While containers provide isolated application execution environment, orchestration platforms allow seamless scaling by helping replicate and manage containers. MinIO extends this by adding isolated storage environment for each tenant.

        <iframe class="w-100 h-100 position-absolute" frameborder="0"
                src="<%=request.getContextPath()%>${serverPath}<%= response.encodeURL("/_plugin/kopf/") %>"
                seamless></iframe>
    </div>

    <jsp:include page="/WEB-INF/view/common/admin/footer.jsp"></jsp:include>
</div>
<jsp:include page="/WEB-INF/view/common/admin/foot.jsp"></jsp:include>
</body>

## MinIO Deployment on Kubernetes

There are multiple options to deploy MinIO on Kubernetes:

- MinIO-Operator: Operator offers seamless way to create and update highly available distributed MinIO clusters. Refer [MinIO Operator documentation](https://github.com/minio/minio-operator/blob/master/README.md) for more details.

	"rows":        contentTypePlain,
	"rowspan":     contentTypePlain,
	"sandbox":     contentTypeUnsafe,
	"spellcheck":  contentTypePlain,
	"scope":       contentTypePlain,
	"scoped":      contentTypePlain,
	"seamless":    contentTypePlain,
	"selected":    contentTypePlain,
	"shape":       contentTypePlain,
	"size":        contentTypePlain,
	"sizes":       contentTypePlain,
	"span":        contentTypePlain,

| <<xctest_plugin.adoc#xctest_plugin,XCTest>>
| Adds a target representing the XCTest bundle to the project file.
| The tested target is also configured to enable a seamless workflow from the IDE.
|===

[[sec:xcode_usage]]
== Usage

.Applying the Xcode Plugin
====
include::sample[dir="snippets/ide/xcode/kotlin",files="build.gradle.kts[tags=apply-plugin]"]

``MinIO`` community welcomes your contribution. To make the process as seamless as possible, we recommend you read this contribution guide.

## Development Workflow

# Tensorflow SavedModel to StableHLO (tf-to-stablehlo-translate)

Converts TensorFlow models (SavedModel or MLIR module) to StableHLO MLIR
modules, preserving model structure and signatures. It enables seamless
integration of TensorFlow models into MLIR-based compiler frameworks for further
optimization and deployment.

## C++ APIs

```bash
tf-to-stablehlo-translate \
    --input-path=/path/to/model \

)

var (
	uploadKubeadmConfigLongDesc = fmt.Sprintf(cmdutil.LongDesc(`
		Upload the kubeadm ClusterConfiguration to a ConfigMap called %s in the %s namespace.
		This enables correct configuration of system components and a seamless user experience when upgrading.

		Alternatively, you can use kubeadm config.
		`), kubeadmconstants.KubeadmConfigConfigMap, metav1.NamespaceSystem)

	uploadKubeadmConfigExample = cmdutil.Examples(`

oor.io/series/fastapi-react-poll-app title: Building the Poll App From Django Tutorial With FastAPI And React - author: Silvan Melchior author_link: https://github.com/silvanmelchior link: https://blog.devgenius.io/seamless-fastapi-configuration-with-confz-90949c14ea12 title: Seamless FastAPI Configuration with ConfZ - author: Kaustubh Gupta author_link: https://medium.com/@kaustubhgupta1828/ link: https://levelup.gitconnected.com/5-advance-features-of-fastapi-you-should-try-7c0ac7eebb3e title: 5...

// If the cluster is removed, the T.Close() method will be called.
// Constructors MUST not do blocking IO; they will block other operations.
// During a cluster update, a new component is constructed before the old one is removed for seamless migration.
func BuildMultiClusterComponent[T ComponentConstraint](c ComponentBuilder, constructor func(cluster *Cluster) T) *Component[T] {
	comp := &Component[T]{
		constructor: constructor,