* You can now set `VolumeMount.Propagation` to `Bidirectional` to enable a particular mount for a container to propagate itself to the host or other containers.

* [alpha] Improve Flex volume deployment

  * Flex volume driver deployment is simplified in the following ways:

    * New driver files can now be automatically discovered and initialized without requiring a kubelet or controller-manager restart.

The main web server to run **FastAPI** applications.

You can also use the `--workers` command line option to have an asynchronous multi-process server.

Check more details in the [Deployment](deployment/index.md){.internal-link target=_blank} section.

///

## Benchmarks and speed

O principal servidor web para rodar aplicações **FastAPI**.

Você pode combinar ele com o Gunicorn, para ter um servidor multi-processos assíncrono.

Verifique mais detalhes na seção [Deployment](deployment/index.md){.internal-link target=_blank}.

///

## Performance e velocidade

								"elapsed",
								"",
							))
						ctx := logger.SetReqInfo(GlobalContext,
							reqInfo)
						bootLogOnceIf(ctx, fmt.Errorf("%s resolves to localhost in a containerized deployment, waiting for it to resolve to a valid IP",
							endpoints[i].Hostname()), endpoints[i].Hostname(), logger.ErrorKind)
					}

					continue
				}

				// return err if not Docker or Kubernetes

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

## `async` and `await`

Hauptwebserver zum Ausführen von **FastAPI**-Anwendungen.

Sie können ihn mit Gunicorn kombinieren, um einen asynchronen Multiprozess-Server zu erhalten.

Weitere Details finden Sie im Abschnitt [Deployment](deployment/index.md){.internal-link target=_blank}.

///

## Benchmarks und Geschwindigkeit

Wie Sie diese Parallelität in der Produktion erreichen, erfahren Sie im Abschnitt über [Deployment](deployment/index.md){.internal-link target=_blank}.

## `async` und `await`.

  - [**Notable Features**](#notable-features)
  - [Kubefed](#kubefed)
    - [**Kubernetes API**](#kubernetes-api)
      - [User Provided Extensions](#user-provided-extensions)
    - [**Application Deployment**](#application-deployment)
      - [StatefulSet](#statefulset)
      - [DaemonSet](#daemonset-1)
      - [Deployments](#deployments)
      - [PodDisruptionBudget](#poddisruptionbudget)
    - [**Security**](#security)

/// info | "정보"

FastAPI는 0.95.0 버전부터 `Annotated`에 대한 지원을 (그리고 이를 사용하기 권장합니다) 추가했습니다.

옛날 버전을 가지고 있는 경우, `Annotated`를 사용하려 하면 에러를 맞이하게 될 것입니다.

`Annotated`를 사용하기 전에 최소 0.95.1로 [FastAPI 버전 업그레이드](../../deployment/versions.md#fastapi_2){.internal-link target=_blank}를 확실하게 하세요.

///

### `Depends` 불러오기

//// tab | Python 3.10+

```Python hl_lines="3"
{!> ../../docs_src/dependencies/tutorial001_an_py310.py!}

并且，您也可以利用并行和多进程（让多个进程并行运行）的优点来处理与机器学习系统中类似的 **CPU 密集型** 工作。

这一点，再加上 Python 是**数据科学**、机器学习（尤其是深度学习）的主要语言这一简单事实，使得 **FastAPI** 与数据科学/机器学习 Web API 和应用程序（以及其他许多应用程序）非常匹配。

了解如何在生产环境中实现这种并行性，可查看此文 [Deployment](deployment/index.md){.internal-link target=_blank}。

## `async` 和 `await`

现代版本的 Python 有一种非常直观的方式来定义异步代码。这使它看起来就像正常的"顺序"代码，并在适当的时候"等待"。

当有一个操作需要等待才能给出结果，且支持这个新的 Python 特性时，您可以编写如下代码：

```Python