services:
  # ============================================================================
  # FlyGuard: Универсальный производственный запуск (NVIDIA GPU / CUDA 12)
  # Поддерживает Ada Lovelace (RTX 4070 Ti Super 16GB) / Ampere / CPU Fallback
  # ============================================================================

  # --- Главный сервис общего запуска конвейера по всем бэгам лидара ---
  pipeline:
    build:
      context: .
      dockerfile: Dockerfile.offline
    image: flyguard:latest
    container_name: flyguard-pipeline
    command: ["pipeline", "--all", "--verbose"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - FLYGUARD_DEVICE=auto
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Полный прогон тестового набора ядра и роутинга устройств ---
  test:
    image: flyguard:latest
    container_name: flyguard-test
    command: ["test"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - FLYGUARD_DEVICE=auto
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Диагностика доступности GPU и характеристик оборудования ---
  info:
    image: flyguard:latest
    container_name: flyguard-info
    command: ["info"]
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

  # --- Оценка метрик детекции (Folds Cross-Validation / AUC) ---
  evaluate:
    image: flyguard:latest
    container_name: flyguard-evaluate
    command: ["evaluate", "--mbon-dir", "artifacts/mbon_folds", "--device", "auto"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Генерация синтетического бенчмарка с GPU DoG и MBON ---
  benchmark:
    image: flyguard:latest
    container_name: flyguard-benchmark
    command: [
      "benchmark",
      "--memory", "artifacts/mushroom_body.npz",
      "--mbon-dir", "artifacts/mbon_folds",
      "--augment",
      "--device", "auto",
      "--out", "artifacts/benchmark_gpu.json"
    ]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Обучение MBON Readout на 50 000 клеток Кеньона на GPU ---
  train-mbon:
    image: flyguard:latest
    container_name: flyguard-train-mbon
    command: [
      "train-mbon",
      "--device", "auto",
      "--n-kc", "50000",
      "--active", "100",
      "--epochs", "100",
      "--save-folds", "artifacts/mbon_folds_50k",
      "--out", "artifacts/mbon_readout_50k.npz"
    ]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Обучение классификатора треков TrackReadout ---
  train-track:
    image: flyguard:latest
    container_name: flyguard-train-track
    command: [
      "train-track",
      "--device", "auto",
      "--epochs", "300",
      "--baseline",
      "--save-folds", "artifacts/track_folds",
      "--out", "artifacts/track_readout.npz"
    ]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    shm_size: '8gb'

  # --- Интерактивная Bash-сессия разработчика ---
  shell:
    image: flyguard:latest
    container_name: flyguard-shell
    command: ["bash"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    stdin_open: true
    tty: true
    shm_size: '8gb'

  # ============================================================================
  # СЕРВИСЫ ДЛЯ ЧИСТОГО CPU (Stand-alone без NVIDIA GPU)
  # ============================================================================
  cpu-test:
    build:
      context: .
      dockerfile: Dockerfile.offline
      target: cpu
    image: flyguard:cpu
    container_name: flyguard-cpu-test
    command: ["test"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - FLYGUARD_DEVICE=cpu
    shm_size: '2gb'

  cpu-pipeline:
    image: flyguard:cpu
    container_name: flyguard-cpu-pipeline
    command: ["pipeline", "--all"]
    volumes:
      - ./artifacts:/app/artifacts
      - ./data:/data:ro
    environment:
      - PYTHONUNBUFFERED=1
      - FLYGUARD_DATA=/data
      - FLYGUARD_DEVICE=cpu
    shm_size: '2gb'