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by mlthoughts2018 2747 days ago
I’ve had the opposite experience with numba in production. It works almost flawlessly, very easy to reason about the generated code and inspect annotations, easy to debug.
2 comments

Do you interact with numpy or other compiled numerical packages? this is where it usually breaks for me. The thing is that I use numpy, scipy, keras, tensorflow, etc. in literally every project, making numba not too useful
Check out github.com/google/jax, it’s NumPy on the GPU with automatic differentiation, JIT and autobatching.
That’s very cool. Numba and Cython work extremely well with virtually no overhead or extra effort on my part, so jax doesn’t seem like it would buy me much for most of my work. But I can imagine a lot of projects where jax woukd be useful, and I plan to keep current on best practices for it.