Multi-user server for Jupyter notebooks

Overview

Technical Overview | Installation | Configuration | Docker | Contributing | License | Help and Resources


Please note that this repository is participating in a study into the sustainability of open source projects. Data will be gathered about this repository for approximately the next 12 months, starting from 2021-06-11.

Data collected will include the number of contributors, number of PRs, time taken to close/merge these PRs, and issues closed.

For more information, please visit our informational page or download our participant information sheet.


JupyterHub

Latest PyPI version Latest conda-forge version Documentation build status GitHub Workflow Status - Test DockerHub build status Test coverage of code GitHub Discourse Gitter

With JupyterHub you can create a multi-user Hub that spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server.

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.

Technical overview

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.

Installation

Check prerequisites

  • A Linux/Unix based system

  • Python 3.6 or greater

  • nodejs/npm

    • 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

  • Domain name

Install packages

Using conda

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

Using pip

JupyterHub can be installed with pip, and the proxy with npm:

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:

jupyterhub

Visit https://localhost:8000 in your browser, and sign in with your unix PAM credentials.

Note: To allow multiple users to sign in to the server, you will need to run the jupyterhub command as a privileged user, such as root. The wiki describes how to run the server as a less privileged user, which requires more configuration of the system.

Configuration

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:

jupyterhub --generate-config

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

Authenticators

Authenticator Description
PAMAuthenticator Default, built-in authenticator
OAuthenticator OAuth + JupyterHub Authenticator = OAuthenticator
ldapauthenticator Simple LDAP Authenticator Plugin for JupyterHub
kerberosauthenticator Kerberos Authenticator Plugin for JupyterHub

Spawners

Spawner Description
LocalProcessSpawner Default, built-in spawner starts single-user servers as local processes
dockerspawner Spawn single-user servers in Docker containers
kubespawner Kubernetes spawner for JupyterHub
sudospawner Spawn single-user servers without being root
systemdspawner Spawn single-user notebook servers using systemd
batchspawner Designed for clusters using batch scheduling software
yarnspawner Spawn single-user notebook servers distributed on a Hadoop cluster
wrapspawner WrapSpawner and ProfilesSpawner enabling runtime configuration of spawners

Docker

A starter docker image for JupyterHub gives a baseline deployment of JupyterHub using Docker.

Important: This 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 jupyterhub/jupyterhub jupyterhub

This command will create a container named jupyterhub that you can stop and resume with docker stop/start.

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 a 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.

The command 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.

Contributing

If you would like to contribute to the project, please read our contributor documentation and the CONTRIBUTING.md. The CONTRIBUTING.md file 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

License

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

We encourage you to ask questions and share ideas on the Jupyter community forum. You can also talk with us on our JupyterHub Gitter channel.

JupyterHub follows the Jupyter Community Guides.


Technical Overview | Installation | Configuration | Docker | Contributing | License | Help and Resources

Owner
JupyterHub
Official JupyterHub-related repositories
JupyterHub
A way to integrate Latex, VSCode, and Inkscape in macOS. Adopted the whole workflow from Gilles Castel.

VSCode-LaTeX-Inkscape A way to integrate LaTeX, VSCode, and Inkscape in macOS Abstract I use LaTeX heavily in past two years for both academic work an

Pingbang Hu 62 Dec 14, 2022
pyiron - an integrated development environment (IDE) for computational materials science.

pyiron pyiron - an integrated development environment (IDE) for computational materials science. It combines several tools in a common platform: Atomi

pyiron 20 Dec 22, 2022
VSCode extension to sort and refactor python imports using reorder-python-imports.

reorder-python-imports VSCode extension to sort and refactor python imports using reorder-python-imports. Unlike other import organizers, reorder-pyth

Ryan Butler 3 Aug 26, 2022
Python Indent - Correct python indentation in Visual Studio Code.

Python Indent Correct python indentation in Visual Studio Code. See the extension on the VSCode Marketplace and its source code on GitHub. Please cons

Kevin Rose 57 Dec 15, 2022
Jarvide - A powerful AI mixed with a powerful IDE.

Jarvide About Jarvide Welcome to Jarvide. A powerful AI mixed with a powerful ID

Caeden 23 Oct 28, 2022
Live coding in Python with PyCharm, Emacs, Sublime Text, or even a browser

Live Coding in Python Visualize your Python code while you type it in PyCharm, Emacs, Sublime Text, or even your browser. To see how to use one of the

Don Kirkby 256 Dec 14, 2022
An echo kernel for JupyterLite

jupyterlite-echo-kernel An echo kernel for JupyterLite. Requirements JupyterLite = 0.1.0a10 Install To install the extension, execute: pip install ju

JupyterLite 7 Dec 07, 2022
Integrate clang-format with Sublime Text

Sublime Text Clang Format Plugin This is a minimal plugin integrating clang-format with Sublime Text, with emphasis on the word minimal. It is not rea

Jon Palmisciano 1 Dec 17, 2021
Python IDE or notebook to generate a basic Kepler.gl data visualization

geospatial-data-analysis [readme] Use this code in your Python IDE or notebook to generate a basic Kepler.gl data visualization, without pre-configura

2 Sep 05, 2022
Launch a ready-to-code Wagtail Live development environment with a single click.

Wagtail Live Gitpod Launch a ready-to-code Wagtail Live development environment with a single click. Steps: Click the Open in Gitpod button. Relax: a

Coen van der Kamp 6 Oct 29, 2021
Python 3 patcher for Sublime Text v4107-4114 Windows x64

sublime-text-4-patcher Python 3 patcher for Sublime Text v4107-4114 Windows x64 Credits for signatures and patching logic goes to https://github.com/l

187 Dec 27, 2022
Gaphor is a UML and SysML modeling application written in Python.

Gaphor is a UML and SysML modeling application written in Python. It is designed to be easy to use, while still being powerful. Gaphor implements a fully-compliant UML 2 data model, so it is much mor

Gaphor 1.3k Jan 07, 2023
Shows Odin Lang errors in Sublime Text.

OdinErrors Shows Odin Lang errors in Sublime Text. Config Collections and defines are stored in ols.json (Hijacked from ols). { "collections": [

Gus 3 Nov 20, 2021
Blender add-on for baking your scene to textures

Bake Scene This add-on bakes your scene to textures. This is useful in many situations: Creating trim sheets Creating decals Creating hair cards Creat

5 Sep 20, 2022
Automatically detect obfuscated code and other state machines

Scripts to automatically detect obfuscated code and state machines in binaries.

Aaron 110 Dec 04, 2022
Multi-user server for Jupyter notebooks

Technical Overview | Installation | Configuration | Docker | Contributing | License | Help and Resources Please note that this repository is participa

JupyterHub 7k Jan 02, 2023
Spyder - The Scientific Python Development Environment

Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, ana

Spyder IDE 7.3k Jan 08, 2023
Wasm powered Jupyter running in the browser 💡

JupyterLite JupyterLite is a JupyterLab distribution that runs entirely in the browser built from the ground-up using JupyterLab components and extens

JupyterLite 3k Jan 04, 2023
A Sublime Text package that allows a user to view all the available core/plugin commands for Sublime Text and Sublime Merge, along with their documentation/source.

CommandsBrowser A Sublime Text package that allows a user to view all the available core/plugin commands for Sublime Text and Sublime Merge, along wit

Sublime Instincts 26 Nov 15, 2022
Joy is a tiny creative coding library in Python.

Joy Joy is a tiny creative coding library in Python. Installation The easiest way to install it is download joy.py and place it in your directory. The

FOSS United Foundation 181 Dec 04, 2022