Skip to content
master
Go to file
Code

README.md

DarkForest, the Facebook Go engine

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.

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}
}

Architecture

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.

Build

Dependencies:

  1. Install torch7.
  2. Install CUDA / CuDNN
  3. Install a few packages
luarocks install class
luarocks install image
luarocks install tds
luarocks install cudnn

This program supports 1 to 4 GPUs.

Then just compile with the following command:

sh ./compile.sh

GCC 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.

Usage

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_gpu the number of GPUs (1-8) you have for the current machine.
  • pipe file path The 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 running cnnPlayerMCTSV2.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 10

or (if you want to use a set of plausibly good parameters):

th cnnPlayerMCTSV2.lua --use_formal_params --num_gpu [num_gpu] --time_limit 10

To 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_board to clear the board
  • genmove b to genmove the black move.
  • play w Q4 to play a move at Q4 for specific color.
  • quit to 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...]
quit

For more commands, please use command list_commands, check the details of GTP protocol or take a look at the source code.

Training

To train the policy network from scratch, please run ./train.sh. 1 GPU is needed. Please install torchnet first (e.g., luarocks install torchnet).

Differences with the award-winning versions

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.

Troubleshooting

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".

Code Overview

The system consists of the following parts.

  • ./CNNPlayerV2 Lua (terminal) interface for Go.
  1. CNNPlayerV3.lua Run Pure-DCNN player
  2. CNNPlayerMCTSV2.lua Run player with DCNN + MCTS
  • ./board Things about board and its evaluations. Board data structure and different playout policy.

  • ./mctsv2 Implementation of Monte Carlo Tree Search

  • ./local_evaluator Simple GPU-based server. Communication with search threads via pipe.

  • ./utils Simple utilities, e.g., read/write sgf files.

  • ./test Test utilities.

  • ./train Training code

  • ./dataset Dataset used for training. Please download them here and save to the ./dataset directory.

  • ./models All pre-trained models. Please download them here and save to the ./models directory.

  • ./sgfs Some exemplar sgf files.

License

Please check the LICENSE file for the license of Facebook DarkForest Go engine.

About

DarkForest, the Facebook Go engine.

Resources

License

Releases

No releases published
You can’t perform that action at this time.