Distributed Training - Selfplay Diversity Fixes

If you're a new user, don't forget to check out this section for getting started and basic usage!

KataGo is continuing to improve at https://katagotraining.org/ and if you'd like to donate your spare GPU cycles and support it, it could use your help there!

If you don't know which version to choose (OpenCL, CUDA, Eigen, Eigen AVX2), read this: https://github.com/lightvector/KataGo#opencl-vs-cuda-vs-eigen

What's New This Version

If you are a user who helps with distributed training on https://katagotraining.org/ it would be great if you could update to this version, which is also the new tip of the stable branch as soon as it is convenient! And let me know in the issues if there are any new problems you encounter with it. Thanks!

This is a minor release mainly of interest to contributors to KataGo's distributed run, or to users who run KataGo self-play training on their own GPUs. This release doesn't make any changes to the KataGo engine itself, but it does fix an issue that was believed to be limiting the diversity in KataGo's self-play games. Switching to this version should over the long term of training, improve KataGo's learning, particularly on small boards, as well as enable a few further parameter changes in the future once most people have upgraded which should also further improve opening diversity.

And, still coming hopefully not too long after this should be a release with some strength and performance improvements for general users. :)

Changes

• Separately sample komi for initializing the board versus actually playing the game
• Rework and refactor komi initialization to make komi randomization more consistent. Newly consistent applies it to some cases missed before (e.g. cleanup training)
• Polish up and improve many aspects of the logic for a few commands of interest to devs who run specialized training, such as dataminesgfs.