[DOC] Fix docs sanity

.github/workflows/linting.yml

@@ -2,15 +2,11 @@ name: Linting
 
 on:
   push:
-    branches: 
-    - develop
+    branches:
     - main
-    - staging
   pull_request:
-    branches: 
-    - develop
+    branches:
     - main
-    - staging
 
 jobs:
   call-workflow-passing-data:

.markdownlint-cli2.jsonc

@@ -0,0 +1,7 @@
+{
+  "globs": ["**/*.md"],
+  "ignores": [
+    "**/vendor/**",
+    "**/.git/**"
+  ]
+}

.wordlist.txt

@@ -1,61 +1,203 @@
+amd
+AFID
+Affectioned
+AGFHC
+Allocatable
+ACS
+AKS
+ARI
 Autobuild
+bb
+burnin
+CheckUnitStatus
+CleanupPreState
 CLI
+CN
 CNI
+computePartition
+ConfigMap
+ConfigMaps
+ConditionalWorkflows
+CoreOS
+CPX
+CrashLoopBackOff
 CRD
-DKMS
-DNS
-DockerHub
-GPUs
-HTTPS
-KMM
-MOK
-NFD
-OLM
-PCI
-RBAC
-ROCm
-TLS
-YAML
-allocatable
-bool
-calico
-clusterIP
-config
-configmap
-cryptographic
+CRDs
+CRs
+CronJob
+Customizable
 daemonset
+daemonsets
+DaemonSet
+Daemonsets
+DCM
+dcm
+Depricated
+deivce
 DeviceConfig
+DeviceIDs
+DevicePlugin
+DevicePluginArguments
+DevicePluginImage
+DevicePluginImagePullPolicy
+DevicePluginSpec
+DKMS
+dma
+DMC
+DNS
 Dockerfile
+DockerHub
+DPX
+DriverToolkit
+ECC
+EnableNodeLabeller
+ErrImagePull
 flannel
+GPUs
+gpup
+Grafana
+GracePeriodSeconds
+gst
+gpuagent
+gpuClientSystemdServices
+GKE
+hbm
+HealthThresholds
+Helmify
 hostname
 hostnames
+HSIO
+HTTPS
+iet
+IfNotPresent
+IgnoreDaemonSets
+IgnoreNamespaces
+ImageStream
+jq
 json
 kaniko
+KMM
+kmod
+kubectl
+Kubelet
+KubeVirt
+Kuberntes
 Kubernetes
 kubeconfig
 labeller
+Labeler
 lifecycle
+lvl
 MachineConfig
-modprobe
+MachineConfigOperator
+MCO
+Mericsclient
+MaxParallelWorkflows
+MaxUnavailable
+MCO
+memoryPartition
+MetricsExporter
+MetricsExporterSpec
+MinIO
+Minio
+MOK
+MTLS
 namespace
+NFD
 NMC
+NodeCondition
+NodeDrainPolicy
+NodeIP
+NodeLabeller
+NodeLabellerArguments
+NodeLabellerImage
+NodeLabellerImagePullPolicy
+Nodelabeller
+nodename
+Nodeport
 NodePort
+NodeRemediationLabels
+NodeRemediationTaints
+NoExecute
+NPD
 NotReady
-OperatorHub
+numGPUsAssigned
+Observability
+oc
+OLM
+OOM
 OpenShift
+OperatorHub
+Openshift
+parition
+paritioning
+pbqt
+pebb
+PCI
+pcie
+perf
+PFs
+Perses
 plugin
+PodIP
 PreFlight
+PreStateDB
 prometheus
+Promethues
 quay
-rocminfo
+QPX
+RAS
+RBAC
+Redhat
 RedHat
+RHCOS
+RMA
+rocminfo
+rochpl
+ROCm
 runtime
+SAR
 schedulable
 SDK
+selfcheck
+ServiceAccounts
+ServiceMonitor
+ServiecMonitor
+skippedGPUs
+Slinkproject
+SlinkProject
+Slrum
+Slurm
+SPX
+StopOnFailure
+SubjectAccessReview
 systemd
+TestCategory
+TesterImage
+TimeoutSeconds
+TokenReview
+Tolerations
+TODO
+TLS
+tolerations
+tst
+TtlForFailedWorkflows
 ubuntu
+UI
+UID
+UNCORRECT
+Uncordoning
+uninstallation
 unschedulable
+Upgrademgr
+UpgradePolicy
 validation
 verison
+VC
+VCN
+VFIO
+VFs
+VMs
 webhook
-uninstallation
+xgmi
+YAML

README.md

@@ -83,20 +83,19 @@ Installation Options
 > It is strongly recommended to use AMD-optimized KMM images included in the operator release. This is not required when installing the GPU Operator on Red Hat OpenShift.
 
 ### 3. Install Custom Resource
-After the installation of AMD GPU Operator:
-  * By default there will be a default `DeviceConfig` installed. If you are using default `DeviceConfig`, you can modify the default `DeviceConfig` to adjust the config for your own use case. `kubectl edit deviceconfigs -n kube-amd-gpu default`
-  * If you installed without default `DeviceConfig` (either by using `--set crds.defaultCR.install=false` or installing a chart prior to v1.3.0), you need to create the `DeviceConfig` custom resource in order to trigger the operator start to work. By preparing the `DeviceConfig` in the YAML file, you can create the resouce by running ```kubectl apply -f deviceconfigs.yaml```.
-  * For custom resource definition and more detailed information, please refer to [Custom Resource Installation Guide](https://dcgpu.docs.amd.com/projects/gpu-operator/en/latest/installation/kubernetes-helm.html#install-custom-resource).
 
-  * Potential Failures with default `DeviceConfig`: 
+After the installation of AMD GPU Operator:
 
-    a. Operand pods are stuck in ```Init:0/1``` state: It means your GPU worker doesn't have inbox GPU driver loaded. We suggest check the [Driver Installation Guide]([./drivers/installation.md](https://instinct.docs.amd.com/projects/gpu-operator/en/latest/drivers/installation.html#driver-installation-guide)) then modify the default `DeviceConfig` to ask Operator to install the out-of-tree GPU driver for your worker nodes.
+* By default there will be a default `DeviceConfig` installed. If you are using default `DeviceConfig`, you can modify the default `DeviceConfig` to adjust the config for your own use case. `kubectl edit deviceconfigs -n kube-amd-gpu default`
+* If you installed without default `DeviceConfig` (either by using `--set crds.defaultCR.install=false` or installing a chart prior to v1.3.0), you need to create the `DeviceConfig` custom resource in order to trigger the operator start to work. By preparing the `DeviceConfig` in the YAML file, you can create the resouce by running ```kubectl apply -f deviceconfigs.yaml```.
+* For custom resource definition and more detailed information, please refer to [Custom Resource Installation Guide](https://dcgpu.docs.amd.com/projects/gpu-operator/en/latest/installation/kubernetes-helm.html#install-custom-resource).
+* Potential Failures with default `DeviceConfig`:
+  a. Operand pods are stuck in ```Init:0/1``` state: It means your GPU worker doesn't have inbox GPU driver loaded. We suggest check the [Driver Installation Guide]([./drivers/installation.md](https://instinct.docs.amd.com/projects/gpu-operator/en/latest/drivers/installation.html#driver-installation-guide)) then modify the default `DeviceConfig` to ask Operator to install the out-of-tree GPU driver for your worker nodes.
   `kubectl edit deviceconfigs -n kube-amd-gpu default`
-
-    b. No operand pods showed up: It is possible that default `DeviceConfig` selector `feature.node.kubernetes.io/amd-gpu: "true"` cannot find any matched node.
-      * Check node label `kubectl get node -oyaml | grep -e "amd-gpu:" -e "amd-vgpu:"`
-      * If you are using GPU in the VM, you may need to change the default `DeviceConfig` selector to `feature.node.kubernetes.io/amd-vgpu: "true"`
-      * You can always customize the node selector of the `DeviceConfig`.
+  b. No operand pods showed up: It is possible that default `DeviceConfig` selector `feature.node.kubernetes.io/amd-gpu: "true"` cannot find any matched node.
+  * Check node label `kubectl get node -oyaml | grep -e "amd-gpu:" -e "amd-vgpu:"`
+  * If you are using GPU in the VM, you may need to change the default `DeviceConfig` selector to `feature.node.kubernetes.io/amd-vgpu: "true"`
+  * You can always customize the node selector of the `DeviceConfig`.
 
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