dagnostics/docker-compose.training.yml at master · pesnik/dagnostics · GitHub

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# Docker Compose for Training Environment
# Separate training container with GPU support

version: '3.8'

services:
  dagnostics-training:
    build:
      context: .
      dockerfile: Dockerfile.training
    container_name: dagnostics-training
    volumes:
      # Mount data directories
      - ./data:/app/data
      - ./models:/app/models
      - ./config:/app/config
      - ./logs:/app/logs
      # Mount source for development
      - ./src:/app/src
    environment:
      - DAGNOSTICS_CONFIG=/app/config/config.yaml
      - PYTHONPATH=/app/src
      - CUDA_VISIBLE_DEVICES=0  # Use first GPU
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    command: >
      bash -c "
        echo 'DAGnostics Training Environment Ready' &&
        echo 'Available commands:' &&
        echo '  python -m dagnostics.training.dataset_generator' &&
        echo '  python -m dagnostics.training.fine_tuner' &&
        echo '  dagnostics training generate-dataset' &&
        echo '  dagnostics training train-model' &&
        tail -f /dev/null
      "
    networks:
      - dagnostics-net

  # Main DAGnostics service (no ML deps)
  dagnostics-main:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: dagnostics-main
    ports:
      - "8000:8000"  # API
      - "8080:8080"  # Web UI
    volumes:
      - ./data:/app/data
      - ./config:/app/config
      - ./logs:/app/logs
      - ./src:/app/src
    environment:
      - DAGNOSTICS_CONFIG=/app/config/config.yaml
      - PYTHONPATH=/app/src
    command: >
      bash -c "
        echo 'Starting DAGnostics Main Service...' &&
        dagnostics daemon start
      "
    networks:
      - dagnostics-net
    depends_on:
      - dagnostics-training

networks:
  dagnostics-net:
    driver: bridge