Burn: A Friendly Deep-Dive into the Next-Gen Deep Learning Framework for Everyone

A practical walk-through for junior college graduates and working engineers who want to train, tune, and ship models—without juggling three different languages.

Burn

Why yet another framework?
What exactly is Burn?
Performance in plain English
Hardware support at a glance
Training & inference—end-to-end
Your first model in five minutes
Moving models in and out of Burn
Real examples you can run today
Common questions & answers
Where to go next

Why yet another framework?

Every popular framework solves part of the problem, but it often leaves you with:

Python for research, C++/CUDA for production—two code bases, twice the bugs.
No painless path from a laptop to a browser or an embedded board.
Hand-written GPU kernels if you want real speed—steep learning curve.

Burn’s goal is simple: one Rust code base handles research, training, and deployment on anything from a Raspberry Pi to an NVIDIA A100. No rewrites, no drama.

What exactly is Burn?

Term you might know	Burn’s counterpart	What it means for you
PyTorch `nn.Module`	`#[derive(Module)]` struct	Same mental model, compile-time safety
CUDA kernels	Auto-generated WGSL	You write high-level math, Burn writes the shader
`torch.save`	`Record` trait + MPK/ZIP	Readable, version-tolerant snapshots
Python GIL headache	Rust ownership	True multi-threading, no locks
ONNX export	Built-in ONNX import	Bring models from TF/PyTorch into Burn

Burn is 100 % open source under MIT + Apache 2.0; you can embed it in commercial products without opening your own code.

Performance in plain English

Burn breaks speed into six practical techniques. Click each heading for the short, no-jargon version.

1. Automatic kernel fusion – “many ops become one GPU call”

When you chain small operations (add → mul → activation), Burn stitches them into one GPU kernel.
Example: the custom GELU below turns into ~60 lines of WGSL at runtime—hand-written speed without hand-written pain.

fn gelu_custom<B: Backend, const D: usize>(x: Tensor<B, D>) -> Tensor<B, D> {
    let x = x.clone() * ((x / SQRT_2).erf() + 1);
    x / 2
}

You keep the readable math; Burn keeps the speed.

2. Asynchronous execution – “CPU and GPU stop waiting for each other”

Burn’s first-party backends queue work in the background.
Your Python-style loop keeps feeding the GPU while the GPU keeps crunching numbers; no blocking, no wasted cycles.

3. Thread-safe building blocks – “multi-GPU without locks”

Each module owns its weights. Clone the module, send it to another thread, compute gradients, send them back—no mutexes, no race conditions.
PyTorch’s .grad mutations simply don’t exist here.

4. Smart memory management – “reuse instead of malloc”

Memory pool keeps frequently used buffers alive.
Ownership rules let Burn mutate tensors in place when safe; small wins add up to large-model savings.

5. Automatic kernel selection – “right kernel, right size, right hardware”

Matrix multiply has dozens of tile/block choices. Burn micro-benchmarks once, caches the best, and reuses the decision. First run slower, every later run faster.

6. Hardware-specific features – “when your chip has a secret weapon, Burn uses it”

NVIDIA Tensor Cores: used in CUDA, LibTorch, Candle, WGPU back-ends.
Apple Metal Performance Shaders: used in Metal back-end.
Future chips will come online as soon as WGSL extensions land.

Hardware support at a glance

Back-end	Devices	Maintainer	Special notes
CUDA	NVIDIA GPUs (Linux, Windows)	Burn team	Tensor Cores active
ROCm	AMD GPUs (Linux)	Burn team
Metal	Apple GPUs (macOS, iOS)	Burn team	Apple Silicon supported
Vulkan	Most desktop GPUs	Burn team	Linux & Windows
Wgpu	Any Vulkan/Metal/DX12 target	Burn team	Also compiles to WebAssembly
NdArray	Any CPU	Community	Works in `no_std`
LibTorch	CPU & GPU (via PyTorch C++)	Community	Reuses PyTorch kernels
Candle	NVIDIA, Apple, CPU	Community	HuggingFace back-end

Combining back-ends (optional decorators)

Autodiff – adds automatic differentiation to any back-end.
Fusion – adds kernel fusion to supported back-ends (WGPU, CUDA today).
Router (Beta) – routes parts of the graph to GPU, others to CPU.
Remote (Beta) – send ops over the network to a GPU box.

Example: a CPU+GPU mixed run in four lines.

type Backend = Router<(Wgpu, NdArray)>;
let gpu = MultiDevice::B1(WgpuDevice::DiscreteGpu(0));
let cpu = MultiDevice::B2(NdArrayDevice::Cpu);

Training & inference

1. Terminal dashboard

A Ratatui-based TUI shows live metrics, checkpoints, and early-stop safety—no TensorBoard required.

2. ONNX import

Bring any ONNX model (exported from TensorFlow, PyTorch, etc.) into Burn:

cargo add burn-import --features onnx

See the ONNX import book section for supported ops.

3. PyTorch & Safetensors weights

Load pre-trained weights directly:

let record = PyTorchFileRecorder::new()
    .load("model.pt".into(), &device)?;
let model = MyModelConfig::new().init(&device).load_record(record)?;

4. Browser inference

Compile Burn + WGPU → WebAssembly and ship a 2 MB .wasm file.
Try the live MNIST demo—no server round-trip.

5. Embedded (`no_std`)

NdArray back-end runs on bare-metal ARM Cortex-M.
Limitation: only CPU inference for now.

Your first model in five minutes

Step 1 — install Rust (once per machine)

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Step 2 — create a project

cargo new hello_burn
cd hello_burn
cargo add burn --features wgpu

Step 3 — define a tiny network

use burn::nn;
use burn::module::Module;
use burn::tensor::{backend::Backend, Tensor};

#[derive(Module, Debug)]
pub struct Mlp<B: Backend> {
    linear1: nn::Linear<B>,
    linear2: nn::Linear<B>,
    relu: nn::Relu,
}

impl<B: Backend> Mlp<B> {
    pub fn forward<const D: usize>(&self, x: Tensor<B, D>) -> Tensor<B, D> {
        let x = self.linear1.forward(x);
        let x = self.relu.forward(x);
        self.linear2.forward(x)
    }
}

Step 4 — train on MNIST

git clone https://github.com/tracel-ai/burn
cd burn/examples/mnist
cargo run --release

First run compiles shaders and benchmarks kernels; subsequent runs start immediately.

Moving models in and out of Burn

From	To Burn	Steps
ONNX	Burn	`burn-import` crate, one function call
PyTorch `.pt`	Burn	Convert to `.safetensors`, then load with `PyTorchFileRecorder`
Burn	ONNX	Export is planned; currently use your own forward loop
Burn	WebAssembly	`cargo build --target wasm32-unknown-unknown`

Real examples you can run today

Example	What you learn	Command
MNIST training	End-to-end CNN training	`cargo run --release`
Custom CSV dataset	Read any tabular file	`cargo run --release`
Image classification web demo	Browser inference	`trunk serve`
Text classification	Transformer fine-tuning	`cargo run --release -- --dataset ag_news`
Custom WGPU kernel	Write your own GPU op	`cargo run --release`

All examples are self-contained; clone and cargo run.

Common questions & answers

Q1: Is Rust hard to learn?

Most engineers pick up the syntax in a weekend. Burn hides the hard parts—ownership errors show up at compile time, not runtime.

Q2: Does Burn work on Windows?

Yes. Vulkan, WGPU, CUDA, and LibTorch back-ends are tested on Windows CI every day.

Q3: Do I need bleeding-edge drivers?

– CUDA: driver ≥ 515
– Vulkan: any driver that supports Vulkan 1.2
– Apple: macOS 12+

Q4: I saved a model last month—will it still load?

Burn 0.14–0.16 can read older snapshots if you enable the `record-backward-compat` feature. Load and re-save once to migrate to the new format.

Q5: How do I write a custom GPU kernel?

Write WGSL in a `.wgsl` file, register it with the WGPU back-end. The [custom-wgpu-kernel](./examples/custom-wgpu-kernel) example has 60 lines of copy-pasteable code.

Where to go next

Read the Burn Book
https://burn.dev/books/burn/
Short chapters: Tensors → Modules → Training → Advanced.
Chat with the community
Discord: https://discord.gg/uPEBbYYDB6
Ask anything—#beginners channel is active.
Contribute
- Architecture overview: contributor-book
- Good first issues: GitHub

License

Burn is dual-licensed under MIT and Apache 2.0. Use it in open-source or commercial projects with no strings attached.

Ready to ship your next model in pure Rust?
Clone the repo, run an example, and see the difference.

Burn Deep Learning Framework: Revolutionizing Cross-Platform AI Development in Rust

Burn: A Friendly Deep-Dive into the Next-Gen Deep Learning Framework for Everyone

Table of Contents

Why yet another framework?

What exactly is Burn?

Performance in plain English

Hardware support at a glance

Combining back-ends (optional decorators)

Training & inference

1. Terminal dashboard

2. ONNX import

3. PyTorch & Safetensors weights

4. Browser inference

5. Embedded (`no_std`)

Your first model in five minutes

Step 1 — install Rust (once per machine)

Step 2 — create a project

Step 3 — define a tiny network

Step 4 — train on MNIST

Moving models in and out of Burn

Real examples you can run today

Common questions & answers

Where to go next

License

Burn Deep Learning Framework: Revolutionizing Cross-Platform AI Development in Rust

Burn: A Friendly Deep-Dive into the Next-Gen Deep Learning Framework for Everyone

Table of Contents

Why yet another framework?

What exactly is Burn?

Performance in plain English

Hardware support at a glance

Combining back-ends (optional decorators)

Training & inference

1. Terminal dashboard

2. ONNX import

3. PyTorch & Safetensors weights

4. Browser inference

5. Embedded (no_std)

Your first model in five minutes

Step 1 — install Rust (once per machine)

Step 2 — create a project

Step 3 — define a tiny network

Step 4 — train on MNIST

Moving models in and out of Burn

Real examples you can run today

Common questions & answers

Where to go next

License

Related Posts

5. Embedded (`no_std`)