}
	}

	return host, port, nil
}

// isLocalHost - checks if the given parameter
// correspond to one of the local IP of the
// current machine
func isLocalHost(host string, port string, localPort string) (bool, error) {
	hostIPs, err := getHostIP(host)
	if err != nil {
		return false, err
	}

	nonInterIPV4s := mustGetLocalIP4().Intersection(hostIPs)

New-Item -ItemType directory -Path $gradleInit
echo "Copy .ci/init.gradle to $gradleInit"
Copy-Item .ci/init.gradle -Destination $gradleInit

[Environment]::SetEnvironmentVariable("JAVA_HOME", $null, "Machine")
$env:PATH="C:\Users\jenkins\.java\$env:ES_BUILD_JAVA\bin\;$env:PATH"
$env:JAVA_HOME=$null
$env:SYSTEM_JAVA_HOME="C:\Users\jenkins\.java\$env:ES_RUNTIME_JAVA"
Remove-Item -Recurse -Force \tmp -ErrorAction Ignore

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

New-Item -ItemType directory -Path $gradleInit
echo "Copy .ci/init.gradle to $gradleInit"
Copy-Item .ci/init.gradle -Destination $gradleInit

[Environment]::SetEnvironmentVariable("JAVA_HOME", $null, "Machine")
$env:PATH="C:\Users\jenkins\.java\$env:ES_BUILD_JAVA\bin\;$env:PATH"
$env:JAVA_HOME=$null
$env:SYSTEM_JAVA_HOME="C:\Users\jenkins\.java\$env:ES_RUNTIME_JAVA"
Remove-Item -Recurse -Force \tmp -ErrorAction Ignore

## `fastapi run` { #fastapi-run }

Exécuter `fastapi run` démarre FastAPI en mode production par défaut.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

### Mémoire du serveur { #server-memory }

Par exemple, si votre code charge un modèle de Machine Learning de **1 Go**, lorsque vous exécutez un processus avec votre API, il consommera au moins 1 Go de RAM. Et si vous démarrez **4 processus** (4 workers), chacun consommera 1 Go de RAM. Donc au total, votre API consommera **4 Go de RAM**.

Et si votre serveur distant ou votre machine virtuelle n'a que 3 Go de RAM, essayer de charger plus de 4 Go de RAM posera problème. 🚨

## What Does Deployment Mean { #what-does-deployment-mean }

To **deploy** an application means to perform the necessary steps to make it **available to the users**.

For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.

### Server Memory { #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. 🚨

**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.