Get TabNet online inferences

  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 
 ); 
  
 });