Sayuri is a GTP-compliant go engine based on Deep Convolutional Neural Network and Monte Carlo Tree Search. Learning the game of Go without strategic knowledge from human with AlphaZero-based algorithm. She is strongly inspired by Leela Zero and KataGo. The board data structure, search algorithm and network format are borrowed from Leela Zero in the beginning. Current version follows the KataGo research, the engine supports variable komi and board size now. Some methods or reports you may see my articles (some are chinese).
First, a executable weights is necessary. You could get the released weights from this page. If you want to load the older network, please use the v0.5 engine at the save-last-v050 branch. Then start the program with GTP mode via the terminal/PowerShell, using 1 thread and 400 visits per move with optimistic policy, please enter
$ ./sayuri -w <weights file> -t 1 -p 400 --use-optimistic-policy
You will see the diagnostic verbose. If the verbose includes Network Version information, it means you success to execute the program with GPT mode. For more arguments, please give the --help option.
$ ./sayuri --help
Or you may execute pure python engine with checkpoint model. The checkpoint models are released after 4th main run in the page. Run it via the terminal/PowerShell.
$ python3 train/torch/pysayuri.py -c model.pt --use-swa
Sayuri is not complete engine. You need a graphical interface for playing with her. She supports any GTP (version 2) interface application. Sabaki and GoGui are recommended because Sayuri supports some specific analysis commands.
- Sabaki analysis mode
- GoGui analysis commands
Please see this section. If you are Windows platform, you may download the executable file from release page.
Sayuri is a fairly fast self-play learning system for the game of Go. The pictute shows the estimated computation of v0.7 engine (purple line) versus KataGo and LeelaZero. Compare sayuri with ELF OpenGo, achieving a around 250x reduction in computation. In detail, spending 3 months on a single RTX4080 device. The result is apparently better than KataGo g104 which claims 50x reduction in computation.
Here will describe how to run the self-play loop.
- Support NHWC format.
- Support distributed computation.
- Support KataGo analysis mode.
- Go Text Protocol, https://www.gnu.org/software/gnugo/gnugo_19.html
- Leela Zero, https://github.com/leela-zero/leela-zero
- KataGo methods, https://github.com/lightvector/KataGo/blob/master/docs/KataGoMethods.md
- YouTube, playing with Pachi.
- Supported analysis commands, analyze.
- AlphaZero 之加速演算法實作 (v0.4~v0.5), describe some methods for old version.
- Journal
The code is released under the GPLv3, except for threadpool.h, cppattributes.h, Eigen and Fast Float, which have specific licenses mentioned in those files.
cglemon000@gmail.com (Hung-Tse Lin)
