JetsonMCP

# Contributing to JetsonMCP Thank you for your interest in contributing to JetsonMCP! This guide will help you get started with contributing to the project. ## 🚀 Getting Started ### Prerequisites - Python 3.8 or higher - Access to an NVIDIA Jetson Nano for testing - Basic knowledge of Python, SSH, and edge computing concepts - Familiarity with MCP (Model Context Protocol) ### Development Setup 1. **Fork and Clone** ```bash git clone https://github.com/your-username/jetsonMCP.git cd jetsonMCP ``` 2. **Create Virtual Environment** ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. **Install Development Dependencies** ```bash make install-dev # Or manually: pip install -e ".[dev,test]" pre-commit install ``` 4. **Configure Environment** ```bash cp .env.example .env # Edit .env with your Jetson Nano connection details ``` 5. **Test Your Setup** ```bash make test-conn make test ``` ## 🛠️ Development Workflow ### Code Organization ``` jetsonmcp/ ├── server.py # Main MCP server implementation ├── config.py # Configuration management ├── tools/ # Individual tool implementations │ ├── base.py # Base tool class │ ├── hardware.py # Hardware management │ ├── ai_workloads.py # AI/ML operations │ ├── containers.py # Docker management │ └── ... # Other specialized tools ├── utils/ # Utility functions └── cli.py # Command-line interface ``` ### Making Changes 1. **Create a Feature Branch** ```bash git checkout -b feature/your-feature-name ``` 2. **Make Your Changes** - Follow the existing code style and patterns - Add docstrings to all functions and classes - Include type hints where appropriate - Update tests for any new functionality 3. **Test Your Changes** ```bash make test # Run all tests make test-cov # Run with coverage make lint # Check code style make format # Auto-format code ``` 4. **Commit Your Changes** ```bash git add . git commit -m "feat: add new hardware monitoring feature" ``` Follow [Conventional Commits](https://conventionalcommits.org/): - `feat:` for new features - `fix:` for bug fixes - `docs:` for documentation changes - `test:` for test additions/modifications - `refactor:` for code refactoring 5. **Push and Create Pull Request** ```bash git push origin feature/your-feature-name ``` ## 🧪 Testing ### Test Categories - **Unit Tests**: Test individual functions and classes - **Integration Tests**: Test tool interactions with real Jetson systems - **Mock Tests**: Test with simulated SSH connections ### Running Tests ```bash # All tests make test # Unit tests only make test-unit # Integration tests (requires Jetson connection) make test-integration # With coverage make test-cov ``` ### Writing Tests ```python import pytest from jetsonmcp.tools.hardware import HardwareTool from jetsonmcp.config import JetsonConfig @pytest.mark.asyncio async def test_hardware_tool_power_mode(): """Test power mode management.""" config = JetsonConfig.load() config.test_mode = True # Enable mock mode tool = HardwareTool(config) result = await tool.execute("manage_hardware", {"action": "get_power_mode"}) assert len(result) == 1 assert "current_power_mode" in result[0] ``` ## 📝 Documentation ### Code Documentation - Use clear, descriptive docstrings for all public functions - Include parameter descriptions and return value information - Add usage examples for complex functions ```python async def deploy_model(self, model_name: str, model_path: str) -> List[Dict[str, Any]]: """ Deploy an AI model for inference on Jetson Nano. Args: model_name: Unique name for the deployed model model_path: Local path to the model file Returns: List containing deployment status and model information Example: >>> tool = AIWorkloadsTool(config) >>> result = await tool.deploy_model("yolov5", "/models/yolov5.onnx") >>> print(result[0]["success"]) True """ ``` ### README Updates - Update the main README.md if you add new features - Include configuration examples for new options - Add troubleshooting information for common issues ## 🎯 Contributing Guidelines ### What We're Looking For - **New Tools**: Additional management capabilities for Jetson systems - **Hardware Support**: Support for other Jetson platforms (AGX Xavier, Orin) - **AI Framework Integration**: Support for new ML frameworks and optimization tools - **Performance Improvements**: Optimizations for SSH connections and command execution - **Documentation**: Better examples, tutorials, and troubleshooting guides - **Testing**: Improved test coverage and integration tests ### Code Style - Follow PEP 8 style guidelines - Use Black for code formatting (`make format`) - Use isort for import sorting - Maximum line length: 88 characters - Use type hints where possible ### Error Handling - Always include proper error handling in tools - Return structured error responses with helpful messages - Log errors appropriately using the logging framework ```python try: result = await self.execute_command(command) return [{"success": True, "data": result["stdout"]}] except JetsonCommandError as e: logger.error(f"Command failed: {e}") return [{"error": str(e), "command": command}] ``` ## 🐛 Reporting Issues ### Bug Reports When reporting bugs, please include: - JetsonMCP version - Jetson Nano hardware details (RAM, storage, JetPack version) - Python version and operating system - Complete error messages and stack traces - Steps to reproduce the issue - Expected vs. actual behavior ### Feature Requests For feature requests, please describe: - The problem you're trying to solve - Your proposed solution - Any alternative solutions you've considered - How this would benefit other users ## 📋 Pull Request Process 1. **Pre-submission Checklist** - [ ] All tests pass - [ ] Code is properly formatted - [ ] Documentation is updated - [ ] Commit messages follow conventional format - [ ] No merge conflicts with main branch 2. **Pull Request Description** - Clear title describing the change - Detailed description of what changed and why - Reference any related issues - Include testing instructions if applicable 3. **Review Process** - At least one maintainer review required - All CI checks must pass - Address any feedback from reviewers ## 🤝 Community ### Communication - **GitHub Issues**: For bug reports and feature requests - **GitHub Discussions**: For questions and general discussion - **Email**: ajeetraina@gmail.com for direct contact ### Code of Conduct We are committed to providing a welcoming and inclusive environment for all contributors. Please: - Be respectful and constructive in all interactions - Welcome newcomers and help them get started - Focus on what's best for the community - Show empathy towards other community members ## 🏆 Recognition Contributors will be: - Listed in the project's AUTHORS file - Mentioned in release notes for significant contributions - Invited to join the maintainer team for sustained contributions ## 📚 Resources - [NVIDIA Jetson Documentation](https://developer.nvidia.com/embedded/jetson-nano-developer-kit) - [MCP Protocol Specification](https://spec.modelcontextprotocol.io/) - [Python AsyncIO Documentation](https://docs.python.org/3/library/asyncio.html) - [Paramiko SSH Library Documentation](https://docs.paramiko.org/) --- Thank you for contributing to JetsonMCP! Your efforts help make edge AI development more accessible to everyone. 🚀