Training Restart Stuck
Training restarts after failure can get stuck if cleanup didn't complete properly, blocking new training jobs.
Training restarts after failure can get stuck if cleanup didn't complete properly, blocking new training jobs.
What this failure is
Training Restart Stuck is a Reliability failure seen during ML training runs. Training restarts after failure can get stuck if cleanup didn't complete properly, blocking new training jobs. Common tags: Restart, Stuck, Cleanup, Reliability.
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Why it happens (the mechanism)
Previous training process not fully killed. CUDA context not released. Distributed training group not destroyed. File handles not closed properly. 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
- Restarted training hangs at startup
- Previous training's resources still held
- GPU memory not released from previous run
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| New training process hangs at init | Previous training process not fully killed |
| GPU shows memory used by previous process | CUDA context not released |
| SLURM/K8s shows old process still running | Distributed training group not destroyed |
Which systems are affected
- Training with elastic restart
- Training after manual restart
- Training after node failure
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: New training process hangs at init
- ✓Verified signal present: GPU shows memory used by previous process
- ✓Verified signal present: SLURM/K8s shows old process still running
- ✓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.
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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.
Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- Previous training process not fully killed
- CUDA context not released
- Distributed training group not destroyed
- File handles not closed properly
The fix and how to prevent it
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Don't just read the fix, diagnose your run
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