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全部 > AI技術 > AI編程 > AI代碼生成 > 總結Dockerfile自動生成規則

總結Dockerfile自動生成規則

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的輕量級鏡像作為基礎鏡像 FROM python:3.11-slim # 設置工作目錄 WORKDIR /app # 復制依賴文件到容器中 COPY requirements.txt . # 安裝依賴 RUN pip install --no-cache-dir -r requirements.txt # 復制應用代碼到容器中 COPY . . # 暴露應用運行的端口 EXPOSE 8000 # 設置容器啟動命令 CMD ["gunicorn", "app:app"] ``` ### 關鍵指令解釋 1. **FROM python:3.11-slim**: 指定基礎鏡像為Python 3.11的輕量級版本,適合生產環境。 2. **WORKDIR /app**: 設置工作目錄為`/app`,后續的所有命令將在此目錄下執行。 3. **COPY requirements.txt .**: 將本地的`requirements.txt`文件復制到容器的工作目錄中,以便安裝依賴。 4. **RUN pip install --no-cache-dir -r requirements.txt**: 安裝依賴,`--no-cache-dir`選項用于減少鏡像大小。 5. **COPY . .**: 將當前目錄下的所有應用代碼復制到容器的工作目錄中。 6. **EXPOSE 8000**: 聲明容器將監聽8000端口,供外部訪問。 7. **CMD ["gunicorn", "app:app"]**: 指定容器啟動時執行的命令,使用Gunicorn運行應用。 該Dockerfile符合Docker官方最佳實踐,適合用于生產部署的Python Web應用。

通義千問2.5-Coder-7B(阿里云百煉)

```markdown ## Dockerfile Source Code ```dockerfile # 使用Python 3.11-slim作為基礎鏡像 FROM python:3.11-slim as builder # 設置工作目錄 WORKDIR /app # 復制項目文件到工作目錄 COPY requirements.txt . # 安裝項目依賴 RUN pip install --no-cache-dir -r requirements.txt # 構建最終的應用鏡像 FROM python:3.11-slim # 設置工作目錄 WORKDIR /app # 從builder階段復制依賴安裝結果 COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages # 復制項目文件到工作目錄 COPY . . # 暴露8000端口 EXPOSE 8000 # 設置環境變量 ENV FLASK_APP=app.py # 啟動應用 CMD ["gunicorn", "-b", ":8000", "app:app"] ``` ## Command Explanations - **FROM python:3.11-slim as builder**: 使用輕量級的Python 3.11鏡像作為構建階段的基礎鏡像,并命名為`builder`。 - **WORKDIR /app**: 設置工作目錄為`/app`。 - **COPY requirements.txt .**: 將本地的`requirements.txt`文件復制到鏡像中的`/app`目錄。 - **RUN pip install --no-cache-dir -r requirements.txt**: 在構建階段安裝項目的依賴包。 - **FROM python:3.11-slim**: 重新使用輕量級的Python 3.11鏡像作為運行階段的基礎鏡像。 - **COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages**: 從構建階段復制已安裝的依賴包到運行階段鏡像中。 - **COPY . .**: 將本地的項目文件復制到鏡像中的`/app`目錄。 - **EXPOSE 8000**: 聲明容器將監聽8000端口。 - **ENV FLASK_APP=app.py**: 設置環境變量`FLASK_APP`為`app.py`。 - **CMD ["gunicorn", "-b", ":8000", "app:app"]**: 設置容器啟動時執行的命令,使用Gunicorn運行Flask應用。 ``` ### 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 精簡版基礎鏡像 FROM python:3.11-slim # 設置工作目錄 WORKDIR /app # 安裝系統依賴,升級 pip RUN apt-get update && \ apt-get install -y --no-install-recommends gcc && \ pip install --upgrade pip && \ rm -rf /var/lib/apt/lists/* # 復制依賴文件并安裝 Python 依賴 COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # 復制項目代碼 COPY . . # 暴露應用端口 EXPOSE 8000 # 啟動應用,建議在生產環境采用非 root 用戶(可選步驟,視需求添加新用戶) # USER nobody # 容器啟動時執行的命令 CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:8000"] ``` --- ## Command Explanations - FROM python:3.11-slim 選擇官方提供的 Python 3.11 精簡基礎鏡像,減少鏡像體積且安全可靠。 - WORKDIR /app 設置應用工作目錄為 /app,后續命令都在該目錄下執行。 - RUN apt-get update ... 更新系統倉庫,安裝 GCC 等構建型依賴,并升級 pip,最后清理無用緩存,減小鏡像大小。 - COPY requirements.txt . 僅復制依賴文件以便分層加速依賴安裝,無需每次代碼變更都重新安裝。 - RUN pip install --no-cache-dir -r requirements.txt 按照項目依賴文件 requirements.txt 安裝所有 Python 庫,并不緩存安裝包,節省空間。 - COPY . . 復制項目所有代碼到容器的 /app 目錄。 - EXPOSE 8000 聲明容器監聽 8000 端口,便于外部流量接入(需配合 gunicorn 參數設置綁定端口)。 - CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:8000"] 容器啟動時運行 gunicorn,基于 WSGI 啟動 app 應用監聽 8000 端口,生產環境推薦 gunicorn。 > 如需進一步安全加固,可添加創建非 root 用戶并采用 USER 指令啟動(生產建議)。 ```

模型名稱 模型提供商 響應總時長(s) 生成 token 數 首 token 延遲(s) 生成速率(tokens/s)
7.22
響應最快
594
0.96
82.23
速度最快
9.73
0
1.64
0
15.99
659
0.41
延遲最小
41.21
10.03
664
內容最多
1.73
66.18
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