Why You Should (or Shouldn't) Be Using JAX in 2022

JAX is a numerical computing library that incorporates composable function transformations. It is not a Deep Learning framework or library, but it can be used for scientific computing and has the potential to significantly increase computation speed through various function transformations such as grad(), vmap(), pmap(), and jit(). While JAX is still considered experimental and requires diligence when using, its growing popularity in research communities suggests promising future developments.