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Tom Softreck
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‎.gitignore‎

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.idea
2-
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.old
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old
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cleanup_old_repo.sh
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poetry.lock
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]

‎MANIFEST.in‎

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include README.md
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include LICENSE
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include CHANGELOG.md
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include requirements.txt
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recursive-include dialogchain *.py
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recursive-include examples *
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recursive-include tests *

‎examples/examplescamera_routesyaml.py‎

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# Advanced Camera Processing Routes
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# Demonstrates multi-stage processing with different languages
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routes:
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- name: "advanced_camera_detection"
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from: "rtsp://{{CAMERA_USER}}:{{CAMERA_PASS}}@{{CAMERA_IP}}/stream1"
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processors:
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# Stage 1: Python YOLO object detection
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- type: "external"
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command: "python scripts/detect_objects.py"
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input_format: "json"
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output_format: "json"
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config:
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confidence_threshold: 0.6
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model: "yolov8n.pt"
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target_objects: ["person", "car", "motorcycle", "bicycle"]
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batch_processing: false
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# Stage 2: Go-based risk assessment and zone analysis
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- type: "external"
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command: "go run scripts/image_processor.go"
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input_format: "json"
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output_format: "json"
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config:
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zone_mapping: "entrance:critical,parking:medium,garden:low"
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threat_threshold: 0.7
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risk_factors: "time_of_day,object_type,zone,confidence"
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# Stage 3: C++ performance optimization and NMS
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- type: "external"
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command: "./bin/cpp_postprocessor"
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input_format: "json"
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output_format: "json"
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config:
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algorithm: "fast_nms"
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nms_threshold: 0.5
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confidence_threshold: 0.7
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# Stage 4: Filter only high-risk detections
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- type: "filter"
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condition: "{{threat_level}} in ['high', 'critical']"
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# Stage 5: Node.js business rules engine
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- type: "external"
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command: "node scripts/business_rules.js"
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input_format: "json"
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output_format: "json"
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config:
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rules_file: "security_rules.json"
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business_hours: "09:00-17:00"
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escalation_time: 300
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# Stage 6: Transform to alert format
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- type: "transform"
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template: |
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🚨 SECURITY ALERT - {{camera_name}}
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Threat Level: {{threat_level}} ({{business_priority}})
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Time: {{timestamp}}
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Location: {{zone}} ({{position}})
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Detections:
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{{#enhanced_detections}}
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• {{object_type}} - Confidence: {{confidence}}% - Risk: {{risk_score}}
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Recommended Action: {{recommended_action}}
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{{/enhanced_detections}}
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Business Context:
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• Business Hours: {{#business_context.is_business_hours}}Yes{{/business_context.is_business_hours}}{{^business_context.is_business_hours}}No{{/business_context.is_business_hours}}
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• Zone Risk: {{business_context.zone_risk_level}}
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• Immediate Response Required: {{business_context.requires_immediate_response}}
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to:
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- "email://{{SMTP_SERVER}}:{{SMTP_PORT}}?user={{SMTP_USER}}&password={{SMTP_PASS}}&to={{SECURITY_EMAIL}}"
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- "http://{{SECURITY_WEBHOOK}}/critical-alert"
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- "mqtt://{{MQTT_BROKER}}:1883/security/alerts/{{CAMERA_NAME}}"
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- "log://logs/security_{{CAMERA_NAME}}.log"
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- name: "camera_health_monitoring"
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from: "timer://2m"
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processors:
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- type: "external"
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command: "python scripts/camera_health_check.py"
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config:
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camera_endpoints: "{{CAMERA_HEALTH_ENDPOINTS}}"
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timeout: 10
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expected_fps: 25
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- type: "filter"
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condition: "{{status}} != 'healthy'"
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to: "email://{{SMTP_SERVER}}:{{SMTP_PORT}}?user={{SMTP_USER}}&password={{SMTP_PASS}}&to={{ADMIN_EMAIL}}"
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- name: "motion_detection_backup"
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from: "rtsp://{{BACKUP_CAMERA_USER}}:{{BACKUP_CAMERA_PASS}}@{{BACKUP_CAMERA_IP}}/stream1"
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processors:
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- type: "external"
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command: "python scripts/motion_detector.py"
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config:
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sensitivity: 0.3
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min_area: 5000
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background_subtraction: true
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- type: "aggregate"
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strategy: "collect"
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timeout: "30s"
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max_size: 5
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- type: "transform"
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template: "Motion detected on backup camera: {{count}} events in last 30s"
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to: "log://logs/motion_backup.log"
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env_vars:
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- CAMERA_USER
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- CAMERA_PASS
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- CAMERA_IP
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- CAMERA_NAME
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- BACKUP_CAMERA_USER
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- BACKUP_CAMERA_PASS
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- BACKUP_CAMERA_IP
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- SMTP_SERVER
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- SMTP_PORT
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- SMTP_USER
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- SMTP_PASS
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- SECURITY_EMAIL
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- ADMIN_EMAIL
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- SECURITY_WEBHOOK
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- MQTT_BROKER
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- CAMERA_HEALTH_ENDPOINTS
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settings:
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max_concurrent_routes: 5
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default_timeout: 60
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log_level: "info"
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metrics_enabled: true
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health_check_port: 8080
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frame_skip_ratio: 3
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max_memory_usage: "2GB"

‎examples/examplesgrpc_routesyaml.py‎

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# gRPC-based ML Pipeline Routes
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# Demonstrates microservices architecture with gRPC communication
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routes:
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- name: "ml_inference_pipeline"
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from: "grpc://0.0.0.0:50051/ImageProcessingService/ProcessBatch"
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processors:
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# Rust preprocessing for performance
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- type: "external"
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command: "cargo run --bin data_preprocessor"
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input_format: "json"
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output_format: "json"
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config:
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batch_size: 32
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normalize: true
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parallel: true
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simd_enabled: true
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# Python ML inference
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- type: "external"
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command: "python scripts/grpc_ml_client.py"
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input_format: "json"
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output_format: "json"
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config:
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grpc_server: "{{ML_GRPC_SERVER}}"
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model_name: "{{ML_MODEL_NAME}}"
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timeout: 30
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batch_mode: true
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# Go post-processing and validation
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- type: "external"
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command: "go run scripts/inference_validator.go"
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config:
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validation_rules: "{{VALIDATION_RULES}}"
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confidence_threshold: 0.8
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# C++ optimization for real-time requirements
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- type: "external"
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command: "./bin/cpp_postprocessor"
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config:
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algorithm: "fast_nms"
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optimization: "speed"
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max_latency_ms: 100
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to:
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- "grpc://{{RESULT_GRPC_SERVER}}/ResultService/StoreResults"
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- "mqtt://{{MQTT_BROKER}}:1883/ml/results"
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- name: "model_performance_monitoring"
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from: "timer://5m"
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processors:
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- type: "external"
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command: "python scripts/model_metrics_collector.py"
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config:
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models: "{{MONITORED_MODELS}}"
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metrics: "accuracy,latency,throughput,error_rate"
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- type: "filter"
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condition: "{{accuracy}} < 0.9 or {{latency_ms}} > 500"
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- type: "transform"
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template: |
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Model Performance Alert:
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Model: {{model_name}}
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Accuracy: {{accuracy}}
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Latency: {{latency_ms}}ms
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Error Rate: {{error_rate}}%
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to: "email://{{SMTP_SERVER}}:{{SMTP_PORT}}?user={{SMTP_USER}}&password={{SMTP_PASS}}&to={{ML_TEAM_EMAIL}}"
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- name: "distributed_batch_processing"
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from: "file:///data/batch_queue/*.json"
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processors:
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- type: "aggregate"
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strategy: "collect"
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timeout: "1m"
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max_size: 100
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- type: "external"
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command: "python scripts/batch_distributor.py"
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config:
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worker_nodes: "{{WORKER_NODES}}"
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distribution_strategy: "round_robin"
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max_workers: 10
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to: "grpc://{{BATCH_RESULT_SERVER}}/BatchService/ProcessResults"
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env_vars:
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- ML_GRPC_SERVER
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- ML_MODEL_NAME
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- RESULT_GRPC_SERVER
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- MQTT_BROKER
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- VALIDATION_RULES
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- MONITORED_MODELS
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- ML_TEAM_EMAIL
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- WORKER_NODES
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- BATCH_RESULT_SERVER
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settings:
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grpc_max_message_size: "100MB"
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grpc_keepalive_time: "30s"
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grpc_keepalive_timeout: "5s"
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grpc_keepalive_permit_without_calls: true
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max_concurrent_routes: 3
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default_timeout: 120

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