This page covers how to customize the components that kubeadm deploys. For control plane components you can use flags in the ClusterConfiguration structure or patches per-node. For the kubelet and kube-proxy you can use KubeletConfiguration and KubeProxyConfiguration, accordingly. All of these options are possible via the kubeadm configuration API. For more details on each field in the configuration you can navigate to our API reference pages. Note:Customizing the CoreDNS deployment of kubead...| Kubernetes
Different ways to change the behavior of your Kubernetes cluster.| Kubernetes
VPC and Subnet Considerations| docs.aws.amazon.com
Optimize GPU obtainability for large-scale batch and AI workloads using GPUs, ProvisioningRequest, and Dynamic Workload Scheduler on GKE.| Google Cloud
Pods are the smallest deployable units of computing that you can create and manage in Kubernetes. A Pod (as in a pod of whales or pea pod) is a group of one or more containers, with shared storage and network resources, and a specification for how to run the containers. A Pod's contents are always co-located and co-scheduled, and run in a shared context. A Pod models an application-specific "logical host": it contains one or more application containers which are relatively tightly coupled.| Kubernetes
Kubernetes runs your workload by placing containers into Pods to run on Nodes. A node may be a virtual or physical machine, depending on the cluster. Each node is managed by the control plane and contains the services necessary to run Pods. Typically you have several nodes in a cluster; in a learning or resource-limited environment, you might have only one node. The components on a node include the kubelet, a container runtime, and the kube-proxy.| Kubernetes
Production-Grade Container Orchestration| Kubernetes
This page describes running Kubernetes across multiple zones. Background Kubernetes is designed so that a single Kubernetes cluster can run across multiple failure zones, typically where these zones fit within a logical grouping called a region. Major cloud providers define a region as a set of failure zones (also called availability zones) that provide a consistent set of features: within a region, each zone offers the same APIs and services.| Kubernetes
When you specify a Pod, you can optionally specify how much of each resource a container needs. The most common resources to specify are CPU and memory (RAM); there are others. When you specify the resource request for containers in a Pod, the kube-scheduler uses this information to decide which node to place the Pod on. When you specify a resource limit for a container, the kubelet enforces those limits so that the running container is not allowed to use more of that resource than the limit ...| Kubernetes
This page describes the lifecycle of a Pod. Pods follow a defined lifecycle, starting in the Pending phase, moving through Running if at least one of its primary containers starts OK, and then through either the Succeeded or Failed phases depending on whether any container in the Pod terminated in failure. Like individual application containers, Pods are considered to be relatively ephemeral (rather than durable) entities. Pods are created, assigned a unique ID (UID), and scheduled to run on ...| Kubernetes
Role-based access control (RBAC) is a method of regulating access to computer or network resources based on the roles of individual users within your organization. RBAC authorization uses the rbac.authorization.k8s.io API group to drive authorization decisions, allowing you to dynamically configure policies through the Kubernetes API. To enable RBAC, start the API server with the --authorization-config flag set to a file that includes the RBAC authorizer; for example: apiVersion: apiserver.| Kubernetes
You can constrain a Pod so that it is restricted to run on particular node(s), or to prefer to run on particular nodes. There are several ways to do this and the recommended approaches all use label selectors to facilitate the selection. Often, you do not need to set any such constraints; the scheduler will automatically do a reasonable placement (for example, spreading your Pods across nodes so as not place Pods on a node with insufficient free resources).| Kubernetes
