Examples with OutputConfig com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig used on opensource projects

Example 6 with OutputConfig

use of com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig in project java-aiplatform by googleapis.

the class ExportModelSample method exportModelSample.

static void exportModelSample(String project, String modelId, String gcsDestinationOutputUriPrefix, String exportFormat) throws IOException, InterruptedException, ExecutionException, TimeoutException {
    ModelServiceSettings modelServiceSettings = ModelServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
        String location = "us-central1";
        GcsDestination.Builder gcsDestination = GcsDestination.newBuilder();
        gcsDestination.setOutputUriPrefix(gcsDestinationOutputUriPrefix);
        ModelName modelName = ModelName.of(project, location, modelId);
        ExportModelRequest.OutputConfig outputConfig = ExportModelRequest.OutputConfig.newBuilder().setExportFormatId(exportFormat).setArtifactDestination(gcsDestination).build();
        OperationFuture<ExportModelResponse, ExportModelOperationMetadata> exportModelResponseFuture = modelServiceClient.exportModelAsync(modelName, outputConfig);
        System.out.format("Operation name: %s\n", exportModelResponseFuture.getInitialFuture().get().getName());
        System.out.println("Waiting for operation to finish...");
        ExportModelResponse exportModelResponse = exportModelResponseFuture.get(300, TimeUnit.SECONDS);
        System.out.format("Export Model Response: %s\n", exportModelResponse);
    }
}

Example 7 with OutputConfig

use of com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig in project java-aiplatform by googleapis.

the class ExportModelTabularClassificationSample method exportModelTableClassification.

static void exportModelTableClassification(String gcsDestinationOutputUriPrefix, String project, String modelId) throws IOException, ExecutionException, InterruptedException, TimeoutException {
    ModelServiceSettings modelServiceSettings = ModelServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
        String location = "us-central1";
        ModelName modelName = ModelName.of(project, location, modelId);
        GcsDestination.Builder gcsDestination = GcsDestination.newBuilder();
        gcsDestination.setOutputUriPrefix(gcsDestinationOutputUriPrefix);
        ExportModelRequest.OutputConfig outputConfig = ExportModelRequest.OutputConfig.newBuilder().setExportFormatId("tf-saved-model").setArtifactDestination(gcsDestination).build();
        OperationFuture<ExportModelResponse, ExportModelOperationMetadata> exportModelResponseFuture = modelServiceClient.exportModelAsync(modelName, outputConfig);
        System.out.format("Operation name: %s\n", exportModelResponseFuture.getInitialFuture().get().getName());
        System.out.println("Waiting for operation to finish...");
        ExportModelResponse exportModelResponse = exportModelResponseFuture.get(300, TimeUnit.SECONDS);
        System.out.format("Export Model Tabular Classification Response: %s", exportModelResponse.toString());
    }
}

Example 8 with OutputConfig

use of com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig in project java-aiplatform by googleapis.

the class ExportModelVideoActionRecognitionSample method exportModelVideoActionRecognitionSample.

static void exportModelVideoActionRecognitionSample(String project, String modelId, String gcsDestinationOutputUriPrefix, String exportFormat) throws IOException, ExecutionException, InterruptedException {
    ModelServiceSettings settings = ModelServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient client = ModelServiceClient.create(settings)) {
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
        ExportModelRequest.OutputConfig outputConfig = ExportModelRequest.OutputConfig.newBuilder().setArtifactDestination(gcsDestination).setExportFormatId(exportFormat).build();
        ModelName name = ModelName.of(project, location, modelId);
        OperationFuture<ExportModelResponse, ExportModelOperationMetadata> response = client.exportModelAsync(name, outputConfig);
        // 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("Operation name: %s\n", response.getInitialFuture().get().getName());
        // OperationFuture.get() will block until the operation is finished.
        ExportModelResponse exportModelResponse = response.get();
        System.out.format("exportModelResponse: %s\n", exportModelResponse);
    }
}

Example 9 with OutputConfig

use of com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig in project java-aiplatform by googleapis.

the class CreateBatchPredictionJobBigquerySample method createBatchPredictionJobBigquerySample.

static void createBatchPredictionJobBigquerySample(String project, String displayName, String model, String instancesFormat, String bigquerySourceInputUri, String predictionsFormat, String bigqueryDestinationOutputUri) throws IOException {
    JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
        JsonObject jsonModelParameters = new JsonObject();
        Value.Builder modelParametersBuilder = Value.newBuilder();
        JsonFormat.parser().merge(jsonModelParameters.toString(), modelParametersBuilder);
        Value modelParameters = modelParametersBuilder.build();
        BigQuerySource bigquerySource = BigQuerySource.newBuilder().setInputUri(bigquerySourceInputUri).build();
        BatchPredictionJob.InputConfig inputConfig = BatchPredictionJob.InputConfig.newBuilder().setInstancesFormat(instancesFormat).setBigquerySource(bigquerySource).build();
        BigQueryDestination bigqueryDestination = BigQueryDestination.newBuilder().setOutputUri(bigqueryDestinationOutputUri).build();
        BatchPredictionJob.OutputConfig outputConfig = BatchPredictionJob.OutputConfig.newBuilder().setPredictionsFormat(predictionsFormat).setBigqueryDestination(bigqueryDestination).build();
        String modelName = ModelName.of(project, location, model).toString();
        BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(displayName).setModel(modelName).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
        LocationName parent = LocationName.of(project, location);
        BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
        System.out.format("response: %s\n", response);
        System.out.format("\tName: %s\n", response.getName());
    }
}

Example 10 with OutputConfig

use of com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig in project java-aiplatform by googleapis.

the class CreateBatchPredictionJobSample method createBatchPredictionJobSample.

static void createBatchPredictionJobSample(String project, String displayName, String model, String instancesFormat, String gcsSourceUri, String predictionsFormat, String gcsDestinationOutputUriPrefix) throws IOException {
    JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
        // Passing in an empty Value object for model parameters
        Value modelParameters = ValueConverter.EMPTY_VALUE;
        GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();
        BatchPredictionJob.InputConfig inputConfig = BatchPredictionJob.InputConfig.newBuilder().setInstancesFormat(instancesFormat).setGcsSource(gcsSource).build();
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
        BatchPredictionJob.OutputConfig outputConfig = BatchPredictionJob.OutputConfig.newBuilder().setPredictionsFormat(predictionsFormat).setGcsDestination(gcsDestination).build();
        MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-2").setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80).setAcceleratorCount(1).build();
        BatchDedicatedResources dedicatedResources = BatchDedicatedResources.newBuilder().setMachineSpec(machineSpec).setStartingReplicaCount(1).setMaxReplicaCount(1).build();
        String modelName = ModelName.of(project, location, model).toString();
        BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(displayName).setModel(modelName).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).setDedicatedResources(dedicatedResources).build();
        LocationName parent = LocationName.of(project, location);
        BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
        System.out.format("response: %s\n", response);
        System.out.format("\tName: %s\n", response.getName());
    }
}