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 in elastic cloud clusters (like aws spot instances), the compute node itself may be terminated by the cloud provider before slurm's slurmctld can successfully execute the requeue command. alternatively, `jobrequeue=1` is not set in `slurm.conf`.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Job Scheduling 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 job scheduling 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 SLURM 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 #Requeue Configuration. 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 Requeue Configuration is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Jobs submitted with `#SBATCH --requeue` are fully canceled when preempted instead of going back into the queue. 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.

SLURM Requeue Fails on Preemption in Cloud Environments

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 in elastic cloud clusters (like aws spot instances), the compute node itself may be terminated by the cloud provider before slurm's slurmctld can successfully execute the requeue command. alternatively, `jobrequeue=1` is not set in `slurm.conf`. 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.

In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`.

Quick answer

In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command.

Root cause: In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`.
Recommended fix: trap 'scontrol requeue ${SLURM_JOB_ID}; exit 15' 15
How Denpex helps: Denpex matches SLURM Requeue Fails on Preemption in Cloud Environments 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.

Job Scheduling#Requeue Configuration

What this failure is

SLURM Requeue Fails on Preemption in Cloud Environments is a Job Scheduling failure seen during ML training runs. In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`. Common tags: Requeue Configuration.

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Why it happens (the mechanism)

The user explicitly set `--requeue` and expects a seamless restart, but the job is killed, leading them to believe the flag is broken.

What you'll observe

Job state changes to CANCELLED instead of PENDING after preemption
scontrol: error: requeue failed

Common symptoms and what they mean

Symptom	Why it happens
Jobs submitted with `#SBATCH --requeue` are fully canceled when preempted instead of going back into the queue.	In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`.
Compute node is lost abruptly.	In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`.

Which systems are affected

SLURM
AWS ParallelCluster

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 `scontrol show config | grep JobRequeue`.
✓Verify `PreemptMode` setting in slurm.conf (needs to include REQUEUE).
✓Review slurmctld logs to see if the node was marked DOWN before the requeue could process.

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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Slurm GPU errors in context

Slurm GPU failures can occur before allocation, during cgroup creation or inside the workload. The hub joins job reason, GRES, memory, node-health and distributed-launch evidence.

Compare every slurm gpu error side by side

Root cause

In elastic cloud clusters (like AWS Spot instances), the compute node itself may be terminated by the cloud provider before SLURM's slurmctld can successfully execute the requeue command. Alternatively, `JobRequeue=1` is not set in `slurm.conf`.

The fix and how to prevent it

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References

↗StackOverflow - SLURM preemption and requeue

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