```Python
    from main import app
    ```

!!! warning
    Uvicorn and others support a `--reload` option that is useful during development.

    The `--reload` option consumes much more resources, is more unstable, etc.

    It helps a lot during **development**, but you **shouldn't** use it in **production**.

## Hypercorn with Trio

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.

production Python API, I would highly recommend **FastAPI**. It is **beautifully designed**, **simple to use** and **highly scalable**, it has become a **key component** in our API first development strategy and is driving many automations and services such as our Virtual TAC Engineer._"

<div style="text-align: right; margin-right: 10%;">Deon Pillsbury - <strong>Cisco</strong> <a href="https://www.linkedin.com/posts/deonpillsbury_cisco-cx-python-activity-6963242628536487936-trAp/" target="...

* Muchas características extra (gracias a Starlette) como:
    * **WebSockets**
    * **GraphQL**
    * pruebas extremadamente fáciles con HTTPX y `pytest`
    * **CORS**
    * **Cookie Sessions**
    * ...y mucho más.

## Rendimiento

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

Containers also have their own **isolated** running processes (commonly just one process), file system, and network, simplifying deployment, security, development, etc.

## What is a Container Image

### Create the `Settings` object

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

production Python API, I would highly recommend **FastAPI**. It is **beautifully designed**, **simple to use** and **highly scalable**, it has become a **key component** in our API first development strategy and is driving many automations and services such as our Virtual TAC Engineer._"

<div style="text-align: right; margin-right: 10%;">Deon Pillsbury - <strong>Cisco</strong> <a href="https://www.linkedin.com/posts/deonpillsbury_cisco-cx-python-activity-6963242628536487936-trAp/" target="...

    * Code-Vervollständigung.
    * Typprüfungen.
* Validierung von Daten:
    * Automatische und eindeutige Fehler, wenn die Daten ungültig sind.
    * Validierung auch für tief verschachtelte JSON-Objekte.
* <abbr title="auch bekannt als: Serialisierung, Parsen, Marshalling">Konvertierung</abbr> von Eingabedaten: Aus dem Netzwerk kommend, zu Python-Daten und -Typen. Lesen von:
    * JSON.
    * Pfad-Parametern.

production Python API, I would highly recommend **FastAPI**. It is **beautifully designed**, **simple to use** and **highly scalable**, it has become a **key component** in our API first development strategy and is driving many automations and services such as our Virtual TAC Engineer._"

<div style="text-align: right; margin-right: 10%;">Deon Pillsbury - <strong>Cisco</strong> <a href="https://www.linkedin.com/posts/deonpillsbury_cisco-cx-python-activity-6963242628536487936-trAp/" target="...