"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

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

  - [**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)

Se você utiliza uma versão anterior, ocorrerão erros ao tentar utilizar `Annotated`.

Certifique-se de [Atualizar a versão do FastAPI](../../deployment/versions.md#atualizando-as-versoes-do-fastapi){.internal-link target=_blank} para pelo menos 0.95.1 antes de usar `Annotated`.

///

### Importando `Depends`

//// tab | Python 3.10+

		if s.hasReplicationUsage() {
			return true
		}
	}
	return false
}

// ReplicationStats holds the global in-memory replication stats
type ReplicationStats struct {
	// map of site deployment ID to site replication status
	// for site replication - maintain stats at global level
	srStats *SRStats
	// active worker stats
	workers *ActiveWorkerStat
	// queue stats cache
	qCache queueCache

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

## `async` und `await`.

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

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

            A unique identifier for a deployment location. This is used to match the
            site to configuration in the {@code settings.xml} file, for example.
          </description>
          <type>String</type>
        </field>
        <field>
          <name>name</name>
          <version>4.0.0+</version>
          <description>Human readable name of the deployment location.</description>

FastAPI added support for `Annotated` (and started recommending it) in version 0.95.0.

If you have an older version, you would get errors when trying to use `Annotated`.

Make sure you [Upgrade the FastAPI version](../deployment/versions.md#upgrading-the-fastapi-versions){.internal-link target=_blank} to at least 0.95.1 before using `Annotated`.

///

## Use `Annotated` in the type for the `q` parameter