# 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`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/models/get) API method directly or by using the client libraries > [!NOTE] > **Note:** Getting information about models by querying the `INFORMATION_SCHEMA` views is unsupported. ## Required permissions To get model metadata, you must be assigned the [`READER`](https://docs.cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_dataset) 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](https://docs.cloud.google.com/bigquery/docs/access-control). ## Get model metadata To get metadata about models: ### Console 1. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 2. In the **Explorer** pane, expand the project, click **Datasets**, and then select the dataset. 3. Click the **Models** tab, and then click a model name to select the model. 4. Click the **Details** tab. 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`](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#global_flags) 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`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/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](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries). For more information, see the [BigQuery Go API reference documentation](https://godoc.org/cloud.google.com/go/bigquery). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs). 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](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries). For more information, see the [BigQuery Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/overview). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs). import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryException.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryOptions.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.Model.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ModelId.html; 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. https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html bigquery = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryOptions.html.getDefaultInstance().getService(); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ModelId.html modelId = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ModelId.html.of(datasetName, modelName); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.Model.html model = bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html#com_google_cloud_bigquery_BigQuery_getModel_com_google_cloud_bigquery_ModelId_com_google_cloud_bigquery_BigQuery_ModelOption____(modelId); System.out.println("Model: " + model.getDescription()); System.out.println("Successfully retrieved model"); } catch (https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryException.html 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](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries). For more information, see the [BigQuery Node.js API reference documentation](https://googleapis.dev/nodejs/bigquery/latest/index.html). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs). // Import the Google Cloud client library const {BigQuery} = require('https://docs.cloud.google.com/nodejs/docs/reference/bigquery/latest/overview.html'); const bigquery = new https://docs.cloud.google.com/nodejs/docs/reference/bigquery/latest/bigquery/bigquery.html(); 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.https://docs.cloud.google.com/nodejs/docs/reference/bigquery/latest/bigquery/dataset.html(modelId).get(); console.log('Model:'); console.log(https://docs.cloud.google.com/nodejs/docs/reference/bigquery/latest/bigquery/dataset.html.metadata.modelReference); }
### Python Before trying this sample, follow the Python setup instructions in the [BigQuery quickstart using client libraries](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries). For more information, see the [BigQuery Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/bigquery/latest). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs). from google.cloud import https://docs.cloud.google.com/python/docs/reference/bigquery/latest # Construct a BigQuery client object. client = https://docs.cloud.google.com/python/docs/reference/bigquery/latest.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html() # TODO(developer): Set model_id to the ID of the model to fetch. # model_id = 'your-project.your_dataset.your_model' model = client.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html#google_cloud_bigquery_client_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) )
## What's next - For an overview of BigQuery ML, see [Introduction to BigQuery ML](https://docs.cloud.google.com/bigquery/docs/bqml-introduction). - To get started using BigQuery ML, see [Create machine learning models in BigQuery ML](https://docs.cloud.google.com/bigquery/docs/create-machine-learning-model). - To learn more about working with models, see: - [List models](https://docs.cloud.google.com/bigquery/docs/listing-models) - [Update model metadata](https://docs.cloud.google.com/bigquery/docs/updating-model-metadata) - [Manage models](https://docs.cloud.google.com/bigquery/docs/managing-models) - [Delete models](https://docs.cloud.google.com/bigquery/docs/deleting-models)