```

</div>

Em seguida, faça o deploy da sua aplicação:

<div class="termy">

```console
$ fastapi deploy

Deploying to FastAPI Cloud...

✅ Deployment successful!

🐔 Ready the chicken! Your app is ready at https://myapp.fastapicloud.dev
```

</div>

É isso! Agora você pode acessar sua aplicação nessa URL. ✨

You are logged in to FastAPI Cloud 🚀
```

</div>

Luego despliega tu app:

<div class="termy">

```console
$ fastapi deploy

Deploying to FastAPI Cloud...

✅ Deployment successful!

🐔 Ready the chicken! Your app is ready at https://myapp.fastapicloud.dev
```

</div>

¡Eso es todo! Ahora puedes acceder a tu app en esa URL. ✨

* the type annotation
* the type hint

* the server worker
* the Uvicorn worker
* the Gunicorn Worker
* the worker process
* the worker class
* the workload

* the deployment
* to deploy

* the SDK
* the software development kit

* the `APIRouter`
* the `requirements.txt`
* the Bearer Token
* the breaking change
* the bug
* the button
* the callable

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

## `async` und `await` { #async-and-await }

		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

## Notable Changes to Existing Behavior

### Deployments

- ReplicaSets of paused Deployments are now scaled while the Deployment is paused. This is retroactive to existing Deployments.
- When scaling a Deployment during a rollout, the ReplicaSets of all Deployments are now scaled proportionally based on the number of replicas they each have instead of only scaling the newest ReplicaSet.

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 { #benchmarks-and-speed }

Указав `--proxy-headers`, FastAPI сможет редиректить на корректный адрес. 😎

```
https://mysuperapp.com/items/
```

/// tip | Совет

Если хотите узнать больше об HTTPS, смотрите руководство [О HTTPS](../deployment/https.md){.internal-link target=_blank}.

///

### Как работают пересылаемые заголовки прокси { #how-proxy-forwarded-headers-work }

FastAPI додав підтримку `Annotated` (і почав рекомендувати його) у версії 0.95.0.

Якщо у Вас старіша версія, під час використання `Annotated` можуть виникати помилки.

Переконайтеся, що Ви [оновили версію FastAPI](../deployment/versions.md#upgrading-the-fastapi-versions){.internal-link target=_blank} до принаймні 0.95.1, перш ніж використовувати `Annotated`.

///

## Використання `Annotated` у типі параметра `q`

并且，你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，你可以编写如下代码：

```Python