Burn Framework (Part 1): Deep Learning in Pure Rust

Burn ↗ is a comprehensive deep learning framework written entirely in Rust. Designed with flexibility, high performance, and extreme portability in mind, Burn empowers developers to train and deploy models everywhere from cloud servers to WebAssembly (WASM).

Why Burn for Deep Learning?#

While Python dominates the ML space with PyTorch and TensorFlow, Rust offers distinct production advantages:

Memory Safety & Strong Type System: Catch model architecture and shape mismatch bugs at compile-time.
No Heavy C++ Dependencies: Clean single-binary deployment.
CubeCL Compiler: Automatically fuses and optimizes GPU kernels across diverse hardware.

[!NOTE] Burn seamlessly targets NVIDIA (CUDA/WGPU), Apple Silicon (Metal), AMD GPUs, and CPUs.

Setting Up Your First Burn Project#

Create a standard Cargo project to get started:

cargo new burn_hello_world
cd burn_hello_world

bash

Update your Cargo.toml:

Cargo.toml

[dependencies]
burn = { version = "0.14", features = ["wgpu"] }

toml

src/main.rs

use burn::tensor::Tensor;
use burn::backend::Wgpu;

fn main() {
    type MyBackend = Wgpu;
    
    // [!code focus]
    // Create a 2D Tensor on WGPU backend
    let tensor: Tensor<MyBackend, 2> = Tensor::from_data([[1.0, 2.0], [3.0, 4.0]]);
    println!("Tensor:\n{}", tensor);
}

rust

Key Highlights of Burn#

Multi-Backend Architecture: Easily switch between WGPU, LibTorch, Candle, and NdArray backends.
Asynchronous Execution: High-throughput computation graphs with non-blocking transfers.
WASM Support: Run trained models natively in web browsers.

Why Burn for Deep Learning?#

Setting Up Your First Burn Project#

Key Highlights of Burn#

References#