Create a specific version of a user-managed notebooks instance

Create a specific version of a user-managed notebooks instance

Vertex AI Workbench user-managed notebooks is deprecated. On January 30, 2025, support for user-managed notebooks will end and the ability to create user-managed notebooks instances will be removed. Existing instances will continue to function but patches, updates, and upgrades won't be available. To continue using Vertex AI Workbench, we recommend that you migrate your user-managed notebooks instances to Vertex AI Workbench instances.

This page describes how you can create a user-managed notebooks instance based on a specific Deep Learning VM Images version.

Why you might want to create a specific version

To ensure that your user-managed notebooks instance has software that is compatible with your code or application, you might want to create a specific version.

User-managed notebooks instances are created by using Deep Learning VM images. Deep Learning VM images are updated frequently, and specific versions of preinstalled software and packages vary from version to version.

To learn more about specific Deep Learning VM versions, see the Deep Learning VM release notes.

After you create a specific version of a user-managed notebooks instance, you can upgrade it. Upgrading the instance updates the preinstalled software and packages. For more information, see Upgrade a user-managed notebooks instance's environment.

Before you begin

Before you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Notebooks API.

   Enable the API

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

7. Enable the Notebooks API.

   Enable the API

8. If you plan to use GPUs with your user-managed notebooks instance, check the quotas page in the Google Cloud console to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page, or you require additional GPU quota, you can request a quota increase. See Requesting an increase in quota on the Compute Engine Resource quotas page.

Required roles

If you created the project, you have the Owner (roles/owner) IAM role on the project, which includes all required permissions. Skip this section and start creating your user-managed notebooks instance. If you didn't create the project yourself, continue in this section.

To ensure that your user account has the necessary permissions to create a Vertex AI Workbench user-managed notebooks instance, ask your administrator to grant your user account the following IAM roles on the project:

• Notebooks Admin (roles/notebooks.admin)
• Service Account User (roles/iam.serviceAccountUser)

For more information about granting roles, see Manage access to projects, folders, and organizations.

Your administrator might also be able to give your user account the required permissions through custom roles or other predefined roles.

Find the specific version that you want

To create a user-managed notebooks instance based on a specific Deep Learning VM version, you must know the image name of the specific Deep Learning VM version that you want to use.

Each release of Deep Learning VM includes updates to many different images, and each image in the release has its own image name.

To find the specific image name that you want:

1. Find the Deep Learning VM release number that you want to get image names for. Release numbers are included in the Deep Learning VM release notes. Release numbers are in the form of an M followed by the number of the release, for example, M79.

2. To list the image names for a specific Deep Learning VM release, run the following command.

   gcloud compute images list --project="deeplearning-platform-release" \
       --format="value(name)" \
       --filter="labels.release=RELEASE_NUMBER" \
       --show-deprecated

   Replace RELEASE_NUMBER with a Deep Learning VM release number, such as M79.

3. Find the image name that you want to use.

Create a specific version from the command line

To create a specific version of a user-managed notebooks instance from the command line, complete the following steps:

1. Run the following gcloud notebooks command:

   gcloud notebooks instances create INSTANCE_NAME \
       --vm-image-project="deeplearning-platform-release" \
       --vm-image-name=VM_IMAGE_NAME \
       --machine-type=MACHINE_TYPE \
       --location=LOCATION

   Replace the following:

   • INSTANCE_NAME: the name of your new instance
   • VM_IMAGE_NAME: the image name that you want to use to create your instance
   • MACHINE_TYPE: the machine type of your instance's VM
   • LOCATION: the Google Cloud location where you want your new instance to be

2. Access your instance from the Google Cloud console.

What's next

• Learn more about upgrading user-managed notebooks instances to ensure that your instance upgrades only when you are ready.

• Install dependencies on your new user-managed notebooks instance.

• Learn more about Deep Learning VM instances in the Deep Learning VM documentation.

• Learn about monitoring the health status of your user-managed notebooks instance.