List models

This page shows you how to list BigQuery ML models in a dataset. You can list BigQuery ML models by:

• Using the Google Cloud console.
• Using the bq ls command in the bq command-line tool.
• Calling the models.list API method directly or by using the client libraries.

Required permissions

To list models in a dataset, 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.list permissions. If you are granted bigquery.models.list permissions at the project level, you can list models in any dataset in the project. The following predefined, project-level IAM roles include bigquery.models.list permissions:

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

For more information on IAM roles and permissions in BigQuery ML, see Access control. For more information on dataset-level roles, see Basic roles for datasets.

List models

To list models in a dataset:

Console

1. In the Google Cloud console, go to the BigQuery page.

   Go to the BigQuery page

2. In the left pane, click Explorer:

   

   If you don't see the left pane, click Expand left pane to open the pane.

3. In the Explorer pane, expand your project and click Datasets.

4. Click the dataset that contains your model.

5. Click the Models tab.

bq

Issue the bq ls command with the --models or -m flag. The --format flag can be used to control the output. If you are listing models in a project other than your default project, add the project ID to the dataset in the following format: [PROJECT_ID]:[DATASET].

bq ls -m --format=pretty PROJECT_ID:DATASET

Replace the following:

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

The command output looks like the following when the --format=pretty flag is used. --format=pretty produces formatted table output. The Model Type column displays the model type, for example, KMEANS.

+-------------------------+------------+--------+-----------------+
|           Id            | Model Type | Labels |  Creation Time  |
+-------------------------+------------+--------+-----------------+
| mymodel                 | KMEANS     |        | 03 May 03:02:27 |
+-------------------------+------------+--------+-----------------+

Examples:

Enter the following command to list models in dataset mydataset in your default project.

bq ls --models --format=pretty mydataset

Enter the following command to list models in dataset mydataset in myotherproject. This command uses the -m shortcut to list models.

bq ls -m --format=pretty myotherproject:mydataset

API

To list models by using the API, call the models.list method and provide the projectId and datasetId.

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"
	"google.golang.org/api/iterator"
)

// listModels demonstrates iterating through the collection of ML models in a dataset
// and printing a basic identifier of the model.
func listModels(w io.Writer, projectID, datasetID string) error {
	// projectID := "my-project-id"
	// datasetID := "mydataset"
	ctx := context.Background()

	client, err := bigquery.NewClient(ctx, projectID)
	if err != nil {
		return fmt.Errorf("bigquery.NewClient: %w", err)
	}
	defer client.Close()

	fmt.Fprintf(w, "Models contained in dataset %q\n", datasetID)
	it := client.Dataset(datasetID).Models(ctx)
	for {
		m, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return err
		}
		fmt.Fprintf(w, "Model: %s\n", m.FullyQualifiedName())
	}
	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.api.gax.paging.Page;
import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQuery.ModelListOption;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Model;

public class ListModels {

  public static void runListModels() {
    // TODO(developer): Replace these variables before running the sample.
    String datasetName = "MY_DATASET_NAME";
    listModels(datasetName);
  }

  public static void listModels(String datasetName) {
    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();

      Page<Model> models = bigquery.listModels(datasetName, ModelListOption.pageSize(100));
      if (models == null) {
        System.out.println("Dataset does not contain any models.");
        return;
      }
      models
          .iterateAll()
          .forEach(model -> System.out.printf("Success! Model ID: %s", model.getModelId()));
    } catch (BigQueryException e) {
      System.out.println("Models not listed in dataset due to error: \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 listModels() {
  // Lists all existing models in the dataset.

  /**
   * TODO(developer): Uncomment the following lines before running the sample.
   */
  // const datasetId = "my_dataset";

  const dataset = bigquery.dataset(datasetId);

  dataset.getModels().then(data => {
    const models = data[0];
    console.log('Models:');
    models.forEach(model => console.log(model.metadata));
  });
}

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 dataset_id to the ID of the dataset that contains
#                  the models you are listing.
# dataset_id = 'your-project.your_dataset'

models = client.list_models(dataset_id)  # Make an API request.

print("Models contained in '{}':".format(dataset_id))
for model in models:
    full_model_id = "{}.{}.{}".format(
        model.project, model.dataset_id, model.model_id
    )
    friendly_name = model.friendly_name
    print("{}: friendly_name='{}'".format(full_model_id, friendly_name))

What's next

• For an overview of BigQuery ML, see Introduction to BigQuery ML.
• To get started using BigQuery ML, see Create machine learning models in BigQuery ML.
• To learn more about working with models, see:
  • Get model metadata
  • Update model metadata
  • Manage models
  • Delete models