Updated ecosystem list

patrick-kidger · Apr 20, 2024 · 2905390 · 2905390
1 parent bad41ba
commit 2905390
Show file tree

Hide file tree

Showing 2 changed files with 36 additions and 16 deletions.
diff --git a/README.md b/README.md
@@ -81,28 +81,32 @@ If you found this library to be useful in academic work, then please cite: ([arX
 
 ## See also: other libraries in the JAX ecosystem
 
-[jaxtyping](https://github.com/google/jaxtyping): type annotations for shape/dtype of arrays.
+#### Always useful
+
+[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.
+
+#### Deep learning
 
 [Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
 
-[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
+[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
 
-[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
 
-[Lineax](https://github.com/google/lineax): linear solvers.
+#### Scientific computing
 
-[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
+[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
 
-[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
+[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
 
-[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
 
-[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
+[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
 
-[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
 
 [PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)
 
-## Disclaimer
+#### Awesome JAX
 
-Equinox is maintained by Patrick Kidger at Google X, but this is not an official Google product.
+[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.
diff --git a/docs/index.md b/docs/index.md
@@ -70,16 +70,32 @@ If this quick start has got you interested, then have a read of [All of Equinox]
 
 ## See also: other libraries in the JAX ecosystem
 
+#### Always useful
+
+[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.
+
+#### Deep learning
+
 [Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
 
+[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
+
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+
+#### Scientific computing
+
 [Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
 
-[Lineax](https://github.com/google/lineax): linear solvers and linear least squares.
+[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
 
-[jaxtyping](https://github.com/google/jaxtyping): type annotations for shape/dtype of arrays.
+[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
 
-[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
+[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
 
-[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
 
-[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)
+
+#### Awesome JAX
+
+[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.