## 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>.

If you create a Python file that imports and uses FastAPI, and run it with the Python from your local environment, it will use your cloned local FastAPI source code.

And if you update that local FastAPI source code when you run that Python file again, it will use the fresh version of FastAPI you just edited.

That way, you don't have to "install" your local version to be able to test every change.

!!! note "Technical Details"

### In a Remote Server

When you set up a remote server (a cloud server, a virtual machine, etc.) the simplest thing you can do is to use `fastapi run`, Uvicorn (or similar) manually, the same way you do when developing locally.

And it will work and will be useful **during development**.

But if your connection to the server is lost, the **running process** will probably die.

* You can ask them to provide a <a href="https://stackoverflow.com/help/minimal-reproducible-example" class="external-link" target="_blank">minimal, reproducible, example</a>, that you can **copy-paste** and run locally to see the same error or behavior they are seeing, or to understand their use case better.

<font color="#4E9A06">INFO</font>:     Application startup complete.
</pre>
```

</div>

It is **HIGHLY encouraged** that you write or copy the code, edit it and run it locally.

Using it in your editor is what really shows you the benefits of FastAPI, seeing how little code you have to write, all the type checks, autocompletion, etc.

---

## Install FastAPI

        code = list(lang_dict.keys())[0]
        url = lang_dict[code]
        if code not in local_language_names:
            print(
                f"Missing language name for: {code}, "
                "update it in docs/language_names.yml"
            )
            raise typer.Abort()
        use_name = f"{code} - {local_language_names[code]}"
        new_alternate.append({"link": url, "name": use_name})

4. Install the package dependencies in the requirements file.

    The `--no-cache-dir` option tells `pip` to not save the downloaded packages locally, as that is only if `pip` was going to be run again to install the same packages, but that's not the case when working with containers.

    !!! note

Você também pode declarar parâmetros de **corpo**, **rota** e **consulta**, ao mesmo tempo.

O **FastAPI** irá reconhecer cada um deles e retirar a informação do local correto.

```Python hl_lines="18"
{!../../../docs_src/body/tutorial004.py!}
```

Os parâmetros da função serão reconhecidos conforme abaixo:

En el output, hay una línea que dice más o menos:

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

Esa línea muestra la URL dónde se está sirviendo tu app en tu maquina local.

### Revísalo

Abre tu navegador en <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

Verás la respuesta en JSON:

```JSON
{"message": "Hello World"}
```

Então, nesse caso, poderia ser mais simples ter **um único contêiner** com **múltiplos processos**, e uma ferramenta local (por exemplo, um exportador do Prometheus) no mesmo contêiner coletando métricas do Prometheus para todos os processos internos e expor essas métricas no único contêiner.

---