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Contributing to ECG-RAMBA

We welcome contributions! Please read this guide before submitting.

Getting Started

Prerequisites

Requirement Version
Python 3.10+
CUDA 11.8+
PyTorch 2.0+
GPU VRAM 10GB+ (20GB+ recommended for full training)

Setup

# Clone the repository
git clone https://github.com/BrianNguyen29/ECG-RAMBA.git
cd ECG-RAMBA

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

# Install dependencies
pip install -r requirements.txt

Development Guidelines

Code Style

  • Follow PEP 8 for Python code
  • Use descriptive variable names
  • Add docstrings to all functions and classes
  • Keep functions focused and under 50 lines when possible

Documentation

  • Update README.md if adding new features
  • Add comments for non-obvious logic
  • Document any new configuration parameters in configs/config.py

Testing

Before submitting:

# Verify imports work
python -c "from src.model import ECGRambaV7Advanced; print('OK')"

# Run sanity check
python -c "from src.model import run_sanity_check; run_sanity_check()"

Web App Development

If you are contributing to the Clinical Dashboard (web_app/):

Backend (FastAPI)

  • Located in web_app/backend/
  • Run tests: pytest web_app/backend/tests/
  • Add new endpoints in app/api/endpoints/

Frontend (React)

  • Located in web_app/frontend/
  • Use Tailwind CSS for styling
  • Components must be responsive and support Dark Mode

Contribution Types

🐛 Bug Fixes

  1. Open an issue describing the bug
  2. Fork the repository
  3. Create a branch: git checkout -b fix/issue-description
  4. Make your fix
  5. Submit a pull request

✨ New Features

  1. Open an issue to discuss the feature first
  2. Fork and create a branch: git checkout -b feature/feature-name
  3. Implement with tests
  4. Update documentation
  5. Submit a pull request

📖 Documentation

  • Improvements to README, docstrings, or comments are always welcome
  • Create a branch: git checkout -b docs/description

Pull Request Process

  1. Ensure your code follows the style guidelines
  2. Update documentation as needed
  3. Test your changes locally
  4. Write a clear PR description explaining:
    • What changes were made
    • Why they were made
    • Any breaking changes

Hardware Notes

Training Requirements

Configuration GPU VRAM Batch Size Training Time (5 folds)
Full 24 GB 192 ~4 hours
Reduced 12 GB 64 ~8 hours
Minimal 8 GB 32 ~12 hours

Inference Requirements

  • Inference is significantly less demanding
  • CPU-only inference is possible (but slow)
  • Minimum 4GB VRAM for GPU inference

Code of Conduct

  • Be respectful and constructive
  • Welcome newcomers
  • Focus on the code, not the person

Questions?

Open an issue with the question label or contact the maintainers.

Thank you for contributing! 🎉