使用 deploy_model 方法为自定义训练模型部署模型。
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代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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)
后续步骤
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