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    Get model metadata

    This page shows you how to get information or metadata about BigQuery ML models. You can get model metadata by:

    • Using the Google Cloud console
    • Using the bq show CLI command
    • Calling the models.get API method directly or by using the client libraries

    Required permissions

    To get model metadata, you must be assigned the READER role on the dataset, or you must be assigned a project-level Identity and Access Management (IAM) role that includes bigquery.models.getMetadata permissions. If you are granted bigquery.models.getMetadata permissions at the project level, you can get metadata on models in any dataset in the project. The following predefined, project-level IAM roles include bigquery.models.getMetadata permissions:

    • bigquery.dataViewer
    • bigquery.dataEditor
    • bigquery.dataOwner
    • bigquery.metadataViewer
    • bigquery.admin

    For more information on IAM roles and permissions in BigQuery ML, see Access control .

    Get model metadata

    To get metadata about models:

    Console

    1. In the Explorerpane, expand your project and then expand a dataset.

    2. Expand the Modelsfolder in the dataset, and then click a model name to select the model.

    3. Click the Detailstab. This tab displays the model's metadata, including the description, labels, model type, and training options.

    bq

    Issue the bq show command with the --model or -m flag to display model metadata. The --format flag can be used to control the output.

    To see only the feature columns for your model, use the --schema flag with the --model flag. When you use the --schema flag, --format must be set to either json or prettyjson .

    If you are getting information about a model in a project other than your default project, add the project ID to the dataset in the following format: [PROJECT_ID]:[DATASET] .

    bq show --model --format=prettyjson PROJECT_ID 
: DATASET 
. MODEL

    Replace the following:

    • PROJECT_ID is your project ID.
    • DATASET is the name of the dataset.
    • MODEL is the name of the model.

    The command output looks like the following when the --format=pretty flag is used. To see full details, use the --format=prettyjson format. The sample output shows metadata for a logistic regression model.

    +--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+
|      Id      |     Model Type      |   Feature Columns   |       Label Columns       | Labels |  Creation Time  | Expiration Time |
+--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+
| sample_model | LOGISTIC_REGRESSION | |- column1: string  | |- label_column: int64    |        | 03 May 23:14:42 |                 |
|              |                     | |- column2: bool    |                           |        |                 |                 |
|              |                     | |- column3: string  |                           |        |                 |                 |
|              |                     | |- column4: int64   |                           |        |                 |                 |
+--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+

    Examples:

    Enter the following command to display all information about mymodel in mydataset . mydataset is in your default project.

     bq show --model --format=prettyjson mydataset.mymodel

    Enter the following command to display all information about mymodel in mydataset . mydataset is in myotherproject , not your default project.

     bq show --model --format=prettyjson myotherproject:mydataset.mymodel

    Enter the following command to display only the feature columns for mymodel in mydataset . mydataset is in myotherproject , not your default project.

     bq show --model --schema --format=prettyjson \
myotherproject:mydataset.mymodel

    API

    To get model metadata by using the API, call the models.get method and provide the projectId , datasetId , and modelId .

    Go

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

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

      import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 "cloud.google.com/go/bigquery" 
 ) 
 // printModelInfo demonstrates fetching metadata about a BigQuery ML model and printing some of 
 // it to an io.Writer. 
 func 
  
 printModelInfo 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
 , 
  
 datasetID 
 , 
  
 modelID 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "my-project-id" 
  
 // datasetID := "mydataset" 
  
 // modelID := "mymodel" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 bigquery 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
 projectID 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "bigquery.NewClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 meta 
 , 
  
 err 
  
 := 
  
 client 
 . 
 Dataset 
 ( 
 datasetID 
 ). 
 Model 
 ( 
 modelID 
 ). 
 Metadata 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "couldn't retrieve metadata: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Got model '%q' with friendly name '%q'\n" 
 , 
  
 modelID 
 , 
  
 meta 
 . 
 Name 
 ) 
  
 return 
  
 nil 
 }

    Java

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

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

      import 
  
 com.google.cloud.bigquery. BigQuery 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryException 
 
 ; 
 import 
  
 com.google.cloud.bigquery. BigQueryOptions 
 
 ; 
 import 
  
 com.google.cloud.bigquery. Model 
 
 ; 
 import 
  
 com.google.cloud.bigquery. ModelId 
 
 ; 
 public 
  
 class 
 GetModel 
  
 { 
  
 public 
  
 static 
  
 void 
  
 runGetModel 
 () 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 datasetName 
  
 = 
  
 "MY_DATASET_NAME" 
 ; 
  
 String 
  
 modelName 
  
 = 
  
 "MY_MODEL_ID" 
 ; 
  
 getModel 
 ( 
 datasetName 
 , 
  
 modelName 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 getModel 
 ( 
 String 
  
 datasetName 
 , 
  
 String 
  
 modelName 
 ) 
  
 { 
  
 try 
  
 { 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. 
  
  BigQuery 
 
  
 bigquery 
  
 = 
  
  BigQueryOptions 
 
 . 
 getDefaultInstance 
 (). 
 getService 
 (); 
  
  ModelId 
 
  
 modelId 
  
 = 
  
  ModelId 
 
 . 
 of 
 ( 
 datasetName 
 , 
  
 modelName 
 ); 
  
  Model 
 
  
 model 
  
 = 
  
 bigquery 
 . 
  getModel 
 
 ( 
 modelId 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model: " 
  
 + 
  
 model 
 . 
 getDescription 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Successfully retrieved model" 
 ); 
  
 } 
  
 catch 
  
 ( 
  BigQueryException 
 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Cannot retrieve model \n" 
  
 + 
  
 e 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 }

    Node.js

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

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

      // Import the Google Cloud client library 
 const 
  
 { 
 BigQuery 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/bigquery 
' 
 ); 
 const 
  
 bigquery 
  
 = 
  
 new 
  
  BigQuery 
 
 (); 
 async 
  
 function 
  
 getModel 
 () 
  
 { 
  
 // Retrieves model named "my_existing_model" in "my_dataset". 
  
 /** 
 * TODO(developer): Uncomment the following lines before running the sample 
 */ 
  
 // const datasetId = "my_dataset"; 
  
 // const modelId = "my_existing_model"; 
  
 const 
  
 dataset 
  
 = 
  
 bigquery 
 . 
 dataset 
 ( 
 datasetId 
 ); 
  
 const 
  
 [ 
 model 
 ] 
  
 = 
  
 await 
  
 dataset 
 . 
  model 
 
 ( 
 modelId 
 ). 
 get 
 (); 
  
 console 
 . 
 log 
 ( 
 'Model:' 
 ); 
  
 console 
 . 
 log 
 ( 
  model 
 
 . 
 metadata 
 . 
 modelReference 
 ); 
 }

    Python

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

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries .

      from 
  
 google.cloud 
  
 import 
  bigquery 
 
 # Construct a BigQuery client object. 
 client 
 = 
  bigquery 
 
 . 
  Client 
 
 () 
 # TODO(developer): Set model_id to the ID of the model to fetch. 
 # model_id = 'your-project.your_dataset.your_model' 
 model 
 = 
 client 
 . 
  get_model 
 
 ( 
 model_id 
 ) 
 # Make an API request. 
 full_model_id 
 = 
 " 
 {} 
 . 
 {} 
 . 
 {} 
 " 
 . 
 format 
 ( 
 model 
 . 
 project 
 , 
 model 
 . 
 dataset_id 
 , 
 model 
 . 
 model_id 
 ) 
 friendly_name 
 = 
 model 
 . 
 friendly_name 
 print 
 ( 
 "Got model ' 
 {} 
 ' with friendly_name ' 
 {} 
 '." 
 . 
 format 
 ( 
 full_model_id 
 , 
 friendly_name 
 ) 
 )

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