Contributing

🤝 Contributing

We welcome contributions! Whether it's adding new benchmarks, supporting new models, or submitting your hardware results.

Development Setup

1. Fork and clone the repository

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

2. Install dependencies including dev tools

   uv sync --group quality --group dev
   

3. Install pre-commit hooks

   pre-commit install
   

This sets up automatic code quality checks that run before each commit:

• ruff — Fast Python linter and formatter
• mypy — Static type checking
• bandit — Security vulnerability scanner
• Standard checks (trailing whitespace, YAML syntax, etc.)

Making Changes

1. Create a new branch

   git checkout -b feature/your-feature-name
   

2. Make your changes

3. Write code following the existing patterns
4. Add type hints where applicable

5. Update documentation if needed

6. Test your changes

   # Run benchmarks to ensure they work
   uv run python main.py
   
   # Update benchmark results tables (if you modified results)
   make
   
   # Run code quality checks manually (optional - pre-commit will run them automatically)
   make format
   

Available Makefile commands:

• make — Generate benchmark results tables (default)
• make generate — Generate benchmark results tables
• make format — Run pre-commit hooks on all files
• make lint — Run ruff linter only
• make clean — Clean Python cache files

• make help — Show all available commands

• Commit your changes

  git add .
  git commit -m "feat: your descriptive commit message"
  

Pre-commit hooks will automatically: - Format your code - Check for type errors - Scan for security issues - Fix common issues (trailing whitespace, etc.)

If any check fails, fix the issues and commit again.

1. Push and create a Pull Request

   git push origin feature/your-feature-name
   

Code Quality Standards

All contributions must pass:

• Ruff linting and formatting
• Mypy type checking
• Bandit security checks

These are enforced automatically via pre-commit hooks.

Adding New Benchmarks

See CLAUDE.md for detailed instructions on: - Adding new models to existing benchmarks - Creating new benchmark categories - Data loading patterns - Memory management best practices

Tip: Add CLAUDE.md when working with your AI coding assistant — it helps provide full project context.

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