Professional AI-Powered DDR5 Memory Optimization Platform
Advanced memory tuning with artificial intelligence, real-time hardware integration, 3D visualizations, and comprehensive safety systems.
Professional DDR5 optimization interface with simulation and AI optimization features
Comprehensive feature hub with dark/light theme, 3D charts, real-time monitoring, and AI assistant
Real hardware control with safety locks, advanced integration, and comprehensive databases
Linux/macOS:
git clone https://github.com/your-repo/ddr5-ai-sandbox-simulator.git
cd ddr5-ai-sandbox-simulator
chmod +x setup.sh && ./setup.shWindows:
git clone https://github.com/your-repo/ddr5-ai-sandbox-simulator.git
cd ddr5-ai-sandbox-simulator
setup.batLaunch:
streamlit run main.py- Dark/Light Theme with smooth animations
- Custom CSS styling and metric cards
- Progress indicators and loading spinners
- Responsive design for all screen sizes
- 3D Performance Charts - Interactive surface plots
- Real-time Graphs - Live memory bandwidth/latency
- Configuration Comparisons - Side-by-side analysis
- Optimization Landscapes - AI fitness visualization
- Multiple AI Engines - Genetic Algorithm, Neural Networks, RL
- AutoML Pipeline - Automated model training with Optuna
- LLM Integration - OpenAI, Anthropic, Ollama, Local models
- Plain English explanations and recommendations
- Live Tuning with safety locks and confirmations
- WebSocket Monitoring - Real-time metrics streaming
- Hardware Detection - Automatic system profiling
- Emergency Recovery - Instant parameter restoration
- Multi-level Confirmations for hardware changes
- Predictive Health Monitoring - Component lifespan estimation
- JEDEC Compliance checking and validation
- Damage Prevention - Voltage/timing safety limits
- Tool Imports/Exports - ASUS AI Suite, MSI Dragon Center, Intel XTU
- Database Integration - CPU, Motherboard, Memory Kit databases
- Cross-Brand Compatibility - Intel, AMD, NVIDIA platforms
- Benchmark Integration - Performance validation
| Component | Minimum | Recommended |
|---|---|---|
| Python | 3.9+ | 3.11+ |
| OS | Windows 10/Linux | Ubuntu 22.04+/Fedora 38+ |
| RAM | 0GB | 8-16GB+ |
| Storage | 2GB | 5GB+ |
| Hardware Access | User | Root (for live tuning) |
| GPU | Optional | CUDA/ROCm (for AI acceleration) |
| Tab | Description | Features |
|---|---|---|
| 🎯 Simulation | Parameter tuning sandbox | Manual configuration, validation, JEDEC compliance |
| 🧠 AI Optimization | Automated optimization | Multiple AI engines, hyperparameter tuning |
| ⚡ Live Tuning | Real hardware control | Safety locks, backup/restore, emergency stops |
| 🚀 Enhanced Features | Advanced tools hub | 7 specialized sub-tabs with cutting-edge features |
| 🔧 Advanced Integration | Hardware databases | CPU/MB/RAM detection, cross-platform compatibility |
# Run comprehensive test suite
pytest tests/ -v
# Expected: 29/29 tests passing (100%)
# Code quality checks
black src/ tests/ main.py
flake8 src/ tests/ main.py
mypy src/ --ignore-missing-imports
# Security scanning
bandit -r src/
safety checkCurrent Status:
- ✅ 29/29 tests passing (100% success rate)
- ✅ All imports resolved (no dependency issues)
- ✅ Type hints coverage (90%+ of codebase)
- ✅ Security validated (no known vulnerabilities)
- Multi-level confirmations before applying changes
- Automatic parameter backup and instant recovery
- Real-time validation of voltage/timing relationships
- Emergency stop buttons with immediate effect
- Bounded optimization within safe parameter ranges
- JEDEC compliance checking for all configurations
- Stability scoring for parameter combinations
- Gradual tuning with incremental steps
- Privilege escalation warnings and confirmations
- System monitoring during live tuning sessions
- Rollback mechanisms for failed configurations
- Health monitoring with predictive maintenance
| Metric | Value | Status |
|---|---|---|
| Version | 6.0.0 | ✅ Current |
| Tests | 29/29 passing | ✅ All Green |
| Code Quality | A+ Grade | ✅ Excellent |
| Security | No Issues | ✅ Secure |
| Performance | <100ms response | ✅ Fast |
| Platform Support | Linux/Windows/macOS | ✅ Cross-platform |
| AI Models | 5+ engines | ✅ Advanced |
| Hardware Support | Intel/AMD/NVIDIA | ✅ Universal |
We welcome contributions! Here's how to get started:
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Add tests for new functionality
- Run the test suite:
pytest tests/ - Submit a pull request
git clone https://github.com/your-repo/ddr5-ai-sandbox-simulator.git
cd ddr5-ai-sandbox-simulator
pip install -r requirements.txt -r requirements-dev.txt
pre-commit install
Your support helps fund:
- Hardware testing on diverse platforms
- AI model training and optimization
- New feature development
- Documentation and tutorials
This project is licensed under the MIT License - see the LICENSE file for details.
- 📖 Documentation: GitHub Pages
- 🐛 Bug Reports: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📦 Releases: GitHub Releases
Built with ❤️ for the DDR5 optimization community
Empowering enthusiasts and professionals with AI-driven memory tuning