Kubernetes v1.10 [beta]
Kubernetes includes experimental support for managing AMD and NVIDIA GPUs (graphical processing units) across several nodes.
This page describes how users can consume GPUs across different Kubernetes versions and the current limitations.
Using device plugins
Kubernetes implements Device Plugins to let Pods access specialized hardware features such as GPUs.
As an administrator, you have to install GPU drivers from the corresponding hardware vendor on the nodes and run the corresponding device plugin from the GPU vendor:
When the above conditions are true, Kubernetes will expose
nvidia.com/gpu as a schedulable resource.
You can consume these GPUs from your containers by requesting
<vendor>.com/gpu the same way you request
However, there are some limitations in how you specify the resource requirements
when using GPUs:
- GPUs are only supposed to be specified in the
limitssection, which means:
- You can specify GPU
requestsbecause Kubernetes will use the limit as the request value by default.
- You can specify GPU in both
requestsbut these two values must be equal.
- You cannot specify GPU
- You can specify GPU
- Containers (and Pods) do not share GPUs. There's no overcommitting of GPUs.
- Each container can request one or more GPUs. It is not possible to request a fraction of a GPU.
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: "registry.k8s.io/cuda-vector-add:v0.1" resources: limits: nvidia.com/gpu: 1 # requesting 1 GPU
Deploying AMD GPU device plugin
The official AMD GPU device plugin has the following requirements:
- Kubernetes nodes have to be pre-installed with AMD GPU Linux driver.
To deploy the AMD device plugin once your cluster is running and the above requirements are satisfied:
kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/v1.10/k8s-ds-amdgpu-dp.yaml
You can report issues with this third-party device plugin by logging an issue in RadeonOpenCompute/k8s-device-plugin.
Deploying NVIDIA GPU device plugin
There are currently two device plugin implementations for NVIDIA GPUs:
Official NVIDIA GPU device plugin
The official NVIDIA GPU device plugin has the following requirements:
- Kubernetes nodes have to be pre-installed with NVIDIA drivers.
- Kubernetes nodes have to be pre-installed with nvidia-docker 2.0
- Kubelet must use Docker as its container runtime
nvidia-container-runtimemust be configured as the default runtime for Docker, instead of runc.
- The version of the NVIDIA drivers must match the constraint ~= 384.81.
To deploy the NVIDIA device plugin once your cluster is running and the above requirements are satisfied:
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
You can report issues with this third-party device plugin by logging an issue in NVIDIA/k8s-device-plugin.
NVIDIA GPU device plugin used by GCE
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.
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-1.14/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml
You can report issues with using or deploying this third-party device plugin by logging an issue in GoogleCloudPlatform/container-engine-accelerators.
Google publishes its own instructions for using NVIDIA GPUs on GKE .
Clusters containing different types of GPUs
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
Automatic node labelling
At the moment, that controller can add labels for:
- Device ID (-device-id)
- VRAM Size (-vram)
- Number of SIMD (-simd-count)
- Number of Compute Unit (-cu-count)
- Firmware and Feature Versions (-firmware)
- GPU Family, in two letters acronym (-family)
- SI - Southern Islands
- CI - Sea Islands
- KV - Kaveri
- VI - Volcanic Islands
- CZ - Carrizo
- AI - Arctic Islands
- RV - Raven
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 …
With the Node Labeller in use, you can 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: "registry.k8s.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.