Is this a hardware or a software issue?

Synchronization failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a synchronization failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so PyTorch is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #NCCL Timeout. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after NCCL Timeout is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Training hangs at the very end of an epoch. is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

DDP Hang from Uneven Dataset Sizes Across Ranks

Q: What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is when using distributedsampler without drop_last=true, the dataset size might not be perfectly divisible by the number of gpus. consequently, some ranks might have n batches, while others have n-1. the ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify when using distributedsampler without drop_last=true, the dataset size might not be perfectly divisible by the number of gpus. consequently, some ranks might have n batches, while others have n-1. the ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks.

Quick answer

When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs.

Symptom: RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp
Root cause: When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.
Recommended fix: Use the Join context manager for training loops. from torch.distributed.algorithms.join import Join with Join([model]): for batch in dataloader: ... The Join context manager shadows collective communications for ranks that finish early, satisfying the all-reduce expectations of the active ranks.
How Denpex helps: Denpex matches DDP Hang from Uneven Dataset Sizes Across Ranks across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Synchronization#NCCL Timeout

What this failure is

DDP Hang from Uneven Dataset Sizes Across Ranks is a Synchronization failure seen during ML training runs. When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks. Common tags: NCCL Timeout.

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Why it happens (the mechanism)

The timeout happens up to 30 minutes after the actual discrepancy occurred. Engineers often mistake this for a transient network issue or hardware failure on the cluster because the logs point to NCCL.

What you'll observe

RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp
nccl timeout (Watchdog detected a stall)

Common symptoms and what they mean

Symptom	Why it happens
Training hangs at the very end of an epoch.	When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks.
The job ultimately fails with a NCCL Watchdog Timeout after a long delay (default 30 minutes).	When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks.

Which systems are affected

PyTorch
NCCL
DistributedDataParallel (DDP)

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.

✓Check the length of the dataloader on all ranks: print(f'Rank {rank} batches: {len(dataloader)}')
✓Enable NCCL_DEBUG=INFO to see which ranks are timing out.

Searchable error signature

search key

RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

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.

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NCCL errors in context

NCCL is where a distributed job reports failure, which is not the same as where it failed. The hub lists every common NCCL error next to what it actually indicates, and the environment variables that tell them apart.

Compare every nccl error side by side

Root cause

When using DistributedSampler without drop_last=True, the dataset size might not be perfectly divisible by the number of GPUs. Consequently, some ranks might have N batches, while others have N-1. The ranks that finish early exit the training loop, while the remaining ranks enter the next batch and block indefinitely on dist.all_reduce waiting for the finished ranks.

The fix and how to prevent it

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References

↗Uneven Inputs in PyTorch DDP

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Related Synchronization errors

DDP Hang Due to Unused Parameters in Forward Pass

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SyncBatchNorm Deadlock During Single-Rank Evaluation

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dist.all_gather Timeout from Dynamic Tensor Shapes

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