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Being a cluster administrator can come with its own challenges, especially with environments that carry out-of-tree (OOT) cluster modules. Upgrading device plug-ins or different kernel versions can be prone to errors when doing so one-by-one. This is where the Kernel Module Management Operator (KMM) comes in, allowing admins to build, sign, and deploy multiple kernel versions for any kernel module.

KMM is designed to accommodate multiple kernel versions at once for any kernel module. Using this operator can also leverage the hardware acceleration capabilities of Intel Center GPU Flex, allowing for seamless node upgrades, faster application processing, and quicker module deployment.

Setting up KMM

KMM requires an already working OpenShift environment and a registry to push images to. KMM can be installed using OperatorHub in the OpenShift console or via the following kmm.yaml:

apiVersion: v1
kind: Namespace
  name: openshift-kmm
kind: OperatorGroup
  name: kernel-module-management
  namespace: openshift-kmm
kind: Subscription
  name: kernel-module-management
  namespace: openshift-kmm
  channel: "stable"
  installPlanApproval: Automatic
  name: kernel-module-management
  source: redhat-operators
  sourceNamespace: openshift-marketplace


oc apply -f kmm.yaml

Enabling hardware acceleration

Once installed, KMM can compile and install kernel module drivers for your hardware. Admins can then integrate with the Node Feature Discovery Operator (NFD), which detects hardware features on nodes and labels them for selector use later. NFD automatically adds labels to the nodes that present some characteristics, including if the node has a GPU and which GPU it has.

In using NFD labels, specific custom kernel versions can be targeted for your module deployment and enablement, so that only hosts with the required kernel and the required hardware are enabled for driver activation. This ensures that only compatible drivers are installed on nodes with a supported kernel, which is what makes KMM so valuable.

With NFD integration, KMM can more easily deploy Intel GPU kernels to the intended nodes, while leaving any other nodes unaffected. This process is detailed more in the site:

Final thoughts

This is just one aspect of KMM and kernel modules that can be utilized to reduce the amount of effort required to manage updates in multiple nodes. KMM will let you handle out-of-tree kernel modules in a seamless fashion, until you can later incorporate your drivers upstream and include them in your distribution.

KMM is a community project, which you can test on upstream Kubernetes. There is also a Slack community channel where you can chat with fellow developers and experts about more ways to apply KMM to your own environment.

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