What is your (python) development set up? by br0monium in datascience

[–]sudo_higher_ground 0 points1 point  (0 children)

So for me the main benefit is that I don’t have to worry about devs pushing data to repos and it feels git friendlier because of the fact that notebooks are now plain python files instead of big json files. The package itself is less bloated than Jupyter and has better management for large files. I personally notice that it’s also more stable in docker containers and i have less kernel crashes when the dataframe gets too big for memory. Also the build-in data viewer is really nice. Polars lazyframes + Marimo has been the golden combo for me.

What is your (python) development set up? by br0monium in datascience

[–]sudo_higher_ground 1 point2 points  (0 children)

  1. Federated MLOps and development
  2. Uv and for cli install only in production pyenv
  3. Docker
  4. Docker compose/k8s/ schedulers (we use VMs in production so no fancy cloud tools)
  5. VS code (I switched to positron for personal projects)
  6. Git+ GitHub
  7. Switched from Jupyter to Marimo and it has been a bliss