What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is file descriptor exhaustion: each dataloader worker opens files for dataset items, and the cumulative total exceeds the per-process ulimit -n limit. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Data Pipeline failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a data pipeline failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so Training with DataLoader(num_workers > 0) where workers share resources is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #dataloader, #worker, #data-pipeline. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after dataloader is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. RuntimeError: DataLoader worker (pid X) is killed by signal: Terminated is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Dataloader Worker Failure

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify file descriptor exhaustion: each dataloader worker opens files for dataset items, and the cumulative total exceeds the per-process ulimit -n limit through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

Dataloader worker failures stall training or produce corrupted batches.

Quick answer

Dataloader worker failures stall training or produce corrupted batches.

Symptom: RuntimeError: DataLoader worker (pid X) is killed by signal: Terminated
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.
Recommended fix: Reduce num_workers to 0 (disable multiprocessing) as a diagnostic: if the error disappears, the issue is in the worker architecture
How Denpex helps: Denpex investigates Dataloader Worker Failure using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Data Pipeline#dataloader#worker#data-pipeline#multiprocessing#crash#batch

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.

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

Searchable error signature

search key

RuntimeError: DataLoader worker (pid X) is killed by signal: Terminated
CUDA error: an illegal memory access was encountered. Caused by corrupted batch data from a crashed worker
dmesg shows OOM killer or segfault originating from Python worker processes

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

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