com.google.api.gax.longrunning.OperationFuture Java Examples

static void undeployModel(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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");
  }
}
 

/**
 * Call the Video Intelligence Cloud AI service and return annotation results.
 *
 * @param elementURI This or elementContents is required. GCS address of video to be annotated
 * @param elementContents this or elementURI is required. Hex-encoded contents of video to be
 *     annotated
 * @param videoContext Optional context for video annotation.
 * @return
 */
List<VideoAnnotationResults> getVideoAnnotationResults(
    String elementURI, ByteString elementContents, VideoContext videoContext)
    throws InterruptedException, ExecutionException {
  AnnotateVideoRequest.Builder requestBuilder =
      AnnotateVideoRequest.newBuilder().addAllFeatures(featureList);
  if (elementURI != null) {
    requestBuilder.setInputUri(elementURI);
  } else if (elementContents != null) {
    requestBuilder.setInputContent(elementContents);
  } else {
    throw new IllegalArgumentException("Either elementURI or elementContents should be non-null");
  }
  if (videoContext != null) {
    requestBuilder.setVideoContext(videoContext);
  }
  AnnotateVideoRequest annotateVideoRequest = requestBuilder.build();
  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> annotateVideoAsync =
      videoIntelligenceServiceClient.annotateVideoAsync(annotateVideoRequest);
  return annotateVideoAsync.get().getAnnotationResultsList();
}
 

static void deployModel(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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");
  }
}
 

@After
public void teardown() throws IOException, InterruptedException, ExecutionException {
  blob.delete();
  bucket.delete();

  ClusterControllerSettings clusterControllerSettings =
      ClusterControllerSettings.newBuilder().setEndpoint(ENDPOINT).build();

  try (ClusterControllerClient clusterControllerClient =
      ClusterControllerClient.create(clusterControllerSettings)) {
    for (Cluster element :
        clusterControllerClient.listClusters(PROJECT_ID, REGION).iterateAll()) {
      if (element.getClusterName() == CLUSTER_NAME) {
        OperationFuture<Empty, ClusterOperationMetadata> deleteClusterAsyncRequest =
            clusterControllerClient.deleteClusterAsync(PROJECT_ID, REGION, CLUSTER_NAME);
        deleteClusterAsyncRequest.get();
        break;
      }
    }
  }
}
 

@Before
public void setUp() throws IOException, ExecutionException, InterruptedException {
  bout = new ByteArrayOutputStream();
  System.setOut(new PrintStream(bout));

  ClusterControllerSettings clusterControllerSettings =
      ClusterControllerSettings.newBuilder().setEndpoint(ENDPOINT).build();

  try (ClusterControllerClient clusterControllerClient =
      ClusterControllerClient.create(clusterControllerSettings)) {
    // Create the Dataproc cluster.
    Cluster cluster = Cluster.newBuilder().setClusterName(CLUSTER_NAME).build();
    OperationFuture<Cluster, ClusterOperationMetadata> createClusterAsyncRequest =
        clusterControllerClient.createClusterAsync(PROJECT_ID, REGION, cluster);
    createClusterAsyncRequest.get();
  }
}
 

static void deployModel(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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");
  }
}
 

static void undeployModel(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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");
  }
}
 

static void visionObjectDetectionDeployModelNodeCount(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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);
    ImageObjectDetectionModelDeploymentMetadata metadata =
        ImageObjectDetectionModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
    DeployModelRequest request =
        DeployModelRequest.newBuilder()
            .setName(modelFullId.toString())
            .setImageObjectDetectionModelDeploymentMetadata(metadata)
            .build();
    OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);

    future.get();
    System.out.println("Model deployment finished");
  }
}
 

static void visionClassificationDeployModelNodeCount(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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);
    ImageClassificationModelDeploymentMetadata metadata =
        ImageClassificationModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
    DeployModelRequest request =
        DeployModelRequest.newBuilder()
            .setName(modelFullId.toString())
            .setImageClassificationModelDeploymentMetadata(metadata)
            .build();
    OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);

    future.get();
    System.out.println("Model deployment finished");
  }
}
 

public static void deleteGlossary(String projectId, String glossaryId)
    throws InterruptedException, ExecutionException, IOException {

  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // the "close" method on the client to safely clean up any remaining background resources.
  try (TranslationServiceClient client = TranslationServiceClient.create()) {
    // Supported Locations: `global`, [glossary location], or [model location]
    // Glossaries must be hosted in `us-central1`
    // Custom Models must use the same location as your model. (us-central1)
    GlossaryName glossaryName = GlossaryName.of(projectId, "us-central1", glossaryId);
    DeleteGlossaryRequest request =
        DeleteGlossaryRequest.newBuilder().setName(glossaryName.toString()).build();

    // Start an asynchronous request
    OperationFuture<DeleteGlossaryResponse, DeleteGlossaryMetadata> future =
        client.deleteGlossaryAsync(request);

    System.out.println("Waiting for operation to complete...");
    DeleteGlossaryResponse response = future.get();
    System.out.format("Deleted Glossary: %s\n", response.getName());
  }
}
 

/**
 * Performs transcription on remote FLAC file and prints the transcription.
 *
 * @param gcsUri the path to the remote FLAC audio file to transcribe.
 */
public static void transcribeGcsWithAutomaticPunctuation(String gcsUri) throws Exception {
  try (SpeechClient speechClient = SpeechClient.create()) {
    // Configure request with raw PCM audio
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.FLAC)
            .setLanguageCode("en-US")
            .setSampleRateHertz(16000)
            .setEnableAutomaticPunctuation(true)
            .build();

    // Set the remote path for the audio file
    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speechClient.longRunningRecognizeAsync(config, audio);

    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }

    // Just print the first result here.
    SpeechRecognitionResult result = response.get().getResultsList().get(0);

    // There can be several alternative transcripts for a given chunk of speech. Just use the
    // first (most likely) one here.
    SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);

    // Print out the result
    System.out.printf("Transcript : %s\n", alternative.getTranscript());
  }
}
 

/**
 * Transcribe a remote audio file with multi-language recognition
 *
 * @param gcsUri the path to the remote audio file
 */
public static void transcribeMultiLanguageGcs(String gcsUri) throws Exception {
  try (SpeechClient speechClient = SpeechClient.create()) {

    ArrayList<String> languageList = new ArrayList<>();
    languageList.add("es-ES");
    languageList.add("en-US");

    // Configure request to enable multiple languages
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.LINEAR16)
            .setSampleRateHertz(16000)
            .setLanguageCode("ja-JP")
            .addAllAlternativeLanguageCodes(languageList)
            .build();

    // Set the remote path for the audio file
    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speechClient.longRunningRecognizeAsync(config, audio);

    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }

    for (SpeechRecognitionResult result : response.get().getResultsList()) {

      // There can be several alternative transcripts for a given chunk of speech. Just use the
      // first (most likely) one here.
      SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);

      // Print out the result
      System.out.printf("Transcript : %s\n\n", alternative.getTranscript());
    }
  }
}
 

/**
 * Demonstrates using the AutoML client to create a model.
 *
 * @param projectId the Id of the project.
 * @param computeRegion the Region name.
 * @param dataSetId the Id of the dataset to which model is created.
 * @param modelName the Name of the model.
 */
public static void createModel(
    String projectId, String computeRegion, String dataSetId, String modelName)
    throws IOException, InterruptedException, ExecutionException {
  // Instantiates a client
  try (AutoMlClient client = AutoMlClient.create()) {

    // A resource that represents Google Cloud Platform location.
    LocationName projectLocation = LocationName.of(projectId, computeRegion);

    // Set model metadata.
    TranslationModelMetadata translationModelMetadata =
        TranslationModelMetadata.newBuilder().setBaseModel("").build();

    // Set model name, dataset and metadata.
    Model myModel =
        Model.newBuilder()
            .setDisplayName(modelName)
            .setDatasetId(dataSetId)
            .setTranslationModelMetadata(translationModelMetadata)
            .build();

    // Create a model with the model metadata in the region.
    OperationFuture<Model, OperationMetadata> response =
        client.createModelAsync(projectLocation, myModel);

    System.out.println(
        String.format(
            "Training operation name: %s", response.getInitialFuture().get().getName()));
    System.out.println("Training started...");
  }
}
 

@After
public void tearDown() throws IOException, InterruptedException, ExecutionException {

  ClusterControllerSettings clusterControllerSettings =
      ClusterControllerSettings.newBuilder().setEndpoint(ENDPOINT).build();

  try (ClusterControllerClient clusterControllerClient =
      ClusterControllerClient.create(clusterControllerSettings)) {
    OperationFuture<Empty, ClusterOperationMetadata> deleteClusterAsyncRequest =
        clusterControllerClient.deleteClusterAsync(PROJECT_ID, REGION, CLUSTER_NAME);
    deleteClusterAsyncRequest.get();
  }
}
 

static void createModel(String projectId, String datasetId, String displayName)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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.
    ImageObjectDetectionModelMetadata metadata =
        ImageObjectDetectionModelMetadata.newBuilder().build();
    Model model =
        Model.newBuilder()
            .setDisplayName(displayName)
            .setDatasetId(datasetId)
            .setImageObjectDetectionModelMetadata(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...");
  }
}
 

static void batchPredict(String projectId, String modelId, String inputUri, String outputUri)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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.");
  }
}
 

static void importDataset(String projectId, String datasetId, String path)
    throws IOException, ExecutionException, InterruptedException, TimeoutException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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);

    // 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;
  }
}
 

public static void purgeOrphanProducts(String projectId, String computeRegion) throws Exception {

    // String projectId = "YOUR_PROJECT_ID";
    // String computeRegion = "us-central1";
    // boolean force = true;

    try (ProductSearchClient client = ProductSearchClient.create()) {
      String parent = LocationName.format(projectId, computeRegion);

      // The purge operation is async.
      PurgeProductsRequest request =
          PurgeProductsRequest.newBuilder()
              .setDeleteOrphanProducts(true)
              // The operation is irreversible and removes multiple products.
              // The user is required to pass in force=True to actually perform the
              // purge.
              // If force is not set to True, the service raises an exception.
              .setForce(true)
              .setParent(parent)
              .build();

      OperationFuture response = client.purgeProductsAsync(request);
      response.getPollingFuture().get(180, TimeUnit.SECONDS);

      System.out.println("Orphan products deleted.");
    }
  }
 

static void createDataset(String projectId, String displayName)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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");

    ImageObjectDetectionDatasetMetadata metadata =
        ImageObjectDetectionDatasetMetadata.newBuilder().build();
    Dataset dataset =
        Dataset.newBuilder()
            .setDisplayName(displayName)
            .setImageObjectDetectionDatasetMetadata(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);
  }
}
 

static void createDataset(String projectId, String displayName)
    throws IOException, ExecutionException, InterruptedException {
  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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);
  }
}
 

/**
 * Performs non-blocking speech recognition on remote FLAC file and prints the transcription.
 *
 * @param gcsUri the path to the remote LINEAR16 audio file to transcribe.
 */
public static void asyncRecognizeGcs(String gcsUri) throws Exception {
  // Instantiates a client with GOOGLE_APPLICATION_CREDENTIALS
  try (SpeechClient speech = SpeechClient.create()) {

    // Configure remote file request for FLAC
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.FLAC)
            .setLanguageCode("en-US")
            .setSampleRateHertz(16000)
            .build();
    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speech.longRunningRecognizeAsync(config, audio);
    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }

    List<SpeechRecognitionResult> results = response.get().getResultsList();

    for (SpeechRecognitionResult result : results) {
      // There can be several alternative transcripts for a given chunk of speech. Just use the
      // first (most likely) one here.
      SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
      System.out.printf("Transcription: %s\n", alternative.getTranscript());
    }
  }
}
 

public static void purgeProductsInProductSet(
    String projectId, String location, String productSetId) throws Exception {

  // String projectId = "YOUR_PROJECT_ID";
  // String location = "us-central1";
  // String productSetId = "YOUR_PRODUCT_SET_ID";
  // boolean force = true;

  try (ProductSearchClient client = ProductSearchClient.create()) {

    String parent = LocationName.format(projectId, location);
    ProductSetPurgeConfig productSetPurgeConfig =
        ProductSetPurgeConfig.newBuilder().setProductSetId(productSetId).build();

    PurgeProductsRequest request =
        PurgeProductsRequest.newBuilder()
            .setParent(parent)
            .setProductSetPurgeConfig(productSetPurgeConfig)
            // The operation is irreversible and removes multiple products.
            // The user is required to pass in force=True to actually perform the
            // purge.
            // If force is not set to True, the service raises an exception.
            .setForce(true)
            .build();

    OperationFuture<Empty, BatchOperationMetadata> response = client.purgeProductsAsync(request);
    response.getPollingFuture().get(180, TimeUnit.SECONDS);

    System.out.println("Products removed from product set.");
  }
}
 

static void objectDetectionDeployModelNodeCount(String projectId, String modelId)
    throws IOException, ExecutionException, InterruptedException {
  // String projectId = "YOUR_PROJECT_ID";
  // String modelId = "YOUR_MODEL_ID";

  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // 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);

    // Set how many nodes the model is deployed on
    ImageObjectDetectionModelDeploymentMetadata deploymentMetadata =
        ImageObjectDetectionModelDeploymentMetadata.newBuilder().setNodeCount(2).build();

    DeployModelRequest request =
        DeployModelRequest.newBuilder()
            .setName(modelFullId.toString())
            .setImageObjectDetectionModelDeploymentMetadata(deploymentMetadata)
            .build();
    // Deploy the model
    OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
    future.get();
    System.out.println("Model deployment on 2 nodes finished");
  }
}
 

/**
 * Import images of different products in the product set.
 *
 * @param projectId - Id of the project.
 * @param computeRegion - Region name.
 * @param gcsUri - Google Cloud Storage URI.Target files must be in Product Search CSV format.
 * @throws Exception - on client errors.
 */
public static void importProductSets(String projectId, String computeRegion, String gcsUri)
    throws Exception {
  try (ProductSearchClient client = ProductSearchClient.create()) {

    // A resource that represents Google Cloud Platform location.
    String formattedParent = ProductSearchClient.formatLocationName(projectId, computeRegion);
    Builder gcsSource = ImportProductSetsGcsSource.newBuilder().setCsvFileUri(gcsUri);

    // Set the input configuration along with Google Cloud Storage URI
    ImportProductSetsInputConfig inputConfig =
        ImportProductSetsInputConfig.newBuilder().setGcsSource(gcsSource).build();

    // Import the product sets from the input URI.
    OperationFuture<ImportProductSetsResponse, BatchOperationMetadata> response =
        client.importProductSetsAsync(formattedParent, inputConfig);

    System.out.println(String.format("Processing operation name: %s", response.getName()));
    ImportProductSetsResponse results = response.get();
    System.out.println("Processing done.");
    System.out.println("Results of the processing:");

    for (int i = 0; i < results.getStatusesCount(); i++) {
      System.out.println(
          String.format(
              "Status of processing line %s of the csv: %s", i, results.getStatuses(i)));
      // Check the status of reference image.
      if (results.getStatuses(i).getCode() == 0) {
        ReferenceImage referenceImage = results.getReferenceImages(i);
        System.out.println(referenceImage);
      } else {
        System.out.println("No reference image.");
      }
    }
  }
}
 

@Test
public void executeDdlStrings_createsDatabaseIfMissing() throws Exception {
	when(this.mockDatabasePage.getValues()).thenReturn(Arrays.asList(
			new Database(this.databaseId, State.READY, this.databaseAdminClient)));

	OperationFuture<Void, UpdateDatabaseDdlMetadata> mockFuture = mock(OperationFuture.class);
	when(this.databaseAdminClient.updateDatabaseDdl("fakeinstance", "fakedb", this.ddlList, null)).thenReturn(mockFuture);
	when(mockFuture.get()).thenReturn(null);

	this.spannerDatabaseAdminTemplate.executeDdlStrings(this.ddlList, true);

	verify(this.databaseAdminClient, times(0)).createDatabase("fakeinstance", "fakedb", Arrays.asList("describe Something"));
	verify(this.databaseAdminClient).updateDatabaseDdl("fakeinstance", "fakedb", this.ddlList, null);
}
 

/**
 * Drops a single table.
 */
public void deleteSingleTable() {
  OperationFuture<Void, UpdateDatabaseDdlMetadata> ddlFuture =
      this.databaseAdminClient.updateDatabaseDdl(
          INSTANCE_NAME,
          DATABASE_NAME,
          Collections.singletonList("DROP TABLE " + AIRPORT_TABLE), null);

  try {
    ddlFuture.get();
  } catch (Exception e) {
    throw new RuntimeException(e);
  }
}
 

/**
 * Creates an experiment by issuing an asynchronous operation and immediately blocking to get the
 * result.
 *
 * @param googleAdsClient the API client to use.
 * @param customerId the customer ID to operate on.
 * @param baseCampaignId the original campaign ID.
 * @param draftId the draft campaign ID.
 * @return a newly created experiment.
 */
private CreateCampaignExperimentMetadata createExperiment(
    GoogleAdsClient googleAdsClient, long customerId, long baseCampaignId, long draftId) {
  // Defines the experiment to be created.
  CampaignExperiment experiment =
      CampaignExperiment.newBuilder()
          .setCampaignDraft(
              StringValue.of(ResourceNames.campaignDraft(customerId, baseCampaignId, draftId)))
          .setName(StringValue.of("Campaign experiment #" + System.currentTimeMillis()))
          .setTrafficSplitPercent(Int64Value.of(50))
          .setTrafficSplitType(CampaignExperimentTrafficSplitType.RANDOM_QUERY)
          .build();

  // Creates an API service client.
  try (CampaignExperimentServiceClient campaignExperimentServiceClient =
      googleAdsClient.getLatestVersion().createCampaignExperimentServiceClient()) {
    // Issues the create request.
    OperationFuture<Empty, CreateCampaignExperimentMetadata> campaignExperimentAsync =
        campaignExperimentServiceClient.createCampaignExperimentAsync(
            String.valueOf(customerId), experiment);

    // Block on the long running (i.e. async) operation result.
    try {
      return campaignExperimentAsync.getMetadata().get();
    } catch (InterruptedException | ExecutionException e) {
      throw new RuntimeException("Create operation failed", e);
    }
  }
}
 

/**
 * Polls the server until the mutate job execution finishes by setting the total time to wait
 * before time-out.
 *
 * @param operationResponse the operation response used to poll the server.
 */
private void pollMutateJob(OperationFuture operationResponse) {
  try {
    operationResponse.get(MAX_TOTAL_POLL_INTERVAL_SECONDS, TimeUnit.SECONDS);
  } catch (InterruptedException | ExecutionException | TimeoutException e) {
    System.err.printf("Failed polling the mutate job. Exception: %s%n", e);
    System.exit(1);
  }
}
 

/**
 * Requests the API to run the mutate job for executing all uploaded mutate job operations.
 *
 * @param mutateJobServiceClient the mutate job service client.
 * @param mutateJobResourceName the resource name of mutate job to be run.
 * @return the operation response from running mutate job.
 */
private OperationFuture runMutateJob(
    MutateJobServiceClient mutateJobServiceClient, String mutateJobResourceName) {
  OperationFuture operationResponse =
      mutateJobServiceClient.runMutateJobAsync(mutateJobResourceName);
  System.out.printf(
      "Mutate job with resource name '%s' has been executed.%n", mutateJobResourceName);

  return operationResponse;
}
 

/**
 * Transcribe a remote audio file with multi-channel recognition
 *
 * @param gcsUri the path to the audio file
 */
public static void transcribeMultiChannelGcs(String gcsUri) throws Exception {

  try (SpeechClient speechClient = SpeechClient.create()) {

    // Configure request to enable multiple channels
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            .setSampleRateHertz(44100)
            .setAudioChannelCount(2)
            .setEnableSeparateRecognitionPerChannel(true)
            .build();

    // Set the remote path for the audio file
    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speechClient.longRunningRecognizeAsync(config, audio);

    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }
    // Just print the first result here.
    for (SpeechRecognitionResult result : response.get().getResultsList()) {

      // There can be several alternative transcripts for a given chunk of speech. Just use the
      // first (most likely) one here.
      SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);

      // Print out the result
      System.out.printf("Transcript : %s\n", alternative.getTranscript());
      System.out.printf("Channel Tag : %s\n\n", result.getChannelTag());
    }
  }
}