python3 -m venv env
source env/bin/activate
pip install python-chess==0.31.4 pytorch-lightning torch "ray[tune]" torchvision

This requires a C++17 compiler.

Windows:

compile_data_loader.bat

Linux/Mac:

sh compile_data_loader.bat

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

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

python train.py --gpus 1 ...

By default the trainer uses a factorized HalfKP feature set (named "HalfKP^") If you wish to change the feature set used then you can use the --features=NAME option. For the list of available features see --help The default is:

python train.py ... --features="HalfKP^"

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 an existing SF NNUE network to the pytorch network format.

python serialize.py nn.nnue converted.pt

pip install tensorboard
tensorboard --logdir=logs

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

• 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)