Getting model metadata

Overview

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

  • Using the 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 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. For more information on dataset-level roles, see Primitive roles for datasets in the BigQuery documentation.

Getting model metadata

To get metadata about models:

Console

  1. In the navigation panel, in the Resources section, expand your project and select a dataset.

  2. Click the model name.

  3. Below the query editor box, click Details. This page displays the model's metadata including the description, labels, details, and training options.

    Model metadata in the Google Cloud Console

CLI

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]

Where:

  • [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

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: %v", err)
	}
	defer client.Close()

	meta, err := client.Dataset(datasetID).Model(modelID).Metadata(ctx)
	if err != nil {
		return fmt.Errorf("Metadata: %v", err)
	}
	fmt.Fprintf(w, "Got model '%q' with friendly name '%q'\n", modelID, meta.Name)
	return nil
}

Java

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

// 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


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)
)

What's next