Getting started with JupyterHub

JupyterHub lets you 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 using Kubernetes. 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.

Verifying JupyterHub dependencies

At this point, you should have completed Step Zero and have an operational Kubernetes cluster. You will already have a cloud provider/infrastructure and kubernetes and docker installed.

If you need to create a Kubernetes cluster, see Creating a Kubernetes Cluster.

We also depend on Helm and the JupyterHub Helm chart for your JupyterHub deployment. We’ll deploy them in this section. Let’s begin by moving on to Setting up Helm.


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