Flax is a neural network library and ecosystem for JAX, designed for flexibility and ease of use in research. It targets researchers and developers working with JAX, offering a Pythonic API for building, inspecting, and debugging neural networks, with benefits like simplified state management and mutability.

How It Works

Flax NNX, the latest iteration, introduces first-class support for Python reference semantics, allowing models to be expressed as regular Python objects. This enables natural reference sharing and mutability, simplifying complex model architectures and state management compared to more rigid functional approaches. It builds upon the earlier Flax Linen API, integrating core neural network components and utilities for distributed training and serialization.

Quick Start & Requirements

• Install via pip: pip install flax
• Requires Python 3.8+ and JAX (installation instructions vary by hardware: CPU, GPU, TPU).
• For additional dependencies: pip install "flax[all]"
• Documentation: https://flax.readthedocs.io/en/latest/
• Flax NNX Basics: https://flax.readthedocs.io/en/latest/flax.nnx/basics.html

Highlighted Details

• Offers a comprehensive neural network API including Linear, Conv, BatchNorm, LayerNorm, Attention, LSTMCell, and GRUCell.
• Includes utilities for replicated training, serialization, checkpointing, metrics, and on-device prefetching.
• Provides educational examples such as MNIST, Gemma language model inference, and Transformer LM1B.
• Developed in close collaboration with the JAX team.

Maintenance & Community

• Maintained by a dedicated team at Google DeepMind.
• Feedback and discussions are encouraged via GitHub discussions: https://github.com/google/flax/discussions
• Direct contact: flax-dev@google.com

Licensing & Compatibility

• Apache 2.0 License.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The project is actively developed, with NNX being a newer API. While the core API is expected to remain stable, users should be aware of potential evolution and follow changelogs for any deprecations or minor breaking changes.