Dynamo Troubleshoot
NVIDIAOfficialCreated by Dan GilDistributed by NVIDIA
- Skill
- Review and audit
- Cloud and DevOps
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Dynamo Troubleshoot provides a structured, top-down methodology for diagnosing issues within Dynamo deployments. It utilizes a read-only debug bundle collection script to gather cluster state, events, and resource status, allowing operators to classify failures into specific buckets such as storage, GPU scheduling, or operator reconciliation. The skill emphasizes a systematic approach to troubleshooting, ensuring that users identify the root cause before applying minimal, reversible changes to their cluster configuration.
What it does
- Collects a read-only debug bundle containing pods, events, jobs, and PVC status.
- Provides a decision tree to classify failures into specific operational buckets.
- Guides users through a top-down diagnostic order from infrastructure to application endpoints.
- Suggests specific remediation steps for common failure modes like PVC binding or GPU scheduling.
- Ensures safe troubleshooting by never executing mutations or collecting sensitive secrets.
When to use it
- When a Dynamo deployment fails to reach a healthy state.
- When pods, model-cache jobs, or benchmark jobs enter a failed or pending status.
- When frontend or router services are unresponsive or failing to register workers.
- When you need to audit the status of a DynamoGraphDeployment resource.
Advantages
- Standardizes the diagnostic process for Dynamo environments.
- Minimizes risk by focusing on read-only data collection and manual remediation.
- Reduces mean time to resolution by categorizing failures into actionable buckets.
- Prevents accidental exposure of sensitive information like Hugging Face tokens.
Usage examples
- “Collect debug information for the dynamo-demo namespace.”
- The agent runs the collection script and returns a bundle of cluster state, events, and resource status for the specified namespace.
- “Troubleshoot a failing deployment named qwen-vllm-disagg in the production namespace.”
- The agent collects scoped debug data for that specific deployment and provides a classification of the failure based on the decision tree.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/dynamo-troubleshoot/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip dynamo-troubleshoot.zip -d .agents/skills
Documentation by Dan Gil
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.
kubectlconfigured with read access to the target namespace.Permission to read pods, events, jobs, PVCs, and
DynamoGraphDeploymentresources (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:
namespace, storage class, GPU nodes, and HF secret existence
PVC and model-download job
DynamoGraphDeploymentstatus and eventspod status,
describe pod, and container logsfrontend service and port-forward
/v1/models/v1/chat/completionsbenchmark 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
storageClassNamepatch 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-demoScope to a single failing deployment:
python3 scripts/collect_dynamo_debug_bundle.py \
--namespace dynamo-demo \
--deployment-name qwen-vllm-disaggEquivalent 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-checkfor 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.mdfor bucket-specific checks.Use
scripts/collect_dynamo_debug_bundle.pyfor read-only bundle collection.
Files
- ATTRIBUTION.mdAdded by AutoGineer916 B
- BENCHMARK.md3.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.0 KiB
- evals/evals.json3.2 KiB
- references/failure-decision-tree.md3.7 KiB
- scripts/collect_dynamo_debug_bundle.pyScript8.3 KiB
- skill-card.md2.8 KiB
- skill.oms.sig5.0 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python
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Attribution and licence
- Created by
- Dan Gil
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/dynamo-troubleshoot · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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