RAF RunPod PyTorch Template

Version: 4.0.0 (Stable) Status: Production Ready

Overview

Custom PyTorch Docker template for RunPod optimized for machine learning workloads with integrated HuggingFace and Rclone support.

Features

Current Stable (v4.0.0)

• Base: PyTorch 2.4.0, Python 3.11, CUDA 12.4.1
• Integrated Services:
  • Jupyter Lab (Port 8888)
  • SSH Server (Port 22)
  • Nginx Proxy (Port 80)
• ML Integration:
  • HuggingFace with token support
  • Rclone/Dropbox synchronization
  • Network volume optimization

Quick Start

# Pull the stable image
docker pull rafrafraf/rnpd-pytorch240:latest

# Run locally
docker run -p 8888:8888 -p 22:22 -p 80:80 \
  -v /path/to/workspace:/workspace \
  -e HF_TOKEN=your_token \
  rafrafraf/rnpd-pytorch240:latest

# Access points
- Jupyter: http://localhost:8888 (password: 1234)
- SSH: ssh -p 22 root@localhost (password: 1234)

Workspace Structure

/workspace/
├── .cache/
│   └── huggingface/    # HF cache directory
├── .config/
│   └── rclone/        # Rclone configuration
├── SimpleTuner/       # ML workspace
│   ├── config/       # Training configurations
│   ├── datasets/     # Training data
│   └── output/       # Training results
└── StableSwarmUI/     # Model management

RunPod Deployment

1. Template: rafrafraf/rnpd-pytorch240:latest
2. Environment Variables:
   • JUPYTER_PASSWORD: Custom password (default: 1234)
   • HF_TOKEN: Your HuggingFace token
   • SSH_PORT: 22 (fixed)
   • JUPYTER_PORT: 8888 (fixed)
3. Volume Configuration:
   • Mount point: /workspace
   • Network volume required for persistence

Integration Features

HuggingFace

• Automatic token configuration
• Persistent cache directory
• Model and dataset management
• Token verification via huggingface_hub.HfApi().whoami()

Rclone

• Network volume configuration
• Dropbox synchronization support
• Automatic configuration loading
• Persistent settings

Container Services

• Jupyter Lab with terminal support
• SSH access for remote development
• Nginx reverse proxy
• Automatic workspace setup
• Network volume optimization

Version History

• v4.0.0: Current stable
  • Working HuggingFace integration
  • Working Rclone/Dropbox support
  • Network volume optimization

Development

For custom development:

1. Fork the repository
2. Local testing:

docker build -t mytest:latest .
docker run -p 8888:8888 -v /path/to/workspace:/workspace mytest:latest

Contributing

1. Fork the repository
2. Create your feature branch: git checkout -b feature/my-new-feature
3. Commit your changes: git commit -am 'feat: Add some feature'
4. Push to the branch: git push origin feature/my-new-feature
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues and feature requests, please use the GitHub issue tracker.