332 lines
13 KiB
Markdown
332 lines
13 KiB
Markdown
# Development Roadmap: Visual Parallel Inference Pipeline Designer
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## 🎯 Mission Statement
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Transform Cluster4NPU into an intuitive visual tool that enables users to create parallel AI inference pipelines without coding knowledge, with clear visualization of speedup benefits and performance optimization.
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## 🚨 Critical Missing Features Analysis
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### 1. **Parallel Processing Visualization** (CRITICAL)
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**Current Gap**: Users can't see how parallel processing improves performance
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**Impact**: Core value proposition not visible to users
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**Missing Components**:
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- Visual representation of parallel execution paths
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- Real-time speedup metrics (2x, 3x, 4x faster)
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- Before/after performance comparison
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- Parallel device utilization visualization
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### 2. **Performance Benchmarking System** (CRITICAL)
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**Current Gap**: No systematic way to measure and compare performance
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**Impact**: Users can't quantify benefits of parallel processing
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**Missing Components**:
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- Automated benchmark execution
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- Single vs multi-device comparison
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- Throughput and latency measurement
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- Performance regression testing
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### 3. **Device Management Dashboard** (HIGH)
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**Current Gap**: Limited visibility into hardware resources
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**Impact**: Users can't optimize device allocation
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**Missing Components**:
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- Visual device status monitoring
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- Device health and temperature tracking
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- Manual device assignment interface
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- Load balancing visualization
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### 4. **Real-time Performance Monitoring** (HIGH)
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**Current Gap**: Basic status bar insufficient for performance analysis
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**Impact**: Users can't monitor and optimize running pipelines
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**Missing Components**:
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- Live performance graphs (FPS, latency)
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- Resource utilization charts
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- Bottleneck identification
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- Performance alerts
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## 📋 Detailed Implementation Plan
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### Phase 1: Performance Visualization Foundation (Weeks 1-2)
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#### 1.1 Performance Benchmarking Engine
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**Location**: `core/functions/performance_benchmarker.py`
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```python
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class PerformanceBenchmarker:
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def run_single_device_benchmark(pipeline_config, test_data)
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def run_multi_device_benchmark(pipeline_config, test_data, device_count)
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def calculate_speedup_metrics(single_results, multi_results)
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def generate_performance_report(benchmark_results)
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```
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**Features**:
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- Automated test execution with standardized datasets
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- Precise timing measurements (inference time, throughput)
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- Statistical analysis (mean, std, percentiles)
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- Speedup calculation: `speedup = single_device_time / parallel_time`
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#### 1.2 Performance Dashboard Widget
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**Location**: `ui/components/performance_dashboard.py`
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```python
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class PerformanceDashboard(QWidget):
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def __init__(self):
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# Real-time charts using matplotlib or pyqtgraph
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self.fps_chart = LiveChart("FPS")
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self.latency_chart = LiveChart("Latency (ms)")
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self.speedup_display = SpeedupWidget()
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self.device_utilization = DeviceUtilizationChart()
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```
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**UI Elements**:
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- **Speedup Indicator**: Large, prominent display (e.g., "3.2x FASTER")
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- **Live Charts**: FPS, latency, throughput over time
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- **Device Utilization**: Bar charts showing per-device usage
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- **Performance Comparison**: Side-by-side single vs parallel metrics
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#### 1.3 Benchmark Integration in Dashboard
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**Location**: `ui/windows/dashboard.py` (enhancement)
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```python
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class IntegratedPipelineDashboard:
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def create_performance_panel(self):
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# Add performance dashboard to right panel
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self.performance_dashboard = PerformanceDashboard()
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def run_benchmark_test(self):
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# Automated benchmark execution
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# Show progress dialog
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# Display results in performance dashboard
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```
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### Phase 2: Device Management Enhancement (Weeks 3-4)
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#### 2.1 Advanced Device Manager
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**Location**: `core/functions/device_manager.py`
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```python
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class AdvancedDeviceManager:
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def detect_all_devices(self) -> List[DeviceInfo]
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def get_device_health(self, device_id) -> DeviceHealth
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def monitor_device_performance(self, device_id) -> DeviceMetrics
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def assign_devices_to_stages(self, pipeline, device_allocation)
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def optimize_device_allocation(self, pipeline) -> DeviceAllocation
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```
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**Features**:
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- Real-time device health monitoring (temperature, utilization)
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- Automatic device allocation optimization
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- Device performance profiling and history
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- Load balancing across available devices
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#### 2.2 Device Management Panel
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**Location**: `ui/components/device_management_panel.py`
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```python
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class DeviceManagementPanel(QWidget):
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def __init__(self):
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self.device_list = DeviceListWidget()
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self.device_details = DeviceDetailsWidget()
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self.allocation_visualizer = DeviceAllocationWidget()
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self.health_monitor = DeviceHealthWidget()
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```
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**UI Features**:
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- **Device Grid**: Visual representation of all detected devices
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- **Health Indicators**: Color-coded status (green/yellow/red)
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- **Assignment Interface**: Drag-and-drop device allocation to pipeline stages
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- **Performance History**: Charts showing device performance over time
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#### 2.3 Parallel Execution Visualizer
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**Location**: `ui/components/parallel_visualizer.py`
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```python
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class ParallelExecutionVisualizer(QWidget):
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def show_execution_flow(self, pipeline, device_allocation)
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def animate_data_flow(self, pipeline_data)
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def highlight_bottlenecks(self, performance_metrics)
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def show_load_balancing(self, device_utilization)
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```
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**Visual Elements**:
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- **Execution Timeline**: Show parallel processing stages
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- **Data Flow Animation**: Visual representation of data moving through pipeline
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- **Bottleneck Highlighting**: Red indicators for performance bottlenecks
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- **Load Distribution**: Visual representation of work distribution
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### Phase 3: Pipeline Optimization Assistant (Weeks 5-6)
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#### 3.1 Optimization Engine
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**Location**: `core/functions/optimization_engine.py`
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```python
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class PipelineOptimizationEngine:
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def analyze_pipeline_bottlenecks(self, pipeline, metrics)
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def suggest_device_allocation(self, pipeline, available_devices)
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def predict_performance(self, pipeline, device_allocation)
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def generate_optimization_recommendations(self, analysis)
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```
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**Optimization Strategies**:
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- **Bottleneck Analysis**: Identify slowest stages in pipeline
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- **Device Allocation**: Optimal distribution of devices across stages
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- **Queue Size Tuning**: Optimize buffer sizes for throughput
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- **Preprocessing Optimization**: Suggest efficient preprocessing strategies
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#### 3.2 Optimization Assistant UI
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**Location**: `ui/dialogs/optimization_assistant.py`
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```python
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class OptimizationAssistant(QDialog):
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def __init__(self, pipeline):
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self.analysis_results = OptimizationAnalysisWidget()
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self.recommendations = RecommendationListWidget()
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self.performance_prediction = PerformancePredictionWidget()
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self.apply_optimizations = OptimizationApplyWidget()
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```
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**Features**:
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- **Automatic Analysis**: One-click pipeline optimization analysis
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- **Recommendation List**: Prioritized list of optimization suggestions
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- **Performance Prediction**: Estimated speedup from each optimization
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- **One-Click Apply**: Easy application of recommended optimizations
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#### 3.3 Configuration Templates
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**Location**: `core/templates/pipeline_templates.py`
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```python
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class PipelineTemplates:
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def get_fire_detection_template(self, device_count)
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def get_object_detection_template(self, device_count)
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def get_classification_template(self, device_count)
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def create_custom_template(self, pipeline_config)
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```
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**Template Categories**:
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- **Common Use Cases**: Fire detection, object detection, classification
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- **Device-Optimized**: Templates for 2, 4, 8 device configurations
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- **Performance-Focused**: High-throughput vs low-latency configurations
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- **Custom Templates**: User-created and shared templates
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### Phase 4: Advanced Monitoring and Analytics (Weeks 7-8)
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#### 4.1 Real-time Analytics Engine
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**Location**: `core/functions/analytics_engine.py`
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```python
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class AnalyticsEngine:
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def collect_performance_metrics(self, pipeline)
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def analyze_performance_trends(self, historical_data)
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def detect_performance_anomalies(self, current_metrics)
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def generate_performance_insights(self, analytics_data)
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```
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**Analytics Features**:
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- **Performance Trending**: Track performance over time
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- **Anomaly Detection**: Identify unusual performance patterns
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- **Predictive Analytics**: Forecast performance degradation
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- **Comparative Analysis**: Compare different pipeline configurations
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#### 4.2 Advanced Visualization Components
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**Location**: `ui/components/advanced_charts.py`
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```python
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class AdvancedChartComponents:
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class ParallelTimelineChart: # Show parallel execution timeline
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class SpeedupComparisonChart: # Compare different configurations
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class ResourceUtilizationHeatmap: # Device usage over time
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class PerformanceTrendChart: # Long-term performance trends
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```
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**Chart Types**:
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- **Timeline Charts**: Show parallel execution stages over time
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- **Heatmaps**: Device utilization and performance hotspots
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- **Comparison Charts**: Side-by-side performance comparisons
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- **Trend Analysis**: Long-term performance patterns
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#### 4.3 Reporting and Export
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**Location**: `core/functions/report_generator.py`
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```python
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class ReportGenerator:
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def generate_performance_report(self, benchmark_results)
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def create_optimization_report(self, before_after_metrics)
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def export_configuration_summary(self, pipeline_config)
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def generate_executive_summary(self, project_metrics)
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```
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**Report Types**:
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- **Performance Reports**: Detailed benchmark results and analysis
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- **Optimization Reports**: Before/after optimization comparisons
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- **Configuration Documentation**: Pipeline setup and device allocation
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- **Executive Summaries**: High-level performance and ROI metrics
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## 🎨 User Experience Enhancements
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### Enhanced Pipeline Editor
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**Location**: `ui/windows/pipeline_editor.py` (new)
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```python
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class EnhancedPipelineEditor(QMainWindow):
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def __init__(self):
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self.node_graph = NodeGraphWidget()
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self.performance_overlay = PerformanceOverlayWidget()
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self.device_allocation_panel = DeviceAllocationPanel()
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self.optimization_assistant = OptimizationAssistantPanel()
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```
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**New Features**:
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- **Performance Overlay**: Show performance metrics directly on pipeline nodes
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- **Device Allocation Visualization**: Color-coded nodes showing device assignments
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- **Real-time Feedback**: Live performance updates during pipeline execution
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- **Optimization Hints**: Visual suggestions for pipeline improvements
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### Guided Setup Wizard
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**Location**: `ui/dialogs/setup_wizard.py`
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```python
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class PipelineSetupWizard(QWizard):
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def __init__(self):
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self.use_case_selection = UseCaseSelectionPage()
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self.device_configuration = DeviceConfigurationPage()
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self.performance_targets = PerformanceTargetsPage()
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self.optimization_preferences = OptimizationPreferencesPage()
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```
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**Wizard Steps**:
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1. **Use Case Selection**: Choose from common pipeline templates
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2. **Device Configuration**: Automatic device detection and allocation
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3. **Performance Targets**: Set FPS, latency, and throughput goals
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4. **Optimization Preferences**: Choose between speed vs accuracy tradeoffs
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## 📊 Success Metrics and Validation
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### Key Performance Indicators
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1. **Time to First Pipeline**: < 5 minutes from launch to working pipeline
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2. **Speedup Visibility**: Clear display of performance improvements (2x, 3x, etc.)
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3. **Optimization Impact**: Measurable performance gains from suggestions
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4. **User Satisfaction**: Intuitive interface requiring minimal training
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### Validation Approach
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1. **Automated Testing**: Comprehensive test suite for all new components
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2. **Performance Benchmarking**: Systematic testing across different hardware configurations
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3. **User Testing**: Feedback from non-technical users on ease of use
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4. **Performance Validation**: Verify actual speedup matches predicted improvements
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## 🛠 Technical Implementation Notes
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### Architecture Principles
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- **Modular Design**: Each component should be independently testable
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- **Performance First**: All visualizations must not impact inference performance
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- **User-Centric**: Every feature should directly benefit the end user experience
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- **Scalable**: Design for future expansion to more device types and use cases
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### Integration Strategy
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- **Extend Existing**: Build on current InferencePipeline and dashboard architecture
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- **Backward Compatible**: Maintain compatibility with existing pipeline configurations
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- **Progressive Enhancement**: Add features incrementally without breaking existing functionality
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- **Clean Interfaces**: Well-defined APIs between components for maintainability
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## 🎯 Expected Outcomes
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### For End Users
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- **Dramatic Productivity Increase**: Create parallel pipelines in minutes instead of hours
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- **Clear ROI Demonstration**: Visual proof of performance improvements and cost savings
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- **Optimized Performance**: Automatic suggestions leading to better hardware utilization
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- **Professional Results**: Production-ready pipelines without deep technical knowledge
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### For the Platform
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- **Market Differentiation**: Unique visual approach to parallel AI inference
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- **Reduced Support Burden**: Self-service optimization reduces need for expert consultation
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- **Scalable Business Model**: Platform enables users to handle larger, more complex projects
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- **Community Growth**: Easy-to-use tools attract broader user base
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This roadmap transforms Cluster4NPU from a functional tool into an intuitive platform that makes parallel AI inference accessible to non-technical users while providing clear visualization of performance benefits. |