Python Jax-comln Resources

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

KFAC-JAX - Second Order Optimization with Approximate Curvature in JAX Installation | Quickstart | Documentation | Examples | Citing KFAC-JAX KFAC-JAX

Squirrel Core Share, load, and transform data in a collaborative, flexible, and efficient way What is Squirrel? Squirrel is a Python library that enab

SOFIMA SOFIMA (Scalable Optical Flow-based Image Montaging and Alignment) is a tool for stitching, aligning and warping large 2d, 3d and 4d microscopy

KoCLIP This repository contains code for KoCLIP, a Korean port of OpenAI's CLIP. This project was conducted as part of Hugging Face's Flax/JAX communi

MUP for Haiku This is a (very preliminary) port of Yang and Hu et al.'s μP repo to Haiku and JAX. It's not feature complete, and I'm very open to sugg

Pre-trained image classification models for Jax/Haiku Jax/Haiku Applications are deep learning models that are made available alongside pre-trained we

Seq2Seq Speech in JAX A JAX/Flax repository for combining a pre-trained speech encoder model (e.g. Wav2Vec2, HuBERT, WavLM) with a pre-trained text de

Continuous-Time Meta-Learning with Forward Mode Differentiation ICLR 2022 (Spotlight) - Installation - Example - Citation This repository contains the

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Kompressor Branch CI Coverage main (active) main development A neural compression framework built on top of JAX. Install setup.py assumes a compatible

Diffrax Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. Diffrax is a JAX-based library providing numerical differe

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces JAXMAPP is a JAX-based library for multi-agent path planning (MAPP) in c

pybaum provides tools to work with pytrees which is a concept burrowed from JAX.

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

JaxTon 💯 JAX exercises Mission 🚀 To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beg

Jaxtorch (a jax nn library) This is my jax based nn library. I created this because I was annoyed by the complexity and 'magic'-ness of the popular ja

Prompt Tuning This is the code to reproduce the experiments from the EMNLP 2021 paper "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lest

Evolving Neural Networks in JAX This repository holds code displaying techniques for applying evolutionary network training strategies in JAX. Each sc

JAX Models Table of Contents About The Project Getting Started Prerequisites Installation Usage Contributing License Contact About The Project The JAX

Trax — Deep Learning with Clear Code and Speed Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively us

Relaxed Machines Explorations in neuro-symbolic differentiable interpreters. Baby steps: inc_stop Libraries JAX Haiku Optax Resources Chapter 3 (∂4: A

Advantage Actor Critic (A2C): jax + flax implementation Current version supports only environments with continious action spaces and was tested on muj

tree-math: mathematical operations for JAX pytrees tree-math makes it easy to implement numerical algorithms that work on JAX pytrees, such as iterati

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

PPO Lagrangian in JAX This repository implements PPO in JAX. Implementation is tested on the safety-gym benchmark. Usage Install dependencies using th

learned_optimization: Training and evaluating learned optimizers in JAX learned_optimization is a research codebase for training learned optimizers. I

Memory Efficient Attention This is unofficial implementation of Self-attention Does Not Need O(n^2) Memory for Jax and PyTorch. Implementation is almo

Table of contents Mesh Transformer JAX Updates Pretrained Models GPT-J-6B Links Acknowledgments License Model Details Zero-Shot Evaluations Architectu

English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Veros is the versatile ocean simulator -- it aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Because Veros

Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o

Treeo A small library for creating and manipulating custom JAX Pytree classes Light-weight: has no dependencies other than jax. Compatible: Treeo Tree

jax-variational-diffwave Jax/Flax implementation of Variational-DiffWave. (Zhifeng Kong et al., 2020, Diederik P. Kingma et al., 2021.) DiffWave with

JAX Gravity This repo contains a demo of how to use JAX to create a simple gravity simulation. It uses JAX's experimental ode package to solve the dif

learn-nerf This is a JAX implementation of Neural Radiance Fields for learning purposes. I've been curious about NeRF and its follow-up work for a whi

JAX bindings to FINUFFT This package provides a JAX interface to (a subset of) the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) lib

Reinforcement learning library in JAX.

Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX Foolbox is a Python li

GAN JAX - A toy project to generate images from GANs with JAX This project aims to bring the power of JAX, a Python framework developped by Google and

Flaxformer is a transformer library for primarily NLP and multimodal research at Google.

Einshape: DSL-based reshaping library for JAX and other frameworks. The jnp.einsum op provides a DSL-based unified interface to matmul and tensordot o

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

jaxdf - JAX-based Discretization Framework Overview | Example | Installation | Documentation ⚠️ This library is still in development. Breaking changes

v-diffusion-jax v objective diffusion inference code for JAX, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The models

Flaxformer: transformer architectures in JAX/Flax Flaxformer is a transformer library for primarily NLP and multimodal research at Google. It is used

JAXGA - JAX Geometric Algebra GitHub | Docs JAXGA is a Geometric Algebra package on top of JAX. It can handle high dimensional algebras by storing onl

Map single-cell transcriptomes to copy number evolutionary trees. Check out the tutorial for more information. Installation $ pip install scatrex SCA

Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil

The purpose of this repo is to make it easy to get started with JAX. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning JAX.

A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:

JAXDL: JAX (Flax) Deep Learning Library Simple and clean JAX/Flax deep learning algorithm implementations: Soft-Actor-Critic (arXiv:1812.05905) Transf

English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrai

jax-multi-gpu-resnet50-example This repo shows how to use jax for multi-node multi-GPU training. The example is adapted from the resnet50 example in d

Use Jax functions in Pytorch with DLPack

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

JAX + Attention Learn To Solve Routing Problems Reinplementation of the paper Attention, Learn to Solve Routing Problems! using Jax and Flax. Fully su

functorch is a prototype of JAX-like composable function transforms for PyTorch.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Rockpool Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build network

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

Scenic Scenic is a codebase with a focus on research around attention-based models for computer vision. Scenic has been successfully used to develop c

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video compression models, and metrics for image and video evaluation.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported

RoBERTa base model for Marathi Language (मराठी भाषा) Pretrained model on Marathi language using a masked language modeling (MLM) objective. RoBERTa wa

PIX PIX is an image processing library in JAX, for JAX. Overview JAX is a library resulting from the union of Autograd and XLA for high-performance ma

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators. It's also a suite of learning algorithms to train agents to operate in these environments (PPO, SAC, evolutionary strategy, and direct trajectory optimization are implemented).

flax-sentence-embeddings This repository will be used to share code for the Flax / JAX community event to train sentence embeddings on 1B+ training pa

ProGen - (wip) Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper:

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators. It's also a suite of learning algorithms to train agents to operate in these environments (PPO, SAC, evolutionary strategy, and direct trajectory optimization are implemented).

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

Toy implementations of some popular ML optimizers using Python/JAX

NesT is a simple method, which aggragrates nested local transformers on image blocks. The idea makes vision transformers attain better accuracy, data efficiency, and convergence on the ImageNet benchmark. NesT can be scaled to small datasets to match convnet accuracy.

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

jax_dataclasses jax_dataclasses provides a wrapper around dataclasses.dataclass for use in JAX, which enables automatic support for: Pytree registrati

SYMPAIS: Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Overview | Installation | Documentation | Examples | Notebo

Factor graphs + nonlinear optimization in JAX

Local Attention - Flax Autoregressive Local Attention - Flax module for Jax Install $ pip install local-attention-flax Usage from jax import random fr

MLP GPT - Jax (wip) A GPT, made only of MLPs, in Jax. The specific MLP to be used are gMLPs with the Spatial Gating Units. Working Pytorch implementat

Mixed precision training [0] is a technique that mixes the use of full and half precision floating point numbers during training to reduce the memory bandwidth requirements and improve the computational efficiency of a given model.

Vision Transformers are Robust Learners This repository contains the code for the paper Vision Transformers are Robust Learners by Sayak Paul* and Pin

Sabertooth Sabertooth is standalone pre-training recipe based on JAX+Flax, with data pipelines implemented in Rust. It runs on CPU, GPU, and/or TPU, b

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Bayesian optimization in JAX

coax is built on top of JAX, but it doesn't have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version.

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Funsor Funsor is a tensor-like library for functions and distributions. See Functional tensors for probabilistic programming for a system description.

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Mesh Transformer Jax A haiku library using the new(ly documented) xmap operator in Jax for model parallelism of transformers. See enwik8_example.py fo

Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha

Very Deep VAEs in JAX/Flax Implementation of the experiments in the paper Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on I

sympy2jax Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions. All SymPy floats become trainable input parameters. S