Contributing to Open Source

KataGo is an active open source project with multiple ways to contribute.

There are four main ways to contribute to KataGo: donate GPU computing power to the distributed training network (katagotraining.org), report issues on GitHub, improve the documentation, and contribute C++/Python code. The lowest-barrier option is contributing computing power by running self-play on a computer with a GPU.

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Contribution Methods Overview

Method	Difficulty	Requirements
Contribute computing power	Low	Computer with GPU
Report issues	Low	GitHub account
Improve documentation	Medium	Familiar with technical content
Contribute code	High	C++/Python development skills

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Contribute Computing Power: Distributed Training

KataGo Training Introduction

KataGo Training is a global distributed training network:

• Volunteers contribute GPU power for self-play
• Self-play data uploaded to central server
• Server periodically trains new models
• New models distributed to volunteers for continued play

Website: https://katagotraining.org/

Participation Steps

1. Create Account

Go to https://katagotraining.org/ and register.

2. Download KataGo

# Download latest version
wget https://github.com/lightvector/KataGo/releases/download/v1.15.3/katago-v1.15.3-cuda11.1-linux-x64.zip
unzip katago-v1.15.3-cuda11.1-linux-x64.zip

3. Configure Contribute Mode

# First run will guide you through setup
./katago contribute -username YOUR_USERNAME -password YOUR_PASSWORD

The system will automatically:

• Download latest model
• Run self-play
• Upload game data

4. Background Execution

# Use screen or tmux for background execution
screen -S katago
./katago contribute -username YOUR_USERNAME -password YOUR_PASSWORD
# Ctrl+A, D to detach from screen

Contribution Statistics

You can view at https://katagotraining.org/contributions/:

• Your contribution ranking
• Total contributed games
• Recently trained models

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Report Issues

Where to Report

• GitHub Issues: https://github.com/lightvector/KataGo/issues
• Discord: https://discord.gg/bqkZAz3

Good Issue Reports Include

1. KataGo version: katago version
2. Operating system: Windows/Linux/macOS
3. Hardware: GPU model, memory
4. Complete error message: Copy full log
5. Reproduction steps: How to trigger the issue

Example

## Problem Description
Out of memory error when running benchmark

## Environment
- KataGo version: 1.15.3
- Operating system: Ubuntu 22.04
- GPU: RTX 3060 12GB
- Model: kata-b40c256.bin.gz

## Error Message

CUDA error: out of memory

## Reproduction Steps
1. Run `katago benchmark -model kata-b40c256.bin.gz`
2. Wait about 30 seconds
3. Error appears

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Improve Documentation

Documentation Locations

• README: README.md
• GTP docs: docs/GTP_Extensions.md
• Analysis docs: docs/Analysis_Engine.md
• Training docs: python/README.md

Contribution Process

1. Fork project
2. Create new branch
3. Edit documentation
4. Submit Pull Request

git clone https://github.com/YOUR_USERNAME/KataGo.git
cd KataGo
git checkout -b improve-docs
# Edit documentation
git add .
git commit -m "Improve documentation for Analysis Engine"
git push origin improve-docs
# Create Pull Request on GitHub

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Contribute Code

Development Environment Setup

# Clone project
git clone https://github.com/lightvector/KataGo.git
cd KataGo

# Compile (Debug mode)
cd cpp
mkdir build && cd build
cmake .. -DUSE_BACKEND=OPENCL -DCMAKE_BUILD_TYPE=Debug
make -j$(nproc)

# Run tests
./katago runtests

Code Style

KataGo uses the following code style:

C++:

• 2 space indent
• Braces on same line
• Variables use camelCase
• Classes use PascalCase

class ExampleClass {
public:
  void exampleMethod() {
    int localVariable = 0;
    if(condition) {
      doSomething();
    }
  }
};

Python:

• Follow PEP 8
• 4 space indent

Contribution Areas

Area	File Location	Skills Required
Core engine	cpp/	C++, CUDA/OpenCL
Training	python/	Python, PyTorch
GTP protocol	cpp/command/gtp.cpp	C++
Analysis API	cpp/command/analysis.cpp	C++, JSON
Tests	cpp/tests/	C++

Pull Request Process

1. Create Issue: Discuss your intended changes first
2. Fork & Clone: Create your own branch
3. Develop & Test: Ensure all tests pass
4. Submit PR: Describe changes in detail
5. Code Review: Respond to maintainer feedback
6. Merge: Maintainer merges your code

PR Example

## Change Description
Add support for New Zealand rules

## Changes Made
- Add NEW_ZEALAND rules in rules.cpp
- Update GTP commands to support `kata-set-rules nz`
- Add unit tests

## Test Results
- All existing tests pass
- New tests pass

## Related Issue
Fixes #123

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Community Resources

Official Links

Resource	Link
GitHub	https://github.com/lightvector/KataGo
Discord	https://discord.gg/bqkZAz3
Training Network	https://katagotraining.org/

Discussion Forums

• Discord: Real-time discussion, technical Q&A
• GitHub Discussions: Long-form discussions, feature proposals
• Reddit r/baduk: General Go AI discussion

Related Projects

Project	Description	Link
KaTrain	Teaching analysis tool	github.com/sanderland/katrain
Lizzie	Analysis interface	github.com/featurecat/lizzie
Sabaki	Game record editor	sabaki.yichuanshen.de
BadukAI	Online analysis	baduk.ai

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Recognition & Rewards

Contributors List

All contributors are listed on:

• GitHub Contributors page
• KataGo Training contribution leaderboard

Learning Benefits

Benefits of participating in open source:

• Learn industrial-grade AI system architecture
• Exchange with global developers
• Build open source contribution record
• Deep understanding of Go AI technology

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Further Reading

• Source Code Guide — Understanding code structure
• KataGo Training Mechanism — Local training experiments
• Understanding Go AI — Technical principles