Purpose
Provide troubleshooting steps when a GPU Miscount alert is triggered, indicating that the system detects an incorrect number of GPUs on a node.
Alert Name
GPU Miscount
What the Alert Is
This alert indicates that the number of GPUs detected on the node does not match the expected GPU count.
The system monitoring tool compares the expected GPU count with the GPUs currently detected by the node.
Summary
Incorrect number of GPUs detected on the node.
Possible causes include:
-
GPU not detected by the OS
-
PCIe device detection issue
-
GPU driver issue
-
GPU allocation problem in Kubernetes (k3s)
-
Hardware failure
Severity
P3 – Medium Impact
The node may not fully utilize GPU resources, which can impact workloads and cluster scheduling.
L1 Engineer Actions
Follow these steps when the alert is received.
Step 1 – Log the Alert
Step 2 – Create Ticket
Step 3 – Escalation (If Required)
If the issue appears significant:
Step 4 – Documentation
After confirmation with L2:
L2 Engineer Actions
L2 engineers should perform detailed checks on the node.
Step 1 – Check GPU Detection
Run:
Verify the number of GPUs detected by the system.
Step 2 – Check GPU Allocation from HGX side and from K3s Perspective from login node / K3s.
Verify whether GPUs are properly allocated or restricted in k3s.
Check:
Example Command: for
n in $(kubectl get nodes | awk '{print $1}'); do echo $n; kubectl describe node
$n | grep Allocatable -A 10 | grep nvidia.com/gpu | grep -v 8; done
Step 3 – Verify GPU Detection at PCI Level
Run:
This confirms whether the operating system detects the GPUs at the PCIe hardware level.
Step 4 – Check GPU Driver Errors
Run:
Look for NVIDIA Xid errors, which may indicate:
-
GPU hardware failure
-
Driver crash
-
GPU reset events
Escalation
Escalate to platform/OEM support if:
-
GPUs are not visible in lspci
-
Persistent Xid errors occur
-
GPUs disappear after reboot
-
Hardware failure is suspected
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