dstRecords, err = globalDNSConfig.Get(dstBucket)
		if err != nil {
			writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
			return
		}

		// Send PutObject request to appropriate instance (in federated deployment)
		core, rerr := getRemoteInstanceClient(r, getHostFromSrv(dstRecords))
		if rerr != nil {
			writeErrorResponse(ctx, w, toAPIError(ctx, rerr), r.URL)
			return
		}

		popts := minio.PutObjectPartOptions{

- ReplicaSets and Deployments should always count `.status.availableReplicas` at the correct time without a delay. This results in faster reconciliation of Deployment conditions and faster, unblocked Deployment rollouts. ([#132121](https://github.com/kubernetes/kubernetes/pull/132121), [@atiratree](https://github.com/atiratree)) [SIG Apps]

但你也可以利用平行與多行程 (multiprocessing)（讓多個行程同時運行） 的優勢來處理機器學習系統中的 **CPU 密集型**工作。

這一點，再加上 Python 是 **資料科學**、機器學習，尤其是深度學習的主要語言，讓 **FastAPI** 成為資料科學/機器學習 Web API 和應用程式（以及許多其他應用程式）的絕佳選擇。

想了解如何在生產環境中實現這種平行性，請參見 [部屬](deployment/index.md){.internal-link target=_blank}。

## `async` 和 `await`

現代 Python 版本提供一種非常直觀的方式定義非同步程式碼。這使得它看起來就像正常的「順序」程式碼，並在適當的時機「等待」。

當某個操作需要等待才能回傳結果，並且支援這些新的 Python 特性時，你可以像這樣編寫程式碼：

```Python

// uptime, etc..
type ServerProperties struct {
	Uptime       int64    `json:"uptime"`
	Version      string   `json:"version"`
	CommitID     string   `json:"commitID"`
	DeploymentID string   `json:"deploymentID"`
	Region       string   `json:"region"`
	SQSARN       []string `json:"sqsARN"`
}

// serverConnStats holds transferred bytes from/to the server
type serverConnStats struct {

build:android_x86_64 --platforms=@org_tensorflow//tensorflow/tools/toolchains/android:x86_64

# Build everything statically for Android since all static libs are later
# bundled together into a single .so for deployment.
build:android --dynamic_mode=off
# TODO(belitskiy): Remove once on Clang 20.
build:android --define=xnn_enable_avxvnniint8=false

# Sets the default Apple platform to macOS.

  Multi vCenter support is deprecated as of v1.21. If you have a Kubernetes cluster spanning across multiple vCenter servers, please consider moving all k8s nodes to a single vCenter Server. vSphere CSI Driver does not support Kubernetes deployment spanning across multiple vCenter servers.
  
  Support for these deprecations will be available till Kubernetes v1.24. ([#98546](https://github.com/kubernetes/kubernetes/pull/98546), [@divyenpatel](https://github.com/divyenpatel))

		return false, toAPIError(ctx, BucketRemoteTargetNotFound{Bucket: bucket})
	}
	return sameTarget, toAPIError(ctx, nil)
}

// performs a http request to remote endpoint to check if deployment id of remote endpoint is same as
// local cluster deployment id. This is to prevent replication to self, especially in case of a loadbalancer
// in front of MinIO.
func checkRemoteEndpoint(ctx context.Context, epURL *url.URL) error {
	reqURL := &url.URL{

파이썬이 **데이터 사이언스**, 머신러닝과 특히 딥러닝에 의 주된 언어라는 간단한 사실에 더해서, 이것은 FastAPI를 데이터 사이언스 / 머신러닝 웹 API와 응용프로그램에 (다른 것들보다) 좋은 선택지가 되게 합니다.

배포시 병렬을 어떻게 가능하게 하는지 알고싶다면, [배포](deployment/index.md){.internal-link target=_blank}문서를 참고하십시오.

## `async`와  `await`

최신 파이썬 버전에는 비동기 코드를 정의하는 매우 직관적인 방법이 있습니다. 이는 이것을 평범한 "순차적" 코드로 보이게 하고, 적절한 순간에 당신을 위해 "대기"합니다.

### Feature

- 'kubeadm: enhanced the "patches" functionality to be able to patch coredns
  deployment. The new patch target is called "corednsdeployment" (e.g. patch file
  "corednsdeployment+json.json"). This makes it possible to apply custom patches

- Kube-controller-manager: Fixes bug setting headless service labels on endpoints ([#85361](https://github.com/kubernetes/kubernetes/pull/85361),...