Choose a notebook solution

This page describes the differences between Vertex AI's notebookenvironment options so that you can choose the best one for your project.

Vertex AI provides two notebook environment solutions:

• Colab Enterprise: A collaborative,managed notebook environment with the security and compliance capabilitiesof Google Cloud. If your project's priorities are to collaboratewith others and to avoid spending time managing infrastructure,Colab Enterprise might be the best option for you.See the followingColab Enterprisesection.

• Vertex AI Workbench: A Jupyter notebook-based environmentprovided through virtual machine (VM) instances with features that supportthe entire data science workflow. If your project's priorities are controland customizability, Vertex AI Workbench might be the best optionfor you. See the followingVertex AI Workbenchsection.

Colab Enterprise

Learn about a few of Colab Enterprise's strengths in thesections that follow. For more information, seeIntroduction toColab Enterprise.

Share and collaborate

Colab Enterprise lets you share notebooks and collaboratewith others. You can share a notebook with a single user, Google group,or Google Workspace domain. You control this accessthrough Identity and Access Management (IAM).

Managed compute

Colab Enterprise lets you work in notebooks without havingto manage infrastructure. Colab Enterprise provisionsa runtime for you when you need it. If you want to, you can configureruntimes for specific needs, but Colab Enterprise startsthem for you and shuts them down when you no longer need them.

Integrated into the Google Cloud console

Colab Enterprise's integrations with Google Cloud servicesmake it easier to use notebooks that interact with those services.You can use Colab Enterprise from within the Google Cloud console,with features built into both Vertex AI andBigQuery.

Write code with Gemini assistance

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of theService Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see thelaunch stage descriptions.

You can use Gemini in Vertex AI, which is a product intheGemini for Google Cloud portfolio,to help you write and generate code in a Vertex AI notebook.Gemini in Vertex AI can generate code completionsuggestions while you type in a code cell. You can also use theHelp me code tool to generate code based on a descriptionof what you want. To learn more, seeWrite codewith Gemini assistance.

Vertex AI Workbench

Learn about a few of Vertex AI Workbench's strengths in thesections that follow. For more information, seeIntroduction toVertex AI Workbench.

Overview

All Vertex AI Workbench instances provide the following:

• Prepackaged withJupyterLab.
• A preinstalled suite of deep learning packages, including support forthe TensorFlow and PyTorch frameworks.
• Support for GPU accelerators.
• The ability to sync with aGitHub repository.
• Google Cloud authentication and authorization.

Add conda environments

Vertex AI Workbench instances usekernelsbased on conda environments.You can add a conda environment to your Vertex AI Workbench instance,and the environment appears as a kernel in your instance's JupyterLab interface.

Adding conda environments lets you use kernels that aren't available in thedefault Vertex AI Workbench instance.For example, you can add conda environments for R and Apache Beam. Or youcan add conda environments for specific earlier versions of the availableframeworks, such as TensorFlow, PyTorch, or Python.

For more information, seeAdd a conda environment.

Access to data

You can work more efficiently by accessing your data without leavingthe JupyterLab interface.

From within JupyterLab's navigation menu ona Vertex AI Workbench instance, you canuse theCloud Storage integrationto browse data and other files that you have access to.

Also from within the navigation menu, you canuse theBigQuery integrationto browse tables that you have access to,write queries, preview results, and load data into your notebook.

Automated notebook runs

You canset a notebook to run on a recurringschedule.Even while your instance is shut down, Vertex AI Workbench willrun your notebook file and save the resultsfor you to look at and share with others.

Automated shutdown for idle instances

To help manage costs, you can setyour Vertex AI Workbench instanceto shut down after being idle for a specific time period.For more information,seeIdle shutdown.

Custom containers

You can create a Vertex AI Workbench instance based on a custom container.Start with a Google-provided base container image, and modify it foryour needs. Then create an instance based on your custom container.

For more information, seeCreate an instance using acustom container.

Use third party credentials

You can create and manage Vertex AI Workbench instances withthird party credentials provided by Workforce Identity Federation.Workforce Identity Federation uses your external identity provider (IdP)to grant a group of users access to Vertex AI Workbench instancesthrough a proxy.

For more information, seeCreate an instance withthird party credentials.

Health status monitoring

To help ensure that your Vertex AI Workbench instanceis working properly, you canmonitor the healthstatus.

Editable Deep Learning VM instances

Vertex AI Workbench provides API methods for modifying theunderlying VM through the Notebooks API.

• Create a Colab Enterprisenotebook.

• Create a Vertex AI Workbench instance.