Collecting GPU Pod Monitoring Metrics and Setting Up a Grafana Dashboard

Application Scenarios

Monitoring metrics for GPU pods, such as gpu_pod_core_percentage_total, gpu_pod_core_percentage_used, gpu_pod_memory_total, and gpu_pod_memory_used, have been added to CCE AI Suite (NVIDIA GPU) of versions 2.1.30, 2.7.46, and later. These metrics allow for comprehensive monitoring of the resource usage of a GPU card and the compute and memory usage of the GPU workloads that run on a GPU. By using these metrics, you can create monitoring dashboards at different levels (for example, by pod or node) to stay informed about cluster information in real-time. For details about GPU metrics, see GPU Metrics.

The Cloud Native Cluster Monitoring add-on does not automatically collect these metrics. To view them on Grafana dashboards, you must manually configure the Cloud Native Cluster Monitoring add-on for data collection and set up a dashboard. This section provides an example using pod monitoring to explain how to collect GPU pod monitoring metrics and establish a dashboard.

Prerequisites

• A NVIDIA GPU node is running properly in the cluster.
• The CCE AI Suite (NVIDIA GPU) add-on has been installed in the cluster and its version is 2.1.30, 2.7.46, or later. For details about how to install the add-on, see CCE AI Suite (NVIDIA GPU).
• The Cloud Native Cluster Monitoring add-on and Grafana add-on have been installed in the cluster, and public access has been enabled for Grafana. For details about how to install these add-ons, see Cloud Native Cluster Monitoring and Grafana.
  • To use AOM data sources (Configuring an AOM Data Source), enable Report Monitoring Data to AOM in the Cloud Native Cluster Monitoring add-on and Interconnect with AOM in the Grafana add-on. In addition, make sure to use the same AOM instances for both add-ons.
  • To use Prometheus data sources (Configuring a Prometheus Data Source), enable Local Data Storage in the Cloud Native Cluster Monitoring add-on.

Process

Step 1: Collect GPU Pod Monitoring Metrics

The Cloud Native Cluster Monitoring add-on does not automatically collect GPU pod monitoring metrics. To view these metrics in the monitoring center, you need to manually configure the add-on for data collection.

1. Log in to the CCE console and click the cluster name to access the cluster console.
2. In the navigation pane, choose Cluster > Settings. In the right pane, click the Monitoring tab. In the Monitoring Settings area, locate Preset Policies under Collection Settings and click Enable. In the dialog box displayed, click OK. If you have already enabled Preset Policies, skip this step.
   

   The Preset Policies feature is used to configure preset collection policies for the Cloud Native Cluster Monitoring add-on. Enabling this feature will result in the deletion of preset ServiceMonitor and PodMonitor policies. Any manually created collection configurations will remain unaffected. Once this feature is enabled, customizing preset collection tasks becomes effortless.

   Figure 1 Enabling the Preset Policies feature
   

3. In the Monitoring Settings area, locate Metric Settings and click Refresh to obtain data. After you enabled Preset Policies, the Cloud Native Cluster Monitoring add-on will be automatically upgraded. Then, choose Monitoring Settings > Collection Settings > Preset Policies and click Manage.
   On the Collection Settings page, click the search box, select Task Name, and choose nvidia-gpu-device-plugin from the drop-down list. In the Collection Metric column, click Edit Trustlist.

   Figure 2 Editing trustlist
   

4. In the upper left corner of the Trustlist metrics page, click Add Metric and add the gpu_pod_core_percentage_total, gpu_pod_core_percentage_used, gpu_pod_memory_total, and gpu_pod_memory_used metrics. After completing the operation, close the current page.

Step 2: Configure a Data Source for Grafana

Grafana supports:

• AOM data sources: Grafana creates the prometheus-aom data source automatically. Make sure this data source can be properly connected to Grafana.
• Prometheus data sources: You can use the prometheus data source preset in Grafana. Make sure this data source can be properly connected to Grafana.

Step 3: Configure a Grafana Dashboard

Grafana dashboards are essential for centralized monitoring and visualizing data from various data sources. They provide real-time insights into system statuses and service metrics using charts, graphs, and alarms. Grafana can generate monitoring dashboards based on GPU pod metrics, categorizing them by pod, GPU, node, and cluster. This section focuses on configuring a Grafana dashboard for pods as an example. Additional PromQL statements for different monitoring panels are available in PromQL Statement. You can perform the following operations to set up related dashboards.

1. Create a Grafana dashboard to show GPU pod monitoring metrics.
   1. On the Grafana GUI, click to open the menu bar on the left and click Dashboards. In the upper right corner of the Dashboards page, click New and choose New dashboard from the drop-down list.
      Figure 5 Creating a dashboard
      
   2. In the upper right corner of the New dashboard page, click to name the dashboard. Enter GPU Pod Dashboard for Title and click Save dashboard in the upper right corner. On the Save dashboard page displayed, click Save. In this example, the dashboard is named GPU Pod Dashboard. You can use another name.
      Figure 6 Renaming a dashboard
      

2. Configure variables on the Grafana dashboard.
   Grafana variables allow you to create dynamic, customizable, templated dashboards. By changing variable values within the dashboard, you can modify the queries and panels. There is no need to create separate dashboards for each service or metric. Table 1 outlines the variables to configure in this example. Using instance as an example, this step demonstrates how to configure variables on a Grafana dashboard. You can refer to the instance example to add the variables listed in Table 1 in order.

   Table 1 Variables

   Variable	Description	PromQL Statement
   instance	Pod name of dp. It is used to obtain GPU nodes.	label_values(up{job=~".*nvidia-gpu-device-plugin.*"},instance)
   gpu_index	GPU card number, for example, 0, 1, or 2	label_values(cce_gpu_temperature{instance=~"$instance"}, gpu_index)
   namespace	Namespace where the GPU workload is in	label_values(gpu_pod_memory_used{instance=~"$instance",gpu_index=~"$gpu_index"}, namespace)
   pod	Pod name of a GPU workload	label_values(gpu_pod_memory_used{instance=~"$instance",gpu_index=~"$gpu_index",namespace=~"$namespace"}, pod)

   1. In the upper right corner of the New dashboard page, click . On the Settings page, click the Variables tab and click Add variable.
      Figure 7 Adding a variable
      
   2. Configure Select variable type and General. For details, see Figure 8 and Table 2.
      Figure 8 Configuring Select variable type and General
      

      Table 2 Configuration example for Select variable type and General

      Parameter	Example Value	Description
      Select variable type	Query	Choose a variable type and select Query to obtain the values of variables in Table 1 from data source queries.
      Name	instance	Configure a variable name.

   3. Configure Query options. For details, see Figure 9 and Table 3.
      Figure 9 Configuring Query options
      

      Table 3 Configuration example for Query options

      Parameter	Example Value	Description
      Query type	Classic query	Select the data query mode and display mode. In this example, select Classic query, which is a text-based query mode where you can write query statements (such as PromQL) directly to retrieve data.
      Classic Query	label_values(up{job=~".*nvidia-gpu-device-plugin.*"},instance)	Enter a Prometheus query statement. The query statement in this example is used to get the up metrics whose job labels contain nvidia-gpu-device-plugin from Prometheus and extract the instance label values of these metrics.
      Sort	Alphabetical (asc)	Select the sorting mode of variable values. You can select a value as needed. In this example, Alphabetical (asc) is selected, which means the records are sorted in ascending alphabetical order.
      Refresh	On time range change	Manage the update timing of variable values to specify when they are retrieved and refreshed from the data source. You can select a value as needed. In this example, On time range change is selected, meaning variable values are fetched from the data source again when the dashboard's time window changes.

   4. Configure Selection options. For details, see Figure 10 and Table 4.
      Figure 10 Configuring Selection options
      

      Table 4 Configuration example for Selection options

      Parameter	Example Value	Description
      Multi-value	Selected	After this function is enabled, you can select multiple values of a variable.
      Include All option	Selected	After this function is enabled, the All option is added to the variable drop-down list, allowing you to select all values with just one click.
      Preview of values	-	Variable value of instance. It is the pod name of dp.

      Repeat the preceding steps to add variables listed in Table 1 in order. After the variables are configured, appears on the right of the variables, showing a message that indicates the variables are not being referenced. These variables will be referenced when you configure the monitoring panel.

   5. Click Save dashboard in the upper right corner. On the Save dashboard page displayed, click Save to save the variables.

3. Configure the Grafana monitoring panels.

   The following uses the monitoring panels listed in the below table as examples to describe how to create different types of monitoring panels. For details, see Table 5. You can set up different monitoring panels as needed. For details, see Table 6.

   Table 5 Monitoring panels

   Panel Name	Description	Panel Type
   Total Pods	Total number of GPU pods running on non-sharable GPUs	Stat
   Pod Details	Summary of information such as the pod names, allocated GPU memory, and used GPU memory in a table	Table
   Pod GPU Memory Allocation	Amount of GPU memory allocated to pods of the GPU workloads	Bar gauge
   Pod GPU Memory Allocation Usage Rate	Usage of GPU memory allocated to pods of the GPU workloads	Gauge

   Example of Stat Visualization: Total Pods

   1. In the upper left corner of the current page, click GPU Pod Dashboard, click Add on the right, and choose Visualization from the drop-down list box to create a panel.
   2. On the Query tab in the lower left corner of the Edit panel page, select the data source configured in Step 2: Configure a Data Source for Grafana for Data source. Click on the left of A, click Code on the right of the expanded content, and enter the corresponding PromQL statements in Metrics browser to collect data. For details about PromQL statements, see Table 6.
      Figure 11 Entering PromQL statements
      
   3. StatPanel optionsTitleTotal Pods

      Figure 12 Configuring the panel type and title
      

   4. Configure the color mode and graph mode in Stat styles. In this example, Color mode is set to Background Gradient and Graph mode is set to None. You can customize the values as required.
      Figure 13 Configuring Stat styles
      
   5. Click Save in the upper right corner. On the Save dashboard page displayed, click Save again to save the panel configuration. In the upper right corner, click Apply to go to the dashboard page. The Total Pods panel has been created. The number in Figure 14 is 3, which means that there are three pods in the cluster.
      Figure 14 Checking the total number of pods
      

   Example of Table Visualization: Pod Details

   1. On the right, click Add and choose Visualization from the drop-down list to create a panel.
   2. In the upper right corner of the Edit panel page, switch the panel type to Table and enter a panel title in Panel options > Title. In this example, the title is set to Pod Details. You can use another title.
   3. On the Query tab in the lower left corner, click Add query twice and enter three PromQL statements in Metrics browser of A, B, and C in order. For details about PromQL statements, see Table 6.
      Figure 15 Entering PromQL statements
      

      In the Options of A, B, and C, set Format to Table and Type to Instant. This setting will display the query results in a table and retrieve only the data at the current time point.

      Figure 16 Changing the display mode and query mode of query results
      
   4. Switch to the Transform data tab, click Add transformation, and select Merge series/tables to merge the added PromQL statements.
      Figure 17 Configuring Merge series/tables
      
   5. In the lower part of the Transform data tab, click Add another transformation and select Organize fields by name to configure the table header. Additionally, Organize fields by name supports column hiding, column sequence adjustment, and column name setting. For details, see Figure 19. You can customize the settings.
      Figure 18 Configuring Organize fields by name
      
      Figure 19 Configuring the column name
      
   6. SaveSave dashboardSaveApplyPod Details

      Figure 20 Checking pod details
      

   Example of Bar Gauge Visualization: GPU Memory Allocated to Pods

   1. On the right, click Add and choose Visualization from the drop-down list to create a panel.
   2. In the lower left corner of the Edit panel page, enter PromQL statements in Metrics browser of A. For details about PromQL statements, see Table 6. After entering the PromQL statements, select Custom in Options > Legend and enter {{Pod}}. This operation is used to replace the label in the legend with the value of the variable pod.
      Figure 21 Adding PromQL statements
      
   3. Bar gaugePanel optionsTitlePod GPU Memory Allocation
   4. In Standard options, set Unit to Data > bytes (IEC) to convert the data unit to GiB and set Min to 0 to display data more intuitively.
      Figure 22 Configuring Standard options
      
   5. Click Save in the upper right corner. On the Save dashboard page displayed, click Save again to save the panel configuration. In the upper right corner, click Apply to go to the dashboard page. The Pod GPU Memory Allocation panel has been created.
      Figure 23 Checking the pod GPU memory allocation
      

   Dashboard Panel Example: The Usage of GPU Memory Allocated to Pods

   1. On the right, click Add and choose Visualization from the drop-down list to create a panel.
   2. Edit panelMetrics browserACustomOptionsLegend{{Pod}}

      Table 6

      Figure 24 Modifying a legend label
      
   3. In the upper right corner, switch the panel type to Gauge and enter a panel title in Panel options > Title. In this example, the title is set to Pod GPU Memory Allocation Usage Rate. You can use another title.
   4. In Standard options, set Unit to Misc > Percent (0-100) to convert the data unit to percentage ranging from 0% to 100%.
      Figure 25 Changing data unit
      
   5. Click Save in the upper right corner. On the Save dashboard page displayed, click Save again to save the panel configuration. In the upper right corner, click Apply to go to the dashboard page. The Pod GPU Memory Allocation Usage Rate panel has been created.
      Figure 26 Checking the usage of GPU memory allocated to pods
      

PromQL Statement

This section provides PromQL statements for each monitoring panel in different dimensions. You can use these statements to create Grafana dashboards in various dimensions.

If the allocated GPU memory or compute is 0, calculating their utilization is meaningless. The PromQL statements here exclude cases where GPU memory or compute allocation is 0 when calculating their utilization.