Schedule GPUs
Kubernetes includes experimental support for managing NVIDIA GPUs spread across nodes. This page describes how users can consume GPUs and the current limitations.
Before you begin
- Kubernetes nodes have to be pre-installed with Nvidia drivers. Kubelet will not detect Nvidia GPUs otherwise. Try to re-install Nvidia drivers if kubelet fails to expose Nvidia GPUs as part of Node Capacity. After installing the driver, run
nvidia-docker-pluginto confirm that all drivers have been loaded. - A special alpha feature gate
Acceleratorshas to be set to true across the system:--feature-gates="Accelerators=true". - Nodes must be using
docker engineas the container runtime.
The nodes will automatically discover and expose all Nvidia GPUs as a schedulable resource.
API
Nvidia GPUs can be consumed via container level resource requirements using the resource name alpha.kubernetes.io/nvidia-gpu.
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod
spec:
containers:
-
name: gpu-container-1
image: gcr.io/google_containers/pause:2.0
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 2 # requesting 2 GPUs
-
name: gpu-container-2
image: gcr.io/google_containers/pause:2.0
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 3 # requesting 3 GPUs
- GPUs are only supposed to be specified in the
limitssection, which means:- You can specify GPU
limitswithout specifyingrequestsbecause Kubernetes will use the limit as the request value by default. - You can specify GPU in both
limitsandrequestsbut these two values must equal. - You cannot specify GPU
requestswithout specifyinglimits.
- You can specify GPU
- Containers (and pods) do not share GPUs.
- Each container can request one or more GPUs.
- It is not possible to request a portion of a GPU.
- Nodes are expected to be homogenous, i.e. run the same GPU hardware.
If your nodes are running different versions of GPUs, then use Node Labels and Node Selectors to schedule pods to appropriate GPUs. Following is an illustration of this workflow:
As part of your Node bootstrapping, identify the GPU hardware type on your nodes and expose it as a node label.
NVIDIA_GPU_NAME=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 | sed -e 's/ /-/g')
source /etc/default/kubelet
KUBELET_OPTS="$KUBELET_OPTS --node-labels='alpha.kubernetes.io/nvidia-gpu-name=$NVIDIA_GPU_NAME'"
echo "KUBELET_OPTS=$KUBELET_OPTS" > /etc/default/kubelet
Specify the GPU types a pod can use via Node Affinity rules.
kind: pod
apiVersion: v1
metadata:
annotations:
scheduler.alpha.kubernetes.io/affinity: >
{
"nodeAffinity": {
"requiredDuringSchedulingIgnoredDuringExecution": {
"nodeSelectorTerms": [
{
"matchExpressions": [
{
"key": "alpha.kubernetes.io/nvidia-gpu-name",
"operator": "In",
"values": ["Tesla K80", "Tesla P100"]
}
]
}
]
}
}
}
spec:
containers:
-
name: gpu-container-1
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 2
This will ensure that the pod will be scheduled to a node that has a Tesla K80 or a Tesla P100 Nvidia GPU.
Warning
The API presented here will change in an upcoming release to better support GPUs, and hardware accelerators in general, in Kubernetes.
Access to CUDA libraries
As of now, CUDA libraries are expected to be pre-installed on the nodes.
To mitigate this, you can copy the libraries to a more permissive folder in /var/lib/ or change the permissions directly. (Future releases will automatically perform this operation)
Pods can access the libraries using hostPath volumes.
kind: Pod
apiVersion: v1
metadata:
name: gpu-pod
spec:
containers:
- name: gpu-container-1
image: gcr.io/google_containers/pause:2.0
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 1
volumeMounts:
- mountPath: /usr/local/nvidia/bin
name: bin
- mountPath: /usr/lib/nvidia
name: lib
volumes:
- hostPath:
path: /usr/lib/nvidia-375/bin
name: bin
- hostPath:
path: /usr/lib/nvidia-375
name: lib
Future
- Support for hardware accelerators is in its early stages in Kubernetes.
- GPUs and other accelerators will soon be a native compute resource across the system.
- Better APIs will be introduced to provision and consume accelerators in a scalable manner.
- Kubernetes will automatically ensure that applications consuming GPUs get the best possible performance.
- Key usability problems like access to CUDA libraries will be addressed.