I rebuilt the distributed killall-go solver from Game Solving with Online Fine-Tuning (Wu et al., NeurIPS 2023; rlglab/online-fine-tuning-solver) and ran it on a single RTX 5090 (WSL2, no Docker). It solved 7×7 killall-go opening JA with the same verdict as the authors' 12× GTX 1080Ti / 384-slot run. Same code/problem/PCN/settings, only the hardware changed, so it's a clean cross-generation benchmark: 2.0× per-slot throughput and 41% less total search (1.01B vs 1.73B nodes) — the node reduction tracks a 19× higher online PCN update rate from colocating the trainer on the same GPU. Wall-clock was 4.6× slower (41 h vs 9 h), but on 1/16 the parallel hardware.

Report (JP, EN abstract): https://soy-tuber.github.io/killallgo-rtx5090/

EN writeup: https://media.patentllm.org/blog/gpu-inference/rtx-5090-vs-12-gpu-cluster-go-proof-benchmark

I am running a very cheap VPS with just 1 core CPU, no graphics card, 1gb ram. I've downloaded the the latest non-avx Pachi release, which has katago_cpu included https://github.com/pasky/pachi/releases.

The model is the one that comes with release. It's only about 11mb in size.

g170e-b10c128-s1141046784-d204142634.bin.gz

I am using

numSearchThreads = 1
maxPlayouts = 70
maxvisits = 50

I am satisfied with the model's strength, even though it's old and I am not using a lot of playouts/visits.

However, when I run even just one instance of the engine, the CPU load spikes to 100% during the game. If someone has experience with tuning for optimization - I would like to know if it's possible to run multiple such engines at the same time without overloading the CPU, and how many cores (and RAM) that would require.

Ideally, I want to be able to have at least 20 simultaneous games at the same time. I know some load can be taken off using pooling, but still, if I do not get a VPS with a GPU, and only use CPU, what is the minimum amount of cores I'd need for that?

Hi, I'm a student at UCSC. I trained an AI for Go using an AlphaGo-Zero-Style training framework, and it worked, but not that well. I trained it on a 5x5 and 9x9 board since I didn't want to wait forever for training. It got to about a 20-15kyu level on 9x9, good enough to beat people new to the game, but the learning process seemed to slow down drastically.

I'm wondering if anyone might have worked on a similar project or has insight as to why my model stopped learning. I have the source code linked on my GitHub. https://github.com/colinHuang314/AlphaZero-Style-Go-Bot

P.S. Sorry if the code is messy. Also, during training, I had different hyperparameters than shown on TrainingLoop.py, which are just some default ones.

Updated for 2025.

Hi friends:

23 years ago , Guido Adam republished issues of the "Computer Go Bulletin" as pdf files in sixteen issues on his site http://www.daogo.org . But Now www.daogo.org is unavailable. I need Computer Go Bulletin for the research about Explainable Go AI. Anyone still has the files? Please  tell me where to download the files, or e-mail me a copy. My email address is [[email protected]](mailto:[email protected]) .

Appeciate for your time and help!

I've found some of the existing options for running matches between go bots to be a little limiting - gomill is in python2 rather than python3, gogui-twogtp seems like it's a bit awkward for certain kinds of custom scripting and tournaments and commands, etc. So I tried writing a simple fresh one in python3. Posting the link here in case some other person at some point happens to find it useful.

I am looking for a script that plays AtariGo. I made one myself, and it actually looks like Go when viewed from the outside, but I lose every time. Is there an open source library that has been made before? The main challenge is, I want to use it on my MacBook Air. MCTS is too much for this kind of task

Hi, someone recommended me posting it here, you can find my original post in the link.
Any help is most welcome

I'm surprised that thing like this doesn't exist, or at least I can't find it, but is there something similar to KaTrain that runs in the browser that I can self host?

黑A时,白B是无理手，黑如何教训白棋？

when black A, white B is overplay. what's the next move for black?

https://youtube.com/shorts/Q0h3C4kcqCU?si=SOUUgE31ai_WvSgp

I've noticed quite a few expensive KataGo-based apps on the App Store. Since KataGo itself is open-source and free, I believe apps built on it should ideally follow the same spirit: accessible to everyone.

So, I decided to create my own KataGo app for Apple devices. It includes:

• The latest KataGo search algorithm
• The latest network model
• Human Supervised Learning (SL) profiles
• An optimized backend for Apple Silicon
• A native SwiftUI interface

The app is completely open-source and available on GitHub. I built it both to improve my own Baduk skills and to learn SwiftUI app development. I'm not looking to make a profit from this, at least not right now. My main goal is to contribute something useful and open to the Go community.

In the latest version (v2.13), I’ve added a feature that lets the AI auto-play using a chosen human profile, like a pro player from 1850, or a 5k pre-AlphaGo amateur. These profiles are based on the Human SL model in KataGo. However, tuning their actual strength to match the intended ranks is tough to do alone.

If you’re interested in Go, KataGo, or AI development, I’d love your help testing and improving the SL parameters so that the AI strength better reflects each rank. Feedback, pull requests, and bug reports are all welcome!

When I try to make a video with sora about two men playing go, there are many basic mistakes about the boards, stones, and bowls, and techniques of picking up and putting a stone, etc.. I can desribe the environments of where they play and characteristics of the two human being (or animals), but I tried and failed to make them play correctly with correct equipments. Any ideas? Thanks in advance.

Hi,

Looking to practice/continue learning Go by being able to play against low-level (or adjustable) AI in Sabaki. It seems KataGo is what people use. Are there other recommendations/resources (aside from playing with people IRL or on OGS).

Thanks!

[EDIT] I'm sorry, I'm just stupid. Only after posting this it occurred to me: In case of no captures the N of stones should equal N of moves. So then only counting the N of stones on the board actually accounts for the captures by virtue of net difference in N of stones between the players... Not sure if I should leave the post or delete it

Hey there!

Anyone here who's read this book? I've just started, but now trying to implement scoring, but I can't find either in the book or on the GitHub repo (link) any implementation of scoring prisoners/captures. What? I'm totally confused.

So the scoring works like: 2 random bots playout games till there's no other legal moves except filling their own eyes (even eyes being single points surrounded by one color). Then it counts the territory and number of stones on the board but if in the process one player captured any amount of opponent stones it does not record that... This description is totally accurate as there are few different versions of the program depending which chapter you're reading, but NONE of them account for prisoners and literal captures whatsoever. Does that not matter at all? How useful is that bot then?

Apologies for the little rant, but I'd love some insights on this from someone who's read the book and understands this.