Burn is a comprehensive deep learning framework written entirely in Rust, combining PyTorch's flexibility with Rust's performance and safety guarantees — providing dynamic computation graphs, backend-agnostic model definitions (swap between CUDA, Metal, Vulkan, and CPU without changing model code), and compilation to single-binary executables for production deployment in environments where Python's runtime overhead and GIL limitations are unacceptable.

What Is Burn?

• Definition: A Rust-native deep learning framework that provides both training and inference capabilities — unlike Candle (inference-focused), Burn supports the full ML lifecycle including model definition, training loops, optimizers, and data loading, all in pure Rust.
• Backend Agnostic: Model code is written against Burn's abstract tensor API — the same model runs on wgpu (WebGPU/Vulkan), LibTorch (PyTorch C++ backend), ndarray (CPU), CUDA, and Metal by changing a single type parameter, with no model code modifications.
• Rust Safety: Rust's ownership system prevents data races, null pointer dereferences, and memory leaks at compile time — critical for production ML systems where a segfault in a Python C extension can crash the entire serving pipeline.
• Single Binary Deployment: Compile your trained model and inference server into a single executable — no Python interpreter, no pip dependencies, no Docker container with gigabytes of framework code.

Key Features

• Dynamic Graphs: Like PyTorch, Burn uses eager execution with dynamic computation graphs — debug with standard Rust tooling, use conditional logic and loops in model definitions.
• Autodiff: Full automatic differentiation for training — compute gradients through arbitrary Rust code, not just predefined operations.
• Backend Swapping: type Backend = Wgpu; or type Backend = LibTorch; — change one line to switch the entire computation backend.
• Model Import: Import ONNX models and PyTorch state dicts — use models trained in Python with Burn's Rust inference engine.
• no_std Support: Burn can compile without the Rust standard library — enabling deployment on bare-metal embedded systems and microcontrollers.

Burn vs Alternatives

Burn is the full-featured Rust deep learning framework for teams that need production-grade ML without Python — providing training and inference with backend-agnostic model definitions, Rust's compile-time safety guarantees, and single-binary deployment for embedded systems, high-frequency trading, and edge AI where Python's overhead is unacceptable.