- Setup script now creates both venv-cuda and venv-rocm by default
- Build script builds both CUDA and ROCm worker versions
- Start script reads configuration to launch appropriate backend workers
- Added pip3 detection and --break-system-packages support
- Windows batch files updated with pip3 detection
- Runtime can switch between CUDA/ROCm implementations based on config

- Remove invalid local version labels from torch requirements
- Use proper pip syntax: torch>=2.0.0 with --index-url for CUDA/ROCm
- Fix pip installation errors for CUDA and ROCm builds

- Add network configuration settings: web_host, web_port, backend_host, backend_web_port, backend_worker_port
- Update web.py to use configurable host and port for Flask app
- Update backend.py to use configurable ports for socket servers
- Update worker processes to use configurable backend worker port
- Add network configuration section to admin panel
- Update command line arguments to accept network settings
- Maintain backward compatibility with default values

- Add MySQL support as alternative to SQLite with full configuration
- Implement database abstraction layer supporting both SQLite and MySQL
- Add database configuration section to admin panel
- Update requirements files with PyMySQL dependency
- Complete multi-process architecture with web, backend, and worker processes
- Add user registration system with email confirmation
- Implement token-based usage system with payment processing
- Add comprehensive documentation and licensing
- Create professional SaaS-style web interface
- Implement REST API for programmatic access
- Add email notification system and payment processors

🎯 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.

- Add detailed system architecture diagrams to README
- Create visual data flow and usage workflow diagrams
- Enhance docs/architecture.md with process and communication diagrams
- Add cluster architecture visualization with load balancing flows
- Include cross-platform deployment diagrams
- Provide visual representation of inter-process communication
- Document cluster communication protocols with ASCII schemas

- Create build.bat, start.bat, setup.bat, and clean.bat equivalents
- Update compatibility module with Windows-specific functions
- Add platform detection and script execution utilities
- Update README with Windows-specific instructions
- Ensure full Windows compatibility without requiring bash

- Implement cluster master/client architecture with token-based authentication
- Add cluster configuration options (host, port, token, client mode)
- Create cluster communication protocol for distributed workload management
- Implement load balancing with configurable process weights
- Add cluster management web interface for monitoring and control
- Support mixed local/remote worker deployment
- Enable/disable processes across cluster nodes
- Update queue manager for distributed job execution
- Add cluster documentation and configuration examples

- Add estimated_tokens and used_tokens fields to processing_queue table
- Implement token estimation based on request type and content
- Track actual token usage during job processing
- Display estimated and used tokens in web interface queue views
- Update dashboard, queue list, and job details to show token information
- Simulate realistic token usage in queue processing

- Add comm_type configuration option (unix/tcp, default unix)
- Command line argument --comm-type for runtime selection
- Web configuration page includes communication type selection
- SocketCommunicator and SocketServer support both Unix and TCP
- Updated all processes to use configured communication type
- Documentation updated to reflect both socket types
- Unix sockets provide better performance for local communication

- Multi-process architecture: web, backend, analysis/training workers
- SQLite database for persistent configuration and system prompts
- Configurable CUDA/ROCm backends with command line override
- TCP socket-based inter-process communication
- Web interface with comprehensive configuration management
- GPLv3 licensing with copyright notices on all files
- Complete documentation: README, architecture docs, changelog
- Build and deployment scripts for different GPU backends
- Git repository setup with .gitignore for build artifacts