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总结Dockerfile自动生成规则

gpt-4o(OpenAI)

```markdown # Dockerfile Source Code ```dockerfile # Use a multi-stage build for better efficiency and security # Stage 1: Builder FROM python:3.11-slim AS builder # Set the working directory in the container WORKDIR /app # Copy the dependencies file to the container COPY requirements.txt . # Install the dependencies RUN pip install --no-cache-dir -r requirements.txt # Stage 2: Runner FROM python:3.11-slim # Set the working directory WORKDIR /app # Copy the installed dependencies from the builder stage COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages # Copy the rest of the application code to the container COPY . . # Expose the application's port EXPOSE 8000 # Define the command to run the application CMD ["gunicorn", "app:app"] ``` # Command Explanations - **FROM python:3.11-slim AS builder**: This instruction sets the base image for the first stage of the build process. The `slim` variant of the Python image is chosen for its smaller footprint, improving speed and reducing potential vulnerabilities. - **WORKDIR /app**: Sets the working directory inside the container. All subsequent commands will be executed relative to this path. - **COPY requirements.txt .**: Copies the `requirements.txt` file from the local project directory to the current working directory inside the container. - **RUN pip install --no-cache-dir -r requirements.txt**: Installs the Python dependencies specified in `requirements.txt`. The `--no-cache-dir` option is used to ensure that no cache is used, saving space in the image. - **FROM python:3.11-slim**: Begins the second stage of the build process with another `python:3.11-slim` base image, aimed specifically at running the application. - **COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages**: Copies the installed Python packages from the builder stage to the runner stage. - **COPY . .**: Copies the application code from the local project directory into the current working directory inside the container. - **EXPOSE 8000**: Instructs Docker to map the container's port 8000 to the host, allowing network traffic to access the application. - **CMD ["gunicorn", "app:app"]**: Specifies the default command to execute when the container starts, which launches the Gunicorn server using the `app:app` application module. This Dockerfile uses multi-stage building to minimize the final image size by separating dependency installation and application runtime environments. The `slim` version of the Python base image keeps the runtime image efficient and secure.

claude3.5 sonnet 20241022(Anthropic)

### Dockerfile for Python Web Application ```dockerfile # 使用Python 3.11的轻量级镜像作为基础镜像 FROM python:3.11-slim # 设置工作目录 WORKDIR /app # 复制依赖文件到容器中 COPY requirements.txt . # 安装依赖 RUN pip install --no-cache-dir -r requirements.txt # 复制应用代码到容器中 COPY . . # 暴露应用运行的端口 EXPOSE 8000 # 设置容器启动命令 CMD ["gunicorn", "app:app"] ``` ### 关键指令解释 1. **FROM python:3.11-slim**: 指定基础镜像为Python 3.11的轻量级版本,适合生产环境。 2. **WORKDIR /app**: 设置工作目录为`/app`,后续的所有命令将在此目录下执行。 3. **COPY requirements.txt .**: 将本地的`requirements.txt`文件复制到容器的工作目录中,以便安装依赖。 4. **RUN pip install --no-cache-dir -r requirements.txt**: 安装依赖,`--no-cache-dir`选项用于减少镜像大小。 5. **COPY . .**: 将当前目录下的所有应用代码复制到容器的工作目录中。 6. **EXPOSE 8000**: 声明容器将监听8000端口,供外部访问。 7. **CMD ["gunicorn", "app:app"]**: 指定容器启动时执行的命令,使用Gunicorn运行应用。 该Dockerfile符合Docker官方最佳实践,适合用于生产部署的Python Web应用。

通义千问2.5-Coder-7B(阿里云百炼)

```markdown ## Dockerfile Source Code ```dockerfile # 使用Python 3.11-slim作为基础镜像 FROM python:3.11-slim as builder # 设置工作目录 WORKDIR /app # 复制项目文件到工作目录 COPY requirements.txt . # 安装项目依赖 RUN pip install --no-cache-dir -r requirements.txt # 构建最终的应用镜像 FROM python:3.11-slim # 设置工作目录 WORKDIR /app # 从builder阶段复制依赖安装结果 COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages # 复制项目文件到工作目录 COPY . . # 暴露8000端口 EXPOSE 8000 # 设置环境变量 ENV FLASK_APP=app.py # 启动应用 CMD ["gunicorn", "-b", ":8000", "app:app"] ``` ## Command Explanations - **FROM python:3.11-slim as builder**: 使用轻量级的Python 3.11镜像作为构建阶段的基础镜像,并命名为`builder`。 - **WORKDIR /app**: 设置工作目录为`/app`。 - **COPY requirements.txt .**: 将本地的`requirements.txt`文件复制到镜像中的`/app`目录。 - **RUN pip install --no-cache-dir -r requirements.txt**: 在构建阶段安装项目的依赖包。 - **FROM python:3.11-slim**: 重新使用轻量级的Python 3.11镜像作为运行阶段的基础镜像。 - **COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages**: 从构建阶段复制已安装的依赖包到运行阶段镜像中。 - **COPY . .**: 将本地的项目文件复制到镜像中的`/app`目录。 - **EXPOSE 8000**: 声明容器将监听8000端口。 - **ENV FLASK_APP=app.py**: 设置环境变量`FLASK_APP`为`app.py`。 - **CMD ["gunicorn", "-b", ":8000", "app:app"]**: 设置容器启动时执行的命令,使用Gunicorn运行Flask应用。 ``` ### Build Instructions 1. Save the above Dockerfile content to a file named `Dockerfile`. 2. Ensure your project files (including `requirements.txt`) are in the same directory as the Dockerfile. 3. Build the Docker image using the following command: ```sh docker build -t my-python-app . ``` 4. Run the Docker container using the following command: ```sh docker run -d -p 8000:8000 my-python-app ``` This will build and run your Python web application inside a Docker container, exposing port 8000 on the host machine.

gpt-4.1(OpenAI)

```markdown ## Dockerfile Source Code ```dockerfile # 使用官方 Python 3.11 精简版基础镜像 FROM python:3.11-slim # 设置工作目录 WORKDIR /app # 安装系统依赖,升级 pip RUN apt-get update && \ apt-get install -y --no-install-recommends gcc && \ pip install --upgrade pip && \ rm -rf /var/lib/apt/lists/* # 复制依赖文件并安装 Python 依赖 COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # 复制项目代码 COPY . . # 暴露应用端口 EXPOSE 8000 # 启动应用,建议在生产环境采用非 root 用户(可选步骤,视需求添加新用户) # USER nobody # 容器启动时执行的命令 CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:8000"] ``` --- ## Command Explanations - FROM python:3.11-slim 选择官方提供的 Python 3.11 精简基础镜像,减少镜像体积且安全可靠。 - WORKDIR /app 设置应用工作目录为 /app,后续命令都在该目录下执行。 - RUN apt-get update ... 更新系统仓库,安装 GCC 等构建型依赖,并升级 pip,最后清理无用缓存,减小镜像大小。 - COPY requirements.txt . 仅复制依赖文件以便分层加速依赖安装,无需每次代码变更都重新安装。 - RUN pip install --no-cache-dir -r requirements.txt 按照项目依赖文件 requirements.txt 安装所有 Python 库,并不缓存安装包,节省空间。 - COPY . . 复制项目所有代码到容器的 /app 目录。 - EXPOSE 8000 声明容器监听 8000 端口,便于外部流量接入(需配合 gunicorn 参数设置绑定端口)。 - CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:8000"] 容器启动时运行 gunicorn,基于 WSGI 启动 app 应用监听 8000 端口,生产环境推荐 gunicorn。 > 如需进一步安全加固,可添加创建非 root 用户并采用 USER 指令启动(生产建议)。 ```

模型名称 模型提供商 响应总时长(s) 生成 token 数 首 token 延迟(s) 生成速率(tokens/s)
7.22
响应最快
594
0.96
82.23
速度最快
9.73
0
1.64
0
15.99
659
0.41
延迟最小
41.21
10.03
664
内容最多
1.73
66.18
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