1. What Is Burn and Why Use It

Burn is a modern deep learning framework written in Rust. It provides a full toolkit for building, training, and deploying neural networks with a focus on performance, memory safety, and flexibility. Unlike traditional frameworks like PyTorch or TensorFlow, Burn leverages all of Rust's advantages: zero-cost abstractions, compile-time safety guarantees, and no garbage collector. This makes it an ideal choice for systems where predictable performance and low resource consumption are critical — for example, in robotics, embedded systems, high-load servers, and edge devices.

Burn offers a unique architecture based on the concept of "backend neutrality." This means the same code can run on CPU (via its own tensor engine), GPU (via CUDA/WGPU), and in the future — on specialized accelerators. The framework supports automatic differentiation, optimizers (Adam, SGD, AdamW), loss functions, layers (Linear, Conv2d, BatchNorm, Dropout, and more), as well as built-in datasets and DataLoader. A key feature is the ability to compile models into static libraries for deployment without Python or CUDA runtime dependencies.

Why use Burn? First, for projects that demand maximum performance and memory control. Second, for building cross-platform solutions that work on CPU, GPU, and even WebAssembly. Third, for safely deploying models in production where runtime errors (like null pointers or type mismatches) are unacceptable. Burn is actively developed by the community and is already used in areas such as computer vision, NLP, recommendation systems, and reinforcement learning.

2. Installation

To install Burn, you need Rust (version 1.75 or newer recommended). If Rust isn't installed yet, use rustup.

Create a new project and add dependencies to Cargo.toml:

cargo new my_burn_project
cd my_burn_project

Add to Cargo.toml:

[dependencies]
burn = { version = "0.15", features = ["ndarray"] }  # for CPU
# or for GPU (CUDA):
# burn = { version = "0.15", features = ["cuda"] }
# or for WGPU (cross-platform GPU):
# burn = { version = "0.15", features = ["wgpu"] }

Then run:

cargo build

For CUDA, you'll need CUDA Toolkit 11.x or 12.x installed. For WGPU, you'll need Vulkan, Metal, or DirectX 12 (depending on your OS).

3. Quick Start — Minimal Working Example

Let's build a simple neural network for a regression task on synthetic data. This example demonstrates the core steps: defining a model, creating data, training, and inference.

use burn::{
    module::Module,
    nn::{Linear, Relu},
    tensor::{backend::Backend, Tensor},
    train::{Adam, Learner},
    data::dataloader::DataLoaderBuilder,
};

// Define the model structure
#[derive(Module, Debug)]
struct MyModel {
    linear1: Linear,
    linear2: Linear,
    activation: Relu,
}

impl MyModel {
    fn new() -> Self {
        Self {
            linear1: Linear::new(1, 16),
            linear2: Linear::new(16, 1),
            activation: Relu::new(),
        }
    }

    fn forward(&self, input: Tensor) -> Tensor {
        let x = self.linear1.forward(input);
        let x = self.activation.forward(x);
        se