18:08 sunpoet 

• math/py-jax 0.1.72 Differentiate, compile, and transform Numpy code

Add py-jax 0.1.72

JAX is Autograd and XLA, brought together for high-performance machine learning
research.

With its updated version of Autograd, JAX can automatically differentiate native
Python and NumPy functions. It can differentiate through loops, branches,
recursion, and closures, and it can take derivatives of derivatives of
derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation)
via grad as well as forward-mode differentiation, and the two can be composed
arbitrarily to any order.

What's new is that JAX uses XLA to compile and run your NumPy programs on GPUs
and TPUs. Compilation happens under the hood by default, with library calls
getting just-in-time compiled and executed. But JAX also lets you just-in-time

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