A domain-specific ML library, generally used for training NNUE-style networks for many of the strongest chess engines in the world due to its best-in-class performance, chess-specific tooling and ease of use.

Usage for NNUE/Value Network Training

Before attempting to use, check out the docs. They contain all the main information about building bullet, managing training data and the network output format.

Most people simply clone the repo and edit one of the examples to their taste. If you want to create your own example file to ease pulling from upstream, you need to add the example to bullet_lib's Cargo.toml.

Alternatively, import the bullet_lib crate with

bullet = { git = "https://github.com/jw1912/bullet", package = "bullet_lib" }

Specific API documentation is covered by Rust's docstrings. You can create local documentations with cargo doc.

Constituent Crates

• acyclib
  • Core ML library for directed acyclic tensor graphs
  • Graphs are defined once (ahead of use), then optimised and compiled for a given backend device
  • A token single-threaded CPU backend is included for verifying correctness of the crate and other backend implementations
  • Contains training abstractions to take care of asynchronous data loading and transfer from CPU to GPU
  • See the MNIST example for using it at a lower level
• bullet_cuda_backend
• bullet_hip_backend
• bullet_lib
  • Provides a high-level wrapper around the above crates specifically for training networks to do with chess (and other games e.g. Ataxx) easily
  • Value network training for games with ValueTrainer
    • The simple example shows ease-of-use in training the simplest NNUE architectures
    • The progression examples show how to incrementally improve your NNUE architecture
    • Read the documentation for compilation instructions
• bullet-utils
  • Various utilities mostly to do with handling chess data

Help/Feedback

• Please open an issue to file any bug reports/feature requests.
• Feel free to use the dedicated #bullet channel in the Engine Programming discord server if you run into any issues.
• For general training discussion the Engine Programming non-#bullet channels are appropriate, or #engines-dev in the Stockfish discord.