JAX

JAX combines Autograd and XLA to deliver high-performance numerical computing and machine learning research at scale.

JAX transforms NumPy code into hardware-accelerated kernels using a functional API. It leverages XLA (Accelerated Linear Algebra) to target GPUs and TPUs, achieving massive throughput for deep learning and scientific simulations. Key primitives like jit (just-in-time compilation), vmap (automatic vectorization), and grad (arbitrary-order differentiation) allow developers to write pure Python while executing at native speeds. By treating programs as composable transformations, JAX eliminates the overhead typical of standard Python execution and provides a unified framework for modern AI research.

https://github.com/google/jax