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/dynamo-troubleshoot

Diagnose failed or unhealthy Dynamo deployments. Use when pods, model-cache jobs, PVCs, workers, frontend/router health, endpoints, or benchmark jobs fail; use recipe-runner/router-starter before this for normal bring-up.

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SKILL.md

dynamo-troubleshoot.SKILL.md
name: dynamo-troubleshoot
description: Diagnose failed or unhealthy Dynamo deployments. Use when pods, model-cache jobs, PVCs, workers, frontend/router health, endpoints, or benchmark jobs fail; use recipe-runner/router-starter before this for normal bring-up.
license: Apache-2.0
metadata:
  author: Dan Gil <dagil@nvidia.com>
  tags:
    - dynamo
    - kubernetes
    - troubleshooting
    - day-2

Dynamo Troubleshoot

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

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

| Script | Purpose | Arguments | |---|---|---| | `scripts/collect_dynamo_debug_bundle.py` | Collect a read-only debug bundle (pods, events, jobs, PVCs, CR status) | `--namespace`, `--deployment-name`, `--output-dir` |

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

| Symptom | Likely cause | Next step | |---|---|---| | `kubectl` returns Forbidden on events/pods | Service account lacks read RBAC | Ask operator for read-only role binding on the namespace | | Bundle missing `DynamoGraphDeployment` status | Operator not installed or different namespace | Verify `dynamo-platform` operator is installed and watching the namespace | | Model-download job in `Pending` | PVC unbound or HF secret missing | Fix PVC binding or create the named HF secret, then rerun the job | | Worker pods `CrashLoopBackOff` | Image/runtime mismatch or GPU not available | Inspect container logs; check `nvidia.com/gpu` allocatable on nodes |

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.
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