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Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library.
In JAX in Action you will learn how to:
Use JAX for numerical calculations
Build differentiable models with JAX primitives
Run distributed and parallelized computations with JAX
Use high-level neural network libraries such as Flax and Haiku
Leverage libraries and modules from the JAX ecosystem
The JAX numerical computing library tackles the core performance challenges at the heart of Deep Learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations.
JAX in Action is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment.
about the technology
The JAX Python mathematics library is used by many successful deep learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level deep learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, federated learning, and much more! JAX brings a functional programming mindset to Python deep learning, letting you improve your composability and parallelization in a cluster.
FIRST STEPS
Intro to JAX
Your first program in JAX
CORE JAX
Working with tensors
Autodiff
Compiling your code
Parallelizing and vectorizing your code
Random numbers in JAX
Complex structures in JAX
ECOSYSTEM
Optax — optimization in JAX
Flax — a high-level neural network library
Haiku — sonnet for JAX
When you still need TensorFlow/PyTorch
Writing reliable JAX code
Other members of the ecosystem
PPENDIXES
A Installing JAX
B Using Google Colab