Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high-performance computing group.
Three main actors make up JupyterHub:
- multi-user Hub (tornado process)
- configurable http proxy (node-http-proxy)
- multiple single-user Jupyter notebook servers (Python/Jupyter/tornado)
Basic principles for operation are:
- Hub launches a proxy.
- The Proxy forwards all requests to Hub by default.
- Hub handles login and spawns single-user servers on demand.
- Hub configures proxy to forward URL prefixes to the single-user notebook servers.
JupyterHub also provides a REST API for administration of the Hub and its users.
A Linux/Unix based system
Python 3.6 or greater
If you are using
conda, the nodejs and npm dependencies will be installed for you by conda.
If you are using
pip, install a recent version (at least 12.0) of nodejs/npm.
If using the default PAM Authenticator, a pluggable authentication module (PAM).
TLS certificate and key for HTTPS communication
To install JupyterHub along with its dependencies including nodejs/npm:
conda install -c conda-forge jupyterhub
If you plan to run notebook servers locally, install JupyterLab or Jupyter notebook:
conda install jupyterlab
conda install notebook
JupyterHub can be installed with
pip, and the proxy with
npm install -g configurable-http-proxy
python3 -m pip install jupyterhub
If you plan to run notebook servers locally, you will need to install JupyterLab or Jupyter notebook:
python3 -m pip install --upgrade jupyterlab
python3 -m pip install --upgrade notebook
Run the Hub server
To start the Hub server, run the command:
http://localhost:8000 in your browser, and sign in with your system username and password.
Note: To allow multiple users to sign in to the server, you will need to
jupyterhub command as a privileged user, such as root.
describes how to run the server as a less privileged user, which requires
more configuration of the system.
The Getting Started section of the documentation explains the common steps in setting up JupyterHub.
The JupyterHub tutorial provides an in-depth video and sample configurations of JupyterHub.
Create a configuration file
To generate a default config file with settings and descriptions:
Start the Hub
To start the Hub on a specific url and port
10.0.1.2:443 with https:
jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
|Default, built-in authenticator
|OAuth + JupyterHub Authenticator = OAuthenticator
|Simple LDAP Authenticator Plugin for JupyterHub
|Kerberos Authenticator Plugin for JupyterHub
|Default, built-in spawner starts single-user servers as local processes
|Spawn single-user servers in Docker containers
|Kubernetes spawner for JupyterHub
|Spawn single-user servers without being root
|Spawn single-user notebook servers using systemd
|Designed for clusters using batch scheduling software
|Spawn single-user notebook servers distributed on a Hadoop cluster
|WrapSpawner and ProfilesSpawner enabling runtime configuration of spawners
A starter docker image for JupyterHub gives a baseline deployment of JupyterHub using Docker.
quay.io/jupyterhub/jupyterhub image contains only the Hub itself,
with no configuration. In general, one needs to make a derivative image, with
at least a
jupyterhub_config.py setting up an Authenticator and/or a Spawner.
To run the single-user servers, which may be on the same system as the Hub or
not, Jupyter Notebook version 4 or greater must be installed.
The JupyterHub docker image can be started with the following command:
docker run -p 8000:8000 -d --name jupyterhub quay.io/jupyterhub/jupyterhub jupyterhub
This command will create a container named
jupyterhub that you can
stop and resume with
The Hub service will be listening on all interfaces at port 8000, which makes this a good choice for testing JupyterHub on your desktop or laptop.
If you want to run docker on a computer that has a public IP then you should (as in MUST) secure it with ssl by adding ssl options to your docker configuration or by using an ssl enabled proxy.
Mounting volumes will allow you to store data outside the docker image (host system) so it will be persistent, even when you start a new image.
docker exec -it jupyterhub bash will spawn a root shell in your docker
container. You can use the root shell to create system users in the container.
These accounts will be used for authentication in JupyterHub's default configuration.
If you would like to contribute to the project, please read our
explains how to set up a development installation, how to run the test suite,
and how to contribute to documentation.
For a high-level view of the vision and next directions of the project, see the JupyterHub community roadmap.
A note about platform support
JupyterHub is supported on Linux/Unix based systems.
JupyterHub officially does not support Windows. You may be able to use JupyterHub on Windows if you use a Spawner and Authenticator that work on Windows, but the JupyterHub defaults will not. Bugs reported on Windows will not be accepted, and the test suite will not run on Windows. Small patches that fix minor Windows compatibility issues (such as basic installation) may be accepted, however. For Windows-based systems, we would recommend running JupyterHub in a docker container or Linux VM.
Additional Reference: Tornado's documentation on Windows platform support
We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.
All code is licensed under the terms of the revised BSD license.
Help and resources
- Reporting Issues
- JupyterHub tutorial
- Documentation for JupyterHub
- Documentation for JupyterHub's REST API
- Documentation for Project Jupyter
- Project Jupyter website
- Project Jupyter community
JupyterHub follows the Jupyter Community Guides.