VGPU-Manager

A k8s device plugin for managing and allocating vGPU devices, support multi container and multi GPU virtualization and rich scheduling strategies.

Project objectives:

• Efficient scheduling performance
• Ensure the security of container resource isolation
• Simplify GRPC within containers
• Support CUDA 12.x driver version
• Support CGroupV1 and CGroupV2
• Dual scheduling policy for nodes and devices
• Provide GPU monitoring indicators
• Idle computing power of dynamic balancing equipment
• GPU device uses virtual memory after exceeding memory limit
• Rescheduling device allocation failed pod
• Webhook dynamic admission, fixing some non-standard pod configurations
• Provide the optimal topology allocation for NUMA and NVLink
• Compatible with open-gpu-kernel-modules
• Compatible with hot swappable devices and expansion capabilities
• Compatible with Volcano Batch Scheduler
• Support dynamic resource allocation (DRA)

Note: Checking indicates that the function has been completed, while unchecking indicates that the function has not been completed or is planned to be implemented.

Prerequisite

• Kubernetes v1.17+ (Install using helm chart method)
• docker / containerd / cri-o (other container runtime not tested)
• Nvidia Container Toolkit (Configure nvidia container runtime)

Build

• compile binary

make build

Note: After the program compilation is completed, three binary files will be generated in the /bin directory

• build docker image and push it

make docker-build docker-push IMG=<tag>

Deploy

precondition: nvidia-container-toolkit must be installed and correctly configure the default container runtime

Label the node where the device plugin will be deployed: vgpu-manager-enable=enable

kubectl label node <nodename> vgpu-manager-enable=enable

Deploy directly using YAML files

kubectl apply -f deploy/vgpu-manager-scheduler.yaml
kubectl apply -f deploy/vgpu-manager-deviceplugin.yaml

Note that the scheduler version needs to be modified according to the cluster version, If the scheduler version is v1.25.x or above, you can directly modify the imageTag for use, otherwise you need to modify the scheduler configuration file.

      containers:
        - image: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.28.15
          imagePullPolicy: IfNotPresent
          name: scheduler

If you want to install the webhook service component, please ensure that the cluster has installed cert-manager.

The Webhook service requires the use of cert-manager to generate HTTPS certificates and manage certificate renewal policies.

kubectl apply -f deploy/vgpu-manager-webhook.yaml

Helm charts deployment

Modify the configuration in values.yaml according to the node environment and requirements.

If you want to install the webhook service component, please ensure that the cluster has installed cert-manager, then modify file helm/values.yaml values.webhook.enabled=true.

Use the following command for deployment

helm install vgpu-manager ./helm/ -n kube-system

Verify the installation of vgpu-manager-device-plugin and vgpu-manager-scheduler

kubectl get pods -n kube-system

Uninstall

Uninstall directly according to YAML

kubectl delete -f deploy/vgpu-manager-scheduler.yaml
kubectl delete -f deploy/vgpu-manager-deviceplugin.yaml
kubectl delete -f deploy/vgpu-manager-webhook.yaml

Helm charts uninstallation

helm uninstall vgpu-manager -n kube-system 

Example of use

Submit a VGPU container application with 10% computing power and 1GB of memory

Note: vGPU pod requires specifying the scheduler name and the number of vGPU devices to be requested by the container.

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
  namespace: default
spec:    
  schedulerName: vgpu-scheduler  # Specify scheduler (default: vgpu-manager)
  terminationGracePeriodSeconds: 0
  containers:
  - name: default
    image: nvidia/cuda:12.4.1-devel-ubuntu20.04
    command: ["sleep", "9999999"]
    resources:
      limits:
        cpu: 2
        memory: 4Gi
        nvidia.com/vgpu-number: 1     # Allocate one gpu
        nvidia.com/vgpu-cores: 10      # Allocate 10% of computing power
        nvidia.com/vgpu-memory: 1024  # Allocate memory (default: Mib)

Check that the container meets expectations

root@gpu-pod1:/# nvidia-smi 
[vGPU INFO(34|loader.c|1043)]: loaded nvml libraries
[vGPU INFO(34|loader.c|1171)]: loaded cuda libraries
Mon Mar  3 03:04:34 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.86.10              Driver Version: 570.86.10      CUDA Version: 12.8     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce GTX 1050 Ti     Off |   00000000:01:00.0 Off |                  N/A |
| N/A   41C    P8             N/A / 5001W |       0MiB /   1024MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Scheduling Policy

Support scheduling policies for both node and device dimensions

• binpack: Choose the busiest nodes or devices to improve resource utilization and reduce fragmentation.
• spread: Select the most idle node or device to distribute tasks and isolate faults.

Usage

Add annotations on the vGPU pod: nvidia.com/node-scheduler-policy or nvidia.com/device-scheduler-policy

metadata:
  annotations:
    nvidia.com/node-scheduler-policy: spread
    nvidia.com/device-scheduler-policy: binpack

Select Devices

Support using annotations to select the device type and uuid to be selected for the pod.

Device TYPE

Add annotations to vGPU pod to select or exclude device types to be scheduled: nvidia.com/include-gpu-type nvidia.com/exclude-gpu-type

Example: Choose to use A10 and exclude A100

metadata:
  annotations:
    nvidia.com/include-gpu-type: "A10"  
    nvidia.com/exclude-gpu-type: "A100"

Note: If there are multiple devices separated by commas

Device UUID

Add annotations to vGPU pod to select or exclude device uuids to be scheduled: nvidia.com/include-gpu-uuid nvidia.com/exclude-gpu-uuid

Example: Select a GPU uuid

metadata:
  annotations:
    nvidia.com/include-gpu-uuid: GPU-49aa2e6a-33f3-99dd-e08b-ea4beb0e0d28

Example: Excluded a GPU uuid

metadata:
  annotations:
    nvidia.com/exclude-gpu-uuid: GPU-49aa2e6a-33f3-99dd-e08b-ea4beb0e0d28

Note: If there are multiple devices separated by commas

Compute Policy

Support the use of annotations on nodes or pods to configure the computing policy to be used: nvidia.com/vgpu-compute-policy

Supported policy values:

• fixed: Fixed GPU core limit to ensure that task core utilization does not exceed the limit (Default strategy)
• balance: Allow tasks to run beyond the limit when there are still remaining resources on the GPU, improving the overall core utilization of the GPU
• none: No core restriction effect, competing for computing power on its own

Note: If policies are configured on both Node and Pod, the configuration on Pod takes priority; otherwise, the policy on Node is used.

Feature Gate

The device plugin of vgpu-manager has implemented some special functions that require adding the command-line parameter --feature-gates to enable.

CorePlugin

• action scope: device-plugin

Opening the core plugin will report the number of virtual cores to the kubelet node.

Use the command --feature-gates=CorePlugin=true to open the feature.

After opening the feature gate, check the status of the corresponding node to see the registered resource name nvidia.com/vgpu-cores.

status:
  allocatable:
    nvidia.com/vgpu-cores: "200"
  capacity:
    nvidia.com/vgpu-cores: "200"

Tips: It may be useful in scenarios where node resource constraints such as ResourceQuota are required.

MemoryPlugin

• action scope: device-plugin

Opening the memory plugin will report virtual memory to the kubelet node.

Use the command --feature-gates=MemoryPlugin=true to open the feature.

After opening the feature gate, check the status of the corresponding node to see the registered resource name nvidia.com/vgpu-memory.

status:
  allocatable:
    nvidia.com/vgpu-memory: "8192"
  capacity:
    nvidia.com/vgpu-memory: "8192"

Tips: It may be useful in scenarios where node resource constraints such as ResourceQuota are required.

Reschedule

• action scope: device-plugin

Opening the reschedule will rearrange nodes and devices for certain pods that have failed allocation.

Use the command --feature-gates=Reschedule=true to open the feature.

Tips: In scenarios where multiple Pods are created and scheduled in parallel, device plugins may experience allocation errors. Enabling this feature can restore the erroneous Pods.

SerialBindNode

• action scope: scheduler-extender

Enable serial binding of nodes to the scheduler, this will reduce the performance of the scheduler, but it will increase the success rate of device allocation.

Use the command --feature-gates=SerialBindNode=true to open the feature.

GPUTopology

• action scope: scheduler-extender, device-plugin

Opening the GPU topology through the device plugin will reveal GPU topology information to the nodes.

When the scheduler opens the GPU topology, it will affect the device allocation of Pods in link topology mode. nvidia.com/device-topology-mode: link

Use the command --feature-gates=GPUTopology=true to open the feature.