github lightvector/KataGo v1.1
Strong Neural Net, LCB, and many bugfixes

latest releases: v1.15.1, v1.15.0, v1.14.1...
5 years ago

KataGo has a new run ("g104") that has reached as strong as or slightly stronger than LZ-ELFv2! As a result of some improved hyperparameters and other training improvements, starting from scratch this run surpassed the old 1-week run in the previous release in only 3.5 days, and reached perhaps slightly stronger than ELF and near LZ200 after a total 19 days on an average of less than 27 Nvidia V100 GPUs.

g104 models are trained on 9x9 to 19x19 square boards. They will probably generalize somewhat to other sizes, including rectangular boards, but are not guaranteed to play well for boards outside that size range. They are trained on area scoring, positional and situational superko, and suicide on or off. They will behave erratically for other scoring rules or other ko rules.

For match games, this release also features an implementation of LCB (leela-zero/leela-zero#2282) giving a noticeable boost in match strength, as well as a variety of bugfixes and improvements. Self-play training data written by this release is not compatible with training data produced using the prior release, due to a slight format change in the .npz data files.

For the full history of models and training data (rather than only the strongest of each size), see here. If you are curious to look at SGFs of the self-play games, they are included as well.

Also attached for convenience is a precompiled Linux executable, which is the main OS I've tested on, still requiring CUDA 10 and CUDNN to be installed, for now. Compiling yourself may actually be a bit more flexible though - I've actually gotten a working compile on a system with CUDA 9 for example. Support for other OSes, including Windows, and elimination of dependence on CUDA, is being worked on and is not ready yet, but should not be too much longer.

Enjoy!

NOTE: If you're on windows, and you want to try a pre-compiled version that another user has managed to get working there, you can check out #2. Among other things though, you will need to explicitly unzip the neural net ".txt.gz" file to get the neural net to load properly in that version, as well as to ensure you have up-to-date version of your GPU drivers and the right version of cudnn64_7.dll. More-official windows support still in progress.

(edit: 2019-06-18, 2019-06-19 - bumped the tag for this release with some GTP bugfixes/improvements)

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