describes the peculiarities that apply when writing assembly code to interact with Go.
</p>

<p>
The most important thing to know about Go's assembler is that it is not a direct representation of the underlying machine.
Some of the details map precisely to the machine, but some do not.
This is because the compiler suite (see
<a href="https://9p.io/sys/doc/compiler.html">this description</a>)
needs no assembler pass in the usual pipeline.

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.

### 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.

{
    "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"
        }

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)

* 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")

 |
 |                 -s /path/to/user/settings.xml
 |
 |  2. Installation Level.
 |                 This settings.xml file provides configuration for all Maven
 |                 users on a machine (assuming they're all using the same Maven
 |                 installation). It's normally provided in
 |                 ${maven.installation.conf}/settings.xml.
 |