These crates are not maintained by Tracel, but they are part of the same Rust AI story. Anything that helps you load data, build environments, or ship models belongs here. Built something that fits? Open a PR to add it!

In application code, autodiff is runtime context carried by tensors. Devices provide the default context for newly created tensors, and each tensor can later enable or remove autodiff independently. Enabling autodiff permits graph recording; it does not by itself make a tensor retain gradients.

Internally, Burn implements this by decorating a concrete backend, so autodiff cannot execute by itself. Enable it on a device before creating tensors or initializing a model. With the autodiff and wgpu features enabled:

use burn::tensor::{Device, Distribution, Tensor};

fn main() {
    let device = Device::wgpu(Default::default()).autodiff();

    let x: Tensor<2> = Tensor::random([32, 32], Distribution::Default, &device);
    let y: Tensor<2> = Tensor::random([32, 32], Distribution::Default, &device).require_grad();

    let tmp = x.clone() + y.clone();
    let tmp = tmp.matmul(x);
    let tmp = tmp.exp();

    let grads = tmp.backward();
    let y_grad = y.grad(&grads).unwrap();
    println!("{y_grad}");
}

backward() checks graph participation at runtime. Enable autodiff before the forward pass and call require_grad() on source leaves whose gradients you need. is_autodiff(), is_tracked(), and is_require_grad() inspect autodiff association, graph participation, and gradient retention respectively. See the autodiff guide.

See the Autodiff Backend README for more details.

This backend decorator enhances a backend with kernel fusion, provided that the inner backend supports it. Note that you can compose this backend with other backend decorators such as Autodiff. All first-party accelerated backends (like WGPU and CUDA) use Fusion by default (burn/fusion feature flag), so you typically don’t need to apply it manually.

#[cfg(not(feature = "fusion"))]
pub type Cube = burn_cubecl::CubeBackend;

#[cfg(feature = "fusion")]
pub type Cube = burn_fusion::Fusion<burn_cubecl::CubeBackend>;

Device::autodiff().gradient_checkpointing() enables the balanced gradient-checkpointing strategy, which trades recomputation for reduced activation storage during training.

See the Fusion Backend README for more details.

Remote execution has a client and a server. The server’s devices select the compute backend; clients use a remote Device with the same tensor API. Iroh, the default transport, reaches a server across any network, authenticated and encrypted; see the server example and the distributed computing guide. On a trusted network, WebSocket is the simplest setup: enable remote-server, remote-websocket, and cuda on the server, and remote-websocket plus autodiff on the client:

use burn::remote::RemoteHost;
use burn::server::{RemoteServer, ServeError, WebSocketTransport};
use burn::tensor::{Device, Distribution, Tensor};

fn main_server() -> Result<(), ServeError> {
    RemoteServer::new([Device::cuda(0)]).serve(WebSocketTransport::new(3000))
}

fn main_client() -> Result<(), burn::remote::ConnectError> {
    let host = RemoteHost::websocket("ws://localhost:3000");
    let device = Device::remote_options(&host).init()?.autodiff();
    let tensor_gpu = Tensor::<2>::random([3, 3], Distribution::Default, &device);
    Ok(())
}

As you can see in the previous video (click on the picture!), a new terminal UI dashboard based on the Ratatui crate allows users to follow their training with ease without having to connect to any external application.

You can visualize your training and validation metrics updating in real-time and analyze the lifelong progression or recent history of any registered metrics using only the arrow keys. Break from the training loop without crashing, allowing potential checkpoints to be fully written or important pieces of code to complete without interruption 🛡

Burn supports importing ONNX (Open Neural Network Exchange) models through the burn-onnx crate, allowing you to easily port models from TensorFlow or PyTorch to Burn. The ONNX model is converted into Rust code that uses Burn’s native APIs, enabling the imported model to run on any Burn backend (CPU, GPU, WebAssembly) and benefit from all of Burn’s optimizations like automatic kernel fusion.

Our ONNX support is further described in this section of the Burn Book 🔥.

Note: This crate is in active development and currently supports a limited set of ONNX operators.

You can load weights from PyTorch or Safetensors formats directly into your Burn-defined models. This makes it easy to reuse existing models while benefiting from Burn’s performance and deployment features.

Learn more in the Saving & Loading Models section of the Burn Book.

Several of our backends can run in WebAssembly environments: Flex for CPU execution, and WGPU for GPU acceleration via WebGPU. This means that you can run inference directly within a browser. We provide several examples of this:

• MNIST where you can draw digits and a small convnet tries to find which one it is! 2️⃣ 7️⃣ 😰
• Image Classification where you can upload images and classify them! 🌄

Burn’s core components support no_std. This means it can run in bare metal environment such as embedded devices without an operating system.

As of now, only the Flex backend can be used in a no_std environment.

To begin working effectively with Burn, it is crucial to understand its key components and philosophy. This is why we highly recommend new users to read the first sections of The Burn Book 🔥. It provides detailed examples and explanations covering every facet of the framework, including building blocks like tensors, modules, and optimizers, all the way to advanced usage, like coding your own GPU kernels.

The project is constantly evolving, and we try as much as possible to keep the book up to date with new additions. However, we might miss some details sometimes, so if you see something weird, let us know! We also gladly accept Pull Requests 😄

Let’s start with a code snippet that shows how intuitive the framework is to use! In the following, we declare a neural network module with some parameters along with its forward pass.

use burn::nn;
use burn::module::Module;
use burn::tensor::Tensor;

#[derive(Module, Debug)]
pub struct PositionWiseFeedForward {
    linear_inner: nn::Linear,
    linear_outer: nn::Linear,
    dropout: nn::Dropout,
    gelu: nn::Gelu,
}

impl PositionWiseFeedForward {
    pub fn forward<const D: usize>(&self, input: Tensor<D>) -> Tensor<D> {
        let x = self.linear_inner.forward(input);
        let x = self.gelu.forward(x);
        let x = self.dropout.forward(x);

        self.linear_outer.forward(x)
    }
}

We have a somewhat large amount of examples in the repository that shows how to use the framework in different scenarios.

Following the book:

• Basic Workflow : Creates a custom CNN Module to train on the MNIST dataset and use for inference.
• Custom Training Loop : Implements a basic training loop instead of using the Learner.
• Custom WGPU Kernel : Learn how to create your own custom operation with the WGPU backend.

Additional examples:

• Custom CSV Dataset : Implements a dataset to parse CSV data for a regression task.
• Regression : Trains a simple MLP on the California Housing dataset to predict the median house value for a district.
• Custom Image Dataset : Trains a simple CNN on custom image dataset following a simple folder structure.
• Custom Renderer : Implements a custom renderer to display the Learner progress.
• Image Classification Web : Image classification web browser demo using Burn, WGPU and WebAssembly.
• MNIST Inference on Web : An interactive MNIST inference demo in the browser.
• MNIST Training : Demonstrates how to train a custom Module (MLP) with the Learner configured to log metrics and keep training checkpoints.
• PyTorch Import Inference : Imports a PyTorch model pre-trained on MNIST to perform inference on a sample image with Burn.
• Text Classification : Trains a text classification transformer model on the AG News or DbPedia dataset. The trained model can then be used to classify a text sample.
• Text Generation : Trains a text generation transformer model on the DbPedia dataset.
• Wasserstein GAN MNIST : Trains a WGAN model to generate new handwritten digits based on MNIST.

For more practical insights, you can clone the repository and run any of them directly on your computer!

We keep an updated and curated list of models and examples built with Burn, see the tracel-ai/models repository for more details.

Don’t see the model you want? Don’t hesitate to open an issue, and we may prioritize it. Built a model using Burn and want to share it? You can also open a Pull Request and add your model under the community section!

Deep Learning is a special form of software where you need very high level abstractions as well as extremely fast execution time. Rust is the perfect candidate for that use case since it provides zero-cost abstractions to easily create neural network modules, and fine-grained control over memory to optimize every detail. To this day, the mainstream solution has been to offer APIs in Python but rely on bindings to low-level languages such as C/C++. This reduces portability, increases complexity and creates friction between researchers and engineers. Rust’s approach to abstractions is versatile enough to tackle this two-language dichotomy, and Cargo makes it easy to build, test and deploy from any environment, which is usually a pain in Python.

Rust’s AI ecosystem is young, but it is real and growing quickly. Foundational pieces are already here: Burn and CubeCL for training and compute, candle for inference, Hugging Face’s tokenizers and safetensors, and polars and ndarray for data. Betting on Rust today means betting on a stack that is growing, and one where contributors still shape the direction. The pieces that don’t exist yet are opportunities rather than dead-ends (see Contributing).

Rust is also what makes one-stack-everywhere possible: a single self-contained binary with no Python runtime to ship, running from servers down to no_std embedded targets.

Burn 0.22 uses burnpack for native records and cannot directly read legacy Recorder formats such as .mpk, .bin, or JSON. Load the checkpoint in a compatible older Burn project, export the model weights through burn-store, and import them into your 0.22 model. See Migrating checkpoints for an example and the distinction between transferring weights and resuming training state.

For records saved before 0.14.0, an earlier migration step may also be needed: use Burn 0.14, 0.15, or 0.16 with the record-backward-compat feature to load and re-save the record with the newer tensor-data representation. For legacy binary records, first use the version that wrote them to export a self-describing format such as NamedMpkFileRecorder. This historical migration does not convert records to the format required by 0.22; follow the checkpoint migration guide afterward.