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Version: v0.7

Device Scheduling

We provide a fine-grained mechanism for managing GPUs and other devices such as RDMA and FPGA, defines a set of APIs to describe device information on nodes, including GPU, RDMA, and FPGA, and a new set of resource names to flexibly support users to apply at a finer granularity GPU resources. This mechanism is the basis for subsequent other GPU scheduling capabilities such as GPU Share, GPU Overcommitment, etc.

Introduction​

GPU devices have very strong computing power, but are expensive. How to make better use of GPU equipment, give full play to the value of GPU and reduce costs is a problem that needs to be solved. In the existing GPU allocation mechanism of the K8s community, the GPU is allocated by the kubelet, and it is a complete device allocation. This method is simple and reliable, but similar to the CPU and memory, the GPU will also be wasted. Therefore, some users expect to use only a portion of the GPU's resources and share the rest with other workloads to save costs. Moreover, GPU has particularities. For example, the NVLink and oversold scenarios supported by NVIDIA GPU mentioned below both require a central decision through the scheduler to obtain globally optimal allocation results.

Setup​

Prerequisite​

  • Kubernetes >= 1.18
  • Koordinator >= 0.71

Installation​

Please make sure Koordinator components are correctly installed in your cluster. If not, please refer to Installation.

Configurations​

DeviceScheduling is Enabled by default. You can use it without any modification on the koord-scheduler config.

Use DeviceScheduling​

Quick Start​

1.check device crd:

$ kubectl get device host04 -o yaml
apiVersion: scheduling.koordinator.sh/v1alpha1
kind: Device
metadata:
creationTimestamp: "2022-10-08T09:26:42Z"
generation: 1
managedFields:
- apiVersion: scheduling.koordinator.sh/v1alpha1
fieldsType: FieldsV1
fieldsV1:
f:metadata:
f:ownerReferences: {}
f:spec:
.: {}
f:devices: {}
f:status: {}
manager: koordlet
operation: Update
time: "2022-10-08T09:26:42Z"
name: host04
ownerReferences:
- apiVersion: v1
blockOwnerDeletion: true
controller: true
kind: Node
name: host04
uid: 09c4f912-6026-467a-85d2-6b2147c9557e
resourceVersion: "39011943"
selfLink: /apis/scheduling.koordinator.sh/v1alpha1/devices/host04
uid: 5a498e1f-1357-4518-b74c-cab251d6c18c
spec:
devices:
- health: true
id: GPU-04cea5cd-966f-7116-1d58-1ac34421541b
minor: 0
resources:
kubernetes.io/gpu-core: "100"
kubernetes.io/gpu-memory: 16Gi
kubernetes.io/gpu-memory-ratio: "100"
type: gpu
- health: true
id: GPU-3680858f-1753-371e-3c1a-7d8127fc7113
minor: 1
resources:
kubernetes.io/gpu-core: "100"
kubernetes.io/gpu-memory: 16Gi
kubernetes.io/gpu-memory-ratio: "100"
type: gpu
status: {}

We can find this node has two gpu cards, we can find the detail info of each gpu card here.

2.check node allocatable resource:

$ kubectl get node host04 -o yaml
apiVersion: v1
kind: Node
metadata:
annotations:
flannel.alpha.coreos.com/backend-data: '{"VtepMAC":"5a:69:48:10:29:25"}'
creationTimestamp: "2022-08-29T09:12:55Z"
labels:
beta.kubernetes.io/os: linux
status:
addresses:
- address: 10.15.0.37
type: InternalIP
- address: host04
type: Hostname
allocatable:
cpu: "6"
ephemeral-storage: "200681483926"
kubernetes.io/gpu: "200"
kubernetes.io/gpu-core: "200"
kubernetes.io/gpu-memory: 32Gi
kubernetes.io/gpu-memory-ratio: "200"
memory: 59274552Ki
nvidia.com/gpu: "2"
pods: "220"
capacity:
cpu: "8"
kubernetes.io/gpu: "200"
kubernetes.io/gpu-core: "200"
kubernetes.io/gpu-memory: 32Gi
kubernetes.io/gpu-memory-ratio: "200"
memory: 61678904Ki
nvidia.com/gpu: "2"
pods: "220"

We can find the node allocatable resource has merged each gpu card resource.

3.apply pod:

apiVersion: v1
kind: Pod
metadata:
name: pod-example
namespace: default
spec:
schedulerName: koord-scheduler
containers:
- command:
- sleep
- 365d
image: busybox
imagePullPolicy: IfNotPresent
name: curlimage
resources:
limits:
cpu: 40m
memory: 40Mi
requests:
cpu: 40m
memory: 40Mi
kubernetes.io/gpu: "100"
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
restartPolicy: Always
$ kubectl get pod -n default pod-example -o yaml
apiVersion: v1
kind: Pod
metadata:
annotations:
scheduling.koordinator.sh/device-allocated: '{"gpu":[{"minor":0,"resources":{"kubernetes.io/gpu-core":"100","kubernetes.io/gpu-memory":"12508288Ki","kubernetes.io/gpu-memory-ratio":"100"}}]}'
creationTimestamp: "2022-10-08T09:33:07Z"
name: pod-example
namespace: default
resourceVersion: "39015044"
selfLink: /api/v1/namespaces/xlf/pods/gpu-pod7
uid: 6bf1ac3c-0c9f-472a-8b86-de350bbfa795
spec:
containers:
- command:
- sleep
- 365d
image: busybox
imagePullPolicy: IfNotPresent
name: curlimage
resources:
limits:
cpu: "1"
kubernetes.io/gpu: "100"
memory: 256Mi
requests:
cpu: "1"
kubernetes.io/gpu: "100"
memory: 256Mi
status:
conditions:
...
hostIP: 10.0.0.149
phase: Running
podIP: 10.244.2.45
podIPs:
- ip: 10.244.2.45
qosClass: Guaranteed
startTime: "2022-10-08T09:33:07Z"

You can find the concrete device allocate result through annotation scheduling.koordinator.sh/device-allocated.

4.more apply protocol:

apiVersion: v1
kind: Pod
...
spec:
...
resources:
requests:
cpu: 40m
memory: 40Mi
nvidia.com/gpu: "100"
apiVersion: v1
kind: Pod
...
spec:
...
resources:
requests:
cpu: 40m
memory: 40Mi
kubernetes.io/gpu-core: "100"
kubernetes.io/gpu-memory-ratio: "100"
apiVersion: v1
kind: Pod
...
spec:
...
resources:
requests:
cpu: 40m
memory: 40Mi
kubernetes.io/gpu-core: "100"
kubernetes.io/gpu-memory: "16Mi"

4.device resource debug api:

$ kubectl -n koordinator-system get lease koord-scheduler --no-headers | awk '{print $2}' | cut -d'_' -f1 | xargs -I {} kubectl -n koordinator-system get pod {} -o wide --no-headers | awk '{print $6}'
10.244.0.64

$ curl 10.244.0.64:10251/apis/v1/plugins/DeviceShare/nodeDeviceSummaries
$ curl 10.244.0.64:10251/apis/v1/plugins/DeviceShare/nodeDeviceSummaries/host04
{
"allocateSet": {
"gpu": {
"xlf/gpu-pod7": {
"0": {
"kubernetes.io/gpu-core": "100",
"kubernetes.io/gpu-memory": "12508288Ki",
"kubernetes.io/gpu-memory-ratio": "100"
}
}
}
},
"deviceFree": {
"kubernetes.io/gpu-core": "0",
"kubernetes.io/gpu-memory": "0",
"kubernetes.io/gpu-memory-ratio": "0"
},
"deviceFreeDetail": {
"gpu": {
"0": {
"kubernetes.io/gpu-core": "0",
"kubernetes.io/gpu-memory": "0",
"kubernetes.io/gpu-memory-ratio": "0"
}
}
},
"deviceTotal": {
"kubernetes.io/gpu-core": "100",
"kubernetes.io/gpu-memory": "12508288Ki",
"kubernetes.io/gpu-memory-ratio": "100"
},
"deviceTotalDetail": {
"gpu": {
"0": {
"kubernetes.io/gpu-core": "100",
"kubernetes.io/gpu-memory": "12508288Ki",
"kubernetes.io/gpu-memory-ratio": "100"
}
}
},
"deviceUsed": {
"kubernetes.io/gpu-core": "100",
"kubernetes.io/gpu-memory": "12508288Ki",
"kubernetes.io/gpu-memory-ratio": "100"
},
"deviceUsedDetail": {
"gpu": {
"0": {
"kubernetes.io/gpu-core": "100",
"kubernetes.io/gpu-memory": "12508288Ki",
"kubernetes.io/gpu-memory-ratio": "100"
}
}
}
}