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    Deploy a model by using the gcloud CLI or Vertex AI API

    To deploy a model to a public endpoint by using the gcloud CLI or Vertex AI API, you need to get the endpoint ID for an existing endpoint and then deploy the model to it.

    Get the endpoint ID

    You need the endpoint ID to deploy the model.

    gcloud

    The following example uses the gcloud ai endpoints list command :

       
gcloud  
ai  
endpoints  
list  
 \ 
  
--region = 
 LOCATION_ID 
  
 \ 
  
--filter = 
 display_name 
 = 
 ENDPOINT_NAME

    Replace the following:

    • LOCATION_ID : The region where you are using Vertex AI.
    • ENDPOINT_NAME : The display name for the endpoint.

    Note the number that appears in the ENDPOINT_ID column. Use this ID in the following step.

    REST

    Before using any of the request data, make the following replacements:

    • LOCATION_ID : The region where you are using Vertex AI.
    • PROJECT_ID : Your project ID .
    • ENDPOINT_NAME : The display name for the endpoint.

    HTTP method and URL:

    GET https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/endpoints?filter=display_name= ENDPOINT_NAME

    To send your request, expand one of these options:

    You should receive a JSON response similar to the following:

    {
  "endpoints": [
    {
      "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION_ID 
/endpoints/ ENDPOINT_ID 
",
      "displayName": " ENDPOINT_NAME 
",
      "etag": "AMEw9yPz5pf4PwBHbRWOGh0PcAxUdjbdX2Jm3QO_amguy3DbZGP5Oi_YUKRywIE-BtLx",
      "createTime": "2020-04-17T18:31:11.585169Z",
      "updateTime": "2020-04-17T18:35:08.568959Z"
    }
  ]
}
    Note the ENDPOINT_ID .

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

    Replace the following:

    • PROJECT_ID : Your project ID.
    • LOCATION_ID : The region where you are using Vertex AI.
    • ENDPOINT_NAME : The display name for the endpoint.
      from 
  
 google.cloud 
  
 import 
 aiplatform 
 PROJECT_ID 
 = 
 " PROJECT_ID 
" 
 LOCATION 
 = 
 " LOCATION_ID 
" 
 ENDPOINT_NAME 
 = 
 " ENDPOINT_NAME 
" 
 aiplatform 
 . 
  init 
 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 , 
 ) 
 endpoint 
 = 
 aiplatform 
 . 
  Endpoint 
 
 . 
 list 
 ( 
 filter 
 = 
 'display_name=ENDPOINT_NAME' 
 , 
 ) 
 endpoint_id 
 = 
 endpoint 
 . 
 name 
 . 
 split 
 ( 
 "/" 
 )[ 
 - 
 1 
 ]

    Deploy the model

    Select the tab below for your language or environment:

    gcloud

    The following examples use the gcloud ai endpoints deploy-model command .

    The following example deploys a Model to an Endpoint without using GPUs to accelerate prediction serving and without splitting traffic between multiple DeployedModel resources:

    Before using any of the command data below, make the following replacements:

    • ENDPOINT_ID : The ID for the endpoint.
    • LOCATION_ID : The region where you are using Vertex AI.
    • MODEL_ID : The ID for the model to be deployed.
    • DEPLOYED_MODEL_NAME : A name for the DeployedModel . You can use the display name of the Model for the DeployedModel as well.
    • MIN_REPLICA_COUNT : The minimum number of nodes for this deployment. The node count can be increased or decreased as required by the inference load, up to the maximum number of nodes and never fewer than this number of nodes.
    • MAX_REPLICA_COUNT : The maximum number of nodes for this deployment. The node count can be increased or decreased as required by the inference load, up to this number of nodes and never fewer than the minimum number of nodes. If you omit the --max-replica-count flag, then maximum number of nodes is set to the value of --min-replica-count .

    Execute the gcloud ai endpoints deploy-model command:

    Linux, macOS, or Cloud Shell

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
 \ 
  
--region = 
 LOCATION_ID 
  
 \ 
  
--model = 
 MODEL_ID 
  
 \ 
  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
 \ 
  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
 \ 
  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
 \ 
  
--traffic-split = 
 0 
 = 
 100

    Windows (PowerShell)

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
 ` 
  
--region = 
 LOCATION_ID 
  
 ` 
  
--model = 
 MODEL_ID 
  
 ` 
  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
 ` 
  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
 ` 
  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
 ` 
  
--traffic-split = 
 0 
 = 
 100

    Windows (cmd.exe)

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
^  
--region = 
 LOCATION_ID 
  
^  
--model = 
 MODEL_ID 
  
^  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
^  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
^  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
^  
--traffic-split = 
 0 
 = 
 100

    Splitting traffic

    The --traffic-split=0=100 flag in the preceding examples sends 100% of prediction traffic that the Endpoint receives to the new DeployedModel , which is represented by the temporary ID 0 . If your Endpoint already has other DeployedModel resources, then you can split traffic between the new DeployedModel and the old ones. For example, to send 20% of traffic to the new DeployedModel and 80% to an older one, run the following command.

    Before using any of the command data below, make the following replacements:

    • OLD_DEPLOYED_MODEL_ID : the ID of the existing DeployedModel .

    Execute the gcloud ai endpoints deploy-model command:

    Linux, macOS, or Cloud Shell

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
 \ 
  
--region = 
 LOCATION_ID 
  
 \ 
  
--model = 
 MODEL_ID 
  
 \ 
  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
 \ 
  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
 \ 
  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
 \ 
  
 --traffic-split = 
 0 
 = 
 20 
, OLD_DEPLOYED_MODEL_ID 
 = 
 80

    Windows (PowerShell)

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
 ` 
  
--region = 
 LOCATION_ID 
  
 ` 
  
--model = 
 MODEL_ID 
  
 ` 
  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
 \ 
  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
 ` 
  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
 ` 
  
 --traffic-split = 
 0 
 = 
 20 
, OLD_DEPLOYED_MODEL_ID 
 = 
 80

    Windows (cmd.exe)

    gcloud  
ai  
endpoints  
deploy-model  
 ENDPOINT_ID 
^  
--region = 
 LOCATION_ID 
  
^  
--model = 
 MODEL_ID 
  
^  
--display-name = 
 DEPLOYED_MODEL_NAME 
  
 \ 
  
--min-replica-count = 
 MIN_REPLICA_COUNT 
  
^  
--max-replica-count = 
 MAX_REPLICA_COUNT 
  
^  
 --traffic-split = 
 0 
 = 
 20 
, OLD_DEPLOYED_MODEL_ID 
 = 
 80

    REST

    Deploy the model.

    Before using any of the request data, make the following replacements:

    • LOCATION_ID : The region where you are using Vertex AI.
    • PROJECT_ID : Your project ID .
    • ENDPOINT_ID : The ID for the endpoint.
    • MODEL_ID : The ID for the model to be deployed.
    • DEPLOYED_MODEL_NAME : A name for the DeployedModel . You can use the display name of the Model for the DeployedModel as well.
    • MACHINE_TYPE : Optional. The machine resources used for each node of this deployment. Its default setting is n1-standard-2 . Learn more about machine types.
    • ACCELERATOR_TYPE : The type of accelerator to be attached to the machine. Optional if ACCELERATOR_COUNT is not specified or is zero. Not recommended for AutoML models or custom-trained models that are using non-GPU images. Learn more .
    • ACCELERATOR_COUNT : The number of accelerators for each replica to use. Optional. Should be zero or unspecified for AutoML models or custom-trained models that are using non-GPU images.
    • MIN_REPLICA_COUNT : The minimum number of nodes for this deployment. The node count can be increased or decreased as required by the inference load, up to the maximum number of nodes and never fewer than this number of nodes. This value must be greater than or equal to 1.
    • MAX_REPLICA_COUNT : The maximum number of nodes for this deployment. The node count can be increased or decreased as required by the inference load, up to this number of nodes and never fewer than the minimum number of nodes.
    • REQUIRED_REPLICA_COUNT : Optional. The required number of nodes for this deployment to be marked as successful. Must be greater than or equal to 1 and fewer than or equal to the minimum number of nodes. If not specified, the default value is the minimum number of nodes.
    • TRAFFIC_SPLIT_THIS_MODEL : The percentage of the prediction traffic to this endpoint to be routed to the model being deployed with this operation. Defaults to 100. All traffic percentages must add up to 100. Learn more about traffic splits .
    • DEPLOYED_MODEL_ID_N : Optional. If other models are deployed to this endpoint, you must update their traffic split percentages so that all percentages add up to 100.
    • TRAFFIC_SPLIT_MODEL_N : The traffic split percentage value for the deployed model id key.
    • PROJECT_NUMBER : Your project's automatically generated project number

    HTTP method and URL:

    POST https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/endpoints/ ENDPOINT_ID 
:deployModel

    Request JSON body:

    {
  "deployedModel": {
    "model": "projects/ PROJECT 
/locations/us-central1/models/ MODEL_ID 
",
    "displayName": " DEPLOYED_MODEL_NAME 
",
    "dedicatedResources": {
       "machineSpec": {
         "machineType": " MACHINE_TYPE 
",
         "acceleratorType": " ACCELERATOR_TYPE 
",
         "acceleratorCount": " ACCELERATOR_COUNT 
"
       },
       "minReplicaCount": MIN_REPLICA_COUNT 
,
       "maxReplicaCount": MAX_REPLICA_COUNT 
,
       "requiredReplicaCount": REQUIRED_REPLICA_COUNT 
},
  },
  "trafficSplit": {
    "0": TRAFFIC_SPLIT_THIS_MODEL 
,
    " DEPLOYED_MODEL_ID_1 
": TRAFFIC_SPLIT_MODEL_1 
,
    " DEPLOYED_MODEL_ID_2 
": TRAFFIC_SPLIT_MODEL_2 
},
}

    To send your request, expand one of these options:

    You should receive a JSON response similar to the following:

    {
  "name": "projects/ PROJECT_ID 
/locations/ LOCATION 
/endpoints/ ENDPOINT_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployModelOperationMetadata",
    "genericMetadata": {
      "createTime": "2020-10-19T17:53:16.502088Z",
      "updateTime": "2020-10-19T17:53:16.502088Z"
    }
  }
}

    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. DedicatedResources 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeployModelOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeployModelResponse 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DeployedModel 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. EndpointServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. MachineSpec 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ModelName 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.HashMap 
 ; 
 import 
  
 java.util.Map 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 public 
  
 class 
 DeployModelCustomTrainedModelSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "PROJECT" 
 ; 
  
 String 
  
 endpointId 
  
 = 
  
 "ENDPOINT_ID" 
 ; 
  
 String 
  
 modelName 
  
 = 
  
 "MODEL_NAME" 
 ; 
  
 String 
  
 deployedModelDisplayName 
  
 = 
  
 "DEPLOYED_MODEL_DISPLAY_NAME" 
 ; 
  
 deployModelCustomTrainedModelSample 
 ( 
 project 
 , 
  
 endpointId 
 , 
  
 modelName 
 , 
  
 deployedModelDisplayName 
 ); 
  
 } 
  
 static 
  
 void 
  
 deployModelCustomTrainedModelSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 endpointId 
 , 
  
 String 
  
 model 
 , 
  
 String 
  
 deployedModelDisplayName 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
  EndpointServiceSettings 
 
  
 settings 
  
 = 
  
  EndpointServiceSettings 
 
 . 
 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. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  EndpointServiceClient 
 
  
 client 
  
 = 
  
  EndpointServiceClient 
 
 . 
 create 
 ( 
 settings 
 )) 
  
 { 
  
  MachineSpec 
 
  
 machineSpec 
  
 = 
  
  MachineSpec 
 
 . 
 newBuilder 
 (). 
  setMachineType 
 
 ( 
 "n1-standard-2" 
 ). 
 build 
 (); 
  
  DedicatedResources 
 
  
 dedicatedResources 
  
 = 
  
  DedicatedResources 
 
 . 
 newBuilder 
 (). 
 setMinReplicaCount 
 ( 
 1 
 ). 
 setMachineSpec 
 ( 
 machineSpec 
 ). 
 build 
 (); 
  
 String 
  
 modelName 
  
 = 
  
  ModelName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 model 
 ). 
 toString 
 (); 
  
  DeployedModel 
 
  
 deployedModel 
  
 = 
  
  DeployedModel 
 
 . 
 newBuilder 
 () 
  
 . 
 setModel 
 ( 
 modelName 
 ) 
  
 . 
 setDisplayName 
 ( 
 deployedModelDisplayName 
 ) 
  
 // `dedicated_resources` must be used for non-AutoML models 
  
 . 
 setDedicatedResources 
 ( 
 dedicatedResources 
 ) 
  
 . 
 build 
 (); 
  
 // key '0' assigns traffic for the newly deployed model 
  
 // Traffic percentage values must add up to 100 
  
 // Leave dictionary empty if endpoint should not accept any traffic 
  
 Map<String 
 , 
  
 Integer 
>  
 trafficSplit 
  
 = 
  
 new 
  
 HashMap 
<> (); 
  
 trafficSplit 
 . 
 put 
 ( 
 "0" 
 , 
  
 100 
 ); 
  
  EndpointName 
 
  
 endpoint 
  
 = 
  
  EndpointName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 endpointId 
 ); 
  
 OperationFuture<DeployModelResponse 
 , 
  
 DeployModelOperationMetadata 
>  
 response 
  
 = 
  
 client 
 . 
  deployModelAsync 
 
 ( 
 endpoint 
 , 
  
 deployedModel 
 , 
  
 trafficSplit 
 ); 
  
 // You can use OperationFuture.getInitialFuture to get a future representing the initial 
  
 // response to the request, which contains information while the operation is in progress. 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Operation name: %s\n" 
 , 
  
 response 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 // OperationFuture.get() will block until the operation is finished. 
  
  DeployModelResponse 
 
  
 deployModelResponse 
  
 = 
  
 response 
 . 
  get 
 
 (); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "deployModelResponse: %s\n" 
 , 
  
 deployModelResponse 
 ); 
  
 } 
  
 } 
 }

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 deploy_model_with_dedicated_resources_sample 
 ( 
  
 project 
 , 
  
 location 
 , 
  
 model_name 
 : 
  
 str 
 , 
  
 machine_type 
 : 
  
 str 
 , 
  
 endpoint 
 : 
  
 Optional 
 [ 
 aiplatform.Endpoint 
 ] 
  
 = 
  
 None 
 , 
  
 deployed_model_display_name 
 : 
  
 Optional 
 [ 
 str 
 ] 
  
 = 
  
 None 
 , 
  
 traffic_percentage 
 : 
  
 Optional 
 [ 
 int 
 ] 
  
 = 
  
 0 
 , 
  
 traffic_split 
 : 
  
 Optional 
 [ 
 Dict[str, int 
 ] 
 ] 
  
 = 
  
 None 
 , 
  
 min_replica_count 
 : 
  
 int 
  
 = 
  
 1 
 , 
  
 max_replica_count 
 : 
  
 int 
  
 = 
  
 1 
 , 
  
 accelerator_type 
 : 
  
 Optional 
 [ 
 str 
 ] 
  
 = 
  
 None 
 , 
  
 accelerator_count 
 : 
  
 Optional 
 [ 
 int 
 ] 
  
 = 
  
 None 
 , 
  
 explanation_metadata 
 : 
  
 Optional 
 [ 
 explain.ExplanationMetadata 
 ] 
  
 = 
  
 None 
 , 
  
 explanation_parameters 
 : 
  
 Optional 
 [ 
 explain.ExplanationParameters 
 ] 
  
 = 
  
 None 
 , 
  
 metadata 
 : 
  
 Optional 
 [ 
 Sequence[Tuple[str, str 
 ] 
 ]] 
  
 = 
  
 (), 
  
 sync 
 : 
  
 bool 
  
 = 
  
 True 
 , 
 ) 
 : 
  
 """ 
 model_name: A fully-qualified model resource name or model ID. 
 Example: " 
 projects 
 / 
 123 
 / 
 locations 
 / 
 us 
 - 
 central1 
 / 
 models 
 / 
 456 
 " or 
 " 
 456 
 " when project and location are initialized or passed. 
 """ 
  
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
  
 location 
 = 
 location 
 ) 
  
 model 
  
 = 
  
 aiplatform 
 . 
 Model 
 ( 
 model_name 
 = 
 model_name 
 ) 
  
 # 
  
 The 
  
 explanation_metadata 
  
 and 
  
 explanation_parameters 
  
 should 
  
 only 
  
 be 
  
 # 
  
 provided 
  
 for 
  
 a 
  
 custom 
  
 trained 
  
 model 
  
 and 
  
 not 
  
 an 
  
 AutoML 
  
 model 
 . 
  
 model 
 . 
 deploy 
 ( 
  
 endpoint 
 = 
 endpoint 
 , 
  
 deployed_model_display_name 
 = 
 deployed_model_display_name 
 , 
  
 traffic_percentage 
 = 
 traffic_percentage 
 , 
  
 traffic_split 
 = 
 traffic_split 
 , 
  
 machine_type 
 = 
 machine_type 
 , 
  
 min_replica_count 
 = 
 min_replica_count 
 , 
  
 max_replica_count 
 = 
 max_replica_count 
 , 
  
 accelerator_type 
 = 
 accelerator_type 
 , 
  
 accelerator_count 
 = 
 accelerator_count 
 , 
  
 explanation_metadata 
 = 
 explanation_metadata 
 , 
  
 explanation_parameters 
 = 
 explanation_parameters 
 , 
  
 metadata 
 = 
 metadata 
 , 
  
 sync 
 = 
 sync 
 , 
  
 ) 
  
 model 
 . 
 wait 
 () 
  
 print 
 ( 
 model 
 . 
 display_name 
 ) 
  
 print 
 ( 
 model 
 . 
 resource_name 
 ) 
  
 return 
  
 model

    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 .

      const 
  
 automl 
  
 = 
  
 require 
 ( 
 ' @google-cloud/automl 
' 
 ); 
 const 
  
 client 
  
 = 
  
 new 
  
 automl 
 . 
 v1beta1 
 . 
  AutoMlClient 
 
 (); 
 /** 
 * Demonstrates using the AutoML client to create a model. 
 * TODO(developer): Uncomment the following lines before running the sample. 
 */ 
 // const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project"; 
 // const computeRegion = '[REGION_NAME]' e.g., "us-central1"; 
 // const datasetId = '[DATASET_ID]' e.g., "TBL2246891593778855936"; 
 // const tableId = '[TABLE_ID]' e.g., "1991013247762825216"; 
 // const columnId = '[COLUMN_ID]' e.g., "773141392279994368"; 
 // const modelName = '[MODEL_NAME]' e.g., "testModel"; 
 // const trainBudget = '[TRAIN_BUDGET]' e.g., "1000", 
 // `Train budget in milli node hours`; 
 // A resource that represents Google Cloud Platform location. 
 const 
  
 projectLocation 
  
 = 
  
 client 
 . 
 locationPath 
 ( 
 projectId 
 , 
  
 computeRegion 
 ); 
 // Get the full path of the column. 
 const 
  
 columnSpecId 
  
 = 
  
 client 
 . 
 columnSpecPath 
 ( 
  
 projectId 
 , 
  
 computeRegion 
 , 
  
 datasetId 
 , 
  
 tableId 
 , 
  
 columnId 
 ); 
 // Set target column to train the model. 
 const 
  
 targetColumnSpec 
  
 = 
  
 { 
 name 
 : 
  
 columnSpecId 
 }; 
 // Set tables model metadata. 
 const 
  
 tablesModelMetadata 
  
 = 
  
 { 
  
 targetColumnSpec 
 : 
  
 targetColumnSpec 
 , 
  
 trainBudgetMilliNodeHours 
 : 
  
 trainBudget 
 , 
 }; 
 // Set datasetId, model name and model metadata for the dataset. 
 const 
  
 myModel 
  
 = 
  
 { 
  
 datasetId 
 : 
  
 datasetId 
 , 
  
 displayName 
 : 
  
 modelName 
 , 
  
 tablesModelMetadata 
 : 
  
 tablesModelMetadata 
 , 
 }; 
 // Create a model with the model metadata in the region. 
 client 
  
 . 
 createModel 
 ({ 
 parent 
 : 
  
 projectLocation 
 , 
  
 model 
 : 
  
 myModel 
 }) 
  
 . 
 then 
 ( 
 responses 
  
 = 
>  
 { 
  
 const 
  
 initialApiResponse 
  
 = 
  
 responses 
 [ 
 1 
 ]; 
  
 console 
 . 
 log 
 ( 
 `Training operation name: 
 ${ 
 initialApiResponse 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Training started...' 
 ); 
  
 }) 
  
 . 
 catch 
 ( 
 err 
  
 = 
>  
 { 
  
 console 
 . 
 error 
 ( 
 err 
 ); 
  
 });

    Learn how to change the default settings for inference logging .

    Get operation status

    Some requests start long-running operations that require time to complete. These requests return an operation name, which you can use to view the operation's status or cancel the operation. Vertex AI provides helper methods to make calls against long-running operations. For more information, see Working with long-running operations .

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