LT",lastDay:"[Gister om] LT",lastWeek:"[Laas] dddd [om] LT",sameElse:"L"},relativeTime:{future:"oor %s",past:"%s gelede",s:"'n paar sekondes",ss:"%d sekondes",m:"'n minuut",mm:"%d minute",h:"'n uur",hh:"%d ure",d:"'n dag",dd:"%d dae",M:"'n maand",MM:"%d maande",y:"'n jaar",yy:"%d jaar"},dayOfMonthOrdinalParse:/\d{1,2}(ste|de)/,ordinal:function(e){return e+(1===e||8===e||20<=e?"ste":"de")},week:{dow:1,doy:4}});function es(e){return 0===e?0:1===e?1:2===e?2:3<=e%100&&e%100<=10?3:11<=e%100?4:5}function...

#!/bin/bash

cd `dirname $0`
. ./_project.sh

FIRST_ARG=$1
SECOND_ARG=$2

sh $DBFLUTE_HOME/etc/cmd/_df-manage.sh $MY_PROPERTIES_PATH $FIRST_ARG $SECOND_ARG
taskReturnCode=$?

if [ $taskReturnCode -ne 0 ];then
  exit $taskReturnCode;

%~d0
cd %~p0
call _project.bat

:: tilde to remove double quotation
set FIRST_ARG=%~1
if "%FIRST_ARG%"=="" set FIRST_ARG=""
set SECOND_ARG=%2
if "%SECOND_ARG%"=="" set SECOND_ARG=""

call %DBFLUTE_HOME%\etc\cmd\_df-manage.cmd %MY_PROPERTIES_PATH% "%FIRST_ARG%" %SECOND_ARG%

if "%pause_at_end%"=="y" (
  pause

labels.facet_timestamp_title=Periode
labels.facet_timestamp_1day=Afgelopen 24 uur
labels.facet_timestamp_1week=Afgelopen week
labels.facet_timestamp_1month=Afgelopen maand
labels.facet_timestamp_1year=Afgelopen jaar
labels.facet_timestamp_3month=Afgelopen 3 maanden
labels.facet_timestamp_6month=Afgelopen 6 maanden
labels.facet_timestamp_2year=Afgelopen 2 jaar
labels.facet_timestamp_3year=Afgelopen 3 jaar
labels.facet_contentLength_title=Grootte

Como parte disso, eu precisava investigar, testar e usar muitas alternativas.

A história do **FastAPI** é, em grande parte, a história de seus predecessores.

Como dito na seção [Alternativas](alternatives.md):

<blockquote markdown="1">

**FastAPI** não existiria se não pelo trabalho anterior de outros.

* **Aprendizado de Máquina**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.
* **Deep Learning**: Esse é um subcampo do Aprendizado de Máquina, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.

,EAAAA,QAAE,QACIoT,aAAaK,QAAb,WAAgC2B,MAEhChR,GACd1C,KAAKkC,SAASgS,wBAChBhO,EAAMtC,SAAS,mBAAmBA,SAASlB,IAAsB+B,MAAM,IAAIC,OAAM,WAC/EpG,EAAAA,QAAE0B,MAAMgE,YAAY,mBACpB1F,EAAAA,QAAE0B,MAAM4E,aAGVsB,EAAMtC,SAASlB,IAER1C,KAAKkC,SAASgS,wBACvBhO,EAAMtC,SAAS,mBAAmBI,YAAYtB,IAAsB+B,MAAM,IAAIC,OAAM,WAClFpG,EAAAA,QAAE0B,MAAMgE,YAAY,mBACpB1F,EAAAA,QAAE0B,MAAM4E,aAGVsB,EAAMlC,YAAYtB,QAMtBd,MAAA,WAAQ,IAAAC,EAAA7B,KACNA,KAAKsU,WACLtU,KAAKqU,eAEL/V,EAAAA,QAAEwI,QAAQC,QAAO,WACflF,EAAKwS,cAAa,SAItBzT,YAAA,WAAc,I...

## Container Images { #container-images }

Docker has been one of the main tools to create and manage **container images** and **containers**.

And there's a public [Docker Hub](https://hub.docker.com/) with pre-made **official container images** for many tools, environments, databases, and applications.

For example, there's an official [Python Image](https://hub.docker.com/_/python).

<img src="/img/tutorial/path-operation-configuration/image01.png">

### Utiliser des tags avec Enum { #tags-with-enums }

Si vous avez une grande application, vous pourriez finir par accumuler **plusieurs tags**, et vous voudrez vous assurer d'utiliser toujours le **même tag** pour les *chemins d'accès* associés.

Le résultat de son appel est quelque chose qui peut être encodé avec la fonction standard de Python [`json.dumps()`](https://docs.python.org/3/library/json.html#json.dumps).

Elle ne renvoie pas une grande `str` contenant les données au format JSON (sous forme de chaîne). Elle renvoie une structure de données standard de Python (par ex. un `dict`) avec des valeurs et sous-valeurs toutes compatibles avec JSON.

/// note | Remarque