These bots are close in strength but their weaknesses differ. Katago enjoys ladder vision and is therefore superior in positions with any ladder potential. This means in other positions like this one it is significantly weaker (as they match ~50%).

Does that follow? It seems to me that it means there must be more weaknesses overall in positions not featuring ladders, but it's still plausible that it will judge some positions better and some worse than LZ.

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Sure, just like a kyu may judge a position better than a dan sometimes. But generally we can expect it to be significantly worse since the ladder gain also seems quite significant winrate-wise.

I dont think that follows at all. You are making many implicit assumptions about the frequency and severity of misjudged ladders.

Never mind.

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Visualize whirled peas.

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There were factual reports on this. And a few percent of actual losses on misplayed ladders suggest much more winrate loss because of search innefficiencies (misjudged HIDDEN ladders).

Bill: Are you sure B threat (g17) makes no difference?

I thought so, but after you asked, I changed my mind. But now I think I see White's reply after Black completes the threat, so that White wins by a few points. There is still a lot of play afterwards, and I don't want to post something erroneous. So I am checking the play.

OTOH, there is no question at all if White plays the ko at move 240.

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

katago v1.2-alpha https://github.com/leela-zero/leela-zero/issues/2448. Has anyone figured out how to run katago on windows?

Have a look at https://github.com/lightvector/KataGo/issues/2 for a windows version

If you can manage to compile Lizzie by yourself, you can make it work with that. There has already been made changes that supports KataGo, but they are not yet officially released

Also, you need an NVIDIA GPU with CUDA support

it seems there is a way easier and without problems with cuda

Ah seems you're right - appears there is a non-cuda version in your link.

You still need to build a newer version of Lizzie if you want more than just winrates and move suggestions though

The most confusing part is to compile the cudahelpers.cu into an obj file. I made a batch file to run in command prompt with this contents:

NVCC cudahelpers.cu -c --cudart shared -gencode arch=compute_30,code=sm_30 -gencode arch=compute_30,code=compute_30 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_53,code=compute_53 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_70,code=compute_70

Then you just need to put all the source code as defined in the cmakelist.txt into a project with the cudahelper.obj and compile in VS2017 IDE. Of course you also have to download any packages needed.

Version 1.2-beta is now available with precompiled Windows OpenCL binaries
https://github.com/lightvector/KataGo/releases

OpenCL and Windows support are new, so there maybe be some further bumpiness in getting it working, so let me know if you run into problems or have questions. Happy to fix the inevitable bugs that arise.

For anyone in the boat of "compiling myself is way too scary"/"I don't have an nvidia GPU"/"The CUDA version didn't work for me", maybe this is all working now? I hope!

Enjoy!

@splee99 - not exactly sure what your compile set up looks like, but I didn't ever have to manually invoke nvcc compiling for CUDA, was there some issue you ran into, or maybe you were on a sufficiently much older version that something was fixed between then and now? I compiled the CUDA version attached in the release more-or-less by following the same instructions I posted to the latest readme at https://github.com/lightvector/KataGo#windows

Parameters for sabaki, 1.2-beta opencl:

manage engines...
katago
d:\katago\katago.exe
gtp -model d:\katago\model.txt -config d:\katago\gtp_example.cfg

can set the time, for example time_settings 0 10 1

in the folder d:\katago place the network file for example from https://github.com/lightvector/KataGo/releases g104-b15c192-s297383936-d140330251.zip, unpack, use model.txt

This Shin Jinseo game illustrates a situation in which KataGo arrives at the correct positional evaluation very quickly compared to pure zero bots, such as LZ.

http://www.go4go.net/go/games/sgfview/84421

On Reddit (https://www.reddit.com/r/baduk/comments ... nt/eu0xgjz) uberdude shows that even with large numbers of playouts LZ has trouble. However, even at a couple thousand playouts KataGo is able to see that white has a crushing advantage.

MiniGo and Elfv2 were much better at quickly evaluating the position correctly, so not all zero bots fail as badly as LZ here. I've not studied ladder problems much in recent networks, but a while ago I noticed a trend that (locally speaking irrespective of the truth of what's on the diagonal) Elfv1 tended to not see ladders or assume they didn't work, whilst 15b LZ tended to assume ladders do work. So in cases the ladder DOESN'T really work as here LZ will fail badly, and in cases the ladder DOES work Elf fails badly. I don't know about MiniGo.

That's definitely pretty strange. I have noticed that on low playouts certain versions of LZ seemed to assume ladders work, but never realized there was a pattern. I've been really liking KataGo 1.1 for analysis, since I end up playing handicap games a lot on kgs.

It's more complex these days. In a recently discussed game LZ definitely looked the opposite corner where it had a few stones, got convinced the ladder doesn't work and pulled out. A nice step forward (except this ladder worked, but search couldn't see this since policy didn't suggest the - assumed to be broken - ladder moves).

How can I check performance, for example, visits or n/s? interesting to compare OpenCL and cuda

Download them both, try them out on a variety of positions to adjust how many threads you should use for best performance for each one, and see how long searches with different numbers of visits takes?