Init Seed Mismatch
Different random seeds across runs cause non-reproducible results, making experiments hard to compare.
Different random seeds across runs cause non-reproducible results, making experiments hard to compare.
What this failure is
Init Seed Mismatch is a Training Stability failure seen during ML training runs. Different random seeds across runs cause non-reproducible results, making experiments hard to compare. Common tags: Seed, Reproducibility, Init, Training Stability.
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Why it happens (the mechanism)
Random seed not set. Seed set but not for all RNG sources. CUDA non-deterministic operations. DataLoader workers have different seeds. Distributed training not synced. 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
- Same code gives different results
- Cannot reproduce previous training
- Hyperparameter search noisy
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Different loss curves on re-run | Random seed not set |
| Different accuracy on re-run | Seed set but not for all RNG sources |
| Distributed training non-deterministic | CUDA non-deterministic operations |
Which systems are affected
- Research experiments
- Hyperparameter search
- Ablation studies
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: Different loss curves on re-run
- ✓Verified signal present: Different accuracy on re-run
- ✓Verified signal present: Distributed training non-deterministic
- ✓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
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.
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Root cause
- Random seed not set
- Seed set but not for all RNG sources
- CUDA non-deterministic operations
- DataLoader workers have different seeds
- Distributed training not synced
The fix and how to prevent it
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Don't just read the fix, diagnose your run
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