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This repository provides a reference deployment of JupyterHub, a multi-user Jupyter Notebook environment, on a single host using Docker.

This deployment:

  • Runs the JupyterHub components in a Docker container on the host
  • Uses DockerSpawner to spawn single-user Jupyter Notebook servers in separate Docker containers on the same host
  • Persists JupyterHub data in a Docker volume on the host
  • Persists user notebook directories in Docker volumes on the host
  • Uses OAuthenticator and GitHub OAuth to authenticate users

Use Cases

Possible use cases for this deployment may include, but are not limited to:

  • A JupyterHub demo environment that you can spin up relatively quickly.
  • A multi-user Jupyter Notebook environment for small classes, teams, or departments.


This deployment is NOT intended for a production environment.


From here on, we'll assume you are set up with docker,
via a local installation or docker-machine.
At this point,

docker ps

should work.

Setup GitHub Authentication

This deployment uses GitHub OAuth to authenticate users.
It requires that you create a GitHub application.
You will need to specify an OAuth callback URL in the following form:


You must pass the secrets that GitHub provides for your application to JupyterHub at runtime.
You can do this by setting the GITHUB_CLIENT_ID, GITHUB_CLIENT_SECRET,
and OAUTH_CALLBACK_URL environment variables when you run the JupyterHub container,
or you can add them to the .env file in the root directory of this repository. For example,


Note: The .env file is a special file that Docker Compose uses to lookup environment variables.
If you choose to place the GitHub secrets in this file,
you should ensure that this file remains private
(e.g., do not commit the secrets to source control).

Build the JupyterHub Docker image

Configure JupyterHub and build it into a Docker image.

  1. Copy the TLS certificate chain and key files for the JupyterHub server to a directory named secrets within this repository directory. These will be added to the JupyterHub Docker image at build time. If you do not have a certificate chain and key, you can either create self-signed versions, or obtain real ones from Let's Encrypt (see the letsencrypt example for instructions).

     mkdir -p secrets
     cp jupyterhub.crt jupyterhub.key secrets/
  2. Create a userlist file with a list of authorized users. At a minimum, this file should contain a single admin user. The username should be a GitHub username. For example:

    jtyberg admin

    The admin user will have the ability to add more users in the JupyterHub admin console.

  3. Use docker-compose to build the
    JupyterHub Docker image on the active Docker machine host:

     make build

Prepare the Jupyter Notebook Image

You can configure JupyterHub to spawn Notebook servers from any Docker image, as
long as the image's ENTRYPOINT and/or CMD starts a single-user instance of
Jupyter Notebook server that is compatible with JupyterHub.

To specify which Notebook image to spawn for users, you set the value of the
DOCKER_NOTEBOOK_IMAGE environment variable to the desired container image.
You can set this variable in the .env file, or alternatively, you can
override the value in this file by setting DOCKER_NOTEBOOK_IMAGE in the
environment where you launch JupyterHub.

Whether you build a custom Notebook image or pull an image from a public or
private Docker registry, the image must reside on the host.

If the Notebook image does not exist on host, Docker will attempt to pull the
image the first time a user attempts to start his or her server. In such cases,
JupyterHub may timeout if the image being pulled is large, so it is better to
pull the image to the host before running JupyterHub.

This deployment defaults to the
Notebook image, which is built from the scipy-notebook
Docker stacks. (Note that the Docker
stacks *-notebook images tagged 2d878db5cbff include the script required to start a single-user instance of the
Notebook server that is compatible with JupyterHub).

You can pull the image using the following command:

make notebook_image

Run JupyterHub

Run the JupyterHub container on the host.

To run the JupyterHub container in detached mode:

docker-compose up -d

Once the container is running, you should be able to access the JupyterHub console at


To bring down the JupyterHub container:

docker-compose down

Behind the scenes

make build does a few things behind the scenes, to set up the environment for JupyterHub:

Create a Docker Network

Create a Docker network for inter-container communication. The benefits of using a Docker network are:

  • container isolation - only the containers on the network can access one another
  • name resolution - Docker daemon runs an embedded DNS server to provide automatic service discovery for containers connected to user-defined networks. This allows us to access containers on the same network by name.

Here we create a Docker network named jupyterhub-network. Later, we will configure the JupyterHub and single-user Jupyter Notebook containers to run attached to this network.

docker network create jupyterhub-network

Create a JupyterHub Data Volume

Create a Docker volume to persist JupyterHub data. This volume will reside on the host machine. Using a volume allows user lists, cookies, etc., to persist across JupyterHub container restarts.

docker volume create --name jupyterhub-data


How can I view the logs for JupyterHub or users' Notebook servers?

Use docker logs <container>. For example, to view the logs of the jupyterhub container

docker logs jupyterhub

How do I specify the Notebook server image to spawn for users?

In this deployment, JupyterHub uses DockerSpawner to spawn single-user
Notebook servers. You set the desired Notebook server image in a
DOCKER_NOTEBOOK_IMAGE environment variable.

JupyterHub reads the Notebook image name from, which
reads the Notebook image name from the DOCKER_NOTEBOOK_IMAGE environment

# DockerSpawner setting in
c.DockerSpawner.container_image = os.environ['DOCKER_NOTEBOOK_IMAGE']

By default, theDOCKER_NOTEBOOK_IMAGE environment variable is set in the
.env file.

# Setting in the .env file

To use a different notebook server image, you can either change the desired
container image value in the .env file, or you can override it
by setting the DOCKER_NOTEBOOK_IMAGE variable to a different Notebook
image in the environment where you launch JupyterHub. For example, the
following setting would be used to spawn single-user pyspark notebook servers:

export DOCKER_NOTEBOOK_IMAGE=jupyterhub/pyspark-notebook:2d878db5cbff

docker-compose up -d

If I change the name of the Notebook server image to spawn, do I need to restart JupyterHub?

Yes. JupyterHub reads its configuration which includes the container image
name for DockerSpawner. JupyterHub uses this configuration to determine the
Notebook server image to spawn during startup.

If you change DockerSpawner's name of the Docker image to spawn, you will
need to restart the JupyterHub container for changes to occur.

In this reference deployment, cookies are persisted to a Docker volume on the
Hub's host. Restarting JupyterHub might cause a temporary blip in user
service as the JupyterHub container restarts. Users will not have to login
again to their individual notebook servers. However, users may need to
refresh their browser to re-establish connections to the running Notebook

How can I backup a user's notebook directory?

There are multiple ways to backup and restore data in Docker containers.

Suppose you have the following running containers:

docker ps --format "table {{.ID}}\t{{.Image}}\t{{.Names}}"

CONTAINER ID        IMAGE                    NAMES
bc02dd6bb91b        jupyter/minimal-notebook jupyter-jtyberg
7b48a0b33389        jupyterhub               jupyterhub

In this deployment, the user's notebook directories (/home/jovyan/work) are backed by Docker volumes.

docker inspect -f '{{ .Mounts }}' jupyter-jtyberg

[{jtyberg /var/lib/docker/volumes/jtyberg/_data /home/jovyan/work local rw true rprivate}]

We can backup the user's notebook directory by running a separate container that mounts the user's volume and creates a tarball of the directory.

docker run --rm \
  -u root \
  -v /tmp:/backups \
  -v jtyberg:/notebooks \
  jupyter/minimal-notebook \
  tar cvf /backups/jtyberg-backup.tar /notebooks

The above command creates a tarball in the /tmp directory on the host.

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