common:rbe_windows_x86_cpu_2022 --platforms="//tensorflow/tools/toolchains/win2022:windows_ltsc2022_clang_new"

# RBE configuration for ML Actions (hermetic toolchains, new pool)
# Note: Using NCCL 2.28.9 to match the requirements of newer hermetic CUDA toolchains used in this environment.
common:rbe_windows_x86_cpu_ml_actions --repo_env=HERMETIC_NCCL_VERSION="2.28.9"

	if err != nil {
		t.Fatal(err)
	}

	defer removeRoots(fsDirs)

	pools := mustGetPoolEndpoints(0, fsDirs[:16]...)
	pools = append(pools, mustGetPoolEndpoints(1, fsDirs[16:]...)...)

	// Everything is fine, should return nil
	objLayer, _, err := initObjectLayer(ctx, pools)
	if err != nil {
		t.Fatal(err)
	}

	bucket := getRandomBucketName()
	object := getRandomObjectName()

- Updated the Partitionable Devices feature to support referencing counter sets across ResourceSlices within the same resource pool. Devices from incomplete pools were no longer considered for allocation. This change introduced backwards-incompatible updates to the alpha feature, requiring any ResourceSlices using it to be removed before upgrading or downgrading between v1.34 and v1.35. ([#13...

	if err != nil {
		removeRoots(fsDirs)
		return nil, nil, err
	}

	// Wait up to 10 seconds for disks to come online.
	pools := obj.(*erasureServerPools)
	t := time.Now()
	for _, pool := range pools.serverPools {
		for _, sets := range pool.erasureDisks {
			for _, s := range sets {
				if !s.IsLocal() {
					for !s.IsOnline() {
						time.Sleep(100 * time.Millisecond)

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///

Pour éviter de bloquer la boucle d'événements, vous pouvez simplement déclarer la *fonction de chemin d'accès* avec un `def` classique au lieu de `async def`. Ainsi, FastAPI l'exécutera dans un worker de pool de threads, afin d'éviter de bloquer la boucle principale.

{* ../../docs_src/stream_data/tutorial002_py310.py ln[30:34] hl[31] *}

/// tip | Astuce

Dies kann sehr nützlich sein, um **Ressourcen** einzurichten, die Sie in der gesamten App verwenden wollen und die von Requests **gemeinsam genutzt** werden und/oder die Sie anschließend **aufräumen** müssen. Zum Beispiel ein Pool von Datenbankverbindungen oder das Laden eines gemeinsam genutzten Modells für maschinelles Lernen.

## Anwendungsfall { #use-case }

        }
        throw new SearchEngineClientException("client is not HttpClient.");
    }

    //
    // Fesen Client
    //

    /**
     * Gets the thread pool used by the client.
     *
     * @return the thread pool
     */
    @Override
    public ThreadPool threadPool() {
        return client.threadPool();
    }

    /**

Isso pode ser muito útil para configurar **recursos** que você precisa usar por toda a aplicação, e que são **compartilhados** entre as requisições e/ou que você precisa **limpar** depois. Por exemplo, um pool de conexões com o banco de dados ou o carregamento de um modelo de Aprendizado de Máquina compartilhado.

## Caso de uso { #use-case }

Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

```Python
contents = myfile.file.read()
```

/// note | Détails techniques `async`

Lorsque vous utilisez les méthodes `async`, **FastAPI** exécute les méthodes de fichier dans un pool de threads et les attend.

///

/// note | Détails techniques Starlette