ServerImageBuilder

The repo builds the docker images used as workspace containers for JupyterHub on the CVIP servers in Computing.

Knowledge Requirements

You will need to have some experience of:

• General sysadmin
• Docker (bonus points for Docker BuildKit, multi-stage builds)
• JupyterHub admin / config
• Shell scripting

We use the base images created by each deep learning framework (Matlab, tensorflow and pytorch) as a starting point. Our images add the necessary components and dependencies for running with Jupyterhub and doing things like SSH etc on the CVIP servers. All our images are available at https://hub.docker.com/uodcvip/.

Building images

Use the run-prod.sh script to build the images. For Tensorflow images:

./run.sh tensorflow

Matlab is a special case as it requires an extra argument network address for licensing:

./run.sh matlab <PORT-NUMBER>@<DNS-ADDRESS>

Hint: To find the correct matlab licensing argument run env | grep MLM inside an already built matlab image.

Updating available images

Update the <framework>_image_tags.txt.<version> file with new tags for the base image files. Then run the script as above.

How it works

There are 3 main stages to each build:

1. Install base OS dependencies
2. Configure the jupyter environment
3. Configure the framework environment/build
4. Add the entrypoint scripts for JupyterHub and SSH.

The JupyterHub entrypoint scripts are required to get images running correctly -- i.e. switching to the OS user account and mounting their home directory.