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构建 Bento 并将其容器化

将代码、模型和依赖项打包进可部署的 Bento

构建 Bento 并将其容器化 是 CoddyKit 上的免费 MLOps Academy 课时。 这是第 4 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。

What a Bento Is

A Bento is the deployable bundle BentoML builds. It packs your service code, the model, and the exact dependencies into one archive. 📦

The bentofile

You describe the build in a bentofile.yaml. It names the service entry point and lists what to include.

service: "service:svc"

Include Your Files

You tell BentoML which source files to bundle. The include key takes globs so only the code you need ships.

include:
  - "*.py"

Pin Python Packages

Dependencies live right in the bentofile. Under python you list packages so the build resolves them into the bundle.

python:
  packages:
    - scikit-learn

Build the Bento

One command assembles everything. bentoml build reads the bentofile and produces a versioned Bento in your store.

bentoml build

List Your Bentos

Built Bentos show up alongside your models. Run bentoml list to see each one with its name and version tag.

bentoml list

Why Containerize

A Bento still needs a runtime. Turning it into a Docker image makes it run identically on any machine or cloud. 🐳

One Command to an Image

BentoML writes the Dockerfile for you. bentoml containerize takes a Bento tag and builds a ready-to-run image.

bentoml containerize iris_classifier:latest

Run the Container

Now it is a normal image. You start it with docker run, mapping the port so requests reach the service inside.

docker run -p 3000:3000 iris_classifier:latest

Ship It Anywhere

That image is your deployable artifact. Push it to a registry and any cloud runtime or Kubernetes cluster can pull and serve it. 🚀

Set a Docker Base Image

You can control the runtime under your image. The docker section of the bentofile lets you pick a Python version or base.

docker:
  python_version: "3.11"

Quick Check

You ran bentoml build and got a Bento. What does bentoml containerize add on top of that?

Recap

You defined a bentofile, ran bentoml build to bundle code and deps, then containerized it into a Docker image ready to ship to any cloud. End to end! 🙌

常见问题解答

「构建 Bento 并将其容器化」课时是免费的吗？

是的 — 「构建 Bento 并将其容器化」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。

「构建 Bento 并将其容器化」这节课中我会学到什么？

将代码、模型和依赖项打包进可部署的 Bento 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 4 节课，共 4 节。

「构建 Bento 并将其容器化」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

  1. 将模型保存到 Bento 存储库
  2. 定义服务及其 API
  3. 启用自适应微批处理
  4. 构建 Bento 并将其容器化
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