Dataset Cache Memory
Dataset cache memory usage grows when transformations or augmentations are applied before batching.
Dataset cache memory usage grows when transformations or augmentations are applied before batching.
What this failure is
Dataset Cache Memory is a Memory failure seen during ML training runs. Dataset cache memory usage grows when transformations or augmentations are applied before batching. Common tags: Dataset Cache, Hf Datasets, Webdataset, Cache.
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Why it happens (the mechanism)
HF datasets cache on small / partition. Cache not pruned. Transformations applied in __init__ and stored. Augmentation pipeline stored all transformed samples. 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
- Memory grows with cache size
- Pre-batched dataset uses too much RAM
- Augmentation cache causes OOM
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| GPU memory or RAM grows with cached transformations | HF datasets cache on small / partition |
| WebDataset cache directory is large | Cache not pruned |
| HF datasets cache is on small disk | Transformations applied in __init__ and stored |
Which systems are affected
- Training with HF datasets caching
- ImageFolder with transforms cached
- WebDataset with cached decode
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: GPU memory or RAM grows with cached transformations
- ✓Verified signal present: WebDataset cache directory is large
- ✓Verified signal present: HF datasets cache is on small disk
- ✓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
- HF datasets cache on small / partition
- Cache not pruned
- Transformations applied in __init__ and stored
- Augmentation pipeline stored all transformed samples
The fix and how to prevent it
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