use of com.google.cloud.aiplatform.v1.EndpointServiceSettings in project java-aiplatform by googleapis.
the class CreateEndpointSample method createEndpointSample.
static void createEndpointSample(String project, String endpointDisplayName) 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";
LocationName locationName = LocationName.of(project, location);
Endpoint endpoint = Endpoint.newBuilder().setDisplayName(endpointDisplayName).build();
OperationFuture<Endpoint, CreateEndpointOperationMetadata> endpointFuture = endpointServiceClient.createEndpointAsync(locationName, endpoint);
System.out.format("Operation name: %s\n", endpointFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
Endpoint endpointResponse = endpointFuture.get(300, TimeUnit.SECONDS);
System.out.println("Create Endpoint Response");
System.out.format("Name: %s\n", endpointResponse.getName());
System.out.format("Display Name: %s\n", endpointResponse.getDisplayName());
System.out.format("Description: %s\n", endpointResponse.getDescription());
System.out.format("Labels: %s\n", endpointResponse.getLabelsMap());
System.out.format("Create Time: %s\n", endpointResponse.getCreateTime());
System.out.format("Update Time: %s\n", endpointResponse.getUpdateTime());
}
}
use of com.google.cloud.aiplatform.v1.EndpointServiceSettings in project java-aiplatform by googleapis.
the class DeleteEndpointSample method deleteEndpointSample.
static void deleteEndpointSample(String project, String endpointId) 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);
OperationFuture<Empty, DeleteOperationMetadata> operationFuture = endpointServiceClient.deleteEndpointAsync(endpointName);
System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
Empty deleteResponse = operationFuture.get(300, TimeUnit.SECONDS);
System.out.format("Delete Endpoint Response: %s\n", deleteResponse);
}
}
use of com.google.cloud.aiplatform.v1.EndpointServiceSettings 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);
}
}
use of com.google.cloud.aiplatform.v1.EndpointServiceSettings 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);
}
}
use of com.google.cloud.aiplatform.v1.EndpointServiceSettings 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());
}
}
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