github lightvector/KataGo v1.14.1
Minor fixes, restore support for TensorRT 8.5

latest releases: v1.15.1, v1.15.0
4 months ago

If you're a new user, this section has tips for getting started and basic usage! If you don't know which version to choose (OpenCL, CUDA, TensorRT, Eigen, Eigen AVX2), see here. Also, download the latest neural nets to use with this engine release at

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

Summary and Notes

This is primarily a bugfix release. If you're contributing to distributed training for KataGo, this release also includes a minor adjustment to the bonuses that incentivize KataGo to finish the game cleanly, which might slightly improve robustness of training.

Both this and the prior release support an upcoming larger and stronger "b28" neural net that is currently being trained and will likely be ready soon!

As a reminder, for 9x9 boards, see here for a special neural net better than any other net on 9x9, which was used to generate the 9x9 opening books at

Available below are both the standard and "bs29" versions of KataGo. The "bs29" versions are just for fun, and don't support distributed training but DO support board sizes up to 29x29. They may also be slower and will use much more memory, even when only playing on 19x19, so use them only when you really want to try large boards.

The Linux executables were compiled on a 20.04 Ubuntu machine. Some users have encountered issues with libzip or other library compatibility issues in the past. If you have this issue, you may be able to work around it by compiling from source, which is usually not so hard on Linux, see the "TLDR" instructions for Linux here.

Changes in v1.14.1

  • Restores support for TensorRT 8.5. Although the precompiled executables are still for TensorRT 8.6 and CUDA 12.1, if you are building from source TensorRT 8.5 along with a suitable CUDA version such as 11.8 should work as well. Thanks to @hyln9 - #879
  • Changes ending score bonus to not discourage capture moves, encouraging selfplay to more frequently sample mild resistances and and refute bad endgame cleanup.
  • Python neural net training code now randomizes history masking, instead of using a static mask that is generated at data generation time. This should very slightly improve data diversity when reusing data rows.
  • Python neural net training code now will clear out nans from running training statistics, so that the stats can remain useful if a neural net during training experiences an exploded gradient but still manages to recover from it.
  • Various minor cleanups to code and documentation, including a new document about graph search.

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