| Name | equinox JSON |
| Version |
0.11.10
JSON |
| download |
| home_page | None |
| Summary | Elegant easy-to-use neural networks in JAX. |
| upload_time | 2024-12-08 02:44:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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| keywords |
deep-learning
equinox
jax
neural-networks
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
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|
<h1 align='center'>Equinox</h1>
Equinox is your one-stop [JAX](https://github.com/google/jax) library, for everything you need that isn't already in core JAX:
- neural networks (or more generally any model), with easy-to-use PyTorch-like syntax;
- filtered APIs for transformations;
- useful PyTree manipulation routines;
- advanced features like runtime errors;
and best of all, Equinox isn't a framework: everything you write in Equinox is compatible with anything else in JAX or the ecosystem.
If you're completely new to JAX, then start with this [CNN on MNIST example](https://docs.kidger.site/equinox/examples/mnist/).
_Coming from [Flax](https://github.com/google/flax) or [Haiku](https://github.com/deepmind/haiku)? The main difference is that Equinox (a) offers a lot of advanced features not found in these libraries, like PyTree manipulation or runtime errors; (b) has a simpler way of building models: they're just PyTrees, so they can pass across JIT/grad/etc. boundaries smoothly._
## Installation
```bash
pip install equinox
```
Requires Python 3.9+ and JAX 0.4.13+.
## Documentation
Available at [https://docs.kidger.site/equinox](https://docs.kidger.site/equinox).
## Quick example
Models are defined using PyTorch-like syntax:
```python
import equinox as eqx
import jax
class Linear(eqx.Module):
weight: jax.Array
bias: jax.Array
def __init__(self, in_size, out_size, key):
wkey, bkey = jax.random.split(key)
self.weight = jax.random.normal(wkey, (out_size, in_size))
self.bias = jax.random.normal(bkey, (out_size,))
def __call__(self, x):
return self.weight @ x + self.bias
```
and are fully compatible with normal JAX operations:
```python
@jax.jit
@jax.grad
def loss_fn(model, x, y):
pred_y = jax.vmap(model)(x)
return jax.numpy.mean((y - pred_y) ** 2)
batch_size, in_size, out_size = 32, 2, 3
model = Linear(in_size, out_size, key=jax.random.PRNGKey(0))
x = jax.numpy.zeros((batch_size, in_size))
y = jax.numpy.zeros((batch_size, out_size))
grads = loss_fn(model, x, y)
```
Finally, there's no magic behind the scenes. All `eqx.Module` does is register your class as a PyTree. From that point onwards, JAX already knows how to work with PyTrees.
## Citation
If you found this library to be useful in academic work, then please cite: ([arXiv link](https://arxiv.org/abs/2111.00254))
```bibtex
@article{kidger2021equinox,
author={Patrick Kidger and Cristian Garcia},
title={{E}quinox: neural networks in {JAX} via callable {P}y{T}rees and filtered transformations},
year={2021},
journal={Differentiable Programming workshop at Neural Information Processing Systems 2021}
}
```
(Also consider starring the project on GitHub.)
## 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.
[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.
Raw data
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"description": "<h1 align='center'>Equinox</h1>\n\nEquinox is your one-stop [JAX](https://github.com/google/jax) library, for everything you need that isn't already in core JAX:\n\n- neural networks (or more generally any model), with easy-to-use PyTorch-like syntax;\n- filtered APIs for transformations;\n- useful PyTree manipulation routines;\n- advanced features like runtime errors;\n\nand best of all, Equinox isn't a framework: everything you write in Equinox is compatible with anything else in JAX or the ecosystem.\n\nIf you're completely new to JAX, then start with this [CNN on MNIST example](https://docs.kidger.site/equinox/examples/mnist/).\n\n_Coming from [Flax](https://github.com/google/flax) or [Haiku](https://github.com/deepmind/haiku)? The main difference is that Equinox (a) offers a lot of advanced features not found in these libraries, like PyTree manipulation or runtime errors; (b) has a simpler way of building models: they're just PyTrees, so they can pass across JIT/grad/etc. boundaries smoothly._\n\n## Installation\n\n```bash\npip install equinox\n```\n\nRequires Python 3.9+ and JAX 0.4.13+.\n\n## Documentation\n\nAvailable at [https://docs.kidger.site/equinox](https://docs.kidger.site/equinox).\n\n## Quick example\n\nModels are defined using PyTorch-like syntax:\n\n```python\nimport equinox as eqx\nimport jax\n\nclass Linear(eqx.Module):\n weight: jax.Array\n bias: jax.Array\n\n def __init__(self, in_size, out_size, key):\n wkey, bkey = jax.random.split(key)\n self.weight = jax.random.normal(wkey, (out_size, in_size))\n self.bias = jax.random.normal(bkey, (out_size,))\n\n def __call__(self, x):\n return self.weight @ x + self.bias\n```\n\nand are fully compatible with normal JAX operations:\n\n```python\n@jax.jit\n@jax.grad\ndef loss_fn(model, x, y):\n pred_y = jax.vmap(model)(x)\n return jax.numpy.mean((y - pred_y) ** 2)\n\nbatch_size, in_size, out_size = 32, 2, 3\nmodel = Linear(in_size, out_size, key=jax.random.PRNGKey(0))\nx = jax.numpy.zeros((batch_size, in_size))\ny = jax.numpy.zeros((batch_size, out_size))\ngrads = loss_fn(model, x, y)\n```\n\nFinally, there's no magic behind the scenes. All `eqx.Module` does is register your class as a PyTree. From that point onwards, JAX already knows how to work with PyTrees.\n\n## Citation\n\nIf you found this library to be useful in academic work, then please cite: ([arXiv link](https://arxiv.org/abs/2111.00254))\n\n```bibtex\n@article{kidger2021equinox,\n author={Patrick Kidger and Cristian Garcia},\n title={{E}quinox: neural networks in {JAX} via callable {P}y{T}rees and filtered transformations},\n year={2021},\n journal={Differentiable Programming workshop at Neural Information Processing Systems 2021}\n}\n```\n\n(Also consider starring the project on GitHub.)\n\n## See also: other libraries in the JAX ecosystem\n\n**Always useful** \n[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays. \n\n**Deep learning** \n[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers. \n[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device). \n[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs). \n\n**Scientific computing** \n[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers. \n[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares. \n[Lineax](https://github.com/patrick-kidger/lineax): linear solvers. \n[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling. \n[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent. \n[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!) \n\n**Awesome JAX** \n[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects. \n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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