use of com.google.cloud.datalabeling.v1beta1.GcsDestination in project java-aiplatform by googleapis.
the class ExportModelTabularClassificationSample method exportModelTableClassification.
static void exportModelTableClassification(String gcsDestinationOutputUriPrefix, String project, String modelId) throws IOException, ExecutionException, InterruptedException, TimeoutException {
ModelServiceSettings modelServiceSettings = ModelServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
// the "close" method on the client to safely clean up any remaining background resources.
try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
String location = "us-central1";
ModelName modelName = ModelName.of(project, location, modelId);
GcsDestination.Builder gcsDestination = GcsDestination.newBuilder();
gcsDestination.setOutputUriPrefix(gcsDestinationOutputUriPrefix);
ExportModelRequest.OutputConfig outputConfig = ExportModelRequest.OutputConfig.newBuilder().setExportFormatId("tf-saved-model").setArtifactDestination(gcsDestination).build();
OperationFuture<ExportModelResponse, ExportModelOperationMetadata> exportModelResponseFuture = modelServiceClient.exportModelAsync(modelName, outputConfig);
System.out.format("Operation name: %s\n", exportModelResponseFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
ExportModelResponse exportModelResponse = exportModelResponseFuture.get(300, TimeUnit.SECONDS);
System.out.format("Export Model Tabular Classification Response: %s", exportModelResponse.toString());
}
}
use of com.google.cloud.datalabeling.v1beta1.GcsDestination in project java-aiplatform by googleapis.
the class ExportModelVideoActionRecognitionSample method exportModelVideoActionRecognitionSample.
static void exportModelVideoActionRecognitionSample(String project, String modelId, String gcsDestinationOutputUriPrefix, String exportFormat) throws IOException, ExecutionException, InterruptedException {
ModelServiceSettings settings = ModelServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
String location = "us-central1";
// the "close" method on the client to safely clean up any remaining background resources.
try (ModelServiceClient client = ModelServiceClient.create(settings)) {
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
ExportModelRequest.OutputConfig outputConfig = ExportModelRequest.OutputConfig.newBuilder().setArtifactDestination(gcsDestination).setExportFormatId(exportFormat).build();
ModelName name = ModelName.of(project, location, modelId);
OperationFuture<ExportModelResponse, ExportModelOperationMetadata> response = client.exportModelAsync(name, outputConfig);
// You can use OperationFuture.getInitialFuture to get a future representing the initial
// response to the request, which contains information while the operation is in progress.
System.out.format("Operation name: %s\n", response.getInitialFuture().get().getName());
// OperationFuture.get() will block until the operation is finished.
ExportModelResponse exportModelResponse = response.get();
System.out.format("exportModelResponse: %s\n", exportModelResponse);
}
}
use of com.google.cloud.datalabeling.v1beta1.GcsDestination in project java-aiplatform by googleapis.
the class CreateBatchPredictionJobSample method createBatchPredictionJobSample.
static void createBatchPredictionJobSample(String project, String displayName, String model, String instancesFormat, String gcsSourceUri, String predictionsFormat, String gcsDestinationOutputUriPrefix) throws IOException {
JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
String location = "us-central1";
// the "close" method on the client to safely clean up any remaining background resources.
try (JobServiceClient client = JobServiceClient.create(settings)) {
// Passing in an empty Value object for model parameters
Value modelParameters = ValueConverter.EMPTY_VALUE;
GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();
BatchPredictionJob.InputConfig inputConfig = BatchPredictionJob.InputConfig.newBuilder().setInstancesFormat(instancesFormat).setGcsSource(gcsSource).build();
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
BatchPredictionJob.OutputConfig outputConfig = BatchPredictionJob.OutputConfig.newBuilder().setPredictionsFormat(predictionsFormat).setGcsDestination(gcsDestination).build();
MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-2").setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80).setAcceleratorCount(1).build();
BatchDedicatedResources dedicatedResources = BatchDedicatedResources.newBuilder().setMachineSpec(machineSpec).setStartingReplicaCount(1).setMaxReplicaCount(1).build();
String modelName = ModelName.of(project, location, model).toString();
BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(displayName).setModel(modelName).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).setDedicatedResources(dedicatedResources).build();
LocationName parent = LocationName.of(project, location);
BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
System.out.format("response: %s\n", response);
System.out.format("\tName: %s\n", response.getName());
}
}
use of com.google.cloud.datalabeling.v1beta1.GcsDestination in project java-aiplatform by googleapis.
the class CreateBatchPredictionJobTextEntityExtractionSample method createBatchPredictionJobTextEntityExtractionSample.
static void createBatchPredictionJobTextEntityExtractionSample(String project, String location, String displayName, String modelId, String gcsSourceUri, String gcsDestinationOutputUriPrefix) throws IOException {
// The AI Platform services require regional API endpoints.
JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
// the "close" method on the client to safely clean up any remaining background resources.
try (JobServiceClient client = JobServiceClient.create(settings)) {
try {
String modelName = ModelName.of(project, location, modelId).toString();
GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();
BatchPredictionJob.InputConfig inputConfig = BatchPredictionJob.InputConfig.newBuilder().setInstancesFormat("jsonl").setGcsSource(gcsSource).build();
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
BatchPredictionJob.OutputConfig outputConfig = BatchPredictionJob.OutputConfig.newBuilder().setPredictionsFormat("jsonl").setGcsDestination(gcsDestination).build();
BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(displayName).setModel(modelName).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
LocationName parent = LocationName.of(project, location);
BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
System.out.format("response: %s\n", response);
System.out.format("\tname:%s\n", response.getName());
} catch (ApiException ex) {
System.out.format("Exception: %s\n", ex.getLocalizedMessage());
}
}
}
use of com.google.cloud.datalabeling.v1beta1.GcsDestination in project java-aiplatform by googleapis.
the class CreateBatchPredictionJobVideoActionRecognitionSample method createBatchPredictionJobVideoActionRecognitionSample.
static void createBatchPredictionJobVideoActionRecognitionSample(String project, String displayName, String model, String gcsSourceUri, String gcsDestinationOutputUriPrefix) throws IOException {
JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
String location = "us-central1";
// the "close" method on the client to safely clean up any remaining background resources.
try (JobServiceClient client = JobServiceClient.create(settings)) {
Value modelParameters = ValueConverter.EMPTY_VALUE;
GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();
BatchPredictionJob.InputConfig inputConfig = BatchPredictionJob.InputConfig.newBuilder().setInstancesFormat("jsonl").setGcsSource(gcsSource).build();
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
BatchPredictionJob.OutputConfig outputConfig = BatchPredictionJob.OutputConfig.newBuilder().setPredictionsFormat("jsonl").setGcsDestination(gcsDestination).build();
String modelName = ModelName.of(project, location, model).toString();
BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(displayName).setModel(modelName).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
LocationName parent = LocationName.of(project, location);
BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
System.out.format("response: %s\n", response);
System.out.format("\tName: %s\n", response.getName());
}
}
Aggregations