Back up and restore files by using Cloud Storage

This page describes how to use Cloud Storage to back up and restore fileson your Vertex AI Workbench instance.

Overview

This guide describes two ways to use Cloud Storage to help youback up and restore files on your Vertex AI Workbench instance:

Before you begin

  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.

    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

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Notebooks API.

    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 API

  5. 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

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the Notebooks API.

    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 API

Required roles

To get the permissions that you need to mount a Cloud Storage bucket to a Vertex AI Workbench instance, ask your administrator to grant you the following IAM roles:

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

You might also be able to get the required permissions throughcustom roles or otherpredefined roles.

Mount a Cloud Storage bucket

Vertex AI Workbench instances include a Cloud Storage integrationthat lets you mount a Cloud Storage bucket. This means you canbrowse the contents of the bucket and work with compatible files from withinthe JupyterLab interface.

You can access any of the Cloud Storage buckets and files thatyour instance has access to within the same project asyour Vertex AI Workbench instance.

Note: Your Vertex AI Workbench instance's access toCloud Storage is determined by the single user or service accountthat you used to grant access to your instance. For example,if you granted a specific service account access to your instance,you must also grant that service account access to theCloud Storage buckets that you want to use in JupyterLab.

Required permission for enabling shared storage mounting

To enable shared storage mounting in your Vertex AI Workbench instance,ask your administrator to grant your Vertex AI Workbench instance'sservice account thestorage.buckets.list permission on the project.

Thestorage.buckets.list permission is required for theMount shared storage button to appear in the JupyterLab interface of yourVertex AI Workbench instance.

Create a bucket and a Vertex AI Workbench instance

You must have access to at least one Cloud Storage bucket in thesame project as your Vertex AI Workbench instance.
  1. If you need to create a Cloud Storage bucket, seeCreate a bucket.
  2. If you haven't already,create a Vertex AI Workbench instance in the same project as your Cloud Storage bucket.

Open JupyterLab

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

    Go to Instances

  2. Next to your Vertex AI Workbench instance's name, clickOpen JupyterLab.

    Your Vertex AI Workbench instance opens JupyterLab.

Mount the Cloud Storage bucket

To mount and then access a Cloud Storage bucket, do the following:

  1. In JupyterLab, make sure the File Browser tab is selected.

  2. In the left sidebar, click the Mount shared storage button. If you don't see the button, drag the right side of the sidebar to expand the sidebar until you see the button.

    The Mount shared storage button in the top right corner of the left sidebar

  3. In theBucket name field, enter the Cloud Storage bucket name that you want to mount.

  4. ClickMount.

  5. Your Cloud Storage bucket appears as a folder in theFile browser tab of the left sidebar. Double-click the folder to open it and browse the contents.

Export to and restore files from Cloud Storage

This section describes how to export files to Cloud Storage andrestore files located in a Cloud Storage bucket.

Export to Cloud Storage

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

    Go to Instances

  2. Next to your Vertex AI Workbench instance's name, clickOpen JupyterLab.

    Your Vertex AI Workbench instance opens JupyterLab.

  3. In JupyterLab, make sure the File Browser tab is selected.

  4. In the left sidebar, click the Export to GCS button. If you don't see the button, drag the right side of the sidebar to expand the sidebar until you see the button.

  5. In theProvide export location dialog, enter a Cloud Storage bucket name that you want to export files to. If you need to create a Cloud Storage bucket, seeCreate a bucket.

    Vertex AI Workbench opens a new notebook that includes code for exporting your instance's files to Cloud Storage.

  6. Run the code in this cell.

Restore files from Cloud Storage

To restore a file to your Vertex AI Workbench instance, you can usegcloud storage cp to copy the file.

Run the following code in a cell of one of your instance's notebooks:

!gcloudstoragecpURI/home/jupyter/FILE_NAME

Replace the following:

  • URI: the gsutil URI of the file that you want tocopy, for example:gs://BUCKET_NAME/ZONE/INSTANCE_ID/FILE_NAME
  • FILE_NAME: the name of the file to copy

For more information, seeDownload the object from yourbucket.

What's next

Except as otherwise noted, the content of this page is licensed under theCreative Commons Attribution 4.0 License, and code samples are licensed under theApache 2.0 License. For details, see theGoogle Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-12-15 UTC.