This repository mostly consists of third-party code related to
- (1) Move Evaluation in Go using Deep Convolutional Neural Networks
- (2) Mastering the game of Go with deep neural networks and tree search
- (3) Mastering the game of Go without human knowledge
Currently, the code implements supervised learning applied to high-level human play from the KGS server (data found here). This is mostly explored in (1) (although it is also used in (2)). The neural network used is a clone of the network from (3).
To download the data, run
bin/download_kgs.sh
To setup the data for training, run
python -m deepgo prepare
Run
python -m deepgo train --batch 512 --checkpoint checkpoint --keep_all_checkpoints --workers 8
04/08/2019 In deepgo/cpp/external/ wget https://download.pytorch.org/libtorch/nightly/cu100/libtorch-shared-with-deps-latest.zip
rm -rf build mkdir build cd build cmake -DCMAKE_PREFIX_PATH=/home/bryanhe/deepgo/cpp/external/libtorch .. make
https://discuss.pytorch.org/t/libtorch-cmake-issues/28246
grep culibos find .
export LD_LIBRARY_PATH=/home/bryanhe/deepgo/cpp/external/libtorch/:${LD_LIBRARY_PATH}