Durch die Berücksichtigung dieser Konzepte können Sie die beste Variante des Deployments **Ihrer eigenen APIs** **evaluieren und konzipieren**.

In den nächsten Kapiteln werde ich Ihnen mehr **konkrete Rezepte** für das Deployment von FastAPI-Anwendungen geben.

<div class="termy">

```console
$ pip install "fastapi[standard]"

---> 100%
```

</div>

**Remarque** : Vous devez vous assurer de mettre « fastapi[standard] » entre guillemets pour garantir que cela fonctionne dans tous les terminaux.

## Exemple { #example }

### Créer { #create-it }

Créez un fichier `main.py` avec :

```Python
from fastapi import FastAPI

app = FastAPI()

  //    it would just add an edge such that if done() observed non-null, then it would also
  //    definitely observe all earlier writes, but we still have no guarantee that done() would see
  //    the initial write (just stronger guarantees if it does).
  //
  // See: http://cs.oswego.edu/pipermail/concurrency-interest/2015-January/013800.html

   * returned, containing the values of {@code array}, and zeroes in the remaining places.
   *
   * @param array the source array
   * @param minLength the minimum length the returned array must guarantee
   * @param padding an extra amount to "grow" the array by if growth is necessary
   * @throws IllegalArgumentException if {@code minLength} or {@code padding} is negative

    private fun invalidLengths(strings: List<String>): Nothing = throw IOException("unexpected journal line: $strings")

    /**
     * Returns a snapshot of this entry. This opens all streams eagerly to guarantee that we see a
     * single published snapshot. If we opened streams lazily then the streams could come from
     * different edits.
     */
    internal fun snapshot(): Snapshot? {
      assertLockHeld()

* **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.

    * Le contenu est **crypté**, même s'il est envoyé avec le **protocole HTTP**.

   * returned, containing the values of {@code array}, and zeroes in the remaining places.
   *
   * @param array the source array
   * @param minLength the minimum length the returned array must guarantee
   * @param padding an extra amount to "grow" the array by if growth is necessary
   * @throws IllegalArgumentException if {@code minLength} or {@code padding} is negative

   * returned, containing the values of {@code array}, and zeroes in the remaining places.
   *
   * @param array the source array
   * @param minLength the minimum length the returned array must guarantee
   * @param padding an extra amount to "grow" the array by if growth is necessary
   * @throws IllegalArgumentException if {@code minLength} or {@code padding} is negative

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  private transient @Nullable Object table;