Sanger2000/nnue-pytorch

NNUE (Chess evaluation) trainer in Pytorch

Setup

python3 -m venv env
source env/bin/activate
pip install python-chess==0.31.4 pytorch-lightning torch

Build the fast DataLoader

This requires a C++17 compiler.

Windows:

compile_data_loader.bat

Linux/Mac:

sh compile_data_loader.bat

Train a network

source env/bin/activate
python train.py train_data.bin val_data.bin

Resuming from a checkpoint

python train.py --resume_from_checkpoint <path> ...

Training on GPU

python train.py --gpus 1 ...

Export a network

Using either a checkpoint (.ckpt) or serialized model (.pt), you can export to SF NNUE format. This will convert last.ckpt to nn.nnue, which you can load directly in SF.

python serialize.py last.ckpt nn.nnue

Import a network

Import an existing SF NNUE network to the pytorch network format.

python serialize.py nn.nnue converted.pt

Logging

pip install tensorboard
tensorboard --logdir=logs

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

Thanks

• Sopel - for the amazing fast sparse data loader
• connormcmonigle - https://github.com/connormcmonigle/seer-nnue, and loss function advice.
• syzygy - http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75506
• https://github.com/DanielUranga/TensorFlowNNUE
• https://hxim.github.io/Stockfish-Evaluation-Guide/
• dkappe - Suggesting ranger (https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer)