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This document provides a quick overview of essential JAX features, so you can get started with JAX quickly: JAX provides a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings. JAX features built-in Just-In-Time (JIT) compilation via Open XLA, an open-source machine learning compiler ecosystem.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. If you’re looking to train neural networks, use Flax and start with its documentation.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd , JAX can automatically differentiate native Python and NumPy functions.
Using JAX requires installing two packages: jax, which is pure Python and cross-platform, and jaxlib which contains compiled binaries, and requires different builds for different operating systems and accelerators. TL;DR For most users, a typical JAX installation may look something like this: CPU-only (Linux/macOS/Windows) pip install -U jax.
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Set up a JAX-transformable function for a custom VJP rule definition. custom_gradient (fun) Convenience function for defining custom VJP rules (aka custom gradients).
Jax with U.GG's best data for every build. The highest win rate Jax build, from rune set to skill order to item path, for Top. LoL Patch 14.14.
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