importcom.google.api.gax.longrunning.OperationFuture;importcom.google.cloud.aiplatform.v1.DedicatedResources;importcom.google.cloud.aiplatform.v1.DeployModelOperationMetadata;importcom.google.cloud.aiplatform.v1.DeployModelResponse;importcom.google.cloud.aiplatform.v1.DeployedModel;importcom.google.cloud.aiplatform.v1.EndpointName;importcom.google.cloud.aiplatform.v1.EndpointServiceClient;importcom.google.cloud.aiplatform.v1.EndpointServiceSettings;importcom.google.cloud.aiplatform.v1.MachineSpec;importcom.google.cloud.aiplatform.v1.ModelName;importjava.io.IOException;importjava.util.HashMap;importjava.util.Map;importjava.util.concurrent.ExecutionException;publicclassDeployModelCustomTrainedModelSample{publicstaticvoidmain(String[]args)throwsIOException,ExecutionException,InterruptedException{// TODO(developer): Replace these variables before running the sample.Stringproject="PROJECT";StringendpointId="ENDPOINT_ID";StringmodelName="MODEL_NAME";StringdeployedModelDisplayName="DEPLOYED_MODEL_DISPLAY_NAME";deployModelCustomTrainedModelSample(project,endpointId,modelName,deployedModelDisplayName);}staticvoiddeployModelCustomTrainedModelSample(Stringproject,StringendpointId,Stringmodel,StringdeployedModelDisplayName)throwsIOException,ExecutionException,InterruptedException{EndpointServiceSettingssettings=EndpointServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();Stringlocation="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(EndpointServiceClientclient=EndpointServiceClient.create(settings)){MachineSpecmachineSpec=MachineSpec.newBuilder().setMachineType("n1-standard-2").build();DedicatedResourcesdedicatedResources=DedicatedResources.newBuilder().setMinReplicaCount(1).setMachineSpec(machineSpec).build();StringmodelName=ModelName.of(project,location,model).toString();DeployedModeldeployedModel=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 trafficMap<String,Integer>trafficSplit=newHashMap<>();trafficSplit.put("0",100);EndpointNameendpoint=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.DeployModelResponsedeployModelResponse=response.get();System.out.format("deployModelResponse: %s\n",deployModelResponse);}}}

defdeploy_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)#Theexplanation_metadataandexplanation_parametersshouldonlybe#providedforacustomtrainedmodelandnotanAutoMLmodel.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)returnmodel

constautoml=require('@google-cloud/automl');constclient=newautoml.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.constprojectLocation=client.locationPath(projectId,computeRegion);// Get the full path of the column.constcolumnSpecId=client.columnSpecPath(projectId,computeRegion,datasetId,tableId,columnId);// Set target column to train the model.consttargetColumnSpec={name:columnSpecId};// Set tables model metadata.consttablesModelMetadata={targetColumnSpec:targetColumnSpec,trainBudgetMilliNodeHours:trainBudget,};// Set datasetId, model name and model metadata for the dataset.constmyModel={datasetId:datasetId,displayName:modelName,tablesModelMetadata:tablesModelMetadata,};// Create a model with the model metadata in the region.client.createModel({parent:projectLocation,model:myModel}).then(responses=>{constinitialApiResponse=responses[1];console.log(`Training operation name:${initialApiResponse.name}`);console.log('Training started...');}).catch(err=>{console.error(err);});