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SLURM Time Limit

SLURM time limit causes training jobs to be killed when they exceed their requested walltime, losing progress.

Quick answer

SLURM time limit causes training jobs to be killed when they exceed their requested walltime, losing progress.

Infrastructure#slurm#time-limit#walltime#checkpoint#infrastructure

What this failure is

SLURM Time Limit is a Infrastructure failure seen during ML training runs. SLURM time limit causes training jobs to be killed when they exceed their requested walltime, losing progress. Common tags: Slurm, Time Limit, Walltime, Checkpoint.

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Why it happens (the mechanism)

Time limit set too short for training duration. No checkpoint at end of training. No resume logic for SLURM jobs. No walltime warning handling. 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 is killed at time limit
  • SLURM job ends unexpectedly
  • No checkpoint saved before time limit

Common symptoms and what they mean

SymptomWhy it happens
slurmstepd: Job ... exceeded its time limitTime limit set too short for training duration
Job cancelled with TIMEOUT reasonNo checkpoint at end of training
Training reaches 99% then diesNo resume logic for SLURM jobs

Which systems are affected

  • Long-running training on SLURM
  • Training with checkpointing
  • Shared cluster training jobs

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: slurmstepd: Job ... exceeded its time limit
  • ✓Verified signal present: Job cancelled with TIMEOUT reason
  • ✓Verified signal present: Training reaches 99% then dies
  • ✓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

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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

Related failures to investigate next

Lambda Bgp Route Withdrawal Region Outage often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related primary cause next. lambda bgp route withdrawal region outage →
Kubernetes Gpu Pod Pending often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. kubernetes gpu pod pending →
Slurm Node Oom often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. slurm node oom →

Root cause

  • Time limit set too short for training duration
  • No checkpoint at end of training
  • No resume logic for SLURM jobs
  • No walltime warning handling

The fix and how to prevent it

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References

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