当GPU监控大盘异常或无数据时，您可以按照本文描述的操作步骤排查GPU监控常见问题。
操作步骤
步骤一：查看集群中是否有GPU节点
- 登录容器服务管理控制台。
- 在控制台左侧导航栏，单击集群。
- 在集群列表页面，单击目标集群名称或者目标集群右侧操作列下的详情。
- 在集群管理页左侧导航栏，选择。
在节点页面，查看目标集群中是否有GPU节点。
说明在节点页面的配置列，如果配置名称包含****ecs.gn****，则说明该集群中有GPU节点。
步骤二：查看ack-arms-prometheus是否正确安装
查看目标集群是否安装ack-arms-prometheus。具体操作，请参见开启阿里云Prometheus监控。
如果已安装ack-arms-prometheus，执行以下命令查看ack-arms-prometheus的Pod状态。
kubectl get pods -n arms-prom预期输出：
NAME READY STATUS RESTARTS AGE arms-prom-ack-arms-prometheus-866cfd9f8f-x8jxl 1/1 Running 0 26d如果是Running状态，说明Pod运行正常。如果Pod的状态不是Running，则执行
kubectl describe pod命令，查看Pod状态不正常原因。
步骤三：检查ack-prometheus-gpu-exporter是否成功部署
执行以下命令，查看Pod的运行状态和数量。
kubectl get pods -n arms-prom预期输出：
NAME READY STATUS RESTARTS AGE
ack-prometheus-gpu-exporter-6kpj7 1/1 Running 0 7d19h
ack-prometheus-gpu-exporter-bkbf8 1/1 Running 0 18h
ack-prometheus-gpu-exporter-blbnq 1/1 Running 0 18h从上述输出信息，可以知道Pod数量和GPU节点数一致，且Pod的状态是Running，说明ack-prometheus-gpu-exporter已在相应GPU节点成功部署。如果Pod的状态不是Running，则执行kubectl describe pod命令，查看Pod状态不正常原因。
步骤四：检查ack-prometheus-gpu-exporter是否成功采集到数据
执行以下命令，SSH登录到目标集群节点。
sudo ssh root@198.51.XX.XXroot：用户自定义用户名。
198.51.XX.XX：目标集群的公网IP访问地址。
执行以下命令，查看Pod的内网IP。
kubectl get pods -n arms-prom -o wide预期输出：
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES ack-prometheus-gpu-exporter-4rdtl 1/1 Running 0 7h6m 172.21.XX.XX cn-beijing.192.168.0.22 <none> <none> ack-prometheus-gpu-exporter-vdkqf 1/1 Running 0 6d16h 172.21.XX.XX cn-beijing.192.168.94.7 <none> <none> ack-prometheus-gpu-exporter-x7v48 1/1 Running 0 7h6m 172.21.XX.XX cn-beijing.192.168.0.23 <none> <none>执行以下命令，调用
gpu exporter服务，获取GPU指标信息。说明ack-prometheus-gpu-exporter的默认端口是9445。
sudo curl 172.21.XX.XX:9445 | grep "nvidia_gpu"预期输出：
% Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 7518 100 7518 0 0 101k 0 --:--:-- --:--:-- --:--:-- 101k # HELP nvidia_gpu_duty_cycle Percent of time over the past sample period during which one or more kernels were executing on the GPU device # TYPE nvidia_gpu_duty_cycle gauge nvidia_gpu_duty_cycle{allocate_mode="exclusive",container_name="tfserving-gpu",minor_number="0",name="Tesla T4",namespace_name="default",node_name="cn-beijing.192.168.0.22",pod_name="fashion-mnist-eci-2-predictor-0-tfserving-proxy-tfserving-v789b",uuid="GPU-293f6608-281a-cc66-fcb3-0d366f32a31d"} 0 # HELP nvidia_gpu_memory_total_bytes Total memory of the GPU device # TYPE nvidia_gpu_memory_total_bytes gauge nvidia_gpu_memory_total_bytes{allocate_mode="exclusive",container_name="tfserving-gpu",minor_number="0",name="Tesla T4",namespace_name="default",node_name="cn-beijing.192.168.0.22",pod_name="fashion-mnist-eci-2-predictor-0-tfserving-proxy-tfserving-v789b",uuid="GPU-293f6608-281a-cc66-fcb3-0d366f32a31d"} 1.5811477504e+10 # HELP nvidia_gpu_memory_used_bytes Memory used by the GPU device # TYPE nvidia_gpu_memory_used_bytes gauge nvidia_gpu_memory_used_bytes{allocate_mode="exclusive",container_name="tfserving-gpu",minor_number="0",name="Tesla T4",namespace_name="default",node_name="cn-beijing.192.168.0.22",pod_name="fashion-mnist-eci-2-predictor-0-tfserving-proxy-tfserving-v789b",uuid="GPU-293f6608-281a-cc66-fcb3-0d366f32a31d"} 1.488453632e+10 # HELP nvidia_gpu_num_devices Number of GPU devices # TYPE nvidia_gpu_num_devices gauge nvidia_gpu_num_devices{node_name="cn-beijing.192.168.0.22"} 1 # HELP nvidia_gpu_power_usage_milliwatts Power usage of the GPU device in watts # TYPE nvidia_gpu_power_usage_milliwatts gauge nvidia_gpu_power_usage_milliwatts{allocate_mode="exclusive",container_name="tfserving-gpu",minor_number="0",name="Tesla T4",namespace_name="default",node_name="cn-beijing.192.168.0.22",pod_name="fashion-mnist-eci-2-predictor-0-tfserving-proxy-tfserving-v789b",uuid="GPU-293f6608-281a-cc66-fcb3-0d366f32a31d"} 27000 # HELP nvidia_gpu_temperature_celsius Temperature of the GPU device in celsius # TYPE nvidia_gpu_temperature_celsius gauge nvidia_gpu_temperature_celsius{allocate_mode="exclusive",container_name="tfserving-gpu",minor_number="0",name="Tesla T4",namespace_name="default",node_name="cn-beijing.192.168.0.22",pod_name="fashion-mnist-eci-2-predictor-0-tfserving-proxy-tfserving-v789b",uuid="GPU-293f6608-281a-cc66-fcb3-0d366f32a31d"} 44如果输出的数据中有
nvidia_gpu开头的指标信息，说明ack-prometheus-gpu-exporter可以成功采集数据。