Disk Full During Training
Full disks crash training by preventing checkpoint writes.
Full disks crash training by preventing checkpoint writes.
What this failure is
Disk Full During Training is a Infrastructure failure seen during ML training runs. Full disks crash training by preventing checkpoint writes. Common tags: Disk, Storage, Checkpoint, Filesystem.
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Why it happens (the mechanism)
GPU memory fragmentation spills to system RAM or swap. Dataloader prefetch and caching fills /tmp with dataset shards. Multiple concurrent checkpoint saves on the same NFS mount consume the filesystem's inode or capacity allocation. Docker or container overlay filesystem grows unbounded as training logs, metrics, and intermediate checkpoints accumulate. 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 crashes mid-write with No space left on device
- Checkpoint save fails corrupting current save
- Crash appears as generic CUDA error wrapping IO exception
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| OSError: [Errno 28] No space left on device | GPU memory fragmentation spills to system RAM or swap |
| Training loop halts at save step with no explicit error | Dataloader prefetch and caching fills /tmp with dataset shards |
| nvidia-smi shows normal GPU but df -h shows 100% | Multiple concurrent checkpoint saves on the same NFS mount consume the filesystem's inode or capacity allocation |
Which systems are affected
- Frequent checkpointing on shared filesystems
- NFS-mounted checkpoint directories
- Small root partitions on preemptible instances
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: OSError: [Errno 28] No space left on device
- ✓Verified signal present: Training loop halts at save step with no explicit error
- ✓Verified signal present: nvidia-smi shows normal GPU but df -h shows 100%
- ✓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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Diagnose this failure in VS Code
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Root cause
- GPU memory fragmentation spills to system RAM or swap
- Dataloader prefetch and caching fills /tmp with dataset shards
- Multiple concurrent checkpoint saves on the same NFS mount consume the filesystem's inode or capacity allocation
- Docker or container overlay filesystem grows unbounded as training logs, metrics, and intermediate checkpoints accumulate
The fix and how to prevent it
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