Burn

Burn is a next-generation deep learning framework developed in Rust, designed to offer exceptional performance, flexibility, and portability for both training and inference. It is tailored for machine learning engineers and researchers, providing a fresh perspective distinct from PyTorch or TensorFlow. Burn focuses on maximizing composability and reliability without compromising efficiency, leveraging Rust’s memory safety and concurrency features.

Burn excels in several key areas: it is blazingly fast, ensuring high performance; it is flexible, allowing the realization of ambitious ideas; and it is portable, abstracting backend implementation to support development across various hardware devices, from laptops and cloud environments to embedded systems. The framework also features a dynamic computational graph with a custom just-in-time compiler, enhancing flexibility and efficiency.

Burn supports multiple backend implementations for both CPU and GPU, and includes comprehensive features for logging, metrics, and checkpointing during model training. It is community-driven and open-source, backed by a vibrant community that contributes to its continuous improvement.

The framework’s unique architecture, based on tensor operation streams and optimized at runtime, benefits from Rust’s robust type system to track tensor usage precisely. This allows for the creation of highly dynamic models with the performance of an optimized static graph. Burn adheres to Rust’s best practices, enhancing development speed and reliability, and aims to reduce debugging time, enabling users to focus more on building and innovating.