Burn v0.10.0 sees the addition of the burn-compute crate to simplify the process of creating new custom backends, a new training dashboard and the possibility of using the GPU in the browser along with a web demo. Additionally, numerous new features, bug fixes, and CI improvements have been made.

Warning: there are breaking changes, see below.

Changes

Burn Compute

• Introduction of burn-compute, a new Burn crate making it easier to create async backends with custom kernels. @nathanielsimard, @louisfd

• Add new memory management strategies @louisfd, @nathanielsimard

• Add autotune capabilities @louisfd

Burn Import

• Add more ONNX record types @antimora

• Support no-std for ONNX imported models @antimora

• Add custom file location for loading record with ONNX models @antimora

• Support importing erf operation to ONNX @AuruTus

Burn Tensor

• Add covariance and diagonal operations @ArvidHammarlund

• [Breaking] Reading operations are now async when compiling to wasm, except when wasm-sync feature is enabled. @nathanielsimard @AlexErrant

• [Breaking] Improved Clamp API @nathanielsimard

• Add unfold tensor operation @agelas, @nathanielsimard

• Improve tensor display implementation with ellipsis for large tensors: @macroexpansion

Burn Dataset

• Improved speed of SqLite Dataset @antimora

• Use gix-tempfile only when sqlite is enabled @AlexErrant

Burn Common

• Add benchmark abstraction @louisfd

• Use thread-local RNG to generate IDs @dae

Burn Autodiff

• Use AtomicU64 for node ids improving performance @dae

Burn WGPU

• Enable non-blocking reads when compiling to wasm to fully support WebGPU @nathanielsimard

• Add another faster matmul kernel @louisfd

• [Breaking] Massive refactor to use burn-compute @nathanielsimard

Burn Candle

• Candle backend is now available as a crate and updated with Candle advances @louisfd @agelas

Burn Train

• New training cli dashboard using ratatui @nathanielsimard

• [Breaking] Heavy refactor of burn-train making it more extensible and easier to work with @nathanielsimard

• Checkpoints can be customized with criteria based on collected metrics @nathanielsimard

• Add the possibility to do early stopping based on collected metrics @nathanielsimard

Examples

• Add image classifier web demo using different backends, including WebGPU, @antimora

Bugfixes

• Epoch and iteration were swapped. (#838) @daniel-vainsencher

• RNN (Gru & LSTM) were not generic over the batch size @agelas, @EddieMataEwy

• Other device adaptors in WGPU were ignored when best available device was used @chistophebiocca

Documentation

• Update book @nathanielsimard

• Doc improvements with std feature flag: @ArvidHammarlund

Chores

• Update all dependencies @antimora

• Lots and lots of CI Improvements with coverage information @Luni-4, @DrChat, @antimora, @dae, @nathanielsimard

Thanks

Thanks to all aforemetioned contributors and to our sponsors @smallstepman, @0x0177b11f and @premAI-io.