### Concorrência + Paralelismo: Web + Aprendizado de Máquina { #concurrency-parallelism-web-machine-learning }

It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and validated.

/// check | Inspired **FastAPI** to

			}
		},
	}
	session := DB.Session(&gorm.Session{Logger: tracer})

	DB.Migrator().DropTable(model1)
	var modifySql []string
	if err := session.AutoMigrate(model1); err != nil {
		t.Fatalf("failed to auto migrate, got error: %v", err)
	}
	if err := session.AutoMigrate(model1); err != nil {
		t.Fatalf("failed to auto migrate, got error: %v", err)
	}
	tracer2 := Tracer{

        "//third_party/py/tf_keras/...",
        "//third_party/yggdrasil_decision_forests/...",
        "//waymo/accelerator/...",
        "//waymo/ml/cn/...",
        "//waymo/ml/models/...",
    ],
)

package_group(
    name = "ndarray_tensor_allow_list",
    packages = [
        "//learning/gemini/gemax/...",
        "//third_party/py/courier/...",

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

from collections.abc import Sequence
from typing import Annotated, Any, Callable, Optional, Union

from annotated_doc import Doc
from fastapi import params
from fastapi._compat import Undefined
from fastapi.openapi.models import Example
from pydantic import AliasChoices, AliasPath
from typing_extensions import Literal, deprecated

_Unset: Any = Undefined


def Path(  # noqa: N802
    default: Annotated[
        Any,
        Doc(

* Sicherheit und Authentifizierung, einschließlich Unterstützung für **OAuth2** mit **JWT-Tokens** und **HTTP Basic** Authentifizierung.
* Fortgeschrittenere (aber ebenso einfache) Techniken zur Deklaration **tief verschachtelter JSON-Modelle** (dank Pydantic).
* **GraphQL**-Integration mit <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> und anderen Bibliotheken.
* Viele zusätzliche Features (dank Starlette) wie:

### Concurrence + Parallélisme : Web + Machine Learning

Avec **FastAPI** vous pouvez bénéficier de la concurrence qui est très courante en développement web (c'est l'attrait principal de NodeJS).

### Nebenläufigkeit + Parallelität: Web + maschinelles Lernen { #concurrency-parallelism-web-machine-learning }