use of com.google.cloud.vision.v1p4beta1.OperationMetadata in project java-automl by googleapis.
the class ImportDataset method importDataset.
// Import a dataset
static void importDataset(String projectId, String datasetId, String path) throws IOException, ExecutionException, InterruptedException, TimeoutException {
Duration totalTimeout = Duration.ofMinutes(45);
RetrySettings retrySettings = RetrySettings.newBuilder().setTotalTimeout(totalTimeout).build();
AutoMlSettings.Builder builder = AutoMlSettings.newBuilder();
builder.importDataSettings().setRetrySettings(retrySettings).build();
AutoMlSettings settings = builder.build();
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create(settings)) {
// Get the complete path of the dataset.
DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
// Get multiple Google Cloud Storage URIs to import data from
GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
// Import data from the input URI
InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
System.out.println("Processing import...");
// Start the import job
OperationFuture<Empty, OperationMetadata> operation = client.importDataAsync(datasetFullId, inputConfig);
System.out.format("Operation name: %s%n", operation.getName());
// If you want to wait for the operation to finish, adjust the timeout appropriately. The
// operation will still run if you choose not to wait for it to complete. You can check the
// status of your operation using the operation's name.
Empty response = operation.get(45, TimeUnit.MINUTES);
System.out.format("Dataset imported. %s%n", response);
} catch (TimeoutException e) {
System.out.println("The operation's polling period was not long enough.");
System.out.println("You can use the Operation's name to get the current status.");
System.out.println("The import job is still running and will complete as expected.");
throw e;
}
}
use of com.google.cloud.vision.v1p4beta1.OperationMetadata in project java-automl by googleapis.
the class TablesBatchPredictBigQuery 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);
// Configure the source of the file from BigQuery
BigQuerySource bigQuerySource = BigQuerySource.newBuilder().setInputUri(inputUri).build();
BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().setBigquerySource(bigQuerySource).build();
// Configure where to store the output in BigQuery
BigQueryDestination bigQueryDestination = BigQueryDestination.newBuilder().setOutputUri(outputUri).build();
BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().setBigqueryDestination(bigQueryDestination).build();
// Build the request that will be sent to the API
BatchPredictRequest request = BatchPredictRequest.newBuilder().setName(name.toString()).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
// Start an asynchronous request
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 BigQuery.");
}
}
use of com.google.cloud.vision.v1p4beta1.OperationMetadata in project java-automl by googleapis.
the class UndeployModel method undeployModel.
// Undeploy a model from prediction
static void undeployModel(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);
UndeployModelRequest request = UndeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(request);
future.get();
System.out.println("Model undeployment finished");
}
}
use of com.google.cloud.vision.v1p4beta1.OperationMetadata in project java-automl by googleapis.
the class LanguageEntityExtractionCreateModel 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.
TextExtractionModelMetadata metadata = TextExtractionModelMetadata.newBuilder().build();
Model model = Model.newBuilder().setDisplayName(displayName).setDatasetId(datasetId).setTextExtractionModelMetadata(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.vision.v1p4beta1.OperationMetadata in project java-automl by googleapis.
the class LanguageSentimentAnalysisCreateDataset 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 text classification type for the dataset.
TextSentimentDatasetMetadata metadata = TextSentimentDatasetMetadata.newBuilder().setSentimentMax(// Possible max sentiment score: 1-10
4).build();
Dataset dataset = Dataset.newBuilder().setDisplayName(displayName).setTextSentimentDatasetMetadata(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);
}
}
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