Masonmason
a099c56bb5
- Rebrand README from InferencePipeline to Cluster4NPU UI Visual Pipeline Designer - Focus documentation on PyQt5-based GUI and drag-and-drop workflow - Update PROJECT_SUMMARY with current capabilities and focused development priorities - Streamline DEVELOPMENT_ROADMAP with 4-phase implementation plan - Remove redundant Chinese technical summary files (STAGE_IMPROVEMENTS_SUMMARY.md, UI_FIXES_SUMMARY.md, STATUS_BAR_FIXES_SUMMARY.md) - Align all documentation with actual three-panel UI architecture and NodeGraphQt integration 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
138 lines
4.6 KiB
Markdown
138 lines
4.6 KiB
Markdown
# Cluster4NPU UI - Project Summary
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## Vision
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Create an intuitive visual tool that enables users to design parallel AI inference pipelines for Kneron NPU dongles without coding knowledge, with clear visualization of performance benefits and hardware utilization.
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## Current System Status
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### ✅ Current Capabilities
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**Visual Pipeline Designer:**
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- Drag-and-drop node-based interface using NodeGraphQt
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- 5 node types: Input, Model, Preprocess, Postprocess, Output
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- Real-time pipeline validation and stage counting
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- Property configuration panels with type-aware widgets
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- Pipeline persistence in .mflow JSON format
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**Professional UI:**
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- Three-panel layout (templates, editor, configuration)
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- Global status bar with live statistics
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- Real-time connection analysis and error detection
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- Integrated project management and recent files
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**Inference Engine:**
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- Multi-stage pipeline orchestration with threading
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- Kneron NPU dongle integration (KL520, KL720, KL1080)
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- Hardware auto-detection and device management
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- Real-time performance monitoring (FPS, latency)
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### 🎯 Core Use Cases
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**Pipeline Flow:**
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```
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Input → Preprocess → Model → Postprocess → Output
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↓ ↓ ↓ ↓ ↓
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Camera Resize NPU Inference Format Display
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```
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**Supported Sources:**
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- USB cameras with configurable resolution/FPS
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- Video files (MP4, AVI, MOV) with frame processing
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- Image files (JPG, PNG, BMP) for batch processing
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- RTSP streams for live video (basic support)
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## 🚀 Development Priorities
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### Immediate Goals
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1. **Performance Visualization**: Show clear speedup benefits of parallel processing
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2. **Device Management**: Enhanced control over NPU dongle allocation
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3. **Benchmarking System**: Automated performance testing and comparison
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4. **Real-time Dashboard**: Live monitoring of pipeline execution
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## 🚨 Key Missing Features
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### Performance Visualization
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- Parallel vs sequential execution comparison
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- Visual device allocation and load balancing
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- Speedup calculation and metrics display
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- Performance improvement charts
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### Advanced Monitoring
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- Live performance graphs (FPS, latency, throughput)
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- Resource utilization visualization
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- Bottleneck identification and alerts
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- Historical performance tracking
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### Device Management
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- Visual device status dashboard
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- Manual device assignment interface
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- Device health monitoring and profiling
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- Optimal allocation recommendations
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### Pipeline Optimization
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- Automated benchmark execution
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- Performance prediction before deployment
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- Configuration templates for common use cases
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- Optimization suggestions based on analysis
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## 🛠 Technical Architecture
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### Current Foundation
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- **Core Processing**: `InferencePipeline` with multi-stage orchestration
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- **Hardware Integration**: `Multidongle` with NPU auto-detection
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- **UI Framework**: PyQt5 with NodeGraphQt visual editor
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- **Pipeline Analysis**: Real-time validation and stage detection
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### Key Components Needed
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1. **PerformanceBenchmarker**: Automated speedup measurement
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2. **DeviceManager**: Advanced NPU allocation and monitoring
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3. **VisualizationDashboard**: Live performance charts and metrics
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4. **OptimizationEngine**: Automated configuration suggestions
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## 🎯 Implementation Roadmap
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### Phase 1: Performance Visualization
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- Implement parallel vs sequential benchmarking
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- Add speedup calculation and display
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- Create performance comparison charts
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- Build real-time monitoring dashboard
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### Phase 2: Device Management
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- Visual device allocation interface
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- Device health monitoring and profiling
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- Manual assignment capabilities
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- Load balancing optimization
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### Phase 3: Advanced Features
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- Pipeline optimization suggestions
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- Configuration templates
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- Performance prediction
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- Advanced analytics and reporting
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## 🎨 User Experience Goals
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### Target Workflow
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1. **Design**: Drag-and-drop pipeline creation (< 5 minutes)
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2. **Configure**: Automatic device detection and allocation
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3. **Preview**: Performance prediction before execution
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4. **Monitor**: Real-time speedup visualization
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5. **Optimize**: Automated suggestions for improvements
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### Success Metrics
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- Clear visualization of parallel processing benefits
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- Intuitive interface requiring minimal training
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- Measurable performance improvements from optimization
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- Professional-grade monitoring and analytics
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## 📈 Business Value
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**For Users:**
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- No-code parallel processing setup
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- Clear ROI demonstration through speedup metrics
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- Optimal hardware utilization without expert knowledge
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**For Platform:**
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- Unique visual approach to AI inference optimization
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- Lower barrier to entry for complex parallel processing
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- Scalable foundation for enterprise features |