Public | Automated Build

Last pushed: 10 months ago
Short Description
Launch scripts and containers for our Jupyterhub server.
Full Description

Gifford lab Jupyterhub server

This is an image and launching script for running a Jupyterhub
(master) server inside Docker, where it is able to launch user Jupyter
(client) notebook servers as separate Docker containers on the same
outer host machine.

It is based on the compmodels Docker
and the

Custom authentication using MIT accounts is provided by

is the client notebook server image that contains all the client-side
libraries, and it's based on this
It has many R and Python packages installed, as well as full CUDA
capability along with Theano and related libraries (which will work
only if the server is running on a GPU machine). Each user only has
write access to /cluster/$USERNAME and read-only access to all of
/cluster. There are automatic Docker builds with these names hooked
to the Github repository.

The overall goal is to have a very thin layer of extra code around the core
Jupyter functionality, so that we can stay up to date and not create
more headaches down the road.


Browsing through and should give an
idea of what needs to be set up on the server before starting the hub:

  • Docker installed and the client image
    giffordlab/jupyter-systemuser and the hub image
    giffordlab/jupyterhub pulled

  • The file /root/setup_oauth_tokens.list, a Docker env-file that
    OAUTH_CLIENT_SECRET, which are all obtained by following the

  • Certificate files at /root/${HOSTNAME}_all.pem and
    /root/${HOSTNAME}_all_certs.pem, obtained by following the

Using the hub

Run the script Make sure the port and persistent storage
directory (mapped to /srv/jupyterhub) values are correct.


  • Check the hub and client logs (via Docker) for any error messages.

  • Double check which version of python or pip you're using, and
    make sure libraries are installed for the correct location (and Python
    version). Everything inside the notebook should use the Conda
    installation at $CONDA_DIR.

Updating the notebook image

If you want more features or packages inside your notebook, you have
several choices:

  1. Install a Python or R package with pip, conda, or R. The
    user's home directory inside the image is set to /cluster/$USERNAME,
    so user-installed packages will go in subdirectories there and persist
    across container restarts. Note that the default versions of python
    and pip in the container are for Python 3; to install Python 2
    packages first run source activate python2 or make sure to use
    binaries in $CONDA_DIR/envs/python2/bin.

  2. Log into the running container (it will be named
    jupyter-$USERNAME) as root and install a system package.

  3. File a pull request that updates the source
    then pull a fresh giffordlab/jupyter-systemuser image on the hub
    machine. Manually stop and remove (docker stop and docker rm)
    your running image so that when you log in again, a fresh container is
    launched. This option is slower but better for improvements that
    should be shared.


Inside the per-user container, HOME is set to /cluster/$USERNAME.
There is no AFS access in the container.

Access is currently restricted to the cgs group. See the details at

Docker Pull Command
Source Repository

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