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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. With its updated version of Autograd , JAX can automatically differentiate native Python and NumPy functions.
JaxCore is a beautiful, functional, customizable set of tools and widgets to spice up your Windows desktop. JaxCore runs on Rainmeter, so there's no limit for customization.
The Jackson Laboratory's mission is to discover precise genomic solutions for disease and empower the global biomedical community in the shared quest to improve human health.
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.
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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)
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