When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

But you can also install an ASGI server manually:

=== "Uvicorn"

## Que signifie le déploiement

**Déployer** une application signifie effectuer les étapes nécessaires pour la rendre **disponible pour les
utilisateurs**.

Pour une **API Web**, cela implique normalement de la placer sur une **machine distante**, avec un **programme serveur**
qui offre de bonnes performances, une bonne stabilité, _etc._, afin que vos **utilisateurs** puissent **accéder** à
l'application efficacement et sans interruption ni problème.

* **Facile**: Progettato per essere facile da usare e imparare. Si riduce il tempo da dedicare alla lettura della documentazione.

Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

!!! tip "Astuce"
    Pour ceux qui se demandent, "AlexNet", "ResNet", et "LeNet" sont juste des noms de <abbr title="Techniquement, des architectures de modèles">modèles</abbr> de Machine Learning.

### Déclarer un paramètre de chemin

Créez ensuite un *paramètre de chemin* avec une annotation de type désignant l'énumération créée précédemment (`ModelName`) :

```Python hl_lines="16"

server["Server on http://127.0.0.1:8000/app"]

browser --> proxy
proxy --> server
```

!!! tip
    The IP `0.0.0.0` is commonly used to mean that the program listens on all the IPs available in that machine/server.

The docs UI would also need the OpenAPI schema to declare that this API `server` is located at `/api/v1` (behind the proxy). For example:

```JSON hl_lines="4-8"
{
    "openapi": "3.1.0",

In the output, there's a line with something like:

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

That line shows the URL where your app is being served, in your local machine.

### Check it

Open your browser at <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

You will see the JSON response as:

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

### 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**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

## Caso de uso

Vamos iniciar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

Vamos imaginar que você tem alguns **modelos de _machine learning_** que deseja usar para lidar com as requisições. 🤖