NumPyro / JAX / PyTorch just seems like the most versatile offering out there right now
JAX
It was just a quick demonstration of how easily one can use Theano as a generalized graph "front-end", while also preserving its more unique and programmable symbolic optimization capabilities. JAX was one of a few "backends" I considered, and, due to the JAX Python library, it also looked like the most straightforward one to implement first.
on the contrary, i recently was looking for python libraries for some bayesian computations for some greenfield development and pymc3 was at the top of the list. with statistical libraries, i prioritize well-tested, large community, and extensive documentation. if others share the same priorities, there's a long future for pymc3.
I welcome and applaud the choice of JAX as it shows a lot of promise with autograd and flexible execution targets.