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Productive, portable, and performant GPU programming in Python
...It uses JIT compilation (via LLVM and its runtime TiRT) to offload compute-heavy code to CPUs, GPUs, mobile devices, and embedded systems. With built-in support for sparse data structures (SNode), automaticdifferentiation, AOT deployment, and compatibility with CUDA, Vulkan, Metal, and OpenGL ES, it empowers disciplines like simulation, graphics, AI, and robotics
Composable transformations of Python+NumPy programs
...But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. Compilation and automaticdifferentiation can be composed arbitrarily, so you can express sophisticated algorithms and get maximal performance without leaving Python.