Dataloader num_workers=0 Too Slow
DataLoader with num_workers=0 is often too slow for GPU training, causing the GPU to wait for data.
DataLoader with num_workers=0 is often too slow for GPU training, causing the GPU to wait for data.
What this failure is
Dataloader num_workers=0 Too Slow is a Data Pipeline failure seen during ML training runs. DataLoader with num_workers=0 is often too slow for GPU training, causing the GPU to wait for data. Common tags: Dataloader, Num Workers, Performance, Pipeline.
Is this what broke your run? Paste your log.
You're reading about Dataloader num_workers=0 Too Slow. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
Data loading is single-threaded. GPU is faster than data loading pipeline. CPU bottleneck in data augmentation. Num_workers=0 means no parallel data loading. 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 is low (e.g., 30-50%)
- Training is slower than expected
- DataLoader iteration is the bottleneck
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Low GPU-SM utilization | Data loading is single-threaded |
| High CPU data loading time | GPU is faster than data loading pipeline |
| GPU waiting for data | CPU bottleneck in data augmentation |
Which systems are affected
- Single-GPU training with num_workers=0
- Production training with eager data loading
- Training with simple data pipeline
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: Low GPU-SM utilization
- ✓Verified signal present: High CPU data loading time
- ✓Verified signal present: GPU waiting for data
- ✓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
The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card. Daily allowance follows verified trust tier. Instant access.
Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionRelated failures to investigate next
Root cause
- Data loading is single-threaded
- GPU is faster than data loading pipeline
- CPU bottleneck in data augmentation
- num_workers=0 means no parallel data loading
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.