jax-js: A JavaScript Implementation of Google's JAX Machine Learning Framework for Web Browsers
By
ekzhang
If you only eat one bagel today, this is the bagel.
Summary
The article announces jax-js, a new machine learning library for the web that reimplements Google DeepMind's JAX framework in pure JavaScript. The library runs completely in the browser using WebGPU and Wasm kernels, enabling cross-platform machine learning on the frontend web. The developer created it to provide a way to run numerical programs and machine learning in JavaScript, complementing Python's dominance in ML while leveraging JavaScript's ubiquity on the web.
Key quotes
· 5 pulledI'm excited to release jax-js, a machine learning library for the web.
You can think of it as a reimplementation of Google DeepMind's JAX framework (similar to PyTorch) in pure JavaScript.
jax-js runs completely in the browser by generating fast WebGPU and Wasm kernels.
Starting in February this year, I spent nights and weekends working on a new ML library for the browser.
I wanted a cross-platform way to run numerical programs on the frontend web, so you can do machine learning.
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