Upload a model

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. LocationName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. Model 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelContainerSpec 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. UploadModelOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. UploadModelResponse 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 UploadModelSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
 , 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 modelDisplayName 
  
 = 
  
 "YOUR_MODEL_DISPLAY_NAME" 
 ; 
  
 String 
  
 metadataSchemaUri 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml" 
 ; 
  
 String 
  
 imageUri 
  
 = 
  
 "YOUR_IMAGE_URI" 
 ; 
  
 String 
  
 artifactUri 
  
 = 
  
 "gs://your-gcs-bucket/artifact_path" 
 ; 
  
 uploadModel 
 ( 
 project 
 , 
  
 modelDisplayName 
 , 
  
 metadataSchemaUri 
 , 
  
 imageUri 
 , 
  
 artifactUri 
 ); 
  
 } 
  
 static 
  
 void 
  
 uploadModel 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 modelDisplayName 
 , 
  
 String 
  
 metadataSchemaUri 
 , 
  
 String 
  
 imageUri 
 , 
  
 String 
  
 artifactUri 
 ) 
  
 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" 
 ; 
  
  LocationName 
 
  
 locationName 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  ModelContainerSpec 
 
  
 modelContainerSpec 
  
 = 
  
  ModelContainerSpec 
 
 . 
 newBuilder 
 (). 
 setImageUri 
 ( 
 imageUri 
 ). 
 build 
 (); 
  
  Model 
 
  
 model 
  
 = 
  
  Model 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 modelDisplayName 
 ) 
  
 . 
 setMetadataSchemaUri 
 ( 
 metadataSchemaUri 
 ) 
  
 . 
 setArtifactUri 
 ( 
 artifactUri 
 ) 
  
 . 
 setContainerSpec 
 ( 
 modelContainerSpec 
 ) 
  
 . 
 build 
 (); 
  
 OperationFuture<UploadModelResponse 
 , 
  
 UploadModelOperationMetadata 
>  
 uploadModelResponseFuture 
  
 = 
  
 modelServiceClient 
 . 
  uploadModelAsync 
 
 ( 
 locationName 
 , 
  
 model 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s\n" 
 , 
  
 uploadModelResponseFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  UploadModelResponse 
 
  
 uploadModelResponse 
  
 = 
  
 uploadModelResponseFuture 
 . 
  get 
 
 ( 
 5 
 , 
  
 TimeUnit 
 . 
 MINUTES 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Upload Model Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model: %s\n" 
 , 
  
 uploadModelResponse 
 . 
  getModel 
 
 ()); 
  
 } 
  
 } 
 } 
 

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 */ 
 // const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME'; 
 // const metadataSchemaUri = 'YOUR_METADATA_SCHEMA_URI'; 
 // const imageUri = 'YOUR_IMAGE_URI'; 
 // const artifactUri = 'YOUR_ARTIFACT_URI'; 
 // 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 
  
 uploadModel 
 () 
  
 { 
  
 // Configure the parent resources 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 // Configure the model resources 
  
 const 
  
 model 
  
 = 
  
 { 
  
 displayName 
 : 
  
 modelDisplayName 
 , 
  
 metadataSchemaUri 
 : 
  
 '' 
 , 
  
 artifactUri 
 : 
  
 artifactUri 
 , 
  
 containerSpec 
 : 
  
 { 
  
 imageUri 
 : 
  
 imageUri 
 , 
  
 command 
 : 
  
 [], 
  
 args 
 : 
  
 [], 
  
 env 
 : 
  
 [], 
  
 ports 
 : 
  
 [], 
  
 predictRoute 
 : 
  
 '' 
 , 
  
 healthRoute 
 : 
  
 '' 
 , 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 , 
  
 model 
 , 
  
 }; 
  
 console 
 . 
 log 
 ( 
 'PARENT AND MODEL' 
 ); 
  
 console 
 . 
 log 
 ( 
 parent 
 , 
  
 model 
 ); 
  
 // Upload Model request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 modelServiceClient 
 . 
 uploadModel 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 const 
  
 result 
  
 = 
  
 response 
 . 
 result 
 ; 
  
 console 
 . 
 log 
 ( 
 'Upload model response ' 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tModel : 
 ${ 
 result 
 . 
 model 
 } 
 ` 
 ); 
 } 
 uploadModel 
 (); 
 

  from 
  
 google.cloud 
  
 import 
 aiplatform 
 def 
  
 upload_model_sample 
 ( 
 project 
 : 
 str 
 , 
 display_name 
 : 
 str 
 , 
 metadata_schema_uri 
 : 
 str 
 , 
 image_uri 
 : 
 str 
 , 
 artifact_uri 
 : 
 str 
 , 
 location 
 : 
 str 
 = 
 "us-central1" 
 , 
 api_endpoint 
 : 
 str 
 = 
 "us-central1-aiplatform.googleapis.com" 
 , 
 timeout 
 : 
 int 
 = 
 1800 
 , 
 ): 
 # 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 
 ) 
 model 
 = 
 { 
 "display_name" 
 : 
 display_name 
 , 
 "metadata_schema_uri" 
 : 
 metadata_schema_uri 
 , 
 # The artifact_uri should be the path to a GCS directory containing 
 # saved model artifacts.  The bucket must be accessible for the 
 # project's AI Platform service account and in the same region as 
 # the api endpoint. 
 "artifact_uri" 
 : 
 artifact_uri 
 , 
 "container_spec" 
 : 
 { 
 "image_uri" 
 : 
 image_uri 
 , 
 "command" 
 : 
 [], 
 "args" 
 : 
 [], 
 "env" 
 : 
 [], 
 "ports" 
 : 
 [], 
 "predict_route" 
 : 
 "" 
 , 
 "health_route" 
 : 
 "" 
 , 
 }, 
 } 
 parent 
 = 
 f 
 "projects/ 
 { 
 project 
 } 
 /locations/ 
 { 
 location 
 } 
 " 
 response 
 = 
 client 
 . 
  upload_model 
 
 ( 
 parent 
 = 
 parent 
 , 
 model 
 = 
 model 
 ) 
 print 
 ( 
 "Long running operation:" 
 , 
 response 
 . 
 operation 
 . 
 name 
 ) 
 upload_model_response 
 = 
 response 
 . 
 result 
 ( 
 timeout 
 = 
 timeout 
 ) 
 print 
 ( 
 "upload_model_response:" 
 , 
 upload_model_response 
 )