DDP NaN Detected on One Rank
DDP NaN detection on a single rank often indicates inconsistent data or hardware between ranks.
DDP NaN detection on a single rank often indicates inconsistent data or hardware between ranks.
What this failure is
DDP NaN Detected on One Rank is a Distributed Training failure seen during ML training runs. DDP NaN detection on a single rank often indicates inconsistent data or hardware between ranks. Common tags: Ddp, Nan, Distributed, Rank.
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Why it happens (the mechanism)
One rank has failing GPU causing NaN computations. Data corruption on specific rank's input. Random seed not synchronized causing different drops. Numerical precision difference across GPU types. 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
- One rank reports NaN but others don't
- Loss differs wildly across ranks
- Training halts when NaN propagates through allreduce
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss NaN on rank X only | One rank has failing GPU causing NaN computations |
| Gradient norm inf on rank Y | Data corruption on specific rank's input |
| AllReduce propagates NaN to all ranks | Random seed not synchronized causing different drops |
Which systems are affected
- Heterogeneous GPU clusters
- Different data between ranks
- One rank has failing hardware
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: Loss NaN on rank X only
- ✓Verified signal present: Gradient norm inf on rank Y
- ✓Verified signal present: AllReduce propagates NaN to all ranks
- ✓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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Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
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Root cause
- One rank has failing GPU causing NaN computations
- Data corruption on specific rank's input
- Random seed not synchronized causing different drops
- Numerical precision difference across GPU types
The fix and how to prevent it
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