Vertex AI Workbench managed notebooks isdeprecated. On April 14, 2025, support for managed notebooks ended and the ability to create managed notebooks instances was removed. Existing instances will continue to function until March 30, 2026, but patches, updates, and upgrades won't be available. To continue using Vertex AI Workbench, we recommend that youmigrate your managed notebooks instances to Vertex AI Workbench instances.

This page shows you how to usethe Vertex AI Workbench managed notebooks executorto run a Python notebook file on an hourly schedule.

Before you begin

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.

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

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

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

Verify that billing is enabled for your Google Cloud project.

Enable the Notebooks and Vertex AI APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the APIs

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

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

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

Verify that billing is enabled for your Google Cloud project.

Enable the Notebooks and Vertex AI APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the APIs

Required roles

To ensure that your instance's service account has the necessary permissions to interact with the Vertex AI Workbench executor, ask your administrator to grant your instance's service account the following IAM roles on the project:

• Notebooks Viewer (roles/notebooks.viewer)
• Vertex AI User (roles/aiplatform.user)
• Storage Admin (roles/storage.admin)

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

Your administrator might also be able to give your instance's service account the required permissions throughcustom roles or otherpredefined roles.

Create a managed notebooks instance and example notebook file

1. Create a managed notebooksinstance.

2. Open JupyterLab.

3. Open a new notebook file.

4. In the first cell of the notebook file, enter the following:

   # Import datetimeimportdatetime# Get the time and print itdatetime.datetime.now()print(datetime.datetime.now())

5. To make sure your notebook file is saved, selectFile > Save Notebook.

Schedule a run

1. In the Google Cloud console, go to theManaged notebooks page.

   Go to Managed notebooks

2. Next to the managed notebooks instancethat you want to use,clickOpen JupyterLab.

   Your managed notebooks instance opens JupyterLab.

3. In thefolder File Browser,double-click the example notebook file to open it.

4. Click the Execute button.

5. In theSubmit notebooks to Executor dialog, in theType field,selectSchedule-based recurring executions.

   By default, the executor runs your notebook fileevery hour at the00 minute of the hour.

6. InAdvanced options,select theRegion where you want to run your notebook.

7. In theCloud Storage bucket field, enter a name for your bucket,and then clickCreate and select.The executor stores your notebook outputin the Cloud Storage bucket.

8. ClickSubmit.

   Your notebook file runs automaticallyon the schedule that you set.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, seeClean up.

View, share, and import an executed notebook file

By using your managed notebooks instance's JupyterLab interface,you can view your notebook output, share the results with others,and import the executed notebook file into JupyterLab.

View the execution results

1. In JupyterLab's navigation menu, click the Notebook Executor button.

2. Click theExecutions tab.

3. Under the execution that you want to view, clickView result.

   Executor opens your result in a new browser tab.

Share the execution results

1. In your managed notebooks instance'sJupyterLab user interface,in the navigation menu, click the Notebook Executor button.

2. Click theExecutions tab.

3. Next to the execution that you want to share,click themore_vert options menu,and selectShare execution result.

4. Follow the directions in the dialogto grant users access to the execution result.

Import the executed notebook into JupyterLab

1. In your managed notebooks instance'sJupyterLab user interface,in the navigation menu, click the Notebook Executor button.

2. Click theExecutions tab.

3. Next to the execution that you want to import,click themore_vert options menu,and selectImport executed notebook.

4. If theSelect Kernel dialog appears,select the kernel that you want to open the notebook.

   The executor opens the executed notebook filein JupyterLab, and stores this notebook file inthe JupyterLab File Browser in a folder namedimported_notebook_jobs.

View or delete a schedule

You can view and delete schedules by using either the Google Cloud console oryour managed notebooks instance's JupyterLab user interface.

View a schedule

View a schedule to see the frequency settings of the scheduleor to view the five most recent results of the notebook file execution.

Delete a schedule

Deleting a schedule doesn't delete the executions that weregenerated from that schedule.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

Delete the instance

1. In the Google Cloud console, go to theManaged notebooks page.

   Go to Managed notebooks

2. Select theRegion that contains your instance.

3. Select the managed notebooks instance that you wantto delete.

4. Clickdelete Delete.

Delete the project

If you used resources outside ofyour managed notebooks instance,such as the Cloud Storage bucket requiredfor creating a schedule,you might want to delete your project to avoid incurring additional charges.

Caution: Deleting a project has the following effects:
• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as anappspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to theManage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then clickDelete.
3. In the dialog, type the project ID, and then clickShut down to delete the project.

What's next

• Vertex AI Workbench managed notebooks instances are deprecated. Toschedule a notebook run in a Vertex AI Workbench instance, seeSchedule a notebook run.