Kubernetes includes experimental support for managing AMD and NVIDIA GPUs spread across nodes. The support for NVIDIA GPUs was added in v1.6 and has gone through multiple backwards incompatible iterations. The support for AMD GPUs was added in v1.9 via device plugin.
This page describes how users can consume GPUs across different Kubernetes versions and the current limitations.
From 1.8 onwards, the recommended way to consume GPUs is to use device plugins.
To enable GPU support through device plugins before 1.10, the
feature gate has to be explicitly set to true across the system:
--feature-gates="DevicePlugins=true". This is no longer required starting
When the above conditions are true, Kubernetes will expose
amd.com/gpu as a schedulable resource.
You can consume these GPUs from your containers by requesting
<vendor>.com/gpu just like you request
However, there are some limitations in how you specify the resource requirements
when using GPUs:
limitssection, which means:
requestsbecause Kubernetes will use the limit as the request value by default.
requestsbut these two values must be equal.
Here’s an example:
apiVersion: v1 kind: Pod metadata: name: cuda-vector-add spec: restartPolicy: OnFailure containers: - name: cuda-vector-add # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile image: "k8s.gcr.io/cuda-vector-add:v0.1" resources: limits: nvidia.com/gpu: 1 # requesting 1 GPU
The official AMD GPU device plugin has the following requirements:
To deploy the AMD device plugin once your cluster is running and the above requirements are satisfied:
# For Kubernetes v1.9 kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.9/k8s-ds-amdgpu-dp.yaml # For Kubernetes v1.10 kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.10/k8s-ds-amdgpu-dp.yaml
Report issues with this device plugin to RadeonOpenCompute/k8s-device-plugin.
There are currently two device plugin implementations for NVIDIA GPUs:
The official NVIDIA GPU device plugin has the following requirements:
To deploy the NVIDIA device plugin once your cluster is running and the above requirements are satisfied:
# For Kubernetes v1.8 kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.8/nvidia-device-plugin.yml # For Kubernetes v1.9 kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.9/nvidia-device-plugin.yml
Report issues with this device plugin to NVIDIA/k8s-device-plugin.
The NVIDIA GPU device plugin used by GCE doesn’t require using nvidia-docker and should work with any container runtime that is compatible with the Kubernetes Container Runtime Interface (CRI). It’s tested on Container-Optimized OS and has experimental code for Ubuntu from 1.9 onwards.
On your 1.12 cluster, you can use the following commands to install the NVIDIA drivers and device plugin:
# Install NVIDIA drivers on Container-Optimized OS: kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/daemonset.yaml # Install NVIDIA drivers on Ubuntu (experimental): kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/nvidia-driver-installer/ubuntu/daemonset.yaml # Install the device plugin: kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/release-1.12/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml
Report issues with this device plugin and installation method to GoogleCloudPlatform/container-engine-accelerators.
Instructions for using NVIDIA GPUs on GKE are here
If different nodes in your cluster have different types of GPUs, then you can use Node Labels and Node Selectors to schedule pods to appropriate nodes.
# Label your nodes with the accelerator type they have. kubectl label nodes <node-with-k80> accelerator=nvidia-tesla-k80 kubectl label nodes <node-with-p100> accelerator=nvidia-tesla-p100
For AMD GPUs, you can deploy Node Labeller, which automatically labels your nodes with GPU properties. Currently supported properties:
$ kubectl describe node cluster-node-23 Name: cluster-node-23 Roles: <none> Labels: beta.amd.com/gpu.cu-count.64=1 beta.amd.com/gpu.device-id.6860=1 beta.amd.com/gpu.family.AI=1 beta.amd.com/gpu.simd-count.256=1 beta.amd.com/gpu.vram.16G=1 beta.kubernetes.io/arch=amd64 beta.kubernetes.io/os=linux kubernetes.io/hostname=cluster-node-23 Annotations: kubeadm.alpha.kubernetes.io/cri-socket: /var/run/dockershim.sock node.alpha.kubernetes.io/ttl: 0 ......
Specify the GPU type in the pod spec:
apiVersion: v1 kind: Pod metadata: name: cuda-vector-add spec: restartPolicy: OnFailure containers: - name: cuda-vector-add # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile image: "k8s.gcr.io/cuda-vector-add:v0.1" resources: limits: nvidia.com/gpu: 1 nodeSelector: accelerator: nvidia-tesla-p100 # or nvidia-tesla-k80 etc.
This will ensure that the pod will be scheduled to a node that has the GPU type you specified.
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