vgpu-manager: enable kernel module configuration via KernelModuleConfig

[shivakunv]

VF-8 Ability to set vGPU host driver options
vGPU host driver options are set via nvidia.ko kernel module parameters in /etc/modprobe.d/nvidia.conf. For example:

options nvidia NVreg_RegistryDwords="RmPVMRL=value"

GPU Operator currently supports setting of kernel module parameters for NVIDIA guest drivers via a module.conf file that's passed to GPU Operator via a ConfigMap.

A similar mechanism will be used to support module parameters for the vGPU host kernel driver.

testing

# git clone https://github.com/NVIDIA/gpu-driver-container.git
# before mering of this PR: 
# git fetch -all 
# git checkout gpumanagerkernelmodulespec 
#build vgpu-manager
# copy NVIDIA-Linux-x86_64-570.124.06-vgpu-kvm.run from smb server to vgpu-manager 
export VGPU_DRIVER_VERSION="570.124.06"
export IMAGE_HOST_NAME="vgpu-manager"
export DIST=ubuntu22.04
export IMAGE_NAME=nvcr.io/ea-cnt/nv_only
#build
VGPU_HOST_DRIVER_VERSION=${VGPU_DRIVER_VERSION} IMAGE_NAME=${IMAGE_NAME}/${IMAGE_HOST_NAME} make build-vgpuhost-${DIST}
#push
VGPU_GUEST_DRIVER_VERSION=${DRIVER_VERSION} IMAGE_NAME=${IMAGE_NAME}/${IMAGE_GUEST_NAME} make push-vgpuguest-${DIST}


# on system (collossus)
GRUB_CMDLINE_LINUX_DEFAULT="quiet splash intel_iommu=on iommu=pt"
sudo update-grub
# reboot


# Complete cleanup
kubectl delete namespace nvidia-gpu-operator --wait=true
kubectl delete secret -n nvidia-gpu-operator ngc-secret
kubectl delete namespace nvidia-gpu-operator
kubectl delete clusterrole gpu-operator
kubectl delete clusterrolebinding  gpu-operator
kubectl delete ClusterPolicy  gpu-operator
kubectl delete clusterpolicy  gpu-operator
kubectl delete clusterpolicy cluster-policy
kubectl delete clusterrole gpu-operator-node-feature-discovery
kubectl delete clusterrole gpu-operator-node-feature-discovery-gc
kubectl delete clusterrolebinding  gpu-operator-node-feature-discovery
kubectl delete clusterrolebinding  gpu-operator-node-feature-discovery-gc
kubectl delete crd -n nvidia-gpu-operator clusterpolicies.nvidia.com nvidiadrivers.nvidia.com
helm uninstall -n nvidia-gpu-operator $(helm list -n nvidia-gpu-operator -q)
kubectl delete clusterpolicy --all



export NGC_API_KEY=ngc_api_key
export REGISTRY_SECRET_NAME=ngc-secret
export PRIVATE_REGISTRY=nvcr.io/ea-cnt/nv_only
export VGPU_DRIVER_VERSION=570.124.06


# Wait 30 seconds for cleanup
sleep 30

# Recreate everything
kubectl create namespace nvidia-gpu-operator
kubectl label --overwrite namespace nvidia-gpu-operator pod-security.kubernetes.io/enforce=privileged

kubectl create secret docker-registry ${REGISTRY_SECRET_NAME} \
  --docker-server=${PRIVATE_REGISTRY} \
  --docker-username='$oauthtoken' \
  --docker-password=${NGC_API_KEY} \
  -n nvidia-gpu-operator

cat <<EOF > nvidia.conf
options nvidia NVreg_EnableGpuFirmwareLogs=1
EOF


# create params configmap
kubectl create configmap kernel-module-params \
  -n nvidia-gpu-operator \
  --from-file=nvidia.conf=./nvidia.conf

# Remove the failing vGPU config label
kubectl label node ipp1-0555 nvidia.com/vgpu.config-

# Install with vGPU Manager
helm install gpu-operator nvidia/gpu-operator \
  -n nvidia-gpu-operator \
  --set operator.repository=ghcr.io/nvidia \
  --set operator.version=280c5460 \
  --set driver.enabled=false \
  --set vgpuManager.enabled=true \
  --set vgpuManager.repository=${PRIVATE_REGISTRY} \
  --set vgpuManager.image=vgpu-manager \
  --set vgpuManager.version="${VGPU_DRIVER_VERSION}" \
  --set vgpuManager.imagePullSecrets[0]="${REGISTRY_SECRET_NAME}" \
  --set vgpuManager.kernelModuleConfig.name="kernel-module-params"




#copy nvidia.com_clusterpolicies.yaml from config/crd/bases/nvidia.com_clusterpolicies.yaml to test system 

kubectl apply -f nvidia.com_clusterpolicies.yaml

kubectl patch clusterpolicy cluster-policy --type='json' -p='[                                                                           
  {
    "op": "add",
    "path": "/spec/vgpuManager/kernelModuleConfig",
    "value": {
      "name": "kernel-module-params"
    }
  }
]'


kubectl patch clusterpolicy cluster-policy --type='merge' -p '{"spec":{"sandboxWorkloads":{"enabled":true,"defaultWorkload":"vm-vgpu"}}}'

kubectl label node ipp1-0555 nvidia.com/gpu.workload.config=vm-vgpu


cat /proc/driver/nvidia/params  | grep EnableGpuFirmwareLogs

should o/p updated value from nvidia.conf configmap 

[coveralls]

coverage: 25.65% (+0.2%) from 25.43%
when pulling a54b7e1 on gpumanagerkernelmodulespec
into 96351fc on main.

[shivakunv]

PTAL:
NVIDIA/gpu-driver-container#512

[rajathagasthya]

@shivakunv Could you rebase this PR?

[shivakunv]

@shivakunv Could you rebase this PR?

done

[cdesiniotis]

This should be removed. The transformPeerMemoryContainer() function is only relevant for the main driver daemonset, not the vGPU manager daemonset. The vGPU manager daemonset does not install the nvidia-peermem module.

Suggested change

	if config.VGPUManager.KernelModuleConfig != nil && config.VGPUManager.KernelModuleConfig.Name != "" {
	// note: transformVGPUManagerContainer() will have already created a Volume backed by the ConfigMap.
	// Only add a VolumeMount for nvidia-vgpu-manager-ctr.
	volumeMounts, _, err := createConfigMapVolumeMounts(n, config.VGPUManager.KernelModuleConfig.Name, driversDir)
	if err != nil {
	return fmt.Errorf("failed to create ConfigMap VolumeMounts for vGPU manager kernel module configuration: %w", err)
	}
	obj.Spec.Template.Spec.Containers[i].VolumeMounts = append(obj.Spec.Template.Spec.Containers[i].VolumeMounts, volumeMounts...)
	}

[cdesiniotis]

Can we add some unit tests to transforms_test.go for TransformVGPUManager()? One of the unit tests can verify that the kernel module config map is getting rendered correctly.