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nvidia-smi Missing or Broken

nvidia-smi is missing or broken when NVIDIA driver is not properly installed, blocking GPU access entirely.

Quick answer

nvidia-smi is missing or broken when NVIDIA driver is not properly installed, blocking GPU access entirely.

Infrastructure#nvidia-smi#driver#cuda#container#gpu-detection#infrastructure

What this failure is

nvidia-smi Missing or Broken is a Infrastructure failure seen during ML training runs. nvidia-smi is missing or broken when NVIDIA driver is not properly installed, blocking GPU access entirely. Common tags: Nvidia Smi, Driver, Cuda, Container.

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Why it happens (the mechanism)

NVIDIA driver not installed. Driver version older than CUDA requires. Container started without --gpus flag. NVIDIA Container Toolkit not installed. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • nvidia-smi: command not found
  • GPU not visible to PyTorch
  • No GPUs detected by training script

Common symptoms and what they mean

SymptomWhy it happens
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driverNVIDIA driver not installed
nvidia-smi: command not foundDriver version older than CUDA requires
CUDA driver version is insufficient for CUDA runtime versionContainer started without --gpus flag

Which systems are affected

  • Fresh GPU server setup
  • Container without NVIDIA runtime
  • Driver version mismatch with CUDA

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • ✓Verified signal present: NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver
  • ✓Verified signal present: nvidia-smi: command not found
  • ✓Verified signal present: CUDA driver version is insufficient for CUDA runtime version
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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CUDA errors in context

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.

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Related failures to investigate next

Lambda Bgp Route Withdrawal Region Outage often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related primary cause next. lambda bgp route withdrawal region outage →
Kubernetes Gpu Pod Pending often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. kubernetes gpu pod pending →
Slurm Node Oom often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. slurm node oom →

Root cause

  • NVIDIA driver not installed
  • Driver version older than CUDA requires
  • Container started without --gpus flag
  • NVIDIA Container Toolkit not installed

The fix and how to prevent it

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References

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