Dataloader Worker Failure
Dataloader worker failures stall training or produce corrupted batches.
Dataloader worker failures stall training or produce corrupted batches.
What this failure is
Dataloader Worker Failure is a Data Pipeline failure seen during ML training runs. Dataloader worker failures stall training or produce corrupted batches. Common tags: Dataloader, Worker, Data Pipeline, Multiprocessing.
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Why it happens (the mechanism)
File descriptor exhaustion: each dataloader worker opens files for dataset items, and the cumulative total exceeds the per-process ulimit -n limit. Shared memory collision: workers writing augmentation results to /dev/shm collide when using the same temporary file names. Worker segfault in a third-party library (PIL, OpenCV, librosa, etc.) triggered by a specific sample in the dataset. Deadlock in worker multiprocessing queue when the main process is blocked on NCCL while workers fill the prefetch queue. 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 stalls with no error after random batch count
- Error points to NCCL but cause is data pipeline
- Restart sometimes works, fails at different step
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: DataLoader worker (pid X) is killed by signal: Terminated | File descriptor exhaustion: each dataloader worker opens files for dataset items, and the cumulative total exceeds the per-process ulimit -n limit |
| Training loss plateaus or degrades after worker crash causes batch padding with zeros | Shared memory collision: workers writing augmentation results to /dev/shm collide when using the same temporary file names |
| CUDA error: an illegal memory access was encountered. Caused by corrupted batch data from a crashed worker | Worker segfault in a third-party library (PIL, OpenCV, librosa, etc.) triggered by a specific sample in the dataset |
| dmesg shows OOM killer or segfault originating from Python worker processes | Deadlock in worker multiprocessing queue when the main process is blocked on NCCL while workers fill the prefetch queue |
Which systems are affected
- Training with DataLoader(num_workers > 0) where workers share resources
- Custom dataset implementations with file I/O or database connections in __getitem__
- Multi-modal datasets that load images, audio, and text simultaneously
- Training on shared filesystems with limited file handle limits
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 by signal: Terminated
- ✓Verified signal present: Training loss plateaus or degrades after worker crash causes batch padding with zeros
- ✓Verified signal present: CUDA error: an illegal memory access was encountered. Caused by corrupted batch data from a crashed worker
- ✓Verified signal present: dmesg shows OOM killer or segfault originating from Python worker processes
- ✓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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Root cause
- File descriptor exhaustion: each dataloader worker opens files for dataset items, and the cumulative total exceeds the per-process ulimit -n limit
- Shared memory collision: workers writing augmentation results to /dev/shm collide when using the same temporary file names
- Worker segfault in a third-party library (PIL, OpenCV, librosa, etc.) triggered by a specific sample in the dataset
- Deadlock in worker multiprocessing queue when the main process is blocked on NCCL while workers fill the prefetch queue
The fix and how to prevent it
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