burn

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  <img src="assets/burn-symbol-colour.svg" alt="The Burn logo" height="200"/>
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[![Rust 1.95 or later](https://img.shields.io/badge/Rust-1.95%2B-f15a24?style=flat-square&logo=rust&logoColor=white)](https://crates.io/crates/burn)
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  <p><b>A tensor library and deep learning framework for numerical computing, training and inference.</b></p>
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Training and inference usually live in separate worlds. Models are typically trained in Python then
exported to an open format like ONNX or optimized for production engines like vLLM, ONNX Runtime, or
TensorRT. This export step is often brittle and lossy, ruling out complex architectures and advanced
deployment use cases.

Burn unifies the two. By executing multi-platform tensor operations via a single, unified API, the
exact code used for training is the exact code that runs in production. This makes workloads like
on-device personalization and federated learning straightforward, while enabling teams to go from
prototype to deployment in a single codebase.

Burn preserves the intuitive ergonomics of PyTorch, with dynamic shapes and graphs, but JIT-compiles
streams of tensor operations, performing automatic kernel fusion. You get the flexibility of dynamic
graphs without the performance drop.

## Rust for Research?

Rust used to be a tough sell for research: long compilation times disrupted the fast
edit-compile-run loop that draws researchers to Python. Burn changes this paradigm. Designed around
incremental compilation, modifying model code recompiles in under 5 seconds, even in release mode.
This delivers a Python-like feedback loop with the speed and safety of Rust.

## Ecosystem

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<img align="right" src="https://raw.githubusercontent.com/tracel-ai/burn/main/assets/ember-blazingly-fast.png" height="96px"/>

Burn is the core of a growing, fully open-source Rust AI ecosystem. You are not adopting a single
library, you are joining a stack that spans GPU compute, model interop and domain toolkits, with
plenty of room to