Create a batch prediction job for video classification
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Creates a batch prediction job for video classification using the create_batch_prediction_job method.
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For detailed documentation that includes this code sample, see the following:
Code sample
Before trying this sample, follow the Java setup instructions in the
Vertex AI quickstart using
client libraries.
For more information, see the
Vertex AI Java API
reference documentation.
To authenticate to Vertex AI, set up Application Default Credentials.
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Set up authentication for a local development environment.
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());
}
}
}
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