Use Notebooks with Vertex AI

This page describes the main features of Notebooks and provides examples of how you can use it in Vertex AI.

Overview of Notebooks

Using Notebooks, you can create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.

Notebooks instances have a pre-installed suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can configure either CPU-only or GPU-enabled instances, to best suit your needs.

Your Notebooks instances are protected by Google Cloud authentication and authorization, and are available using a Notebooks instance URL. Notebooks instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.

See the Notebooks documentation to learn more about the Notebooks product.

Considerations for using Notebooks with Vertex AI

Notebooks is a separate product from Vertex AI that is accessible from both Vertex AI and AI Platform. The Notebooks instances you create are available to use with both products.

In Cloud Console, you can view all of your Notebooks instances from within both Vertex AI and AI Platform. However, the code that you run inside an Notebooks instance determines where a job, model, or other resource is stored. In other words, Vertex AI API requests create resources in Vertex AI, and AI Platform API requests create resources in AI Platform.

Using Notebooks

You can use a Notebooks instance as a part of your work in Vertex AI. Use a notebook to perform feature engineering and data preparation on a dataset, build and iterate on a custom training model, or use one of the Vertex AI client libraries for complex model creation, and then store these resources for use in Vertex AI.

You create and manage Notebooks instances on the Notebooks instances page of the Google Cloud Console. You can also go to notebook.new to go directly to the Advanced options instance creation dialog box.

Go to the Notebooks instances page

What's next