From 56798d63f557dc92979b884b556cfa8b98257e3e Mon Sep 17 00:00:00 2001 From: Neil Girdhar Date: Fri, 30 Aug 2024 08:51:30 -0400 Subject: [PATCH] Add Optimistix and NNX --- readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/readme.md b/readme.md index d1d0b94..74db7c9 100644 --- a/readme.md +++ b/readme.md @@ -36,10 +36,10 @@ This is a curated list of awesome JAX libraries, projects, and other resources. - [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. +- [Optax](https://github.com/deepmind/optax) - Gradient processing and optimization library. - [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.