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 slurm manages job steps independently, while torchrun attempts to spawn its own child processes, causing environment variables like slurm_procid to desync. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Distributed Training 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 distributed training 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 HPC clusters using SLURM workload manager 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 #slurm, #torchrun, #distributed. 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 slurm is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Torchrun elastic agent fails to discover all peers 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.

Torchrun and SLURM Job Step Incompatibility

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 slurm manages job steps independently, while torchrun attempts to spawn its own child processes, causing environment variables like slurm_procid to desync 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.

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.

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.
Recommended fix: Avoid torchrun when using srun; initialize process groups directly using SLURM environment variables
How Denpex helps: Denpex investigates Torchrun and SLURM Job Step Incompatibility using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

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

Symptom	Why it happens
Torchrun elastic agent fails to discover all peers	SLURM 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 container	Multiple torchrun instances on the same node bind to the same rendezvous port
Process exits with code 137 or timeout during init_process_group	SLURM 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.

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

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