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Step by Step Guide
https://developer.nvidia.com/cuda-gpus
https://developer.nvidia.com/cuda-downloads
https://github.com/fairy-stockfish/variant-nnue-tools/releases
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.exe
for Windows, others for Linux -
-largeboard
are for variants with boards >8x8 - Depending on your CPU use
x86-64-bmi2
,x86-64-modern
, or `x86-64. In doubt use the latter.
If you aren't sure, use fairy-stockfish-tools-largeboard_x86-64.exe
.
If you installed git, download the training code with
git clone https://github.com/fairy-stockfish/variant-nnue-pytorch.git
Otherwise you can download the code as a ZIP and extract it.
https://github.com/fairy-stockfish/variant-nnue-pytorch#setup
Now you should be able to run python3 train.py -h
. If there are problems with the installed dependencies it might report errors here, so you need to address these before continuing.
https://github.com/fairy-stockfish/variant-nnue-pytorch/wiki/NNUE-training#code-changes
compile_data_loader.bat
https://github.com/fairy-stockfish/variant-nnue-pytorch/wiki/NNUE-training#training-example
Now you finally generated your first own NNUE network.
If you are doing the training for the first time, you likely want to first validate if the generated NNUE file really works. For that, e.g., load it at https://fairy-stockfish-nnue-wasm.vercel.app/ under the nnue file
. If it reports an ERROR
please check again if your definition in variant.py
fits to the variant you wanted to train.
Depending on the variant there are different options how to compare playing strength to the previous best NNUE network. One option that works for all variants is https://github.com/ianfab/variantfishtest.
If testing shows that your NNUE network performs well, please upload it at https://forms.gle/8Am9LTqXQJo43ps79 in order to share it with others. It will automatically be made available at https://drive.google.com/drive/folders/1m5PpiI3Kjzk_ow7F5RkwKnbO0Td-qb9J?usp=sharing then. The file name will be derived from the variant name and the value you added for the SHA256.