And you get all the editor support with that resulting object.

An example from the official Pydantic docs:

{* ../../docs_src/python_types/tutorial011_py310.py *}

/// info

To learn more about <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic, check its docs</a>.

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

          may be construed as permission to assert or imply that You
          are, or that Your use of the Licensed Material is, connected
          with, or sponsored, endorsed, or granted official status by,
          the Licensor or others designated to receive attribution as
          provided in Section 3(a)(1)(A)(i).

  b. Other rights.

       1. Moral rights, such as the right of integrity, are not

Later, for your production application, you might want to use a database server like **PostgreSQL**.

/// tip

### Imagen Base de Docker { #base-docker-image }

Solía haber una imagen official de Docker de FastAPI: <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi-docker</a>. Pero ahora está obsoleta. ⛔️

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

That's why, as said in the official website:

> Requests is one of the most downloaded Python packages of all time

The way you use it is very simple. For example, to do a `GET` request, you would write:

```Python

  The "source code" for a work means the preferred form of the work
for making modifications to it.  "Object code" means any non-source
form of a work.

  A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.

当然，在某些**特殊情况**，你可能希望拥有 **一个容器**，其中包含 **Gunicorn 进程管理器**，并在其中启动多个 **Uvicorn worker进程**。

在这些情况下，你可以使用 **官方 Docker 镜像**，其中包含 **Gunicorn** 作为运行多个 **Uvicorn 工作进程** 的进程管理器，以及一些默认设置来根据当前情况调整工作进程数量 自动CPU核心。 我将在下面的 [Gunicorn - Uvicorn 官方 Docker 镜像](#official-docker-image-with-gunicorn-uvicorn) 中告诉你更多相关信息。

下面一些什么时候这种做法有意义的示例：


#### 一个简单的应用程序

如果你的应用程序**足够简单**，你不需要（至少现在不需要）过多地微调进程数量，并且你可以使用自动默认值，那么你可能需要容器中的进程管理器 （使用官方 Docker 镜像），并且你在**单个服务器**而不是集群上运行它。

*   TensorFlow Windows Build:

    *   Clang is now the default compiler to build TensorFlow CPU wheels on the
        Windows Platform starting with this release. The currently supported
        version is LLVM/clang 17. The official Wheels-published on PyPI will be
        based on Clang; however, users retain the option to build wheels using
        the MSVC compiler following the steps mentioned in

그런 경우에, 여러분들은 **Gunicorn**을 프로세스 매니저로 포함하는 **공식 도커 이미지**를 사용할 수 있습니다. 이 프로세스 매니저는 다중 **Uvicorn 워커 프로세스들**을 실행하며, 디폴트 세팅으로 현재 CPU 코어에 기반하여 자동으로 워커 개수를 조정합니다. 이 사항에 대해서는 아래의 [Gunicorn과 함께하는 공식 도커 이미지 - Uvicorn](#official-docker-image-with-gunicorn-uvicorn)에서 더 다루겠습니다.

이런 경우에 해당하는 몇가지 예시가 있습니다:

#### 단순한 앱