Dockerfile

@@ -1,50 +1,96 @@
 # ============================================================================
-# FlyGuard: ML-ядро обнаружения препятствий в тоннеле метро (Кейс 05, ЛЦТ-2026)
+# FlyGuard: Bionic Obstacle Detection Core (LCT-2026, Case 05)
+# Hesai Pandar128 LiDAR (128 beams, 10 Hz / 100ms)
+# Universal Production Image: NVIDIA CUDA 12.x / Ada Lovelace / Graceful CPU Fallback
 # ============================================================================
-FROM python:3.11-slim-bookworm AS base
 
-# System configuration & environment
+# ----------------------------------------------------------------------------
+# Stage 1: Optional lightweight CPU-only image (build with: --target cpu)
+# ----------------------------------------------------------------------------
+FROM python:3.11-slim-bookworm AS cpu
+
 ENV DEBIAN_FRONTEND=noninteractive \
     PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
     PYTHONPATH="/app:/app/tools" \
-    FLYGUARD_DATA="/data"
+    FLYGUARD_DATA="/data" \
+    FLYGUARD_DEVICE="cpu"
 
-# Install runtime system dependencies (libgomp1 is required by LightGBM/OpenMP)
 RUN apt-get update && apt-get install -y --no-install-recommends \
     libgomp1 \
     ca-certificates \
     && rm -rf /var/lib/apt/lists/*
 
-# Create non-root user and directories
+RUN useradd -m -u 1000 -s /bin/bash flyguard && \
+    mkdir -p /app /data /app/artifacts && \
+    chown -R flyguard:flyguard /app /data
+
+WORKDIR /app
+COPY --chown=flyguard:flyguard requirements.txt /app/
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+COPY --chown=flyguard:flyguard . /app/
+RUN chmod +x /app/docker-entrypoint.sh
+
+USER flyguard
+VOLUME ["/data", "/app/artifacts"]
+ENTRYPOINT ["/app/docker-entrypoint.sh"]
+CMD ["default"]
+
+
+# ----------------------------------------------------------------------------
+# Stage 2: Universal Production Image with NVIDIA GPU acceleration & CPU fallback
+# (Default target when building with: docker build -t flyguard:latest .)
+# ----------------------------------------------------------------------------
+FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 AS production
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONPATH="/app:/app/tools" \
+    FLYGUARD_DATA="/data" \
+    FLYGUARD_DEVICE="auto" \
+    NVIDIA_VISIBLE_DEVICES=all \
+    NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# Install Python 3.11, pip, and system runtime libraries (OpenMP for LightGBM/Torch)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common \
+    ca-certificates \
+    libgomp1 \
+    curl \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update && apt-get install -y --no-install-recommends \
+    python3.11 \
+    python3.11-distutils \
+    && curl -sS https://bootstrap.pypa.io/get-pip.py | python3.11 \
+    && ln -sf /usr/bin/python3.11 /usr/bin/python3 \
+    && ln -sf /usr/bin/python3.11 /usr/bin/python \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Create non-root user for container security compliance
 RUN useradd -m -u 1000 -s /bin/bash flyguard && \
     mkdir -p /app /data /app/artifacts && \
     chown -R flyguard:flyguard /app /data
 
 WORKDIR /app
 
-# Cache layer: copy only requirements first
+# Cache layer: install Python dependencies with PyTorch CUDA 12.1+ wheels
-COPY --chown=flyguard:flyguard requirements.txt /app/
+COPY --chown=flyguard:flyguard requirements-gpu.txt /app/
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel && \
+    pip install --no-cache-dir -r requirements-gpu.txt
 
-# Install python dependencies
+# Copy source code and artifacts
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir -r requirements.txt
-
-# Copy application source code
 COPY --chown=flyguard:flyguard . /app/
-
-# Make sure entrypoint script is executable
 RUN chmod +x /app/docker-entrypoint.sh
 
-# Switch to non-root user for security
 USER flyguard
-
-# Volume mount points
 VOLUME ["/data", "/app/artifacts"]
 
-# Container healthcheck
+# Container healthcheck: verifies Python runtime and graceful device detection
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
-    CMD python3 -c "import flyguard, numpy, scipy, lightgbm; print('healthy')" || exit 1
+    CMD python3 -c "import flyguard; from flyguard.device import get_device_info; print('healthy', get_device_info())" || exit 1
 
 ENTRYPOINT ["/app/docker-entrypoint.sh"]
-CMD ["test"]
+CMD ["default"]

Dockerfile.gpu

@@ -1,15 +1,16 @@
 # ============================================================================
 # FlyGuard GPU: NVIDIA RTX / CUDA 12 Production Image (Кейс 05, ЛЦТ-2026)
 # Архитектура: Ada Lovelace (RTX 4070 Ti Super 16GB) / Ampere / Turing
+# Полная совместимость с Dockerfile (Stage: production)
 # ============================================================================
-FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 AS base
+FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04 AS production
 
-# System configuration & environment
 ENV DEBIAN_FRONTEND=noninteractive \
     PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
     PYTHONPATH="/app:/app/tools" \
     FLYGUARD_DATA="/data" \
+    FLYGUARD_DEVICE="auto" \
     NVIDIA_VISIBLE_DEVICES=all \
     NVIDIA_DRIVER_CAPABILITIES=compute,utility
 
@@ -18,11 +19,11 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     software-properties-common \
     ca-certificates \
     libgomp1 \
+    curl \
     && add-apt-repository -y ppa:deadsnakes/ppa \
     && apt-get update && apt-get install -y --no-install-recommends \
     python3.11 \
     python3.11-distutils \
-    curl \
     && curl -sS https://bootstrap.pypa.io/get-pip.py | python3.11 \
     && ln -sf /usr/bin/python3.11 /usr/bin/python3 \
     && ln -sf /usr/bin/python3.11 /usr/bin/python \
@@ -44,15 +45,13 @@ RUN pip install --no-cache-dir --upgrade pip setuptools wheel && \
 
 # Copy application source code
 COPY --chown=flyguard:flyguard . /app/
-
 RUN chmod +x /app/docker-entrypoint.sh
 
 USER flyguard
-
 VOLUME ["/data", "/app/artifacts"]
 
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
-    CMD python3 -c "import flyguard, torch; print('healthy', torch.cuda.is_available())" || exit 1
+    CMD python3 -c "import flyguard; from flyguard.device import get_device_info; print('healthy', get_device_info())" || exit 1
 
 ENTRYPOINT ["/app/docker-entrypoint.sh"]
-CMD ["test"]
+CMD ["default"]

README.md

@@ -99,20 +99,27 @@ artifacts/         обученные модели
 
 ## Как запустить
 
-### Вариант 1. Запуск через Docker / Docker Compose
+### Вариант 1. Запуск через Docker / Docker Compose (NVIDIA GPU + CPU Fallback)
+
+Единый универсальный production-контейнер с поддержкой CUDA 12 и автоматическим переключением на CPU при отсутствии GPU:
 
 ```bash
-# Тесты на CPU
+# Сборка универсального образа с поддержкой NVIDIA GPU
-docker compose run --rm test
+./docker-run.sh build
+# (или напрямую: docker build -t flyguard:latest .)
 
-# Тесты с ускорением NVIDIA GPU
+# 1. Общий запуск конвейера по всем бэгам лидара (с GPU-ускорением или CPU fallback)
-docker compose run --rm gpu-test
+./docker-run.sh run
+# (или: docker run --rm -it --gpus all -v /path/to/data:/data:ro flyguard)
 
-# Обучение MBON на 50 000 клеток Кеньона на GPU
+# 2. Запуск через Docker Compose (главный сервис)
-docker compose run --rm train-mbon-gpu
+docker compose up
-
+# или отдельными сервисами:
-# Прогон бенчмарка с аугментациями на GPU
+docker compose run --rm test         # Прогон 40+ unit-тестов
-docker compose run --rm gpu-benchmark
+docker compose run --rm info         # Диагностика GPU и CUDA
+docker compose run --rm train-mbon   # Обучение MBON на 50k клеток Кеньона на GPU
+docker compose run --rm benchmark    # Прогон бенчмарка на GPU
+docker compose run --rm evaluate     # Оценка метрик детекции
 ```
 
 ### Вариант 2. Локальный запуск (Python)

docker-compose.yml

@@ -1,12 +1,38 @@
 services:
-  # ==========================================================================
+  # ============================================================================
-  # CPU СЕРВИСЫ (Стандартный запуск без GPU / Standalone)
+  # FlyGuard: Универсальный производственный запуск (NVIDIA GPU / CUDA 12)
-  # ==========================================================================
+  # Поддерживает Ada Lovelace (RTX 4070 Ti Super 16GB) / Ampere / CPU Fallback
-  test:
+  # ============================================================================
+
+  # --- Главный сервис общего запуска конвейера по всем бэгам лидара ---
+  pipeline:
     build:
       context: .
       dockerfile: Dockerfile
     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:
@@ -15,75 +41,39 @@ services:
     environment:
       - PYTHONUNBUFFERED=1
       - FLYGUARD_DATA=/data
-    shm_size: '2gb'
+      - 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"]
+    command: ["evaluate", "--mbon-dir", "artifacts/mbon_folds", "--device", "auto"]
-    volumes:
-      - ./artifacts:/app/artifacts
-      - ./data:/data:ro
-    environment:
-      - PYTHONUNBUFFERED=1
-      - FLYGUARD_DATA=/data
-    shm_size: '2gb'
-
-  benchmark:
-    image: flyguard:latest
-    container_name: flyguard-benchmark
-    command: [
-      "benchmark",
-      "--memory", "artifacts/mushroom_body.npz",
-      "--mbon-dir", "artifacts/mbon_folds",
-      "--augment",
-      "--out", "artifacts/benchmark.json"
-    ]
-    volumes:
-      - ./artifacts:/app/artifacts
-      - ./data:/data:ro
-    environment:
-      - PYTHONUNBUFFERED=1
-      - FLYGUARD_DATA=/data
-    shm_size: '2gb'
-
-  pipeline:
-    image: flyguard:latest
-    container_name: flyguard-pipeline
-    command: ["pipeline", "--all", "--memory", "artifacts/mushroom_body.npz"]
-    volumes:
-      - ./artifacts:/app/artifacts
-      - ./data:/data:ro
-    environment:
-      - PYTHONUNBUFFERED=1
-      - FLYGUARD_DATA=/data
-    shm_size: '2gb'
-
-  shell:
-    image: flyguard:latest
-    container_name: flyguard-shell
-    command: ["bash"]
-    volumes:
-      - ./artifacts:/app/artifacts
-      - ./data:/data:ro
-    environment:
-      - PYTHONUNBUFFERED=1
-      - FLYGUARD_DATA=/data
-    stdin_open: true
-    tty: true
-    shm_size: '2gb'
-
-  # ==========================================================================
-  # NVIDIA GPU СЕРВИСЫ (NVIDIA GeForce RTX 4070 Ti Super 16GB / CUDA 12.x)
-  # Использование: docker compose run --rm <имя-сервиса>
-  # ==========================================================================
-  gpu-test:
-    build:
-      context: .
-      dockerfile: Dockerfile.gpu
-    image: flyguard:gpu
-    container_name: flyguard-gpu-test
-    command: ["test"]
     volumes:
       - ./artifacts:/app/artifacts
       - ./data:/data:ro
@@ -101,15 +91,16 @@ services:
               capabilities: [gpu]
     shm_size: '8gb'
 
-  gpu-benchmark:
+  # --- Генерация синтетического бенчмарка с GPU DoG и MBON ---
-    image: flyguard:gpu
+  benchmark:
-    container_name: flyguard-gpu-benchmark
+    image: flyguard:latest
+    container_name: flyguard-benchmark
     command: [
       "benchmark",
       "--memory", "artifacts/mushroom_body.npz",
       "--mbon-dir", "artifacts/mbon_folds",
       "--augment",
-      "--device", "cuda",
+      "--device", "auto",
       "--out", "artifacts/benchmark_gpu.json"
     ]
     volumes:
@@ -129,33 +120,13 @@ services:
               capabilities: [gpu]
     shm_size: '8gb'
 
-  gpu-evaluate:
+  # --- Обучение MBON Readout на 50 000 клеток Кеньона на GPU ---
-    image: flyguard:gpu
+  train-mbon:
-    container_name: flyguard-gpu-evaluate
+    image: flyguard:latest
-    command: ["evaluate", "--mbon-dir", "artifacts/mbon_folds", "--device", "cuda"]
+    container_name: flyguard-train-mbon
-    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'
-
-  train-mbon-gpu:
-    image: flyguard:gpu
-    container_name: flyguard-train-mbon-gpu
     command: [
       "train-mbon",
-      "--device", "cuda",
+      "--device", "auto",
       "--n-kc", "50000",
       "--active", "100",
       "--epochs", "100",
@@ -179,12 +150,13 @@ services:
               capabilities: [gpu]
     shm_size: '8gb'
 
-  train-track-gpu:
+  # --- Обучение классификатора треков TrackReadout ---
-    image: flyguard:gpu
+  train-track:
-    container_name: flyguard-train-track-gpu
+    image: flyguard:latest
+    container_name: flyguard-train-track
     command: [
       "train-track",
-      "--device", "cuda",
+      "--device", "auto",
       "--epochs", "300",
       "--baseline",
       "--save-folds", "artifacts/track_folds",
@@ -207,37 +179,10 @@ services:
               capabilities: [gpu]
     shm_size: '8gb'
 
-  gpu-pipeline:
+  # --- Интерактивная Bash-сессия разработчика ---
-    image: flyguard:gpu
+  shell:
-    container_name: flyguard-gpu-pipeline
+    image: flyguard:latest
-    command: [
+    container_name: flyguard-shell
-      "pipeline",
-      "--all",
-      "--memory", "artifacts/mushroom_body.npz",
-      "--readout", "artifacts/mbon_readout.npz",
-      "--device", "cuda",
-      "--verbose"
-    ]
-    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'
-
-  gpu-shell:
-    image: flyguard:gpu
-    container_name: flyguard-gpu-shell
     command: ["bash"]
     volumes:
       - ./artifacts:/app/artifacts
@@ -257,3 +202,36 @@ services:
     stdin_open: true
     tty: true
     shm_size: '8gb'
+
+  # ============================================================================
+  # СЕРВИСЫ ДЛЯ ЧИСТОГО CPU (Stand-alone без NVIDIA GPU)
+  # ============================================================================
+  cpu-test:
+    build:
+      context: .
+      dockerfile: Dockerfile
+      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'

docker-entrypoint.sh

@@ -1,47 +1,116 @@
 #!/usr/bin/env bash
 set -e
 
-# Default to run_tests if no arguments provided
+# ==============================================================================
-if [ $# -eq 0 ]; then
+# FlyGuard: Bionic Obstacle Detection Core (LCT-2026, Case 05)
-    echo "==> FlyGuard container started without arguments. Running verification tests..."
+# Hesai Pandar128 LiDAR (128 beams, 10 Hz / 100ms)
+# Universal Entrypoint: NVIDIA GPU Acceleration (CUDA 12.x) with CPU Fallback
+# ==============================================================================
+
+print_banner() {
+    echo "============================================================================"
+    echo "  FlyGuard · Блиц-обнаружение препятствий в тоннеле метро (Кейс 05, ЛЦТ-2026)"
+    echo "  Hesai Pandar128 (128 лучей, 10 Гц / 100 мс) · NVIDIA CUDA 12 / CPU Fallback"
+    echo "============================================================================"
+}
+
+print_device_info() {
+    python3 -c "
+try:
+    import flyguard.device as dev
+    info = dev.get_device_info()
+    if info['cuda_available']:
+        print(f'==> [HARDWARE] NVIDIA GPU: {info[\"device_name\"]} (Compute {info[\"compute_capability\"]}, {info[\"total_memory_gb\"]} GB VRAM)')
+        print(f'==> [ACCELERATION] CUDA 12: АКТИВНО (PyTorch {info[\"torch_version\"]})')
+    else:
+        print('==> [HARDWARE] CPU Mode: CUDA недоступна или не передана в контейнер.')
+        print('==> [FALLBACK] Активирован автоматический Graceful Fallback на CPU.')
+except Exception as e:
+    print(f'==> Проверка окружения: {e}')
+"
+}
+
+CMD="${1:-default}"
+
+case "$CMD" in
+    default|run)
+        print_banner
+        print_device_info
+        DATA_DIR="${FLYGUARD_DATA:-/data}"
+        FOUND_BAG=$(find "$DATA_DIR" -name '*.db3' 2>/dev/null | head -n 1 || true)
+        if [ -d "$DATA_DIR" ] && [ -n "$FOUND_BAG" ]; then
+            echo "==> Обнаружены бэги лидара в $DATA_DIR. Запуск конвейера FlyGuard по всем записям..."
+            exec python3 tools/run_pipeline.py --all \
+                --memory artifacts/mushroom_body.npz \
+                --readout artifacts/mbon_readout.npz \
+                --device auto \
+                --verbose
+        else
+            echo "==> В $DATA_DIR не найдены файлы .db3 (датасет не смонтирован через -v /путь/к/data:/data)."
+            echo "==> Выполняется системный прогон верификационных тестов и роутинга..."
             exec python3 tests/run_tests.py
         fi
-
+        ;;
-# Subcommand shortcuts
+    pipeline|run-pipeline)
-case "$1" in
+        shift
+        print_banner
+        print_device_info
+        exec python3 tools/run_pipeline.py \
+            --memory artifacts/mushroom_body.npz \
+            --readout artifacts/mbon_readout.npz \
+            --device auto "$@"
+        ;;
+    benchmark|make-benchmark)
+        shift
+        print_banner
+        print_device_info
+        exec python3 tools/make_benchmark.py \
+            --memory artifacts/mushroom_body.npz \
+            --mbon-dir artifacts/mbon_folds \
+            --device auto "$@"
+        ;;
+    evaluate)
+        shift
+        print_banner
+        print_device_info
+        exec python3 tools/evaluate.py \
+            --mbon-dir artifacts/mbon_folds \
+            --device auto "$@"
+        ;;
+    train-mbon)
+        shift
+        print_banner
+        print_device_info
+        exec python3 tools/train_mbon.py \
+            --device auto "$@"
+        ;;
+    train-track)
+        shift
+        print_banner
+        print_device_info
+        exec python3 tools/train_track.py \
+            --device auto "$@"
+        ;;
     test|tests|run-tests)
         shift
+        print_banner
+        print_device_info
         exec python3 tests/run_tests.py "$@"
         ;;
     pytest)
         shift
         exec pytest tests "$@"
         ;;
-    pipeline|run-pipeline)
+    info|device|gpu|gpu-info)
-        shift
+        print_banner
-        exec python3 tools/run_pipeline.py "$@"
+        python3 -c "
-        ;;
+import json
-    benchmark|make-benchmark)
+import flyguard.device as dev
-        shift
+print(json.dumps(dev.get_device_info(), indent=2, ensure_ascii=False))
-        exec python3 tools/make_benchmark.py "$@"
+"
-        ;;
-    evaluate)
-        shift
-        exec python3 tools/evaluate.py "$@"
-        ;;
-    train-mbon)
-        shift
-        exec python3 tools/train_mbon.py "$@"
-        ;;
-    train-track)
-        shift
-        exec python3 tools/train_track.py "$@"
-        ;;
-    plot|plot-benchmark)
-        shift
-        exec python3 tools/plot_benchmark.py "$@"
         ;;
     bash|sh)
+        shift
         exec /bin/bash "$@"
         ;;
     *)

docker-run.sh

@@ -0,0 +1,134 @@
+#!/usr/bin/env bash
+# ==============================================================================
+# FlyGuard: Удобный скрипт сборки и запуска Docker-контейнера
+# Автоматически определяет наличие NVIDIA GPU и передает флаги --gpus all
+# ==============================================================================
+set -e
+
+DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+IMAGE_NAME="flyguard:latest"
+DATA_DIR="${FLYGUARD_DATA:-$DIR/data}"
+ARTIFACTS_DIR="$DIR/artifacts"
+
+mkdir -p "$DATA_DIR" "$ARTIFACTS_DIR"
+
+# Определение наличия драйвера NVIDIA
+GPU_FLAG=""
+if command -v nvidia-smi &>/dev/null; then
+    GPU_FLAG="--gpus all"
+fi
+
+usage() {
+    echo "Использование: $0 [команда]"
+    echo ""
+    echo "Команды:"
+    echo "  build         Собрать универсальный образ с поддержкой NVIDIA GPU ($IMAGE_NAME)"
+    echo "  build-cpu     Собрать облегченный CPU-образ (flyguard:cpu)"
+    echo "  run           Общий запуск конвейера (по умолчанию обработка всех бэгов)"
+    echo "  pipeline      Запуск конвейера с дополнительными аргументами"
+    echo "  test          Запуск всех 40+ тестов верификации"
+    echo "  benchmark     Генерация бенчмарка с ускорением на GPU"
+    echo "  evaluate      Оценка качества моделей и кросс-валидация"
+    echo "  train-mbon    Обучение клеток Кеньона (50 000 KC) на GPU"
+    echo "  train-track   Обучение классификатора треков"
+    echo "  info          Вывод телеметрии и статуса GPU из контейнера"
+    echo "  shell         Интерактивная командная строка (bash) внутри контейнера"
+    echo ""
+    echo "Примеры:"
+    echo "  $0 build"
+    echo "  $0 run"
+    echo "  $0 pipeline --all --verbose"
+    echo "  $0 train-mbon --device cuda --n-kc 50000"
+}
+
+CMD="${1:-run}"
+
+case "$CMD" in
+    build)
+        echo "==> Сборка универсального образа FlyGuard ($IMAGE_NAME)..."
+        docker build -t "$IMAGE_NAME" -f "$DIR/Dockerfile" "$DIR"
+        ;;
+    build-cpu)
+        echo "==> Сборка облегченного CPU-образа FlyGuard (flyguard:cpu)..."
+        docker build --target cpu -t flyguard:cpu -f "$DIR/Dockerfile" "$DIR"
+        ;;
+    run|default)
+        shift 2>/dev/null || true
+        echo "==> Запуск контейнера FlyGuard (GPU: ${GPU_FLAG:-CPU Fallback})..."
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" "$@"
+        ;;
+    pipeline)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" pipeline "$@"
+        ;;
+    test|tests)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" test "$@"
+        ;;
+    benchmark)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" benchmark "$@"
+        ;;
+    evaluate)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" evaluate "$@"
+        ;;
+    train-mbon)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" train-mbon "$@"
+        ;;
+    train-track)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" train-track "$@"
+        ;;
+    info|gpu|device)
+        docker run --rm $GPU_FLAG "$IMAGE_NAME" info
+        ;;
+    shell|bash)
+        shift
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" bash "$@"
+        ;;
+    help|--help|-h)
+        usage
+        exit 0
+        ;;
+    *)
+        docker run --rm -it $GPU_FLAG \
+            -v "$DATA_DIR":/data:ro \
+            -v "$ARTIFACTS_DIR":/app/artifacts \
+            --shm-size=8g \
+            "$IMAGE_NAME" "$@"
+        ;;
+esac

tools/run_pipeline.py

@@ -80,8 +80,16 @@ def main() -> None:
     readout = MbonReadout.load(args.readout) if args.readout else None
     track_readout = TrackReadout.load(args.track_readout) if args.track_readout else None
 
+    if not args.all and not args.bag:
+        args.all = True
+
     params = Params(fov_deg=args.fov, device=args.device)
-    bags = find_bags(B.DATA / "for_hackathon") if args.all else [args.bag]
+    bag_root = (B.DATA / "for_hackathon") if (B.DATA / "for_hackathon").exists() else B.DATA
+    bags = find_bags(bag_root) if args.all else ([args.bag] if args.bag else [])
+    if not bags:
+        print(f"Внимание: бэги не найдены в {bag_root}. Убедитесь, что каталог смонтирован в FLYGUARD_DATA.")
+        return
+
     for b in bags:
         run(b, params, memory, readout=readout, track_readout=track_readout,
             limit=args.limit, verbose=args.verbose)