docker-stacks

Ubuntu! SchoolTool also started 20 years ago. Then Open edX, and it looks like e.g. Canvas for LMS these days. To run an Ubuntu container with podman: apt-get install -y podman distrobox podman run --rm --it docker.io/ubuntu:24.04 bash --login podman run --rm --it docker.io/ubuntu:24.10 bash --login GitHub Codespaces are Ubuntu devcontainers (codespaces-linux) Windows Subsystem for Linux (WSL) installs Ubuntu (otherwise there's GitBash and podman-desktop for over there) The jupyter/docker-stacks containers build FROM Ubuntu:24.04 LTS: https://github.com/jupyter/docker-stacks/blob/main/images/do... : docker run -p 10000:8888 quay.io/jupyter/scipy-notebook:2024-10-07

Given https://github.com/jupyter/docker-stacks Docker might be appropriate. No matter which route you decide to take: make it easy. If you are using pipenv or poetry do so while offering a Makefile or documentation on what commands to run. A Makefile with a decent "help" target would be perfect even if you do use Docker. I'd suggest looking at https://marmelab.com/blog/2016/02/29/auto-documented-makefil... to spare you some work with maintaining the help target.

Why not seriously? Many data science environments are pretty much based on Anaconda installations. (See e.g. these dockers: https://github.com/jupyter/docker-stacks/tree/master/scipy-n...) I mean, all packages I know DONT require Anaconda. But if you need the whole environment, sometimes Anaconda is the easiest tool too install all dependencies.

Try adapting the Julia via Dockerfile example: https://github.com/binder-project/example-dockerfile Or https://github.com/jupyter/docker-stacks/blob/master/r-noteb...

Totally agree. Jupyter/IPython is an impressive tool. I started to play with it recently and I am very happy with what it can do. It's, also, very easy to install in a docker container: https://github.com/jupyter/docker-stacks (I haven't managed to make work the persistence part when stopping the container yet, but this is due to my inexperience with docker).