Awesome JAX

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!

Contents

• Libraries
• Models and Projects
• Videos
• Papers
• Tutorials and 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.
  • 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.
  • HuggingFace - Ecosystem of pretrained Transformers for a wide range of natural language tasks (Flax).
  • Equinox - Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.
• NumPyro - Probabilistic programming based on the Pyro library.
• Chex - Utilities to write and test reliable JAX code.
• Optax - Gradient processing and optimization library.
• RLax - Library for implementing reinforcement learning agents.
• 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.
• Distrax - Reimplementation of TensorFlow Probability, containing probability distributions and bijectors.
• cvxpylayers - Construct differentiable convex optimization layers.
• TensorLy - Tensor learning made simple.
• NetKet - Machine Learning toolbox for Quantum Physics.

• Neural Network Libraries
  • FedJAX - Federated learning in JAX, built on Optax and Haiku.
  • Equivariant MLP - Construct equivariant neural network layers.
  • jax-resnet - Implementations and checkpoints for ResNet variants in Flax.
• 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.
• imax - Image augmentations and transformations.
• FlaxVision - Flax version of TorchVision.
• Oryx - Probabilistic programming language based on program transformations.
• Optimal Transport Tools - Toolbox that bundles utilities to solve optimal transport problems.
• delta PV - A photovoltaic simulator with automatic differentation.
• jaxlie - Lie theory library for rigid body transformations and optimization.
• BRAX - Differentiable physics engine to simulate environments along with learning algorithms to train agents for these environments.
• flaxmodels - Pretrained models for Jax/Flax.
• CR.Sparse - XLA accelerated algorithms for sparse representations and compressive sensing.
• exojax - Automatic differentiable spectrum modeling of exoplanets/brown dwarfs compatible to JAX.
• JAXopt - Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX.
• PIX - PIX is an image processing library in JAX, for JAX.

• Fourier Feature Networks - Official implementation of Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.
• 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.
• Amortized Bayesian Optimization - Code related to Amortized Bayesian Optimization over Discrete Spaces.
• Accurate Quantized Training - Tools and libraries for running and analyzing neural network quantization experiments in JAX and Flax.
• BNN-HMC - Implementation for the paper What Are Bayesian Neural Network Posteriors Really Like?.
• JAX-DFT - One-dimensional density functional theory (DFT) in JAX, with implementation of Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics.
• Robust Loss - Reference code for the paper A General and Adaptive Robust Loss Function.

Flax

• Performer - Flax implementation of the Performer (linear transformer via FAVOR+) architecture.
• 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.
• JAX RL - Implementations of reinforcement learning algorithms.
• gMLP - Implementation of Pay Attention to MLPs.
• MLP Mixer - Minimal implementation of MLP-Mixer: An all-MLP Architecture for Vision.
• Distributed Shampoo - Implementation of Second Order Optimization Made Practical.
• NesT - Official implementation of Aggregating Nested Transformers.
• XMC-GAN - Official implementation of Cross-Modal Contrastive Learning for Text-to-Image Generation.
• FNet - Official implementation of FNet: Mixing Tokens with Fourier Transforms.
• GFSA - Official implementation of Learning Graph Structure With A Finite-State Automaton Layer.
• IPA-GNN - Official implementation of Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
• Flax Models - Collection of models and methods implemented in Flax.
• Protein LM - Implements BERT and autoregressive models for proteins, as described in Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences and ProGen: Language Modeling for Protein Generation.
• Slot Attention - Reference implementation for Differentiable Patch Selection for Image Recognition.
• Vision Transformer - Official implementation of An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
• FID computation - Port of mseitzer/pytorch-fid to Flax.

Haiku

• AlphaFold - Implementation of the inference pipeline of AlphaFold v2.0, presented in Highly accurate protein structure prediction with AlphaFold.
• Adversarial Robustness - Reference code for Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples and Fixing Data Augmentation to Improve Adversarial Robustness.
• Bootstrap Your Own Latent - Implementation for the paper Bootstrap your own latent: A new approach to self-supervised Learning.
• Gated Linear Networks - GLNs are a family of backpropagation-free neural networks.
• Glassy Dynamics - Open source implementation of the paper Unveiling the predictive power of static structure in glassy systems.
• MMV - Code for the models in Self-Supervised MultiModal Versatile Networks.
• Normalizer-Free Networks - Official Haiku implementation of NFNets.
• NuX - Normalizing flows with JAX.
• OGB-LSC - This repository contains DeepMind’s entry to the PCQM4M-LSC (quantum chemistry) and MAG240M-LSC (academic graph) tracks of the OGB Large-Scale Challenge (OGB-LSC).
• Persistent Evolution Strategies - Code used for the paper Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies.
• WikiGraphs - Baseline code to reproduce results in WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Datase.

Trax

• Reformer - Implementation of the Reformer (efficient transformer) architecture.

• 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.
• Solving y=mx+b with Jax on a TPU Pod slice - Mat Kelcey - A four part YouTube tutorial series with Colab notebooks that starts with Jax fundamentals and moves up to training with a data parallel approach on a v3-32 TPU Pod slice.
• JAX, Flax & Transformers 🤗 - 3 days of talks around JAX / Flax, Transformers, large-scale language modeling and other great topics.

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.
• Deterministic ADVI in JAX by Martin Ingram - Walk through of implementing automatic differentiation variational inference (ADVI) easily and cleanly with JAX.
• Evolved channel selection by Mat Kelcey - Trains a classification model robust to different combinations of input channels at different resolutions, then uses a genetic algorithm to decide the best combination for a particular loss.
• Introduction to JAX by Kevin Murphy - Colab that introduces various aspects of the language and applies them to simple ML problems.
• Writing an MCMC sampler in JAX by Jeremie Coullon - Tutorial on the different ways to write an MCMC sampler in JAX along with speed benchmarks.
• How to add a progress bar to JAX scans and loops by Jeremie Coullon - Tutorial on how to add a progress bar to compiled loops in JAX using the host_callback module.

• JAX GitHub Discussions
• Reddit

Contributing

Contributions welcome! Read the contribution guidelines first.