I have a love/hate relationship with numba. On one hand, the minimal setup is excellent. On the other hand, it has so many limitations and rough patches that occasionally we have been forced to abandon the numba code and reimplement in C -- by which time we've spent more time fighting numba than the C reimplementation takes. This has happened twice out of 5 major recent attempts to use it.

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Numba CPU is pretty good. I'd recommend it, modulo some expectation control:

* In order to actually compile a function, it must be written in a very minimal python subset that feels like C

* Numpy is only partially supported in this subset. Expect to manually reimplement ~1/3 of your simple numpy code and all of your fancy numpy code.

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Numba GPU is not good. I'd avoid by default, unless you're looking for a project to contribute to:

* When we first installed, it was trivially broken by the removal of a long-deprecated CUDA API that had happened a year prior. It was an easy fix, we just chopped the reference out of numba, but it was a bad sign that foretold problems to come.

* It has lingering subtle bugs. One time we passed in a float array, it made us declare the float type three times (silly but not bad), and then it ignored the types and silently binary cast to double (bad). We found a "ghost town" thread where others had the issue.

* It has severe structural limitations. To a first order, GPU performance is all about avoiding stalls, which are usually triggered by CPU-GPU copies, and Numba's limited buffer abstractions sometimes make that difficult (you have to destructure your code) or impossible.

* It doesn't play well with nvidia's perf tools. If you're doing GPU, you're after perf, and GPUs have lots of tricky caveats that can accidentally tank your perf, so this is a much bigger deal than jupyter (until recently) missing a debugger.

* If you're wondering if numba supports CUDA API XYZ, the answer is no.