Pinned Memory Overuse
Pinned (page-locked) memory overuse causes system RAM exhaustion and slow CPU-GPU transfers when over-allocated.
Pinned (page-locked) memory overuse causes system RAM exhaustion and slow CPU-GPU transfers when over-allocated.
What this failure is
Pinned Memory Overuse is a Memory failure seen during ML training runs. Pinned (page-locked) memory overuse causes system RAM exhaustion and slow CPU-GPU transfers when over-allocated. Common tags: Pinned Memory, Page Locked, Dataloader, Cpu Ram.
Is this what broke your run? Paste your log.
You're reading about Pinned Memory Overuse. 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)
Pin_memory=True reserves host RAM for each worker. Each worker uses page-locked memory equal to batch size. Many workers multiply pinned memory usage. Pinned memory not released when DataLoader destroyed. 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
- System RAM is exhausted
- pin_memory=True causes OOM
- GPU transfers are slow despite pin_memory=True
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| free -h shows low available memory | pin_memory=True reserves host RAM for each worker |
| Host OOM during DataLoader setup | Each worker uses page-locked memory equal to batch size |
| DataLoader with pin_memory is slower than without | Many workers multiply pinned memory usage |
Which systems are affected
- Training with DataLoader pin_memory=True
- Large batch sizes with pinned memory
- Training on machines with limited RAM
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: free -h shows low available memory
- ✓Verified signal present: Host OOM during DataLoader setup
- ✓Verified signal present: DataLoader with pin_memory is slower than without
- ✓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
- pin_memory=True reserves host RAM for each worker
- Each worker uses page-locked memory equal to batch size
- Many workers multiply pinned memory usage
- Pinned memory not released when DataLoader destroyed
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.