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

Example 46 with ModelName

use of com.google.cloud.aiplatform.v1.ModelName in project java-automl by googleapis.

the class VisionClassificationPredictTest method setUp.

@Before
public void setUp() throws IOException, ExecutionException, InterruptedException {
    // Verify that the model is deployed for prediction
    try (AutoMlClient client = AutoMlClient.create()) {
        ModelName modelFullId = ModelName.of(PROJECT_ID, "us-central1", MODEL_ID);
        Model model = client.getModel(modelFullId);
        if (model.getDeploymentState() == Model.DeploymentState.UNDEPLOYED) {
            // Deploy the model if not deployed
            DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
            client.deployModelAsync(request).get();
        }
    }
    bout = new ByteArrayOutputStream();
    out = new PrintStream(bout);
    originalPrintStream = System.out;
    System.setOut(out);
}

Example 47 with ModelName

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

the class UndeployModelSample method undeployModelSample.

static void undeployModelSample(String project, String endpointId, String modelId) throws IOException, InterruptedException, ExecutionException, TimeoutException {
    EndpointServiceSettings endpointServiceSettings = EndpointServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (EndpointServiceClient endpointServiceClient = EndpointServiceClient.create(endpointServiceSettings)) {
        String location = "us-central1";
        EndpointName endpointName = EndpointName.of(project, location, endpointId);
        ModelName modelName = ModelName.of(project, location, modelId);
        // key '0' assigns traffic for the newly deployed model
        // Traffic percentage values must add up to 100
        // Leave dictionary empty if endpoint should not accept any traffic
        Map<String, Integer> trafficSplit = new HashMap<>();
        trafficSplit.put("0", 100);
        OperationFuture<UndeployModelResponse, UndeployModelOperationMetadata> operation = endpointServiceClient.undeployModelAsync(endpointName.toString(), modelName.toString(), trafficSplit);
        System.out.format("Operation name: %s\n", operation.getInitialFuture().get().getName());
        System.out.println("Waiting for operation to finish...");
        UndeployModelResponse undeployModelResponse = operation.get(180, TimeUnit.SECONDS);
        System.out.format("Undeploy Model Response: %s\n", undeployModelResponse);
    }
}

Example 48 with ModelName

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

the class DeployModelCustomTrainedModelSample method deployModelCustomTrainedModelSample.

static void deployModelCustomTrainedModelSample(String project, String endpointId, String model, String deployedModelDisplayName) throws IOException, ExecutionException, InterruptedException {
    EndpointServiceSettings settings = EndpointServiceSettings.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 (EndpointServiceClient client = EndpointServiceClient.create(settings)) {
        MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-2").build();
        DedicatedResources dedicatedResources = DedicatedResources.newBuilder().setMinReplicaCount(1).setMachineSpec(machineSpec).build();
        String modelName = ModelName.of(project, location, model).toString();
        DeployedModel deployedModel = DeployedModel.newBuilder().setModel(modelName).setDisplayName(deployedModelDisplayName).setDedicatedResources(dedicatedResources).build();
        // key '0' assigns traffic for the newly deployed model
        // Traffic percentage values must add up to 100
        // Leave dictionary empty if endpoint should not accept any traffic
        Map<String, Integer> trafficSplit = new HashMap<>();
        trafficSplit.put("0", 100);
        EndpointName endpoint = EndpointName.of(project, location, endpointId);
        OperationFuture<DeployModelResponse, DeployModelOperationMetadata> response = client.deployModelAsync(endpoint, deployedModel, trafficSplit);
        // 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.
        DeployModelResponse deployModelResponse = response.get();
        System.out.format("deployModelResponse: %s\n", deployModelResponse);
    }
}

Example 49 with ModelName

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

the class DeployModelSample method deployModelSample.

static void deployModelSample(String project, String deployedModelDisplayName, String endpointId, String modelId) throws IOException, InterruptedException, ExecutionException, TimeoutException {
    EndpointServiceSettings endpointServiceSettings = EndpointServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (EndpointServiceClient endpointServiceClient = EndpointServiceClient.create(endpointServiceSettings)) {
        String location = "us-central1";
        EndpointName endpointName = EndpointName.of(project, location, endpointId);
        // key '0' assigns traffic for the newly deployed model
        // Traffic percentage values must add up to 100
        // Leave dictionary empty if endpoint should not accept any traffic
        Map<String, Integer> trafficSplit = new HashMap<>();
        trafficSplit.put("0", 100);
        ModelName modelName = ModelName.of(project, location, modelId);
        AutomaticResources automaticResourcesInput = AutomaticResources.newBuilder().setMinReplicaCount(1).setMaxReplicaCount(1).build();
        DeployedModel deployedModelInput = DeployedModel.newBuilder().setModel(modelName.toString()).setDisplayName(deployedModelDisplayName).setAutomaticResources(automaticResourcesInput).build();
        OperationFuture<DeployModelResponse, DeployModelOperationMetadata> deployModelResponseFuture = endpointServiceClient.deployModelAsync(endpointName, deployedModelInput, trafficSplit);
        System.out.format("Operation name: %s\n", deployModelResponseFuture.getInitialFuture().get().getName());
        System.out.println("Waiting for operation to finish...");
        DeployModelResponse deployModelResponse = deployModelResponseFuture.get(20, TimeUnit.MINUTES);
        System.out.println("Deploy Model Response");
        DeployedModel deployedModel = deployModelResponse.getDeployedModel();
        System.out.println("\tDeployed Model");
        System.out.format("\t\tid: %s\n", deployedModel.getId());
        System.out.format("\t\tmodel: %s\n", deployedModel.getModel());
        System.out.format("\t\tDisplay Name: %s\n", deployedModel.getDisplayName());
        System.out.format("\t\tCreate Time: %s\n", deployedModel.getCreateTime());
        DedicatedResources dedicatedResources = deployedModel.getDedicatedResources();
        System.out.println("\t\tDedicated Resources");
        System.out.format("\t\t\tMin Replica Count: %s\n", dedicatedResources.getMinReplicaCount());
        MachineSpec machineSpec = dedicatedResources.getMachineSpec();
        System.out.println("\t\t\tMachine Spec");
        System.out.format("\t\t\t\tMachine Type: %s\n", machineSpec.getMachineType());
        System.out.format("\t\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());
        System.out.format("\t\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());
        AutomaticResources automaticResources = deployedModel.getAutomaticResources();
        System.out.println("\t\tAutomatic Resources");
        System.out.format("\t\t\tMin Replica Count: %s\n", automaticResources.getMinReplicaCount());
        System.out.format("\t\t\tMax Replica Count: %s\n", automaticResources.getMaxReplicaCount());
    }
}

Example 50 with ModelName

use of com.google.cloud.aiplatform.v1.ModelName 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);
    }
}