#Flax

EasyDeL is an open-source framework designed to enhance machine learning model training with a focus on JAX/Flax optimization for TPU and GPU. It supports a variety of model architectures and includes specialized trainers and efficient serving engines. Features include customizable sharding strategies, advanced quantization, and a flexible framework suitable for cutting-edge ML research. Regular updates provide access to the latest technologies, enabling scalable model training and experimentation with easy-to-use APIs.

Explore CommonLoopUtils, a repository aimed at simplifying ML training loops with common, flexible functionalities. Access resources including a Colab notebook and view examples in the Flax GitHub repository. Engage in discussions on the Flax GitHub page. Contributions are not accepted, but forking for personal extensions is encouraged.

Haiku is a compact neural network library for JAX that offers an object-oriented programming approach integrated with JAX function transformations. Created by the developers of Sonnet for TensorFlow, Haiku focuses on efficient parameter and state management without adding extra frameworks. While currently in maintenance mode focused on bug fixes and compatibility, Haiku still offers key features like hk.Module and hk.transform, facilitating the transition from TensorFlow to JAX. It caters to large-scale project requirements and supports the incorporation of stochastic models and non-trainable states, extending to distributed model training through jax.pmap. Well-documented resources and examples assist users in leveraging Haiku effectively.