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为模型 API 编写 Dockerfile

逐步将 FastAPI 服务容器化

为模型 API 编写 Dockerfile 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Why a Dockerfile

Your model API runs on your laptop but breaks elsewhere. A Dockerfile is a recipe that builds the exact same environment everywhere.

Pick a Base Image

Every Dockerfile starts FROM a base image. A slim Python image gives you the interpreter without a bloated operating system.

FROM python:3.11-slim

Set a Working Directory

The WORKDIR instruction picks the folder your commands run in. It is created if missing, so later paths stay clean and predictable.

WORKDIR /app

Copy Requirements First

Copy requirements.txt alone before your code. Docker caches this layer, so installs are skipped when only your app changes.

COPY requirements.txt .

Install Dependencies

Run pip install inside the image. The --no-cache-dir flag skips the pip cache and keeps the final image a little smaller.

RUN pip install --no-cache-dir -r requirements.txt

Copy Your Code

Now COPY the rest of your project in. Doing this after the install means code edits do not bust the dependency cache layer.

COPY . .

Document the Port

The EXPOSE instruction declares which port your API listens on. It is documentation for humans and tools, not an actual open.

EXPOSE 8000

Define the Start Command

The CMD sets what runs when the container starts. Here it launches Uvicorn to serve your FastAPI model app.

CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

Bind to All Interfaces

Inside a container you must serve on 0.0.0.0, not 127.0.0.1. Otherwise the host cannot reach your model through the mapped port.

Build the Image

Turn the recipe into an image with docker build. The -t flag tags it with a name you can run and push later.

docker build -t model-api .

Layers Are Cached

Each instruction becomes a cached layer. Order them from least to most frequently changed so rebuilds stay fast.

Quick Check

Let us check the most cache-friendly build order.

Recap

You wrote a Dockerfile: base image, workdir, cached install, copy, expose, and a start command. Your model API now builds the same anywhere. 🐳

常见问题解答

「为模型 API 编写 Dockerfile」课时是免费的吗？

是的 — 「为模型 API 编写 Dockerfile」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「为模型 API 编写 Dockerfile」这节课中我会学到什么？

逐步将 FastAPI 服务容器化你通过在浏览器中直接运行的动手代码来练习 MLOps Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「为模型 API 编写 Dockerfile」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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