CONTRIBUTING.md

Contributing to TorchForge

Thank you for your interest in contributing to TorchForge! This document provides guidelines and instructions for contributing.

Code of Conduct

We are committed to providing a welcoming and inclusive environment. Please be respectful and professional in all interactions.

How to Contribute

Reporting Bugs

Before creating a bug report:

1. Check the existing issues
2. Verify you're using the latest version
3. Collect relevant information (Python version, PyTorch version, OS, etc.)

Create a bug report with:

• Clear, descriptive title
• Steps to reproduce
• Expected behavior
• Actual behavior
• Code sample (if applicable)
• Error messages and stack traces

Suggesting Features

We welcome feature suggestions! Please:

1. Check existing feature requests
2. Describe the problem your feature would solve
3. Explain your proposed solution
4. Consider alternative approaches

Pull Requests

Setup Development Environment

# Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/torchforge.git
cd torchforge

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Development Workflow

1. Create a branch

   git checkout -b feature/your-feature-name
   

2. Make your changes

   • Write clear, documented code
   • Follow existing code style
   • Add tests for new functionality
   • Update documentation as needed

3. Run tests

   # Run all tests
   pytest tests/ -v
   
   # Run with coverage
   pytest tests/ --cov=torchforge --cov-report=html
   
   # Run specific test
   pytest tests/test_core.py::TestForgeModel::test_model_creation
   

4. Format code

   # Format with black
   black torchforge/ tests/
   
   # Sort imports
   isort torchforge/ tests/
   
   # Check style
   flake8 torchforge/
   
   # Type check
   mypy torchforge/
   

5. Commit changes

   git add .
   git commit -m "feat: add new feature description"
   

   Follow Conventional Commits:

   • feat: New feature
   • fix: Bug fix
   • docs: Documentation changes
   • test: Adding or updating tests
   • refactor: Code refactoring
   • perf: Performance improvements
   • chore: Build process or auxiliary tool changes

6. Push and create PR

   git push origin feature/your-feature-name
   

   Then create a Pull Request on GitHub with:

   • Clear description of changes
   • Link to related issues
   • Screenshots (if UI changes)
   • Test results

Code Style Guidelines

Python Style

• Follow PEP 8
• Use type hints
• Maximum line length: 100 characters
• Use docstrings for all public functions/classes

Documentation Style

def function_name(param1: str, param2: int) -> bool:
    """
    Short description of function.
    
    Longer description with more details about what the function
    does and when to use it.
    
    Args:
        param1: Description of param1
        param2: Description of param2
        
    Returns:
        Description of return value
        
    Raises:
        ValueError: When param1 is invalid
        
    Example:
        >>> result = function_name("test", 42)
        >>> print(result)
        True
    """
    pass

Testing Guidelines

• Write tests for all new features
• Aim for >80% code coverage
• Use descriptive test names
• Include edge cases and error conditions

def test_feature_name_with_valid_input():
    """Test that feature works with valid input."""
    # Arrange
    model = create_test_model()
    
    # Act
    result = model.some_method()
    
    # Assert
    assert result.status == "success"

Documentation

When adding new features:

1. Update relevant documentation files
2. Add docstrings to all public APIs
3. Include code examples
4. Update CHANGELOG.md

Areas for Contribution

We especially welcome contributions in:

Core Features

• Additional compliance frameworks (EU AI Act, ISO 42001)
• Advanced monitoring capabilities
• Performance optimizations
• Cloud provider integrations

Documentation

• Tutorial improvements
• Example notebooks
• API documentation
• Translation to other languages

Testing

• Test coverage improvements
• Performance benchmarks
• Integration tests

Infrastructure

• CI/CD improvements
• Docker optimizations
• Kubernetes best practices

Community

• GitHub Discussions: Ask questions, share ideas
• GitHub Issues: Bug reports and feature requests
• LinkedIn: Follow @anilsprasad for updates

Recognition

Contributors will be:

• Listed in CONTRIBUTORS.md
• Mentioned in release notes
• Recognized in the annual contributor report

Thank you for contributing to TorchForge! 🚀