Create an endpoint

Creates an endpoint using the create_endpoint method.

Explore further

For detailed documentation that includes this code sample, see the following:

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. CreateEndpointOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. Endpoint 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. LocationName 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 CreateEndpointSample 
  
 { 
  
 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 
  
 endpointDisplayName 
  
 = 
  
 "YOUR_ENDPOINT_DISPLAY_NAME" 
 ; 
  
 createEndpointSample 
 ( 
 project 
 , 
  
 endpointDisplayName 
 ); 
  
 } 
  
 static 
  
 void 
  
 createEndpointSample 
 ( 
 String 
  
 project 
 , 
  
 String 
  
 endpointDisplayName 
 ) 
  
 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" 
 ; 
  
  LocationName 
 
  
 locationName 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  Endpoint 
 
  
 endpoint 
  
 = 
  
  Endpoint 
 
 . 
 newBuilder 
 (). 
 setDisplayName 
 ( 
 endpointDisplayName 
 ). 
 build 
 (); 
  
 OperationFuture<Endpoint 
 , 
  
 CreateEndpointOperationMetadata 
>  
 endpointFuture 
  
 = 
  
 endpointServiceClient 
 . 
  createEndpointAsync 
 
 ( 
 locationName 
 , 
  
 endpoint 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Operation name: %s\n" 
 , 
  
 endpointFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  Endpoint 
 
  
 endpointResponse 
  
 = 
  
 endpointFuture 
 . 
  get 
 
 ( 
 300 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Create Endpoint Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 endpointResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 endpointResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Description: %s\n" 
 , 
  
 endpointResponse 
 . 
  getDescription 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %s\n" 
 , 
  
 endpointResponse 
 . 
  getLabelsMap 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 endpointResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 endpointResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 } 
  
 } 
 } 
 

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 endpointDisplayName = 'YOUR_ENDPOINT_DISPLAY_NAME'; 
 // 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 
  
 createEndpoint 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 const 
  
 endpoint 
  
 = 
  
 { 
  
 displayName 
 : 
  
 endpointDisplayName 
 , 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 , 
  
 endpoint 
 , 
  
 }; 
  
 // Get and print out a list of all the endpoints for this resource 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 endpointServiceClient 
 . 
 createEndpoint 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 const 
  
 result 
  
 = 
  
 response 
 . 
 result 
 ; 
  
 console 
 . 
 log 
 ( 
 'Create endpoint response' 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tName : 
 ${ 
 result 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tDisplay name : 
 ${ 
 result 
 . 
 displayName 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tDescription : 
 ${ 
 result 
 . 
 description 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tLabels : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 . 
 labels 
 ) 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tCreate time : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 . 
 createTime 
 ) 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tUpdate time : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 . 
 updateTime 
 ) 
 } 
 ` 
 ); 
 } 
 createEndpoint 
 (); 
 

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 
  
 create_endpoint_sample 
 ( 
 project 
 : 
 str 
 , 
 display_name 
 : 
 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 
 ) 
 endpoint 
 = 
 { 
 "display_name" 
 : 
 display_name 
 } 
 parent 
 = 
 f 
 "projects/ 
 { 
 project 
 } 
 /locations/ 
 { 
 location 
 } 
 " 
 response 
 = 
 client 
 . 
  create_endpoint 
 
 ( 
 parent 
 = 
 parent 
 , 
 endpoint 
 = 
 endpoint 
 ) 
 print 
 ( 
 "Long running operation:" 
 , 
 response 
 . 
 operation 
 . 
 name 
 ) 
 create_endpoint_response 
 = 
 response 
 . 
 result 
 ( 
 timeout 
 = 
 timeout 
 ) 
 print 
 ( 
 "create_endpoint_response:" 
 , 
 create_endpoint_response 
 ) 
 

Terraform

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands . For more information, see the Terraform provider reference documentation .

  # Endpoint name must be unique for the project 
 resource 
  
 "random_id" 
  
 "endpoint_id" 
  
 { 
  
 byte_length 
  
 = 
  
 4 
 } 
 resource 
  
 "google_vertex_ai_endpoint" 
  
 "default" 
  
 { 
  
 name 
  
 = 
  
 substr 
 ( 
 random_id.endpoint_id.dec 
 , 
  
 0 
 , 
  
 10 
 ) 
  
 display_name 
  
 = 
  
 "sample-endpoint" 
  
 description 
  
 = 
  
 "A sample Vertex AI endpoint" 
  
 location 
  
 = 
  
 "us-central1" 
  
 labels 
  
 = 
  
 { 
  
 label-one 
  
 = 
  
 "value-one" 
  
 } 
 } 
 

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .

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