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CONTRIBUTING.md

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🚀 Contributing to CYBER

Thanks for your interest in contributing to CYBER! We welcome contributions of all kinds, from bug fixes to new features.

🌟 Ways to Contribute

1. 🐛 Report Bugs & Issues

  • Use our bug report template when opening issues
  • Include clear reproduction steps
  • Provide environment details (OS, Python version, GPU)
  • Add relevant logs or screenshots
  • Tag issues appropriately (bug, documentation, enhancement, etc.)

2. 💡 Share Ideas & Discussions

  • Join our Discussions for:
    • Feature proposals
    • Architecture discussions
    • Best practices
    • Use cases & applications
    • Questions & answers

3. 🔧 Submit Code Changes

  • Bug fixes
  • Performance improvements
  • Documentation updates
  • New features
  • Test coverage improvements

4. 🤖 Contribute Models

  • Share new model architectures
  • Add model implementations
  • Contribute pre-trained checkpoints
  • Improve existing models
  • Add model benchmarks

5. 📊 Share Datasets

  • Contribute new datasets
  • Add data processing scripts
  • Improve data loading
  • Share data augmentation techniques
  • Add dataset documentation

6. 📚 Improve Documentation

  • Fix typos and clarify explanations
  • Add code examples
  • Write tutorials
  • Create diagrams
  • Translate documentation

💬 Getting Help

📜 License

By contributing, you agree that your contributions will be licensed under the Apache 2.0 License.

🛠️ Development Setup Guide

Environment Setup

  1. Fork and Clone

    git clone https://github.com/YOUR_USERNAME/Cyber.git
cd Cyber

  2. Set up Poetry Environment

    # Install poetry if you haven't (see https://python-poetry.org/docs/)
# IMPORTANT: Poetry should be installed in its own isolated environment, NOT in your project's environment.

# Install dependencies
poetry install

# Activate the virtual environment
poetry shell

  3. Install Pre-commit Hooks

    poetry run pre-commit install

Code Standards

We use several tools to maintain code quality:

  1. Ruff for linting and formatting:

    • Line length: 160 characters
    • Python version: 3.10+
    • Auto-fixes available for many issues
    • Run: poetry run ruff check .

  2. MyPy for type checking:

    • Strict optional checking
    • Run: poetry run mypy .

  3. Pre-commit Hooks check:

    • Formatting (ruff-format)
    • Linting (ruff)
    • Type checking (mypy)
    • Run manually: poetry run pre-commit run --all-files

PR Workflow

We follow the GitHub Flow for all contributions. Here's a detailed breakdown:

  1. Create a Branch

    git checkout -b feature/your-feature-name
    • Use descriptive branch names (e.g., add-vision-model, fix-memory-leak)
    • Create a separate branch for each set of unrelated changes

  2. Make Changes

    • Write clear, documented code
    • Add tests for new features
    • Update documentation if needed
    • Commit and push changes regularly to backup your work
    • Each commit should contain an isolated, complete change

  3. Quality Checks

    # Run all pre-commit hooks
poetry run pre-commit run --all-files

# Run tests
poetry run pytest tests/
    • All CI checks must pass
    • Code must follow our style guidelines
    • Tests must pass

  4. Create Pull Request

    • Write a clear PR description explaining the changes
    • Link related issues using keywords (e.g., "Fixes #123")
    • Include screenshots or examples if relevant
    • Mark as draft if you want early feedback
    • Request reviews from relevant team members

  5. Address Reviews

    • Respond to all review comments
    • Make requested changes
    • Push additional commits as needed
    • Get required approvals

  6. Merge

    • PR must have required approvals
    • All CI checks must pass
    • No merge conflicts
    • Squash commits if requested by reviewers

  7. Clean Up

    • Delete your branch after merging
    • Close related issues if not auto-closed

For more detailed information about the GitHub Flow process, please refer to the GitHub Flow documentation.

Best Practices

  • Code Organization

    • Keep files focused and modular
    • Use appropriate directory structure
    • Follow existing patterns

  • Documentation

    • Document new functions/classes
    • Update README if needed
    • Add docstrings in NumPy format

  • Testing

    • Write unit tests for new features
    • Ensure existing tests pass
    • Add integration tests if needed

  • PR Size

    • Keep PRs focused on single changes
    • Split large changes into smaller PRs
    • Aim for reviewable chunks