Avant d'essayer cet exemple, suivez les instructions de configuration pour Java décrites dans le guide de démarrage rapide de Vertex AI à l'aide des bibliothèques clientes.
Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Java.
Pour vous authentifier auprès de Vertex AI, configurez le service Identifiants par défaut de l'application.
Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.
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());
}
}
}