DDP Hang at Epoch Boundary
DDP training hangs at the end of an epoch when one rank exhausts its data partition before others.
DDP training hangs at the end of an epoch when one rank exhausts its data partition before others.
What this failure is
DDP Hang at Epoch Boundary is a Distributed Training failure seen during ML training runs. DDP training hangs at the end of an epoch when one rank exhausts its data partition before others. Common tags: Ddp, Uneven, Data, Distributed.
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Why it happens (the mechanism)
One rank exhausts its dataset partition before others. DDP requires all ranks to call the same number of backward passes. Drop_last=False causes epoch boundary mismatch. 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
- DDP forward pass hangs at epoch transition
- Some ranks finish the dataset before others
- Training stalls between epochs
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| DDP forward pass hangs after last batch | One rank exhausts its dataset partition before others |
| Some ranks report end of epoch while others are processing | DDP requires all ranks to call the same number of backward passes |
| NCCL timeout at the end of the last batch | Drop_last=False causes epoch boundary mismatch |
Which systems are affected
- DDP training with uneven dataset sizes
- Datasets with random sampling
- Distributed training with streaming data
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: DDP forward pass hangs after last batch
- ✓Verified signal present: Some ranks report end of epoch while others are processing
- ✓Verified signal present: NCCL timeout at the end of the last batch
- ✓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
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Root cause
- One rank exhausts its dataset partition before others
- DDP requires all ranks to call the same number of backward passes
- Drop_last=False causes epoch boundary mismatch
The fix and how to prevent it
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