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bf5ddb3e7a
- 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>
52 lines
1.4 KiB
Docker
52 lines
1.4 KiB
Docker
# ============================================================
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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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