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DataLoader Failure

DataLoader failures cause training to crash or hang when data loading is misconfigured or data is corrupted.

Quick answer

DataLoader failures cause training to crash or hang when data loading is misconfigured or data is corrupted.

Reliability#dataloader#worker#failure#data-pipeline#reliability

What this failure is

DataLoader Failure is a Reliability failure seen during ML training runs. DataLoader failures cause training to crash or hang when data loading is misconfigured or data is corrupted. Common tags: Dataloader, Worker, Failure, Data Pipeline.

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Why it happens (the mechanism)

Num_workers=0 causes GPU starvation. Too many workers exhaust memory. Worker process dies with OOM. Shared memory limit exceeded for shared tensors. Data corruption in dataset. 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

  • Training hangs at first batch
  • DataLoader worker crashes
  • DataLoader raises error mid-training

Common symptoms and what they mean

SymptomWhy it happens
RuntimeError: DataLoader worker (pid X) is killednum_workers=0 causes GPU starvation
BrokenPipeErrorToo many workers exhaust memory
CUDA error in DataLoader workerWorker process dies with OOM
DataLoader hangs at first iterationShared memory limit exceeded for shared tensors

Which systems are affected

  • Training with PyTorch DataLoader
  • Multi-worker data loading
  • Distributed data loading

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: RuntimeError: DataLoader worker (pid X) is killed
  • ✓Verified signal present: BrokenPipeError
  • ✓Verified signal present: CUDA error in DataLoader worker
  • ✓Verified signal present: DataLoader hangs at first iteration
  • ✓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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Related failures to investigate next

Bytedance Sequence Length Straggler often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related primary cause next. bytedance sequence length straggler →
Training Checkpoint Corruption On Write often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. training checkpoint corruption on write →
Checkpoint Partial Save often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. checkpoint partial save →

Root cause

  • num_workers=0 causes GPU starvation
  • Too many workers exhaust memory
  • Worker process dies with OOM
  • Shared memory limit exceeded for shared tensors
  • Data corruption in dataset

The fix and how to prevent it

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References

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NIXL Firmware Page Registration Fan-Out Triggers Host OOM Kills on HGX H200 and B200

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MTTF Scaling Inversely with GPU Count in Large ML Research Clusters

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