Prompt
Why do my GPU jobs keep failing after driver updates?
Latest observation
GPU jobs often fail after driver updates because the update changes the runtime environment your workloads depend on. Common causes include:
- Driver/CUDA mismatch: Your app or container may have been built for a different NVIDIA driver/CUDA version than the one now installed.
- Kernel module issues: The new driver may not have loaded correctly, or may conflict with the current kernel.
- Library incompatibility: Frameworks like PyTorch/TensorFlow may rely on specific CUDA, cuDNN, or NCCL versions that no longer line up.
- Container runtime mismatch: If you use Docker, the NVIDIA container toolkit/host driver pairing may be broken after the update.
- Cluster node inconsistency: On multi-node systems, some nodes may have updated drivers while others have not, causing jobs to fail only on certain machines.
- Reboot required: The new driver may not take effect until after a reboot, or an old module may still be in use.
- Device permissions or persistence changes: Driver updates can reset settings that affect access to
/dev/nvidia*or GPU persistence mode.
What to check:
nvidia-smion the affected node- Kernel logs:
dmesg | grep -i nvidia - CUDA/runtime version expected by your framework
- Whether the driver version satisfies the minimum required by your CUDA version
- Container setup if applicable
- Whether all nodes have the same driver version
If you want, I can help you diagnose this with the exact error message, your driver version, CUDA version, and whether you’re using bare metal or containers.
Observation history
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