</p>
<p align="center">
    FastAPI framework, high performance, easy to learn, fast to code, ready for production
</p>
<p align="center">
<a href="https://github.com/tiangolo/fastapi/actions?query=workflow%3ATest+event%3Apush+branch%3Amaster" target="_blank">

"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""

__version__ = "0.110.2"

from starlette import status as status

from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from .exceptions import HTTPException as HTTPException
from .exceptions import WebSocketException as WebSocketException

   * Returns a string that would replace the given character in the specified escaper, or {@code
   * null} if no replacement should be made. This method is intended for use in tests through the
   * {@code EscaperAsserts} class; production users of {@link CharEscaper} should limit themselves
   * to its public interface.
   *
   * @param c the character to escape if necessary
   * @return the replacement string, or {@code null} if no escaping was needed

TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several...

identifiers:
repository-code: 'https://github.com/tiangolo/fastapi'
url: 'https://fastapi.tiangolo.com'
abstract: >-
  FastAPI framework, high performance, easy to learn, fast to code,
  ready for production
keywords:
  - fastapi
  - pydantic
  - starlette

## Creating a hotfix branch

Customers in MinIO are allowed LTS on any release they choose to standardize. Production setups seldom change and require maintenance. Hotfix branches are such maintenance branches that allow customers to operate a production cluster without drastic changes to their deployment.

## Backporting a fix

	if _, maxLimit, err = sys.GetMaxOpenFileLimit(); err != nil {
		return err
	}

	if maxLimit < 4096 && runtime.GOOS != globalWindowsOSName {
		logger.Info("WARNING: maximum file descriptor limit %d is too low for production servers. At least 4096 is recommended. Fix with \"ulimit -n 4096\"",
			maxLimit)
	}

	if err = sys.SetMaxOpenFileLimit(maxLimit, maxLimit); err != nil {
		return err
	}

                    "https://prod.example.com/",
                    # TODO: remove when deprecating Pydantic v1
                    "https://prod.example.com",
                ),
                "description": "Production environment",
            },
        ],
        "paths": {
            "/app": {
                "get": {
                    "summary": "Read Main",
                    "operationId": "read_main_app_get",

    boolean is(String propertyKey);

    /**
     * Get the value for the key 'lasta_di.smart.deploy.mode'. <br>
     * The value is, e.g. warm <br>
     * comment: The mode of Lasta Di's smart-deploy, should be cool in production (e.g. hot, cool, warm)
     * @return The value of found property. (NotNull: if not found, exception but basically no way)
     */
    String getLastaDiSmartDeployMode();

    /**

!!! note
    Vous pouvez également l'installer pièce par pièce.

    C'est ce que vous feriez probablement une fois que vous voudrez déployer votre application en production :

    ```
    pip install fastapi
    ```

    Installez également `uvicorn` pour qu'il fonctionne comme serveur :

    ```
    pip install uvicorn
    ```