Split k0RDENT/k0S to it's own page as it's a separate product from MKE

partner-validated/index.rst

@@ -27,6 +27,7 @@ About Partner-Validated Configurations
    :hidden:
 
    self
+   k0rdent.rst
    mirantis-mke.rst
 
 Partner-validated configurations help end users who want to use
@@ -153,4 +154,4 @@ What happens if the partner requires changes to the NVIDIA GPU Operator that are
 
 How are CVE fixes managed for partner software that is used by the NVIDIA GPU Operator?
   The partner is responsible for managing security issues and is advised to proactively notify users of issues and fixes.
-  When the partner provides users with software, such as a containerized GPU driver, the partner is responsible for notifying and resolving issues with the container image.
+  When the partner provides users with software, such as a containerized GPU driver, the partner is responsible for notifying and resolving issues with the container image.

partner-validated/k0rdent.rst

@@ -0,0 +1,161 @@
+.. headings # #, * *, =, -, ^, "
+
+.. |prod-name-long| replace:: Mirantis k0RDENT
+.. |prod-name-short| replace:: k0RDENT
+
+#############################################
+|prod-name-long| with the NVIDIA GPU Operator
+#############################################
+
+
+*********************************************
+About |prod-name-short| with the GPU Operator
+*********************************************
+
+|prod-name-short| is as a "super control plane" designed to ensure the consistent provisioning and lifecycle
+management of Kubernetes clusters and the services that make them useful. The goal of the k0rdent project is
+to provide platform engineers with the means to deliver a distributed container management environment (DCME) 
+and enable them to compose unique internal developer platforms (IDP) to support a diverse range of complex 
+modern application workloads.
+
+The NVIDIA GPU Operator uses the operator framework within Kubernetes to automate
+both the deployment and management of all NVIDIA software components needed to provision NVIDIA GPUs.
+These components include the NVIDIA GPU drivers to enable CUDA, Kubernetes device plugin for GPUs,
+the NVIDIA Container Toolkit, automatic node labeling using GFD, DCGM based monitoring and others.
+
+
+******************************
+Validated Configuration Matrix
+******************************
+
+|prod-name-long| has self-validated with the following components and versions:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Version
+     - | NVIDIA
+       | GPU
+       | Operator
+     - | Operating
+       | System
+     - | Container
+       | Runtime
+     - Kubernetes
+     - Helm
+     - NVIDIA GPU
+     - Hardware Model
+
+   * - k0rdent 0.2.0 / k0s v1.31.5+k0s
+     - v24.9.2
+     - | Ubuntu 22.04
+     - containerd v1.7.24  with the NVIDIA Container Toolkit v1.17.4
+     - 1.31.5
+     - Helm v3
+     - | 2x NVIDIA RTX 4000 SFF Ada 20GB GDDR6 (ECC)
+     - | Supermicro SuperServer 6028U-E1CNR4T+
+
+       | 1000W Supermicro PWS-1K02A-1R
+
+       | 2x Intel Xeon E5-2630v4, 10C/20T 2.2/3.1 GHz LGA 2011-3 25MB 85W
+
+       | 32GB DDR4-2666 RDIMM, M393A4K40BB2-CTD6Q
+
+       | NVMe 960GB PM983 NVMe M.2, MZ1LB960HAJQ-00007
+
+       | 2 x NVIDIA RTX 4000 SFF Ada 20GB GDDR6 (ECC), 70W, PCIe 4.0x16, 4x
+
+       | 4x Mini DisplayPort 1.4a
+
+
+*************
+Prerequisites
+*************
+
+* A running |prod-name-short| managed cluster with at least one control plane node and two worker nodes.
+  The recommended configuration is at least three control plane nodes and at least two worker nodes.
+
+* At least one worker node with an NVIDIA GPU physically installed.
+  The GPU Operator can locate the GPU and label the node accordingly.
+
+* The kubeconfig file for the |prod-name-short| managed cluster on the seed node.
+  You can get the file from the |prod-name-short| control plane.
+
+* You have access to the |prod-name-short| cluster.
+
+
+*********
+Procedure
+*********
+
+Perform the following steps to prepare the |prod-name-short| cluster:
+
+#. Install template to k0rdent
+
+   .. code-block:: console
+
+      $ helm install gpu-operator oci://ghcr.io/k0rdent/catalog/charts/gpu-operator-service-template \
+          --version 24.9.2 -n kcm-system
+
+#. Verify service template:
+
+   .. code-block:: console
+
+      $ kubectl get servicetemplates -A
+
+   *Example Output*
+
+   .. code-block:: output
+
+      NAMESPACE    NAME                          VALID
+      kcm-system   gpu-operator-24-9-2           true
+
+#. Deploy service template to child cluster:
+
+   .. code-block:: console
+
+      apiVersion: k0rdent.mirantis.com/v1alpha1
+      kind: MultiClusterService
+      metadata:
+        name: gpu-operator
+      spec:
+        clusterSelector:
+          matchLabels:
+            group: demo
+      serviceSpec:
+        services:
+        - template: gpu-operator-24-9-2
+          name: gpu-operator
+          namespace: gpu-operator
+          values: |
+            operator:
+              defaultRuntime: containerd
+            toolkit:
+              env:
+                - name: CONTAINERD_CONFIG
+                value: /etc/k0s/containerd.d/nvidia.toml
+                - name: CONTAINERD_SOCKET
+                value: /run/k0s/containerd.sock
+                - name: CONTAINERD_RUNTIME_CLASS
+                value: nvidia
+
+
+The |prod-name-short| managed clusters will now have the NVIDIA GPU operator
+
+*************************************************
+Verifying |prod-name-short| with the GPU Operator
+*************************************************
+
+Refer to :external+gpuop:ref:`running sample gpu applications` to verify the installation.
+
+***************
+Getting Support
+***************
+
+Refer to the k0RDENT product documentation for information about working with k0RDENT.
+
+*******************
+Related information
+*******************
+
+* https://docs.k0rdent.io/v0.2.0/

partner-validated/mirantis-mke.rst

@@ -44,27 +44,6 @@ Validated Configuration Matrix
      - NVIDIA GPU
      - Hardware Model
 
-   * - k0s v1.31.5+k0s / k0rdent 0.1.0
-     - v24.9.2
-     - | Ubuntu 22.04
-     - containerd v1.7.24  with the NVIDIA Container Toolkit v1.17.4
-     - 1.31.5
-     - Helm v3
-     - | 2x NVIDIA RTX 4000 SFF Ada 20GB GDDR6 (ECC)
-     - | Supermicro SuperServer 6028U-E1CNR4T+
-
-       | 1000W Supermicro PWS-1K02A-1R
-
-       | 2x Intel Xeon E5-2630v4, 10C/20T 2.2/3.1 GHz LGA 2011-3 25MB 85W
-
-       | 32GB DDR4-2666 RDIMM, M393A4K40BB2-CTD6Q
-
-       | NVMe 960GB PM983 NVMe M.2, MZ1LB960HAJQ-00007
-
-       | 2 x NVIDIA RTX 4000 SFF Ada 20GB GDDR6 (ECC), 70W, PCIe 4.0x16, 4x
-
-       | 4x Mini DisplayPort 1.4a
-
    * - MKE 3.8
      - v24.9.2
      - | Ubuntu 22.04