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Data Pipeline / DataLoader Stalls

Training is bottlenecked or completely halted due to the DataLoader failing to fetch the next batch in time.

Quick answer

Training is bottlenecked or completely halted due to the DataLoader failing to fetch the next batch in time.

Data Pipeline#dataloader#stall#io#storage#performance

What this failure is

Data Pipeline / DataLoader Stalls is a Data Pipeline failure seen during ML training runs. Training is bottlenecked or completely halted due to the DataLoader failing to fetch the next batch in time. Common tags: Dataloader, Stall, Io, Storage.

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Why it happens (the mechanism)

Underlying storage latency or IOPS limits reached. CPU bottleneck in data preprocessing/augmentation. DataLoader workers killed by OOM killer due to memory leaks in the preprocessing pipeline. 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

  • GPU utilization drops to 0%
  • DataLoader workers stall

Common symptoms and what they mean

SymptomWhy it happens
next(iterator) taking longer than X secondsUnderlying storage latency or IOPS limits reached.
DataLoader worker (pid(s) X) exited unexpectedlyCPU bottleneck in data preprocessing/augmentation.
getting batch took X secondsDataLoader workers killed by OOM killer due to memory leaks in the preprocessing pipeline.
GPU utilization drops to 0%Underlying storage latency or IOPS limits reached.

Which systems are affected

  • PyTorch DataLoader
  • Network attached storage (NFS, Lustre, S3)

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: next(iterator) taking longer than X seconds
  • ✓Verified signal present: DataLoader worker (pid(s) X) exited unexpectedly
  • ✓Verified signal present: getting batch took X seconds
  • ✓Verified signal present: GPU utilization drops to 0%
  • ✓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

Augmentation Pipeline Error often co-occurs with this failure because both share underlying assumptions about data pipeline; if you fix this entry but still see drift, validate the related primary cause next. augmentation pipeline error →
Lmdb Corruption often co-occurs with this failure because both share underlying assumptions about data pipeline; if you fix this entry but still see drift, validate the related neighboring cause next. lmdb corruption →
Text Encoding Mismatch often co-occurs with this failure because both share underlying assumptions about data pipeline; if you fix this entry but still see drift, validate the related neighboring cause next. text encoding mismatch →

Root cause

  • Underlying storage latency or IOPS limits reached.
  • CPU bottleneck in data preprocessing/augmentation.
  • DataLoader workers killed by OOM killer due to memory leaks in the preprocessing pipeline.

The fix and how to prevent it

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References

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