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).
Guest attributes

Enables guest attributes. Required for running idle shutdown.

For more information, see Requirements for running idle shutdown.

enable-guest-attributes
  • true (default): Enables guest attributes.
  • false: Turns off guest attributes.
    		•
    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.

    The Add metadata button in the Environment section

    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

    To update the metadata of a Vertex AI Workbench instance, do the following:

    1. In the Google Cloud console, go to the Instances page.

      Go to Instances

    2. In the list of instances, click the name of the instance that you want to update.

    3. On the Instance details page, click Software and security.

    4. In the Metadata section, update the metadata key-value pairs that you want to change.

    5. Click Submit.

    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

    To remove metadata from a Vertex AI Workbench instance, do the following:

    1. In the Google Cloud console, go to the Instances page.

      Go to Instances

    2. In the list of instances, click the name of the instance that you want to modify.

    3. On the Instance details page, click Software and security.

    4. In the Metadata section, to the right of a key-value pair that you want to delete, click  Delete.

    5. Click Submit.

    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.