PyTorch Lightning Fault-Tolerant Resume Fails with DataLoader num_workers>1
Resuming a run with PyTorch Lightning's experimental fault-tolerant training raises a MisconfigurationException about the worker state when the DataLoader uses num_workers>1. The feature never reliably reconstructed per-worker iterator state and was removed in Lightning 2.0, so the only durable fix is a custom resumable sampler.
Resuming a run with PyTorch Lightning's experimental fault-tolerant training raises a MisconfigurationException about the worker state when the DataLoader uses num_workers>1.
What this failure is
PyTorch Lightning Fault-Tolerant Resume Fails with DataLoader num_workers>1 is a Data Pipeline failure seen during ML training runs. Resuming a run with PyTorch Lightning's experimental fault-tolerant training raises a MisconfigurationException about the worker state when the DataLoader uses num_workers>1. The feature never reliably reconstructed per-worker iterator state and was removed in Lightning 2.0, so the only durable fix is a custom resumable sampler. Common tags: Pytorch Lightning, Fault Tolerance, Dataloader, Num_workers.
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Why it happens (the mechanism)
The experimental fault-tolerant sampler cached per-worker iteration state assuming a fixed worker count, so rotating the cached worker indices fails when the saved state length does not equal num_workers-1. The feature was never hardened for multi-worker map datasets and was removed entirely in Lightning 2.0, so it cannot be fixed in place. 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
- Resuming from a fault-tolerant checkpoint crashes instead of continuing
- The failure only appears when DataLoader num_workers is greater than 1
- Restarting from scratch wastes days of completed training
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| pytorch_lightning.utilities.exceptions.MisconfigurationException: The `state` should contain `num_workers - 1` values | The experimental fault-tolerant sampler cached per-worker iteration state assuming a fixed worker count, so rotating the cached worker indices fails when the saved state length does not equal num_workers-1 |
| Traceback through utilities/auto_restart.py in _rotate_worker_indices | The feature was never hardened for multi-worker map datasets and was removed entirely in Lightning 2.0, so it cannot be fixed in place |
| Works with num_workers<=1 but breaks with multiple workers | The experimental fault-tolerant sampler cached per-worker iteration state assuming a fixed worker count, so rotating the cached worker indices fails when the saved state length does not equal num_workers-1 |
Which systems are affected
- PyTorch Lightning 1.6-1.9 experimental fault-tolerant training
- Map-style datasets iterated with multiple DataLoader workers
- Long runs that depend on mid-epoch resumability
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: pytorch_lightning.utilities.exceptions.MisconfigurationException: The `state` should contain `num_workers - 1` values
- ✓Verified signal present: Traceback through utilities/auto_restart.py in _rotate_worker_indices
- ✓Verified signal present: Works with num_workers<=1 but breaks with multiple workers
- ✓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
- The experimental fault-tolerant sampler cached per-worker iteration state assuming a fixed worker count, so rotating the cached worker indices fails when the saved state length does not equal num_workers-1
- The feature was never hardened for multi-worker map datasets and was removed entirely in Lightning 2.0, so it cannot be fixed in place
The fix and how to prevent it
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