Create a custom job

Creates a custom job using the create_custom_job method.

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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.cloud.aiplatform.v1. AcceleratorType 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ContainerSpec 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. CustomJob 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. CustomJobSpec 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. JobServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. JobServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. LocationName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. MachineSpec 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. WorkerPoolSpec 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 // Create a custom job to run machine learning training code in Vertex AI 
 public 
  
 class 
 CreateCustomJobSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "PROJECT" 
 ; 
  
 String 
  
 displayName 
  
 = 
  
 "DISPLAY_NAME" 
 ; 
  
 // Vertex AI runs your training application in a Docker container image. A Docker container 
  
 // image is a self-contained software package that includes code and all dependencies. Learn 
  
 // more about preparing your training application at 
  
 // https://cloud.google.com/vertex-ai/docs/training/overview#prepare_your_training_application 
  
 String 
  
 containerImageUri 
  
 = 
  
 "CONTAINER_IMAGE_URI" 
 ; 
  
 createCustomJobSample 
 ( 
 project 
 , 
  
 displayName 
 , 
  
 containerImageUri 
 ); 
  
 } 
  
 static 
  
 void 
  
 createCustomJobSample 
 ( 
 String 
  
 project 
 , 
  
 String 
  
 displayName 
 , 
  
 String 
  
 containerImageUri 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
  JobServiceSettings 
 
  
 settings 
  
 = 
  
  JobServiceSettings 
 
 . 
 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. 
  
 try 
  
 ( 
  JobServiceClient 
 
  
 client 
  
 = 
  
  JobServiceClient 
 
 . 
 create 
 ( 
 settings 
 )) 
  
 { 
  
  MachineSpec 
 
  
 machineSpec 
  
 = 
  
  MachineSpec 
 
 . 
 newBuilder 
 () 
  
 . 
  setMachineType 
 
 ( 
 "n1-standard-4" 
 ) 
  
 . 
  setAcceleratorType 
 
 ( 
  AcceleratorType 
 
 . 
 NVIDIA_TESLA_T4 
 ) 
  
 . 
  setAcceleratorCount 
 
 ( 
 1 
 ) 
  
 . 
 build 
 (); 
  
  ContainerSpec 
 
  
 containerSpec 
  
 = 
  
  ContainerSpec 
 
 . 
 newBuilder 
 (). 
 setImageUri 
 ( 
 containerImageUri 
 ). 
 build 
 (); 
  
  WorkerPoolSpec 
 
  
 workerPoolSpec 
  
 = 
  
  WorkerPoolSpec 
 
 . 
 newBuilder 
 () 
  
 . 
 setMachineSpec 
 ( 
 machineSpec 
 ) 
  
 . 
 setReplicaCount 
 ( 
 1 
 ) 
  
 . 
 setContainerSpec 
 ( 
 containerSpec 
 ) 
  
 . 
 build 
 (); 
  
  CustomJobSpec 
 
  
 customJobSpecJobSpec 
  
 = 
  
  CustomJobSpec 
 
 . 
 newBuilder 
 (). 
  addWorkerPoolSpecs 
 
 ( 
 workerPoolSpec 
 ). 
 build 
 (); 
  
  CustomJob 
 
  
 customJob 
  
 = 
  
  CustomJob 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 displayName 
 ) 
  
 . 
  setJobSpec 
 
 ( 
 customJobSpecJobSpec 
 ) 
  
 . 
 build 
 (); 
  
  LocationName 
 
  
 parent 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  CustomJob 
 
  
 response 
  
 = 
  
 client 
 . 
 createCustomJob 
 ( 
 parent 
 , 
  
 customJob 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "response: %s\n" 
 , 
  
 response 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 response 
 . 
  getName 
 
 ()); 
  
 } 
  
 } 
 }

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 customJobDisplayName = 'YOUR_CUSTOM_JOB_DISPLAY_NAME'; 
 // const containerImageUri = 'YOUR_CONTAINER_IMAGE_URI'; 
 // const project = 'YOUR_PROJECT_ID'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // Imports the Google Cloud Job Service Client library 
 const 
  
 { 
 JobServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 jobServiceClient 
  
 = 
  
 new 
  
  JobServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 createCustomJob 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 const 
  
 customJob 
  
 = 
  
 { 
  
 displayName 
 : 
  
 customJobDisplayName 
 , 
  
 jobSpec 
 : 
  
 { 
  
 workerPoolSpecs 
 : 
  
 [ 
  
 { 
  
 machineSpec 
 : 
  
 { 
  
 machineType 
 : 
  
 'n1-standard-4' 
 , 
  
 acceleratorType 
 : 
  
 'NVIDIA_TESLA_T4' 
 , 
  
 acceleratorCount 
 : 
  
 1 
 , 
  
 }, 
  
 replicaCount 
 : 
  
 1 
 , 
  
 containerSpec 
 : 
  
 { 
  
 imageUri 
 : 
  
 containerImageUri 
 , 
  
 command 
 : 
  
 [], 
  
 args 
 : 
  
 [], 
  
 }, 
  
 }, 
  
 ], 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
 parent 
 , 
  
 customJob 
 }; 
  
 // Create custom job request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 jobServiceClient 
 . 
 createCustomJob 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 'Create custom job response:\n' 
 , 
  
 JSON 
 . 
 stringify 
 ( 
 response 
 )); 
 } 
 createCustomJob 
 ();

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_custom_job_sample 
 ( 
 project 
 : 
 str 
 , 
 display_name 
 : 
 str 
 , 
 container_image_uri 
 : 
 str 
 , 
 location 
 : 
 str 
 = 
 "us-central1" 
 , 
 api_endpoint 
 : 
 str 
 = 
 "us-central1-aiplatform.googleapis.com" 
 , 
 ): 
 # 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 
 . 
  JobServiceClient 
 
 ( 
 client_options 
 = 
 client_options 
 ) 
 custom_job 
 = 
 { 
 "display_name" 
 : 
 display_name 
 , 
 "job_spec" 
 : 
 { 
 "worker_pool_specs" 
 : 
 [ 
 { 
 "machine_spec" 
 : 
 { 
 "machine_type" 
 : 
 "n1-standard-4" 
 , 
 "accelerator_type" 
 : 
 aiplatform 
 . 
 gapic 
 . 
  AcceleratorType 
 
 . 
 NVIDIA_TESLA_K80 
 , 
 "accelerator_count" 
 : 
 1 
 , 
 }, 
 "replica_count" 
 : 
 1 
 , 
 "container_spec" 
 : 
 { 
 "image_uri" 
 : 
 container_image_uri 
 , 
 "command" 
 : 
 [], 
 "args" 
 : 
 [], 
 }, 
 } 
 ] 
 }, 
 } 
 parent 
 = 
 f 
 "projects/ 
 { 
 project 
 } 
 /locations/ 
 { 
 location 
 } 
 " 
 response 
 = 
 client 
 . 
  create_custom_job 
 
 ( 
 parent 
 = 
 parent 
 , 
 custom_job 
 = 
 custom_job 
 ) 
 print 
 ( 
 "response:" 
 , 
 response 
 )

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Code sample

Java

Node.js

Python

What's next

Create a custom job