed = response.resourcesConsumed;
  console.log('\tResource consumed');
  if (!resourcesConsumed) {
    console.log('\t\tReplica Hours: {}');
  } else {
    console.log(`\t\tReplica Hours: ${resourcesConsumed.replicaHours}`);
  }

  const completionStats = response.completionStats;
  console.log('\tCompletion status');
  if (!completionStats) {
    console.log('\t\tSuccessful count: {}');
    console.log('\t\tFailed count: {}');
    console.log('\t\tIncomplete count: {}');
  } else {
    console.log(`\t\tSuccessful count: ${completionStats.successfulCount}`);
    console.log(`\t\tFailed count: ${completionStats.failedCount}`);
    console.log(`\t\tIncomplete count: ${completionStats.incompleteCount}`);
  }
}
getBatchPredictionJob();

Python

Pour savoir comment installer et utiliser la bibliothèque cliente pour Vertex AI, consultez la page Bibliothèques clientes Vertex AI. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Python.

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)

Étape suivante

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