## Testing locally with Traefik

You can easily run the experiment locally with a stripped path prefix using <a href="https://docs.traefik.io/" class="external-link" target="_blank">Traefik</a>.

<a href="https://github.com/containous/traefik/releases" class="external-link" target="_blank">Download Traefik</a>, it's a single binary, you can extract the compressed file and run it directly from the terminal.

Then create a file `traefik.toml` with:

!!! info
    如果你使用 Kubernetes，这可能是 <a href="https://kubernetes.io/docs/concepts/workloads/pods/init-containers/" class="external-link" target="_blank">Init Container</a>。

如果在你的用例中，运行前面的步骤**并行多次**没有问题（例如，如果你没有运行数据库迁移，而只是检查数据库是否已准备好），那么你也可以将它们放在开始主进程之前在每个容器中。

### 单容器

前の章で詳しく説明したように、Uvicornワーカーで動作するGunicornを含む公式のDockerイメージがあります： [Server Workers - Gunicorn と Uvicorn](server-workers.md){.internal-link target=_blank}で詳しく説明しています。

このイメージは、主に上記で説明した状況で役に立つでしょう： [複数のプロセスと特殊なケースを持つコンテナ（Containers with Multiple Processes and Special Cases）](#containers-with-multiple-processes-and-special-cases)

* <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>.

```console
$ docker build -t myimage .

---> 100%
```

</div>

### Inicie o container Docker

* Rode um container baseado em sua imagem:

<div class="termy">

```console
$ docker run -d --name mycontainer -p 80:80 myimage
```

</div>

Agora você tem um servidor FastAPI otimizado em um container Docker. Auto-ajustado para seu servidor atual (e número de núcleos de CPU).

### Verifique

    For example, in `app/main.py` you could have a line like:

    ```
    from app.routers import items
    ```

* The `app` directory contains everything. And it has an empty file `app/__init__.py`, so it is a "Python package" (a collection of "Python modules"): `app`.
* It contains an `app/main.py` file. As it is inside a Python package (a directory with a file `__init__.py`), it is a "module" of that package: `app.main`.

import logging
import subprocess
import sys
from collections import Counter, defaultdict
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Container, DefaultDict, Dict, List, Set, Union

import httpx
import yaml
from github import Github
from pydantic import BaseModel, SecretStr
from pydantic_settings import BaseSettings

github_graphql_url = "https://api.github.com/graphql"

the `response_model` (e.g. you return a `UserInDB` that inherits from `User` but contains extra fields, like `hashed_password`, and `User` is used in the `response_model`), it would still pass the validation (because `UserInDB` is a subclass of `User`) and the object would be returned as-is, including the `hashed_password`. To fix this, the declared `response_model` is cloned, if it is a Pydantic model class (or contains Pydantic model classes in it, e.g. in a `List[Item]`), the Pydantic model class(es)...

    * But it needs authentication for that specific endpoint.
    * So, to authenticate with our API, it sends a header `Authorization` with a value of `Bearer ` plus the token.
    * If the token contains `foobar`, the content of the `Authorization` header would be: `Bearer foobar`.

## **FastAPI**'s `OAuth2PasswordBearer`

`note.dict()` returns a `dict` with its data, something like:

```Python
{
    "text": "Some note",
    "completed": False,
}
```

but it doesn't have the `id` field.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

It will take the `dict` at `result.value`, and take each of its keys and values and pass them as key-values to `UserInDB` as keyword arguments.

So, if the `dict` contains:

```Python
{
    "username": "johndoe",
    "hashed_password": "some_hash",
}
```

It will be passed to `UserInDB` as:

```Python