Can be repeated to define multiple symbols.
	-I dir1 -I dir2
		Search for #include files in dir1, dir2, etc,
		after consulting $GOROOT/pkg/$GOOS_$GOARCH.
	-S
		Print assembly and machine code.
	-V
		Print assembler version and exit.
	-debug
		Dump instructions as they are parsed.
	-dynlink
		Support references to Go symbols defined in other shared libraries.
	-e

Chroot allows user based namespace isolation on many standard Linux deployments.

## 1. Prerequisites

- Familiarity with [chroot](http://man7.org/linux/man-pages/man2/chroot.2.html)
- Chroot installed on your machine.

## 2. Install MinIO in Chroot

> **Note:** MinIO community edition is now distributed as source code only. Pre-compiled binaries are no longer provided for new releases.

* plugin: плагин
* plug-in: плагин
* full stack: full stack (do not translate)
* full-stack: full-stack (do not translate)
* loop (as in async loop): цикл событий
* Machine Learning: Машинное обучение
* Deep Learning: Глубокое обучение
* callback hell: callback hell (clarify as `ад обратных вызовов`)
* on the fly: на лету
* scratch the surface: поверхностно ознакомиться

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

Das Ausführen von `fastapi run` startet FastAPI standardmäßig im Produktionsmodus.

	flag.BoolVar(&DebugV, "v", false, "print debug output")
	flag.Var(objabi.NewDebugFlag(&DebugFlags, nil), "d", "enable debugging settings; try -d help")
	objabi.AddVersionFlag() // -V
	objabi.Flagcount("S", "print assembly and machine code", &PrintOut)

	DebugFlags.CompressInstructions = 1
}

// MultiFlag allows setting a value multiple times to collect a list, as in -I=dir1 -I=dir2.
type MultiFlag []string

Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

### El abbr da una explicación { #the-abbr-gives-an-explanation }

* <abbr title="Un grupo de máquinas configuradas para estar conectadas y trabajar juntas de alguna manera.">clúster</abbr>
* <abbr title="Un método de machine learning que usa redes neuronales artificiales con numerosas capas ocultas entre las capas de entrada y salida, desarrollando así una estructura interna completa">Deep Learning</abbr>

  html_url: https://github.com/zhanymkanov/fastapi-best-practices
  stars: 14644
  owner_login: zhanymkanov
  owner_html_url: https://github.com/zhanymkanov
- name: machine-learning-zoomcamp
  html_url: https://github.com/DataTalksClub/machine-learning-zoomcamp
  stars: 12320
  owner_login: DataTalksClub
  owner_html_url: https://github.com/DataTalksClub
- name: fastapi_mcp
  html_url: https://github.com/tadata-org/fastapi_mcp

st ``` The MinIO deployment starts using default root credentials `minioadmin:minioadmin`. You can test the deployment using the MinIO Console, an embedded web-based object browser built into MinIO Server. Point a web browser running on the host machine to <http://127.0.0.1:9000> and log in with the root credentials. You can use the Browser to create buckets, upload objects, and browse the contents of the MinIO server. You can also connect using any S3-compatible tool, such as the MinIO Client `mc`...

MinIO uses [`klauspost/compress/s2`](https://github.com/klauspost/compress/tree/master/s2)
streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.