Google JAX Cookbook

This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across machine learning and numerical computing projects.

The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.

If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.

Key Learnings

Get your calculations done faster by moving from NumPy to JAX's optimized framework.

Make your training pipelines more efficient by profiling how long things take and how much memory they use.

Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.

Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.

Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.

Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.

Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.

Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.

Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.

Table of Content

Transition NumPy to JAX

Profiling Computation and Device Memory

Debugging Runtime Values and Errors

Mastering Pytrees for Data Structures

Exporting and Serialization

Type Promotion Semantics and Mixed Precision

Integrating Foreign Functions (FFI)

Training Neural Networks with JAX