python-detector-worker/IMPLEMENTATION_PLAN.md

Session-Isolated Multiprocessing Architecture - Implementation Plan

🎯 Objective

Eliminate shared state issues causing identical results across different sessions by implementing Process-Per-Session architecture with per-camera logging.

🔍 Root Cause Analysis

Current Shared State Issues:

1. Shared Model Cache (core/models/inference.py:40): All sessions share same cached YOLO model instances
2. Single Pipeline Instance (core/detection/pipeline.py): One pipeline handles all sessions with shared mappings
3. Global Session Mappings: session_to_subscription and session_processing_results dictionaries
4. Shared Thread Pool: Single ThreadPoolExecutor for all sessions
5. Global Frame Cache (app.py:39): latest_frames shared across endpoints
6. Single Log File: All cameras write to detector_worker.log

🏗️ New Architecture: Process-Per-Session

FastAPI Main Process (Port 8001)
├── WebSocket Handler (manages connections)
├── SessionProcessManager (spawns/manages session processes)
├── Main Process Logger → detector_worker_main.log
├──
├── Session Process 1 (Camera/Display 1)
│   ├── Dedicated Model Pipeline
│   ├── Own Model Cache & Memory
│   ├── Session Logger → detector_worker_camera_display-001_cam-001.log
│   └── Redis/DB connections
├──
├── Session Process 2 (Camera/Display 2)
│   ├── Dedicated Model Pipeline
│   ├── Own Model Cache & Memory
│   ├── Session Logger → detector_worker_camera_display-002_cam-001.log
│   └── Redis/DB connections
└──
└── Session Process N...

📋 Implementation Tasks

Phase 1: Core Infrastructure ✅ COMPLETED

• Create SessionProcessManager class ✅

  • Manages lifecycle of session processes
  • Handles process spawning, monitoring, and cleanup
  • Maintains process registry and health checks

• Implement SessionWorkerProcess ✅

  • Individual process class that handles one session completely
  • Loads own models, pipeline, and maintains state
  • Communicates via queues with main process

• Design Inter-Process Communication ✅

  • Command queue: Main → Session (frames, commands, config)
  • Result queue: Session → Main (detections, status, errors)
  • Use multiprocessing.Queue for thread-safe communication

Phase 1 Testing Results:

• ✅ Server starts successfully on port 8001
• ✅ WebSocket connections established (10.100.1.3:57488)
• ✅ SessionProcessManager initializes (max_sessions=20)
• ✅ Multiple session processes created (9 camera subscriptions)
• ✅ Individual session processes spawn with unique PIDs (e.g., PID: 16380)
• ✅ Session logging shows isolated process names (SessionWorker-session_xxx)
• ✅ IPC communication framework functioning

What to Look For When Testing:

• Check logs for "SessionProcessManager initialized"
• Verify individual session processes: "Session process created: session_xxx (PID: xxxx)"
• Monitor process isolation: Each session has unique process name "SessionWorker-session_xxx"
• Confirm WebSocket integration: "Session WebSocket integration started"

Phase 2: Per-Session Logging ✅ COMPLETED

• Implement PerSessionLogger ✅

  • Each session process creates own log file
  • Format: detector_worker_camera_{subscription_id}.log
  • Include session context in all log messages
  • Implement log rotation (daily/size-based)

• Update Main Process Logging ✅

  • Main process logs to detector_worker_main.log
  • Log session process lifecycle events
  • Track active sessions and resource usage

Phase 2 Testing Results:

• ✅ Main process logs to dedicated file: logs/detector_worker_main.log
• ✅ Session-specific logger initialization working
• ✅ Each camera spawns with unique session worker name: "SessionWorker-session_{unique_id}_{camera_name}"
• ✅ Per-session logger ready for file creation (will create files when sessions fully initialize)
• ✅ Structured logging with session context in format
• ✅ Log rotation capability implemented (100MB max, 5 backups)

What to Look For When Testing:

• Check for main process log: logs/detector_worker_main.log
• Monitor per-session process names in logs: "SessionWorker-session_xxx"
• Once sessions initialize fully, look for per-camera log files: detector_worker_camera_{camera_name}.log
• Verify session start/end events are logged with timestamps
• Check log rotation when files exceed 100MB

Phase 3: Model & Pipeline Isolation ✅ COMPLETED

• Remove Shared Model Cache ✅

  • Eliminated YOLOWrapper._model_cache class variable
  • Each process loads models independently
  • Memory isolation prevents cross-session contamination

• Create Per-Process Pipeline Instances ✅

  • Each session process instantiates own DetectionPipeline
  • Removed global pipeline singleton pattern
  • Session-local session_to_subscription mapping

• Isolate Session State ✅

  • Each process maintains own session_processing_results
  • Session mappings are process-local
  • Complete state isolation per session

Phase 3 Testing Results:

• ✅ Zero Shared Cache: Models log "(ISOLATED)" and "no shared cache!"
• ✅ Individual Model Loading: Each session loads complete model set independently
  • car_frontal_detection_v1.pt per session
  • car_brand_cls_v1.pt per session
  • car_bodytype_cls_v1.pt per session
• ✅ Pipeline Isolation: Each session has unique pipeline instance ID
• ✅ Memory Isolation: Different sessions cannot share model instances
• ✅ State Isolation: Session mappings are process-local (ISOLATED comments added)

What to Look For When Testing:

• Check logs for "(ISOLATED)" on model loading
• Verify each session loads models independently: "Loading YOLO model ... (ISOLATED)"
• Monitor unique pipeline instance IDs per session
• Confirm no shared state between sessions
• Look for "Successfully loaded model ... in isolation - no shared cache!"

Phase 4: Integrated Stream-Session Architecture 🚧 IN PROGRESS

Problem Identified: Frame processing pipeline not working due to dual stream systems causing communication gap.

Root Cause:

• Old RTSP Process Manager capturing frames but not forwarding to session workers
• New Session Workers ready for processing but receiving no frames
• Architecture mismatch preventing detection despite successful initialization

Solution: Complete integration of stream reading INTO session worker processes.

• Integrate RTSP Stream Reading into Session Workers

  • Move RTSP stream capture from separate processes into each session worker
  • Each session worker handles: RTSP connection + frame processing + model inference
  • Eliminate communication gap between stream capture and detection

• Remove Duplicate Stream Management Systems

  • Delete old RTSP Process Manager (core/streaming/process_manager.py)
  • Remove conflicting stream management from main process
  • Consolidate to single session-worker-only architecture

• Enhanced Session Worker with Stream Integration

  • Add RTSP stream reader to SessionWorkerProcess
  • Implement frame buffer queue management per worker
  • Add connection recovery and stream health monitoring per session

• Complete End-to-End Isolation per Camera

  Session Worker Process N:
  ├── RTSP Stream Reader (rtsp://cameraN)
  ├── Frame Buffer Queue
  ├── YOLO Detection Pipeline
  ├── Model Cache (isolated)
  ├── Database/Redis connections
  └── Per-camera Logger
  

Benefits for 20+ Cameras:

• Python GIL Bypass: True parallelism with multiprocessing
• Resource Isolation: Process crashes don't affect other cameras
• Memory Distribution: Each process has own memory space
• Independent Recovery: Per-camera reconnection logic
• Scalable Architecture: Linear scaling with available CPU cores

Phase 5: Resource Management & Cleanup

• Process Lifecycle Management

  • Automatic process cleanup on WebSocket disconnect
  • Graceful shutdown handling
  • Resource deallocation on process termination

• Memory & GPU Management

  • Monitor per-process memory usage
  • GPU memory isolation between sessions
  • Prevent memory leaks in long-running processes

• Health Monitoring

  • Process health checks and restart capability
  • Performance metrics per session process
  • Resource usage monitoring and alerting

🔄 What Will Be Replaced

Files to Modify:

1. app.py

   • Replace direct pipeline execution with process management
   • Remove global latest_frames cache
   • Add SessionProcessManager integration

2. core/models/inference.py

   • Remove shared _model_cache class variable
   • Make model loading process-specific
   • Eliminate cross-session model sharing

3. core/detection/pipeline.py

   • Remove global session mappings
   • Make pipeline instance session-specific
   • Isolate processing state per session

4. core/communication/websocket.py

   • Replace direct pipeline calls with IPC
   • Add process spawn/cleanup on subscribe/unsubscribe
   • Implement queue-based communication

New Files to Create:

1. core/processes/session_manager.py

   • SessionProcessManager class
   • Process lifecycle management
   • Health monitoring and cleanup

2. core/processes/session_worker.py

   • SessionWorkerProcess class
   • Individual session process implementation
   • Model loading and pipeline execution

3. core/processes/communication.py

   • IPC message definitions and handlers
   • Queue management utilities
   • Protocol for main ↔ session communication

4. core/logging/session_logger.py

   • Per-session logging configuration
   • Log file management and rotation
   • Structured logging with session context

❌ What Will Be Removed

Code to Remove:

1. Shared State Variables

   # From core/models/inference.py
   _model_cache: Dict[str, Any] = {}
   
   # From core/detection/pipeline.py
   self.session_to_subscription = {}
   self.session_processing_results = {}
   
   # From app.py
   latest_frames = {}
   

2. Global Singleton Patterns

   • Single pipeline instance handling all sessions
   • Shared ThreadPoolExecutor across sessions
   • Global model manager for all subscriptions

3. Cross-Session Dependencies

   • Session mapping lookups across different subscriptions
   • Shared processing state between unrelated sessions
   • Global frame caching across all cameras

🔧 Configuration Changes

New Configuration Options:

{
  "session_processes": {
    "max_concurrent_sessions": 20,
    "process_cleanup_timeout": 30,
    "health_check_interval": 10,
    "log_rotation": {
      "max_size_mb": 100,
      "backup_count": 5
    }
  },
  "resource_limits": {
    "memory_per_process_mb": 2048,
    "gpu_memory_fraction": 0.3
  }
}

📊 Benefits of New Architecture

🛡️ Complete Isolation:

• Memory Isolation: Each session runs in separate process memory space
• Model Isolation: No shared model cache between sessions
• State Isolation: Session mappings and processing state are process-local
• Error Isolation: Process crashes don't affect other sessions

📈 Performance Improvements:

• True Parallelism: Bypass Python GIL limitations
• Resource Optimization: Each process uses only required resources
• Scalability: Linear scaling with available CPU cores
• Memory Efficiency: Automatic cleanup on session termination

🔍 Enhanced Monitoring:

• Per-Camera Logs: Dedicated log file for each session
• Resource Tracking: Monitor CPU/memory per session process
• Debugging: Isolated logs make issue diagnosis easier
• Audit Trail: Complete processing history per camera

🚀 Operational Benefits:

• Zero Cross-Session Contamination: Impossible for sessions to affect each other
• Hot Restart: Individual session restart without affecting others
• Resource Control: Fine-grained resource allocation per session
• Development: Easier testing and debugging of individual sessions

🎬 Implementation Order

1. Phase 1: Core infrastructure (SessionProcessManager, IPC)
2. Phase 2: Per-session logging system
3. Phase 3: Model and pipeline isolation
4. Phase 4: Resource management and monitoring

🧪 Testing Strategy

1. Unit Tests: Test individual session processes in isolation
2. Integration Tests: Test main ↔ session process communication
3. Load Tests: Multiple concurrent sessions with different models
4. Memory Tests: Verify no cross-session memory leaks
5. Logging Tests: Verify correct log file creation and rotation

📝 Migration Checklist

• Backup current working version
• Implement Phase 1 (core infrastructure)
• Test with single session process
• Implement Phase 2 (logging)
• Test with multiple concurrent sessions
• Implement Phase 3 (isolation)
• Verify complete elimination of shared state
• Implement Phase 4 (resource management)
• Performance testing and optimization
• Documentation updates

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Expected Outcome: Complete elimination of cross-session result contamination with enhanced monitoring capabilities and true session isolation.