---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

<div style="text-align: right; margin-right: 10%;">Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>

---

---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

<div style="text-align: right; margin-right: 10%;">Piero Molino, Yaroslav Dudin, e Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>

---

---

「_我们选择了 **FastAPI** 来创建用于获取**预测结果**的 **REST** 服务。[用于 Ludwig]_」

<div style="text-align: right; margin-right: 10%;">Piero Molino，Yaroslav Dudin 和 Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>

---

---

## **Typer**, интерфейс командной строки для FastAPI

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

Do you want to have an `id` and `email` and not have any `username` in your model? Sure. You can use these same tools.

Do you want to just have a `str`? Or just a `dict`? Or a database class model instance directly? It all works the same way.

    ```

!!! info
    To use `EmailStr`, first install <a href="https://github.com/JoshData/python-email-validator" class="external-link" target="_blank">`email_validator`</a>.

    E.g. `pip install email-validator`
    or `pip install pydantic[email]`.

And we are using this model to declare our input and the same model to declare our output:

=== "Python 3.10+"

```Python hl_lines="14"
{!../../../docs_src/background_tasks/tutorial001.py!}
```

`.add_task()` は以下の引数を受け取ります:

* バックグラウンドで実行されるタスク関数 (`write_notification`)。
* タスク関数に順番に渡す必要のある引数の列 (`email`)。
* タスク関数に渡す必要のあるキーワード引数 (`message="some notification"`)。

## 依存性注入

`BackgroundTasks` の使用は依存性注入システムでも機能し、様々な階層 (*path operations 関数*、依存性 (依存可能性)、サブ依存性など) で `BackgroundTasks` 型のパラメーターを宣言できます。

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.

Here's a small preview of how you could integrate Strawberry with FastAPI:

```Python hl_lines="3  22  25-26"
{!../../../docs_src/graphql/tutorial001.py!}
```

## 其它模型

接下来，直接在*路径操作函数*中获取当前用户，并用 `Depends` 在**依赖注入**系统中处理安全机制。

开发者可以使用任何模型或数据满足安全需求（本例中是 Pydantic 的 `User` 模型）。

而且，不局限于只能使用特定的数据模型、类或类型。

不想在模型中使用 `username`，而是使用 `id` 和 `email`？当然可以。这些工具也支持。

只想使用字符串？或字典？甚至是数据库类模型的实例？工作方式都一样。

实际上，就算登录应用的不是用户，而是只拥有访问令牌的机器人、程序或其它系统？工作方式也一样。

尽管使用应用所需的任何模型、类、数据库。**FastAPI** 通过依赖注入系统都能帮您搞定。


## 代码大小

| `license_info` | `dict` | 🛂 ℹ 🎦 🛠️. ⚫️ 💪 🔌 📚 🏑. <details><summary><code>license_info</code> 🏑</summary><table><thead><tr><...