共享GPU调度

问题背景

全球主要的容器集群服务厂商的Kubernetes服务都提供了Nvidia GPU容器调度能力,但是通常都是将一个GPU卡分配给一个容器。这可以实现比较好的隔离性,确保使用GPU的应用不会被其他应用影响;对于深度学习模型训练的场景非常适合,但是如果对于模型开发和模型预测的场景就会比较浪费。 大家的诉求是能够让更多的预测服务共享同一个GPU卡上,进而提高集群中Nvidia GPU的利用率。而这就需要提供GPU资源的划分,而这里GPU资源划分的维度指的就是GPU显存和Cuda Kernel线程的划分。通常在集群级别谈支持共享GPU,通常是两件事情:

1.调度
2.隔离,我们这里主要讨论的是调度,隔离的方案未来会基于Nvidia的MPS来实现。

而对于细粒度的GPU卡调度,目前Kubernetes社区并没有很好的方案,这是由于Kubernetes对于GPU这类扩展资源的定义仅仅支持整数粒度的加加减减,无法支持复杂资源的分配。比如用户希望使用Pod A占用半张GPU卡,这在目前Kubernetes的架构设计中无法实现资源分配的记录和调用。这里挑战是多卡GPU共享是实际矢量资源问题,而Extened Resource是标量资源的描述。

针对此问题,我们设计了一个outoftree的共享GPU调度方案,该方案依赖于Kubernetes的现有工作机制:

  1. Extended Resource定义
  2. Scheduler Extender机制
  3. Device Plugin机制

设计原则

  1. 明确问题简化设计,第一步只负责调度和部署,后续再实现运行时显存管控。
    有很多的客户明确的诉求是首先可以支持多AI应用可以调度到同一个GPU上,他们可以接受从应用级别控制显存的大小,利用类似gpu_options.per_process_gpu_memory_fraction控制应用的显存使用量。那我们要解决的问题就先简化到以显存为调度标尺,并且把显存使用的大小以参数的方式传递给容器内部。
  2. 不做侵入式修改
    本设计中不会修改Kubernetes核心的Extended Resource的设计, Scheduler的实现,Device Plugin的机制以及Kubelet的相关设计。重用Extended Resource描述共享资源的申请API。这样的好处在于提供一个可以移植的方案,用户可以在原生Kubernetes上使用这个方案。
  3. 按显存和按卡调度的方式可以在集群内并存,但是同一个节点内是互斥的,不支持二者并存;要么是按卡数目,要么是按显存分配。

详细设计

前提

  1. 依旧延用Kubernetes Extended Resource定义,但是衡量维度最小单位从1个GPU卡变为GPU显存的MiB。如果所节点使用的GPU为单卡16GiB显存,它对应的资源就是16276MiB
  2. 由于用户对于共享GPU的诉求在于模型开发和模型预测场景,在此场景下,用户申请的GPU资源上限不会超过一张卡,也就是申请的资源上限为单卡

而我们的工作首先是定义了两个新的Extended Resource: 第一个是gpu-mem, 对应的是GPU显存;第二个是gpu-count,对应的是GPU卡数。 通过两个标量资源描述矢量资源, 并且结合这一资源,提供支持共享GPU的工作机制。下面是基本的架构图:
1

核心功能模块

  • GPU Share Scheduler Extender: 利用Kubernetes的调度器扩展机制,负责在全局调度器Filter和Bind的时候判断节点上单个GPU卡是否能够提供足够的GPU Mem,并且在Bind的时刻将GPU的分配结果通过annotation记录到Pod Spec以供后续Filter检查分配结果。
  • GPU Share Device Plugin: 利用Device Plugin机制,在节点上被Kubelet调用负责GPU卡的分配,依赖scheduler Extender分配结果执行。

具体流程

  1. 资源上报

GPU Share Device Plugin利用nvml库查询到GPU卡的数量和每张GPU卡的显存, 通过ListAndWatch()将节点的GPU总显存(数量 显存)作为另外Extended Resource汇报给Kubelet; Kubelet进一步汇报给Kubernetes API Server。 举例说明,如果节点含有两块GPU卡,并且每块卡包含16276MiB,从用户的角度来看:该节点的GPU资源为16276 2 = 32552; 同时也会将节点上的GPU卡数量2作为另外一个Extended Resource上报。

  1. 扩展调度

GPU Share Scheduler Extender可以在分配gpu-mem给Pod的同时将分配信息以annotation的形式保留在Pod spec中,并且在过滤时刻根据此信息判断每张卡是否包含足够可用的gpu-mem分配。

2.1 Kubernetes默认调度器在进行完所有过滤(filter)行为后会通过http方式调用GPU Share Scheduler Extender的filter方法, 这是由于默认调度器计算Extended Resource时,只能判断资源总量是否有满足需求的空闲资源,无法具体判断单张卡上是否满足需求;所以就需要由GPU Share Scheduler Extender检查单张卡上是否含有可用资源。

以下图为例, 在由3个包含两块GPU卡的节点组成的Kubernetes集群中,当用户申请gpu-mem=8138时,默认调度器会扫描所有节点,发现N1所剩的资源为 (16276 * 2 - 16276 -12207 = 4069 )不满足资源需求,N1节点被过滤掉。
而N2和N3节点所剩资源都为8138MiB,从整体调度的角度看,都符合默认调度器的条件;此时默认调度器会委托GPU Share Scheduler Extender进行二次过滤,在二次过滤中,GPU Share Scheduler Extender需要判断单张卡是否满足调度需求,在查看N2节点时发现该节点虽然有8138MiB可用资源,但是落到每张卡上看,GPU0和分别GPU1只有4069MiB的可用资源,无法满足单卡8138MiB的诉求。而N3节点虽然也是总共有8138MiB可用资源,但是这些可用资源都属于GPU0,满足单卡可调度的需求。由此,通过GPU Share Scheduler Extender的筛选就可以实现精准的条件筛选。
2

2.2 当调度器找到满足条件的节点,就会委托GPU Share Scheduler Extender的bind方法进行节点和Pod的绑定,这里Extender需要做的是两件事情

  • 以binpack的规则找到节点中最优选择的GPU卡id,此处的最优含义是对于同一个节点不同的GPU卡,以binpack的原则作为判断条件,优先选择空闲资源满足条件但同时又是所剩资源最少的GPU卡,并且将其作为ALIYUN_COM_GPU_MEM_IDX保存到Pod的annotation中;同时也保存该Pod申请的GPU Memory作为ALIYUN_COM_GPU_MEM_POD和ALIYUN_COM_GPU_MEM_ASSUME_TIME保存至Pod的annotation中,并且在此时进行Pod和所选节点的绑定。

注意:这时还会保存ALIYUN_COM_GPU_MEM_ASSIGNED的Pod annotation,它被初始化为“false”。它表示该Pod在调度时刻被指定到了某块GPU卡,但是并没有真正在节点上创建该Pod。ALIYUN_COM_GPU_MEM_ASSUME_TIME代表了指定时间。

如果此时发现分配节点上没有GPU资源符合条件,此时不进行绑定,直接不报错退出,默认调度器会在assume超时后重新调度。

  • 调用Kubernetes API执行节点和Pod的绑定
    以下图为例,当GPU Share Scheduler Extender要把gpu-mem:8138的Pod和经过筛选出来的节点N1绑定,首先会比较不同GPU的可用资源,分别为GPU0(12207),GPU1(8138),GPU2(4069),GPU3(16276),其中GPU2所剩资源不满足需求,被舍弃掉;而另外三个满足条件的GPU中, GPU1恰恰是符合空闲资源满足条件但同时又是所剩资源最少的GPU卡,因此GPU1被选出。

3

  1. 节点上运行

当Pod和节点绑定的事件被Kubelet接收到后,Kubelet就会在节点上创建真正的Pod实体,在这个过程中, Kubelet会调用GPU Share Device Plugin的Allocate方法, Allocate方法的参数是Pod申请的gpu-mem。而在Allocate方法中,会根据GPU Share Scheduler Extender的调度决策运行对应的Pod

3.1 会列出该节点中所有状态为Pending并且ALIYUN_COM_GPU_MEM_ASSIGNED为false的GPU Share Pod

3.2 选择出其中Pod Annotation的ALIYUN_COM_GPU_MEM_POD的数量与Allocate申请数量一致的Pod。如果有多个符合这种条件的Pod,就会选择其中ALIYUN_COM_GPU_MEM_ASSUME_TIME最早的Pod。

3.3 将该Pod的annotation ALIYUN_COM_GPU_MEM_ASSIGNED设置为true,并且将Pod annotation中的GPU信息转化为环境变量返回给Kubelet用以真正的创建Pod。

4

安装使用

前提条件

  1. 支持共享GPU调度的节点不能设置CPU Policy为static。
  2. 已通过kubectl工具连接专有版GPU集群。具体操作,请参见通过kubectl工具连接集群。

使用须知

配置 支持版本
Kubernetes 1.12.6及其以上,仅支持专有版集群
NVIDIA驱动版本 418.87.01及以上版本
Docker版本 19.03.5以上
操作系统 CentOS 7.x、Ubuntu 16.04和Ubuntu 18.04
支持显卡 Tesla P4、Tesla P100、 Tesla T4和Tesla v100

部署组件

部署参考

部署 gpushare-schd-extender 组件

  1. 创建scheduler-policy-config.json 文件

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    {
    "kind": "Policy",
    "apiVersion": "v1",
    "extenders": [
    {
    "urlPrefix": "http://127.0.0.1:32766/gpushare-scheduler",
    "filterVerb": "filter",
    "bindVerb": "bind",
    "enableHttps": false,
    "nodeCacheCapable": true,
    "managedResources": [
    {
    "name": "aliyun.com/gpu-mem",
    "ignoredByScheduler": false
    }
    ],
    "ignorable": false
    }
    ]
    }
  2. 在所用调度器节点kube-scheduler.yaml参数中添加策略配置文件参数

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    - --policy-config-file=/etc/kubernetes/scheduler-policy-config.json
  3. 创建gpushare-schd-extender.yaml

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    # rbac.yaml
    ---
    kind: ClusterRole
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
    name: gpushare-schd-extender
    rules:
    - apiGroups:
    - ""
    resources:
    - nodes
    verbs:
    - get
    - list
    - watch
    - apiGroups:
    - ""
    resources:
    - events
    verbs:
    - create
    - patch
    - apiGroups:
    - ""
    resources:
    - pods
    verbs:
    - update
    - patch
    - get
    - list
    - watch
    - apiGroups:
    - ""
    resources:
    - bindings
    - pods/binding
    verbs:
    - create
    - apiGroups:
    - ""
    resources:
    - configmaps
    verbs:
    - get
    - list
    - watch
    ---
    apiVersion: v1
    kind: ServiceAccount
    metadata:
    name: gpushare-schd-extender
    namespace: kube-system
    ---
    kind: ClusterRoleBinding
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
    name: gpushare-schd-extender
    namespace: kube-system
    roleRef:
    apiGroup: rbac.authorization.k8s.io
    kind: ClusterRole
    name: gpushare-schd-extender
    subjects:
    - kind: ServiceAccount
    name: gpushare-schd-extender
    namespace: kube-system

    # deployment yaml
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
    name: gpushare-schd-extender
    namespace: kube-system
    resourceVersion: "27297333"
    spec:
    selector:
    matchLabels:
    app: gpushare
    component: gpushare-schd-extender
    strategy:
    type: Recreate
    template:
    metadata:
    annotations:
    scheduler.alpha.kubernetes.io/critical-pod: ""
    creationTimestamp: null
    labels:
    app: gpushare
    component: gpushare-schd-extender
    spec:
    containers:
    - env:
    - name: LOG_LEVEL
    value: debug
    - name: PORT
    value: "12345"
    image: hub.kce.ksyun.com/ksyun/gpushare-scheduler-extender:latest
    imagePullPolicy: Always
    name: gpushare-schd-extender
    resources: {}
    terminationMessagePath: /dev/termination-log
    terminationMessagePolicy: File
    dnsPolicy: ClusterFirst
    hostNetwork: true
    nodeSelector:
    node-role.kubernetes.io/master: ""
    restartPolicy: Always
    schedulerName: default-scheduler
    securityContext: {}
    serviceAccount: gpushare-schd-extender
    serviceAccountName: gpushare-schd-extender
    terminationGracePeriodSeconds: 30
    tolerations:
    - effect: NoSchedule
    key: node-role.kubernetes.io/master
    operator: Exists
    - effect: NoSchedule
    key: node.cloudprovider.kubernetes.io/uninitialized
    operator: Exists

    # service.yaml
    ---
    apiVersion: v1
    kind: Service
    metadata:
    name: gpushare-schd-extender
    namespace: kube-system
    labels:
    app: gpushare
    component: gpushare-schd-extender
    spec:
    type: NodePort
    ports:
    - port: 12345
    name: http
    targetPort: 12345
    nodePort: 32766
    selector:
    # select app=ingress-nginx pods
    app: gpushare
    component: gpushare-schd-extender

部署 gpushare-device-plugin 组件

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kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: gpushare-device-plugin
rules:
- apiGroups:
- ""
resources:
- nodes
- nodes/proxy
verbs:
- get
- list
- watch
- apiGroups:
- ""
resources:
- events
verbs:
- create
- patch
- apiGroups:
- ""
resources:
- pods
verbs:
- update
- patch
- get
- list
- watch
- apiGroups:
- ""
resources:
- nodes/status
verbs:
- patch
- update
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: gpushare-device-plugin
namespace: kube-system
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
name: gpushare-device-plugin
namespace: kube-system
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: gpushare-device-plugin
subjects:
- kind: ServiceAccount
name: gpushare-device-plugin
namespace: kube-system
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: gpushare-device-plugin-ds
namespace: kube-system
spec:
revisionHistoryLimit: 10
selector:
matchLabels:
app: gpushare
component: gpushare-device-plugin
name: gpushare-device-plugin-ds
template:
metadata:
annotations:
scheduler.alpha.kubernetes.io/critical-pod: ""
creationTimestamp: null
labels:
app: gpushare
component: gpushare-device-plugin
name: gpushare-device-plugin-ds
spec:
containers:
- command:
- gpushare-device-plugin-v2
- -logtostderr
- --v=9
- --memory-unit=GiB
env:
- name: KUBECONFIG
value: /etc/kubernetes/kubelet.conf
- name: NODE_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: spec.nodeName
image: hub.kce.ksyun.com/ksyun/gpushare-device-plugin
imagePullPolicy: Always
name: gpushare
resources:
limits:
cpu: "1"
memory: 300Mi
requests:
cpu: "1"
memory: 300Mi
securityContext:
allowPrivilegeEscalation: false
capabilities:
drop:
- ALL
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
volumeMounts:
- mountPath: /var/lib/kubelet/device-plugins
name: device-plugin
- mountPath: /etc/kubernetes/kubelet.conf
name: kubeconfig
- mountPath: /etc/localtime
name: time-zone
readOnly: true
dnsPolicy: ClusterFirst
hostNetwork: true
nodeSelector:
gpushare: "true"
restartPolicy: Always
schedulerName: default-scheduler
securityContext: {}
serviceAccount: gpushare-device-plugin
serviceAccountName: gpushare-device-plugin
terminationGracePeriodSeconds: 30
volumes:
- hostPath:
path: /var/lib/kubelet/device-plugins
type: ""
name: device-plugin
- hostPath:
path: /etc/kubernetes/kubelet.kubeconfig
type: ""
name: kubeconfig
- hostPath:
path: /etc/localtime
type: ""
name: time-zone
updateStrategy:
rollingUpdate:
maxUnavailable: 1
type: RollingUpdate
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kubectl apply -f gpushare-device-plugin.yaml

需要在要安装设备插件的所有节点上添加标签“gpushare=true”,因为设备插件是 deamonset。

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kubectl label node  gpushare=true

测试样例

首先创建一个使用aliyun.com/gpu-mem的应用

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apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: binpack-1
name: binpack-1
namespace: default
spec:
progressDeadlineSeconds: 600
replicas: 1
revisionHistoryLimit: 10
selector:
matchLabels:
app: binpack-1
strategy:
rollingUpdate:
maxSurge: 25%
maxUnavailable: 25%
type: RollingUpdate
template:
metadata:
creationTimestamp: null
labels:
app: binpack-1
spec:
containers:
- env:
- name: NVIDIA_VISIBLE_DEVICES
value: all
image: hub.kce.ksyun.com/ksyun/gpu-player:v2
imagePullPolicy: IfNotPresent
name: binpack-1
resources:
limits:
aliyun.com/gpu-mem: "1"
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
dnsPolicy: ClusterFirst
restartPolicy: Always
schedulerName: default-scheduler
securityContext: {}
terminationGracePeriodSeconds: 30

查看现象

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root@10-0-20-250:~# kubectl -n kube-system exec -it gpushare-device-plugin-ds-grh24  -- kubectl-inspect-gpushare-v2  -d

NAME: 10.0.20.250
IPADDRESS: 10.0.20.250

NAME NAMESPACE GPU0(Allocated) GPU1(Allocated) GPU2(Allocated) GPU3(Allocated)
binpack-1-767bddd4b5-gzqk7 default 1 0 0 0
Allocated : 1 (1%)
Total : 88
------------------------------------------------------

NAME: 10.0.33.21
IPADDRESS: 10.0.33.21

NAME NAMESPACE GPU0(Allocated) GPU1(Allocated) GPU2(Allocated) GPU3(Allocated) GPU4(Allocated) GPU5(Allocated) GPU6(Allocated)
Allocated : 0 (0%)
Total : 77
------------------------------------------------------


Allocated/Total GPU Memory In Cluster: 1/165 (0%)

登入gpu节点查看

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root@10-0-20-250:~# nvidia-smi 
Thu Sep 30 14:51:03 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P40 Off | 00000000:02:00.0 Off | 0 |
| N/A 34C P0 51W / 250W | 923MiB / 22919MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla P40 Off | 00000000:03:00.0 Off | 0 |
| N/A 23C P8 9W / 250W | 0MiB / 22919MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla P40 Off | 00000000:83:00.0 Off | 0 |
| N/A 24C P8 10W / 250W | 0MiB / 22919MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla P40 Off | 00000000:84:00.0 Off | 0 |
| N/A 23C P8 10W / 250W | 0MiB / 22919MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 41043 C python 913MiB |
+-----------------------------------------------------------------------------+

异常问题

gpu型号不支持

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# gpu型号:  TITAN Xp
Error: failed to start container "binpack-1": Error response from daemon: OCI runtime create failed: container_linux.go:380: starting container process caused: process_linux.go:545: container init caused: Running hook #0:: error running hook: exit status 1, stdout: , stderr: nvidia-container-cli: device error: no-gpu-has-1GiB-to-run: unknown device: unknown

root@10-0-33-21:~# nvidia-smi
Thu Sep 30 14:59:00 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:05:00.0 Off | N/A |
| 23% 26C P8 8W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 TITAN Xp Off | 00000000:08:00.0 Off | N/A |
| 23% 24C P8 9W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 TITAN Xp Off | 00000000:09:00.0 Off | N/A |
| 23% 22C P8 8W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 TITAN Xp Off | 00000000:85:00.0 Off | N/A |
| 23% 23C P8 9W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 TITAN Xp Off | 00000000:86:00.0 Off | N/A |
| 23% 25C P8 9W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 TITAN Xp Off | 00000000:89:00.0 Off | N/A |
| 23% 23C P8 8W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 TITAN Xp Off | 00000000:8A:00.0 Off | N/A |
| 23% 25C P8 8W / 250W | 0MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+

表示gpu卡型号 TITAN Xp不支持

gpu调度插件端口不通

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{
"kind": "Policy",
"apiVersion": "v1",
"extenders": [
{
"urlPrefix": "http://127.0.0.1:32766/gpushare-scheduler",
"filterVerb": "filter",
"bindVerb": "bind",
"enableHttps": false,
"nodeCacheCapable": true,
"managedResources": [
{
"name": "aliyun.com/gpu-mem",
"ignoredByScheduler": false
}
],
"ignorable": false
}
]
}

监听地址32766端口, 在127.0.0.1 下不通,本机ip地址相通