Some chat: Pyro/tf.prob are all more of an operations/stat research oriented tools, I gather but if there are some MC abstractions it might be good choice.
Yes, tested and used Jax for some prototyping but it felt like still has "two language problem". But Taichi looks quite interesting, will check out.
Of course, forgot to mention, it has to be lightweight, torch and tf are now huge platforms. Not sure, probably Julia-lang is much small.
I mostly work with arrays in numpy but sometimes I get stuck with a problem that needs a for loop - only two things I can think of are parallelize the for loop or use numba/jax JIT functions and GPU acceleration.
Both don’t just work out of the box like Julia or MATLAB’s “parfor” loop, but seem to work well enough for non trivial for loop cases.
Have you tried Jax or Taichi? https://www.taichi-lang.org/
For Monte Carlo simulations, Pyro and tensorflow_probability have also nice abstractions.
Some chat: Pyro/tf.prob are all more of an operations/stat research oriented tools, I gather but if there are some MC abstractions it might be good choice.
Yes, tested and used Jax for some prototyping but it felt like still has "two language problem". But Taichi looks quite interesting, will check out.
Of course, forgot to mention, it has to be lightweight, torch and tf are now huge platforms. Not sure, probably Julia-lang is much small.
I mostly work with arrays in numpy but sometimes I get stuck with a problem that needs a for loop - only two things I can think of are parallelize the for loop or use numba/jax JIT functions and GPU acceleration.
Both don’t just work out of the box like Julia or MATLAB’s “parfor” loop, but seem to work well enough for non trivial for loop cases.
Thanks. numba is a good trade-off before moving to Julia completely.