Imported from nvidia/skills (skills/dynamo-troubleshoot/SKILL.md) via skills.sh. Install upstream with npx skills add nvidia/skills --skill dynamo-troubleshoot. Copyright stays with the author (Apache-2.0).

Dynamo Troubleshoot

Purpose

Turn a Dynamo failure into a clear problem class, strongest signal, and next action. Start with read-only evidence, avoid secrets, and fix one layer at a time.

Prerequisites

• Python 3.10+ on the operator machine.
• kubectl configured with read access to the target namespace.
• Permission to read pods, events, jobs, PVCs, and DynamoGraphDeployment resources (NOT secrets).
• Network reachability to the cluster API server.

Instructions

1. Collect A Read-Only Bundle

Run:

python3 scripts/collect_dynamo_debug_bundle.py \
  --namespace "${NAMESPACE}"

If the user names a deployment, include it:

python3 scripts/collect_dynamo_debug_bundle.py \
  --namespace "${NAMESPACE}" \
  --deployment-name <deployment-name>

Do not collect Kubernetes secrets. Do not print Hugging Face tokens.

2. Classify The Failure

Use references/failure-decision-tree.md and classify into one primary bucket:

• cluster/platform
• namespace/secret
• model cache/PVC/download
• image pull/runtime image
• GPU scheduling/resources
• operator/DynamoGraphDeployment reconciliation
• frontend/router
• worker/backend
• endpoint/API
• benchmark/perf job

3. Debug Top Down

Check in this order:

1. namespace, storage class, GPU nodes, and HF secret existence
2. PVC and model-download job
3. DynamoGraphDeployment status and events
4. pod status, describe pod, and container logs
5. frontend service and port-forward
6. /v1/models
7. /v1/chat/completions
8. benchmark job only after endpoint smoke test passes

4. Fix One Layer At A Time

Prefer the smallest reversible change:

• create missing namespace or HF secret
• patch storageClassName
• patch image tag or image pull secret
• reduce GPU request only if the recipe can still be valid
• switch KV router to approximate mode only if workers do not publish events
• restart failed jobs after fixing the underlying config

After each fix, rerun the relevant readiness check before moving deeper.

Available Scripts

Invoke via the agentskills.io run_script() protocol:

run_script("scripts/collect_dynamo_debug_bundle.py", args=["--namespace", "dynamo-demo"])

Examples

Collect everything in a namespace for triage:

python3 scripts/collect_dynamo_debug_bundle.py --namespace dynamo-demo

Scope to a single failing deployment:

python3 scripts/collect_dynamo_debug_bundle.py \
  --namespace dynamo-demo \
  --deployment-name qwen-vllm-disagg

Equivalent through the agent protocol:

run_script("scripts/collect_dynamo_debug_bundle.py", args=["--namespace", "dynamo-demo", "--deployment-name", "qwen-vllm-disagg"])

Output Contract

Return:

• problem class
• evidence checked
• strongest signal
• likely cause
• exact next command or patch
• what was ruled out
• whether it is safe to continue deployment or benchmarking

Limitations

• Read-only. Never mutates the cluster; remediation commands are returned, not executed.
• Will not collect secrets or print Hugging Face tokens; some failure modes (auth) may need user-side inspection.
• Bundle size grows with deployment size; on very large namespaces, scope with --deployment-name.
• Does not validate disagg transport — use dynamo-interconnect-check for that.

Troubleshooting

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

• Read references/failure-decision-tree.md for bucket-specific checks.
• Use scripts/collect_dynamo_debug_bundle.py for read-only bundle collection.