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NNUE PyTorch ​

Setup ​

Docker ​

Use Docker with the NVIDIA PyTorch container. This eliminates the need for local Python environment setup and C++ compilation.

Prerequisites ​

For AMD Users:

  • Docker
  • Up-to-date ROCm driver

For NVIDIA Users:

  • Docker
  • Up-to-date NVIDIA driver
  • NVIDIA Container Toolkit

For driver requirements, check Running ROCm Docker containers (AMD) or the PyTorch container release notes (Nvidia).

The container includes CUDA 12.x / ROCm 6.4.3 and all required dependencies. Your local CUDA/ROCm toolkit version doesn't matter.

Running the container ​

Use the provided script to build and start the container:

./run_docker.sh

You'll be prompted to select the target GPU vendor and the path to your data directory, which will be mounted into the container. Once inside the container, you can run training commands directly.

Building the container will take it's time and disk space (~30-60GB)

Network training and management ​

Hard way: wiki

Easier way: wiki

Logging ​

TODO: Move to wiki. Add setup for easy_train.py

tensorboard --logdir=logs

Then, go to http://localhost:6006/

Automatically run matches to determine the best net generated by a (running) training ​

TODO: Move to wiki

python run_games.py --concurrency 16 --stockfish_exe ./stockfish.master --c_chess_exe ./c-chess-cli --ordo_exe ./ordo --book_file_name ./noob_3moves.epd run96

Automatically converts all .ckpt found under run96 to .nnue and runs games to find the best net. Games are played using c-chess-cli and nets are ranked using ordo. This script runs in a loop, and will monitor the directory for new checkpoints. Can be run in parallel with the training, if idle cores are available.

Thanks ​