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Torchrun and SLURM Job Step Incompatibility

Torchrun's approach to spawning jobs conflicts with SLURM's process management, resulting in hung NCCL initialization, rank mismapping, and job allocation failures.

Quick answer

Torchrun's approach to spawning jobs conflicts with SLURM's process management, resulting in hung NCCL initialization, rank mismapping, and job allocation failures.

Distributed Training#slurm#torchrun#distributed#nccl#hpc#rendezvous

What this failure is

Torchrun and SLURM Job Step Incompatibility is a Distributed Training failure seen during ML training runs. Torchrun's approach to spawning jobs conflicts with SLURM's process management, resulting in hung NCCL initialization, rank mismapping, and job allocation failures. Common tags: Slurm, Torchrun, Distributed, Nccl.

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Why it happens (the mechanism)

SLURM manages job steps independently, while Torchrun attempts to spawn its own child processes, causing environment variables like SLURM_PROCID to desync. Multiple torchrun instances on the same node bind to the same rendezvous port. 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

  • NCCL hangs indefinitely during initialization without throwing an error
  • Ranks are mapped incorrectly across nodes, causing tensor parallel deadlocks
  • SLURM jobs crash with 'Connection refused' during Torch distributed rendezvous

Common symptoms and what they mean

SymptomWhy it happens
Torchrun elastic agent fails to discover all peersSLURM manages job steps independently, while Torchrun attempts to spawn its own child processes, causing environment variables like SLURM_PROCID to desync
SLURM_PROCID does not match the expected LOCAL_RANK inside the containerMultiple torchrun instances on the same node bind to the same rendezvous port
Process exits with code 137 or timeout during init_process_groupSLURM manages job steps independently, while Torchrun attempts to spawn its own child processes, causing environment variables like SLURM_PROCID to desync

Which systems are affected

  • HPC clusters using SLURM workload manager
  • Multi-node PyTorch training jobs launched via srun + torchrun

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: Torchrun elastic agent fails to discover all peers
  • Verified signal present: SLURM_PROCID does not match the expected LOCAL_RANK inside the container
  • Verified signal present: Process exits with code 137 or timeout during init_process_group
  • 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

Root cause

  • SLURM manages job steps independently, while Torchrun attempts to spawn its own child processes, causing environment variables like SLURM_PROCID to desync
  • Multiple torchrun instances on the same node bind to the same rendezvous port

The fix and how to prevent it

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