使用 gcloud CLI 或 Vertex AI API 部署模型

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);     }   } } 

def deploy_model_with_dedicated_resources_sample(     project,     location,     model_name: str,     machine_type: str,     endpoint: Optional[aiplatform.Endpoint] = None,     deployed_model_display_name: Optional[str] = None,     traffic_percentage: Optional[int] = 0,     traffic_split: Optional[Dict[str, int]] = None,     min_replica_count: int = 1,     max_replica_count: int = 1,     accelerator_type: Optional[str] = None,     accelerator_count: Optional[int] = None,     explanation_metadata: Optional[explain.ExplanationMetadata] = None,     explanation_parameters: Optional[explain.ExplanationParameters] = None,     metadata: Optional[Sequence[Tuple[str, str]]] = (),     sync: bool = True, ):     """     model_name: A fully-qualified model resource name or model ID.           Example: "projects/123/locations/us-central1/models/456" or           "456" when project and location are initialized or passed.     """      aiplatform.init(project=project, location=location)      model = aiplatform.Model(model_name=model_name)      # The explanation_metadata and explanation_parameters should only be     # provided for a custom trained model and not an AutoML model.     model.deploy(         endpoint=endpoint,         deployed_model_display_name=deployed_model_display_name,         traffic_percentage=traffic_percentage,         traffic_split=traffic_split,         machine_type=machine_type,         min_replica_count=min_replica_count,         max_replica_count=max_replica_count,         accelerator_type=accelerator_type,         accelerator_count=accelerator_count,         explanation_metadata=explanation_metadata,         explanation_parameters=explanation_parameters,         metadata=metadata,         sync=sync,     )      model.wait()      print(model.display_name)     print(model.resource_name)     return model  

const automl = require('@google-cloud/automl'); const client = new automl.v1beta1.AutoMlClient();  /**  * Demonstrates using the AutoML client to create a model.  * TODO(developer): Uncomment the following lines before running the sample.  */ // const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project"; // const computeRegion = '[REGION_NAME]' e.g., "us-central1"; // const datasetId = '[DATASET_ID]' e.g., "TBL2246891593778855936"; // const tableId = '[TABLE_ID]' e.g., "1991013247762825216"; // const columnId = '[COLUMN_ID]' e.g., "773141392279994368"; // const modelName = '[MODEL_NAME]' e.g., "testModel"; // const trainBudget = '[TRAIN_BUDGET]' e.g., "1000", // `Train budget in milli node hours`;  // A resource that represents Google Cloud Platform location. const projectLocation = client.locationPath(projectId, computeRegion);  // Get the full path of the column. const columnSpecId = client.columnSpecPath(   projectId,   computeRegion,   datasetId,   tableId,   columnId );  // Set target column to train the model. const targetColumnSpec = {name: columnSpecId};  // Set tables model metadata. const tablesModelMetadata = {   targetColumnSpec: targetColumnSpec,   trainBudgetMilliNodeHours: trainBudget, };  // Set datasetId, model name and model metadata for the dataset. const myModel = {   datasetId: datasetId,   displayName: modelName,   tablesModelMetadata: tablesModelMetadata, };  // Create a model with the model metadata in the region. client   .createModel({parent: projectLocation, model: myModel})   .then(responses => {     const initialApiResponse = responses[1];     console.log(`Training operation name: ${initialApiResponse.name}`);     console.log('Training started...');   })   .catch(err => {     console.error(err);   });