Back up and restore files by using Cloud Storage

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

Overview

This guide describes two ways to use Cloud Storage to help you back 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.

    Go to project selector

  3. Verify 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.

    Go to project selector

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

  7. Enable the Notebooks API.

    Enable the API

    8.

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 on the project:

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

You might also be able to get the required permissions through custom roles or other predefined roles.

Mount a Cloud Storage bucket

Vertex AI Workbench instances include a Cloud Storage integration that lets you mount a Cloud Storage bucket. This means you can browse the contents of the bucket and work with compatible files from within the JupyterLab interface.

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

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's service account the storage.buckets.list permission on the project.

The storage.buckets.list permission is required for the Mount shared storage button to appear in the JupyterLab interface of your Vertex AI Workbench instance.

Create a bucket and a Vertex AI Workbench instance

You must have access to at least one Cloud Storage bucket in the same project as your Vertex AI Workbench instance.
  1. If you need to create a Cloud Storage bucket, see Create 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 the Instances page.

    Go to Instances

  2. Next to your Vertex AI Workbench instance's name, click Open 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 the Bucket name field, enter the Cloud Storage bucket name that you want to mount.

  4. Click Mount.

  5. Your Cloud Storage bucket appears as a folder in the File 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 and restore files located in a Cloud Storage bucket.

Export to Cloud Storage

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

    Go to Instances

  2. Next to your Vertex AI Workbench instance's name, click Open 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 the Provide 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, see Create 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 use gcloud storage cp to copy the file.

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

!gcloud storage cp URI /home/jupyter/FILE_NAME

Replace the following:

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

For more information, see Download the object from your bucket.

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