The PyTorch developer's guide to JAX fundamentals
Like many PyTorch users, you may have heard great things about JAX — its high performance, the elegance of its functional programming approach, and its powerful, built-in support for parallel computation. However, you may have also struggled to find what you need to get started: a straightforward, easy-to-follow tutorial to help you understand the basics of JAX by connecting its new concepts to the PyTorch building blocks that you’re already familiar with. So, we created one!
In this tutorial, we explore the basics of the JAX ecosystem from the lens of a PyTorch user, focusing on training a simple neural network in both frameworks for the classic machine learning (ML) task of predicting which passengers survived the Titanic disaster . Along the way, we introduce JAX by demonstrating how many things — from model definitions and instantiation to training — map to their PyTorch equivalents.
You can follow along with full code examples in the accompanying notebook: https://www.kaggle.com/code/anfalatgoogle/pytorch-developer-s-guide-to-jax-fundamentals
