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!
- 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.
- Flax - Centered on flexibility and clarity.
- 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.
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
- 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.
- 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-learnkernel 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.
- bayex - Bayesian Optimization powered by 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.
- 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.
- 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.
- 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_callbackmodule.
Contributions welcome! Read the contribution guidelines first.