返回给客户端之前，要把枚举元素转换为对应的值（本例中为字符串）：

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

客户端中的 JSON 响应如下：

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 包含路径的路径参数

假设*路径操作*的路径为 `/files/{file_path}`。

但需要 `file_path` 中也包含*路径*，比如，`home/johndoe/myfile.txt`。

此时，该文件的 URL 是这样的：`/files/home/johndoe/myfile.txt`。

### Конкурентность + параллелизм: Веб + Машинное обучение { #concurrency-parallelism-web-machine-learning }

С **FastAPI** вы можете использовать преимущества конкурентности, что очень распространено в веб-разработке (это та же основная «фишка» NodeJS).

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

/// note

If you are a Python expert, and you already know everything about type hints, skip to the next chapter.

///

## Motivation { #motivation }

Let's start with a simple example:

### Serverspeicher { #server-memory }

Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.

## Görüşler

"_[...] Bugünlerde **FastAPI**'ı çok fazla kullanıyorum. [...] Aslında bunu ekibimin **Microsoft'taki Machine Learning servislerinin** tamamında kullanmayı planlıyorum. Bunlardan bazıları **Windows**'un ana ürünlerine ve **Office** ürünlerine entegre ediliyor._"

### Memória do servidor { #server-memory }

Por exemplo, se seu código carrega um modelo de Machine Learning com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

Si tu aplicación es **simple**, probablemente esto **no será un problema**, y puede que no necesites especificar límites de memoria estrictos. Pero si estás **usando mucha memoria** (por ejemplo, con modelos de **Machine Learning**), deberías verificar cuánta memoria estás consumiendo y ajustar el **número de contenedores** que se ejecutan en **cada máquina** (y tal vez agregar más máquinas a tu cluster).

afraid of interrupting him,) `I'll give him sixpence.  _I_ don't
believe there's an atom of meaning in it.'

  The jury all wrote down on their slates, `SHE doesn't believe
there's an atom of meaning in it,' but none of them attempted to
explain the paper.

  `If there's no meaning in it,' said the King, `that saves a
world of trouble, you know, as we needn't try to find any.  And

* 📝 Add external link: Building simple E-Commerce with NuxtJS and FastAPI. PR [#3271](https://github.com/tiangolo/fastapi/pull/3271) by [@ShahriyarR](https://github.com/ShahriyarR).
* 📝 Add external link: Serve a machine learning model using Sklearn, FastAPI and Docker. PR [#2974](https://github.com/tiangolo/fastapi/pull/2974) by [@rodrigo-arenas](https://github.com/rodrigo-arenas).