pgcontents VS nbdev

First, the format. The ipynb format does not play nicely with git since it stores the cell's source code and output in the same file. But Jupyter has built-in mechanisms to allow other formats to look like notebooks. For example, here's a library that allows you to store notebooks on a postgres database (I know this isn't practical, but it's a great example). To give more practical advice, jupytext allows you to open .py files as notebooks. So you can develop interactively but in the backend, you're storing .py files.

First, yes, this is a common question. IPython does not try to deal with that, it's just the executing engine.

Notebooks, do not have to be stored in ipynb form, I would suggest to look at https://github.com/mwouts/jupytext, and notebook UI is inherently not design for multi-file and application developpement. So training humans will always be necessary.

Technically Jupyter Notebook does not even care that notebooks are files, you could save then using say postgres (https://github.com/quantopian/pgcontents) , and even sync content between notebooks.

I'm not too well informed anymore on this particular topic, but there are other folks at https://www.quansight.com/ that might be more aware, you can also ask on discourse.jupyter.org, I'm pretty sure you can find threads on those issues.

I think on the Jupyter side we could do a better job curating and exposing many tools to help with that, but there are just so many hours in the day...

I also recommend I don't like notebook from Joel Grus, https://www.youtube.com/watch?v=7jiPeIFXb6U it's a really funny talk, a lot of the points are IMHO invalid as Joel is misinformed on how things can be configured, but still a great watch.

One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev

- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.

9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/

See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.

You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff

nbdev: jupyter notebooks -> python package

Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)