Overview
This page shows you how to get information or metadata about BigQuery ML models. You can get model metadata by:
- Using the GCP Console
- Using the
bq showCLI command - Calling the
models.getAPI 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.dataViewerbigquery.dataEditorbigquery.dataOwnerbigquery.metadataViewerbigquery.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
In the navigation panel, in the Resources section, expand your project and select a dataset.
Click the model name.
Below the query editor box, click Details. This page displays the model's metadata including the description, labels, details, and training options.
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.
What's next
- For an overview of BigQuery ML, see Introduction to BigQuery ML.
- To get started using BigQuery ML, see Getting started with BigQuery ML using the web UI or Getting started with BigQuery ML using Cloud Datalab.
- To learn more about working with models, see:
