<div class="termy">

    ```console
    $ pip install "uvicorn[standard]"

    ---> 100%
    ```

    </div>

    !!! tip
        By adding the `standard`, Uvicorn will install and use some recommended extra dependencies.

        That including `uvloop`, the high-performance drop-in replacement for `asyncio`, that provides the big concurrency performance boost.

## About Starlette

You shouldn't pin the version of `starlette`.

Different versions of **FastAPI** will use a specific newer version of Starlette.

So, you can just let **FastAPI** use the correct Starlette version.

## About Pydantic

## Run it

After that, you just need to install `pytest`:

<div class="termy">

```console
$ pip install pytest

---> 100%
```

</div>

It will detect the files and tests automatically, execute them, and report the results back to you.

Run the tests with:

<div class="termy">

```console
$ pytest

🚥 👆 ❎ 🏎 ✳, 👆 💪 ❎ 🛠️ ⏮️:

<div class="termy">

```console
$ typer --install-completion

zsh completion installed in /home/user/.bashrc.
Completion will take effect once you restart the terminal.
```

</div>

### 📱 &amp; 🩺 🎏 🕰

🚥 👆 🏃 🖼 ⏮️, ✅:

<div class="termy">

```console
$ uvicorn tutorial001:app --reload

* OS上で**実行**している特定のプログラム
    * これはファイルやコードを指すのではなく、OSによって**実行**され、管理されているものを指します。
* どんなプログラムやコードも、それが**実行されているときにだけ機能**します。つまり、**プロセスとして実行されているときだけ**です。
* プロセスは、ユーザーにあるいはOSによって、 **終了**（あるいは "kill"）させることができます。その時点で、プロセスは実行/実行されることを停止し、それ以降は**何もできなくなります**。
* コンピュータで実行されている各アプリケーションは、実行中のプログラムや各ウィンドウなど、その背後にいくつかのプロセスを持っています。そして通常、コンピュータが起動している間、**多くのプロセスが**同時に実行されています。
* **同じプログラム**の**複数のプロセス**が同時に実行されていることがあります。

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

!!! info
    These docs are about to be updated. 🎉

    The current version assumes Pydantic v1.

    The new docs will hopefully use Pydantic v2 and will use <a href="https://art049.github.io/odmantic/" class="external-link" target="_blank">ODMantic</a> with MongoDB.

!!! warning "Deprecated"
    This tutorial is deprecated and will be removed in a future version.

### Technical Details: `root_path`

When you mount a sub-application as described above, FastAPI will take care of communicating the mount path for the sub-application using a mechanism from the ASGI specification called a `root_path`.

That way, the sub-application will know to use that path prefix for the docs UI.

So, the prefix in this case is `/items`.

We can also add a list of `tags` and extra `responses` that will be applied to all the *path operations* included in this router.

And we can add a list of `dependencies` that will be added to all the *path operations* in the router and will be executed/solved for each request made to them.

!!! tip

```

### Create a custom `GzipRoute` class

Next, we create a custom subclass of `fastapi.routing.APIRoute` that will make use of the `GzipRequest`.

This time, it will overwrite the method `APIRoute.get_route_handler()`.

This method returns a function. And that function is what will receive a request and return a response.

Here we use it to create a `GzipRequest` from the original request.

```Python hl_lines="18-26"