Listing models

Overview

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

  • Using the 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 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 in the BigQuery documentation.

Listing models

To list models in a dataset:

Console

  1. Go to the BigQuery page in the Cloud Console.

    Go to the BigQuery page

  2. In the navigation panel, in the Resources section, click your project name.

  3. As you expand each of the datasets in a project, models are listed along with the other BigQuery resources in the datasets. Models are indicated by the model icon: model icon .

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]

Where:

  • [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 

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: %v", 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 

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 


// 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 


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