Jax
Showing 36 of 113 projects
A differentiable, hardware-accelerated molecular dynamics simulation framework built on JAX for computational physics and materials science.
A fast, modular Bayesian inference library for JAX, providing composable samplers for CPU and GPU.
High-performance, end-to-end reinforcement learning implementations fully written in JAX for massive parallelization on GPUs.
A modular Python library for Reinforcement Learning with support for PyTorch, JAX, NVIDIA Warp, and multiple environment interfaces.
Hardware-accelerated, batchable, and differentiable optimization algorithms implemented in JAX for machine learning research.
A pretrained modeling library for Keras 3 offering simple, flexible, and fast access to models for text, image, and audio tasks.
A Python library for probabilistic state space modeling and inference, built on JAX.
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
A library of utilities for writing and testing reliable JAX code, including assertions, debugging tools, and test variants.
Official JAX implementation of Mip-NeRF, a multiscale neural radiance field model for anti-aliased novel view synthesis.
A JAX-based library providing accelerated reinforcement learning environments with full compatibility to the classic gym API.
A collection of transformer-based foundation models for genomics and transcriptomics, enabling tasks like sequence analysis, functional prediction, and conversational DNA exploration.
A diverse suite of scalable reinforcement learning environments written in JAX for hardware-accelerated research.
A collection of tutorials and resources to help developers learn JAX, Flax, and Haiku for machine learning.
An object-oriented machine learning framework built on JAX, designed for simplicity and readability in research.
A comprehensive, high-performance library implementing 30+ Evolution Strategies in JAX for scalable optimization on modern hardware.
JAX (Flax) implementations of reinforcement learning algorithms for continuous action spaces, designed for research.
A JAX-powered library for solving large-scale optimal transport problems, including matching, barycenters, and neural approximations.
A JAX-based framework for training large language models with a focus on legibility, scalability, and reproducibility.
A Python library built on JAX for studying many-body quantum systems using neural networks and machine learning.
A JAX-native library of probability distributions and bijectors, reimplementing a subset of TensorFlow Probability with emphasis on readability and extensibility.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
A collection of GPU-accelerated parallel game simulators for reinforcement learning, built with JAX.
A JAX library for nonlinear optimization including root finding, minimization, fixed points, and least squares.
A JAX-based machine learning framework for configuring and training large-scale models with high efficiency on TPUs and GPUs.
A differentiable, massively parallel Lattice Boltzmann library in Python for physics-based machine learning and fluid dynamics simulations.
An image processing library built on JAX, designed to be optimized and parallelized with JAX transformations.
A collection of CI pipelines, Docker images, and optimized examples to simplify JAX development on NVIDIA GPUs.
A tutorial demonstrating how to extend JAX with custom C++ and CUDA operations for high-performance computing.
A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
A hardware-accelerated Python library for running Quality-Diversity and neuroevolution algorithms in minutes instead of days.
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
A JAX library implementing Lie groups for rigid body transformations in computer vision and robotics.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A JAX library for second-order optimization of neural networks using the K-FAC curvature approximation algorithm.
An efficient open-source Python package for 3D photonic nanostructure simulation and design using GPU-accelerated FDTD with automatic differentiation.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.