> ## Documentation Index > Fetch the complete documentation index at: https://docs.scitix.ai/llms.txt > Use this file to discover all available pages before exploring further. # Using a CKS Cluster > Learn the basic workflow for running containerized applications on CKS clusters, including preparing images, creating clusters, configuring storage, deploying workloads, and viewing status. CKS clusters are suitable for hosting containerized training tasks, inference services, batch processing tasks, or long-running business applications. Without going into specific parameter configurations, this document helps you understand the basic workflow: preparing a business image, creating a cluster, configuring storage, deploying workloads, configuring access endpoints, and monitoring operational status. ## Usage workflow **1. Prepare a Container Image** Kubernetes workloads are typically delivered with their runtime environments through container images. Before submitting a workload, build or obtain the required image for your application, and push it to a container image registry. After the image is prepared, record the image address and tag required for subsequent workloads. For instructions on pushing and pulling images, see [Push and Pull Images](https://docs.scitix.ai/gpu-cloud/registry/push-pull-container-images.md). **2. Create a CKS Cluster** Log in to the [GPU Cloud console](https://console.scitix.ai/gpu-cloud/home), go to the **Clusters** page, and create a CKS cluster. When creating a cluster, you typically need to confirm the Kubernetes version, node pool configuration, and other settings. For details, see [Create a CKS Cluster](https://docs.scitix.ai/gpu-cloud/cluster/create-kubernetes-cluster.md). **3. Configure cluster access and basic resources** After the cluster is created, prepare the access credentials and basic resources according to the subsequent operation method. * If you need to manage the cluster through `kubectl`, obtain the KubeConfig. For details, see [Access a CKS Cluster Using KubeConfig](https://docs.scitix.ai/gpu-cloud/cluster/access-cluster-with-kubeconfig.md). * If you need to isolate resources by team, project, or environment, create namespaces. For details, see [Organize Namespace Resources](https://docs.scitix.ai/gpu-cloud/cluster/organize-resources-with-namespaces.md). * If workloads depend on non-sensitive configuration or sensitive information, create a ConfigMap or Secret. For details, see [Manage ConfigMaps and Secrets](https://docs.scitix.ai/gpu-cloud/cluster/configure-configmap-and-secret.md). * If your business depends on cluster components, select and install components as needed. For details, see [Select and Install Cluster Components](https://docs.scitix.ai/gpu-cloud/cluster/choose-and-install-cluster-components.md). **4. Configure storage and data access** After the cluster becomes available, configure storage and data access based on the workload's data access method. * When multiple nodes or workloads need to share data through a file system, you can use the GPFS file system and make it available to workloads through Kubernetes storage objects such as PVs, PVCs, or StorageClasses. For details, see [CKS cluster persistent storage overview](https://docs.scitix.ai/gpu-cloud/cluster/persistent-storage-overview.md). * When you need to store datasets, model files, inference inputs, training outputs, or archived logs, and upload or download them from a local environment, CES instances, or automated scripts, you can use OSS. For details, see [OSS object storage overview](https://docs.scitix.ai/gpu-cloud/storage/oss-overview.md). If the workload needs to mount a directory directly, prepare persistent storage before deployment. If training data, model weights, or output results need to be retained long term, plan the storage location and access permissions in advance. **5. Submit and run workloads** Once the cluster is available, submit workloads for training, inference, batch processing, or online services based on your business requirements. Before submitting, we recommend confirming the workload type, image address and labels, CPU/memory/GPU resource configuration, namespace, configuration items, storage mounts or object storage access methods, and log output path. For deployment and troubleshooting procedures, see [Deploy and Troubleshoot Workloads](https://docs.scitix.ai/gpu-cloud/cluster/deploy-and-troubleshoot-workloads.md). **6. Configure Service Access** After the workload is running, you can configure a Service or Ingress based on the access scope. * Access endpoints within the cluster are typically provided through Services. For details, see [Create and Manage Services](https://docs.scitix.ai/gpu-cloud/cluster/create-and-manage-service.md). * HTTP or HTTPS access endpoints are typically provided through Ingresses. If your workload needs to be accessed from outside the cluster, confirm the access domain name or port, target service, and access endpoint. For details, see [Expose Cluster Services Using Ingress](https://docs.scitix.ai/gpu-cloud/cluster/expose-services-with-ingress.md). **7. Observe Runtime Status** During workload execution, you can use monitoring to view cluster, node, or workload metrics, and use logs to view task output or application errors. For details, see [View Cluster Overview Monitoring](https://docs.scitix.ai/gpu-cloud/monitoring/view-cluster-overview-monitoring.md) and [Query and Analyze Logs](https://docs.scitix.ai/gpu-cloud/log/query-logs.md). To continuously monitor cluster or workload exceptions, you can configure a notifier. For details, see [Create a Custom Alert Rule](https://docs.scitix.ai/gpu-cloud/alarm/create-custom-alert-rule.md). **8. Clean Up Resources** After the task is complete, clean up workloads, image artifacts, and stored data that are no longer needed based on your business requirements. Before deleting a cluster, repository, or storage resource, ensure that no running tasks will be affected and that any data that needs to be retained has been backed up or migrated. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.