Ultralytics MCP Server

# 📋 Changelog All notable changes to this project will be documented in this file. ## [1.0.0] - 2025-08-08 ### 🎉 Initial Release - Complete Ultralytics MCP Server implementation - One-command deployment with Docker Compose - Full N8N integration with SSE transport ### ✨ Added - **MCP Server** with 7 specialized AI tools - **Streamlit Dashboard** for interactive YOLO model management - **TensorBoard Integration** for training metrics visualization - **Jupyter Lab** environment for development - **CUDA 12.4.1** support for GPU acceleration - **Docker Compose** orchestration for unified deployment ### 🛠️ MCP Tools Implemented 1. `detect_objects` - Real-time object detection 2. `train_model` - Custom YOLO model training 3. `evaluate_model` - Model performance assessment 4. `predict_batch` - Batch processing capabilities 5. `export_model` - Model format conversion 6. `benchmark_model` - Performance benchmarking 7. `analyze_dataset` - Dataset statistics and validation ### 🔧 Technical Features - **CommonJS MCP Server** for N8N compatibility - **SSE Transport** for real-time communication - **Health Check Endpoints** for monitoring - **Container Orchestration** with proper networking - **Build Optimization** with .dockerignore - **Automatic Service Discovery** and startup ### 🎯 Services Available - **Streamlit UI**: Port 8501 - Interactive model interface - **MCP Server**: Port 8092 - N8N integration endpoint - **TensorBoard**: Port 6006 - Training metrics visualization - **Jupyter Lab**: Port 8888 - Development environment ### 🐳 Container Architecture - **ultralytics-container**: CUDA-enabled AI processing - **mcp-connector-container**: Node.js MCP server - **Shared Network**: Seamless inter-container communication - **Volume Persistence**: Data and model storage ### 📁 Project Structure ``` ultralytics_mcp_server/ ├── docker-compose.yml # Service orchestration ├── Dockerfile.ultralytics # AI processing container ├── Dockerfile.mcp-connector # MCP server container ├── src/server.js # MCP implementation ├── main_dashboard.py # Streamlit interface ├── pages/ # Multi-page Streamlit app ├── startup.sh # Service initialization ├── .dockerignore # Build optimization └── README.md # Documentation ``` ### 🔄 Deployment Process 1. One-command startup: `docker-compose up -d` 2. Automatic health checks and service discovery 3. Container restart policies for reliability 4. Build optimization for faster deployments ### 📊 Performance Features - **GPU Memory Management** - Automatic CUDA optimization - **Batch Processing** - Efficient multi-image inference - **Model Caching** - Pre-loaded models for faster response - **Concurrent Processing** - Multi-threaded request handling ### 🔐 Integration Features - **N8N Compatibility** - Full MCP protocol support - **SSE Transport** - Real-time event streaming - **Health Monitoring** - Service status endpoints - **Error Handling** - Comprehensive error management ### 🎮 User Experience - **Single Command Deployment** - `docker-compose up -d` - **Multi-Interface Access** - Web UI, Jupyter, TensorBoard - **Real-time Feedback** - Live training and inference metrics - **Professional Documentation** - Comprehensive README ### 🔧 Development Features - **Hot Reload** - Development-friendly configuration - **Debug Logging** - Comprehensive service logs - **Container Inspection** - Easy debugging tools - **Modular Architecture** - Easy to extend and modify --- ## 🚀 Next Release Plans ### [1.1.0] - Coming Soon - **Enhanced Model Support** - Additional YOLO variants - **API Documentation** - Swagger/OpenAPI integration - **Advanced Metrics** - Extended performance analytics - **Custom Datasets** - Enhanced training workflows ### [1.2.0] - Future - **Multi-GPU Support** - Distributed training capabilities - **Cloud Integration** - AWS/GCP deployment options - **Advanced Automation** - Enhanced N8N workflow templates - **Performance Optimizations** - Faster inference and training --- > **Version 1.0.0** represents a complete, production-ready AI platform for computer vision workflows! 🎉