awesome-awesomeness/terminal/jax2

                                                                       Awesome JAX !Awesome (https://awesome.re/badge.svg) (https://awesome.re) (https://github.com/google/jax)


JAX (https://github.com/google/jax) brings automatic differentiation and the XLA compiler (https://www.tensorflow.org/xla) together through a NumPy (https://numpy.org/)-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 (#libraries)
- Models and Projects (#models-and-projects)
- Videos (#videos)
- Papers (#papers)
- Tutorials and Blog Posts (#tutorials-and-blog-posts)
- Books (#books)
- Community (#community)



Libraries

- Neural Network Libraries
- **Flax** (https://github.com/google/flax) - Centered on flexibility and clarity.                                                                                                            
- **Flax NNX** (https://github.com/google/flax/tree/main/flax/nnx) - An evolution on Flax by the same team                                                                                    
- **Haiku** (https://github.com/deepmind/dm-haiku) - Focused on simplicity, created by the authors of Sonnet at DeepMind.                                                                     
- **Objax** (https://github.com/google/objax) - Has an object oriented design similar to PyTorch.                                                                                             
- **Elegy** (https://poets-ai.github.io/elegy/) - A High Level API for Deep Learning in JAX. Supports Flax, Haiku, and Optax.                                                                 
- **Trax** (https://github.com/google/trax) - "Batteries included" deep learning library focused on providing solutions for common workloads.                                                 
- **Jraph** (https://github.com/deepmind/jraph) - Lightweight graph neural network library.                                                                                                   
- **Neural Tangents** (https://github.com/google/neural-tangents) - High-level API for specifying neural networks of both finite and _infinite_ width.                                        
- **HuggingFace Transformers** (https://github.com/huggingface/transformers) - Ecosystem of pretrained Transformers for a wide range of natural language tasks (Flax).                        
- **Equinox** (https://github.com/patrick-kidger/equinox) - Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.                                                 
- **Scenic** (https://github.com/google-research/scenic) - A Jax Library for Computer Vision Research and Beyond.                                                                             
- **Penzai** (https://github.com/google-deepmind/penzai) - Prioritizes legibility, visualization, and easy editing of neural network models with composable tools and a simple mental model.  
- Levanter (https://github.com/stanford-crfm/levanter) - Legible, Scalable, Reproducible Foundation Models with Named Tensors and JAX.  
- EasyLM (https://github.com/young-geng/EasyLM) - LLMs made easy: Pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.  
- NumPyro (https://github.com/pyro-ppl/numpyro) - Probabilistic programming based on the Pyro library. 
- Chex (https://github.com/deepmind/chex) - Utilities to write and test reliable JAX code. 
- Optax (https://github.com/deepmind/optax) - Gradient processing and optimization library. 
- RLax (https://github.com/deepmind/rlax) - Library for implementing reinforcement learning agents. 
- JAX, M.D. (https://github.com/google/jax-md) - Accelerated, differential molecular dynamics. 
- Coax (https://github.com/coax-dev/coax) - Turn RL papers into code, the easy way. 
- Distrax (https://github.com/deepmind/distrax) - Reimplementation of TensorFlow Probability, containing probability distributions and bijectors. 
- cvxpylayers (https://github.com/cvxgrp/cvxpylayers) - Construct differentiable convex optimization layers. 
- TensorLy (https://github.com/tensorly/tensorly) - Tensor learning made simple. 
- NetKet (https://github.com/netket/netket) - Machine Learning toolbox for Quantum Physics. 
- Fortuna (https://github.com/awslabs/fortuna) - AWS library for Uncertainty Quantification in Deep Learning. 
- BlackJAX (https://github.com/blackjax-devs/blackjax) - Library of samplers for JAX. 



New Libraries

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** (https://github.com/google/fedjax) - Federated learning in JAX, built on Optax and Haiku.                    
- **Equivariant MLP** (https://github.com/mfinzi/equivariant-MLP) - Construct equivariant neural network layers.          
- **jax-resnet** (https://github.com/n2cholas/jax-resnet/) - Implementations and checkpoints for ResNet variants in Flax. 
- **Parallax** (https://github.com/srush/parallax) - Immutable Torch Modules for JAX.                                     
- Nonlinear Optimization
- **Optimistix** (https://github.com/patrick-kidger/optimistix) - Root finding, minimisation, fixed points, and least squares.    
- **JAXopt** (https://github.com/google/jaxopt) - Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. 
- jax-unirep (https://github.com/ElArkk/jax-unirep) - Library implementing the UniRep model (https://www.nature.com/articles/s41592-019-0598-1) for protein machine learning applications. 
- flowjax (https://github.com/danielward27/flowjax) - Distributions and normalizing flows built as equinox modules. 
- jax-flows (https://github.com/ChrisWaites/jax-flows) - Normalizing flows in JAX. 
- sklearn-jax-kernels (https://github.com/ExpectationMax/sklearn-jax-kernels) - scikit-learn kernel matrices using JAX. 
- jax-cosmo (https://github.com/DifferentiableUniverseInitiative/jax_cosmo) - Differentiable cosmology library. 
- efax (https://github.com/NeilGirdhar/efax) - Exponential Families in JAX. 
- mpi4jax (https://github.com/PhilipVinc/mpi4jax) - Combine MPI operations with your Jax code on CPUs and GPUs. 
- imax (https://github.com/4rtemi5/imax) - Image augmentations and transformations. 
- FlaxVision (https://github.com/rolandgvc/flaxvision) - Flax version of TorchVision. 
- Oryx (https://github.com/tensorflow/probability/tree/master/spinoffs/oryx) - Probabilistic programming language based on program transformations.
- Optimal Transport Tools (https://github.com/google-research/ott) - Toolbox that bundles utilities to solve optimal transport problems.
- delta PV (https://github.com/romanodev/deltapv) - A photovoltaic simulator with automatic differentation. 
- jaxlie (https://github.com/brentyi/jaxlie) - Lie theory library for rigid body transformations and optimization. 
- BRAX (https://github.com/google/brax) - Differentiable physics engine to simulate environments along with learning algorithms to train agents for these environments. 
- flaxmodels (https://github.com/matthias-wright/flaxmodels) - Pretrained models for Jax/Flax. 
- CR.Sparse (https://github.com/carnotresearch/cr-sparse) - XLA accelerated algorithms for sparse representations and compressive sensing. 
- exojax (https://github.com/HajimeKawahara/exojax) - Automatic differentiable spectrum modeling of exoplanets/brown dwarfs compatible to JAX. 
- PIX (https://github.com/deepmind/dm_pix) - PIX is an image processing library in JAX, for JAX. 
- bayex (https://github.com/alonfnt/bayex) - Bayesian Optimization powered by JAX. 
- JaxDF (https://github.com/ucl-bug/jaxdf) - Framework for differentiable simulators with arbitrary discretizations. 
- tree-math (https://github.com/google/tree-math) - Convert functions that operate on arrays into functions that operate on PyTrees. 
- jax-models (https://github.com/DarshanDeshpande/jax-models) - Implementations of research papers originally without code or code written with frameworks other than JAX. 
- PGMax (https://github.com/vicariousinc/PGMax) - A framework for building discrete Probabilistic Graphical Models (PGM's) and running inference inference on them via JAX. 
- EvoJAX (https://github.com/google/evojax) - Hardware-Accelerated Neuroevolution 
- evosax (https://github.com/RobertTLange/evosax) - JAX-Based Evolution Strategies 
- SymJAX (https://github.com/SymJAX/SymJAX) - Symbolic CPU/GPU/TPU programming. 
- mcx (https://github.com/rlouf/mcx) - Express & compile probabilistic programs for performant inference. 
- Einshape (https://github.com/deepmind/einshape) - DSL-based reshaping library for JAX and other frameworks. 
- ALX (https://github.com/google-research/google-research/tree/master/alx) - Open-source library for distributed matrix factorization using Alternating Least Squares, more info in _ALX: Large Scale Matrix Factorization on TPUs_ 
(https://arxiv.org/abs/2112.02194).
- Diffrax (https://github.com/patrick-kidger/diffrax) - Numerical differential equation solvers in JAX. 
- tinygp (https://github.com/dfm/tinygp) - The _tiniest_ of Gaussian process libraries in JAX. 
- gymnax (https://github.com/RobertTLange/gymnax) - Reinforcement Learning Environments with the well-known gym API. 
- Mctx (https://github.com/deepmind/mctx) - Monte Carlo tree search algorithms in native JAX. 
- KFAC-JAX (https://github.com/deepmind/kfac-jax) - Second Order Optimization with Approximate Curvature for NNs. 
- TF2JAX (https://github.com/deepmind/tf2jax) - Convert functions/graphs to JAX functions. 
- jwave (https://github.com/ucl-bug/jwave) - A library for differentiable acoustic simulations 
- GPJax (https://github.com/thomaspinder/GPJax) - Gaussian processes in JAX.
- Jumanji (https://github.com/instadeepai/jumanji) - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX. 
- Eqxvision (https://github.com/paganpasta/eqxvision) - Equinox version of Torchvision. 
- JAXFit (https://github.com/dipolar-quantum-gases/jaxfit) - Accelerated curve fitting library for nonlinear least-squares problems (see arXiv paper (https://arxiv.org/abs/2208.12187)). 
- econpizza (https://github.com/gboehl/econpizza) - Solve macroeconomic models with hetereogeneous agents using JAX. 
- SPU (https://github.com/secretflow/spu) - A domain-specific compiler and runtime suite to run JAX code with MPC(Secure Multi-Party Computation). 
- jax-tqdm (https://github.com/jeremiecoullon/jax-tqdm) - Add a tqdm progress bar to JAX scans and loops. 
- safejax (https://github.com/alvarobartt/safejax) - Serialize JAX, Flax, Haiku, or Objax model params with 🤗safetensors. 
- Kernex (https://github.com/ASEM000/kernex) - Differentiable stencil decorators in JAX. 
- MaxText (https://github.com/google/maxtext) - A simple, performant and scalable Jax LLM written in pure Python/Jax and targeting Google Cloud TPUs. 
- Pax (https://github.com/google/paxml) - A Jax-based machine learning framework for training large scale models. 
- Praxis (https://github.com/google/praxis) - The layer library for Pax with a goal to be usable by other JAX-based ML projects. 
- purejaxrl (https://github.com/luchris429/purejaxrl) - Vectorisable, end-to-end RL algorithms in JAX. 
- Lorax (https://github.com/davisyoshida/lorax) - Automatically apply LoRA to JAX models (Flax, Haiku, etc.)
- SCICO (https://github.com/lanl/scico) - Scientific computational imaging in JAX. 
- Spyx (https://github.com/kmheckel/spyx) - Spiking Neural Networks in JAX for machine learning on neuromorphic hardware. 
- Brain Dynamics Programming Ecosystem
- **BrainPy** (https://github.com/brainpy/BrainPy) - Brain Dynamics Programming in Python.                                               
- **brainunit** (https://github.com/chaobrain/brainunit) - Physical units and unit-aware mathematical system in JAX.                     
- **dendritex** (https://github.com/chaobrain/dendritex) - Dendritic Modeling in JAX.                                                    
- **brainstate** (https://github.com/chaobrain/brainstate) - State-based Transformation System for Program Compilation and Augmentation. 
- **braintaichi** (https://github.com/chaobrain/braintaichi) - Leveraging Taichi Lang to customize brain dynamics operators.             
- OTT-JAX (https://github.com/ott-jax/ott) - Optimal transport tools in JAX. 
- QDax (https://github.com/adaptive-intelligent-robotics/QDax) - Quality Diversity optimization in Jax. 
- JAX Toolbox (https://github.com/NVIDIA/JAX-Toolbox) - Nightly CI and optimized examples for JAX on NVIDIA GPUs using libraries such as T5x, Paxml, and Transformer Engine. 
- Pgx (http://github.com/sotetsuk/pgx) - Vectorized board game environments for RL with an AlphaZero example. 
- EasyDeL (https://github.com/erfanzar/EasyDeL) - EasyDeL 🔮 is an OpenSource Library to make your training faster and more Optimized With cool Options for training and serving (Llama, MPT, Mixtral, Falcon, etc) in JAX 
- XLB (https://github.com/Autodesk/XLB) - A Differentiable Massively Parallel Lattice Boltzmann Library in Python for Physics-Based Machine Learning. 
- dynamiqs (https://github.com/dynamiqs/dynamiqs) - High-performance and differentiable simulations of quantum systems with JAX. 
- foragax (https://github.com/i-m-iron-man/Foragax) - Agent-Based modelling framework in JAX.  
- tmmax (https://github.com/bahremsd/tmmax) - Vectorized calculation of optical properties in thin-film structures using JAX. Swiss Army knife tool for thin-film optics research 
- Coreax (https://github.com/gchq/coreax) - Algorithms for finding coresets to compress large datasets while retaining their statistical properties. 
- NAVIX (https://github.com/epignatelli/navix) - A reimplementation of MiniGrid, a Reinforcement Learning environment, in JAX 




Models and Projects

JAX

- Fourier Feature Networks (https://github.com/tancik/fourier-feature-networks) - Official implementation of _Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains_ 
(https://people.eecs.berkeley.edu/~bmild/fourfeat).
- kalman-jax (https://github.com/AaltoML/kalman-jax) - Approximate inference for Markov (i.e., temporal) Gaussian processes using iterated Kalman filtering and smoothing.
- jaxns (https://github.com/Joshuaalbert/jaxns) - Nested sampling in JAX.
- Amortized Bayesian Optimization (https://github.com/google-research/google-research/tree/master/amortized_bo) - Code related to _Amortized Bayesian Optimization over Discrete Spaces_ (http://www.auai.org/uai2020/proceedings/329_main_paper.pdf).
- Accurate Quantized Training (https://github.com/google-research/google-research/tree/master/aqt) - Tools and libraries for running and analyzing neural network quantization experiments in JAX and Flax.
- BNN-HMC (https://github.com/google-research/google-research/tree/master/bnn_hmc) - Implementation for the paper _What Are Bayesian Neural Network Posteriors Really Like?_ (https://arxiv.org/abs/2104.14421).
- JAX-DFT (https://github.com/google-research/google-research/tree/master/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_ (https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.036401).
- Robust Loss (https://github.com/google-research/google-research/tree/master/robust_loss_jax) - Reference code for the paper _A General and Adaptive Robust Loss Function_ (https://arxiv.org/abs/1701.03077).
- Symbolic Functionals (https://github.com/google-research/google-research/tree/master/symbolic_functionals) - Demonstration from _Evolving symbolic density functionals_ (https://arxiv.org/abs/2203.02540).
- TriMap (https://github.com/google-research/google-research/tree/master/trimap) - Official JAX implementation of _TriMap: Large-scale Dimensionality Reduction Using Triplets_ (https://arxiv.org/abs/1910.00204).

Flax

- DeepSeek-R1-Flax-1.5B-Distill (https://github.com/J-Rosser-UK/Torch2Jax-DeepSeek-R1-Distill-Qwen-1.5B) - Flax implementation of DeepSeek-R1 1.5B distilled reasoning LLM.
- Performer (https://github.com/google-research/google-research/tree/master/performer/fast_attention/jax) - Flax implementation of the Performer (linear transformer via FAVOR+) architecture.
- JaxNeRF (https://github.com/google-research/google-research/tree/master/jaxnerf) - Implementation of _NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis_ (http://www.matthewtancik.com/nerf) with multi-device GPU/TPU support.
- mip-NeRF (https://github.com/google/mipnerf) - Official implementation of _Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields_ (https://jonbarron.info/mipnerf).
- RegNeRF (https://github.com/google-research/google-research/tree/master/regnerf) - Official implementation of _RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs_ (https://m-niemeyer.github.io/regnerf/).
- JaxNeuS (https://github.com/huangjuite/jaxneus) - Implementation of _NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction_ (https://lingjie0206.github.io/papers/NeuS/)
- Big Transfer (BiT) (https://github.com/google-research/big_transfer) - Implementation of _Big Transfer (BiT): General Visual Representation Learning_ (https://arxiv.org/abs/1912.11370).
- JAX RL (https://github.com/ikostrikov/jax-rl) - Implementations of reinforcement learning algorithms.
- gMLP (https://github.com/SauravMaheshkar/gMLP) - Implementation of _Pay Attention to MLPs_ (https://arxiv.org/abs/2105.08050).
- MLP Mixer (https://github.com/SauravMaheshkar/MLP-Mixer) - Minimal implementation of _MLP-Mixer: An all-MLP Architecture for Vision_ (https://arxiv.org/abs/2105.01601).
- Distributed Shampoo (https://github.com/google-research/google-research/tree/master/scalable_shampoo) - Implementation of _Second Order Optimization Made Practical_ (https://arxiv.org/abs/2002.09018).
- NesT (https://github.com/google-research/nested-transformer) - Official implementation of _Aggregating Nested Transformers_ (https://arxiv.org/abs/2105.12723).
- XMC-GAN (https://github.com/google-research/xmcgan_image_generation) - Official implementation of _Cross-Modal Contrastive Learning for Text-to-Image Generation_ (https://arxiv.org/abs/2101.04702).
- FNet (https://github.com/google-research/google-research/tree/master/f_net) - Official implementation of _FNet: Mixing Tokens with Fourier Transforms_ (https://arxiv.org/abs/2105.03824).
- GFSA (https://github.com/google-research/google-research/tree/master/gfsa) - Official implementation of _Learning Graph Structure With A Finite-State Automaton Layer_ (https://arxiv.org/abs/2007.04929).
- IPA-GNN (https://github.com/google-research/google-research/tree/master/ipagnn) - Official implementation of _Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks_ (https://arxiv.org/abs/2010.12621).
- Flax Models (https://github.com/google-research/google-research/tree/master/flax_models) - Collection of models and methods implemented in Flax.
- Protein LM (https://github.com/google-research/google-research/tree/master/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_ (https://www.biorxiv.org/content/10.1101/622803v1.full) and _ProGen: Language Modeling for Protein Generation_ (https://www.biorxiv.org/content/10.1101/2020.03.07.982272v2).
- Slot Attention (https://github.com/google-research/google-research/tree/master/ptopk_patch_selection) - Reference implementation for _Differentiable Patch Selection for Image Recognition_ (https://arxiv.org/abs/2104.03059).
- Vision Transformer (https://github.com/google-research/vision_transformer) - Official implementation of _An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale_ (https://arxiv.org/abs/2010.11929).
- FID computation (https://github.com/matthias-wright/jax-fid) - Port of mseitzer/pytorch-fid (https://github.com/mseitzer/pytorch-fid) to Flax.
- ARDM (https://github.com/google-research/google-research/tree/master/autoregressive_diffusion) - Official implementation of _Autoregressive Diffusion Models_ (https://arxiv.org/abs/2110.02037).
- D3PM (https://github.com/google-research/google-research/tree/master/d3pm) - Official implementation of _Structured Denoising Diffusion Models in Discrete State-Spaces_ (https://arxiv.org/abs/2107.03006).
- Gumbel-max Causal Mechanisms (https://github.com/google-research/google-research/tree/master/gumbel_max_causal_gadgets) - Code for _Learning Generalized Gumbel-max Causal Mechanisms_ (https://arxiv.org/abs/2111.06888), with extra code in 
GuyLor/gumbel_max_causal_gadgets_part2 (https://github.com/GuyLor/gumbel_max_causal_gadgets_part2).
- Latent Programmer (https://github.com/google-research/google-research/tree/master/latent_programmer) - Code for the ICML 2021 paper _Latent Programmer: Discrete Latent Codes for Program Synthesis_ (https://arxiv.org/abs/2012.00377).
- SNeRG (https://github.com/google-research/google-research/tree/master/snerg) - Official implementation of _Baking Neural Radiance Fields for Real-Time View Synthesis_ (https://phog.github.io/snerg).
- Spin-weighted Spherical CNNs (https://github.com/google-research/google-research/tree/master/spin_spherical_cnns) - Adaptation of _Spin-Weighted Spherical CNNs_ (https://arxiv.org/abs/2006.10731).
- VDVAE (https://github.com/google-research/google-research/tree/master/vdvae_flax) - Adaptation of _Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images_ (https://arxiv.org/abs/2011.10650), original code at 
openai/vdvae (https://github.com/openai/vdvae).
- MUSIQ (https://github.com/google-research/google-research/tree/master/musiq) - Checkpoints and model inference code for the ICCV 2021 paper _MUSIQ: Multi-scale Image Quality Transformer_ (https://arxiv.org/abs/2108.05997)
- AQuaDem (https://github.com/google-research/google-research/tree/master/aquadem) - Official implementation of _Continuous Control with Action Quantization from Demonstrations_ (https://arxiv.org/abs/2110.10149).
- Combiner (https://github.com/google-research/google-research/tree/master/combiner) - Official implementation of _Combiner: Full Attention Transformer with Sparse Computation Cost_ (https://arxiv.org/abs/2107.05768).
- Dreamfields (https://github.com/google-research/google-research/tree/master/dreamfields) - Official implementation of the ICLR 2022 paper _Progressive Distillation for Fast Sampling of Diffusion Models_ (https://ajayj.com/dreamfields).
- GIFT (https://github.com/google-research/google-research/tree/master/gift) - Official implementation of _Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent_ (https://arxiv.org/abs/2106.06080).
- Light Field Neural Rendering (https://github.com/google-research/google-research/tree/master/light_field_neural_rendering) - Official implementation of _Light Field Neural Rendering_ (https://arxiv.org/abs/2112.09687).
- Sharpened Cosine Similarity in JAX by Raphael Pisoni (https://colab.research.google.com/drive/1KUKFEMneQMS3OzPYnWZGkEnry3PdzCfn?usp=sharing) -  A JAX/Flax implementation of the Sharpened Cosine Similarity layer.
- GNNs for Solving Combinatorial Optimization Problems (https://github.com/IvanIsCoding/GNN-for-Combinatorial-Optimization) -  A JAX + Flax implementation of Combinatorial Optimization with Physics-Inspired Graph Neural Networks 
(https://arxiv.org/abs/2107.01188).
- DETR (https://github.com/MasterSkepticista/detr) - Flax implementation of _DETR: End-to-end Object Detection with Transformers_ (https://github.com/facebookresearch/detr) using Sinkhorn solver and parallel bipartite matching.

Haiku

- AlphaFold (https://github.com/deepmind/alphafold) - Implementation of the inference pipeline of AlphaFold v2.0, presented in _Highly accurate protein structure prediction with AlphaFold_ (https://www.nature.com/articles/s41586-021-03819-2).
- Adversarial Robustness (https://github.com/deepmind/deepmind-research/tree/master/adversarial_robustness) - Reference code for _Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples_ 
(https://arxiv.org/abs/2010.03593) and _Fixing Data Augmentation to Improve Adversarial Robustness_ (https://arxiv.org/abs/2103.01946).
- Bootstrap Your Own Latent (https://github.com/deepmind/deepmind-research/tree/master/byol) - Implementation for the paper _Bootstrap your own latent: A new approach to self-supervised Learning_ (https://arxiv.org/abs/2006.07733).
- Gated Linear Networks (https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks) - GLNs are a family of backpropagation-free neural networks.
- Glassy Dynamics (https://github.com/deepmind/deepmind-research/tree/master/glassy_dynamics) - Open source implementation of the paper _Unveiling the predictive power of static structure in glassy systems_ 
(https://www.nature.com/articles/s41567-020-0842-8).
- MMV (https://github.com/deepmind/deepmind-research/tree/master/mmv) - Code for the models in _Self-Supervised MultiModal Versatile Networks_ (https://arxiv.org/abs/2006.16228).
- Normalizer-Free Networks (https://github.com/deepmind/deepmind-research/tree/master/nfnets) - Official Haiku implementation of _NFNets_ (https://arxiv.org/abs/2102.06171).
- NuX (https://github.com/Information-Fusion-Lab-Umass/NuX) - Normalizing flows with JAX.
- OGB-LSC (https://github.com/deepmind/deepmind-research/tree/master/ogb_lsc) - This repository contains DeepMind's entry to the PCQM4M-LSC (https://ogb.stanford.edu/kddcup2021/pcqm4m/) (quantum chemistry) and MAG240M-LSC 
(https://ogb.stanford.edu/kddcup2021/mag240m/) (academic graph)
tracks of the OGB Large-Scale Challenge (https://ogb.stanford.edu/kddcup2021/) (OGB-LSC).
- Persistent Evolution Strategies (https://github.com/google-research/google-research/tree/master/persistent_es) - Code used for the paper _Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies_ 
(http://proceedings.mlr.press/v139/vicol21a.html).
- Two Player Auction Learning (https://github.com/degregat/two-player-auctions) - JAX implementation of the paper _Auction learning as a two-player game_ (https://arxiv.org/abs/2006.05684).
- WikiGraphs (https://github.com/deepmind/deepmind-research/tree/master/wikigraphs) - Baseline code to reproduce results in _WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Datase_ (https://aclanthology.org/2021.textgraphs-1.7).

Trax

- Reformer (https://github.com/google/trax/tree/master/trax/models/reformer) - Implementation of the Reformer (efficient transformer) architecture.

NumPyro

- lqg (https://github.com/RothkopfLab/lqg) - Official implementation of Bayesian inverse optimal control for linear-quadratic Gaussian problems from the paper _Putting perception into action with inverse optimal control for continuous 
psychophysics_ (https://elifesciences.org/articles/76635)




Videos

- NeurIPS 2020: JAX Ecosystem Meetup (https://www.youtube.com/watch?v=iDxJxIyzSiM) - JAX, its use at DeepMind, and discussion between engineers, scientists, and JAX core team.
- Introduction to JAX (https://youtu.be/0mVmRHMaOJ4) - Simple neural network from scratch in JAX.
- JAX: Accelerated Machine Learning Research | SciPy 2020 | VanderPlas (https://youtu.be/z-WSrQDXkuM) - JAX's core design, how it's powering new research, and how you can start using it.
- Bayesian Programming with JAX + NumPyro — Andy Kitchen (https://youtu.be/CecuWGpoztw) - Introduction to Bayesian modelling using NumPyro.
- JAX: Accelerated machine-learning research via composable function transformations in Python | NeurIPS 2019 | Skye Wanderman-Milne 
(https://slideslive.com/38923687/jax-accelerated-machinelearning-research-via-composable-function-transformations-in-python) - JAX intro presentation in _Program Transformations for Machine Learning_ (https://program-transformations.github.io) 
workshop.
- JAX on Cloud TPUs | NeurIPS 2020 | Skye Wanderman-Milne and James Bradbury (https://drive.google.com/file/d/1jKxefZT1xJDUxMman6qrQVed7vWI0MIn/edit) - Presentation of TPU host access with demo.
- Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond | NeurIPS 2020 (https://slideslive.com/38935810/deep-implicit-layers-neural-odes-equilibrium-models-and-beyond) - Tutorial created by Zico Kolter, David Duvenaud, and Matt 
Johnson with Colab notebooks avaliable in _Deep Implicit Layers_ (http://implicit-layers-tutorial.org).
- Solving y=mx+b with Jax on a TPU Pod slice - Mat Kelcey (http://matpalm.com/blog/ymxb_pod_slice/) - 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 🤗 (https://github.com/huggingface/transformers/blob/9160d81c98854df44b1d543ce5d65a6aa28444a2/examples/research_projects/jax-projects/README.md#talks) - 3 days of talks around JAX / Flax, Transformers, large-scale 
language modeling and other great topics.



Papers

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 (#projects) section.


- __Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_. (https://mlsys.org/Conferences/doc/2018/146.pdf) - 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_. (https://arxiv.org/abs/1912.04232) - 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_. (https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster 
differentially private than existing libraries.
- __XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_. (https://arxiv.org/abs/2311.16080) - White paper describing the XLB library: benchmarks, validations, and 
more details about the library.





Tutorials and Blog Posts

- Using JAX to accelerate our research by David Budden and Matteo Hessel (https://deepmind.com/blog/article/using-jax-to-accelerate-our-research) - Describes the state of JAX and the JAX ecosystem at DeepMind.
- Getting started with JAX (MLPs, CNNs & RNNs) by Robert Lange (https://roberttlange.github.io/posts/2020/03/blog-post-10/) - Neural network building blocks from scratch with the basic JAX operators.
- Learn JAX: From Linear Regression to Neural Networks by Rito Ghosh (https://www.kaggle.com/code/truthr/jax-0) - A gentle introduction to JAX and using it to implement Linear and Logistic Regression, and Neural Network models and using them to 
solve real world problems.
- Tutorial: image classification with JAX and Flax Linen by 8bitmp3 (https://github.com/8bitmp3/JAX-Flax-Tutorial-Image-Classification-with-Linen) - 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 (https://medium.com/swlh/plugging-into-jax-16c120ec3302) - Compares Flax, Haiku, and Objax on the Kaggle flower classification challenge.
- Meta-Learning in 50 Lines of JAX by Eric Jang (https://blog.evjang.com/2019/02/maml-jax.html) - Introduction to both JAX and Meta-Learning.
- Normalizing Flows in 100 Lines of JAX by Eric Jang (https://blog.evjang.com/2019/07/nf-jax.html) - Concise implementation of RealNVP (https://arxiv.org/abs/1605.08803).
- Differentiable Path Tracing on the GPU/TPU by Eric Jang (https://blog.evjang.com/2019/11/jaxpt.html) - Tutorial on implementing path tracing.
- Ensemble networks by Mat Kelcey (http://matpalm.com/blog/ensemble_nets) - Ensemble nets are a method of representing an ensemble of models as one single logical model.
- Out of distribution (OOD) detection by Mat Kelcey (http://matpalm.com/blog/ood_using_focal_loss) - Implements different methods for OOD detection.
- Understanding Autodiff with JAX by Srihari Radhakrishna (https://www.radx.in/jax.html) - Understand how autodiff works using JAX.
- From PyTorch to JAX: towards neural net frameworks that purify stateful code by Sabrina J. Mielke (https://sjmielke.com/jax-purify.htm) - 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 (https://github.com/dfm/extending-jax) - Tutorial demonstrating the infrastructure required to provide custom ops in JAX.
- Evolving Neural Networks in JAX by Robert Tjarko Lange (https://roberttlange.github.io/posts/2021/02/cma-es-jax/) - Explores how JAX can power the next generation of scalable neuroevolution algorithms.
- Exploring hyperparameter meta-loss landscapes with JAX by Luke Metz (http://lukemetz.com/exploring-hyperparameter-meta-loss-landscapes-with-jax/) - 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 (https://martiningram.github.io/deterministic-advi/) - Walk through of implementing automatic differentiation variational inference (ADVI) easily and cleanly with JAX.
- Evolved channel selection by Mat Kelcey (http://matpalm.com/blog/evolved_channel_selection/) - 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 (https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/jax_intro.ipynb) - Colab that introduces various aspects of the language and applies them to simple ML problems.
- Writing an MCMC sampler in JAX by Jeremie Coullon (https://www.jeremiecoullon.com/2020/11/10/mcmcjax3ways/) - 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 (https://www.jeremiecoullon.com/2021/01/29/jax_progress_bar/) - Tutorial on how to add a progress bar to compiled loops in JAX using the host_callback module.
- Get started with JAX by Aleksa Gordić (https://github.com/gordicaleksa/get-started-with-JAX) - A series of notebooks and videos going from zero JAX knowledge to building neural networks in Haiku.
- Writing a Training Loop in JAX + FLAX by Saurav Maheshkar and Soumik Rakshit (https://wandb.ai/jax-series/simple-training-loop/reports/Writing-a-Training-Loop-in-JAX-FLAX--VmlldzoyMzA4ODEy) - A tutorial on writing a simple end-to-end training 
and evaluation pipeline in JAX, Flax and Optax.
- Implementing NeRF in JAX by Soumik Rakshit and Saurav Maheshkar (https://wandb.ai/wandb/nerf-jax/reports/Implementing-NeRF-in-JAX--VmlldzoxODA2NDk2?galleryTag=jax) - A tutorial on 3D volumetric rendering of scenes represented by Neural Radiance
Fields in JAX.
- Deep Learning tutorials with JAX+Flax by Phillip Lippe (https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial2/Introduction_to_JAX.html) - A series of notebooks explaining various deep learning concepts, from basics 
(e.g. intro to JAX/Flax, activiation functions) to recent advances (e.g., Vision Transformers, SimCLR), with translations to PyTorch.
- Achieving 4000x Speedups with PureJaxRL (https://chrislu.page/blog/meta-disco/) - A blog post on how JAX can massively speedup RL training through vectorisation.
- Simple PDE solver + Constrained Optimization with JAX by Philip Mocz (https://levelup.gitconnected.com/create-your-own-automatically-differentiable-simulation-with-python-jax-46951e120fbb?sk=e8b9213dd2c6a5895926b2695d28e4aa) - A simple example 
of solving the advection-diffusion equations with JAX and using it in a constrained optimization problem to find initial conditions that yield desired result.



Books

- Jax in Action (https://www.manning.com/books/jax-in-action) - A hands-on guide to using JAX for deep learning and other mathematically-intensive applications.



Community

- JaxLLM (Unofficial) Discord (https://discord.com/channels/1107832795377713302/1107832795688083561)
- JAX GitHub Discussions (https://github.com/google/jax/discussions)
- Reddit (https://www.reddit.com/r/JAX/)

Contributing

Contributions welcome! Read the contribution guidelines (contributing.md) first.

jax Github: https://github.com/n2cholas/awesome-jax