KataGo is a strong open-source self-play-trained Go engine, with many improvements to accelerate learning (arXiv paper and further techniques since). It can predict score and territory, play handicap games reasonably, and handle many board sizes and rules all with the same neural net.
This site hosts KataGo's first public-distributed training run! With the help of volunteers, we are attempting to resume training from the end of KataGo's previous official run ("g170") that ended in June 2020, and see how much further we can go. If would like to contribute, see below!
Downloading KataGo
If you simply want to run KataGo, the latest releases are here and you can download the latest networks from here.
You very likely want a GUI as well, because the engine alone is command-line-only. Some possible GUIs include KaTrain, Lizzie, and q5Go, more can be found searching online.
How to Contribute
Contributors are much appreciated! If you'd like to contribute your spare GPU cycles to generate training data for the run, the steps are:
First, create an account on this site, picking a username and secure password. Make sure to verify your email so that the site considers your account fully active. Note: the username you pick will be publicly visible in statistics and on the games you contribute.
Then pick one of the following methods.
Likely easiest method, for a home desktop computer:
Inside the GUI menu, select the option for distributed training, provide the username and password for the account you created, and choose a few settings, then just let it run!
Command line method: if running on a remote server, or have already set up KataGo for other things, or if you want a command line that will work in the background without any GUI, or want slightly more flexibility to configure things:
Edit the contribute_example.cfg that came when you downloaded KataGo, use a text editor to fill in your username, password, and the few different settings you want.
Run it on the command line like: ./katago contribute -config contribute_example.cfg on Linux, or katago.exe contribute -config contribute_example.cfg on Windows. If it is working, it should print out various stats as it runs, including when it finishes and uploads a game.
Either way, once some games are finished, you can view the results at https://katagotraining.org/contributions/ - scroll down and find your username! If anything looks unusual or buggy about the games, or KataGo is behaving weirdly on your machine, please let us know, so we can avoid uploading and training on bad data. Or, if you encounter any error messages, feel to ask for help on KataGo's GitHub or the Discord chat.
For advanced users, instead of downloading a release, you can also build it from source. If you do so, use the stable branch, NOT the master branch. The example config can be found in cpp/configs/contribute_example.cfg
And if you're interested contribute to development via coding, or have a cool idea for a tool, check out either KataGo's GitHub or the this website's GitHub, and/or the Discord chat where various devs hang out. If you want to test a change that affects the distributed client and you need a test server to experiment with modified versions of KataGo, it is available at test.katagodistributed.org, contact lightvector or tychota in Discord for a testing account.
Stats for kata1
This run is named kata1 and began on 2020-11-28 20:23:43 UTC.
Data Generation
Across all time, 525 distinct users have uploaded 1,437,389,406 rows of training data, 27,169,354 training games, and 641,384 rating games.
In the last week, 101 distinct users have uploaded 29,964,133 rows of training data, 592,235 new training games, and 9,797 new rating games.
In the last 24h, 58 distinct users have uploaded 2,534,359 rows of training data, 50,342 new training games, and 1,151 new rating games.
Approximate Elo Ratings Graph
Graph is based on about 641,384 rating games using mid to high hundreds of playouts. Ratings might still be mildly inflated due to only playing other KataGo nets, but otherwise are fresh and unbiased and involve a variety of nets to avoid rock-paper-scissors. Vertical bars indicate approximately a 95% confidence interval.
Click and drag to zoom. Double-click or click on a button to reset zoom.
Top Contributors
See here for a full list of contributors for kata1.