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

Example 1 with ImageClassificationModelDeploymentMetadata

use of com.google.cloud.automl.v1beta1.ImageClassificationModelDeploymentMetadata in project java-automl by googleapis.

the class ClassificationDeployModelNodeCount method classificationDeployModelNodeCount.

// Deploy a model with a specified node count
static void classificationDeployModelNodeCount(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);
        // Set how many nodes the model is deployed on
        ImageClassificationModelDeploymentMetadata deploymentMetadata = ImageClassificationModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
        DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).setImageClassificationModelDeploymentMetadata(deploymentMetadata).build();
        // Deploy the model
        OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
        future.get();
        System.out.println("Model deployment on 2 nodes finished");
    }
}

Example 2 with ImageClassificationModelDeploymentMetadata

use of com.google.cloud.automl.v1beta1.ImageClassificationModelDeploymentMetadata in project java-automl by googleapis.

the class VisionClassificationDeployModelNodeCount method visionClassificationDeployModelNodeCount.

// Deploy a model for prediction with a specified node count (can be used to redeploy a model)
static void visionClassificationDeployModelNodeCount(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);
        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");
    }
}