jupyterhub-deploy-docker

jupyterhub-deploy-docker provides a reference deployment of JupyterHub, a multi-user Jupyter Notebook environment, on a single host using Docker.

Possible use cases include:

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

Disclaimer

This deployment is NOT intended for a production environment. It is a reference implementation that does not meet traditional requirements in terms of availability nor scalability.

If you are looking for a more robust solution to host JupyterHub, or you require scaling beyond a single host, please check out the excellent zero-to-jupyterhub-k8s project.

Technical Overview

Key components of this reference deployment are:

Host: Runs the JupyterHub components in a Docker container on the host.
Authenticator: Uses OAuthenticator and GitHub OAuth to authenticate users.
Spawner:Uses DockerSpawner to spawn single-user Jupyter Notebook servers in separate Docker containers on the same host.
Persistence of Hub data: Persists JupyterHub data in a Docker volume on the host.
Persistence of user notebook directories: Persists user notebook directories in Docker volumes on the host.

Prerequisites

Docker

This deployment uses Docker, via Docker Compose, for all the things. Docker Engine 1.12.0 or higher is required.

Use Docker's installation instructions to set up Docker for your environment.
To verify your docker installation, whether running docker as a local installation or using docker-machine, enter these commands:
```
docker version
docker ps
```

HTTPS and SSL/TLS certificate

This deployment configures JupyterHub to use HTTPS. You must provide a certificate and key file in the JupyterHub configuration. To configure:

Obtain the domain name that you wish to use for JupyterHub, for example, myfavoritesite.com or jupiterplanet.org.
If you do not have an existing certificate and key, you can:
- obtain one from Let's Encrypt using the certbot client,
- use the helper script in this repo's letsencrypt example, or
- create a self-signed certificate.
Copy the certificate and key files to a directory named secrets in this repository's root directory. These will be added to the JupyterHub Docker image at build time. For example, create a secrets directory in the root of this repo and copy the certificate and key files (jupyterhub.crt and jupyterhub.key) to this directory:
```
mkdir -p secrets
cp jupyterhub.crt jupyterhub.key secrets/
```

Authenticator setup

This deployment uses GitHub OAuth to authenticate users.

It requires that you create and register a GitHub OAuth application by filling out a form on the GitHub site:

In this form, you will specify the OAuth application's callback URL in this format: https://<myhost.mydomain>/hub/oauth_callback.

After you submit the GitHub form, GitHub registers your OAuth application and assigns a unique Client ID and Client Secret. The Client Secret should be kept private.

At JupyterHub's runtime, you must pass the GitHub OAuth Client ID, Client Secret and OAuth callback url. You can do this by either:

setting the GITHUB_CLIENT_ID, GITHUB_CLIENT_SECRET, and OAUTH_CALLBACK_URL environment variables when you run the JupyterHub container, or
add them to an oauth.env file in the secrets directory of this repository. You may need to create both the secrets directory and the oauth.env file. For example, add the following lines in the oauth.env file:

oauth.env file
```
GITHUB_CLIENT_ID=<github_client_id>
GITHUB_CLIENT_SECRET=<github_client_secret>
OAUTH_CALLBACK_URL=https://<myhost.mydomain>/hub/oauth_callback
```
Note: The oauth.env file is a special file that Docker Compose uses to lookup environment variables. If you choose to place the GitHub OAuth application settings in this file, you should make sure that the file remains private (be careful to not commit the oauth.env file with these secrets to source control).

Build the JupyterHub Docker image

Finish configuring JupyterHub and then build the hub's Docker image. (We'll build the Jupyter Notebook image in the next section.)

Configure userlist: Create a userlist file of authorized JupyterHub users. The list should contain GitHub usernames, and this file should designate at least one admin user. For instance, the example file below contains three users, jtyberg, jenny, and guido, and one designated administrator, jtyberg:

userlist file
```
jtyberg admin
jenny
guido
```
The admin user will have the ability to add more users through JupyterHub's admin console.
Use docker-compose to build the JupyterHub Docker image on the active Docker machine host by running the make build command:
```
make build
```

Spawner: 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 jupyter/scipy-notebook Notebook image, which is built from the scipy-notebook Docker stacks. (Note that the Docker stacks *-notebook images tagged 2d878db5cbff include the start-singleuser.sh 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

file

https://myhost.mydomain

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 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

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

FAQ

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 jupyterhub_config.py, which reads the Notebook image name from the DOCKER_NOTEBOOK_IMAGE environment variable:

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

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

file

# Setting in the .env file
DOCKER_NOTEBOOK_IMAGE=jupyter/scipy-notebook:2d878db5cbff

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 kernels.

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.

maarten-betman/jupyterhub-deploy-docker