Crate burn

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§Burn

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

§Performance

Because we believe the goal of a deep learning framework is to convert computation into useful intelligence, we have made performance a core pillar of Burn. We strive to achieve top efficiency by leveraging multiple optimization techniques:

  • Automatic kernel fusion
  • Asynchronous execution
  • Thread-safe building blocks
  • Intelligent memory management
  • Automatic kernel selection
  • Hardware specific features
  • Custom Backend Extension

§Training & Inference

The whole deep learning workflow is made easy with Burn, as you can monitor your training progress with an ergonomic dashboard, and run inference everywhere from embedded devices to large GPU clusters.

Burn was built from the ground up with training and inference in mind. It’s also worth noting how Burn, in comparison to frameworks like PyTorch, simplifies the transition from training to deployment, eliminating the need for code changes.

§Backends

Burn strives to be as fast as possible on as many hardwares as possible, with robust implementations. We believe this flexibility is crucial for modern needs where you may train your models in the cloud, then deploy on customer hardwares, which vary from user to user.

Compared to other frameworks, Burn has a very different approach to supporting many backends. By design, most code is generic over the Backend trait, which allows us to build Burn with swappable backends. This makes composing backend possible, augmenting them with additional functionalities such as autodifferentiation and automatic kernel fusion.

  • WGPU (WebGPU): Cross-Platform GPU Backend
  • Candle: Backend using the Candle bindings
  • LibTorch: Backend using the LibTorch bindings
  • NdArray: Backend using the NdArray primitive as data structure
  • Autodiff: Backend decorator that brings backpropagation to any backend
  • Fusion: Backend decorator that brings kernel fusion to backends that support it

§Quantization (Beta)

Quantization techniques perform computations and store tensors in lower precision data types like 8-bit integer instead of floating point precision. There are multiple approaches to quantize a deep learning model. In most cases, the model is trained in floating point precision and later converted to the lower precision data type. This is called post-training quantization (PTQ). On the other hand, quantization aware training (QAT) models the effects of quantization during training. Quantization errors are thus modeled in the forward and backward passes, which helps the model learn representations that are more robust to the reduction in precision.

Quantization support in Burn is currently in active development. It supports the following modes on some backends:

  • Static per-tensor quantization to signed 8-bit integer (i8)

§Feature Flags

The following feature flags are available. By default, the feature std is activated.

  • Training
    • train: Enables features dataset and autodiff and provides a training environment
    • tui: Includes Text UI with progress bar and plots
    • metrics: Includes system info metrics (CPU/GPU usage, etc.)
  • Dataset
    • dataset: Includes a datasets library
    • audio: Enables audio datasets (SpeechCommandsDataset)
    • sqlite: Stores datasets in SQLite database
    • sqlite_bundled: Use bundled version of SQLite
    • vision: Enables vision datasets (MnistDataset)
  • Backends
    • wgpu: Makes available the WGPU backend
    • wgpu-spirv: Makes available the wgpu backend with the alternative SPIR-V compiler
    • candle: Makes available the Candle backend
    • tch: Makes available the LibTorch backend
    • ndarray: Makes available the NdArray backend
  • Backend specifications
    • cuda: If supported, CUDA will be used
    • accelerate: If supported, Accelerate will be used
    • blas-netlib: If supported, Blas Netlib will be use
    • openblas: If supported, Openblas will be use
    • openblas-system: If supported, Openblas installed on the system will be use
    • autotune: Enable running benchmarks to select the best kernel in backends that support it.
    • fusion: Enable operation fusion in backends that support it.
  • Backend decorators
    • autodiff: Makes available the Autodiff backend
  • Others:
    • std: Activates the standard library (deactivate for no_std)
    • network: Enables network utilities (currently, only a file downloader with progress bar)
    • experimental-named-tensor: Enables named tensors (experimental)

You can also check the details in sub-crates burn-core and burn-train.

Modules§

  • backend
    Backend module.
  • config
    The configuration module.
  • data
    Data module.
  • grad_clipping
    Gradient clipping module.
  • lr_scheduler
    Learning rate scheduler module.
  • module
    Module for the neural network module.
  • nn
    Neural network module.
  • optim
    Optimizer module.
  • prelude
    Structs and macros used by most projects. Add use burn::prelude::* to your code to quickly get started with Burn.
  • record
    Module for the recorder.
  • serde
    Serde
  • tensor
    Module for the tensor.
  • train
    Train module

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