Like many PyTorch users, you may have heard about JAX, including its superior performance, the elegance of its functional programming approach, and its powerful built-in support for parallel computing. However, you may have struggled to find what you need to get started. This is a quick and easy-to-follow tutorial that helps you understand the basics of JAX by connecting new JAX concepts to the PyTorch building blocks you're already familiar with. So we created it!
In this tutorial, we explore the basics of the JAX ecosystem from the perspective of a PyTorch user and focus on training a simple neural network in both frameworks for the classic machine learning (ML) task of predicting which passengers will survive. Titanic disaster. Along the way, we'll introduce JAX, demonstrating that much of it maps to its PyTorch equivalent, from model definition to instantiation to training.
See the complete code example in the included notebook. https://www.kaggle.com/code/anfalatgoogle/pytorch-developer-s-guide-to-jax-fundamentals
Modularization with JAX
PyTorch users may initially find Jax's highly modular ecosystem to be quite different from what they are used to seeing. JAX is focused on being a high-performance numerical computation library that supports: automatic differentiation. Unlike PyTorch, it does not attempt to provide explicit built-in support for defining neural networks, optimizers, etc. Instead, JAX is designed to be flexible, allowing you to incorporate the framework of your choice and add its functionality.
In this tutorial, Flax Neural Network Library and Optax optimization library — Both are very popular and well-supported libraries. For a very PyTorch-like experience, we'll show you how to train a neural network with the new Flax NNX API, and then show you how to do the same with the older but still widely used Linen API.
