Examples with AutoMlClient com.google.cloud.automl.v1beta1.AutoMlClient used on opensource projects

Example 6 with AutoMlClient

use of com.google.cloud.automl.v1beta1.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");
    }
}

Example 7 with AutoMlClient

use of com.google.cloud.automl.v1beta1.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;
    }
}

Example 8 with AutoMlClient

use of com.google.cloud.automl.v1beta1.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]
        }
    }
}

Example 9 with AutoMlClient

use of com.google.cloud.automl.v1beta1.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);
    }
}

Example 10 with AutoMlClient

use of com.google.cloud.automl.v1beta1.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");
    }
}