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https://github.com/google/flax

Flax is a neural network library for JAX that is designed for flexibility.
https://github.com/google/flax

jax

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Flax is a neural network library for JAX that is designed for flexibility.

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# Flax: A neural network library and ecosystem for JAX designed for flexibility



![Build](https://github.com/google/flax/workflows/Build/badge.svg?branch=main) [![coverage](https://badgen.net/codecov/c/gh/google/flax)](https://codecov.io/gh/google/flax)



[**Overview**](#overview)

| [**Quick install**](#quick-install)

| [**What does Flax look like?**](#what-does-flax-look-like)

| [**Documentation**](https://flax.readthedocs.io/)



**📣 NEW**: Check out the [**NNX**](https://flax.readthedocs.io/en/latest/nnx/index.html) API!



This README is a very short intro. **To learn everything you need to know about Flax, refer to our [full documentation](https://flax.readthedocs.io/).**



Flax was originally started by engineers and researchers within the Brain Team in Google Research (in close collaboration with the JAX team), and is now developed jointly with the open source community.



Flax is being used by a growing

community of hundreds of folks in various Alphabet research departments

for their daily work, as well as a [growing community

of open source

projects](https://github.com/google/flax/network/dependents?dependent_type=REPOSITORY).



The Flax team's mission is to serve the growing JAX neural network

research ecosystem -- both within Alphabet and with the broader community,

and to explore the use-cases where JAX shines. We use GitHub for almost

all of our coordination and planning, as well as where we discuss

upcoming design changes. We welcome feedback on any of our discussion,

issue and pull request threads. We are in the process of moving some

remaining internal design docs and conversation threads to GitHub

discussions, issues and pull requests. We hope to increasingly engage

with the needs and clarifications of the broader ecosystem. Please let

us know how we can help!



Please report any feature requests,

issues, questions or concerns in our [discussion

forum](https://github.com/google/flax/discussions), or just let us

know what you're working on!



We expect to improve Flax, but we don't anticipate significant

breaking changes to the core API. We use [Changelog](https://github.com/google/flax/tree/main/CHANGELOG.md)

entries and deprecation warnings when possible.



In case you want to reach us directly, we're at [email protected].



## Overview



Flax is a high-performance neural network library and ecosystem for

JAX that is **designed for flexibility**:

Try new forms of training by forking an example and by modifying the training

loop, not by adding features to a framework.



Flax is being developed in close collaboration with the JAX team and

comes with everything you need to start your research, including:



* **Neural network API** (`flax.linen`): Dense, Conv, {Batch|Layer|Group} Norm, Attention, Pooling, {LSTM|GRU} Cell, Dropout



* **Utilities and patterns**: replicated training, serialization and checkpointing, metrics, prefetching on device



* **Educational examples** that work out of the box: MNIST, LSTM seq2seq, Graph Neural Networks, Sequence Tagging



* **Fast, tuned large-scale end-to-end examples**: CIFAR10, ResNet on ImageNet, Transformer LM1b



## Quick install



You will need Python 3.6 or later, and a working [JAX](https://github.com/google/jax/blob/main/README.md)

installation (with or without GPU support - refer to [the instructions](https://github.com/google/jax/blob/main/README.md)).

For a CPU-only version of JAX:



```

pip install --upgrade pip # To support manylinux2010 wheels.

pip install --upgrade jax jaxlib # CPU-only

```



Then, install Flax from PyPi:



```

pip install flax

```



To upgrade to the latest version of Flax, you can use:



```

pip install --upgrade git+https://github.com/google/flax.git

```

To install some additional dependencies (like `matplotlib`) that are required but not included

by some dependencies, you can use:



```bash

pip install "flax[all]"

```



## What does Flax look like?



We provide three examples using the Flax API: a simple multi-layer perceptron, a CNN and an auto-encoder.



To learn more about the `Module` abstraction, check out our [docs](https://flax.readthedocs.io/), our [broad intro to the Module abstraction](https://github.com/google/flax/blob/main/docs/linen_intro.ipynb). For additional concrete demonstrations of best practices, refer to our

[guides](https://flax.readthedocs.io/en/latest/guides/index.html) and

[developer notes](https://flax.readthedocs.io/en/latest/developer_notes/index.html).



```py

from typing import Sequence



import numpy as np

import jax

import jax.numpy as jnp

import flax.linen as nn



class MLP(nn.Module):

  features: Sequence[int]



  @nn.compact

  def __call__(self, x):

    for feat in self.features[:-1]:

      x = nn.relu(nn.Dense(feat)(x))

    x = nn.Dense(self.features[-1])(x)

    return x



model = MLP([12, 8, 4])

batch = jnp.ones((32, 10))

variables = model.init(jax.random.key(0), batch)

output = model.apply(variables, batch)

```



```py

class CNN(nn.Module):

  @nn.compact

  def __call__(self, x):

    x = nn.Conv(features=32, kernel_size=(3, 3))(x)

    x = nn.relu(x)

    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))

    x = nn.Conv(features=64, kernel_size=(3, 3))(x)

    x = nn.relu(x)

    x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))

    x = x.reshape((x.shape[0], -1))  # flatten

    x = nn.Dense(features=256)(x)

    x = nn.relu(x)

    x = nn.Dense(features=10)(x)

    x = nn.log_softmax(x)

    return x



model = CNN()

batch = jnp.ones((32, 64, 64, 10))  # (N, H, W, C) format

variables = model.init(jax.random.key(0), batch)

output = model.apply(variables, batch)

```



```py

class AutoEncoder(nn.Module):

  encoder_widths: Sequence[int]

  decoder_widths: Sequence[int]

  input_shape: Sequence[int]



  def setup(self):

    input_dim = np.prod(self.input_shape)

    self.encoder = MLP(self.encoder_widths)

    self.decoder = MLP(self.decoder_widths + (input_dim,))



  def __call__(self, x):

    return self.decode(self.encode(x))



  def encode(self, x):

    assert x.shape[1:] == self.input_shape

    return self.encoder(jnp.reshape(x, (x.shape[0], -1)))



  def decode(self, z):

    z = self.decoder(z)

    x = nn.sigmoid(z)

    x = jnp.reshape(x, (x.shape[0],) + self.input_shape)

    return x



model = AutoEncoder(encoder_widths=[20, 10, 5],

                    decoder_widths=[5, 10, 20],

                    input_shape=(12,))

batch = jnp.ones((16, 12))

variables = model.init(jax.random.key(0), batch)

encoded = model.apply(variables, batch, method=model.encode)

decoded = model.apply(variables, encoded, method=model.decode)

```



## 🤗 Hugging Face



In-detail examples to train and evaluate a variety of Flax models for

Natural Language Processing, Computer Vision, and Speech Recognition are

actively maintained in the [🤗 Transformers repository](https://github.com/huggingface/transformers/tree/main/examples/flax).



As of October 2021, the [19 most-used Transformer architectures](https://huggingface.co/transformers/#supported-frameworks) are supported in Flax

and over 5000 pretrained checkpoints in Flax have been uploaded to the [🤗 Hub](https://huggingface.co/models?library=jax&sort=downloads).



## Citing Flax



To cite this repository:



```

@software{flax2020github,

  author = {Jonathan Heek and Anselm Levskaya and Avital Oliver and Marvin Ritter and Bertrand Rondepierre and Andreas Steiner and Marc van {Z}ee},

  title = {{F}lax: A neural network library and ecosystem for {JAX}},

  url = {http://github.com/google/flax},

  version = {0.8.6},

  year = {2023},

}

```



In the above bibtex entry, names are in alphabetical order, the version number

is intended to be that from [flax/version.py](https://github.com/google/flax/blob/main/flax/version.py), and the year corresponds to the project's open-source release.



## Note



Flax is an open source project maintained by a dedicated team in Google Research, but is not an official Google product.