🎯 RunPod.io Cloud GPU Integration
• Dynamic pod creation and lifecycle management
• On-demand GPU scaling without local hardware costs
• Seamless integration with existing multi-process architecture

🏗️ Core Components Added:
• Dockerfile.runpod - Optimized GPU pod image for RunPod
• create_pod.sh - Automated build and deployment script
• vidai/runpod.py - Complete RunPod API integration module
• Enhanced backend with pod spawning capabilities
• Web interface RunPod configuration section

🔧 Key Features:
• Automatic pod creation for analysis jobs
• Cost optimization with idle pod cleanup (30min timeout)
• Multiple GPU type support (RTX 3090, A4000, A5000, 4090)
• Secure API key management and pod isolation
• Fallback to local processing when pods unavailable

📊 Architecture Enhancements:
• Pod lifecycle: Create → Start → Run → Process → Terminate
• Intelligent routing between local workers and cloud pods
• Real-time pod health monitoring and status tracking
• Persistent pod state management with cache files

🛡️ Production Features:
• Comprehensive error handling and recovery
• Detailed logging and monitoring capabilities
• Security-hardened pod environments
• Resource limits and cost controls

📚 Documentation:
• docs/runpod-integration.md - Complete integration guide
• Updated README.md with RunPod setup instructions
• test_runpod.py - Integration testing and validation
• Inline code documentation and examples

🚀 Benefits:
• Zero idle GPU costs - pay only for actual processing
• Access to latest GPU hardware without maintenance
• Unlimited scaling potential for high-throughput workloads
• Global pod distribution for low-latency processing

This implementation provides a production-ready cloud GPU scaling solution that maintains the system's self-contained architecture while adding powerful on-demand processing capabilities.