We were very heavy numba users at my former company. I would even go so far as to say numba was probably the biggest computational enabler for the product. I’ve also made a small contribution to the library.

It’s a phenomenal library for developing novel computationally intensive algorithms on numpy arrays. It’s also more versatile than Jax.

In presentations, I’ve heard Leland McInnes credits numba often when he speaks of his development of UMAP. We built a very computationally intensive portion of our application with it and it has been running in production, stable, for several years now.

It’s not suitable for all use cases. But I recommend testing it if you need to do somewhat complex calculations iterating over numpy arrays for which standard numpy or scipy functions don’t exist. Even then, often we were surprised that we could speed up some of those calculations by placing them inside numba.

Edit: ex of a very small function I wrote with numba that speeds up an existing numpy function (note - written years ago and numba has undergone quite some amount of changes since!): https://github.com/grej/pure_numba_alias_sampling

Disclosure - I now work for Anaconda, the company that sponsors the numba project.