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发布于:2021-03-17    本文总阅读量:

使用 GPU-Operator 与 KubeSphere 简化深度学习训练与监控 GPU

本文将从 GPU-Operator 概念介绍、安装部署、深度训练测试应用部署,以及在 KubeSphere 使用自定义监控面板对接 GPU 监控,从原理到实践,逐步浅析介绍与实践 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,这是比较困难的和容易出错的。

NVIDIA GPU Operator 由 Nvidia 公司开源,利用了 Kubernetes 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:

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runCpreStart 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.enabledfalse, 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

可参考 Docker 官方文档

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

不同的容器运行时安装方法稍有区别:

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!可以考虑使用离线安装解决该类问题

使用 values.yaml 安装

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

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

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 Nvidia 文档的两个深度训练测试实例

实例一

$ 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

$ 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 cuda 与 notebook 示例应用

$ 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svcep

$ 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 custom/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 custom/gpu-servicemonitor.yaml

检查 GPU 指标是否被采集到(可选)

servicemonitor提交给kubesphere平台后,通过暴露prometheus-k8sNodePort,我们可以在PrometheusUI上验证一下是否采集到的相关指标:

创建 KubeSphere GPU 自定义监控面板

KubeSphere 3.0

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

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。

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。

创建自定义监控

下载nvidia-gpu-dcgm-exporter-dashboard并配置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:

正确导入后:

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:

卸载 GPU-Operator

$ 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

GPU-Operator 常见问题

GPU-Operator 重启后无法使用

答: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。

Nvidia k8s-device-plugin 与 GPU-Operator 方案对比?

我之前针对GPU使用的是 https://github.com/NVIDIA/k8s-device-pluginhttps://github.com/NVIDIA/gpu-monitoring-tools 相结合的方案来监控 GPU,请问这个方案与 GPU-Operator的方案相比,孰优孰劣一些?

答:个人认为 GPU-Operator 更简单易用,其自带 GPU 注入能力不需要构建专用的 OS,并且支持节点发现与可插拔,能够自动化集成管理 GPU 所需的 NVIDIA 设备组件,相对来说还是很省事的。

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

GPU-Operator 快速入门:

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

GPU-Operator 离线安装指南:

https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/getting-started.html#considerations-to-install-in-air-gapped-clusters

KubeSphere 自定义监控使用文档:

https://kubesphere.com.cn/docs/project-user-guide/custom-application-monitoring/examples/monitor-mysql/#

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