Creating and managing models

Training models

When you have a dataset with a solid set of training sentence pairs, you are ready to create and train the custom model.

Web UI

  1. Open the AutoML Translation UI .

    The Datasetspage shows the available datasets for the current project.

  2. Select the dataset you want to use to train the custom model.

    The display name of the selected dataset appears in the title bar, and the page lists the individual items in the dataset along with their respective "Training," "Validation," or "Testing" labels.

  3. When you are done reviewing the dataset, click the Traintab just below the title bar.

    Train tab for the my_dataset dataset

  4. Click Start Training.

    A Train new modeldialog box appears.

  5. Specify a name for the model.

  6. Click Start Trainingto begin training your custom model.

Training a model can take several hours to complete. After the model is successfully trained, you will receive a message at the email address you used to sign up for the program.

REST

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

  • project-id : your Google Cloud Platform project ID
  • model-name : the name of your new model
  • dataset-id : the ID of your dataset. The ID is the last element of the name of your dataset. For example, if the name of your dataset is projects/434039606874/locations/us-central1/datasets/3104518874390609379 , then the ID of your dataset is 3104518874390609379 .

HTTP method and URL:

POST https://automl.googleapis.com/v1/projects/ project-id 
/locations/us-central1/models

Request JSON body:

{
    "displayName": " model-name 
",
    "dataset_id": " dataset-id 
",
    "translationModelMetadata": {
        "base_model" : ""
    }
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/ project-number 
/locations/us-central1/operations/ operation-id 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-10-02T18:40:04.010343Z",
    "updateTime": "2019-10-02T18:40:04.010343Z",
    "createModelDetails": {}
  }
}

Go

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Go API reference documentation .

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

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 automl 
  
 "cloud.google.com/go/automl/apiv1" 
  
 "cloud.google.com/go/automl/apiv1/automlpb" 
 ) 
 // translateCreateModel creates a model for translate. 
 func 
  
 translateCreateModel 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
  
 string 
 , 
  
 location 
  
 string 
 , 
  
 datasetID 
  
 string 
 , 
  
 modelName 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // location := "us-central1" 
  
 // datasetID := "TRL123456789..." 
  
 // modelName := "model_display_name" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 automl 
 . 
 NewClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 req 
  
 := 
  
& automlpb 
 . 
 CreateModelRequest 
 { 
  
 Parent 
 : 
  
 fmt 
 . 
 Sprintf 
 ( 
 "projects/%s/locations/%s" 
 , 
  
 projectID 
 , 
  
 location 
 ), 
  
 Model 
 : 
  
& automlpb 
 . 
 Model 
 { 
  
 DisplayName 
 : 
  
 modelName 
 , 
  
 DatasetId 
 : 
  
 datasetID 
 , 
  
 ModelMetadata 
 : 
  
& automlpb 
 . 
 Model_TranslationModelMetadata 
 { 
  
 TranslationModelMetadata 
 : 
  
& automlpb 
 . 
 TranslationModelMetadata 
 {}, 
  
 }, 
  
 }, 
  
 } 
  
 op 
 , 
  
 err 
  
 := 
  
 client 
 . 
 CreateModel 
 ( 
 ctx 
 , 
  
 req 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "CreateModel: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Processing operation name: %q\n" 
 , 
  
 op 
 . 
 Name 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Training started...\n" 
 ) 
  
 return 
  
 nil 
 } 
 

Java

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Java API reference documentation .

To authenticate to AutoML Translation, 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.automl.v1. AutoMlClient 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. LocationName 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. Model 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. OperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. TranslationModelMetadata 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 class 
 TranslateCreateModel 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 datasetId 
  
 = 
  
 "YOUR_DATASET_ID" 
 ; 
  
 String 
  
 displayName 
  
 = 
  
 "YOUR_DATASET_NAME" 
 ; 
  
 createModel 
 ( 
 projectId 
 , 
  
 datasetId 
 , 
  
 displayName 
 ); 
  
 } 
  
 // Create a model 
  
 static 
  
 void 
  
 createModel 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 datasetId 
 , 
  
 String 
  
 displayName 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
 // 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 
  
 ( 
  AutoMlClient 
 
  
 client 
  
 = 
  
  AutoMlClient 
 
 . 
 create 
 ()) 
  
 { 
  
 // A resource that represents Google Cloud Platform location. 
  
  LocationName 
 
  
 projectLocation 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 projectId 
 , 
  
 "us-central1" 
 ); 
  
  TranslationModelMetadata 
 
  
 translationModelMetadata 
  
 = 
  
  TranslationModelMetadata 
 
 . 
 newBuilder 
 (). 
 build 
 (); 
  
  Model 
 
  
 model 
  
 = 
  
  Model 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 displayName 
 ) 
  
 . 
 setDatasetId 
 ( 
 datasetId 
 ) 
  
 . 
 setTranslationModelMetadata 
 ( 
 translationModelMetadata 
 ) 
  
 . 
 build 
 (); 
  
 // Create a model with the model metadata in the region. 
  
 OperationFuture<Model 
 , 
  
 OperationMetadata 
>  
 future 
  
 = 
  
 client 
 . 
 createModelAsync 
 ( 
 projectLocation 
 , 
  
 model 
 ); 
  
 // OperationFuture.get() will block until the model is created, which may take several hours. 
  
 // 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 
 ( 
 "Training operation name: %s\n" 
 , 
  
 future 
 . 
 getInitialFuture 
 (). 
 get 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Training started..." 
 ); 
  
 } 
  
 } 
 } 
 

Node.js

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Node.js API reference documentation .

To authenticate to AutoML Translation, 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. 
 */ 
 // const projectId = 'YOUR_PROJECT_ID'; 
 // const location = 'us-central1'; 
 // const dataset_id = 'YOUR_DATASET_ID'; 
 // const displayName = 'YOUR_DISPLAY_NAME'; 
 // Imports the Google Cloud AutoML library 
 const 
  
 { 
 AutoMlClient 
 } 
  
 = 
  
 require 
 ( 
 '@google-cloud/automl' 
 ). 
 v1 
 ; 
 // Instantiates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 AutoMlClient 
 (); 
 async 
  
 function 
  
 createModel 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 : 
  
 client 
 . 
 locationPath 
 ( 
 projectId 
 , 
  
 location 
 ), 
  
 model 
 : 
  
 { 
  
 displayName 
 : 
  
 displayName 
 , 
  
 datasetId 
 : 
  
 datasetId 
 , 
  
 translationModelMetadata 
 : 
  
 {}, 
  
 }, 
  
 }; 
  
 // Don't wait for the LRO 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 createModel 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 'Training started...' 
 ); 
  
 console 
 . 
 log 
 ( 
 `Training operation name: 
 ${ 
 operation 
 . 
 name 
 } 
 ` 
 ); 
 } 
 createModel 
 (); 
 

Python

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Python API reference documentation .

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

  from 
  
 google.cloud 
  
 import 
 automl 
 # TODO(developer): Uncomment and set the following variables 
 # project_id = "YOUR_PROJECT_ID" 
 # dataset_id = "YOUR_DATASET_ID" 
 # display_name = "YOUR_MODEL_NAME" 
 client 
 = 
 automl 
 . 
 AutoMlClient 
 () 
 # A resource that represents Google Cloud Platform location. 
 project_location 
 = 
 f 
 "projects/ 
 { 
 project_id 
 } 
 /locations/us-central1" 
 translation_model_metadata 
 = 
 automl 
 . 
 TranslationModelMetadata 
 () 
 model 
 = 
 automl 
 . 
 Model 
 ( 
 display_name 
 = 
 display_name 
 , 
 dataset_id 
 = 
 dataset_id 
 , 
 translation_model_metadata 
 = 
 translation_model_metadata 
 , 
 ) 
 # Create a model with the model metadata in the region. 
 response 
 = 
 client 
 . 
 create_model 
 ( 
 parent 
 = 
 project_location 
 , 
 model 
 = 
 model 
 ) 
 print 
 ( 
 f 
 "Training operation name: 
 { 
 response 
 . 
 operation 
 . 
 name 
 } 
 " 
 ) 
 print 
 ( 
 "Training started..." 
 ) 
 

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.

Getting the status of an operation

You can check the status of a long-running task ( importing items into a dataset or training a model ) using the operation ID from the response when you started the task.

You can only check the status of operations using the AutoML API.

To get the status of your training operation, you must send a GET request to the operations resource. The following shows how to send such a request.

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

  • operation-name : the name of the operation as returned in the response to the original call to the API
  • project-id : your Google Cloud Platform project ID

HTTP method and URL:

GET https://automl.googleapis.com/v1/ operation-name 

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-10-01T22:13:48.155710Z",
    "updateTime": "2019-10-01T22:13:52.321072Z",
    ...
  },
  "done": true,
  "response": {
    "@type": " resource-type 
",
    "name": " resource-name 
"
  }
}

Canceling an Operation

You can cancel an import or training task using the operation ID.

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

  • operation-name : the full name of your operation. The full name has the format projects/ project-id /locations/us-central1/operations/ operation-id .
  • project-id : your Google Cloud Platform project ID

HTTP method and URL:

POST https://automl.googleapis.com/v1/ operation-name 
:cancel

To send your request, expand one of these options:

You should receive a successful status code (2xx) and an empty response.

Managing models

Getting information about a model

When training is complete, you can get information about the newly created model.

The examples in this section return the basic metadata about a model. To get details about a model's accuracy and readiness, see Evaluating models .

REST

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

  • model-name : the full name of your model. The full name of your model includes your project name and location. A model name looks similar to the following example: projects/ project-id /locations/us-central1/models/ model-id .
  • project-id : your Google Cloud Platform project ID

HTTP method and URL:

GET https://automl.googleapis.com/v1/ model-name 

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/ project-number 
/locations/us-central1/models/ model-id 
",
  "displayName": " model-display-name 
",
  "datasetId": " dataset-id 
",
  "createTime": "2019-10-01T21:51:44.115634Z",
  "deploymentState": "DEPLOYED",
  "updateTime": "2019-10-02T00:22:36.330849Z",
  "translationModelMetadata": {
    "sourceLanguageCode": " source-language 
",
    "targetLanguageCode": " target-language 
"
  }
}

Go

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Go API reference documentation .

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

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 automl 
  
 "cloud.google.com/go/automl/apiv1" 
  
 "cloud.google.com/go/automl/apiv1/automlpb" 
 ) 
 // getModel gets a model. 
 func 
  
 getModel 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
  
 string 
 , 
  
 location 
  
 string 
 , 
  
 modelID 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // location := "us-central1" 
  
 // modelID := "TRL123456789..." 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 automl 
 . 
 NewClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 req 
  
 := 
  
& automlpb 
 . 
 GetModelRequest 
 { 
  
 Name 
 : 
  
 fmt 
 . 
 Sprintf 
 ( 
 "projects/%s/locations/%s/models/%s" 
 , 
  
 projectID 
 , 
  
 location 
 , 
  
 modelID 
 ), 
  
 } 
  
 model 
 , 
  
 err 
  
 := 
  
 client 
 . 
 GetModel 
 ( 
 ctx 
 , 
  
 req 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "GetModel: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 // Retrieve deployment state. 
  
 deploymentState 
  
 := 
  
 "undeployed" 
  
 if 
  
 model 
 . 
 GetDeploymentState 
 () 
  
 == 
  
 automlpb 
 . 
 Model_DEPLOYED 
  
 { 
  
 deploymentState 
  
 = 
  
 "deployed" 
  
 } 
  
 // Display the model information. 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model name: %v\n" 
 , 
  
 model 
 . 
 GetName 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model display name: %v\n" 
 , 
  
 model 
 . 
 GetDisplayName 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model create time:\n" 
 ) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\tseconds: %v\n" 
 , 
  
 model 
 . 
 GetCreateTime 
 (). 
 GetSeconds 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\tnanos: %v\n" 
 , 
  
 model 
 . 
 GetCreateTime 
 (). 
 GetNanos 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model deployment state: %v\n" 
 , 
  
 deploymentState 
 ) 
  
 return 
  
 nil 
 } 
 

Java

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Java API reference documentation .

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

  import 
  
 com.google.cloud.automl.v1. AutoMlClient 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. Model 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. ModelName 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 class 
 GetModel 
  
 { 
  
 static 
  
 void 
  
 getModel 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 modelId 
  
 = 
  
 "YOUR_MODEL_ID" 
 ; 
  
 getModel 
 ( 
 projectId 
 , 
  
 modelId 
 ); 
  
 } 
  
 // Get a model 
  
 static 
  
 void 
  
 getModel 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 modelId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // 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 
  
 ( 
  AutoMlClient 
 
  
 client 
  
 = 
  
  AutoMlClient 
 
 . 
 create 
 ()) 
  
 { 
  
 // Get the full path of the model. 
  
  ModelName 
 
  
 modelFullId 
  
 = 
  
  ModelName 
 
 . 
 of 
 ( 
 projectId 
 , 
  
 "us-central1" 
 , 
  
 modelId 
 ); 
  
  Model 
 
  
 model 
  
 = 
  
 client 
 . 
 getModel 
 ( 
 modelFullId 
 ); 
  
 // Display the model information. 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model name: %s\n" 
 , 
  
 model 
 . 
  getName 
 
 ()); 
  
 // To get the model id, you have to parse it out of the `name` field. As models Ids are 
  
 // required for other methods. 
  
 // Name Format: `projects/{project_id}/locations/{location_id}/models/{model_id}` 
  
 String 
 [] 
  
 names 
  
 = 
  
 model 
 . 
  getName 
 
 (). 
 split 
 ( 
 "/" 
 ); 
  
 String 
  
 retrievedModelId 
  
 = 
  
 names 
 [ 
 names 
 . 
 length 
  
 - 
  
 1 
 ] 
 ; 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model id: %s\n" 
 , 
  
 retrievedModelId 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model display name: %s\n" 
 , 
  
 model 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model create time:" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "\tseconds: %s\n" 
 , 
  
 model 
 . 
  getCreateTime 
 
 (). 
 getSeconds 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "\tnanos: %s\n" 
 , 
  
 model 
 . 
  getCreateTime 
 
 (). 
 getNanos 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model deployment state: %s\n" 
 , 
  
 model 
 . 
  getDeploymentState 
 
 ()); 
  
 } 
  
 } 
 } 
 

Node.js

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Node.js API reference documentation .

To authenticate to AutoML Translation, 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. 
 */ 
 // const projectId = 'YOUR_PROJECT_ID'; 
 // const location = 'us-central1'; 
 // const modelId = 'YOUR_MODEL_ID'; 
 // Imports the Google Cloud AutoML library 
 const 
  
 { 
 AutoMlClient 
 } 
  
 = 
  
 require 
 ( 
 '@google-cloud/automl' 
 ). 
 v1 
 ; 
 // Instantiates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 AutoMlClient 
 (); 
 async 
  
 function 
  
 getModel 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 : 
  
 client 
 . 
 modelPath 
 ( 
 projectId 
 , 
  
 location 
 , 
  
 modelId 
 ), 
  
 }; 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 getModel 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Model name: 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
  
 `Model id: 
 ${ 
  
 response 
 . 
 name 
 . 
 split 
 ( 
 '/' 
 )[ 
 response 
 . 
 name 
 . 
 split 
 ( 
 '/' 
 ). 
 length 
  
 - 
  
 1 
 ] 
  
 } 
 ` 
  
 ); 
  
 console 
 . 
 log 
 ( 
 `Model display name: 
 ${ 
 response 
 . 
 displayName 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Model create time' 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tseconds 
 ${ 
 response 
 . 
 createTime 
 . 
 seconds 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tnanos 
 ${ 
 response 
 . 
 createTime 
 . 
 nanos 
  
 / 
  
 1e9 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Model deployment state: 
 ${ 
 response 
 . 
 deploymentState 
 } 
 ` 
 ); 
 } 
 getModel 
 (); 
 

Python

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Python API reference documentation .

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

  from 
  
 google.cloud 
  
 import 
 automl 
 # TODO(developer): Uncomment and set the following variables 
 # project_id = "YOUR_PROJECT_ID" 
 # model_id = "YOUR_MODEL_ID" 
 client 
 = 
 automl 
 . 
 AutoMlClient 
 () 
 # Get the full path of the model. 
 model_full_id 
 = 
 client 
 . 
 model_path 
 ( 
 project_id 
 , 
 "us-central1" 
 , 
 model_id 
 ) 
 model 
 = 
 client 
 . 
 get_model 
 ( 
 name 
 = 
 model_full_id 
 ) 
 # Retrieve deployment state. 
 if 
 model 
 . 
 deployment_state 
 == 
 automl 
 . 
 Model 
 . 
 DeploymentState 
 . 
 DEPLOYED 
 : 
 deployment_state 
 = 
 "deployed" 
 else 
 : 
 deployment_state 
 = 
 "undeployed" 
 # Display the model information. 
 print 
 ( 
 f 
 "Model name: 
 { 
 model 
 . 
 name 
 } 
 " 
 ) 
 print 
 ( 
 "Model id: 
 {} 
 " 
 . 
 format 
 ( 
 model 
 . 
 name 
 . 
 split 
 ( 
 "/" 
 )[ 
 - 
 1 
 ])) 
 print 
 ( 
 f 
 "Model display name: 
 { 
 model 
 . 
 display_name 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Model create time: 
 { 
 model 
 . 
 create_time 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Model deployment state: 
 { 
 deployment_state 
 } 
 " 
 ) 
 

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.

Listing models

A project can include numerous models. This section describes how to retrieve a list of the available models for a project.

Web UI

To see a list of the available models using the AutoML Translation UI click the lightbulb icon in the left navigation bar.

Models tab listing one model

To see the models for a different project, select the project from the drop-down list in the upper right of the title bar.

REST

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

  • project-id : your Google Cloud Platform project ID

HTTP method and URL:

GET https://automl.googleapis.com/v1/projects/ project-id 
/locations/us-central1/models

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "model": [
    {
      "name": "projects/ project-number 
/locations/us-central1/models/ model-id 
",
      "displayName": " model-display-name 
",
      "datasetId": " dataset-id 
",
      "createTime": "2019-10-01T21:51:44.115634Z",
      "deploymentState": "DEPLOYED",
      "updateTime": "2019-10-02T00:22:36.330849Z",
      "translationModelMetadata": {
        "sourceLanguageCode": " source-language 
",
        "targetLanguageCode": " target-language 
"
      }
    }
  ]
}

Go

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Go API reference documentation .

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

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 automl 
  
 "cloud.google.com/go/automl/apiv1" 
  
 "cloud.google.com/go/automl/apiv1/automlpb" 
  
 "google.golang.org/api/iterator" 
 ) 
 // listModels lists existing models. 
 func 
  
 listModels 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
  
 string 
 , 
  
 location 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // location := "us-central1" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 automl 
 . 
 NewClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 req 
  
 := 
  
& automlpb 
 . 
 ListModelsRequest 
 { 
  
 Parent 
 : 
  
 fmt 
 . 
 Sprintf 
 ( 
 "projects/%s/locations/%s" 
 , 
  
 projectID 
 , 
  
 location 
 ), 
  
 } 
  
 it 
  
 := 
  
 client 
 . 
 ListModels 
 ( 
 ctx 
 , 
  
 req 
 ) 
  
 // Iterate over all results 
  
 for 
  
 { 
  
 model 
 , 
  
 err 
  
 := 
  
 it 
 . 
 Next 
 () 
  
 if 
  
 err 
  
 == 
  
 iterator 
 . 
 Done 
  
 { 
  
 break 
  
 } 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "ListModels.Next: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 // Retrieve deployment state. 
  
 deploymentState 
  
 := 
  
 "undeployed" 
  
 if 
  
 model 
 . 
 GetDeploymentState 
 () 
  
 == 
  
 automlpb 
 . 
 Model_DEPLOYED 
  
 { 
  
 deploymentState 
  
 = 
  
 "deployed" 
  
 } 
  
 // Display the model information. 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model name: %v\n" 
 , 
  
 model 
 . 
 GetName 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model display name: %v\n" 
 , 
  
 model 
 . 
 GetDisplayName 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model create time:\n" 
 ) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\tseconds: %v\n" 
 , 
  
 model 
 . 
 GetCreateTime 
 (). 
 GetSeconds 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\tnanos: %v\n" 
 , 
  
 model 
 . 
 GetCreateTime 
 (). 
 GetNanos 
 ()) 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model deployment state: %v\n" 
 , 
  
 deploymentState 
 ) 
  
 } 
  
 return 
  
 nil 
 } 
 

Java

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Java API reference documentation .

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

  import 
  
 com.google.cloud.automl.v1. AutoMlClient 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. ListModelsRequest 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. LocationName 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. Model 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 class 
 ListModels 
  
 { 
  
 static 
  
 void 
  
 listModels 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 listModels 
 ( 
 projectId 
 ); 
  
 } 
  
 // List the models available in the specified location 
  
 static 
  
 void 
  
 listModels 
 ( 
 String 
  
 projectId 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // 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 
  
 ( 
  AutoMlClient 
 
  
 client 
  
 = 
  
  AutoMlClient 
 
 . 
 create 
 ()) 
  
 { 
  
 // A resource that represents Google Cloud Platform location. 
  
  LocationName 
 
  
 projectLocation 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 projectId 
 , 
  
 "us-central1" 
 ); 
  
 // Create list models request. 
  
  ListModelsRequest 
 
  
 listModelsRequest 
  
 = 
  
  ListModelsRequest 
 
 . 
 newBuilder 
 () 
  
 . 
 setParent 
 ( 
 projectLocation 
 . 
  toString 
 
 ()) 
  
 . 
 setFilter 
 ( 
 "" 
 ) 
  
 . 
 build 
 (); 
  
 // List all the models available in the region by applying filter. 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "List of models:" 
 ); 
  
 for 
  
 ( 
  Model 
 
  
 model 
  
 : 
  
 client 
 . 
 listModels 
 ( 
 listModelsRequest 
 ). 
 iterateAll 
 ()) 
  
 { 
  
 // Display the model information. 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model name: %s\n" 
 , 
  
 model 
 . 
 getName 
 ()); 
  
 // To get the model id, you have to parse it out of the `name` field. As models Ids are 
  
 // required for other methods. 
  
 // Name Format: `projects/{project_id}/locations/{location_id}/models/{model_id}` 
  
 String 
 [] 
  
 names 
  
 = 
  
 model 
 . 
 getName 
 (). 
 split 
 ( 
 "/" 
 ); 
  
 String 
  
 retrievedModelId 
  
 = 
  
 names 
 [ 
 names 
 . 
 length 
  
 - 
  
 1 
 ] 
 ; 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model id: %s\n" 
 , 
  
 retrievedModelId 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model display name: %s\n" 
 , 
  
 model 
 . 
 getDisplayName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model create time:" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "\tseconds: %s\n" 
 , 
  
 model 
 . 
 getCreateTime 
 (). 
 getSeconds 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "\tnanos: %s\n" 
 , 
  
 model 
 . 
 getCreateTime 
 (). 
 getNanos 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Model deployment state: %s\n" 
 , 
  
 model 
 . 
 getDeploymentState 
 ()); 
  
 } 
  
 } 
  
 } 
 } 
 

Node.js

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Node.js API reference documentation .

To authenticate to AutoML Translation, 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. 
 */ 
 // const projectId = 'YOUR_PROJECT_ID'; 
 // const location = 'us-central1'; 
 // Imports the Google Cloud AutoML library 
 const 
  
 { 
 AutoMlClient 
 } 
  
 = 
  
 require 
 ( 
 '@google-cloud/automl' 
 ). 
 v1 
 ; 
 // Instantiates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 AutoMlClient 
 (); 
 async 
  
 function 
  
 listModels 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 : 
  
 client 
 . 
 locationPath 
 ( 
 projectId 
 , 
  
 location 
 ), 
  
 filter 
 : 
  
 'translation_model_metadata:*' 
 , 
  
 }; 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 listModels 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 'List of models:' 
 ); 
  
 for 
  
 ( 
 const 
  
 model 
  
 of 
  
 response 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 `Model name: 
 ${ 
 model 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 ` 
 Model id: 
 ${ 
 model 
 . 
 name 
 . 
 split 
 ( 
 '/' 
 )[ 
 model 
 . 
 name 
 . 
 split 
 ( 
 '/' 
 ). 
 length 
  
 - 
  
 1 
 ] 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Model display name: 
 ${ 
 model 
 . 
 displayName 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Model create time' 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tseconds 
 ${ 
 model 
 . 
 createTime 
 . 
 seconds 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `\tnanos 
 ${ 
 model 
 . 
 createTime 
 . 
 nanos 
  
 / 
  
 1e9 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Model deployment state: 
 ${ 
 model 
 . 
 deploymentState 
 } 
 ` 
 ); 
  
 } 
 } 
 listModels 
 (); 
 

Python

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Python API reference documentation .

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

  from 
  
 google.cloud 
  
 import 
 automl 
 # TODO(developer): Uncomment and set the following variables 
 # project_id = "YOUR_PROJECT_ID" 
 client 
 = 
 automl 
 . 
 AutoMlClient 
 () 
 # A resource that represents Google Cloud Platform location. 
 project_location 
 = 
 f 
 "projects/ 
 { 
 project_id 
 } 
 /locations/us-central1" 
 request 
 = 
 automl 
 . 
 ListModelsRequest 
 ( 
 parent 
 = 
 project_location 
 , 
 filter 
 = 
 "" 
 ) 
 response 
 = 
 client 
 . 
 list_models 
 ( 
 request 
 = 
 request 
 ) 
 print 
 ( 
 "List of models:" 
 ) 
 for 
 model 
 in 
 response 
 : 
 # Display the model information. 
 if 
 model 
 . 
 deployment_state 
 == 
 automl 
 . 
 Model 
 . 
 DeploymentState 
 . 
 DEPLOYED 
 : 
 deployment_state 
 = 
 "deployed" 
 else 
 : 
 deployment_state 
 = 
 "undeployed" 
 print 
 ( 
 f 
 "Model name: 
 { 
 model 
 . 
 name 
 } 
 " 
 ) 
 print 
 ( 
 "Model id: 
 {} 
 " 
 . 
 format 
 ( 
 model 
 . 
 name 
 . 
 split 
 ( 
 "/" 
 )[ 
 - 
 1 
 ])) 
 print 
 ( 
 f 
 "Model display name: 
 { 
 model 
 . 
 display_name 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Model create time: 
 { 
 model 
 . 
 create_time 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Model deployment state: 
 { 
 deployment_state 
 } 
 " 
 ) 
 

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.

Deleting a model

The following example deletes a model.

Web UI

  1. In the AutoML Translation UI , click the light bulb icon in the left navigation menu to display the list of available models.

    Models tab listing one model

  2. Click the three-dot menu at the far right of the row you want to delete and select Delete model.

  3. Click Deletein the confirmation dialog box.

REST

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

  • model-name : the full name of your model. The full name of your model includes your project name and location. A model name looks similar to the following example: projects/ project-id /locations/us-central1/models/ model-id .
  • project-id : your Google Cloud Platform project ID

HTTP method and URL:

DELETE https://automl.googleapis.com/v1/ model-name 

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/ project-number 
/locations/us-central1/operations/ operation-id 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata",
    "progressPercentage": 100,
    "createTime": "2018-04-27T02:33:02.479200Z",
    "updateTime": "2018-04-27T02:35:17.309060Z"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Go

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Go API reference documentation .

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

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 automl 
  
 "cloud.google.com/go/automl/apiv1" 
  
 "cloud.google.com/go/automl/apiv1/automlpb" 
 ) 
 // deleteModel deletes a model. 
 func 
  
 deleteModel 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
  
 string 
 , 
  
 location 
  
 string 
 , 
  
 modelID 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // location := "us-central1" 
  
 // modelID := "TRL123456789..." 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 automl 
 . 
 NewClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 req 
  
 := 
  
& automlpb 
 . 
 DeleteModelRequest 
 { 
  
 Name 
 : 
  
 fmt 
 . 
 Sprintf 
 ( 
 "projects/%s/locations/%s/models/%s" 
 , 
  
 projectID 
 , 
  
 location 
 , 
  
 modelID 
 ), 
  
 } 
  
 op 
 , 
  
 err 
  
 := 
  
 client 
 . 
 DeleteModel 
 ( 
 ctx 
 , 
  
 req 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "DeleteModel: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Processing operation name: %q\n" 
 , 
  
 op 
 . 
 Name 
 ()) 
  
 if 
  
 err 
  
 := 
  
 op 
 . 
 Wait 
 ( 
 ctx 
 ); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "Wait: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Model deleted.\n" 
 ) 
  
 return 
  
 nil 
 } 
 

Java

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Java API reference documentation .

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

  import 
  
 com.google.cloud.automl.v1. AutoMlClient 
 
 ; 
 import 
  
 com.google.cloud.automl.v1. ModelName 
 
 ; 
 import 
  
 com.google.protobuf. Empty 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 class 
 DeleteModel 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 modelId 
  
 = 
  
 "YOUR_MODEL_ID" 
 ; 
  
 deleteModel 
 ( 
 projectId 
 , 
  
 modelId 
 ); 
  
 } 
  
 // Delete a model 
  
 static 
  
 void 
  
 deleteModel 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 modelId 
 ) 
  
 throws 
  
 IOException 
 , 
  
 ExecutionException 
 , 
  
 InterruptedException 
  
 { 
  
 // 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 
  
 ( 
  AutoMlClient 
 
  
 client 
  
 = 
  
  AutoMlClient 
 
 . 
 create 
 ()) 
  
 { 
  
 // Get the full path of the model. 
  
  ModelName 
 
  
 modelFullId 
  
 = 
  
  ModelName 
 
 . 
 of 
 ( 
 projectId 
 , 
  
 "us-central1" 
 , 
  
 modelId 
 ); 
  
 // Delete a model. 
  
  Empty 
 
  
 response 
  
 = 
  
 client 
 . 
 deleteModelAsync 
 ( 
 modelFullId 
 ). 
 get 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model deletion started..." 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 String 
 . 
 format 
 ( 
 "Model deleted. %s" 
 , 
  
 response 
 )); 
  
 } 
  
 } 
 } 
 

Node.js

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Node.js API reference documentation .

To authenticate to AutoML Translation, 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. 
 */ 
 // const projectId = 'YOUR_PROJECT_ID'; 
 // const location = 'us-central1'; 
 // const modelId = 'YOUR_MODEL_ID'; 
 // Imports the Google Cloud AutoML library 
 const 
  
 { 
 AutoMlClient 
 } 
  
 = 
  
 require 
 ( 
 '@google-cloud/automl' 
 ). 
 v1 
 ; 
 // Instantiates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 AutoMlClient 
 (); 
 async 
  
 function 
  
 deleteModel 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 : 
  
 client 
 . 
 modelPath 
 ( 
 projectId 
 , 
  
 location 
 , 
  
 modelId 
 ), 
  
 }; 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 deleteModel 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Model deleted: 
 ${ 
 response 
 } 
 ` 
 ); 
 } 
 deleteModel 
 (); 
 

Python

To learn how to install and use the client library for AutoML Translation, see AutoML Translation client libraries . For more information, see the AutoML Translation Python API reference documentation .

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

  from 
  
 google.cloud 
  
 import 
 automl 
 # TODO(developer): Uncomment and set the following variables 
 # project_id = "YOUR_PROJECT_ID" 
 # model_id = "YOUR_MODEL_ID" 
 client 
 = 
 automl 
 . 
 AutoMlClient 
 () 
 # Get the full path of the model. 
 model_full_id 
 = 
 client 
 . 
 model_path 
 ( 
 project_id 
 , 
 "us-central1" 
 , 
 model_id 
 ) 
 response 
 = 
 client 
 . 
 delete_model 
 ( 
 name 
 = 
 model_full_id 
 ) 
 print 
 ( 
 f 
 "Model deleted. 
 { 
 response 
 . 
 result 
 () 
 } 
 " 
 ) 
 

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.

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