use of com.google.cloud.automl.v1.AutoMlClient in project java-automl by googleapis.
the class DeployModel method deployModel.
// Deploy a model for prediction
static void deployModel(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);
DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
future.get();
System.out.println("Model deployment finished");
}
}
use of com.google.cloud.automl.v1.AutoMlClient 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.automl.v1.AutoMlClient in project java-automl by googleapis.
the class ListDatasets method listDatasets.
// List the datasets
static void listDatasets(String projectId) throws IOException {
// 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");
ListDatasetsRequest request = ListDatasetsRequest.newBuilder().setParent(projectLocation.toString()).build();
// List all the datasets available in the region by applying filter.
System.out.println("List of datasets:");
for (Dataset dataset : client.listDatasets(request).iterateAll()) {
// Display the dataset information
System.out.format("%nDataset name: %s%n", dataset.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 = dataset.getName().split("/");
String retrievedDatasetId = names[names.length - 1];
System.out.format("Dataset id: %s%n", retrievedDatasetId);
System.out.format("Dataset display name: %s%n", dataset.getDisplayName());
System.out.println("Dataset create time:");
System.out.format("\tseconds: %s%n", dataset.getCreateTime().getSeconds());
System.out.format("\tnanos: %s%n", dataset.getCreateTime().getNanos());
// [END automl_video_object_tracking_list_datasets_beta]
// [END automl_tables_list_datasets]
System.out.format("Video classification dataset metadata: %s%n", dataset.getVideoClassificationDatasetMetadata());
// [END automl_video_classification_list_datasets_beta]
// [START automl_video_object_tracking_list_datasets_beta]
System.out.format("Video object tracking dataset metadata: %s%n", dataset.getVideoObjectTrackingDatasetMetadata());
// [END automl_video_object_tracking_list_datasets_beta]
// [START automl_tables_list_datasets]
System.out.format("Tables dataset metadata: %s%n", dataset.getTablesDatasetMetadata());
// [START automl_video_classification_list_datasets_beta]
// [START automl_video_object_tracking_list_datasets_beta]
}
}
}
use of com.google.cloud.automl.v1.AutoMlClient in project java-automl by googleapis.
the class TablesImportDataset method importDataset.
// Import a dataset via BigQuery or Google Cloud Storage
static void importDataset(String projectId, String datasetId, String path) 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 complete path of the dataset.
DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
InputConfig.Builder inputConfigBuilder = InputConfig.newBuilder();
// Determine which source type was used for the input path (BigQuery or GCS)
if (path.startsWith("bq")) {
// Get training data file to be imported from a BigQuery source.
BigQuerySource.Builder bigQuerySource = BigQuerySource.newBuilder();
bigQuerySource.setInputUri(path);
inputConfigBuilder.setBigquerySource(bigQuerySource);
} else {
// Get multiple Google Cloud Storage URIs to import data from
GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
inputConfigBuilder.setGcsSource(gcsSource);
}
// Import data from the input URI
System.out.println("Processing import...");
Empty response = client.importDataAsync(datasetFullId, inputConfigBuilder.build()).get();
System.out.format("Dataset imported. %s%n", response);
}
}
use of com.google.cloud.automl.v1.AutoMlClient 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");
}
}
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