Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

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

### 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. 🚨

컨테이너를 사용하지 않고서는, 어플리케이션을 구동하고 재시작하는 것이 매우 번거롭고 어려울 수 있습니다. 하지만 **컨테이너를 사용한다면** 대부분의 경우에 이런 기능은 기본적으로 포함되어 있습니다. ✨

## 복제 - 프로세스 개수

만약 여러분이 **쿠버네티스**와 머신 <abbr title="A group of machines that are configured to be connected and work together in some way.">클러스터</abbr>, 도커 스왐 모드, 노마드, 또는 다른 여러 머신 위에 분산 컨테이너를 관리하는 복잡한 시스템을 다루고 있다면, 여러분은 각 컨테이너에서 (워커와 함께 사용하는 Gunicorn 같은) **프로세스 매니저** 대신 **클러스터 레벨**에서 **복제를 다루**고 싶을 것입니다.

🍵 ⚙️ 📦, ⚒ 🈸 🏃 🔛 🕴 & ⏮️ ⏏ 💪 ⚠ & ⚠. ✋️ 🕐❔ **👷 ⏮️ 📦** 🌅 💼 👈 🛠️ 🔌 🔢. 👶

## 🧬 - 🔢 🛠️

🚥 👆 ✔️ <abbr title="A group of machines that are configured to be connected and work together in some way.">🌑</abbr> 🎰 ⏮️ **☁**, ☁ 🐝 📳, 🖖, ⚖️ ➕1️⃣ 🎏 🏗 ⚙️ 🛠️ 📎 📦 🔛 💗 🎰, ⤴️ 👆 🔜 🎲 💚 **🍵 🧬** **🌑 🎚** ↩️ ⚙️ **🛠️ 👨‍💼** (💖 🐁 ⏮️ 👨‍🏭) 🔠 📦.

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

> even if you have Gradle or Develocity build caching enabled for the project.
> The Gradle Build Tool repository is massive, and it will take ages to build on
> a local machine without necessary parallelization and caching.
> The full test suites are executed on the CI instance for multiple configurations,
> and you can rely on it after doing initial sanity check and targeted local testing.

!!! 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",