DDP Rank Stuck During Training
One DDP rank becomes unresponsive and stalls all ranks waiting for gradient sync.
One DDP rank becomes unresponsive and stalls all ranks waiting for gradient sync.
What this failure is
DDP Rank Stuck During Training is a Distributed Training failure seen during ML training runs. One DDP rank becomes unresponsive and stalls all ranks waiting for gradient sync. Common tags: Ddp, Straggler, Rank, Distributed.
Is this what broke your run? Paste your log.
You're reading about DDP Rank Stuck During Training. 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)
Straggler rank has hardware degradation. Network between straggler rank and others is slower. Straggler rank has contention with other workloads. CUDA error on one rank not propagated to others. 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
- Training stalls with no error
- One rank has zero GPU utilization
- All ranks show same step but no progress
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| One rank takes 10x longer than others | Straggler rank has hardware degradation |
| GPU utilization varies wildly across ranks | Network between straggler rank and others is slower |
| Training throughput plateaus despite more compute | Straggler rank has contention with other workloads |
Which systems are affected
- DDP training with heterogeneous hardware
- Multi-node training with varying network latency
- Shared GPU clusters
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: One rank takes 10x longer than others
- ✓Verified signal present: GPU utilization varies wildly across ranks
- ✓Verified signal present: Training throughput plateaus despite more compute
- ✓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
- Straggler rank has hardware degradation
- Network between straggler rank and others is slower
- Straggler rank has contention with other workloads
- CUDA error on one rank not propagated to others
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.