nvidia-dynamo-interconnect-check

name	nvidia-dynamo-interconnect-check
description	Validate that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after recipe-runner brings a deployment up (especially disagg/multi-node) to confirm the KV transport is correct; use troubleshoot for diagnosing already-failed pods.
license	Apache-2.0
metadata	{"author":"Dan Gil <[e-mail]>","tags":["dynamo","nixl","rdma","disagg","validation"]}

Dynamo Interconnect Check

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

Script	Purpose	Arguments
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
• a clear statement of whether disagg can be trusted, or 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

Symptom	Likely cause	Next step
env reports all critical vars missing	Vars baked into image or injected by operator	Run the node check inside the worker pod to verify actual env
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 or run the standalone transfer test from a debug pod

Benchmark

See BENCHMARK.md for the NVCARPS-EVAL performance report (auto-generated by the NVSkills CI pipeline). To refresh, re-run /nvskills-ci on an upstream PR touching this skill.

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.