burn
A Rust-based deep learning framework and tensor library optimized for flexibility, efficiency, and cross-platform portability.
What is burn?
Burn is a comprehensive deep learning framework and tensor library built in Rust, designed to bridge the gap between high-level model development and low-level performance optimization. It enables seamless training and inference across diverse hardware, from embedded devices to large GPU clusters, without sacrificing flexibility.
Deep learning practitioners and engineers who need a single framework for both research and production deployment, particularly those working in Rust or requiring cross-platform compatibility from embedded systems to web browsers.
Developers choose Burn for its unique combination of Rust's performance and safety with high-level abstractions, eliminating the traditional split between research and deployment environments. Its swappable backend system with decorators like Autodiff and Fusion provides broad hardware support and automatic optimizations without code changes.
Overview
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Use Cases
Best For
- Training and deploying deep learning models across multiple hardware backends (CPU, CUDA, ROCm, Metal, Vulkan, WebGPU) from a single codebase.
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
