</div>

/// tip | Astuce

En ajoutant `standard`, Uvicorn va installer et utiliser quelques dépendances supplémentaires recommandées.

Cela inclut `uvloop`, le remplaçant performant de `asyncio`, qui fournit le gros gain de performance en matière de concurrence.

///

////

//// tab | Hypercorn

    default ProducedArtifact getPomArtifact() {
        return getArtifacts().get(0);
    }

    /**
     * {@return the project main artifact}, which is the artifact produced by this project build, if applicable.
     * This artifact MAY be absent if the project is actually not producing any main artifact (i.e. "pom" packaging).
     *
     * @see #getPackaging()
     * @see org.apache.maven.api.services.ArtifactManager#getPath(Artifact)

- find accounts whose group memberships have changed; access policies available to a credential are updated to reflect the change, i.e. they will lose any privileges associated with a group they are removed from, and gain any privileges associated with a group they are added to.

cela bloquerait le serveur).

Si vous venez d'un autre framework asynchrone qui ne fonctionne pas comme de la façon décrite ci-dessus et que vous êtes habitués à définir des *fonctions de chemin* basiques avec un simple `def` pour un faible gain de performance (environ 100 nanosecondes), veuillez noter que dans **FastAPI**, l'effet serait plutôt contraire. Dans ces cas-là, il vaut mieux utiliser `async def` à moins que votre *fonction de chemin* utilise du code qui effectue des opérations...

a de-facto standard.  To achieve this, non-free programs must be
allowed to use the library.  A more frequent case is that a free
library does the same job as widely used non-free libraries.  In this
case, there is little to gain by limiting the free library to free
software only, so we use the Lesser General Public License.

  In other cases, permission to use a particular library in non-free

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

 * is less error-prone and more readable than code using {@code ReentrantLock}, without significant
 * performance loss. {@code Monitor} even has the potential for performance gain by optimizing the
 * evaluation and signaling of conditions. Signaling is entirely <a
 * href="http://en.wikipedia.org/wiki/Monitor_(synchronization)#Implicit_signaling">implicit</a>. By

					dstOpts.WantChecksum = nil
				}
			} else {
				// S3: All copied objects without checksums and specified destination checksum algorithms
				// automatically gain a CRC-64NVME checksum algorithm.
				dstOpts.WantServerSideChecksumType = hash.ChecksumCRC64NVME
				srcInfo.Reader.AddServerSideChecksumHasher(dstOpts.WantServerSideChecksumType)
				dstOpts.WantChecksum = nil
			}

package main

import (
	"context"
	"io"
	"log"
	"os"

	"github.com/minio/minio-go/v7"
	"github.com/minio/minio-go/v7/pkg/credentials"
)

func main() {
	s3Client, err := minio.New("minio-server-address:9000", &minio.Options{
		Creds: credentials.NewStaticV4("access-key", "secret-key", ""),
	})
	if err != nil {
		log.Fatalln(err)
	}

	var opts minio.GetObjectOptions

	// Add extract header to request:

Sebastián Ramírez <******@****.***> 1756653589 +0200