### 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. 🚨

- Kube-apiserver: resolved a regression that treated `304 Not Modified` responses from aggregated API servers as internal errors ([#112530](https://github.com/kubernetes/kubernetes/pull/112530), [@liggitt](https://github.com/liggitt)) [SIG API Machinery]

## 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.

**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.

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

  being written to the API audit log. (#94986, @tkashem) [SIG API Machinery, Auth, Cloud Provider and Testing]
- Create HPA v2 from v2beta2 with some fields changed. (#102534, @wangyysde) [SIG API Machinery, Apps, Auth, Autoscaling and Testing]

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

Para ver cómo lograr este paralelismo en producción, consulta la sección sobre [Despliegue](deployment/index.md).

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

- Fixes issue where you can't attach more than 15 GCE Persistent Disks to c2, n2, m1, m2 machine types. ([#88602](https://github.com/kubernetes/kubernetes/pull/88602), [@yuga711](https://github.com/yuga711)) [SIG Storage]
- Fixes kubelet crash in client certificate rotation cases ([#88079](https://github.com/kubernetes/kubernetes/pull/88079), [@liggitt](https://github.com/liggitt)) [SIG API Machinery, Auth and Node]

* API key: API key (do not translate to "clave API")
* 100% test coverage: cobertura de tests del 100%
* back and forth: de un lado a otro
* I/O (as in "input and output"): I/O (do not translate to "E/S")
* Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
* Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
* callback hell: callback hell (do not translate to "infierno de callbacks")