Getting started with JupyterHub

The goal of JupyterHub is to create custom computing environments that can be accessed remotely (e.g., at a specific URL) by multiple users.

This guide acts as an assistant to guide you through the process of setting up your JupyterHub deployment. It helps you connect and configure the following things:

  • A cloud provider such Google Cloud, Microsoft Azure, Amazon EC2, and others
  • Kubernetes to manage resources on the cloud
  • Helm to configure and control Kubernetes
  • Docker to use containers that standardize computing environments
  • JupyterHub to manage users and deploy Jupyter notebooks

You already are well on your way to understanding what it means (procedurally) to deploy Jupyterhub.

Deployment Guide

We’ve put together a short walkthrough going from having nothing set up to a complete deployment of jupyterhub on Google Cloud. If you want to follow that comprehensive walkthrough, the next step on your journey is to create a Kubernetes cluster on Google Cloud.

Extending and Customizing JupyterHub

If you’d like to know how to expand and customize your jupyterhub setup, such as increasing the computational resources available to users or changing authentication services, check out Extending your JupyterHub setup.

Dependencies for Deploying a JupyterHub Instance

For a more extensive description of the tools and services that JupyterHub depends upon, see our Tools used in a JupyterHub Deployment page.

Questions or Suggestions?

If you have questions or suggestions, please reach out at our issues page on GitHub.