REGULARChess trainer
Train and play with your own chess bot using nevermind-neu and pleco.
Just follow simple instructions to train your own bot.
Usage:
1) Clone repository (git clone https://github.com/regular-dev/chess_trainer.git && cd chess_trainer)
2) Run cargo build --release
3) Download any lichess pgn database from https://database.lichess.org/ (.pgn.zst) to chess_trainer/py folder, i suggest to choose not large file, for example "2014 - January" - 100 mb.
cd py
wget https://database.lichess.org/standard/lichess_db_standard_rated_2014-01.pgn.zst
unzstd lichess_db_standard_rated_2014-01.pgn.zst - unpack zstd archive
4) Convert pgn file to sqlite3 database with columns - [ fen , stockfish eval ] with python code. For engine evaluation you need to download stockfish engine binary.
For ArchLinux it could be installed from AUR https://aur.archlinux.org/packages/stockfish.
Otherwise you could download it from official site and provide path to stockfish-binary in **pgn_to_db.py**.
python *unpacked_pgn_file*
5) Run training process for both sides(black and white) sequentially from project directory
cargo run --release train --dataset=py/chess_db_white.db --ocl --out=net_white --epochs=55
Then we need to train network evaluate positions from black side
cargo run --release train --dataset=py/chess_db_black.db --ocl --out=net_black --epochs=55
**--ocl** flag enables OpenCL computations on GPU
**--epochs** spicifies number of epochs, could be modified.
cargo run --release play --state_white=*net_white...state* --state_black=*net_black...state* --ocl --unicode --depth=4
**--unicode** flag enables pretty unicode board state displaying
**--depth** specifies the depth of move search for alpha-beta algorithm. I suggest to use values from 1 to 4. Big depth values(more than 4) will make the algorithm take a lot of time to search best move.