839 lines
38 KiB
Markdown
839 lines
38 KiB
Markdown
Real-Time Video Analysis Platform
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Project Overview
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A scalable real-time video streaming and AI analysis platform
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that demonstrates modern cloud-native architecture and machine learning integration.
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The system ingests RTMP video streams (from sources like OBS),
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processes them with computer vision AI models,
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and provides live analysis results through a responsive web dashboard.
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Core Functionality
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Video Ingestion: Accept RTMP streams and convert to HLS for web playback
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AI Processing: Real-time object detection (YOLO) and scene analysis (CLIP) on video segments
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Live Dashboard: Angular frontend with WebSocket-powered real-time analysis overlays
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Scalable Architecture: Kubernetes-deployed microservices with configurable processing modes
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Cloud Integration: GCP services integration while maintaining platform agnostic design
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Technical Stack
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Backend: Django + Django Channels, PostgreSQL, Redis, Celery
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AI/ML: OpenCV, YOLO, CLIP, Whisper (Hugging Face Transformers)
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Frontend: Angular 17+ with HLS.js video player and Canvas overlays
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Infrastructure: Docker containers, Kubernetes, NGINX
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Cloud: Google Cloud Platform integration (Storage, Vision API, Build, Logging)
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Streaming: FFmpeg for RTMP→HLS conversion, WebSocket for real-time data
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Key Features
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Dual processing modes (real-time vs batch) with runtime switching
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Live video analysis overlays (bounding boxes, object labels, confidence scores)
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OBS Studio integration for testing with various video sources
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Kubernetes auto-scaling based on processing queue depth
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Performance monitoring and benchmarking for 1080p30 video streams
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Platform-agnostic design with cloud-specific optimizations
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Architecture Goals
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Demonstrate event-driven microservices architecture
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Showcase AI model deployment and inference at scale
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Implement real-time data streaming and WebSocket communication
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Show proficiency with modern web frameworks and container orchestration
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Prove understanding of video processing pipelines and streaming protocols
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This project serves as a technical showcase for backend development, AI/ML integration, cloud platform expertise, and modern web application architecture - directly addressing the requirements for a streaming media and AI-focused development role.
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Master Implementation Checklist
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Core Infrastructure Setup
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Django project structure - Create apps: streaming, ai_processing, api, dashboard
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Database models - Stream, MediaSegment, VideoAnalysis, ObjectDetection tables
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Docker containers - Django app, PostgreSQL, Redis, Nginx
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Basic Kubernetes manifests - Deployments, services, configmaps, PVCs
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RTMP ingestion endpoint - Accept OBS streams, trigger ffmpeg HLS conversion
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HLS segment monitoring - File watcher to detect new video segments
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AI Processing Pipeline
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Video analysis models setup - CLIP for scene understanding, YOLO for object detection
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Frame extraction service - Extract keyframes from HLS segments
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Real-time vs batch processing abstraction - Strategy pattern implementation
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AI processing worker - Celery tasks for video analysis
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Results storage - Store detection results with timestamps and confidence scores
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Processing queue management - Handle backlog and prioritization
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Real-Time Video Effects Pipeline
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GPU-accelerated shader pipeline - OpenGL/CUDA integration with FFmpeg
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AI-triggered effects system - Apply shaders based on detection results
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Custom filter development - Face blur, object highlighting, scene-based color grading
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WebGL frontend effects - Client-side shader preview and control
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Angular Frontend
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Angular 17+ project setup - Standalone components, signals, new control flow
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WebSocket service - Real-time communication with Django Channels
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Video player component - HLS.js integration for stream playback
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Analysis overlay component - Canvas-based bounding boxes and labels
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Stream control dashboard - Start/stop streams, processing mode toggle
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Real-time results panel - Live object detection and scene analysis display
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Real-time Communication
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Django Channels setup - WebSocket consumers for live data streaming
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WebSocket message protocols - Define message types for analysis results
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Frontend WebSocket integration - Angular service for real-time updates
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Overlay synchronization - Match analysis results with video timeline
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GCP Integration Features
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Cloud Storage integration - Store processed video segments and analysis results
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Cloud Vision API comparison - Benchmark against local models (within free tier)
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Cloud Logging - Structured logging for monitoring and debugging
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Cloud Build CI/CD - Automated Docker builds and deployments
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GKE-specific configurations - Node selectors, resource requests/limits
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Cloud Load Balancer - External access to the application
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Video Processing & Effects
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FFmpeg integration - HLS generation with proper segment duration
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Video quality optimization - Handle 1080p streams efficiently
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Frame rate analysis - Detect motion and activity levels
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Color analysis - Dominant colors and scene brightness
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Performance monitoring - Track processing latency and throughput
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Kubernetes Production Setup
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Resource management - CPU/memory limits for AI processing
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Auto-scaling configuration - HPA based on processing queue length
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Persistent storage - Claims for video segments and database
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Service mesh setup - Istio for traffic management (optional)
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Monitoring stack - Prometheus and Grafana integration
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Ingress configuration - NGINX ingress with SSL termination
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Testing & Validation
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OBS integration testing - Verify RTMP stream ingestion
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AI model accuracy testing - Validate object detection results
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Performance benchmarking - Process 1080p30 streams in real-time
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WebSocket stress testing - Multiple concurrent viewers
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End-to-end pipeline testing - OBS → Processing → Angular display
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Documentation & Deployment
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API documentation - OpenAPI/Swagger specs
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Kubernetes deployment guide - Step-by-step cluster setup
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GCP setup instructions - Service account, IAM, and resource creation
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Architecture diagrams - System overview and data flow
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Performance metrics documentation - Benchmarks and optimization notes
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Suggested Implementation Order:
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Phase 1 (Foundation): Infrastructure Setup → Django + Models → Docker Setup
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Phase 2 (Core Features): RTMP Ingestion → AI Processing Pipeline → Results Storage
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Phase 3 (Frontend): Angular Setup → WebSocket Service → Video Player
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Phase 4 (Integration): Real-time Communication → Analysis Overlays → Stream Control
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Phase 5 (Cloud): GCP Services → Kubernetes Production → Monitoring
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Phase 6 (Polish): Testing → Documentation → Performance Optimization
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This order ensures each component builds on the previous ones and you can test functionality incrementally. The GCP integration is spread throughout to demonstrate platform knowledge without being overwhelming.
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Ready to start with the foundation phase?
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Media Analyzer - Complete Project Structure
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media-analyzer/
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├── README.md
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├── docker-compose.yml # Development environment
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├── requirements.txt # Python dependencies
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├── manage.py # Django management
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│
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├── backend/ # Django application root
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│ ├── media_analyzer/ # Main Django project
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│ │ ├── __init__.py
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│ │ ├── settings/
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│ │ │ ├── __init__.py
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│ │ │ ├── base.py # Base settings
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│ │ │ ├── development.py # Dev overrides
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│ │ │ └── production.py # Prod overrides
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│ │ ├── urls.py # Main URL routing
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│ │ ├── wsgi.py # WSGI application
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│ │ └── asgi.py # ASGI for Django Channels
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│ │
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│ ├── streaming/ # RTMP/HLS handling app
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│ │ ├── __init__.py
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│ │ ├── models.py # Stream, MediaSegment models
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│ │ ├── views.py # RTMP endpoints
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│ │ ├── consumers.py # WebSocket consumers
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│ │ ├── rtmp_handler.py # RTMP server logic
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│ │ ├── hls_monitor.py # File system watcher
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│ │ └── urls.py
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│ │
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│ ├── ai_processing/ # AI analysis app
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│ │ ├── __init__.py
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│ │ ├── models.py # Analysis results models
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│ │ ├── processors/
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│ │ │ ├── __init__.py
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│ │ │ ├── base.py # Processing strategy interface
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│ │ │ ├── realtime.py # Real-time processor
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│ │ │ ├── batch.py # Batch processor
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│ │ │ └── video_analyzer.py # CLIP/YOLO integration
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│ │ ├── tasks.py # Celery tasks
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│ │ └── apps.py # App configuration
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│ │
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│ ├── effects/ # Real-time video effects app
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│ │ ├── __init__.py
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│ │ ├── models.py # Effect presets, shader configs
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│ │ ├── processors/
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│ │ │ ├── __init__.py
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│ │ │ ├── gpu_pipeline.py # OpenGL/CUDA processing
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│ │ │ ├── ffmpeg_filters.py # FFmpeg custom filters
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│ │ │ └── effect_engine.py # Main effects orchestrator
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│ │ ├── shaders/ # GLSL shader files
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│ │ │ ├── vertex/ # Vertex shaders
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│ │ │ ├── fragment/ # Fragment shaders
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│ │ │ ├── blur.glsl # Face blur shader
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│ │ │ ├── highlight.glsl # Object highlight shader
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│ │ │ └── color_grade.glsl # Scene-based color grading
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│ │ ├── triggers/
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│ │ │ ├── __init__.py
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│ │ │ ├── ai_triggers.py # AI detection → effect mapping
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│ │ │ └── manual_triggers.py # User-controlled effects
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│ │ ├── tasks.py # GPU processing Celery tasks
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│ │ └── apps.py
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│ │
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│ └── api/ # REST API app
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│ ├── __init__.py
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│ ├── serializers.py # DRF serializers
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│ ├── views.py # API endpoints
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│ └── urls.py
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│
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├── frontend/ # Angular application
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│ ├── package.json
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│ ├── angular.json
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│ ├── src/
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│ │ ├── app/
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│ │ │ ├── app.component.ts
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│ │ │ ├── app.config.ts # Angular 17+ config
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│ │ │ │
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│ │ │ ├── components/
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│ │ │ │ ├── stream-viewer/
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│ │ │ │ │ ├── stream-viewer.component.ts
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│ │ │ │ │ ├── stream-viewer.component.html
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│ │ │ │ │ └── stream-viewer.component.scss
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│ │ │ │ ├── analysis-panel/
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│ │ │ │ │ └── analysis-panel.component.ts
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│ │ │ │ ├── stream-control/
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│ │ │ │ │ └── stream-control.component.ts
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│ │ │ │ └── effects-panel/
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│ │ │ │ ├── effects-panel.component.ts
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│ │ │ │ ├── effects-panel.component.html
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│ │ │ │ └── shader-editor.component.ts
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│ │ │ │
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│ │ │ ├── services/
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│ │ │ │ ├── websocket.service.ts
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│ │ │ │ ├── stream.service.ts
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│ │ │ │ ├── analysis.service.ts
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│ │ │ │ └── effects.service.ts
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│ │ │ │
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│ │ │ ├── webgl/ # Client-side effects engine
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│ │ │ │ ├── shader.service.ts
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│ │ │ │ ├── effects-engine.ts
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│ │ │ │ ├── webgl-utils.ts
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│ │ │ │ └── shaders/ # WebGL shaders
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│ │ │ │ ├── overlay.vert.glsl
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│ │ │ │ ├── overlay.frag.glsl
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│ │ │ │ ├── particle.vert.glsl
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│ │ │ │ └── particle.frag.glsl
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│ │ │ │
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│ │ │ └── models/
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│ │ │ ├── stream.interface.ts
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│ │ │ ├── analysis.interface.ts
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│ │ │ └── effects.interface.ts
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│ │ │
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│ │ └── main.ts # Angular bootstrap
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│ └── ...
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│
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├── docker/ # Docker configurations
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│ ├── Dockerfile.django # Django app container
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│ ├── Dockerfile.nginx # Nginx for HLS serving
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│ └── docker-compose.override.yml
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│
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├── k8s/ # Kubernetes manifests
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│ ├── base/ # Base configurations
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│ │ ├── namespace.yaml
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│ │ ├── django-deployment.yaml
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│ │ ├── nginx-deployment.yaml
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│ │ ├── postgres-statefulset.yaml
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│ │ ├── redis-deployment.yaml
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│ │ ├── services.yaml
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│ │ └── configmaps.yaml
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│ │
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│ ├── overlays/ # Environment-specific
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│ │ ├── development/
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│ │ │ └── kustomization.yaml
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│ │ └── production/
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│ │ ├── kustomization.yaml
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│ │ └── gcp-specific.yaml
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│ │
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│ └── scripts/
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│ ├── deploy.sh
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│ └── setup-cluster.sh
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│
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├── media/ # Media storage (mounted volume)
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│ ├── segments/ # HLS video segments
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│ ├── playlists/ # HLS playlists
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│ └── uploads/ # Uploaded content
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│
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├── scripts/ # Helper scripts
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│ ├── setup-dev.sh # Development environment setup
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│ ├── start-rtmp-server.sh # RTMP server startup
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│ └── load-ai-models.py # Download and setup AI models
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│
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└── docs/ # Documentation
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├── api.md # API documentation
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├── deployment.md # Deployment guide
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└── architecture.md # System architecture
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vertical separation instead of phases
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Slice 1: Basic Stream Ingestion
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RTMP endpoint (basic)
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HLS conversion (minimal)
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Stream model + API
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Simple Angular video player
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Basic K8s deployment
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Slice 2: AI Processing Foundation
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Video segment detection
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Single AI model integration (YOLO)
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Processing strategy abstraction
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Results storage + API
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Analysis display component
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Slice 3: Real-time Communication
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WebSocket setup
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Live analysis streaming
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Frontend overlay system
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Processing queue monitoring
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Slice 4: GCP Integration
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Cloud Storage for segments
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GKE deployment
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Monitoring integration
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considerations for deployment / testing
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does this planning take into consideration running it on a local kubernetes cluster? I use kind and tilt for running tough a prefer not to depend on it for developing just test the orchestration?
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The current planning assumes traditional development (Django dev server + Angular dev server) with K8s only for testing orchestration. This is actually perfect for your workflow! Let me clarify the development vs testing approach:
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## **Development Mode (Local Servers)**
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```bash
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# Backend development
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cd backend
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python manage.py runserver 8000
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# Frontend development
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cd frontend
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ng serve --port 4200
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# Supporting services (via Docker Compose)
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docker-compose up redis postgres
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```
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**Benefits:**
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- Hot reload for both Django and Angular
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- Easy debugging with IDE integration
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- Fast iteration cycles
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- No K8s complexity during development
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## **Testing Mode (KIND + K8s)**
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```bash
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# Build and deploy to KIND cluster
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kind create cluster --name media-analyzer
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kubectl apply -k k8s/overlays/development
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```
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**For orchestration testing only:**
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- Container builds and registry
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- Service discovery and networking
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- Volume mounts and persistence
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- Resource limits and scaling
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- Inter-service communication
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## **Suggested Development Workflow**## **Key Points for Your Workflow:**
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### **Development (95% of time):**
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- **No K8s dependency** - just Docker Compose for supporting services
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- **Fast hot reload** - Django and Angular dev servers
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- **Easy debugging** - direct access to logs and debugger
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- **Simple database migrations** and model changes
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### **K8s Testing (when needed):**
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- **Container builds** work correctly
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- **Service networking** functions properly
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- **Volume mounts** for media files
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- **Resource limits** don't break anything
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- **Horizontal scaling** works as expected
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### **Perfect for KIND:**
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- KIND cluster only created when testing orchestration
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- No overhead during daily development
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- Easy to tear down and recreate
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- Matches production K8s behavior
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### **Tilt Usage (Optional):**
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- **If you use Tilt**: Only for K8s testing, not development
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- **If you skip Tilt**: Use the simple `kubectl` commands for testing
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This approach gives you:
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- **Fast development** without K8s complexity
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- **Realistic testing** of the actual deployment
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- **No vendor lock-in** to Tilt (though it's great if you want it)
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- **Easy CI/CD** since K8s manifests are tested locally
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The planning already supports this perfectly - you develop fast locally and test orchestration when needed!
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STEP DONE
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now that I have a basic stream pipeline a need to add an AI feature (something simple) before stepping everything to something that scales. I was thinking on detecting my extend hand, but maybe some more bussines oriented feature would work better (read the very first message with the JD in spanish to think about this)
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Edit
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Looking back at the job description, they mention analyzing "millones de emisiones en medios y redes sociales" for communication and advertising optimization. For a business-oriented AI feature that aligns with Seenka's focus, I'd suggest:
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🎯 Brand/Logo Detection
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Business Value: Identify brands and logos in video streams (like TV broadcasts, social media content)
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Detect when competitors appear in content
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Monitor brand visibility and placement
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Track advertising effectiveness
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Technical Implementation:
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Use CLIP for general brand recognition
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Train on common logos/brand imagery
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Output: Brand name, confidence, screen position, duration
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📺 Content Classification
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Business Value: Categorize video content for media monitoring
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Detect content types: "news", "sports", "entertainment", "advertising"
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Identify show segments vs commercial breaks
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Monitor content themes and topics
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Technical Implementation:
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CLIP scene classification with predefined categories
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Frame sampling every few seconds
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Output: Content category, confidence, timestamp
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🗣️ Speaker Detection
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Business Value: Media monitoring and audience analysis
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Count people on screen (audience size estimation)
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Detect when someone is speaking/presenting
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Monitor speaker time and engagement
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Technical Implementation:
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YOLO for person detection
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Basic pose estimation to detect "presentation pose"
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Output: Person count, speaking activity, screen position
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My Recommendation: Content Classification
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Why it's perfect for your demo:
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Business relevant: Directly matches Seenka's media analysis focus
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Technically achievable: CLIP can classify scenes out-of-the-box
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Visually impressive: Live categorization overlay on your OBS stream
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Extensible: Easy to add more categories or confidence thresholds
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Demo scenario: Stream different content types with OBS (news clips, sports, entertainment) and watch the AI classify them in real-time.
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Which direction appeals to you most? The hand detection is cool for personal interaction, but content classification would really showcase understanding of their business domain.
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Im using streams of myself (I have a decent setup) so for now the input will be just that but I think i can be fun if I display logos with my phone or something like that
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Edit
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Perfect! Logo detection with your phone is brilliant - it's both business-relevant (brand monitoring) and interactive demo-friendly. You can hold up logos on your phone screen or physical items and watch the AI detect them in real-time.
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🏷️ Logo Detection Implementation
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Simple Approach (Weekend-friendly):
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CLIP-based Brand Recognition:
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python
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# Use CLIP's zero-shot classification
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brands = ["Apple", "Google", "Nike", "Coca-Cola", "McDonald's", "Amazon"]
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# Hold up logos on phone/laptop and classify scenes
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Demo Flow:
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You stream yourself with OBS
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Hold up different brand logos on your phone/laptop screen
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AI detects and labels them with confidence scores
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Live overlay shows: "Apple Logo detected (87% confidence)"
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Enhanced Approach:
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Combine YOLO + CLIP:
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YOLO: Detect objects in scene (phone, laptop, clothing, etc.)
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CLIP: Classify detected regions for brand/logo content
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Output: "iPhone detected", "Nike logo on shirt", "Starbucks cup visible"
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Fun Demo Scenarios:
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Tech Logos: Show Apple, Google, Microsoft logos on your devices
|
||
Fashion Brands: Wear branded clothing/accessories
|
||
Food/Drink: Hold up branded items from your desk
|
||
Mixed Reality: Point phone camera at branded items around your room
|
||
Business Value Demo:
|
||
Brand Monitoring: "Track when competitors appear in streams"
|
||
Advertising Analysis: "Measure brand visibility duration and positioning"
|
||
Content Compliance: "Detect unauthorized brand usage"
|
||
Implementation in Your Matrix:
|
||
This fits perfectly in AI Analysis → Local Single Server:
|
||
|
||
Frame extraction from HLS ✓
|
||
CLIP logo classification (instead of YOLO objects)
|
||
Analysis results storage
|
||
Results API endpoint
|
||
Want to start with basic CLIP brand classification, or go straight for the object detection + brand recognition combo?
|
||
|
||
|
||
CURRENT STEP
|
||
|
||
Demo Strategy
|
||
For Interviewers:
|
||
|
||
Show local demo: "Here's the system running locally with GCP integration"
|
||
Explain cloud strategy: "This architecture scales to full GCP with these manifests"
|
||
Demonstrate cloud services: "Logo detection uses Cloud Vision API"
|
||
Show cost optimization: "Uses free tier effectively while maintaining production patterns"
|
||
|
||
Scaling Path:
|
||
Local K8s → Single VM K8s → GKE Autopilot → Full GKE
|
||
Questions for You:
|
||
|
||
Comfort with hybrid approach? Local K8s + GCP services vs pure cloud?
|
||
Demo requirements? Do interviewers need to access it remotely, or is screen sharing sufficient?
|
||
AI model preference? Keep local CLIP for demo, or switch to Cloud Vision for "production"?
|
||
Time investment? How much time do you want to spend on infrastructure vs other features?
|
||
|
||
The hybrid approach gives you the best of both worlds - demonstrates K8s and GCP knowledge without the costs!
|
||
|
||
|
||
myself said: def what I prefer 2. would like to extend this not just this interview but as portfolio asset for others and potential people interested in learning how to do it 3. I reckon that design decisions allow for easy switching between the two. 4 today's focus is only infra (only had time for end to end logo detection, so shader effects are postponed indefinitely, I separated the visual properties on a different celery task to put in different queues but is commented out to test just the logo detection end-to-end and that's the feature that must work in after completing this prod-ready phase)
|
||
|
||
|
||
Perfect! Your hybrid approach as a portfolio/learning asset changes everything - much better than a throwaway demo. Let me design this for educational value and real-world applicability.
|
||
Portfolio-Ready Architecture
|
||
Design Principles:
|
||
|
||
Teachable: Others can follow your setup
|
||
Realistic: Mirrors actual production patterns
|
||
Cost-effective: $0 monthly with scaling options
|
||
Demonstrable: Works for interviews and learning
|
||
|
||
Hybrid K8s + GCP Implementation
|
||
Phase 1: Local Foundation (Today's Focus)
|
||
┌──────────────────────────────────────────────────────────┐
|
||
│ Local KIND Cluster │
|
||
├─────────────────┬─────────────────┬─────────────────────┤
|
||
│ Django Pod │ Worker Pod │ Infrastructure │
|
||
│ • Web API │ • Logo Analysis │ • PostgreSQL │
|
||
│ • Admin │ • Visual Tasks │ • Redis │
|
||
│ • WebSocket │ • Celery │ • NGINX │
|
||
└─────────────────┴─────────────────┴─────────────────────┘
|
||
│
|
||
┌─────────────┐
|
||
│ GCP Services│
|
||
│ • Storage │
|
||
│ • Vision AI │
|
||
│ • Logging │
|
||
└─────────────┘
|
||
K8s Manifests Structure:
|
||
k8s/
|
||
├── base/ # Platform-agnostic
|
||
│ ├── django-deployment.yaml # Web application
|
||
│ ├── worker-deployment.yaml # Celery workers
|
||
│ ├── postgres-statefulset.yaml # Database
|
||
│ ├── redis-deployment.yaml # Cache/Queue
|
||
│ ├── nginx-deployment.yaml # Reverse proxy
|
||
│ └── services.yaml # Internal networking
|
||
├── overlays/
|
||
│ ├── local/ # KIND cluster
|
||
│ │ ├── kustomization.yaml # Local configs
|
||
│ │ └── local-storage.yaml # Local volumes
|
||
│ ├── gcp-single-vm/ # Single VM deployment
|
||
│ │ ├── kustomization.yaml # VM-specific configs
|
||
│ │ └── resource-limits.yaml # Memory constraints
|
||
│ └── gcp-production/ # Full GKE deployment
|
||
│ ├── kustomization.yaml # Production configs
|
||
│ ├── hpa.yaml # Auto-scaling
|
||
│ └── monitoring.yaml # Observability
|
||
Today's Infrastructure Implementation
|
||
1. GCP Project Setup (Free Tier)
|
||
bash# Create project and enable APIs
|
||
gcloud projects create media-analyzer-demo
|
||
gcloud config set project media-analyzer-demo
|
||
gcloud services enable vision.googleapis.com
|
||
gcloud services enable storage.googleapis.com
|
||
gcloud services enable logging.googleapis.com
|
||
|
||
# Create service account
|
||
gcloud iam service-accounts create media-analyzer \
|
||
--display-name="Media Analyzer Service Account"
|
||
|
||
# Create and download key
|
||
gcloud iam service-accounts keys create credentials.json \
|
||
--iam-account=media-analyzer@media-analyzer-demo.iam.gserviceaccount.com
|
||
|
||
# Create storage bucket
|
||
gsutil mb gs://media-analyzer-segments
|
||
2. Local K8s with GCP Integration
|
||
yaml# k8s/base/django-deployment.yaml
|
||
apiVersion: apps/v1
|
||
kind: Deployment
|
||
metadata:
|
||
name: django-app
|
||
spec:
|
||
replicas: 2
|
||
template:
|
||
spec:
|
||
containers:
|
||
- name: django
|
||
image: media-analyzer-backend:latest
|
||
env:
|
||
- name: GOOGLE_APPLICATION_CREDENTIALS
|
||
value: "/var/secrets/google/credentials.json"
|
||
- name: GCP_BUCKET_NAME
|
||
value: "media-analyzer-segments"
|
||
- name: USE_CLOUD_VISION
|
||
value: "true"
|
||
volumeMounts:
|
||
- name: gcp-credentials
|
||
mountPath: /var/secrets/google
|
||
readOnly: true
|
||
volumes:
|
||
- name: gcp-credentials
|
||
secret:
|
||
secretName: gcp-credentials
|
||
3. Environment Switching Design
|
||
python# backend/media_analyzer/settings/base.py
|
||
import os
|
||
|
||
# AI Processing Configuration
|
||
USE_CLOUD_VISION = os.getenv('USE_CLOUD_VISION', 'false').lower() == 'true'
|
||
USE_LOCAL_CLIP = not USE_CLOUD_VISION
|
||
|
||
# Storage Configuration
|
||
USE_CLOUD_STORAGE = os.getenv('USE_CLOUD_STORAGE', 'false').lower() == 'true'
|
||
GCP_BUCKET_NAME = os.getenv('GCP_BUCKET_NAME', 'media-analyzer-segments')
|
||
|
||
# AI Analysis Strategy
|
||
if USE_CLOUD_VISION:
|
||
AI_ANALYSIS_BACKEND = 'ai_processing.backends.CloudVisionBackend'
|
||
else:
|
||
AI_ANALYSIS_BACKEND = 'ai_processing.backends.LocalClipBackend'
|
||
Portfolio Documentation Strategy
|
||
README Structure:
|
||
markdown# Media Analyzer - Production K8s Deployment
|
||
|
||
## Architecture Options
|
||
- [Local Development](docs/local-setup.md) - Docker Compose
|
||
- [Hybrid K8s](docs/hybrid-k8s.md) - KIND + GCP Services
|
||
- [Single VM](docs/single-vm-k8s.md) - k3s on GCP e2-micro
|
||
- [Full Production](docs/production-gke.md) - GKE with auto-scaling
|
||
|
||
## Cost Analysis
|
||
- Local: $0/month
|
||
- Hybrid: $0/month (free tier)
|
||
- Single VM: $0/month (free tier e2-micro)
|
||
- Production: ~$150/month (full GKE)
|
||
|
||
## Features Demonstrated
|
||
- ✅ Real-time video streaming (RTMP → HLS)
|
||
- ✅ AI-powered logo detection (CLIP/Cloud Vision)
|
||
- ✅ Kubernetes orchestration
|
||
- ✅ Cloud-native patterns
|
||
- ⏳ Auto-scaling and monitoring
|
||
Deployment Commands for Today:
|
||
bash# 1. Setup GCP credentials in K8s
|
||
kubectl create secret generic gcp-credentials \
|
||
--from-file=credentials.json=./credentials.json
|
||
|
||
# 2. Deploy to local KIND
|
||
kubectl apply -k k8s/overlays/local
|
||
|
||
# 3. Port forward for access
|
||
kubectl port-forward service/nginx 8080:80
|
||
|
||
# 4. Test logo detection
|
||
curl http://localhost:8080/api/streams/
|
||
Learning/Portfolio Value:
|
||
For Others:
|
||
|
||
Step-by-step deployment guides
|
||
Cost optimization strategies
|
||
Environment switching patterns
|
||
Real-world K8s manifests
|
||
|
||
For Interviews:
|
||
|
||
"Here's how I optimized for $0 monthly cost"
|
||
"This scales from laptop to production GKE"
|
||
"Logo detection works with both local and cloud AI"
|
||
"Others have successfully followed my setup guide"
|
||
|
||
Today's Checklist:
|
||
|
||
GCP project setup with service account
|
||
K8s secrets for GCP credentials
|
||
Environment switching for AI backends
|
||
Local deployment with cloud services
|
||
Logo detection end-to-end test
|
||
Documentation for portfolio sharing
|
||
|
||
Ready to tackle the infrastructure setup? This approach gives you a real portfolio asset that others can learn from while showcasing production-ready patterns!
|
||
|
||
Your Infrastructure Focus Today:
|
||
|
||
✅ Hybrid K8s (KIND) + GCP services architecture
|
||
✅ Environment switching between local CLIP and Cloud Vision
|
||
✅ Portfolio-ready documentation approach
|
||
✅ $0 monthly cost with production scaling path
|
||
|
||
Key Deliverable:
|
||
Logo detection working end-to-end in production-ready K8s setup
|
||
|
||
|
||
- non free GCP services should use local k8s services that are preferably easy switchable
|
||
- dont add things to gitignore that you don't know if they'll ever be there, just add what's needed as we go
|
||
|
||
|
||
|
||
|
||
|
||
docker check up
|
||
|
||
Here’s a fast “what changed → what to do” map so you don’t nuke/prune on every edit.
|
||
|
||
# Quick map
|
||
|
||
| You changed… | Do this (fastest first) | Notes / good defaults |
|
||
| -------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
|
||
| **App code** (source files) | Nothing if you have live‑reload. Otherwise `docker compose restart <svc>` | Use bind mounts + a dev runner: Node `nodemon`, Python `uvicorn --reload`/Flask debug, Django `runserver`, Go `air`/`reflex`. |
|
||
| **Runtime config** inside container (env var values, flags) | `docker compose up -d --no-deps <svc>` (recreate) or `restart` | If the env is in `.env` or `compose.yml` `environment:`, a restart/recreate is enough. |
|
||
| **Config files read at startup** (e.g., `nginx.conf`, app config YAML) | `docker compose up -d --no-deps <svc>` | Bind-mount the config so you can `restart` instead of `rebuild`. |
|
||
| **Dependency lockfile** (`package-lock.json`, `poetry.lock`, `requirements.txt`, `go.mod/sum`, `Gemfile.lock`) | `docker compose build <svc>` then `up -d --no-deps <svc>` | Cache layers by copying lockfile before `COPY .`. See Dockerfile pattern below. |
|
||
| **Dockerfile** (but not the base image) | `docker compose build <svc>` then `up -d --no-deps <svc>` | BuildKit keeps layer cache; only changed layers rebuild. |
|
||
| **Base image tag** (e.g., `FROM node:20-bullseye` -> new tag or want latest security updates) | `docker compose build --pull <svc>` then `up -d --no-deps <svc>` | `--pull` refreshes the base. Use pinned tags in prod. |
|
||
| **Build args** (`ARG VAR=...` used in Dockerfile) | `docker compose build --no-cache --build-arg VAR=... <svc>` (if the arg affects earlier layers) | If the arg only affects late layers, drop `--no-cache`. |
|
||
| **Multi-service libraries** (shared package used by multiple services) | Rebuild every consumer: `docker compose build svc1 svc2` then `up -d --no-deps svc1 svc2` | Consider a shared base image stage to centralize caches. |
|
||
| **compose.yml** service definition (ports, volumes, healthchecks) | `docker compose up -d` | Compose detects what must be recreated. |
|
||
| **External dependency** (DB schema, migrations) | Run migration container/task; usually no rebuild | Keep DB in a **named volume** so rebuilds don’t wipe data. |
|
||
| **Static assets** (built by a toolchain) | If built outside: restart only. If built inside: `build` that web service | Prefer building in a separate “builder” stage with a cache. |
|
||
| **Secrets** (files mounted via `secrets:` or env injected at runtime) | `restart` the service | Don’t bake secrets into images → no rebuilds needed. |
|
||
| **Data in bind/named volumes** | Nothing (data persists) | Avoid pruning volumes unless you *want* to reset state. |
|
||
|
||
# Minimal dev patterns that avoid rebuilds
|
||
|
||
**Dockerfile (Node/Python example)**
|
||
|
||
```dockerfile
|
||
# syntax=docker/dockerfile:1.7
|
||
FROM node:20 AS deps
|
||
WORKDIR /app
|
||
COPY package*.json ./
|
||
RUN npm ci
|
||
|
||
FROM node:20 AS dev
|
||
WORKDIR /app
|
||
COPY --from=deps /app/node_modules ./node_modules
|
||
# Copy only what’s needed for runtime; source comes from a bind mount in dev
|
||
COPY package*.json ./
|
||
CMD ["npm","run","dev"] # e.g., vite/next with HMR
|
||
```
|
||
|
||
For Python, copy `pyproject.toml`/`requirements.txt` to a deps layer first, install, then bind‑mount your app code.
|
||
|
||
**docker-compose.yml (dev)**
|
||
|
||
```yaml
|
||
services:
|
||
web:
|
||
build:
|
||
context: .
|
||
target: dev
|
||
volumes:
|
||
- .:/app:cached
|
||
env_file: .env
|
||
ports:
|
||
- "3000:3000"
|
||
command: npm run dev
|
||
```
|
||
|
||
# Everyday command palette
|
||
|
||
* Restart a single service (pick up env/config):
|
||
`docker compose restart <svc>`
|
||
* Recreate a service without touching deps (uses existing image):
|
||
`docker compose up -d --no-deps <svc>`
|
||
* Rebuild only what changed (then recreate):
|
||
`docker compose build <svc> && docker compose up -d --no-deps <svc>`
|
||
* Rebuild with fresh base image:
|
||
`docker compose build --pull <svc> && docker compose up -d --no-deps <svc>`
|
||
* Tail logs:
|
||
`docker compose logs -f <svc>`
|
||
* Exec a shell:
|
||
`docker compose exec <svc> sh` (or `bash`)
|
||
* Clean **containers & images from dangling builds** (keep volumes!):
|
||
`docker image prune -f && docker container prune -f`
|
||
* Clean everything **except named volumes**:
|
||
`docker system prune -a` ← only when truly needed.
|
||
|
||
# Tips that keep you off the prune button
|
||
|
||
1. **Bind-mount code in dev** + a live‑reload command, so edits don’t require rebuilds.
|
||
2. **Layer your Dockerfile** so deps install before copying the whole source:
|
||
|
||
* `COPY package*.json .` → install → **then** `COPY src .`
|
||
3. **Use BuildKit caches** for heavy steps (node, pip, apt):
|
||
|
||
* `RUN --mount=type=cache,target=/root/.cache/pip pip install -r requirements.txt`
|
||
* `RUN --mount=type=cache,target=/root/.npm npm ci`
|
||
4. **Pin base images** (e.g., `node:20-bullseye`) and consciously use `--pull` when you want updates.
|
||
5. **Separate build and runtime** (multi‑stage). Artifacts copied forward make rebuilds smaller.
|
||
6. **Keep data in named volumes**; never in the image. Pruning images won’t touch your DB/files.
|
||
7. **.dockerignore** aggressively: exclude `node_modules` (if you install in image), `.git`, build outputs, tmp.
|
||
8. **Compose profiles** for optional services (e.g., `profiles: ["dev"]`) so you don’t restart the world.
|
||
|
||
# Tiny Makefile (muscle memory)
|
||
|
||
```make
|
||
up: ## start everything
|
||
docker compose up -d
|
||
|
||
logs: ## tail logs from app
|
||
docker compose logs -f web
|
||
|
||
re: ## rebuild + recreate app only
|
||
docker compose build web && docker compose up -d --no-deps web
|
||
|
||
restart: ## restart app only
|
||
docker compose restart web
|
||
```
|
||
|
||
If you tell me your stack (language/framework + how you install deps), I’ll tailor the Dockerfile and compose snippets so most edits are “save → live‑reload,” lockfile changes are “build web,” and prunes are basically never.
|
||
|
||
|
||
|
||
it's docker compose not docker-compose
|
||
- use debug logs not prints |