use of com.google.cloud.aiplatform.v1beta1.Dataset in project java-aiplatform by googleapis.
the class ImportDataImageObjectDetectionSampleTest method tearDown.
@After
public void tearDown() throws InterruptedException, ExecutionException, IOException, TimeoutException {
// delete the temp dataset
DatasetServiceSettings datasetServiceSettings = DatasetServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings)) {
DatasetName datasetName = DatasetName.of(PROJECT, LOCATION, datasetId);
OperationFuture<Empty, DeleteOperationMetadata> operationFuture = datasetServiceClient.deleteDatasetAsync(datasetName);
operationFuture.get();
}
System.out.flush();
System.setOut(originalPrintStream);
}
use of com.google.cloud.aiplatform.v1beta1.Dataset in project java-aiplatform by googleapis.
the class ImportDataVideoActionRecognitionSampleTest method tearDown.
@After
public void tearDown() throws InterruptedException, ExecutionException, IOException {
// delete the temp dataset
DatasetServiceSettings datasetServiceSettings = DatasetServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings)) {
DatasetName datasetName = DatasetName.of(PROJECT, LOCATION, datasetId);
OperationFuture<Empty, DeleteOperationMetadata> operationFuture = datasetServiceClient.deleteDatasetAsync(datasetName);
operationFuture.get();
}
System.out.flush();
System.setOut(originalPrintStream);
}
use of com.google.cloud.aiplatform.v1beta1.Dataset in project java-aiplatform by googleapis.
the class ImportDataVideoClassificationSampleTest method tearDown.
@After
public void tearDown() throws InterruptedException, ExecutionException, IOException {
// delete the temp dataset
DatasetServiceSettings datasetServiceSettings = DatasetServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings)) {
DatasetName datasetName = DatasetName.of(PROJECT, LOCATION, datasetId);
OperationFuture<Empty, DeleteOperationMetadata> operationFuture = datasetServiceClient.deleteDatasetAsync(datasetName);
operationFuture.get();
}
System.out.flush();
System.setOut(originalPrintStream);
}
use of com.google.cloud.aiplatform.v1beta1.Dataset in project java-aiplatform by googleapis.
the class CreateDatasetVideoSample method createDatasetSample.
static void createDatasetSample(String datasetVideoDisplayName, String project) throws IOException, InterruptedException, ExecutionException, TimeoutException {
DatasetServiceSettings datasetServiceSettings = DatasetServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
// the "close" method on the client to safely clean up any remaining background resources.
try (DatasetServiceClient datasetServiceClient = DatasetServiceClient.create(datasetServiceSettings)) {
String location = "us-central1";
String metadataSchemaUri = "gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml";
LocationName locationName = LocationName.of(project, location);
Dataset dataset = Dataset.newBuilder().setDisplayName(datasetVideoDisplayName).setMetadataSchemaUri(metadataSchemaUri).build();
OperationFuture<Dataset, CreateDatasetOperationMetadata> datasetFuture = datasetServiceClient.createDatasetAsync(locationName, dataset);
System.out.format("Operation name: %s\n", datasetFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
Dataset datasetResponse = datasetFuture.get(300, TimeUnit.SECONDS);
System.out.println("Create Dataset Video Response");
System.out.format("Name: %s\n", datasetResponse.getName());
System.out.format("Display Name: %s\n", datasetResponse.getDisplayName());
System.out.format("Metadata Schema Uri: %s\n", datasetResponse.getMetadataSchemaUri());
System.out.format("Metadata: %s\n", datasetResponse.getMetadata());
System.out.format("Create Time: %s\n", datasetResponse.getCreateTime());
System.out.format("Update Time: %s\n", datasetResponse.getUpdateTime());
System.out.format("Labels: %s\n", datasetResponse.getLabelsMap());
}
}
use of com.google.cloud.aiplatform.v1beta1.Dataset in project java-aiplatform by googleapis.
the class CreateTrainingPipelineImageObjectDetectionSample method createTrainingPipelineImageObjectDetectionSample.
static void createTrainingPipelineImageObjectDetectionSample(String project, String trainingPipelineDisplayName, String datasetId, String modelDisplayName) throws IOException {
PipelineServiceSettings pipelineServiceSettings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
// 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_image_object_detection_1.0.0.yaml";
LocationName locationName = LocationName.of(project, location);
AutoMlImageObjectDetectionInputs autoMlImageObjectDetectionInputs = AutoMlImageObjectDetectionInputs.newBuilder().setModelType(ModelType.CLOUD_HIGH_ACCURACY_1).setBudgetMilliNodeHours(20000).setDisableEarlyStopping(false).build();
InputDataConfig trainingInputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).build();
Model model = Model.newBuilder().setDisplayName(modelDisplayName).build();
TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(trainingPipelineDisplayName).setTrainingTaskDefinition(trainingTaskDefinition).setTrainingTaskInputs(ValueConverter.toValue(autoMlImageObjectDetectionInputs)).setInputDataConfig(trainingInputDataConfig).setModelToUpload(model).build();
TrainingPipeline trainingPipelineResponse = pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
System.out.println("Create Training Pipeline Image Object Detection Response");
System.out.format("Name: %s\n", trainingPipelineResponse.getName());
System.out.format("Display Name: %s\n", trainingPipelineResponse.getDisplayName());
System.out.format("Training Task Definition %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
System.out.format("Training Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
System.out.format("Training Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
System.out.format("State: %s\n", trainingPipelineResponse.getState());
System.out.format("Create Time: %s\n", trainingPipelineResponse.getCreateTime());
System.out.format("StartTime %s\n", trainingPipelineResponse.getStartTime());
System.out.format("End Time: %s\n", trainingPipelineResponse.getEndTime());
System.out.format("Update Time: %s\n", trainingPipelineResponse.getUpdateTime());
System.out.format("Labels: %s\n", trainingPipelineResponse.getLabelsMap());
InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();
System.out.println("Input Data Config");
System.out.format("Dataset Id: %s", inputDataConfig.getDatasetId());
System.out.format("Annotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
FractionSplit fractionSplit = inputDataConfig.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 = inputDataConfig.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 = inputDataConfig.getPredefinedSplit();
System.out.println("Predefined Split");
System.out.format("Key: %s\n", predefinedSplit.getKey());
TimestampSplit timestampSplit = inputDataConfig.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 = trainingPipelineResponse.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());
System.out.format("Supported Input Storage Formats: %s\n", modelResponse.getSupportedInputStorageFormatsList());
System.out.format("Supported Output Storage Formats: %s\n", modelResponse.getSupportedOutputStorageFormatsList());
System.out.format("Create Time: %s\n", modelResponse.getCreateTime());
System.out.format("Update Time: %s\n", modelResponse.getUpdateTime());
System.out.format("Labels: %sn\n", modelResponse.getLabelsMap());
PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
System.out.println("Predict Schemata");
System.out.format("Instance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
System.out.format("Parameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
System.out.format("Prediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
System.out.println("Supported Export Format");
System.out.format("Id: %s\n", exportFormat.getId());
}
ModelContainerSpec modelContainerSpec = modelResponse.getContainerSpec();
System.out.println("Container Spec");
System.out.format("Image Uri: %s\n", modelContainerSpec.getImageUri());
System.out.format("Command: %s\n", modelContainerSpec.getCommandList());
System.out.format("Args: %s\n", modelContainerSpec.getArgsList());
System.out.format("Predict Route: %s\n", modelContainerSpec.getPredictRoute());
System.out.format("Health Route: %s\n", modelContainerSpec.getHealthRoute());
for (EnvVar envVar : modelContainerSpec.getEnvList()) {
System.out.println("Env");
System.out.format("Name: %s\n", envVar.getName());
System.out.format("Value: %s\n", envVar.getValue());
}
for (Port port : modelContainerSpec.getPortsList()) {
System.out.println("Port");
System.out.format("Container Port: %s\n", port.getContainerPort());
}
for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
System.out.println("Deployed Model");
System.out.format("Endpoint: %s\n", deployedModelRef.getEndpoint());
System.out.format("Deployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
}
Status status = trainingPipelineResponse.getError();
System.out.println("Error");
System.out.format("Code: %s\n", status.getCode());
System.out.format("Message: %s\n", status.getMessage());
}
}
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