Update[12/11/2017] DarkForestGo has been incorporated into ELF platform.
Update: The training code is open source now. See below for detailed instructions.
DarkForest is a Go game engine powered by Deep Learning and developed at Facebook AI Research.
- It has a stable rank of 5d on the KGS servers
- Pure Policy Network achieves a stable rank of 3d on KGS
- It received the 3rd place in the KGS Go Tournament
- It received the 2nd place in UEC Computer Go Cup
We hope that releasing the source code and pre-trained models are beneficial to the community.
Details of the engine are given in our paper and poster, and if you use our engine in future research, cite our paper:
Better Computer Go Player with Neural Network and Long-term Prediction, ICLR 2016
Yuandong Tian, Yan Zhu
@article{tian2015better,
title={Better Computer Go Player with Neural Network and Long-term Prediction},
author={Tian, Yuandong and Zhu, Yan},
journal={arXiv preprint arXiv:1511.06410},
year={2015}
}
Although DarkForest is standalone and does not depend on external libraries, some portions of the tactics and pattern code were inspired by the Pachi engine.
Dependencies:
- Install torch7.
- Install CUDA / CuDNN
- Install a few packages
luarocks install class
luarocks install image
luarocks install tds
luarocks install cudnnThis program supports 1 to 4 GPUs.
Then just compile with the following command:
sh ./compile.shGCC 4.8+ is required. Depending on the location of your C++ compiler, please change the script accordingly. Tested in CentOS 6.5 and Ubuntu 14.04, 15.04.
Install gcc-4.9 as a second compiler and create symlink as:
sudo ln -s /usr/bin/gcc-4.9 /usr/local/cuda/bin/gcc
sudo ln -s /usr/bin/g++-4.9 /usr/local/cuda/bin/g++
During the installation of torch and cudnn, either change the build script or replace symlink at /usr/bin/cc with:
sudo ln -s /usr/bin/gcc-4.9 /usr/bin/cc
More info at (http://stackoverflow.com/questions/6622454/cuda-incompatible-with-my-gcc-version)
After the compilation cc symlink can be reverted back to latest version.
If you get errors like:
These bindings are for version 5005 or above ...
Download latest cuDNN from nvidia at (https://developer.nvidia.com/rdp/cudnn-download), registration required.
Step 1: Download the models.
Create ./models directory and download trained models.
Step 2: First run the GPU server
cd ./local_evaluator
sh cnn_evaluator.sh [num_gpu] [pipe file path]num_gputhe number of GPUs (1-8) you have for the current machine.pipe file pathThe path that the pipe file is settled. Default is/data/local/go. If you have specific other path, then you need to specify the same when runningcnnPlayerMCTSV2.lua
Example: sh cnn_evaluator.sh 4 /data/local/go
Step 3: Run the main program
cd ./cnnPlayerV2
th cnnPlayerMCTSV2.lua [options]See cnnPlayerV2/cnnPlayerMCTSV2.lua for a lot of options. For a simple first run (assuming you have 4 GPUs), you could use:
th cnnPlayerMCTSV2.lua --num_gpu [num_gpu] --time_limit 10or (if you want to use a set of plausibly good parameters):
th cnnPlayerMCTSV2.lua --use_formal_params --num_gpu [num_gpu] --time_limit 10To load an existing game up to move 23:
th cnnPlayerMCTSV2.lua [other_options] --setup_board "/path/to/sgf 23"When you are in the interactive environment, type
clear_boardto clear the boardgenmove bto genmove the black move.play w Q4to play a move at Q4 for specific color.quitto quit.
A complete game may look like:
clear_board
[MCTS initialization ...]
place_free_handicap 3
genmove b
[MCTS generates moves..e.g., it returns Q16]
play w D4
genmove b
[MCTS generates moves...]
quitFor more commands, please use command list_commands, check the details of GTP protocol or take a look at the source code.
To train the policy network from scratch, please run ./train.sh. 1 GPU is needed. Please install
torchnet first (e.g., luarocks install torchnet).
The difference between this open source version (A) and that in KGS/competitions (B) is the following:
- (A) runs on a single machine and uses pipe as client/server communications. (B) uses thrift RPC services as a way to communicate.
- (B) uses more computational resources.
- We might have tuned parameters for (B) extensively, but not for (A). We will give the tip of parameter tuning soon.
Q: My program hanged on genmove/quit, what happened? A: Make sure you run the GPU server under ./local_evaluator, the server remains active and the pipe file path matches between the server and the client.
If you have any questions or find any bugs, please open a Github issue by clicking "Issues" tab and then click "New Issue".
The system consists of the following parts.
./CNNPlayerV2Lua (terminal) interface for Go.
CNNPlayerV3.luaRun Pure-DCNN playerCNNPlayerMCTSV2.luaRun player with DCNN + MCTS
-
./boardThings about board and its evaluations. Board data structure and different playout policy. -
./mctsv2Implementation of Monte Carlo Tree Search -
./local_evaluatorSimple GPU-based server. Communication with search threads via pipe. -
./utilsSimple utilities, e.g., read/write sgf files. -
./testTest utilities. -
./trainTraining code -
./datasetDataset used for training. Please download them here and save to the./datasetdirectory. -
./modelsAll pre-trained models. Please download them here and save to the./modelsdirectory. -
./sgfsSome exemplar sgf files.
Please check the LICENSE file for the license of Facebook DarkForest Go engine.
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