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
In the Google Cloud console, go to the BigQuery page.
In the navigation panel, in the Resources section, click your project name.
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: .
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
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.
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.
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.
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.
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: