Delete an endpoint

Deletes an endpoint using the delete_endpoint method.

Code sample

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeleteOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceSettings 
 
 ; 
 import 
  
 com.google.protobuf. Empty 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 DeleteEndpointSample 
  
 { 
  
 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 
  
 endpointId 
  
 = 
  
 "YOUR_ENDPOINT_ID" 
 ; 
  
 deleteEndpointSample 
 ( 
 project 
 , 
  
 endpointId 
 ); 
  
 } 
  
 static 
  
 void 
  
 deleteEndpointSample 
 ( 
 String 
  
 project 
 , 
  
 String 
  
 endpointId 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  EndpointServiceSettings 
 
  
 endpointServiceSettings 
  
 = 
  
  EndpointServiceSettings 
 
 . 
 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 
  
 ( 
  EndpointServiceClient 
 
  
 endpointServiceClient 
  
 = 
  
  EndpointServiceClient 
 
 . 
 create 
 ( 
 endpointServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
  EndpointName 
 
  
 endpointName 
  
 = 
  
  EndpointName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 endpointId 
 ); 
  
 // NOTE: Be sure to undeploy any models deployed to the endpoint 
  
 // before attempting to delete the endpoint. 
  
 OperationFuture<Empty 
 , 
  
 DeleteOperationMetadata 
>  
 operationFuture 
  
 = 
  
 endpointServiceClient 
 . 
  deleteEndpointAsync 
 
 ( 
 endpointName 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Operation name: %s\n" 
 , 
  
 operationFuture 
 . 
 getInitialFuture 
 (). 
 get 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  Empty 
 
  
 deleteResponse 
  
 = 
  
 operationFuture 
 . 
 get 
 ( 
 300 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Delete Endpoint Response: %s\n" 
 , 
  
 deleteResponse 
 ); 
  
 } 
  
 } 
 } 
 

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const endpointId = 'YOUR_ENDPOINT_ID'; 
 // const project = 'YOUR_PROJECT_ID'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // Imports the Google Cloud Endpoint Service Client library 
 const 
  
 { 
 EndpointServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 endpointServiceClient 
  
 = 
  
 new 
  
  EndpointServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 deleteEndpoint 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 endpoint 
  
 = 
  
 { 
  
 name 
 : 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 /endpoints/ 
 ${ 
 endpointId 
 } 
 ` 
 , 
  
 }; 
  
 // NOTE: Be sure to undeploy any models deployed to the endpoint before 
  
 // attempting to delete the endpoint. 
  
 // Delete endpoint request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 endpointServiceClient 
 . 
 deleteEndpoint 
 ( 
 endpoint 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 const 
  
 result 
  
 = 
  
 response 
 . 
 result 
 ; 
  
 console 
 . 
 log 
 ( 
 'Delete endpoint response:\n' 
 , 
  
 result 
 ); 
 } 
 deleteEndpoint 
 (); 
 

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  from 
  
 google.cloud 
  
 import 
 aiplatform 
 def 
  
 delete_endpoint_sample 
 ( 
 project 
 : 
 str 
 , 
 endpoint_id 
 : 
 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 
 . 
  EndpointServiceClient 
 
 ( 
 client_options 
 = 
 client_options 
 ) 
 name 
 = 
 client 
 . 
  endpoint_path 
 
 ( 
 project 
 = 
 project 
 , 
 location 
 = 
 location 
 , 
 endpoint 
 = 
 endpoint_id 
 ) 
 response 
 = 
 client 
 . 
  delete_endpoint 
 
 ( 
 name 
 = 
 name 
 ) 
 print 
 ( 
 "Long running operation:" 
 , 
 response 
 . 
 operation 
 . 
 name 
 ) 
 delete_endpoint_response 
 = 
 response 
 . 
 result 
 ( 
 timeout 
 = 
 timeout 
 ) 
 print 
 ( 
 "delete_endpoint_response:" 
 , 
 delete_endpoint_response 
 ) 
 

What's next

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