If numpy is a good fit for it, JAX is a good fit for accelerating it, basically. I think of it as numpy plus program transformations, such as differentiation, J

If numpy is a good fit for it, JAX is a good fit for accelerating it, basically. I think of it as numpy plus program transformations, such as differentiation, JIT, parallelization, compiling to XLA, compiling to TPU/GPU, etc. The magic of JAX is that it keeps all that stuff about as simple as writing numpy code.