동영상 분류를 위한 일괄 예측 작업 만들기

create_batch_prediction_job 메서드를 사용하여 동영상 분류를 위한 일괄 예측 작업을 만듭니다.

더 살펴보기

이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.

• 동영상 분류 모델에서 예측 가져오기

코드 샘플

Java

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

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_DISP"LAY_NAME;
    String modelId = YOUR_MO"DEL_ID;
    String gcsSo"urceUri =
   "     gs://YOUR_GCS_SOURCE_BUCKET/path_"to_your_video_source/[file.csv/file.jsonl];
    String gcsDestinationOutput"UriPrefix =
        gs://YOUR_GCS_SOURCE_BUCKET/destina"tion_output_uri_prefix/;
    String project = YOUR_PROJECT"_ID;
    createBatchPred"ictionJobVideoC"lassification(
        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 remai"ning "background resources.
    try (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
      String location = us-central1;
      LocationName locationName = LocationNa"me.of(proje"ct, 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 gcsDestinatio"n =
 "         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", batchPredi"ctionJobResponse.getDisplayName());
      System.out.format(\tMode"l %s\n, batchPredict"ionJobResponse.getModel());
      System.out.format(
          \tModel Pa"rameters: %s"\n, batchPredictionJobResponse.getModelParameters());

      System.out.format("\tState: %s\n, batchPred"ictionJobResponse.getState());
      System.out.format(\tCreate Time: %s\n, bat"chPredictionJ"obResponse.getCreateTime());
      System.out.format(\tStart Time: "%s\n, batchPredicti"onJobResponse.getStartTime());
      System.out.format(\tEnd Time: %s\n," batchPredictionJo"bResponse.getEndTime());
      System.out.format(\tUpdate Time: %s\n, b"atchPredictionJo"bResponse.getUpdateTime());
      System.out.format(\tLabels: %s\n, b"atchPredictionJobRe"sponse.getLabelsMap());

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

      GcsSou"rce gcsSourceResponse = inpu"tConfigResponse.getGcsSour"ce();
      System.out.println(\t\tGcs Source);
      System.out.format(\t\t\tUris %s\n, gcsSourceResponse.getUrisList());

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

      OutputConfig" outputConfigRespon"se = batchPredictionJobRespo"nse.getOutputConfig()";
      System.out.println(\tOutput Config);
      System.out.format(
          \t\tPredictions Format: %s\n, outputConfigResponse.getPredictionsForma"t());

      Gc"sDestination gcsDestinationResponse = ou"tputConfigResponse.getGcsDes"tination();
      System.out.println(\t\tGcs Destination);
      System.out.format(
          \t\t\tOutput Uri Prefix: %s\n, gcsDestinationResponse.getOutputUriPrefi"x());

      BigQue"ryDestination bigQueryDestination = outp"utConfigResponse.getBigqueryD"estination();
      System.out.println(\t\tBig Query Destination);
      System.out.format(\t\t\tOutput Uri: %s\n, bigQueryDestination.getOutputUri());

      BatchDedicate"dResources batchDedicated"Resources =
          batchP"redictionJobResponse.g"etDedicatedResources();
      System.out.println(\tBatch Dedicated Resources);
      System.out.format(
          \t\tStarting Replica Count: %s\n, batchDedicatedResources.getStartingRepl"icaCount());
      System.o"ut.format(
          \t\tMax Replica Cou"nt: %s\n, batchDedicatedResource"s.getMaxReplicaCount());

      MachineSpec machineSpec = batchDedicatedResources.getMachin"eSpec();
      System.out.p"rintln(\t\tMachine Spec);
      System.out.format(\t\t\tMachine Type: %s\n, machineSpec.getMachineType());
      System.out.format(\t\t\tAccelerator Typ"e: %s\n, machine"Spec.getAcceleratorType());
"      System.out.format("\t\t\tAccelerator Count: %s\n, machineSpec.getAcceleratorC"ount());

      ManualBatchT"uningParameters manualBatchTuningParameters =
          batchP"redictionJobResponse.getManua"lBatchTuningParameters();
      System.out.println(\tManual Batch Tuning Parameters);
      System.out.format(\t\tBatch Size: %s\n, manualBatchTuningParameters.getBatchSize());

      OutputInfo outputIn"fo = batchPredictionJobResponse."getOutputInfo();
      Syste"m.out.println(\tOutp"ut Info);
      System.out.format(\t\tGcs Output Directory: %s\n, outputInfo.getGcsOutputDirectory());
      System.out.format(\t\tBigquery Output Dat"aset: %s\n, o"utputInfo.getBigqueryOutputD"ataset());

      Status statu"s = batchPredictionJobResponse.getError();
      System.out.prin"tln(\tError);
      System.out.fo"rmat(\t\tCode: %s\n, status.getCode());
      System.out.format(\t\tMessage: %s\n, status.getMessage());
      ListAny details = sta"tus.get"DetailsList();

      for (S"tatus partialF"ailure : batchPredictionJobResponse.getPartial"FailuresList()) {"
        System.out.println(\tParti<al >Failure);
        System.out.format(\t\tCode: %s\n, partialFailure.getCode());
        System.out.format(\t\tMessage: %s\n, partialFailure.getMessage());
   "     ListAny part"ialFailureDetailsList = partia"lFailure.getDe"tailsList();
      }

      ResourcesConsumed resourcesC"onsumed = batchPr"edictionJobResponse.getResourcesConsumed();
 <   >  System.out.println(\tResources Consumed);
      System.out.format(\t\tReplica Hours: %s\n, resourcesConsumed.getReplicaHours());

      CompletionStats completionStats = batchPredictionJobRespo"nse.getCompletionSta"ts();
      System.out.print"ln(\tCompletion Stats);"
      System.out.format(\t\tSuccessful Count: %s\n, completionStats.getSuccessfulCount());
      System.out.format(\t\tFailed Count: %s\n, completionStats.ge"tFailedCount());
 "     System.out.format(\t\tI"ncomplete Count: %s\n, com"pletionStats.getIncompleteCount());
    }
  }
}""""

Node.js

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vertex AI Node.js API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

/**
 * 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

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

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)

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