本文将从 GPU-Operator 简介、安装、应用部署及在 KubeSphere 上集成监控等方面出发，从原理到实践，逐步浅析介绍 GPU-Operator。

GPU-Operator简介

众所周知，Kubernetes 平台通过设备插件框架提供对特殊硬件资源的访问，如 NVIDIA gpu、网卡、Infiniband 适配器和其他设备。然而，使用这些硬件资源配置和管理节点需要配置多个软件组件，如驱动程序、容器运行时或其他依赖库，这是困难的和容易出错的。

NVIDIA GPU Operator 由Nvidia公司开源，利用了 kubernetes 平台的 Operator 控制模式，方便地自动化集成管理 GPU 所需的 NVIDIA 设备组件，有效地解决了上述GPU设备集成的痛点。这些组件包括 NVIDIA 驱动程序(用于启用 CUDA )、用于 GPU 的 Kubernetes 设备插件、NVIDIA Container 运行时、自动节点标签、基于 DCGM 的监控等。

NVIDIA GPU Operator 的不仅实现了设备和组件一体化集成，而且它管理 GPU 节点就像管理 CPU 节点一样方便，无需单独为 GPU 节点提供特殊的操作系统。值得关注的是，它将GPU各组件容器化，提供 GPU 能力，非常适合快速扩展和管理规模 GPU 节点。当然，对于已经为GPU组件构建了特殊操作系统的应用场景来说，显得并不是那么合适了。

GPU-Operator 架构原理

前文提到，NVIDIA GPU Operator 管理 GPU 节点就像管理 CPU 节点一样方便，那么它是如何实现这一能力呢？

我们一起来看看 GPU-Operator 运行时的架构图：

291266436

通过图中的描述，我们可以知道， GPU-Operator 是通过实现了Nvidia容器运行时，以runC作为输入，在runC中preStart hook中注入了一个名叫nvidia-container-toolkit的脚本，该脚本调用libnvidia-container CLI设置一系列合适的flags，使得容器运行后具有GPU能力。

GPU-Operator 安装说明

前提条件

在安装 GPU Operator 之前，请配置好安装环境如下：

所有节点不需要预先安装NVIDIA组件(driver,container runtime,device plugin)；
所有节点必须配置Docker,cri-o, 或者containerd.对于 docker 来说，可以参考这里；
如果使用HWE内核(e.g. kernel 5.x) 的 Ubuntu 18.04 LTS 环境下,需要给nouveau driver添加黑名单，需要更新initramfs；

$ sudo vim /etc/modprobe.d/blacklist.conf # 在尾部添加黑名单
blacklist nouveau
options nouveau modeset=0
$ sudo update-initramfs -u
$ reboot
$ lsmod | grep nouveau # 验证nouveau是否已禁用
$ cat /proc/cpuinfo | grep name | cut -f2 -d: | uniq -c  #本文测试时处理器架构代号为Broadwell
16 Intel Core Processor (Broadwell)

节点发现(NFD) 需要在每个节点上配置，默认情况会直接安装，如果已经配置，请在Helm chart变量设置nfd.enabled为false, 再安装;
如果使用 Kubernetes 1.13和1.14, 需要激活 KubeletPodResources；

支持的linux版本

OS Name / Version	Identifier	amd64 / x86_64	ppc64le	arm64 / aarch64
Amazon Linux 1	amzn1	X
Amazon Linux 2	amzn2	X
Amazon Linux 2017.09	amzn2017.09	X
Amazon Linux 2018.03	amzn2018.03	X
Open Suse Leap 15.0	sles15.0	X
Open Suse Leap 15.1	sles15.1	X
Debian Linux 9	debian9	X
Debian Linux 10	debian10	X
Centos 7	centos7	X	X
Centos 8	centos8	X	X	X
RHEL 7.4	rhel7.4	X	X
RHEL 7.5	rhel7.5	X	X
RHEL 7.6	rhel7.6	X	X
RHEL 7.7	rhel7.7	X	X
RHEL 8.0	rhel8.0	X	X	X
RHEL 8.1	rhel8.1	X	X	X
RHEL 8.2	rhel8.2	X	X	X
Ubuntu 16.04	ubuntu16.04	X	X
Ubuntu 18.04	ubuntu18.04	X	X	X
Ubuntu 20.04	ubuntu20.04	X	X	X

支持的容器运行时

OS Name / Version	amd64 / x86_64	ppc64le	arm64 / aarch64
Docker 18.09	X	X	X
Docker 19.03	X	X	X
RHEL/CentOS 8 podman	X
CentOS 8 Docker	X
RHEL/CentOS 7 Docker	X

安装doker环境

参考这里

安装NVIDIA Docker

配置 stable 仓库和 GPG key :

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

更新软件仓库后安装nvidia-docker2并添加运行时配置：

$ sudo apt-get update
$ sudo apt-get install -y nvidia-docker2
-----
What would you like to do about it ?  Your options are:
Y or I  : install the package maintainer's version
N or O  : keep your currently-installed version
D     : show the differences between the versions
Z     : start a shell to examine the situation
-----
# 初次安装，遇到以上交互式问题可选择N
# 如果选择Y会覆盖你的一些默认配置
# 选择N后，将以下配置添加到etc/docker/daemon.json
{
  "runtimes": {
      "nvidia": {
          "path": "/usr/bin/nvidia-container-runtime",
          "runtimeArgs": []
      }
  }
}

重启docker:

$ sudo systemctl restart docker

安装Helm

$ curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 \
   && chmod 700 get_helm.sh \
   && ./get_helm.sh

添加helm仓库

$ helm repo add nvidia https://nvidia.github.io/gpu-operator \
   && helm repo update

安装NVIDIA GPU Operator

docker as runtime

$ kubectl create ns gpu-operator-resources
$ helm install gpu-operator nvidia/gpu-operator -n gpu-operator-resources --wait

如果需要指定驱动版本，可参考如下：

$ helm install gpu-operator nvidia/gpu-operator -n gpu-operator-resources \
--set driver.version="450.80.02"

crio as runtime

helm install gpu-operator nvidia/gpu-operator -n gpu-operator-resources\
   --set operator.defaultRuntime=crio

containerd as runtime

helm install gpu-operator nvidia/gpu-operator -n gpu-operator-resources\
   --set operator.defaultRuntime=containerd
   
Furthermore, when setting containerd as the defaultRuntime the following options are also available:
toolkit:
  env:
  - name: CONTAINERD_CONFIG
    value: /etc/containerd/config.toml
  - name: CONTAINERD_SOCKET
    value: /run/containerd/containerd.sock
  - name: CONTAINERD_RUNTIME_CLASS
    value: nvidia
  - name: CONTAINERD_SET_AS_DEFAULT
    value: true

由于安装的镜像比较大，所以初次安装过程中可能会出现超时的情形，请检查你的镜像是否在拉取中！

使用 values.yaml 安装

$ helm install gpu-operator nvidia/gpu-operator -n gpu-operator-resources -f values.yaml

考虑离线安装

应用部署

检查已部署 operator 服务状态

检查 pods 状态

$ kubectl get pods -n gpu-operator-resources
NAME                                                          READY   STATUS      RESTARTS   AGE
gpu-feature-discovery-4gk78                                   1/1     Running     0          35s
gpu-operator-858fc55fdb-jv488                                 1/1     Running     0          2m52s
gpu-operator-node-feature-discovery-master-7f9ccc4c7b-2sg6r   1/1     Running     0          2m52s
gpu-operator-node-feature-discovery-worker-cbkhn              1/1     Running     0          2m52s
gpu-operator-node-feature-discovery-worker-m8jcm              1/1     Running     0          2m52s
nvidia-container-toolkit-daemonset-tfwqt                      1/1     Running     0          2m42s
nvidia-dcgm-exporter-mqns5                                    1/1     Running     0          38s
nvidia-device-plugin-daemonset-7npbs                          1/1     Running     0          53s
nvidia-device-plugin-validation                               0/1     Completed   0          49s
nvidia-driver-daemonset-hgv6s                                 1/1     Running     0          2m47s

检查节点资源是否处于可分配

$ kubectl describe node worker-gpu-001
---
Allocatable:
  cpu:                15600m
  ephemeral-storage:  82435528Ki
  hugepages-2Mi:      0
  memory:             63649242267
  nvidia.com/gpu:     1  #check here
  pods:               110
---

部署官方文档中的两个实例

实例一

该实例是一个CUDA负载生成器，它在GPU上执行FP16矩阵乘法：

cat cuda-load-generator.yaml 
apiVersion: v1
kind: Pod
metadata:
   name: dcgmproftester
spec:
   restartPolicy: OnFailure
   containers:
   - name: dcgmproftester11
   image: nvidia/samples:dcgmproftester-2.0.10-cuda11.0-ubuntu18.04
   args: ["--no-dcgm-validation", "-t 1004", "-d 120"]
   resources:
      limits:
         nvidia.com/gpu: 1
   securityContext:
      capabilities:
         add: ["SYS_ADMIN"]
EOF

实例二

该实例是一个集成了 TensorFlow 的Jupyter notebook ：

$ curl -LO https://nvidia.github.io/gpu-operator/notebook-example.yml
$ cat notebook-example.yml
apiVersion: v1
kind: Service
metadata:
  name: tf-notebook
  labels:
    app: tf-notebook
spec:
  type: NodePort
  ports:
  - port: 80
    name: http
    targetPort: 8888
    nodePort: 30001
  selector:
    app: tf-notebook
---
apiVersion: v1
kind: Pod
metadata:
  name: tf-notebook
  labels:
    app: tf-notebook
spec:
  securityContext:
    fsGroup: 0
  containers:
  - name: tf-notebook
    image: tensorflow/tensorflow:latest-gpu-jupyter
    resources:
      limits:
        nvidia.com/gpu: 1
    ports:
    - containerPort: 8

基于 Jupyter Notebook 应用运行深度学习训练任务

部署应用

$ kubectl apply -f cuda-load-generator.yaml 
pod/dcgmproftester created
$ kubectl apply -f notebook-example.yml       
service/tf-notebook created
pod/tf-notebook created

查看 GPU 处于已分配状态:

$ kubectl describe node worker-gpu-001
---
Allocated resources:
  (Total limits may be over 100 percent, i.e., overcommitted.)
  Resource           Requests     Limits
  --------           --------     ------
  cpu                1087m (6%)   1680m (10%)
  memory             1440Mi (2%)  1510Mi (2%)
  ephemeral-storage  0 (0%)       0 (0%)
  nvidia.com/gpu     1            1 #check this
Events:              <none>

当有 GPU 任务发布给平台时，GPU 资源从可分配状态转变为已分配状态，安装任务发布的先后顺序，第二个任务在第一个任务运行结束后开始运行：

$ kubectl get pods --watch
NAME             READY   STATUS    RESTARTS   AGE
dcgmproftester   1/1     Running   0          76s
tf-notebook      0/1     Pending   0          58s
------
NAME             READY   STATUS      RESTARTS   AGE
dcgmproftester   0/1     Completed   0          4m22s
tf-notebook      1/1     Running     0          4m4s

获取应用端口信息：

$ kubectl get svc # get the nodeport of the svc, 30001
gpu-operator-1611672791-node-feature-discovery   ClusterIP   10.233.10.222   <none>        8080/TCP       12h
kubernetes                                       ClusterIP   10.233.0.1      <none>        443/TCP        12h
tf-notebook                                      NodePort    10.233.53.116   <none>        80:30001/TCP   7m52s

查看日志，获取登录口令：

$ kubectl logs tf-notebook 
[I 21:50:23.188 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 21:50:23.390 NotebookApp] Serving notebooks from local directory: /tf
[I 21:50:23.391 NotebookApp] The Jupyter Notebook is running at:
[I 21:50:23.391 NotebookApp] http://tf-notebook:8888/?token=3660c9ee9b225458faaf853200bc512ff2206f635ab2b1d9
[I 21:50:23.391 NotebookApp]  or http://127.0.0.1:8888/?token=3660c9ee9b225458faaf853200bc512ff2206f635ab2b1d9
[I 21:50:23.391 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 21:50:23.394 NotebookApp]
   To access the notebook, open this file in a browser:
      file:///root/.local/share/jupyter/runtime/nbserver-1-open.html
   Or copy and paste one of these URLs:
      http://tf-notebook:8888/?token=3660c9ee9b225458faaf853200bc512ff2206f635ab2b1d9
   or http://127.0.0.1:8888/?token=3660c9ee9b225458faaf853200bc512ff2206f635ab2b1d9

通过查看应用的日志，我们拿到登录口令后，就可以顺利进入notebook开发环境：

http:://<your-machine-ip>:30001/?token=3660c9ee9b225458faaf853200bc512ff2206f635ab2b1d9

运行深度学习任务

进入jupyter notebook 环境后，尝试进入终端，运行深度学习任务：

进入terminal后拉取tersorflow测试代码并运行：

与此同时，开启另外一个终端运行nvidia-smi查看 GPU 监控使用情况：

利用KubeSphere自定义监控功能监控GPU

部署ServiceMonitor

gpu-operator帮我们提供了nvidia-dcgm-exporter这个exportor, 只需要将它集成到Prometheus的可采集对象中，也就是ServiceMonitor中，我们就能获取GPU监控数据了:

$ kubectl get pods -n gpu-operator-resources
NAME                                       READY   STATUS      RESTARTS   AGE
gpu-feature-discovery-ff4ng                1/1     Running     2          15h
nvidia-container-toolkit-daemonset-2vxjz   1/1     Running     0          15h
nvidia-dcgm-exporter-pqwfv                 1/1     Running     0          5h27m #here
nvidia-device-plugin-daemonset-42n74       1/1     Running     0          5h27m
nvidia-device-plugin-validation            0/1     Completed   0          5h27m
nvidia-driver-daemonset-dvd9r              1/1     Running     3          15h

可以构建一个busybox查看该exporter暴露的指标:

$ kubectl get svc -n gpu-operator-resources
NAME                                  TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
gpu-operator-node-feature-discovery   ClusterIP   10.233.54.111   <none>        8080/TCP   56m
nvidia-dcgm-exporter                  ClusterIP   10.233.53.196   <none>        9400/TCP   54m
$ kubectl exec -it busybox-sleep -- sh
$ wget http://nvidia-dcgm-exporter.gpu-operator-resources:9400/metrics
$ cat metrics
----
DCGM_FI_DEV_SM_CLOCK{gpu="0",UUID="GPU-eeff7856-475a-2eb7-6408-48d023d9dd28",device="nvidia0",container="tf-notebook",namespace="default",pod="tf-notebook"} 405
DCGM_FI_DEV_MEM_CLOCK{gpu="0",UUID="GPU-eeff7856-475a-2eb7-6408-48d023d9dd28",device="nvidia0",container="tf-notebook",namespace="default",pod="tf-notebook"} 715
DCGM_FI_DEV_GPU_TEMP{gpu="0",UUID="GPU-eeff7856-475a-2eb7-6408-48d023d9dd28",device="nvidia0",container="tf-notebook",namespace="default",pod="tf-notebook"} 30
----

查看nvidia-dcgm-exporter暴露的svc和ep：

$ kubectl describe svc nvidia-dcgm-exporter -n gpu-operator-resources
Name:                     nvidia-dcgm-exporter
Namespace:                gpu-operator-resources
Labels:                   app=nvidia-dcgm-exporter
Annotations:              prometheus.io/scrape: true
Selector:                 app=nvidia-dcgm-exporter
Type:                     NodePort
IP:                       10.233.28.200
Port:                     gpu-metrics  9400/TCP
TargetPort:               9400/TCP
NodePort:                 gpu-metrics  31129/TCP
Endpoints:                10.233.84.54:9400
Session Affinity:         None
External Traffic Policy:  Cluster
Events:                   <none>

配置ServiceMonitor定义清单:

$ cat gpu-servicemonitor.yaml 
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: nvidia-dcgm-exporter
  namespace: gpu-operator-resources 
  labels:
     app: nvidia-dcgm-exporter
spec:
  jobLabel: nvidia-gpu
  endpoints:
  - port: gpu-metrics
    interval: 15s
  selector:
    matchLabels:
      app: nvidia-dcgm-exporter
  namespaceSelector:
    matchNames:
    - gpu-operator-resources
$ kubectl apply -f gpu-servicemonitor.yaml

检查 GPU 指标是否被采集到（可选）

将servicemonitor提交给kubesphere平台后，通过暴露prometheus-k8s为NodePort，我们可以在Prometheus的UI上验证一下是否采集到的相关指标：

589036792

创建 KubeSphere GPU 自定义监控面板

`ks 3.0`

如果部署的ks版本是ks 3.0，需要简单地配置以下几个步骤，便可顺利完成可观察性监控。

首先，登录kubsphere console后，创建一个企业空间名称为ks-monitoring-demo, 名称可按需创建;

其次，需要将servicemonitor所在的目标名称空间gpu-operator-resources分配为已存在的企业空间中，以便纳入监控。

295375863

最后，进入目标企业空间，在纳管的项目找到gpu-operator-resources, 点击后找到可自定义监控界面, 即可添加自定义监控。

270522602

521251649

后续版本

后续版本可选择添加集群监控

创建自定义监控

下载dashboard以及配置namespace:

$ curl -LO https://raw.githubusercontent.com/kubesphere/monitoring-dashboard/master/contrib/gallery/nvidia-gpu-dcgm-exporter-dashboard.yaml
$ cat nvidia-gpu-dcgm-exporter-dashboard.yaml
----
apiVersion: monitoring.kubesphere.io/v1alpha1
kind: Dashboard
metadata:
  name: nvidia-dcgm-exporter-dashboard-rev1
  namespace: gpu-operator-resources  #check here
spec:
-----

可以直接命令行apply或者在自定义监控面板中选择编辑模式进行导入：
021089708

正确导入后：

592402715

在上面创建的jupyter notebook运行深度学习测试任务后，可以明显地观察到相关GPU指标变化：

860536766

卸载

$ helm list -n gpu-operator-resources
NAME            NAMESPACE               REVISION        UPDATED                                 STATUS          CHART                   APP VERSION
gpu-operator    gpu-operator-resources  1               2021-02-20 11:50:56.162559286 +0800 CST deployed        gpu-operator-1.5.2      1.5.2     
$ helm uninstall gpu-operator -n gpu-operator-resources

重启无法使用 GPU

关于已部署正常运行的gpu-operator和AI应用的集群，重启GPU主机后会出现没法用上 GPU 的情况，极有可能是因为插件还没加载，应用优先进行了载入，就会导致这种问题。这时，只需要优先保证插件运行正常，然后重新部署应用即可。

参考资料

官方代码仓库

GitHub: https://github.com/NVIDIA/gpu-operator
GitLab: https://gitlab.com/nvidia/kubernetes/gpu-operator

官方文档

https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/getting-started.html#install-nvidia-gpu-operator