Manage features through metadata

Manage features through metadata

This page describes how to manage some Vertex AI Workbench instance features by modifying the instance's metadata key-value pairs.

Metadata keys

For information about features and their respective metadata keys, see the following table.

Feature	Description	Metadata key	Accepted values and defaults
Enables Cloud Storage FUSE on a container image	Mounts /dev/fuse onto the container and enables gcsfuse for use on the container.	container-allow-fuse	true: Enables Cloud Storage FUSE. false (default): Doesn't enable Cloud Storage FUSE.
nbconvert	Lets you export and download notebooks as a different file type.	notebook-disable-nbconvert	true: Turns off nbconvert. false (default): Enables nbconvert.
Delete to trash	Uses the operating system's trash behavior when deleting from JupyterLab.	notebook-enable-delete-to-trash	true: Enables deleting to the trash. false (default): Uses the default JupyterLab behavior.
Dataproc	Enables access to Dataproc kernels. For more information, see Create a Dataproc-enabled instance.	disable-mixer	true: Turns off access to Dataproc kernels. false (default): Enables access to Dataproc kernels.
Idle shutdown	Enables idle shutdown. For more information, see Idle shutdown.	idle-timeout-seconds	An integer representing the idle time in seconds. The default value is 10800 seconds (180 minutes).
Scheduled OS patches	Schedules automatic OS updates of the instance. This enables Debian's unattended upgrade service and only applies to VM-based images.	install-unattended-upgrades	true: Turns on automatic OS updates. false (default): Turns off automatic OS updates.
Custom Jupyter user	Specifies the name of the default Jupyter user. This setting determines the name of the folder for your notebooks. For example, instead of the default /home/jupyter/ directory, you can change the directory to /home/CUSTOM_NAME. This metadata key doesn't affect access to the instance.	jupyter-user	A string. The default value is jupyter.
File downloading	Lets you download files from JupyterLab.	notebook-disable-downloads	true: Turns off file downloading. false (default): Enables file downloading.
Root access	Enables root access.	notebook-disable-root	true: Turns off root access. false (default): Enables root access.
Terminal access	Enables terminal access.	notebook-disable-terminal	true: Turns off terminal access. false (default): Enables terminal access.
Scheduled upgrades	Schedules automatic upgrades of the instance.	notebook-upgrade-schedule	The weekly or monthly schedule that you set, in unix-cron format, for example, 00 19 * * MON means weekly on Monday, at 1900 hours Greenwich Mean Time (GMT). This feature is off by default.
Post-startup script	Runs a custom script after startup.	post-startup-script	The URI of a post-startup script in Cloud Storage, for example: gs://bucket/hello.sh. This feature is off by default.
Post-startup script behavior	Defines when and how the post-startup script runs.	post-startup-script-behavior	run_once (default): Runs the post-startup script once after instance creation or upgrade. run_every_start: Runs the post-startup script after every start. download_and_run_every_start: Redownloads the post-startup script from its source then runs the script after every start.
Report event health	Checks health every 30 seconds for VM metrics.	report-event-health	true (default): Enables event health reporting. false: Turns off event health reporting.
Enable JupyterLab 4 preview	Enables JupyterLab 4 (Preview) on your instance. For more information, see JupyterLab 4 preview.	enable-jupyterlab4-preview	true: Enables JupyterLab 4. false (default): Enables JupyterLab 3.

Metadata managed by Compute Engine

Some of the metadata keys are predefined by Compute Engine. For more information, see Predefined metadata keys.

Protected metadata keys

Some metadata keys are reserved for system use only. If you assign values to these metadata keys, the new values will be overwritten by the system values.

Reserved metadata keys include and are not limited to:

• data-disk-uri
• enable-oslogin
• framework
• notebooks-api
• notebooks-api-version
• nvidia-driver-gcs-path
• proxy-url
• restriction
• shutdown-script
• title
• version

Create an instance with specific metadata

You can create a Vertex AI Workbench instance with specific metadata by using the Google Cloud console, the Google Cloud CLI, Terraform, or the Notebooks API.

Console

When you create a Vertex AI Workbench instance, you can add metadata in the Environment section of Advanced options.

gcloud

When you create a Vertex AI Workbench instance, you can add metadata by using the following command:

gcloud workbench instances create INSTANCE_NAME --metadata=KEY=VALUE

Terraform

To add metadata, create the resource with metadata key-value pairs.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

resource "google_workbench_instance" "default" {
  name     = "workbench-instance-example"
  location = "us-central1-a"

  gce_setup {
    machine_type = "n1-standard-1"
    vm_image {
      project = "cloud-notebooks-managed"
      family  = "workbench-instances"
    }
    metadata = {
      key = "value"
    }
  }
}

Notebooks API

Use the instances.create method with metadata values to manage the corresponding features.

Update an instance's metadata

You can update the metadata of a Vertex AI Workbench instance by using the Google Cloud console, the Google Cloud CLI, Terraform, or the Notebooks API.

Console

You can update the metadata of a Vertex AI Workbench instance in the Software and security section on its Details page.

gcloud

You can update the metadata on a Vertex AI Workbench instance by using the following command:

gcloud workbench instances update INSTANCE_NAME --metadata=KEY=VALUE

Terraform

You can change the metadata key-value pairs to manage the corresponding features on Vertex AI Workbench instances.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

resource "google_workbench_instance" "default" {
  name     = "workbench-instance-example"
  location = "us-central1-a"

  gce_setup {
    machine_type = "n1-standard-1"
    vm_image {
      project = "cloud-notebooks-managed"
      family  = "workbench-instances"
    }
    metadata = {
      key = "updated_value"
    }
  }
}

Notebooks API

Use the instances.patch method with metadata values and gce_setup.metadata in the updateMask to manage the corresponding features.

Remove metadata from an instance

You can remove metadata from a Vertex AI Workbench instance by using the Google Cloud console, the Google Cloud CLI, Terraform, or the Notebooks API.

Console

You can remove metadata from a Vertex AI Workbench instance in the Software and security section on its Details page.

gcloud

You can remove metadata from a Vertex AI Workbench instance by using the following command:

gcloud workbench instances update INSTANCE_NAME --metadata=KEY

Terraform

You can remove metadata key-value pairs to manage the corresponding features of a Vertex AI Workbench instance.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

resource "google_workbench_instance" "default" {
  name     = "workbench-instance-example"
  location = "us-central1-a"

  gce_setup {
    machine_type = "n1-standard-1"
    vm_image {
      project = "cloud-notebooks-managed"
      family  = "workbench-instances"
    }
    metadata = {
    }
  }
}

Notebooks API

Use the instances.patch method with the metadata value set to an empty string and gce_setup.metadata in the updateMask to remove the corresponding feature.