JAX runs transparently on the GPU or TPU (falling back to CPU if you don’t have one). We’ll also end up composing these in interesting ways. Vmap(), for automatic vectorization or batching. It also comes with a few program transformations that are useful when writing numerical code. JAX is much more than just a GPU-backed NumPy. See Is JAX faster than NumPy? for more comparison of performance characteristics of NumPy and JAX If you have a GPU (or TPU!) these calls run on the accelerator and have the potential to be much faster than on CPU. The behavior of device_put() is equivalent to the function jit(lambda x: x), but it’s faster. The output of device_put() still acts like an NDArray, but it only copies values back to the CPU when they’re needed for printing, plotting, saving to disk, branching, etc. Your own Python functions into XLA-optimized kernels using a one-function API.Ĭompilation and automatic differentiation can be composed arbitrarily, so youĬan express sophisticated algorithms and get maximal performance without havingģ81 ms ± 1.46 ms per loop (mean ± std. But JAX even lets you just-in-time compile
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To compile and run your NumPy code on accelerators, like GPUs and TPUs.Ĭompilation happens under the hood by default, with library calls getting It supports reverse-mode as well as forward-mode differentiation, and the two can be composed arbitrarily Recursion, and closures, and it can even take derivatives of derivatives ofĭerivatives. It canĭifferentiate through a large subset of Python’s features, including loops, ifs, With its updated version of Autograd, JAXĬan automatically differentiate native Python and NumPy code. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research. Using JAX in multi-host and multi-process environmentsĢ026: Custom JVP/VJP rules for JAX-transformable functionsĤ008: Custom VJP and `nondiff_argnums` updateĩ407: Design of Type Promotion Semantics for JAXġ1830: `jax.remat` / `jax.checkpoint` new implementation Named axes and easy-to-revise parallelism Training a Simple Neural Network, with PyTorch Data Loading Training a Simple Neural Network, with tensorflow/datasets Data LoadingĬustom derivative rules for JAX-transformable Python functions Advanced Automatic Differentiation in JAX
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