services:
  webui:
    build:
      context: .
    image: yolo-train-webui:latest
    ports:
      # The training API has no built-in user accounts, so expose it locally only.
      - "127.0.0.1:8000:8000"
    volumes:
      - ./datasets:/workspace/datasets
      - ./runs:/workspace/runs
      - ./models:/workspace/models
      - ./models/.config:/root/.config/Ultralytics
    environment:
      # Use the Compose MLflow service so metadata and artifacts survive container recreation.
      YOLO_WEBUI_MLFLOW_TRACKING_URI: http://mlflow:5000
    depends_on:
      - mlflow
    # Uncomment the block below on Linux with NVIDIA GPU to pass the graphics card into the container:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: all
    #           capabilities: [gpu]
    ipc: host
    restart: unless-stopped

  mlflow:
    build:
      context: .
    image: yolo-train-webui:latest
    command:
      - mlflow
      - server
      - --backend-store-uri
      - sqlite:////mlflow/mlflow.db
      - --artifacts-destination
      - /mlflow/artifacts
      - --host
      - 0.0.0.0
      - --port
      - "5000"
      - --workers
      - "1"
      - --allowed-hosts
      - localhost:5000,127.0.0.1:5000,mlflow:5000
    ports:
      - "127.0.0.1:5000:5000"
    volumes:
      - ./mlflow:/mlflow
    restart: unless-stopped