jupyterlab-gitplus VS pyro

If you are in need of a diff tool for jupter notebooks use https://www.reviewnb.com/ and for word documents use https://www.simuldocs.com/

- GitHub PR code reviews with ReviewNB[4]

Alternatively, if you don't care about cell outputs then Jupytext[5]

Disclaimer: I built ReviewNB. It's a completely bootstrapped business, 5 years in the making and now used by leading DS teams at Meta, AWS, NASA JPL, AirBnB, Lyft, Affirm, AMD, Microsoft & more (https://www.reviewnb.com/#customers)

[1] https://github.com/jupyterlab/jupyterlab-git

Already a GitHub plugin that does this very nicely: ReviewNB

For version control: https://www.reviewnb.com/ helps. Agree with the rest but some experimental notebooks are useful to track/version control.

It's not focused on collaboration, but it does add some critical pieces that otherwise make Jupyter development frustrating when working with a team. Specifically: `nbdev_prepare` ensures that diffs are as small as possible, by removing and standardising notebook metadata; and `nbdev_fix` fixes merge conflicts so that they are cell-level, rather than line level, so they can be opened and fixed in notebooks.

Something else we've found helpful for collaboration (not associated - just happy users) is this: https://www.reviewnb.com/ . It means we can get a nice notebook-based PR workflow.

Real-time collaboration is available in Jupyter nowadays: https://jupyterlab.readthedocs.io/en/stable/user/rtc.html . nbdev doesn't have any extra functionality for it, however -- but it should work fine in this environment.

Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.

Amortized Variational Inference (Like done in pyro.ai with neural networks)

Can you compare this to Pyro, which is also built on top of PyTorch?

Using pyod, statmodels, scikit-learn, Tensorflow and pyro.ai (that is using PyTorch as backend). I always use the same framework for training and for production.