use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.
the class LanguageTextClassificationCreateModel method createModel.
// Create a model
static void createModel(String projectId, String datasetId, String displayName) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// A resource that represents Google Cloud Platform location.
LocationName projectLocation = LocationName.of(projectId, "us-central1");
// Set model metadata.
TextClassificationModelMetadata metadata = TextClassificationModelMetadata.newBuilder().build();
Model model = Model.newBuilder().setDisplayName(displayName).setDatasetId(datasetId).setTextClassificationModelMetadata(metadata).build();
// Create a model with the model metadata in the region.
OperationFuture<Model, OperationMetadata> future = client.createModelAsync(projectLocation, model);
// OperationFuture.get() will block until the model is created, which may take several hours.
// 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("Training operation name: %s\n", future.getInitialFuture().get().getName());
System.out.println("Training started...");
}
}
use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.
the class TranslateCreateDataset method createDataset.
// Create a dataset
static void createDataset(String projectId, String displayName) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// A resource that represents Google Cloud Platform location.
LocationName projectLocation = LocationName.of(projectId, "us-central1");
// Specify the source and target language.
TranslationDatasetMetadata translationDatasetMetadata = TranslationDatasetMetadata.newBuilder().setSourceLanguageCode("en").setTargetLanguageCode("ja").build();
Dataset dataset = Dataset.newBuilder().setDisplayName(displayName).setTranslationDatasetMetadata(translationDatasetMetadata).build();
OperationFuture<Dataset, OperationMetadata> future = client.createDatasetAsync(projectLocation, dataset);
Dataset createdDataset = future.get();
// Display the dataset information.
System.out.format("Dataset name: %s\n", createdDataset.getName());
// To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
// required for other methods.
// Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
String[] names = createdDataset.getName().split("/");
String datasetId = names[names.length - 1];
System.out.format("Dataset id: %s\n", datasetId);
}
}
use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.
the class BatchPredict method batchPredict.
static void batchPredict(String projectId, String modelId, String inputUri, String outputUri) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
GcsSource gcsSource = GcsSource.newBuilder().addInputUris(inputUri).build();
BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().setGcsSource(gcsSource).build();
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
BatchPredictRequest request = BatchPredictRequest.newBuilder().setName(name.toString()).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
OperationFuture<BatchPredictResult, OperationMetadata> future = client.batchPredictAsync(request);
System.out.println("Waiting for operation to complete...");
BatchPredictResult response = future.get();
System.out.println("Batch Prediction results saved to specified Cloud Storage bucket.");
}
}
use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.
the class LanguageEntityExtractionCreateDataset method createDataset.
// Create a dataset
static void createDataset(String projectId, String displayName) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// A resource that represents Google Cloud Platform location.
LocationName projectLocation = LocationName.of(projectId, "us-central1");
TextExtractionDatasetMetadata metadata = TextExtractionDatasetMetadata.newBuilder().build();
Dataset dataset = Dataset.newBuilder().setDisplayName(displayName).setTextExtractionDatasetMetadata(metadata).build();
OperationFuture<Dataset, OperationMetadata> future = client.createDatasetAsync(projectLocation, dataset);
Dataset createdDataset = future.get();
// Display the dataset information.
System.out.format("Dataset name: %s\n", createdDataset.getName());
// To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
// required for other methods.
// Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
String[] names = createdDataset.getName().split("/");
String datasetId = names[names.length - 1];
System.out.format("Dataset id: %s\n", datasetId);
}
}
use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.
the class ClassificationUndeployModel method classificationUndeployModel.
// Deploy a model
static void classificationUndeployModel(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// Get the full path of the model.
ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
// Build deploy model request.
UndeployModelRequest undeployModelRequest = UndeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
// Deploy a model with the deploy model request.
OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(undeployModelRequest);
future.get();
// Display the deployment details of model.
System.out.println("Model undeploy finished");
}
}
Aggregations