Flax
A neural network library for JAX designed for flexibility, enabling researchers to experiment with new training forms by modifying training loops.
What is Flax?
Flax is a neural network library and ecosystem for JAX designed specifically for flexibility in machine learning research. It enables researchers to experiment with new training approaches by modifying training loops directly rather than adding features to a rigid framework. The library provides a comprehensive neural network API, utilities for training and serialization, and educational examples to accelerate research workflows.
Machine learning researchers and engineers working with JAX who need flexibility to experiment with novel neural network architectures and training methodologies. It's particularly valuable for those in academic or industrial research settings exploring cutting-edge deep learning techniques.
Developers choose Flax over other neural network libraries because of its emphasis on flexibility and Pythonic design, allowing direct modification of training loops and model structures. Its close collaboration with the JAX team ensures optimal performance integration, while the new Flax NNX API simplifies network creation, inspection, and debugging with first-class Python reference semantics.
Overview
Flax is a neural network library for JAX that is designed for flexibility.
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