Determinism Broken
Broken determinism causes non-reproducible training runs, making debugging and comparison difficult.
Broken determinism causes non-reproducible training runs, making debugging and comparison difficult.
What this failure is
Determinism Broken is a Data Pipeline failure seen during ML training runs. Broken determinism causes non-reproducible training runs, making debugging and comparison difficult. Common tags: Determinism, Reproducibility, Seed, Data Pipeline.
Is this what broke your run? Paste your log.
You're reading about Determinism Broken. 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)
CUDA non-deterministic operations. DataLoader with multiple workers. Distributed training with all-reduce. Random operations not seeded. Some ops have no deterministic implementation. 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 training run
- Random seed doesn't fix results
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Different loss curves on same data | CUDA non-deterministic operations |
| Different model accuracy on same training | DataLoader with multiple workers |
| Distributed training non-deterministic | Distributed training with all-reduce |
Which systems are affected
- Debugging training issues
- Hyperparameter search
- Production model retraining
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 same data
- ✓Verified signal present: Different model accuracy on same training
- ✓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
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
- CUDA non-deterministic operations
- DataLoader with multiple workers
- Distributed training with all-reduce
- Random operations not seeded
- Some ops have no deterministic implementation
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.