GPU Manager is used for managing the nvidia GPU devices in Kubernetes cluster. It implements the
of Kubernetes. So it's compatible with 1.9+ of Kubernetes release version.
To compare with the combination solution of
nvidia-k8s-plugin, GPU manager will use native
runc without modification but nvidia solution does.
Besides we also support metrics report without deploying new components.
To schedule a GPU payload correctly, GPU manager should work with gpu-admission which is a kubernetes scheduler plugin.
GPU manager also supports the payload with fraction resource of GPU device such as 0.1 card or 100MiB gpu device memory. If you want this kind feature, please refer to vcuda-controller project.
1. Build binary
- CUDA toolkit
2. Build image
Prebuilt image can be found at
GPU Manager is running as daemonset, and because of the RABC restriction and hydrid cluster, you need to do the following steps to make this daemonset run correctly.
- service account and clusterrole
kubectl create sa gpu-manager -n kube-system kubectl create clusterrolebinding gpu-manager-role --clusterrole=cluster-admin --serviceaccount=kube-system:gpu-manager
- label node with
kubectl label node <node> nvidia-device-enable=enable
- submit daemonset yaml
kubectl create -f gpu-manager.yaml
Pod template example
There is nothing special to submit a Pod except the description of GPU resource is no longer 1
. The GPU
resources are described as that 100
tencent.com/vcuda-core for 1 GPU and N
tencent.com/vcuda-memory for GPU memory (1 tencent.com/vcuda-memory means 256Mi
GPU memory). And because of the limitation of extend resource validation of Kubernetes, to support
GPU utilization limitation, you should add
tencent.com/vcuda-core-limit: XX in the annotation
field of a Pod.
Notice: the value of
tencent.com/vcuda-core is either the multiple of 100 or any value
smaller than 100.For example, 100, 200 or 20 is valid value but 150 or 250 is invalid
- Submit a Pod with 0.3 GPU utilization and 7680MiB GPU memory with 0.5 GPU utilization limit
apiVersion: v1 kind: Pod metadata: name: vcuda annotations: tencent.com/vcuda-core-limit: 50 spec: restartPolicy: Never containers: - image: <test-image> name: nvidia command: - /usr/local/nvidia/bin/nvidia-smi - pmon - -d - 10 resources: requests: tencent.com/vcuda-core: 50 tencent.com/vcuda-memory: 30 limits: tencent.com/vcuda-core: 50 tencent.com/vcuda-memory: 30
- Submit a Pod with 2 GPU card
apiVersion: v1 kind: Pod metadata: name: vcuda spec: restartPolicy: Never containers: - image: <test-image> name: nvidia command: - /usr/local/nvidia/bin/nvidia-smi - pmon - -d - 10 resources: requests: tencent.com/vcuda-core: 200 tencent.com/vcuda-memory: 60 limits: tencent.com/vcuda-core: 200 tencent.com/vcuda-memory: 60
If you have some questions about this project, you can first refer to FAQ to find a solution.