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like some other commenters here, https://github.com/CliMA/Oceananigans.jl immediately comes to mind.
> JAX offers more than just a JIT compiler: JAX functions are also differentiable
if the downstream library is completely implemented in JAX (numba) ecosystem. Similar for Julia, except implementing fast code in Julia is natural, but many python library is only differentiable because the 100x more effort in writing C/C++ backend, binding to python, and writing chain rules for foreign functions.
I would imagine Julia to be a good fit for this direction in the future!
True, but unfortunately Pytorch is not quite there yet when it comes to more complex benchmarks:
https://github.com/dionhaefner/pyhpc-benchmarks#example-resu...
JAX really is the only library that comes close to low-level code on CPU, almost always.
https://github.com/FluxML/XLA.jl
When in doubt, piggybacking on (or at least interoperating with) what the large technology companies are investing in is probably savvy, sort of what the OP did.