Trainingspipeline für das Videoobjekt-Tracking erstellen
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Erstellt eine Trainingspipeline für das Tracking von Videoobjekten mit der Methode "create_training_pipeline".
Weitere Informationen
Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:
Codebeispiel
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.
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Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.
import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoObjectTrackingInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoObjectTrackingInputs.ModelType;
import com.google.rpc.Status;
import java.io.IOException;
public class CreateTrainingPipelineVideoObjectTrackingSample {
public static void main(String[] args) throws IOException {
String trainingPipelineVideoObjectTracking =
"YOUR_TRAINING_PIPELINE_VIDEO_OBJECT_TRACKING_DISPLAY_NAME";
String datasetId = "YOUR_DATASET_ID";
String modelDisplayName = "YOUR_MODEL_DISPLAY_NAME";
String project = "YOUR_PROJECT_ID";
createTrainingPipelineVideoObjectTracking(
trainingPipelineVideoObjectTracking, datasetId, modelDisplayName, project);
}
static void createTrainingPipelineVideoObjectTracking(
String trainingPipelineVideoObjectTracking,
String datasetId,
String modelDisplayName,
String project)
throws IOException {
PipelineServiceSettings pipelineServiceSettings =
PipelineServiceSettings.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 (PipelineServiceClient pipelineServiceClient =
PipelineServiceClient.create(pipelineServiceSettings)) {
String location = "us-central1";
String trainingTaskDefinition =
"gs://google-cloud-aiplatform/schema/trainingjob/definition/"
+ "automl_video_object_tracking_1.0.0.yaml";
LocationName locationName = LocationName.of(project, location);
AutoMlVideoObjectTrackingInputs trainingTaskInputs =
AutoMlVideoObjectTrackingInputs.newBuilder().setModelType(ModelType.CLOUD).build();
InputDataConfig inputDataConfig =
InputDataConfig.newBuilder().setDatasetId(datasetId).build();
Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
TrainingPipeline trainingPipeline =
TrainingPipeline.newBuilder()
.setDisplayName(trainingPipelineVideoObjectTracking)
.setTrainingTaskDefinition(trainingTaskDefinition)
.setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs))
.setInputDataConfig(inputDataConfig)
.setModelToUpload(modelToUpload)
.build();
TrainingPipeline createTrainingPipelineResponse =
pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
System.out.println("Create Training Pipeline Video Object Tracking Response");
System.out.format("Name: %s\n", createTrainingPipelineResponse.getName());
System.out.format("Display Name: %s\n", createTrainingPipelineResponse.getDisplayName());
System.out.format(
"Training Task Definition %s\n",
createTrainingPipelineResponse.getTrainingTaskDefinition());
System.out.format(
"Training Task Inputs: %s\n",
createTrainingPipelineResponse.getTrainingTaskInputs().toString());
System.out.format(
"Training Task Metadata: %s\n",
createTrainingPipelineResponse.getTrainingTaskMetadata().toString());
System.out.format("State: %s\n", createTrainingPipelineResponse.getState().toString());
System.out.format(
"Create Time: %s\n", createTrainingPipelineResponse.getCreateTime().toString());
System.out.format("StartTime %s\n", createTrainingPipelineResponse.getStartTime().toString());
System.out.format("End Time: %s\n", createTrainingPipelineResponse.getEndTime().toString());
System.out.format(
"Update Time: %s\n", createTrainingPipelineResponse.getUpdateTime().toString());
System.out.format("Labels: %s\n", createTrainingPipelineResponse.getLabelsMap().toString());
InputDataConfig inputDataConfigResponse = createTrainingPipelineResponse.getInputDataConfig();
System.out.println("Input Data config");
System.out.format("Dataset Id: %s\n", inputDataConfigResponse.getDatasetId());
System.out.format("Annotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());
FractionSplit fractionSplit = inputDataConfigResponse.getFractionSplit();
System.out.println("Fraction split");
System.out.format("Training Fraction: %s\n", fractionSplit.getTrainingFraction());
System.out.format("Validation Fraction: %s\n", fractionSplit.getValidationFraction());
System.out.format("Test Fraction: %s\n", fractionSplit.getTestFraction());
FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
System.out.println("Filter Split");
System.out.format("Training Filter: %s\n", filterSplit.getTrainingFilter());
System.out.format("Validation Filter: %s\n", filterSplit.getValidationFilter());
System.out.format("Test Filter: %s\n", filterSplit.getTestFilter());
PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
System.out.println("Predefined Split");
System.out.format("Key: %s\n", predefinedSplit.getKey());
TimestampSplit timestampSplit = inputDataConfigResponse.getTimestampSplit();
System.out.println("Timestamp Split");
System.out.format("Training Fraction: %s\n", timestampSplit.getTrainingFraction());
System.out.format("Validation Fraction: %s\n", timestampSplit.getValidationFraction());
System.out.format("Test Fraction: %s\n", timestampSplit.getTestFraction());
System.out.format("Key: %s\n", timestampSplit.getKey());
Model modelResponse = createTrainingPipelineResponse.getModelToUpload();
System.out.println("Model To Upload");
System.out.format("Name: %s\n", modelResponse.getName());
System.out.format("Display Name: %s\n", modelResponse.getDisplayName());
System.out.format("Description: %s\n", modelResponse.getDescription());
System.out.format("Metadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
System.out.format("Metadata: %s\n", modelResponse.getMetadata());
System.out.format("Training Pipeline: %s\n", modelResponse.getTrainingPipeline());
System.out.format("Artifact Uri: %s\n", modelResponse.getArtifactUri());
System.out.format(
"Supported Deployment Resources Types: %s\n",
modelResponse.getSupportedDeploymentResourcesTypesList().toString());
System.out.format(
"Supported Input Storage Formats: %s\n",
modelResponse.getSupportedInputStorageFormatsList().toString());
System.out.format(
"Supported Output Storage Formats: %s\n",
modelResponse.getSupportedOutputStorageFormatsList().toString());
System.out.format("Create Time: %s\n", modelResponse.getCreateTime());
System.out.format("Update Time: %s\n", modelResponse.getUpdateTime());
System.out.format("Labels: %s\n", modelResponse.getLabelsMap());
Status status = createTrainingPipelineResponse.getError();
System.out.println("Error");
System.out.format("Code: %s\n", status.getCode());
System.out.format("Message: %s\n", status.getMessage());
}
}
}
Nächste Schritte
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