Deploy a model by using the gcloud CLI or Vertex AI API

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;
publicclass DeployModelCustomTrainedModelSample{
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{
EndpointServiceSettings settings=
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(EndpointServiceClient client=EndpointServiceClient .create(settings)){
MachineSpec machineSpec=MachineSpec .newBuilder().setMachineType ("n1-standard-2").build();
DedicatedResources dedicatedResources=
DedicatedResources .newBuilder().setMinReplicaCount(1).setMachineSpec(machineSpec).build();
StringmodelName=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=newHashMap<>();
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);
}
}
}

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);
});