本篇文章为大家展示了Kubernetes如何通过Device Plugins来使用NVIDIA GPU,内容简明扼要并且容易理解,绝对能使你眼前一亮,通过这篇文章的详细介绍希望你能有所收获。
Device Plugins
Device Pulgins在Kubernetes 1.10中是beta特性,开始于Kubernetes 1.8,用来给第三方设备厂商通过插件化的方式将设备资源对接到Kubernetes,给容器提供Extended Resources。
通过Device Plugins方式,用户不需要改Kubernetes的代码,由第三方设备厂商开发插件,实现Kubernetes Device Plugins的相关接口即可。
目前关注度比较高的Device Plugins实现有:
-
Nvidia提供的GPU插件:NVIDIA device plugin for Kubernetes
-
高性能低延迟RDMA卡插件:RDMA device plugin for Kubernetes
-
低延迟Solarflare万兆网卡驱动:Solarflare Device Plugin
Device plugins启动时,对外暴露几个gRPC Service提供服务,并通过/var/lib/kubelet/device-plugins/kubelet.sock
向kubelet进行注册。
Device Plugins Registration
-
在Kubernetes 1.10之前的版本,默认disable DevicePlugins,用户需要在Feature Gate中enable。
-
在Kubernetes 1.10,默认enable DevicePlugins,用户可以在Feature Gate中disable it。
-
当DevicePlugins Feature Gate enable,kubelet就会暴露一个Register gRPC接口。Device Plugins通过调用Register接口完成Device的注册。
-
Register接口描述如下:
pkg/kubelet/apis/deviceplugin/v1beta1/api.pb.go:440 type RegistrationServer interface { Register(context.Context, *RegisterRequest) (*Empty, error) } pkg/kubelet/apis/deviceplugin/v1beta1/api.pb.go:87 type RegisterRequest struct { // Version of the API the Device Plugin was built against Version string `protobuf:"bytes,1,opt,name=version,proto3" json:"version,omitempty"` // Name of the unix socket the device plugin is listening on // PATH = path.Join(DevicePluginPath, endpoint) Endpoint string `protobuf:"bytes,2,opt,name=endpoint,proto3" json:"endpoint,omitempty"` // Schedulable resource name. As of now it's expected to be a DNS Label ResourceName string `protobuf:"bytes,3,opt,name=resource_name,json=resourceName,proto3" json:"resource_name,omitempty"` // Options to be communicated with Device Manager Options *DevicePluginOptions `protobuf:"bytes,4,opt,name=options" json:"options,omitempty"` }
-
RegisterRequest要求的参数如下:
-
对于nvidia gpu,只有一个PreStartRequired选项,表示每个Container启动前是否要调用Device Plugin的PreStartContainer接口(是Kubernetes 1.10中Device Plugin Interface接口之一),默认为false。
vendor/k8s.io/kubernetes/pkg/kubelet/apis/deviceplugin/v1beta1/api.pb.go:71 func (m *NvidiaDevicePlugin) GetDevicePluginOptions(context.Context, *pluginapi.Empty) (*pluginapi.DevicePluginOptions, error) { return &pluginapi.DevicePluginOptions{}, nil } github.com/NVIDIA/k8s-device-plugin/server.go:80 type DevicePluginOptions struct { // Indicates if PreStartContainer call is required before each container start PreStartRequired bool `protobuf:"varint,1,opt,name=pre_start_required,json=preStartRequired,proto3" json:"pre_start_required,omitempty"` }
-
Version, 目前有v1alpha,v1beta1两个版本。
-
Endpoint, 表示device plugin暴露的socket名称,Register时会根据Endpoint生成plugin的socket放在
/var/lib/kubelet/device-plugins/
目录下,比如Nvidia GPU Device Plugin对应/var/lib/kubelet/device-plugins/nvidia.sock
。 -
ResourceName, 须按照Extended Resource Naming Scheme格式
vendor-domain/resource
,比如nvidia.com/gpu
-
DevicePluginOptions, 作为kubelet与device plugin通信时的额外参数传递。
-
前面提到Device Plugin Interface目前有v1alpha, v1beta1两个版本,每个版本对应的接口如下:
-
/v1beta1.Registration/Register
/v1beta1.Registration/Register pkg/kubelet/apis/deviceplugin/v1beta1/api.pb.go:466 var _Registration_serviceDesc = grpc.ServiceDesc{ ServiceName: "v1beta1.Registration", HandlerType: (*RegistrationServer)(nil), Methods: []grpc.MethodDesc{ { MethodName: "Register", Handler: _Registration_Register_Handler, }, }, Streams: []grpc.StreamDesc{}, Metadata: "api.proto", }
-
/v1beta1.DevicePlugin/ListAndWatch
-
/v1beta1.DevicePlugin/Allocate
-
/v1beta1.DevicePlugin/PreStartContainer
-
/v1beta1.DevicePlugin/GetDevicePluginOptions
pkg/kubelet/apis/deviceplugin/v1beta1/api.pb.go:665 var _DevicePlugin_serviceDesc = grpc.ServiceDesc{ ServiceName: "v1beta1.DevicePlugin", HandlerType: (*DevicePluginServer)(nil), Methods: []grpc.MethodDesc{ { MethodName: "GetDevicePluginOptions", Handler: _DevicePlugin_GetDevicePluginOptions_Handler, }, { MethodName: "Allocate", Handler: _DevicePlugin_Allocate_Handler, }, { MethodName: "PreStartContainer", Handler: _DevicePlugin_PreStartContainer_Handler, }, }, Streams: []grpc.StreamDesc{ { StreamName: "ListAndWatch", Handler: _DevicePlugin_ListAndWatch_Handler, ServerStreams: true, }, }, Metadata: "api.proto", }
-
/deviceplugin.Registration/Register
pkg/kubelet/apis/deviceplugin/v1alpha/api.pb.go:374 var _Registration_serviceDesc = grpc.ServiceDesc{ ServiceName: "deviceplugin.Registration", HandlerType: (*RegistrationServer)(nil), Methods: []grpc.MethodDesc{ { MethodName: "Register", Handler: _Registration_Register_Handler, }, }, Streams: []grpc.StreamDesc{}, Metadata: "api.proto", }
-
/deviceplugin.DevicePlugin/Allocate
-
/deviceplugin.DevicePlugin/ListAndWatch
pkg/kubelet/apis/deviceplugin/v1alpha/api.pb.go:505 var _DevicePlugin_serviceDesc = grpc.ServiceDesc{ ServiceName: "deviceplugin.DevicePlugin", HandlerType: (*DevicePluginServer)(nil), Methods: []grpc.MethodDesc{ { MethodName: "Allocate", Handler: _DevicePlugin_Allocate_Handler, }, }, Streams: []grpc.StreamDesc{ { StreamName: "ListAndWatch", Handler: _DevicePlugin_ListAndWatch_Handler, ServerStreams: true, }, }, Metadata: "api.proto", }
-
v1alpha:
-
v1beta1:
-
当Device Plugin成功注册后,它将通过ListAndWatch向kubelet发送它管理的device列表,kubelet收到数据后通过API Server更新etcd中对应node的status中。
-
然后用户就能在Container Spec request中请求对应的device,注意以下限制:
-
Extended Resource只支持请求整数个device,不支持小数点。
-
不支持超配,即Resource QoS只能是Guaranteed。
-
同一块Device不能多个Containers共享。
Device Plugins Workflow
Device Plugins的工作流如下:
-
初始化:Device Plugin启动后,进行一些插件特定的初始化工作以确定对应的Devices处于Ready状态,对于Nvidia GPU,就是加载NVML Library。
-
启动gRPC服务:通过
/var/lib/kubelet/device-plugins/${Endpoint}.sock
对外暴露gRPC服务,不同的API Version对应不同的服务接口,前面已经提过,下面是每个接口的描述。 -
ListAndWatch
-
Allocate
-
GetDevicePluginOptions
-
PreStartContainer
pkg/kubelet/apis/deviceplugin/v1beta1/api.proto // DevicePlugin is the service advertised by Device Plugins service DevicePlugin { // GetDevicePluginOptions returns options to be communicated with Device // Manager rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {} // ListAndWatch returns a stream of List of Devices // Whenever a Device state change or a Device disapears, ListAndWatch // returns the new list rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {} // Allocate is called during container creation so that the Device // Plugin can run device specific operations and instruct Kubelet // of the steps to make the Device available in the container rpc Allocate(AllocateRequest) returns (AllocateResponse) {} // PreStartContainer is called, if indicated by Device Plugin during registeration phase, // before each container start. Device plugin can run device specific operations // such as reseting the device before making devices available to the container rpc PreStartContainer(PreStartContainerRequest) returns (PreStartContainerResponse) {} }
-
ListAndWatch
-
Allocate
pkg/kubelet/apis/deviceplugin/v1alpha/api.proto // DevicePlugin is the service advertised by Device Plugins service DevicePlugin { // ListAndWatch returns a stream of List of Devices // Whenever a Device state changes or a Device disappears, ListAndWatch // returns the new list rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {} // Allocate is called during container creation so that the Device // Plugin can run device specific operations and instruct Kubelet // of the steps to make the Device available in the container rpc Allocate(AllocateRequest) returns (AllocateResponse) {} }
-
v1alpha:
-
v1beta1:
-
Device Plugin通过
/var/lib/kubelet/device-plugins/kubelet.sock
向kubelet进行注册。 -
注册成功后,Device Plugin就正式进入了Serving模式,提供前面提到的gRPC接口调用服务,下面是v1beta1的每个接口对应的具体分析:
下面是
struct Device
的GPU Sample:struct Device { ID: "GPU-fef8089b-4820-abfc-e83e-94318197576e", State: "Healthy", }
-
PreStartContainer is expected to be called before each container start if indicated by plugin during registration phase.
-
PreStartContainer allows kubelet to pass reinitialized devices to containers.
-
PreStartContainer allows Device Plugin to run device specific operations on the Devices requested.
type PreStartContainerRequest struct { DevicesIDs []string `protobuf:"bytes,1,rep,name=devicesIDs" json:"devicesIDs,omitempty"` } // PreStartContainerResponse will be send by plugin in response to PreStartContainerRequest type PreStartContainerResponse struct { }
-
Allocate is expected to be called during pod creation since allocation failures for any container would result in pod startup failure.
-
Allocate allows kubelet to exposes additional artifacts in a pod's environment as directed by the plugin.
-
Allocate allows Device Plugin to run device specific operations on the Devices requested
type AllocateRequest struct { ContainerRequests []*ContainerAllocateRequest `protobuf:"bytes,1,rep,name=container_requests,json=containerRequests" json:"container_requests,omitempty"` } type ContainerAllocateRequest struct { DevicesIDs []string `protobuf:"bytes,1,rep,name=devicesIDs" json:"devicesIDs,omitempty"` } // AllocateResponse includes the artifacts that needs to be injected into // a container for accessing 'deviceIDs' that were mentioned as part of // 'AllocateRequest'. // Failure Handling: // if Kubelet sends an allocation request for dev1 and dev2. // Allocation on dev1 succeeds but allocation on dev2 fails. // The Device plugin should send a ListAndWatch update and fail the // Allocation request type AllocateResponse struct { ContainerResponses []*ContainerAllocateResponse `protobuf:"bytes,1,rep,name=container_responses,json=containerResponses" json:"container_responses,omitempty"` } type ContainerAllocateResponse struct { // List of environment variable to be set in the container to access one of more devices. Envs map[string]string `protobuf:"bytes,1,rep,name=envs" json:"envs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` // Mounts for the container. Mounts []*Mount `protobuf:"bytes,2,rep,name=mounts" json:"mounts,omitempty"` // Devices for the container. Devices []*DeviceSpec `protobuf:"bytes,3,rep,name=devices" json:"devices,omitempty"` // Container annotations to pass to the container runtime Annotations map[string]string `protobuf:"bytes,4,rep,name=annotations" json:"annotations,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` } // DeviceSpec specifies a host device to mount into a container. type DeviceSpec struct { // Path of the device within the container. ContainerPath string `protobuf:"bytes,1,opt,name=container_path,json=containerPath,proto3" json:"container_path,omitempty"` // Path of the device on the host. HostPath string `protobuf:"bytes,2,opt,name=host_path,json=hostPath,proto3" json:"host_path,omitempty"` // Cgroups permissions of the device, candidates are one or more of // * r - allows container to read from the specified device. // * w - allows container to write to the specified device. // * m - allows container to create device files that do not yet exist. Permissions string `protobuf:"bytes,3,opt,name=permissions,proto3" json:"permissions,omitempty"` }
-
AllocateRequest就是DeviceID列表。
-
AllocateResponse包括需要注入到Container里面的Envs、Devices的挂载信息(包括device的cgroup permissions)以及自定义的Annotations。
-
Allocate:Device Plugin执行device-specific操作,返回
AllocateResponse
给kubelet,kubelet再传给dockerd,由dockerd(调用nvidia-docker)在创建容器时分配device时使用。下面是这个接口的Request和Response的描述。 -
PreStartContainer:
-
GetDevicePluginOptions: 目前只有
PreStartRequired
这一个field。type DevicePluginOptions struct { // Indicates if PreStartContainer call is required before each container start PreStartRequired bool `protobuf:"varint,1,opt,name=pre_start_required,json=preStartRequired,proto3" json:"pre_start_required,omitempty"` }
-
ListAndWatch:监控对应Devices的状态变更或者Disappear事件,返回
ListAndWatchResponse
给kubelet, ListAndWatchResponse就是Device列表。type ListAndWatchResponse struct { Devices []*Device `protobuf:"bytes,1,rep,name=devices" json:"devices,omitempty"` } type Device struct { // A unique ID assigned by the device plugin used // to identify devices during the communication // Max length of this field is 63 characters ID string `protobuf:"bytes,1,opt,name=ID,json=iD,proto3" json:"ID,omitempty"` // Health of the device, can be healthy or unhealthy, see constants.go Health string `protobuf:"bytes,2,opt,name=health,proto3" json:"health,omitempty"` }
异常处理
-
每次kubelet启动(重启)时,都会将/var/lib/kubelet/device-plugins下的所有sockets文件删除。
-
Device Plugin要负责监测自己的socket被删除,然后进行重新注册,重新生成自己的socket。
-
当plugin socket被误删,Device Plugin该怎么办?
我们看看Nvidia Device Plugin是怎么处理的,相关的代码如下:
github.com/NVIDIA/k8s-device-plugin/main.go:15 func main() { ... log.Println("Starting FS watcher.") watcher, err := newFSWatcher(pluginapi.DevicePluginPath) ... restart := true var devicePlugin *NvidiaDevicePlugin L: for { if restart { if devicePlugin != nil { devicePlugin.Stop() } devicePlugin = NewNvidiaDevicePlugin() if err := devicePlugin.Serve(); err != nil { log.Println("Could not contact Kubelet, retrying. Did you enable the device plugin feature gate?") log.Printf("You can check the prerequisites at: https://github.com/NVIDIA/k8s-device-plugin#prerequisites") log.Printf("You can learn how to set the runtime at: https://github.com/NVIDIA/k8s-device-plugin#quick-start") } else { restart = false } } select { case event := <-watcher.Events: if event.Name == pluginapi.KubeletSocket && event.Op&fsnotify.Create == fsnotify.Create { log.Printf("inotify: %s created, restarting.", pluginapi.KubeletSocket) restart = true } case err := <-watcher.Errors: log.Printf("inotify: %s", err) case s := <-sigs: switch s { case syscall.SIGHUP: log.Println("Received SIGHUP, restarting.") restart = true default: log.Printf("Received signal \"%v\", shutting down.", s) devicePlugin.Stop() break L } } } }
-
通过
fsnotify.Watcher
监控/var/lib/kubelet/device-plugins/
目录。 -
如果
fsnotify.Watcher
的Events Channel收到Createkubelet.sock
事件(说明kubelet发生重启),则会触发Nvidia Device Plugin的重启。 -
Nvidia Device Plugin重启的逻辑是:先检查devicePlugin对象是否为空(说明完成了Nvidia Device Plugin的初始化):
-
如果不为空,则先停止Nvidia Device Plugin的gRPC Server。
-
然后调用NewNvidiaDevicePlugin()重建一个新的DevicePlugin实例。
-
调用Serve()启动gRPC Server,并先kubelet注册自己。
因此,这其中只监控了kubelet.sock
的Create事件,能很好处理kubelet重启的问题,但是并没有监控自己的socket是否被删除的事件。所以,如果Nvidia Device Plugin的socket被误删了,那么将会导致kubelet无法与该节点的Nvidia Device Plugin进行socket通信,则意味着Device Plugin的gRPC接口都无法调通:
-
无法ListAndWatch该节点上的Device列表、健康状态,Devices信息无法同步。
-
无法Allocate Device,导致容器创建失败。
因此,建议加上对自己device plugin socket的删除事件的监控,一旦监控到删除,则应该触发restart。
select { case event := <-watcher.Events: if event.Name == pluginapi.KubeletSocket && event.Op&fsnotify.Create == fsnotify.Create { log.Printf("inotify: %s created, restarting.", pluginapi.KubeletSocket) restart = true } // 增加对nvidia.sock的删除事件监控 if event.Name == serverSocket && event.Op&fsnotify.Delete == fsnotify.Delete { log.Printf("inotify: %s deleted, restarting.", serverSocket) restart = true } ... }
Extended Resources
-
Device Plugin是通过Extended Resources来expose宿主机上的资源的,Kubernetes内置的Resources都是隶属于
kubernetes.io
domain的,因此Extended Resource不允许advertise在kubernetes.io
domain下。 -
Node-level Extended Resource
注意:~1 is the encoding for the character / in the patch path。
-
给API Server提交PATCH请求,给node的status.capacity添加新的资源名称和数量;
-
kubelet通过定期更新node status.allocatable到API Server,这其中就包括事先给node打PATCH新加的资源。之后请求了新加资源的Pod就会被scheduler根据node status.allocatable进行FitResources Predicate甩选node。
-
注意:kubelet通过–node-status-update-frequency配置定期更新间隔,默认10s。因此,当你提交完PATCH后,最坏情况下可能要等待10s左右的时间才能被scheduler发现并使用该资源。
-
Device plugin管理的资源
-
其他资源
curl --header "Content-Type: application/json-patch+json" \ --request PATCH \ --data '[{"op": "add", "path": "/status/capacity/example.com~1foo", "value": "5"}]' \ http://k8s-master:8080/api/v1/nodes/k8s-node-1/status
-
Cluster-level Extended Resources
-
通常集群级的Extended Resources是给scheduler extender使用的,用来做Resources的配额管理。
-
当Pod请求的resource中包含该extended resources时,default scheduler才会将这个Pod发给对应的scheduler extender进行二次调度。
-
ignoredByScheduler field如果设置为true,则default scheduler将不会对该资源进行PodFitsResources预选检查,通常都会设置为true,因为Cluster-level不是跟node相关的,不适合进行PodFitResources对Node资源进行检查。
{ "kind": "Policy", "apiVersion": "v1", "extenders": [ { "urlPrefix":"<extender-endpoint>", "bindVerb": "bind", "ManagedResources": [ { "name": "example.com/foo", "ignoredByScheduler": true } ] } ] }
-
API Server限制了Extender Resources只能为整数,比如2,2000m,2Ki,不能为1.5, 1500m。
-
Contaienr resources filed中只配置的Extended Resources必须是Guaranteed QoS。即要么只显示设置了limits(此时requests默认同limits),要么requests和limit显示配置一样。
Scheduler GPU
https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/
这里我们只讨论Kubernetes 1.10中如何调度使用GPU。
在Kubernetes 1.8之前,官方还是建议enable alpha gate feature: Accelerators,通过请求resource alpha.kubernetes.io/nvidia-gpu
来使用gpu,并且要求容器挂载Host上的nvidia lib和driver到容器内。这部分内容,请参考我的博文:如何在Kubernetes集群中利用GPU进行AI训练。
-
从Kubernetes 1.8开始,官方推荐使用Device Plugins方式来使用GPU。
-
需要在Node上pre-install NVIDIA Driver,并建议通过Daemonset部署NVIDIA Device Plugin,完成后Kubernetes才能发现nvidia.com/gpu。
-
因为device plugin通过extended resources来expose gpu resource的,所以在container请求gpu资源的时候要注意resource QoS为Guaranteed。
-
Containers目前仍然不支持共享同一块gpu卡。每个Container可以请求多块gpu卡,但是不支持gpu fraction。
使用官方nvidia driver除了以上注意事项之外,还需注意:
-
Node上需要pre-install nvidia docker 2.0,并使用nvidia docker替换runC作为docker的默认runtime。
-
在CentOS上,参考如下方式安装nvidia docker 2.0 :
# Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | \ sudo tee /etc/yum.repos.d/nvidia-docker.repo # Install nvidia-docker2 and reload the Docker daemon configuration sudo yum install -y nvidia-docker2 sudo pkill -SIGHUP dockerd # Test nvidia-smi with the latest official CUDA image docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
-
以上工作都完成后,Container就可以像请求buit-in resources一样请求gpu资源了:
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: 2 # requesting 2 GPU
使用NodeSelector区分不同型号的GPU服务器
如果你的集群中存在不同型号的GPU服务器,比如nvidia tesla k80, p100, v100等,而且不同的训练任务需要匹配不同的GPU型号,那么先给Node打上对应的Label:
# 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
Pod中通过NodeSelector来指定对应的GPU型号:
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.
思考:其实仅仅使用NodeSelector是不能很好解决这个问题的,这要求所有的pod都要加上对应的NodeSelector。对于V100这样的昂贵稀有的GPU卡,通常还要求不能让别的训练任务使用,只给某些算法训练使用,这个时候我们可以通过给Node打上对应的Taint,给需要的Pod的打上对应Toleration就能完美满足需求了。
Deploy
-
建议通过Daemonset来部署Device Plugin,方便实现failover。
-
Device Plugin Pod必须具有privileged特权才能访问/var/lib/kubelet/device-plugins
-
Device Plugin Pod需将宿主机的hostpath /var/lib/kubelet/device-plugins挂载到容器内相同的目录。
kubernetes 1.8
apiVersion: extensions/v1beta1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset spec: template: metadata: labels: name: nvidia-device-plugin-ds spec: containers: - image: nvidia/k8s-device-plugin:1.8 name: nvidia-device-plugin-ctr securityContext: privileged: true volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins
kubernetes 1.10
apiVersion: extensions/v1beta1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: template: metadata: # Mark this pod as a critical add-on; when enabled, the critical add-on scheduler # reserves resources for critical add-on pods so that they can be rescheduled after # a failure. This annotation works in tandem with the toleration below. annotations: scheduler.alpha.kubernetes.io/critical-pod: "" labels: name: nvidia-device-plugin-ds spec: tolerations: # Allow this pod to be rescheduled while the node is in "critical add-ons only" mode. # This, along with the annotation above marks this pod as a critical add-on. - key: CriticalAddonsOnly operator: Exists containers: - image: nvidia/k8s-device-plugin:1.10 name: nvidia-device-plugin-ctr securityContext: privileged: true volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins
关于Kubernetes对critical pod的处理,越来越有意思了,找个时间单独写个博客再详细聊这个。
Device Plugins原理图
上述内容就是Kubernetes如何通过Device Plugins来使用NVIDIA GPU,你们学到知识或技能了吗?如果还想学到更多技能或者丰富自己的知识储备,欢迎关注云搜网行业资讯频道。