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Getting started¤

Equinox is your one-stop 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.

Coming from Flax or 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.


pip install equinox

Requires Python 3.9+ and JAX 0.4.13+.

Quick example¤

Models are defined using PyTorch-like syntax:

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 may be used alongside normal JAX operations:

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)

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.

Next steps¤

If this quick start has got you interested, then have a read of All of Equinox, which introduces you to basically everything in Equinox.


If you found this library to be useful in academic work, then please cite: (arXiv link)

    author={Patrick Kidger and Cristian Garcia},
    title={{E}quinox: neural networks in {JAX} via callable {P}y{T}rees and filtered transformations},
    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: type annotations for shape/dtype of arrays.

Deep learning
Optax: first-order gradient (SGD, Adam, ...) optimisers.
Orbax: checkpointing (async/multi-host/multi-device).
Levanter: scalable+reliable training of foundation models (e.g. LLMs).

Scientific computing
Diffrax: numerical differential equation solvers.
Optimistix: root finding, minimisation, fixed points, and least squares.
Lineax: linear solvers.
BlackJAX: probabilistic+Bayesian sampling.
sympy2jax: SymPy<->JAX conversion; train symbolic expressions via gradient descent.
PySR: symbolic regression. (Non-JAX honourable mention!)

Awesome JAX
Awesome JAX: a longer list of other JAX projects.