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hyperpod-nccl

Diagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, container OOM / exit-137 / OOMKilled, GPU OOM (CUDA out of memory), CrashLoopBackOff / Pending pods, MASTER_ADDR DNS, NetworkPolicy blocking. Not for single-node hardware faults (→ hyperpod-node-debugger § G) or cluster-creation EFA / SSM failures (→ hyperpod-cluster-debugger § A / § F).

plugins/sagemaker-ai/skills/hyperpod-nccl/SKILL.md

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# HyperPod NCCL Debugger

**Operating policy.** Run read-only diagnostics yourself. Never run a command that changes cluster, node, or workload state — present each one as a **Suggested command (run this yourself)** block and wait for the customer. Destructive order: **investigate → reboot → replace** (replace destroys root + secondary volumes; not supported on Slurm controller nodes). Never discard training state on speculation.

Diagnose NCCL failures on SageMaker HyperPod (EKS and Slurm). `scripts/nccl-diagnose.sh` reads state via AWS APIs, kubectl, and SSM, then prints each issue as `[FAIL] ... → references/<file>.md § <section>`. Read-only.

**Signal sourcing:** `list-cluster-events` carries infrastructure-level state only (lifecycle, bootstrap, EFA health check, capacity, replacement, reboot, AMI rollback). It does **not** carry NCCL timeouts, GPU XID/ECC, or per-pod training signals — those come from pod logs, CloudWatch training streams, on-node SSM probes, and NCCL env audit. "No events" on a training-time NCCL issue is expected, not a clean bill of health.

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## Workflow

1. Collect cluster name, region, namespace/job (EKS), exact NCCL error string.
2. Run the diagnostic (always — the output drives everything else).