Urtzi Alfaro caff49761d Fix training hang caused by nested database sessions and deadlocks
Root Cause:
The training process was hanging at the first progress update due to a
nested database session issue. The main trainer created a session and
repositories, then called prophet_manager.train_bakery_model() which
created another nested session with an advisory lock. This caused a
deadlock where:
1. Outer session had uncommitted UPDATE on model_training_logs
2. Inner session tried to acquire advisory lock
3. Neither could proceed, causing training to hang indefinitely

Changes Made:
1. prophet_manager.py:
   - Added optional 'session' parameter to train_bakery_model()
   - Refactored to use parent session if provided, otherwise create new one
   - Prevents nested session creation during training

2. hybrid_trainer.py:
   - Added optional 'session' parameter to train_hybrid_model()
   - Passes session to prophet_manager to maintain single session context

3. trainer.py:
   - Updated _train_single_product() to accept and pass session
   - Updated _train_all_models_enhanced() to accept and pass session
   - Pass db_session from main training context to all training methods
   - Added explicit db_session.flush() after critical progress update
   - This ensures updates are visible before acquiring locks

Impact:
- Eliminates nested session deadlocks
- Training now proceeds past initial progress update
- Maintains single database session context throughout training
- Prevents database transaction conflicts

Related Issues:
- Fixes training hang during onboarding process
- Not directly related to audit_metadata changes but exposed by them

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-05 16:13:32 +01:00
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🍞 Bakery IA - Multi-Service Architecture

Welcome to Bakery IA, an advanced AI-powered platform for bakery management and optimization. This project implements a microservices architecture with multiple interconnected services to provide comprehensive bakery management solutions.

🚀 Quick Start

Prerequisites

  • Docker Desktop with Kubernetes enabled
  • Docker Compose
  • Node.js (for frontend development)

Running the Application

  1. Clone the repository:

    git clone <repository-url>
    cd bakery-ia
    
  2. Set up environment variables:

    cp .env.example .env
    # Edit .env with your specific configuration
    
  3. Run with Docker Compose:

    docker-compose up --build
    
  4. Or run with Kubernetes (Docker Desktop):

    # Enable Kubernetes in Docker Desktop
    # Run the setup script
    ./scripts/setup-kubernetes-dev.sh
    

🏗️ Architecture Overview

The project follows a microservices architecture with the following main components:

  • Frontend: React-based dashboard for user interaction
  • Gateway: API gateway handling authentication and routing
  • Services: Multiple microservices handling different business domains
  • Infrastructure: Redis, RabbitMQ, PostgreSQL databases

🐳 Kubernetes Infrastructure

🛠️ Services

The project includes multiple services:

  • Auth Service: Authentication and authorization
  • Tenant Service: Multi-tenancy management
  • Sales Service: Sales processing
  • External Service: Integration with external systems
  • Training Service: AI model training
  • Forecasting Service: Demand forecasting
  • Notification Service: Notifications and alerts
  • Inventory Service: Inventory management
  • Recipes Service: Recipe management
  • Suppliers Service: Supplier management
  • POS Service: Point of sale
  • Orders Service: Order management
  • Production Service: Production planning
  • Alert Processor: Background alert processing

📊 Monitoring

The system includes comprehensive monitoring with:

  • Prometheus for metrics collection
  • Grafana for visualization
  • ELK stack for logging (planned)

🚀 Production Deployment

For production deployment on clouding.io with Kubernetes:

  1. Set up your clouding.io Kubernetes cluster
  2. Update image references to your container registry
  3. Configure production-specific values
  4. Deploy using the production kustomization:
    kubectl apply -k infrastructure/kubernetes/environments/production/
    

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

📄 License

This project is licensed under the MIT License.

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