# 围棋AI-KataGo安装-Windows免编译

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KataGo 项目地址: https://github.com/lightvector/KataGo/releases

# 计算平台选择

KataGo has three backends, OpenCL (GPU), CUDA (GPU), and Eigen (CPU).

The quick summary is:

Use OpenCL if you have any good or decent GPU.

Use Eigen with AVX2 if you don’t have a GPU or if your GPU is too old/weak to work with OpenCL, and you just want a plain CPU KataGo.

Use Eigen without AVX2 if your CPU is old or on a low-end device that doesn’t support AVX2.

You can try CUDA you have a top-end NVIDIA FP16 + tensor-core GPU and you are willing to go through the hassle to install CUDA+CUDNN. It might or might not be faster than OpenCL, you can try it out to see.

# 模型选择

Which Network Should I Use?

For weaker or mid-range GPUs, try the final 20-block network.

For top-tier GPUs and/or for the highest-quality analysis if you’re going to use many thousands and thousands of playouts and long thinking times, try the final 40-block network, which is more costly to run but should be the strongest and best.

If you care a lot about theoretical purity - no outside data, bot learns strictly on its own - use the 20 or 40 block nets from this release, which are pure in this way and still much stronger than Leela Zero, but also not quite as strong as these final nets here.

If you want some nets that are much faster to run, and each with their own interesting style of play due to their unique stages of learning, try any of the “b10c128” or “b15c192” Extended Training Nets here which are 10 block and 15 block networks from earlier in the run that are much weaker but still pro-level-and-beyond.

And if you want to see how a super ultra large/slow network performs that nobody has tested until now, try the fat 40-block 384 channel network mentioned a little up above.

# katago 配置

Performance tuning 结束后会有提示如何在sabaki进行配置，如下图

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