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Updated ecosystem

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Patrick Kidger
2024-04-20 11:18:48 +02:00
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README.md
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Available at [https://docs.kidger.site/jaxtyping](https://docs.kidger.site/jaxtyping).
## Finally
## See also: other libraries in the JAX ecosystem
### See also: other libraries in the JAX ecosystem
#### Always useful
[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!
#### 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.
[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
[Lineax](https://github.com/google/lineax): linear solvers.
[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
[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
This is not an official Google product.
[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.## Finally
docs/index.md
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## See also: other libraries in the JAX ecosystem
[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
#### Always useful
[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!
#### 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.
[Lineax](https://github.com/google/lineax): linear solvers and linear least squares.
[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
[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.
[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
[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!)
#### Awesome JAX
[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.## Finally## See also: other libraries in the JAX ecosystem