Google JAX Cookbook

<p>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.</p><p><span style=color: rgba(51 51 51 1)>The book starts with the </span><strong style=color: rgba(51 51 51 1)>move from NumPy to JAX</strong><span style=color: rgba(51 51 51 1)>. It introduces the </span><strong style=color: rgba(51 51 51 1)>best ways to speed up computations handle data types generate random numbers and perform in-place operations</strong><span style=color: rgba(51 51 51 1)>. It then shows you how to </span><strong style=color: rgba(51 51 51 1)>use profiling techniques to monitor computation time and device memory helping you to optimize training and performance</strong><span style=color: rgba(51 51 51 1)>. The debugging section provides </span><strong style=color: rgba(51 51 51 1)>clear and effective strategies for resolving common runtime issues including shape mismatches NaNs and control flow errors</strong><span style=color: rgba(51 51 51 1)>. The book goes on to show you how to </span><strong style=color: rgba(51 51 51 1)>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</strong><span style=color: rgba(51 51 51 1)>.</span></p><p><span style=color: rgba(51 51 51 1)>If you want to optimize training processes this book has you covered. It </span><strong style=color: rgba(51 51 51 1)>includes recipes for efficient data loading building custom neural networks implementing mixed precision and tracking experiments with Penzai</strong><span style=color: rgba(51 51 51 1)>. You'll learn how to </span><strong style=color: rgba(51 51 51 1)>visualize model performance and monitor metrics to assess training progress effectively</strong><span style=color: rgba(51 51 51 1)>. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.</span></p><p></p><h3>Key Learnings</h3><p>Get your calculations done faster by moving from NumPy to JAX's optimized framework.</p><p>Make your training pipelines more efficient by profiling how long things take and how much memory they use.</p><p>Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.</p><p>Get to grips with Pytrees for managing complex nested data structures across various machine learning tasks.</p><p>Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.</p><p>Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.</p><p>Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.</p><p>Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.</p><p>Use serialization techniques to save load and transfer models and training checkpoints efficiently.</p><p></p><h3>Table of Content</h3><p>Transition NumPy to JAX</p><p>Profiling Computation and Device Memory</p><p>Debugging Runtime Values and Errors</p><p>Mastering Pytrees for Data Structures</p><p>Exporting and Serialization</p><p>Type Promotion Semantics and Mixed Precision</p><p>Integrating Foreign Functions (FFI)</p><p>Training Neural Networks with JAX</p>