optax 0.2.7
A gradient processing and optimization library in JAX.
Requires Python: >=3.10
Keywords: python, machine learning, reinforcement-learning

# Optax

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[![Documentation Status](https://readthedocs.org/projects/optax/badge/?version=latest)](http://optax.readthedocs.io)
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## Introduction

Optax is a gradient processing and optimization library for JAX.

Optax is designed to facilitate research by providing building blocks
that can be easily recombined in custom ways.

Our goals are to

*   Provide simple, well-tested, efficient implementations of core components.
*   Improve research productivity by enabling to easily combine low-level
    ingredients into custom optimizers (or other gradient processing components).
*   Accelerate adoption of new ideas by making it easy for anyone to contribute.

We favor focusing on small composable building blocks that can be effectively
combined into custom solutions. Others may build upon these basic components
in more complicated abstractions. Whenever reasonable, implementations prioritize
readability and structuring code to match standard equations, over code reuse.

An initial prototype of this library was made available in JAX's experimental
folder as `jax.experimental.optix`. Given the wide adoption across DeepMind
of `optix`, and after a few iterations on the API, `optix` was eventually moved
out of `experimental` as a standalone open-source library, and renamed `optax`.

Documentation on Optax can be found at [optax.readthedocs.io](https://optax.readthedocs.io/).

## Installation

You can install the latest released version of Optax from PyPI via:

```sh
pip install optax
```

or you can install the latest development version from GitHub:

```sh
pip install git+https://github.com/google-deepmind/optax.git
```

## Quickstart

Optax contains implementations of [many popular optimizers](https://optax.readthedocs.io/en/latest/api/optimizers.html) and
[loss functions](https://optax.readthedocs.io/en/latest/api/losses.html).
For example, the following code snippet uses the Adam optimizer from `optax.adam`
and the mean squared error from `optax.l2_loss`. We initialize the optimizer
state using the `init` function and `params` of the model.

```python
optimizer = optax.adam(learning_rate)
# Obtain the `opt_state` that contains statistics for the optimizer.
params = {'w': jnp.ones((num_weights,))}
opt_state = optimizer.init(params)
```

To write the update loop we need a loss function that can be differentiated by
Jax (with `jax.grad` in this
example) to obtain the gradients.

```python
compute_loss = lambda params, x, y: optax.l2_loss(params['w'].dot(x), y)
grads = jax.grad(compute_loss)(params, xs, ys)
```

The gradients are then converted via `optimizer.update` to obtain the updates
that should be applied to the current parameters to obtain the new ones.
`optax.apply_updates` is a convenience utility to do this.

```python
updates, opt_state = optimizer.update(grads, opt_state)
params = optax.apply_updates(params, updates)
```

You can continue the quick start in [the Optax 🚀 Getting started notebook.](https://github.com/google-deepmind/optax/blob/main/docs/getting_started.ipynb)

## Development

We welcome issues reports and pull requests solving issues or improving
existing functionalities. If you are interested in adding a feature like a new
optimizer, **open an issue first**! We are focused on making optax more
flexible, versatile and easy to use for you to define your own optimizers.

### Source code

You can check the latest sources with the following command.

```sh
git clone https://github.com/google-deepmind/optax.git
```
### Testing

To run the tests, please execute the following script.

```sh
sh test.sh
```

### Documentation

To build the documentation, first ensure that all the dependencies are installed.
```sh
pip install -e ".[docs]"
```
Then, execute the following.
```sh
cd docs
make html
```

### Benchmarking

Some benchmarks

- [Benchmarking Neural Network Training Algorithms, Dahl G. et al, 2023](https://arxiv.org/pdf/2306.07179),

- [Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers, Schmidt R. et al, 2021](https://proceedings.mlr.press/v139/schmidt21a).

Making your own benchmark

- [Benchopt: Reproducible, efficient and collaborative optimization benchmarks, Moreau T. et al, 2022](https://arxiv.org/abs/2206.13424).

Optimizer tuning handbook

- [Deep Learning Tuning Playbook, Godbole V. et al, 2023](https://github.com/google-research/tuning_playbook).

### Other optimization-adjacent libraries in JAX

- [optimistix](https://github.com/patrick-kidger/optimistix): nonlinear solvers: root finding, minimisation, fixed points, and least squares.

- [matfree](https://github.com/pnkraemer/matfree):
matrix free methods useful to study curvature dynamics in deep learning.

## Citing Optax

This repository is part of the DeepMind JAX Ecosystem, to cite Optax
please use the citation:

```bibtex
@software{deepmind2020jax,
  title = {The {D}eep{M}ind {JAX} {E}cosystem},
  author = {DeepMind and Babuschkin, Igor and Baumli, Kate and Bell, Alison and Bhupatiraju, Surya and Bruce, Jake and Buchlovsky, Peter and Budden, David and Cai, Trevor and Clark, Aidan and Danihelka, Ivo and Dedieu, Antoine and Fantacci, Claudio and Godwin, Jonathan and Jones, Chris and Hemsley, Ross and Hennigan, Tom and Hessel, Matteo and Hou, Shaobo and Kapturowski, Steven and Keck, Thomas and Kemaev, Iurii and King, Michael and Kunesch, Markus and Martens, Lena and Merzic, Hamza and Mikulik, Vladimir and Norman, Tamara and Papamakarios, George and Quan, John and Ring, Roman and Ruiz, Francisco and Sanchez, Alvaro and Sartran, Laurent and Schneider, Rosalia and Sezener, Eren and Spencer, Stephen and Srinivasan, Srivatsan and Stanojevi\'{c}, Milo\v{s} and Stokowiec, Wojciech and Wang, Luyu and Zhou, Guangyao and Viola, Fabio},
  url = {http://github.com/google-deepmind},
  year = {2020},
}
```