deploy_model 메서드를 사용하여 커스텀 학습 모델을 위한 모델을 배포합니다.
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코드 샘플
Java
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DedicatedResources;
import com.google.cloud.aiplatform.v1.DeployModelOperationMetadata;
import com.google.cloud.aiplatform.v1.DeployModelResponse;
import com.google.cloud.aiplatform.v1.DeployedModel;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.EndpointServiceClient;
import com.google.cloud.aiplatform.v1.EndpointServiceSettings;
import com.google.cloud.aiplatform.v1.MachineSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.ExecutionException;
public class DeployModelCustomTrainedModelSample {
public static void main(String[] args)
throws IOException, ExecutionException, InterruptedException {
// TODO(developer): Replace these variables before running the sample.
String project = "PROJECT";
String endpointId = "ENDPOINT_ID";
String modelName = "MODEL_NAME";
String deployedModelDisplayName = "DEPLOYED_MODEL_DISPLAY_NAME";
deployModelCustomTrainedModelSample(project, endpointId, modelName, deployedModelDisplayName);
}
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";
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// 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)
// `dedicated_resources` must be used for non-AutoML models
.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);
}
}
}
Node.js
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vertex AI Node.js API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const modelId = "YOUR_MODEL_ID";
// const endpointId = 'YOUR_ENDPOINT_ID';
// const deployedModelDisplayName = 'YOUR_DEPLOYED_MODEL_DISPLAY_NAME';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const modelName = `projects/${project}/locations/${location}/models/${modelId}`;
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
// Imports the Google Cloud Endpoint Service Client library
const {EndpointServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint:
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const endpointServiceClient = new EndpointServiceClient(clientOptions);
async function deployModelCustomTrainedModel() {
// Configure the parent resource
// 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
const trafficSplit = {0: 100};
const deployedModel = {
// format: 'projects/{project}/locations/{location}/models/{model}'
model: modelName,
displayName: deployedModelDisplayName,
// `dedicatedResources` must be used for non-AutoML models
dedicatedResources: {
minReplicaCount: 1,
machineSpec: {
machineType: 'n1-standard-2',
// Accelerators can be used only if the model specifies a GPU image.
// acceleratorType: 'NVIDIA_TESLA_K80',
// acceleratorCount: 1,
},
},
};
const request = {
endpoint,
deployedModel,
trafficSplit,
};
// Get and print out a list of all the endpoints for this resource
const [response] = await endpointServiceClient.deployModel(request);
console.log(`Long running operation : ${response.name}`);
// Wait for operation to complete
await response.promise();
const result = response.result;
console.log('Deploy model response');
const modelDeployed = result.deployedModel;
console.log(`\t\tId : ${modelDeployed.id}`);
console.log(modelDeployed);
}
deployModelCustomTrainedModel();Python
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
from google.cloud import aiplatform
def deploy_model_custom_trained_model_sample(
project: str,
endpoint_id: str,
model_name: str,
deployed_model_display_name: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
timeout: int = 7200,
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.EndpointServiceClient(client_options=client_options)
deployed_model = {
# format: 'projects/{project}/locations/{location}/models/{model}'
"model": model_name,
"display_name": deployed_model_display_name,
# `dedicated_resources` must be used for non-AutoML models
"dedicated_resources": {
"min_replica_count": 1,
"machine_spec": {
"machine_type": "n1-standard-2",
# Accelerators can be used only if the model specifies a GPU image.
# 'accelerator_type': aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
# 'accelerator_count': 1,
},
},
}
# 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
traffic_split = {"0": 100}
endpoint = client.endpoint_path(
project=project, location=location, endpoint=endpoint_id
)
response = client.deploy_model(
endpoint=endpoint, deployed_model=deployed_model, traffic_split=traffic_split
)
print("Long running operation:", response.operation.name)
deploy_model_response = response.result(timeout=timeout)
print("deploy_model_response:", deploy_model_response)
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