Export a model

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportModelOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportModelRequest 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ExportModelResponse 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. GcsDestination 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelServiceSettings 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 ExportModelSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 modelId 
  
 = 
  
 "YOUR_MODEL_ID" 
 ; 
  
 String 
  
 gcsDestinationOutputUriPrefix 
  
 = 
  
 "gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_destination/" 
 ; 
  
 String 
  
 exportFormat 
  
 = 
  
 "YOUR_EXPORT_FORMAT" 
 ; 
  
 exportModelSample 
 ( 
 project 
 , 
  
 modelId 
 , 
  
 gcsDestinationOutputUriPrefix 
 , 
  
 exportFormat 
 ); 
  
 } 
  
 static 
  
 void 
  
 exportModelSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 modelId 
 , 
  
 String 
  
 gcsDestinationOutputUriPrefix 
 , 
  
 String 
  
 exportFormat 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  ModelServiceSettings 
 
  
 modelServiceSettings 
  
 = 
  
  ModelServiceSettings 
 
 . 
 newBuilder 
 () 
  
 . 
 setEndpoint 
 ( 
 "us-central1-aiplatform.googleapis.com:443" 
 ) 
  
 . 
 build 
 (); 
  
 // 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 
  
 ( 
  ModelServiceClient 
 
  
 modelServiceClient 
  
 = 
  
  ModelServiceClient 
 
 . 
 create 
 ( 
 modelServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
  GcsDestination 
 
 . 
 Builder 
  
 gcsDestination 
  
 = 
  
  GcsDestination 
 
 . 
 newBuilder 
 (); 
  
 gcsDestination 
 . 
  setOutputUriPrefix 
 
 ( 
 gcsDestinationOutputUriPrefix 
 ); 
  
  ModelName 
 
  
 modelName 
  
 = 
  
  ModelName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 modelId 
 ); 
  
  ExportModelRequest 
 
 . 
 OutputConfig 
  
 outputConfig 
  
 = 
  
  ExportModelRequest 
 
 . 
 OutputConfig 
 . 
 newBuilder 
 () 
  
 . 
  setExportFormatId 
 
 ( 
 exportFormat 
 ) 
  
 . 
 setArtifactDestination 
 ( 
 gcsDestination 
 ) 
  
 . 
 build 
 (); 
  
 OperationFuture<ExportModelResponse 
 , 
  
 ExportModelOperationMetadata 
>  
 exportModelResponseFuture 
  
 = 
  
 modelServiceClient 
 . 
  exportModelAsync 
 
 ( 
 modelName 
 , 
  
 outputConfig 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s\n" 
 , 
  
 exportModelResponseFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  ExportModelResponse 
 
  
 exportModelResponse 
  
 = 
  
 exportModelResponseFuture 
 . 
  get 
 
 ( 
 300 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Export Model Response: %s\n" 
 , 
  
 exportModelResponse 
 ); 
  
 } 
  
 } 
 } 
 

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 (Not necessary if passing values as arguments) 
 */ 
 // const modelId = 'YOUR_MODEL_ID'; 
 // const gcsDestinationOutputUriPrefix ='YOUR_GCS_DEST_OUTPUT_URI_PREFIX'; 
 //    eg. "gs://<your-gcs-bucket>/destination_path" 
 // const exportFormat = 'YOUR_EXPORT_FORMAT'; 
 // const project = 'YOUR_PROJECT_ID'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // Imports the Google Cloud Model Service Client library 
 const 
  
 { 
 ModelServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 modelServiceClient 
  
 = 
  
 new 
  
  ModelServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 exportModel 
 () 
  
 { 
  
 // Configure the name resources 
  
 const 
  
 name 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /models/ 
 ${ 
 modelId 
 } 
 ` 
 ; 
  
 // Configure the outputConfig resources 
  
 const 
  
 outputConfig 
  
 = 
  
 { 
  
 exportFormatId 
 : 
  
 exportFormat 
 , 
  
 gcsDestination 
 : 
  
 { 
  
 outputUriPrefix 
 : 
  
 gcsDestinationOutputUriPrefix 
 , 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 , 
  
 outputConfig 
 , 
  
 }; 
  
 // Export Model request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 modelServiceClient 
 . 
 exportModel 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 const 
  
 result 
  
 = 
  
 response 
 . 
 result 
 ; 
  
 console 
 . 
 log 
 ( 
 `Export model response : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 ) 
 } 
 ` 
 ); 
 } 
 exportModel 
 (); 
 

  from 
  
 google.cloud 
  
 import 
 aiplatform 
 def 
  
 export_model_sample 
 ( 
 project 
 : 
 str 
 , 
 model_id 
 : 
 str 
 , 
 gcs_destination_output_uri_prefix 
 : 
 str 
 , 
 location 
 : 
 str 
 = 
 "us-central1" 
 , 
 api_endpoint 
 : 
 str 
 = 
 "us-central1-aiplatform.googleapis.com" 
 , 
 timeout 
 : 
 int 
 = 
 300 
 , 
 ): 
 # 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 
 . 
  ModelServiceClient 
 
 ( 
 client_options 
 = 
 client_options 
 ) 
 output_config 
 = 
 { 
 "artifact_destination" 
 : 
 { 
 "output_uri_prefix" 
 : 
 gcs_destination_output_uri_prefix 
 }, 
 # For information about export formats: https://cloud.google.com/ai-platform-unified/docs/export/export-edge-model#aiplatform_export_model_sample-drest 
 "export_format_id" 
 : 
 "tf-saved-model" 
 , 
 } 
 name 
 = 
 client 
 . 
  model_path 
 
 ( 
 project 
 = 
 project 
 , 
 location 
 = 
 location 
 , 
 model 
 = 
 model_id 
 ) 
 response 
 = 
 client 
 . 
  export_model 
 
 ( 
 name 
 = 
 name 
 , 
 output_config 
 = 
 output_config 
 ) 
 print 
 ( 
 "Long running operation:" 
 , 
 response 
 . 
 operation 
 . 
 name 
 ) 
 print 
 ( 
 "output_info:" 
 , 
 response 
 . 
 metadata 
 . 
 output_info 
 ) 
 export_model_response 
 = 
 response 
 . 
 result 
 ( 
 timeout 
 = 
 timeout 
 ) 
 print 
 ( 
 "export_model_response:" 
 , 
 export_model_response 
 )