numba is a true horror to use, because supported subset of python language is an ever-moving target and overall small. cython is good but needs time to meddle with. For loop-intensive, simple function I'd advise FFI.
I disagree that it's a "true horror to use." The set of supported built-in classes grows significantly by the day. It's not as good when used for wholly unstructured streams of data (e.g. tuples of mixed type, dicts with complex objects inside of them), but if you can spend the design time to arange things in a structured manner, it's super easy to use, and can seriously boost performance on simple algos.
I've had a ton of success using it in statistical and computational geometry applications.
Let me explain my line of reasoning here (been there at least three times, situation gave 3 different outcomes) :
- case 1 : need to make calculations with a specialized library in C (precision arithmetics). Build a bare FFI, later replaced with CFFI.
- case 2 : loop-intensive on very simple calculations, time-constrained development. Identified as horrible performance in python : tried numba didn't work, ended up using cython, worked really well.
- case 3 : optimize numpy-intensive routine for performance. Tried numba, didn't work. Too expensive to recode in cython. Look at numpy C/C++ interfaces, and numpy-friendly C++ alternatives. Also try to trick numpy to function better (do not ever try this, it'll give worse results). Ended up doing nothing as time to develop was the main constraint here.
If you have a lot of time and work on a small program, maybe you can spend the time to optimize. Team producing lots of complicated algorithms and no way to re-develop everything, stick to python, identify perf losses and choose wisely what you'll optimize.
Numba has progressed but is still not a "drop-in decorator" as advertized. Can even give worse performance in some cases. Nevertheless the idea is good and I praise the effort, when it's done it'll be massive !
FWIW I agree with you. I’ve always found cython easier than Numba. And more performant.
I think Numba has a lot of potential and will improve as they fill out remaining language coverage and finalize the API. The idea of a LLVM JIT compiler for python makes a ton of sense.
