JAX brings automatic differentiation and the XLA compiler together through a numpy-like API for high performance machine learning research on accelerators like GPUs and TPUs.

This is a curated list of awesome JAX libraries, projects, and other resources. Contributions are welcome!

• Libraries
• Models and Projects
• Videos
• Papers
• Blog Posts
• Community

• Neural Network Libraries
  • Flax - Centered on flexibility and clarity.
  • Haiku - Focused on simplicity, created by the authors of Sonnet at DeepMind.
  • Objax - Has an object oriented design similar to PyTorch.
  • Elegy - A framework-agnostic Trainer interface for the Jax ecosystem. Supports Flax, Haiku, and Optax.
  • RLax - Library for implementing reinforcement learning agents.
  • Trax - "Batteries included" deep learning library focused on providing solutions for common workloads.
  • Jraph - Lightweight graph neural network library.
  • Neural Tangents - High-level API for specifying neural networks of both finite and infinite width.
• NumPyro - Probabilistic programming based on the Pyro library.
• Chex - Utilities to write and test reliable JAX code.
• Optax - Gradient processing and optimization library.
• JAX, M.D. - Accelerated, differential molecular dynamics.
• Coax - Turn RL papers into code, the easy way.
• SymJAX - Symbolic CPU/GPU/TPU programming.
• mcx - Express & compile probabilistic programs for performant inference.

This section contains libraries that are well-made and useful, but have not necessarily been battle-tested by a large userbase yet.

• Neural Network Libraries
  • Parallax - Prototype immutable torch modules for JAX.
  • FedJAX - Federated learning in JAX, built on Optax and Haiku.
• jax-unirep - Library implementing the UniRep model for protein machine learning applications.
• jax-flows - Normalizing flows in JAX.
• sklearn-jax-kernels - scikit-learn kernel matrices using JAX.
• jax-cosmo - Differentiable cosmology library.
• efax - Exponential Families in JAX.
• mpi4jax - Combine MPI operations with your Jax code on CPUs and GPUs.

• Performer - Flax implementation of the Performer (linear transformer via FAVOR+) architecture.
• Reformer - Implementation of the Reformer (efficient transformer) architecture.
• Vision Transformer - Official implementation in Flax of An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
• Fourier Feature Networks - Official implementation of Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.
• Flax Models - Collection of open-sourced Flax models.
• JaxNeRF - Implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis with multi-device GPU/TPU support.
• Big Transfer (BiT) - Implementation of Big Transfer (BiT): General Visual Representation Learning.
• NuX - Normalizing flows with JAX.
• kalman-jax - Approximate inference for Markov (i.e., temporal) Gaussian processes using iterated Kalman filtering and smoothing.
• GPJax - Gaussian processes in JAX.
• jaxns - Nested sampling in JAX.
• Normalizer-Free Networks - Official Haiku implementation of NFNets.

• NeurIPS 2020: JAX Ecosystem Meetup - JAX, its use at DeepMind, and discussion between engineers, scientists, and JAX core team.
• Introduction to JAX - Simple neural network from scratch in JAX.
• JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas - JAX's core design, how it's powering new research, and how you can start using it.
• Bayesian Programming with JAX + NumPyro — Andy Kitchen - Introduction to Bayesian modelling using NumPyro.
• JAX: Accelerated machine-learning research via composable function transformations in Python | NeurIPS 2019 | Skye Wanderman-Milne - JAX intro presentation in Program Transformations for Machine Learning workshop.
• JAX on Cloud TPUs | NeurIPS 2020 | Skye Wanderman-Milne and James Bradbury - Presentation of TPU host access with demo.
• Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond | NeurIPS 2020 - Tutorial created by Zico Kolter, David Duvenaud, and Matt Johnson with Colab notebooks avaliable in Deep Implicit Layers.

This section contains papers focused on JAX (e.g. JAX-based library whitepapers, research on JAX, etc). Papers implemented in JAX are listed in the Models/Projects section.

• Compiling machine learning programs via high-level tracing. Roy Frostig, Matthew James Johnson, Chris Leary. MLSys 2018. - White paper describing an early version of JAX, detailing how computation is traced and compiled.
• JAX, M.D.: A Framework for Differentiable Physics. Samuel S. Schoenholz, Ekin D. Cubuk. NeurIPS 2020. - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more.
• Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. arXiv 2020. - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries.

• Using JAX to accelerate our research by David Budden and Matteo Hessel - Describes the state of JAX and the JAX ecosystem at DeepMind.
• Getting started with JAX (MLPs, CNNs & RNNs) by Robert Lange - Neural network building blocks from scratch with the basic JAX operators.
• Tutorial: image classification with JAX and Flax Linen by 8bitmp3 - Learn how to create a simple convolutional network with the Linen API by Flax and train it to recognize handwritten digits.
• Plugging Into JAX by Nick Doiron - Compares Flax, Haiku, and Objax on the Kaggle flower classification challenge.
• Meta-Learning in 50 Lines of JAX by Eric Jang - Introduction to both JAX and Meta-Learning.
• Normalizing Flows in 100 Lines of JAX by Eric Jang - Concise implementation of RealNVP.
• Differentiable Path Tracing on the GPU/TPU by Eric Jang - Tutorial on implementing path tracing.
• Ensemble networks by Mat Kelcey - Ensemble nets are a method of representing an ensemble of models as one single logical model.
• Out of distribution (OOD) detection by Mat Kelcey - Implements different methods for OOD detection.
• Understanding Autodiff with JAX by Srihari Radhakrishna - Understand how autodiff works using JAX.
• From PyTorch to JAX: towards neural net frameworks that purify stateful code by Sabrina J. Mielke - Showcases how to go from a PyTorch-like style of coding to a more Functional-style of coding.
• Extending JAX with custom C++ and CUDA code by Dan Foreman-Mackey - Tutorial demonstrating the infrastructure required to provide custom ops in JAX.
• Evolving Neural Networks in JAX by Robert Tjarko Lange - Explores how JAX can power the next generation of scalable neuroevolution algorithms.
• Exploring hyperparameter meta-loss landscapes with JAX by Luke Metz - Demonstrates how to use JAX to perform inner-loss optimization with SGD and Momentum, outer-loss optimization with gradients, and outer-loss optimization using evolutionary strategies.

• JAX GitHub Discussions
• Reddit

Contributions welcome! Read the contribution guidelines first.