lightvector/KataGo v1.18.2

CUDA Speedup for Turing (RTX 20xx, etc)

on GitHub

This is a quick minor optimization release for the CUDA backend that helps certain older GPUs (RTX 20xx, and T4, mainly). For best performance, use this release if you have an NVIDIA GPU. For all other backends (AMD GPUs, etc) see the prior release v1.18.1.

Download the latest neural nets to use with this engine release at https://katagotraining.org/.
Also, for 9x9 boards or for boards larger than 19x19, see https://katagotraining.org/extra_networks/ for networks specially trained for those sizes!

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!

Getting Started / Choosing a Backend

If you're a new user, this section has tips for getting started and basic usage! For choosing a backend:

NVIDIA GPU

• You'll also have to install CUDA and one of CUDNN or TensorRT from nvidia depending on your choice.
• CUDA+CUDNN
  • Faster startup times than TensorRT, good performance, and v1.18.x makes it faster for transformer models on most GPUs.
  • Use CUDNN >= 9.8.0. The CUDNN 8.9.7 builds will be a LOT slower when running transformers - consider upgrading if you're still on old CUDNN. We're offering CUDNN 8.9.7 builds only for continuity with prior releases.
• TensorRT
  • Longer startup time. May be faster on older convolutional nets still, but tends to be slower than CUDA+CUDNN on transformers on recent versions and strong GPUs.
  • TensorRT versions older than 10 are not supported.
  • See the prior release v1.18.1 for prebuilt exes.

Other GPUs

See the prior release v1.18.1

New changes in v1.18.2

• Added support for tensor-core attention on Turing GPUs on CUDA sm_75 (RTX 20xx GPUs, Tesla T4). On the GPUs of this generation with tensor cores, this can make transformer models more than 50% faster. Thanks to @nlzy for this improvement!
  • Note: GTX 16xx GPUs are also sm_75 but lack tensor cores and aren't expected to benefit. It in some testing it also seemed to be the case that on such GPUs, FP16 is generally slower than FP32, so if you have a GTX 16xx GPU, try useFP16 = false in your config and you might get a major speedup.