使用nvidia_gpu_expoter配合prometheus+grafana监控GPU性能 _grafana

当筵意气临九霄，星离雨散不终朝。这篇文章主要讲述使用nvidia_gpu_expoter配合prometheus+grafana监控GPU性能相关的知识，希望能为你提供帮助。
项目地址：??GitHub - utkuozdemir/nvidia_gpu_exporter: Nvidia GPU exporter for prometheus using nvidia-smi binary???
根据git上面的nvidia监控项目，可以实现grafana监控GPU，但是git上面提供的utkuozdemir/nvidia_gpu_exporter:0.3.0这个镜像只可以在ubuntu系统上面运行，如果在centos上运行，日志会提示无法获取到GPU信息，也就导致无法接到k8s的prometheus.目前使用的方法是将nvidia_gpu_exporter这个可执行访问下载到centos系统中，然后通过系统命令运行，最终得到一个服务，也就是gpu的metircs。然后在k8s中，创建endpoinst、service、servicemonitor,实现prometheus收集到gpu-metrics信息，最后通过grafana进行可视化展示。下面是具体操作步骤：?
1 在centos系统中有创建nvidia_gpu_exporter服务?

安装nvidia_gpu_exporter服务 # VERSION=0.3.0? # wget ??https://github.com/utkuozdemir/nvidia_gpu_exporter/releases/download/v$VERSION/nvidia_gpu_exporter_$VERSION_linux_x86_64.tar.gz?? # tar -xvzf nvidia_gpu_exporter_$VERSION_linux_x86_64.tar.gz? # mv nvidia_gpu_exporter /usr/local/bin? # ./nvidia_gpu_exporter 此时通过web页面就可查看此台GPU服务器的gpu-metircs信息，如下图? 可以看到GPU相关信息? 创建nvidia_gpu_exporter服务? # vim /etc/systemd/system/nvidia_gpu_exporter.service ? [Unit]? Description=Nvidia GPU Exporter? After=network-online.target? [Service]? Type=simple? User=nvidia_gpu_exporter? Group=nvidia_gpu_exporter? ExecStart=/usr/local/bin/nvidia_gpu_exporter? SyslogIdentifier=nvidia_gpu_exporter? Restart=always? RestartSec=1? NoNewPrivileges=yes? ProtectHome=yes? ProtectSystem=strict? ProtectControlGroups=true? ProtectKernelModules=true? ProtectKernelTunables=yes? ProtectHostname=yes? ProtectKernelLogs=yes? ProtectProc=yes? [Install]? WantedBy=multi-user.target? # systemctl daemon-reload ? [root@k8s-gpu4 ~]# systemctl enable nvidia_gpu_exporter? [root@k8s-gpu4 ~]# systemctl start nvidia_gpu_exporter.service ? [root@k8s-gpu4 ~]# systemctl status nvidia_gpu_exporter.service ? ● nvidia_gpu_exporter.service - Nvidia GPU Exporter? Loaded: loaded (/etc/systemd/system/nvidia_gpu_exporter.service; enabled; vendor preset: disabled)? Active: active (running) since Fri 2022-05-13 17:36:03 CST; 5s ago? Main PID: 80178 (nvidia_gpu_expo)? Tasks: 6? Memory: 5.6M? CGroup: /system.slice/nvidia_gpu_exporter.service? └─80178 /usr/local/bin/nvidia_gpu_exporter? May 13 17:36:03 k8s-gpu4 systemd[1]: Started Nvidia GPU Exporter.? May 13 17:36:04 k8s-gpu4 nvidia_gpu_exporter[80178]: ts=2022-05-13T09:36:04.005Z caller=main.go:68 level=info msg="Listening on add...=:9835? May 13 17:36:04 k8s-gpu4 nvidia_gpu_exporter[80178]: ts=2022-05-13T09:36:04.006Z caller=tls_config.go:195 level=info msg="TLS is di...=false? Hint: Some lines were ellipsized, use -l to show in full.? 服务启动成功，通过页面查看?

2 在k8s中创建endpoints、service、servicemonitor?

创建endpoints? # cat gpu-exporter-endpoint.yaml apiVersion: v1 kind: Endpoints metadata: name: nvidia-gpu-exporter namespace: monitoring subsets: - addresses: - ip: 10.1.12.17 ports: - name: http port: 9835 protocol: TCP 上面的ip为GPU服务器地址，如果是多台GPU，可在下面继续添加，如 - ip: ... - ip: ... # kubectl create -f gpu-exporter-endpoint.yaml endpoints/nvidia-gpu-exporter created # kubectl get endpoints -n monitoring nvidia-gpu-exporter NAME ENDPOINTS AGE nvidia-gpu-exporter 10.1.12.17:9835 39s # kubectl describe endpoints -n monitoring nvidia-gpu-exporter Name: nvidia-gpu-exporter Namespace: monitoring Labels: < none> Annotations: < none> Subsets: Addresses: 10.1.12.17 NotReadyAddresses: < none> Ports: Name Port Protocol ---- ---- -------- http 9835 TCP Events: < none> 创建service? # cat gpu-exporter-svc.yaml ? apiVersion: v1? kind: Service? metadata:? labels:? app: nvidia-gpu-exporter? name: nvidia-gpu-exporter? namespace: monitoring? spec:? ports:? - name: http? protocol: TCP? port: 9835? targetPort: http? type: ClusterIP? # kubectl delete -f gpu-exporter-svc.yaml ? service "nvidia-gpu-exporter" deleted? kubectl create -f gpu-exporter-svc.yaml ? service/nvidia-gpu-exporter created? # kubectl get svc -n monitoring nvidia-gpu-exporter ? NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE? nvidia-gpu-exporter ClusterIP 10.10.75.226 < none> 9835/TCP 12s? # kubectl describe svc -n monitoring nvidia-gpu-exporter ? Name: nvidia-gpu-exporter? Namespace: monitoring? Labels: app=nvidia-gpu-exporter? Annotations: < none> ? Selector: < none> ? Type: ClusterIP? IP: 10.10.235.70? Port: http 9835/TCP? TargetPort: http/TCP? Endpoints: 10.1.12.17:9835? Session Affinity: None? Events: < none> 上面的endpioins一定要为上面创建的endpoints中的IP和port 创建servicemonitor #cat gpu-exporter-serviceMonitor.yaml apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: labels: app: nvidia-gpu-exporter name: nvidia-gpu-exporter namespace: monitoring spec: endpoints: - interval: 30s port: http jobLabel: app selector: matchLabels: app: nvidia-gpu-exporter kubectl create -f gpu-exporter-serviceMonitor.yaml servicemonitor.monitoring.coreos.com/nvidia-gpu-exporter created [root@k8s-master dongtai]# kubectl get servicemonitors.monitoring.coreos.com -n monitoring nvidia-gpu-exporter NAME AGE nvidia-gpu-exporter 12s # kubectl describe servicemonitors.monitoring.coreos.com -n monitoring nvidia-gpu-exporter Name: nvidia-gpu-exporter Namespace: monitoring Labels: app=nvidia-gpu-exporter Annotations: < none> API Version: monitoring.coreos.com/v1 Kind: ServiceMonitor Metadata: Creation Timestamp: 2022-05-13T09:50:35Z Generation: 1 Managed Fields: API Version: monitoring.coreos.com/v1 Fields Type: FieldsV1 fieldsV1: f:metadata: f:labels: .: f:app: f:spec: .: f:endpoints: f:jobLabel: f:selector: .: f:matchLabels: .: f:app: Manager: kubectl-create Operation: Update Time: 2022-05-13T09:50:35Z Resource Version: 14080381 Self Link: /apis/monitoring.coreos.com/v1/namespaces/monitoring/servicemonitors/nvidia-gpu-exporter UID: 7fdb365b-8bcd-4fc2-9772-9ad7de6155bf Spec: Endpoints: Interval: 30s Port: http Job Label: app Selector: Match Labels: App: nvidia-gpu-exporter Events: < none> prometheus页面验证? 在prometheus页面的targets中查看nvidia_gpu_exporter? ? 在Graph页面中进行nvidia搜索? ? 通过搜索可以得到这台GPU服务器有两张3090GPU?

3 在grafana中创建GPU监控面板?

在grafana导入官方提供的json文件? 导入官方的json文件会出现错误提示，原因是这个json文件配置有问题，我们需要进行修改。? 点击右上角进行修改? 【使用nvidia_gpu_expoter配合prometheus+grafana监控GPU性能】点击Variables，点击gpu? 将Query改成如下，改完后，可以得到GPU服务器的IP，最后点击update? 返回监控页后，可以得到如下图：? 最终GPU相关的性能指标能得到很好展示?