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dynamo-interconnect-check

nvidia

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Hardened

This skill has been hardened by Chainguard

Last changed

This version passed the hardening gate. Hardening applied 4 targeted changes and preserved all other text. Classified findings before hardening: 4. Classified findings closed: 1. Classified findings remaining: 3. Unclassified scanner findings remaining: 0. Review findings closed: 5. Review findings remaining: 13.


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SKILL.md

Memory & Context Poisoning

—

High
Closed
SKILL.md

Tool & MCP Manipulation

MCP over-permissioning

Medium
Open
scripts/check_interconnect.py

Malicious Code & Command Execution

Dynamic code execution

Medium
Open
BENCHMARK.md

Sensitive Data & Secret Exposure

Agent snooping

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4 findings


SKILL.md

Dynamo Interconnect Check

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: CC-BY-4.0 -->

Purpose

Confirm that the transport disaggregated serving depends on actually works. A deployment can pass an endpoint smoke test while disagg is silently wrong: if NIXL/UCX cannot reach the peer worker over RDMA or NVLink, KV transfer falls back to a slow or broken path. Catch that with read-only checks before trusting a disagg deployment or its benchmark numbers.

This skill is read-only. It never mutates the cluster and never prints secrets.

Prerequisites

  • Python 3.10+ on the operator machine.
  • kubectl exec access to a worker pod in the target Dynamo deployment.
  • Read access to the recipe directory (recipes/<model>/<framework>/<mode>).
  • For node-capability checks: tools like ibstat, nvidia-smi, lsmod available in the worker pod image (missing tools are reported as skipped, not failures).

When To Use

  • After dynamo-recipe-runner deploys a disagg or multi-node recipe.
  • Before reporting disagg throughput/latency, so numbers reflect the real transport.
  • When agg works but disagg is slow, hangs, or returns wrong output and you suspect the fabric rather than the model.

For diagnosing pods that are already crashing or unschedulable, use dynamo-troubleshoot first.

Instructions

1. Check Transport Env Vars On The Recipe

python3 scripts/check_interconnect.py env recipes/<model>/<framework>/<mode>

Reports which NIXL/UCX/NCCL transport variables are set and flags disagg-critical ones (e.g. UCX_TLS, UCX_NET_DEVICES, NCCL_IB_HCA) that are absent. Missing here is only a warning — they may be baked into the image — so confirm with the node and NIXL checks. See references/interconnect-env-vars.md for what each variable does.

2. Check Node Capabilities

Locally on a GPU node, or inside a running worker pod:

python3 scripts/check_interconnect.py node \
  --namespace "${NAMESPACE}" --pod <worker-pod>

Probes (read-only) for: InfiniBand devices and Active links, GPUDirect RDMA (nvidia_peermem), GDRCopy, and NVLink in the GPU topology. Missing tools are reported as skipped, not failures.

3. Validate NIXL Reachability

python3 scripts/check_interconnect.py nixl \
  --namespace "${NAMESPACE}" --pod <worker-pod>

Looks for NIXL test tooling in the pod and surfaces the exact next step to run a pairwise prefill↔decode transfer test. A full cross-pod transfer test requires two scheduled GPU pods on the fabric.

Available Scripts

ScriptPurposeArguments

scripts/check_interconnect.py env

Inspect NIXL/UCX/NCCL env vars on a recipe

positional recipe path

scripts/check_interconnect.py node

Probe InfiniBand, GPUDirect RDMA, GDRCopy, NVLink on a node or pod

--namespace, --pod

scripts/check_interconnect.py nixl

Surface NIXL transfer-test readiness for a pod

--namespace, --pod

Invoke via the agentskills.io run_script() protocol:

run_script("scripts/check_interconnect.py", args=["env", "recipes/qwen3-coder-480b/sglang/disagg"])
run_script("scripts/check_interconnect.py", args=["node", "--namespace", "dynamo-demo", "--pod", "qwen-worker-0"])

Examples

Verify a disagg recipe's transport env shape before deploy:

python3 scripts/check_interconnect.py env recipes/qwen3-coder-480b/sglang/disagg

After deploy, validate a worker pod's fabric:

python3 scripts/check_interconnect.py node \
  --namespace dynamo-demo --pod qwen-worker-0
python3 scripts/check_interconnect.py nixl \
  --namespace dynamo-demo --pod qwen-worker-0

Equivalent through the agent protocol:

run_script("scripts/check_interconnect.py", args=["nixl", "--namespace", "dynamo-demo", "--pod", "qwen-worker-0"])

Output Contract

Each check returns ok / warn / fail / skipped with a one-line detail, plus a rolled-up verdict on disagg transport readiness. Report:

  • transport env vars present vs. disagg-critical ones missing
  • RDMA / GPUDirect / NVLink capability status
  • whether NIXL reachability was validated, and the next command if not
  • what to fix first

Limitations

  • Read-only fabric probe; does not run a full pairwise NIXL transfer (requires two scheduled GPU pods and the in-pod NIXL test tools).
  • skipped results for missing tools (ibstat, nvidia-smi, lsmod) are inconclusive, not a pass.
  • Env-var check inspects the recipe text; values injected at runtime via initContainers or operator-applied envs are not detected.
  • Single-node agg deployments do not exercise the transport — this skill is for disagg / multi-node validation.

Troubleshooting

SymptomLikely causeNext step

env reports all critical vars missing

Vars baked into image or injected by operator

Run the node check inside the worker pod

node reports no Active IB link

Fabric down or HCA not provisioned to the node

Contact cluster admin; verify kubectl describe node shows nvidia.com/gpu and IB labels

nvidia_peermem missing

GPUDirect RDMA module not loaded

Ask cluster admin to load nvidia-peermem; without it, NIXL falls back to staged copies

nixl finds no test tools

Worker image lacks NIXL test harness

Use a NIXL-enabled image

Benchmark

See BENCHMARK.md for the NVCARPS-EVAL performance report (auto-generated by the NVSkills CI pipeline).

References

  • references/interconnect-env-vars.md — NIXL/UCX/NCCL env var catalog and IB capability checklist.
  • Use scripts/check_interconnect.py for all read-only checks.

Install

Install with chainctl
chainctl skill install public/nvidia/dynamo-interconnect-check:latest
Get started with chainctl
View source

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Provenance

Upstream sourcehttps://github.com/nvidia/skills
Upstream commitefd358a207cd9ab26db8296467f340efe4bfb9d7
OCI reference

skills.cgr.dev/public/github.com/nvidia/skills/dynamo-interconnect-check:latest

License

Apache-2.0

Files

BENCHMARK.md

HARDENING.md

LICENSE

SKILL.md

evals/evals.json

references/interconnect-env-vars.md

scripts/check_interconnect.py

skill-card.md

skill.oms.sig


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