## Features - Customizable, user-friendly neural network [module](#module) 🔥 - Comprehensive [training](#learner) tools, inclusive of `metrics`, `logging`, and `checkpointing` 📈 - Versatile [Tensor](#tensor) crate equipped with pluggable backends 🔧 - [Torch](https://github.com/burn-rs/burn/tree/main/burn-tch) backend, supporting both CPU and GPU 🚀 - [Ndarray](https://github.com/burn-rs/burn/tree/main/burn-ndarray) backend with [`no_std`](#support-for-no_std) compatibility, ensuring universal platform adaptability 👌 - [WebGPU](https://github.com/burn-rs/burn/tree/main/burn-wgpu) backend, offering cross-platform, browser-inclusive, GPU-based computations 🌐 - [Autodiff](https://github.com/burn-rs/burn/tree/main/burn-autodiff) backend that enables differentiability across all backends 🌟 - [Dataset](https://github.com/burn-rs/burn/tree/main/burn-dataset) crate containing a diverse range of utilities and sources 📚 - [Import](https://github.com/burn-rs/burn/tree/main/burn-import) crate that simplifies the integration of pretrained models 📦 ## Supported Platforms ### [Burn-ndarray][1] Backend | Option | CPU | GPU | Linux | MacOS | Windows | Android | iOS | WASM | | :--------- | :-: | :-: | :---: | :---: | :-----: | :-----: | :-: | :--: | | Pure Rust | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes | | Accelerate | Yes | No | No | Yes | No | No | Yes | No | | Netlib | Yes | No | Yes | Yes | Yes | No | No | No | | Openblas | Yes | No | Yes | Yes | Yes | Yes | Yes | No | ### [Burn-tch][2] Backend | Option | CPU | GPU | Linux | MacOS | Windows | Android | iOS | WASM | | :----- | :-: | :-: | :---: | :---: | :-----: | :-----: | :-: | :--: | | CPU | Yes | No | Yes | Yes | Yes | Yes | Yes | No | | CUDA | No | Yes | Yes | No | Yes | No | No | No | | MPS | No | Yes | No | Yes | No | No | No | No | | Vulkan | Yes | Yes | Yes | Yes | Yes | Yes | No | No | ### [Burn-wgpu][3] Backend | Option | CPU | GPU | Linux | MacOS | Windows | Android | iOS | WASM | | :-------- | :-: | :-: | :---: | :---: | :-----: | :-----: | :-: | :--: | | Metal | No | Yes | No | Yes | No | No | Yes | No | | Vulkan | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No | | OpenGL | No | Yes | Yes | Yes | Yes | Yes | Yes | No | | WebGpu | No | Yes | No | No | No | No | No | Yes | | Dx11/Dx12 | No | Yes | No | No | Yes | No | No | No | [1]: https://github.com/burn-rs/burn/tree/main/burn-ndarray [2]: https://github.com/burn-rs/burn/tree/main/burn-tch [3]: https://github.com/burn-rs/burn/tree/main/burn-wgpu ## Get Started The best way to get started with `burn` is to clone the repo and play with the [examples](#examples). This may also be a good idea to take a look the main [components](#components) of `burn` to get a quick overview of the fundamental building blocks. If you're interested in how the framework works, you can read our [architecture document](https://github.com/burn-rs/burn/tree/main/ARCHITECTURE.md). ### Examples - [MNIST](https://github.com/burn-rs/burn/tree/main/examples/mnist) train a model on CPU/GPU using different backends. - [MNIST Inference Web](https://github.com/burn-rs/burn/tree/main/examples/mnist-inference-web) run trained model in the browser for inference. - [Text Classification](https://github.com/burn-rs/burn/tree/main/examples/text-classification) train a transformer encoder from scratch on GPU. - [Text Generation](https://github.com/burn-rs/burn/tree/main/examples/text-generation) train an autoregressive transformer from scratch on GPU. ### Components Understanding the key components and philosophy of `burn` can greatly help when beginning to work with the framework. #### Backend Nearly everything in `burn` is based on the `Backend` trait, which enables you to run tensor operations using different implementations without having to modify your code. While a backend may not necessarily have autodiff capabilities, the `ADBackend` trait specifies when autodiff is needed. This trait not only abstracts operations but also tensor, device and element types, providing each backend the flexibility they need. It's worth noting that the trait assumes eager mode since `burn` fully supports dynamic graphs. However, we may create another API to assist with integrating graph-based backends, without requiring any changes to the user's code. #### Tensor At the core of burn lies the `Tensor` struct, which encompasses multiple types of tensors, including `Float`, `Int`, and `Bool`. The element types of these tensors are specified by the backend and are usually designated as a generic argument (e.g., `NdArrayBackend`). Although the same struct is used for all tensors, the available methods differ depending on the tensor kind. You can specify the desired tensor kind by setting the third generic argument, which defaults to `Float`. The first generic argument specifies the backend, while the second specifies the number of dimensions. ```rust use burn::tensor::backend::Backend; use burn::tensor::{Tensor, Int}; fn function(tensor_float: Tensor) { let _tensor_bool = tensor_float.clone().equal_elem(2.0); // Tensor let _tensor_int = tensor_float.argmax(1) // Tensor } ``` As demonstrated in the previous example, nearly all operations require owned tensors as parameters, which means that calling `Clone` explicitly is necessary when reusing the same tensor multiple times. However, there's no need to worry since the tensor's data won't be copied, it will be flagged as readonly when multiple tensors use the same allocated memory. This enables backends to reuse tensor data when possible, similar to a copy-on-write pattern, while remaining completely transparent to the user. #### Autodiff The 'Backend' trait is highly flexible, enabling backpropagation to be implemented using a simple backend decorator, which makes any backend differentiable. ```rust use burn::tensor::backend::{ADBackend, Backend}; use burn::tensor::{Distribution, Tensor}; use burn_autodiff::ADBackendDecorator; use burn_ndarray::NdArrayBackend; fn linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor { x.matmul(weight) + bias } fn main() { type Backend = NdArrayBackend; let weight = Tensor::random([3, 3], Distribution::Standard); let bias = Tensor::zeros([1, 3]); let x = Tensor::random([3, 3], Distribution::Standard); let y = linear::(x.clone(), weight.clone(), bias.clone()); // y.backward() // Method backward doesn't exist let y = linear::>( Tensor::from_inner(x), Tensor::from_inner(weight).require_grad(), Tensor::from_inner(bias).require_grad(), ); let grads = y.backward(); // Method exists } ``` #### Module The `Module` derive allows you to create your own neural network modules, similar to PyTorch. The derive function only generates the necessary methods to essentially act as a parameter container for your type, it makes no assumptions about how the forward pass is declared. ```rust use burn::nn; use burn::module::Module; use burn::tensor::backend::Backend; #[derive(Module, Debug)] pub struct PositionWiseFeedForward { linear_inner: Linear, linear_outer: Linear, dropout: Dropout, gelu: GELU, } impl PositionWiseFeedForward { pub fn forward(&self, input: Tensor) -> Tensor { let x = self.linear_inner.forward(input); let x = self.gelu.forward(x); let x = self.dropout.forward(x); self.linear_outer.forward(x) } } ``` Note that all fields declared in the struct must also implement the `Module` trait. The `Tensor` struct doesn't implement `Module`, but `Param>` does. #### Config The `Config` derive lets you define serializable and deserializable configurations or hyper-parameters for your [modules](#module) or any components. ```rust use burn::config::Config; #[derive(Config)] pub struct PositionWiseFeedForwardConfig { pub d_model: usize, pub d_ff: usize, #[config(default = 0.1)] pub dropout: f64, } ``` The derive also adds useful methods to your config, similar to a builder pattern. ```rust fn main() { let config = PositionWiseFeedForwardConfig::new(512, 2048); println!("{}", config.d_model); // 512 println!("{}", config.d_ff); // 2048 println!("{}", config.dropout); // 0.1 let config = config.with_dropout(0.2); println!("{}", config.dropout); // 0.2 } ``` #### Learner The `Learner` is the main `struct` that let you train a neural network with support for `logging`, `metric`, `checkpointing` and more. In order to create a learner, you must use the `LearnerBuilder`. ```rust use burn::train::LearnerBuilder; use burn::train::metric::{AccuracyMetric, LossMetric}; use burn::record::DefaultRecordSettings; fn main() { let dataloader_train = ...; let dataloader_valid = ...; let model = ...; let optim = ...; let learner = LearnerBuilder::new("/tmp/artifact_dir") .metric_train_plot(AccuracyMetric::new()) .metric_valid_plot(AccuracyMetric::new()) .metric_train(LossMetric::new()) .metric_valid(LossMetric::new()) .with_file_checkpointer::(2) .num_epochs(10) .build(model, optim); let _model_trained = learner.fit(dataloader_train, dataloader_valid); } ``` See this [example](https://github.com/burn-rs/burn/tree/main/examples/mnist) for a real usage. ## Support for `no_std` Burn, including its `burn-ndarray` backend, can work in a `no_std` environment, provided `alloc` is available for the inference mode. To accomplish this, simply turn off the default features in `burn` and `burn-ndarray` (which is the minimum requirement for running the inference mode). You can find a reference example in [burn-no-std-tests](https://github.com/burn-rs/burn/tree/main/examples/burn-no-std-tests). The `burn-core` and `burn-tensor` crates also support `no_std` with `alloc`. These crates can be directly added as dependencies if necessary, as they are reexported by the `burn` crate. Please be aware that when using the `no_std` mode, a random seed will be generated at build time if one hasn't been set using the `Backend::seed` method. Also, the [spin::mutex::Mutex](https://docs.rs/spin/latest/spin/mutex/struct.Mutex.html) is used instead of [std::sync::Mutex](https://doc.rust-lang.org/std/sync/struct.Mutex.html) in this mode. ## Contributing Before contributing, please take a moment to review our [code of conduct](https://github.com/burn-rs/burn/tree/main/CODE-OF-CONDUCT.md). It's also highly recommended to read our [architecture document](https://github.com/burn-rs/burn/tree/main/ARCHITECTURE.md), which explains our architectural decisions. Please see more details in our [contributing guide](/CONTRIBUTING.md). ## Disclamer Burn is currently in active development, and there will be breaking changes. While any resulting issues are likely to be easy to fix, there are no guarantees at this stage. ## Sponsors You can sponsor the founder of Burn from his [GitHub Sponsors profile](https://github.com/sponsors/nathanielsimard). The Burn-rs organization doesn't yet have a fiscal entity, but other sponsor methods might become available as the project grows. Thanks to all current sponsors 🙏. ## License Burn is distributed under the terms of both the MIT license and the Apache License (Version 2.0). See [LICENSE-APACHE](./LICENSE-APACHE) and [LICENSE-MIT](./LICENSE-MIT) for details. Opening a pull request is assumed to signal agreement with these licensing terms.