Batchvorhersagejob abrufen

Ruft einen Batchvorhersagejob mit der Methode "get_batch_prediction_job" ab.

Codebeispiel

Java

Bevor Sie dieses Beispiel anwenden, folgen Sie den Java-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Java API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

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

Bevor Sie dieses Beispiel anwenden, folgen Sie den Node.js-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Node.js API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

/**
 * 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

Bevor Sie dieses Beispiel anwenden, folgen Sie den Python-Einrichtungsschritten in der Vertex AI-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur Vertex AI Python API.

Richten Sie zur Authentifizierung bei Vertex AI Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

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)

Nächste Schritte

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