基础设施: 统一依赖版本 + 新增 Docker 部署配置
- requirements.txt: GPU 依赖从注释改为正式声明，统一版本 PyTorch 2.1.2+cu121, TensorRT 8.6.1.6, ultralytics 8.3.5 NumPy 1.24→1.26.4, OpenCV 4.8.0.74→76, 新增 onnx/Pillow 等 - Dockerfile: 基于 nvcr.io/nvidia/tensorrt:23.08-py3 (CUDA 12.1 + cuDNN 8.9 + TRT 8.6) - docker-compose.yml: GPU 访问、host 网络、卷挂载、日志限制 - .dockerignore: 排除模型/数据/日志等大文件 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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29
.dockerignore
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.dockerignore
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.git
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__pycache__
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*.pyc
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*.pyo
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.idea
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.vscode
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.env
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.env.*
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!.env.example
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# 模型和数据通过卷挂载
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models/
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data/
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logs/
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# 测试文件
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tests/
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test_*.py
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pytest.ini
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# 文档
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docs/
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*.md
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!CLAUDE.md
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# 临时文件
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*.engine
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*.onnx
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*.pt
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Dockerfile
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Dockerfile
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# ============================================================
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# 基础镜像：NVIDIA TensorRT 23.08
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# 内含：CUDA 12.1.1 | cuDNN 8.9.3 | TensorRT 8.6.1.6 | Python 3.10
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# ============================================================
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FROM nvcr.io/nvidia/tensorrt:23.08-py3
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LABEL maintainer="AI Edge Architecture Team"
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LABEL description="Edge AI Inference Service - YOLOv11n + TensorRT"
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# 设置时区
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ENV TZ=Asia/Shanghai
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RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
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# 系统依赖（视频解码、OpenCV 运行时）
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ffmpeg \
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libsm6 \
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libxext6 \
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libgl1-mesa-glx \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# 先复制依赖文件，利用 Docker 层缓存
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COPY requirements.txt .
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# 安装 PyTorch（CUDA 12.1 版本）+ 其余依赖
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RUN pip install --no-cache-dir \
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torch==2.1.2 torchvision==0.16.2 \
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--index-url https://download.pytorch.org/whl/cu121 \
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&& pip install --no-cache-dir -r requirements.txt
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# 复制项目代码
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COPY __init__.py .
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COPY main.py .
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COPY algorithms.py .
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COPY build_engine.py .
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COPY config/ ./config/
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COPY core/ ./core/
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COPY utils/ ./utils/
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# 模型和数据通过卷挂载，不打入镜像
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# -v /path/to/models:/app/models
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# -v /path/to/data:/app/data
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# 日志目录
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RUN mkdir -p /app/logs /app/data
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EXPOSE 9001
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CMD ["python", "main.py"]
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docker-compose.yml
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docker-compose.yml
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version: "3.8"
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services:
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edge-inference:
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build: .
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image: edge-inference:latest
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container_name: edge-inference
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restart: always
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# GPU 访问
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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# 环境变量
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env_file:
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- .env
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# 卷挂载
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volumes:
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- ./models:/app/models # TensorRT 引擎文件
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- ./data:/app/data # SQLite + 截图缓存
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- ./logs:/app/logs # 运行日志
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- ./.env:/app/.env # 环境配置
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# 网络（需要访问摄像头 RTSP + 云端 API + Redis）
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network_mode: host
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# 健康检查
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healthcheck:
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test: ["CMD", "python", "-c", "import os; assert os.path.exists('/app/main.py')"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 30s
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# 日志限制
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logging:
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driver: json-file
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options:
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max-size: "50m"
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max-file: "5"
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# Edge_Inference_Service 依赖清单
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# 安装命令: pip install -r requirements.txt
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# 备注：所有版本均选择最稳定版本，经过大量验证
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# 环境要求: Python 3.10 | CUDA 12.1 | cuDNN 8.9 | TensorRT 8.6.1
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# Docker 基础镜像: nvcr.io/nvidia/tensorrt:23.08-py3
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# ============================================================
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# 核心依赖（必需）
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# GPU 推理依赖（TensorRT 8.6 + CUDA 12.1）
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# ============================================================
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# 视频处理 - OpenCV 4.8.0，最稳定的4.x版本
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opencv-python==4.8.0.74
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# PyTorch - CUDA 12.1 下最稳定版本
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# 安装命令: pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
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--extra-index-url https://download.pytorch.org/whl/cu121
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torch==2.1.2
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torchvision==0.16.2
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# 数值计算 - NumPy 1.24.0，Python 3.8-3.11完美兼容
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numpy==1.24.0
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# TensorRT Python 绑定（NGC 镜像已预装，裸机需手动安装）
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tensorrt==8.6.1.6
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pycuda==2023.1.1
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# YOLO11 目标检测框架
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ultralytics==8.3.5
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# ONNX 模型转换与优化
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onnx==1.16.0
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onnxsim==0.4.36
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onnxruntime-gpu==1.17.1
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# ============================================================
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# 核心依赖
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# ============================================================
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# 视频处理
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opencv-python==4.8.0.76
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# 数值计算（锁定 1.x，避开 NumPy 2.0 破坏性变更）
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numpy==1.26.4
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# 图像处理
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Pillow==10.2.0
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# ============================================================
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# 数据库依赖
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# ============================================================
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# ORM框架 - SQLAlchemy 2.0.23，长期支持稳定版
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# ORM 框架
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sqlalchemy==2.0.23
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# MySQL驱动 - PyMySQL 1.1.0，成熟稳定版本
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# MySQL 驱动
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pymysql==1.1.0
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# ============================================================
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# 消息队列与缓存
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# ============================================================
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# MQTT客户端 - Paho-MQTT 1.6.1，1.x最终稳定版
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# MQTT 客户端（1.x 最终稳定版）
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paho-mqtt==1.6.1
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# Redis客户端 - Redis 4.6.0，4.x最终稳定版
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# Redis 客户端
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redis==4.6.0
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# 腾讯云COS SDK - 用于截图上传
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# 腾讯云 COS SDK（截图上传）
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cos-python-sdk-v5>=1.9.30
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# ============================================================
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# 工具库
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# ============================================================
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# YAML解析 - PyYAML 6.0.1，安全稳定版
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pyyaml==6.0.1
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requests==2.31.0
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psutil==5.9.8
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python-dotenv==1.0.1
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# ============================================================
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# 测试框架
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# 测试依赖
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# ============================================================
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# 单元测试 - PyTest 7.4.4，7.x最终稳定版
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pytest==7.4.4
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# 覆盖率报告 - PyTest-Cov 4.1.0，成熟稳定版
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pytest-cov==4.1.0
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# ============================================================
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# 可选依赖（按需安装）
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# ============================================================
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# GPU推理框架（需要CUDA 12.1环境）
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# tensorrt==8.6.1.6
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# pycuda==2023.1.1
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# YOLOv8目标检测（按需安装）
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# ultralytics==8.0.228
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