Maximal performance without leaving Python. Compilation and automatic differentiation can beĬomposed arbitrarily, so you can express sophisticated algorithms and get Into XLA-optimized kernels using a one-function API, But JAX also lets you just-in-time compile your own Python functions Under the hood by default, with library calls getting just-in-time compiled andĮxecuted. To compile and run your NumPy programs on GPUs and TPUs. Via grad as well as forward-mode differentiation,Īnd the two can be composed arbitrarily to any order. It supports reverse-mode differentiation (a.k.a. Recursion, and closures, and it can take derivatives of derivatives ofĭerivatives. It can differentiate through loops, branches, JAX can automatically differentiate native Brought together for high-performance machine learning research.
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