cocalc-docker VS ML-Workspace

I recently discovered Sagemath CoCalc.

You can download and use cocalc for free via a docker image: https://github.com/sagemathinc/cocalc-docker

I am the CEO of CoCalc, which is an alternatives to Overleaf that they don't own. I don't know whether or not our rules about personal data collection are compatible with your university requirements. Our default rules are at https://cocalc.com/policies, but we have been able to make modifications to them for particular customers. We also have a small easy to install on premises version here https://github.com/sagemathinc/cocalc-docker, and sell a more complicated Kubernetes-based on prem version of our software, so you control all data. Please feel free to email [[email protected]](mailto:[email protected]) with more detailed questions. In particular, it could be that we've already worked with your university to use CoCalc for collaborative Jupyter notebooks for teaching (that's the main thing CoCalc is used for, not latex), and that's something we can discuss in our support channels.

Mail - probably with mailu; and a multimedia server, but I haven't decided yet which one zenphoto? piwigo? lychee? or others?). Maybe seafile, although I probably don't need it given my use now of Syncthing and a backup strategy. And maybe... CoCalc (https://github.com/sagemathinc/cocalc-docker) as it'd be great to be able to run this from any machine, rather than having to manage software for different platforms.

Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.

The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)

It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)

I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.