JAX: The Hidden Gem of AI Research and High-Performance Computing

Author(s): Harshit Kandoi

Originally published on Towards AI.

Photo by Christian Wiediger on Unsplash

Artificial Intelligence (AI) studies and high-performance computing (HPC) are evolving rapidly, pushing the limits of what`s feasible in deep learning and numerical computation. While TensorFlow and PyTorch have long ruled the AI landscape, a lesser-recognized but noticeably effective framework is emerging — JAX. Developed by Google Research, JAX is designed for high-performance numerical computing, presenting unheard speed and versatility for researchers and engineers working on modern AI models.

What makes JAX stand out? Unlike traditional deep studying libraries, JAX can easily combine with automated differentiation (Autograd), just-in-time (JIT) compilation (thru XLA), and vectorized execution (thru vmap and pmap) to maximise our computational efficiency. These capabilities allow JAX to scale without difficulty, presenting exceptional effects in the course of GPUs and TPUs, making it a hidden gem for those exploring AI beyond conventional frameworks.

In this blog, we can dive deep into JAX`s capabilities, its blessings for TensorFlow and PyTorch, real-world applications, and the way you could get started with this game-changing library. Whether you`re an AI researcher, an HPC enthusiast, or a developer seeking more effective methods to train and deploy deep learning models, JAX comes into frame to offer something that we never noticed.

JAX is an… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI