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Numba: A High Performance Python Compiler

numba.pydata.org

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Re: Numba: A High Performance Python Compiler

#61

Earlier quoted context omitted.

> It’s also more versatile than Jax Does numba do automatic differentiation? I view JAX as primarily an automatic differentiation tool with the bonus that it makes great use of XLA and can easy make use of GPU/TPUs. I don’t usually see numba and JAX as solving the same problem, but would be excited to be wrong

They solve different problems. Numba compiles functions down to machine code or cuda kernels, that's it. XLA is "higher level" than what Numba produces. You may be able to get the equivalent of jax via numba+numpy+autograd[1], but I haven't tried it before. IMHO, jax is best thought of as a numerical computation library that happens to include autograd, vmapping, pmapping and provides a high level interface for XLA.…

> You may be able to get the equivalent of jax via numba+numpy+autograd[1], but I haven't tried it before.

I have tried it before [0]. And it works. As expected. Not much friction.

[0]: sorry no github

Re: Numba: A High Performance Python Compiler

#63
post #62

I will save you the pain: switch to Julia.

I don't recommend Julia for anything critical to production.

I would. It's much safer than Python which has a package ecosystem that is known to be critically unsafe. https://pytorch.org/blog/compromised-nightly-dependency/ is just the latest example of security issues it's been having, https://moyix.blogspot.com/2022/09/someones-been-messing-wit... is general numerical incorrectness which is non-local and cannot be turned off, and that's not even getting to the specific inaccuracies of Numba. I'd switch away from a numerically incorrect security issue today!
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