### 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. 🚨

* **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.

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

!!! 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"}

Articles:
  English:
  - author: Kurtis Pykes - NVIDIA
    link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
    title: Building a Machine Learning Microservice with FastAPI
  - author: Ravgeet Dhillon - Twilio
    link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
    title: Booking Appointments with Twilio, Notion, and FastAPI
  - author: Abhinav Tripathi - Microsoft Blogs

* **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.

* **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.

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).