Praxis
A layer library for JAX-based machine learning projects, optimized for large-scale ML.
What is Praxis?
Praxis is a layer library for JAX-based machine learning projects that provides neural network components optimized for large-scale training. It serves as the foundational layer library for PaxML but is designed to be usable by other JAX-based ML frameworks. The library focuses on delivering high-performance building blocks for modern deep learning architectures.
Machine learning researchers and engineers working with JAX who need scalable, reusable neural network layers for large-scale training. Particularly relevant for teams using or considering PaxML.
Developers choose Praxis for its JAX-native implementation optimized for scale, its compatibility with the PaxML ecosystem, and its goal of providing reusable layers that can benefit the broader JAX ML community beyond just Pax users.
Overview
Praxis is a layer library designed for JAX-based machine learning projects, with a primary focus on supporting large-scale ML workloads. While it serves as the foundational layer library for Pax (PaxML), it aims to be broadly usable across the JAX ecosystem.
Key Features
- JAX Integration — Built on JAX for high-performance numerical computing and automatic differentiation
- Scalable ML Layers — Provides neural network layers optimized for training at scale
- Modular Architecture — Offers reusable components that can be integrated into various JAX-based projects
- Pax Compatibility — Serves as the core layer library for the PaxML framework
Philosophy
Praxis emphasizes performance at scale while maintaining flexibility for broader JAX ecosystem adoption, balancing specialized optimization with general usability.
Use Cases
Related Projects
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