Criar um job de previsão em lote para classificação de vídeo

Cria um job de previsão em lote para classificação de vídeo usando o método create_batch_predict_job.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

• Receber previsões de um modelo de classificação de vídeo

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.util.ValueConverter;
import com.google.cloud.aiplatform.v1.BatchDedicatedResources;
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.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.LocationName;
import com.google.cloud.aiplatform.v1.MachineSpec;
import com.google.cloud.aiplatform.v1.ManualBatchTuningParameters;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.cloud.aiplatform.v1.ResourcesConsumed;
import com.google.cloud.aiplatform.v1.schema.predict.params.VideoClassificationPredictionParams;
import com.google.protobuf.Any;
import com.google.protobuf.Value;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.List;

public class CreateBatchPredictionJobVideoClassificationSample {

  public static void main(String[] args) throws IOException {
    String batchPredictionDisplayName = "YOUR_VIDEO_CLASSIFICATION_DISPLAY_NAME";
    String modelId = "YOUR_MODEL_ID";
    String gcsSourceUri =
        "gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_video_source/[file.csv/file.jsonl]";
    String gcsDestinationOutputUriPrefix =
        "gs://YOUR_GCS_SOURCE_BUCKET/destination_output_uri_prefix/";
    String project = "YOUR_PROJECT_ID";
    createBatchPredictionJobVideoClassification(
        batchPredictionDisplayName, modelId, gcsSourceUri, gcsDestinationOutputUriPrefix, project);
  }

  static void createBatchPredictionJobVideoClassification(
      String batchPredictionDisplayName,
      String modelId,
      String gcsSourceUri,
      String gcsDestinationOutputUriPrefix,
      String project)
      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";
      LocationName locationName = LocationName.of(project, location);

      VideoClassificationPredictionParams modelParamsObj =
          VideoClassificationPredictionParams.newBuilder()
              .setConfidenceThreshold(((float) 0.5))
              .setMaxPredictions(10000)
              .setSegmentClassification(true)
              .setShotClassification(true)
              .setOneSecIntervalClassification(true)
              .build();

      Value modelParameters = ValueConverter.toValue(modelParamsObj);

      ModelName modelName = ModelName.of(project, location, modelId);
      GcsSource.Builder gcsSource = GcsSource.newBuilder();
      gcsSource.addUris(gcsSourceUri);
      InputConfig inputConfig =
          InputConfig.newBuilder().setInstancesFormat("jsonl").setGcsSource(gcsSource).build();

      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder()
              .setPredictionsFormat("jsonl")
              .setGcsDestination(gcsDestination)
              .build();

      BatchPredictionJob batchPredictionJob =
          BatchPredictionJob.newBuilder()
              .setDisplayName(batchPredictionDisplayName)
              .setModel(modelName.toString())
              .setModelParameters(modelParameters)
              .setInputConfig(inputConfig)
              .setOutputConfig(outputConfig)
              .build();
      BatchPredictionJob batchPredictionJobResponse =
          jobServiceClient.createBatchPredictionJob(locationName, batchPredictionJob);

      System.out.println("Create Batch Prediction Job Video Classification Response");
      System.out.format("\tName: %s\n", batchPredictionJobResponse.getName());
      System.out.format("\tDisplay Name: %s\n", batchPredictionJobResponse.getDisplayName());
      System.out.format("\tModel %s\n", batchPredictionJobResponse.getModel());
      System.out.format(
          "\tModel Parameters: %s\n", batchPredictionJobResponse.getModelParameters());

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

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

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

      BigQuerySource bigQuerySource = inputConfigResponse.getBigquerySource();
      System.out.println("\t\tBigquery Source");
      System.out.format("\t\t\tInput_uri: %s\n", bigQuerySource.getInputUri());

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

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

      BigQueryDestination bigQueryDestination = outputConfigResponse.getBigqueryDestination();
      System.out.println("\t\tBig Query Destination");
      System.out.format("\t\t\tOutput Uri: %s\n", bigQueryDestination.getOutputUri());

      BatchDedicatedResources batchDedicatedResources =
          batchPredictionJobResponse.getDedicatedResources();
      System.out.println("\tBatch Dedicated Resources");
      System.out.format(
          "\t\tStarting Replica Count: %s\n", batchDedicatedResources.getStartingReplicaCount());
      System.out.format(
          "\t\tMax Replica Count: %s\n", batchDedicatedResources.getMaxReplicaCount());

      MachineSpec machineSpec = batchDedicatedResources.getMachineSpec();
      System.out.println("\t\tMachine Spec");
      System.out.format("\t\t\tMachine Type: %s\n", machineSpec.getMachineType());
      System.out.format("\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());
      System.out.format("\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());

      ManualBatchTuningParameters manualBatchTuningParameters =
          batchPredictionJobResponse.getManualBatchTuningParameters();
      System.out.println("\tManual Batch Tuning Parameters");
      System.out.format("\t\tBatch Size: %s\n", manualBatchTuningParameters.getBatchSize());

      OutputInfo outputInfo = batchPredictionJobResponse.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 = batchPredictionJobResponse.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> details = status.getDetailsList();

      for (Status partialFailure : batchPredictionJobResponse.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> partialFailureDetailsList = partialFailure.getDetailsList();
      }

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

      CompletionStats completionStats = batchPredictionJobResponse.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

Antes de testar essa amostra, siga as instruções de configuração para Node.js Guia de início rápido da Vertex AI: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Node.js.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

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

// const batchPredictionDisplayName = 'YOUR_BATCH_PREDICTION_DISPLAY_NAME';
// const modelId = 'YOUR_MODEL_ID';
// const gcsSourceUri = 'YOUR_GCS_SOURCE_URI';
// const gcsDestinationOutputUriPrefix = 'YOUR_GCS_DEST_OUTPUT_URI_PREFIX';
//    eg. "gs://<your-gcs-bucket>/destination_path"
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {params} = aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;

// Imports the Google Cloud Job Service Client library
const {JobServiceClient} = require('@google-cloud/aiplatform').v1;

// 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 createBatchPredictionJobVideoClassification() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;
  const modelName = `projects/${project}/locations/${location}/models/${modelId}`;

  // For more information on how to configure the model parameters object, see
  // https://cloud.google.com/ai-platform-unified/docs/predictions/batch-predictions
  const modelParamsObj = new params.VideoClassificationPredictionParams({
    confidenceThreshold: 0.5,
    maxPredictions: 1000,
    segmentClassification: true,
    shotClassification: true,
    oneSecIntervalClassification: true,
  });

  const modelParameters = modelParamsObj.toValue();

  const inputConfig = {
    instancesFormat: 'jsonl',
    gcsSource: {uris: [gcsSourceUri]},
  };
  const outputConfig = {
    predictionsFormat: 'jsonl',
    gcsDestination: {outputUriPrefix: gcsDestinationOutputUriPrefix},
  };
  const batchPredictionJob = {
    displayName: batchPredictionDisplayName,
    model: modelName,
    modelParameters,
    inputConfig,
    outputConfig,
  };
  const request = {
    parent,
    batchPredictionJob,
  };

  // Create batch prediction job request
  const [response] = await jobServiceClient.createBatchPredictionJob(request);

  console.log('Create batch prediction job video classification response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createBatchPredictionJobVideoClassification();

Python

Antes de testar essa amostra, siga as instruções de configuração para Python Guia de início rápido da Vertex AI: 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
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

def create_batch_prediction_job_video_classification_sample(
    project: str,
    display_name: str,
    model_name: str,
    gcs_source_uri: str,
    gcs_destination_output_uri_prefix: 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)
    model_parameters_dict = {
        "confidenceThreshold": 0.5,
        "maxPredictions": 10000,
        "segmentClassification": True,
        "shotClassification": True,
        "oneSecIntervalClassification": True,
    }
    model_parameters = json_format.ParseDict(model_parameters_dict, Value())

    batch_prediction_job = {
        "display_name": display_name,
        # Format: 'projects/{project}/locations/{location}/models/{model_id}'
        "model": model_name,
        "model_parameters": model_parameters,
        "input_config": {
            "instances_format": "jsonl",
            "gcs_source": {"uris": [gcs_source_uri]},
        },
        "output_config": {
            "predictions_format": "jsonl",
            "gcs_destination": {"output_uri_prefix": gcs_destination_output_uri_prefix},
        },
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_batch_prediction_job(
        parent=parent, batch_prediction_job=batch_prediction_job
    )
    print("response:", response)

A seguir

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