Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
kubectl configured with read access to the target namespace.DynamoGraphDeployment resources (NOT secrets).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.
Use references/failure-decision-tree.md and classify into one primary bucket:
Check in this order:
DynamoGraphDeployment status and eventsdescribe pod, and container logs/v1/models/v1/chat/completionsPrefer the smallest reversible change:
storageClassNameAfter each fix, rerun the relevant readiness check before moving deeper.
| 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"])
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"])
Return:
--deployment-name.dynamo-interconnect-check for that.| 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 |
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/failure-decision-tree.md for bucket-specific checks.scripts/collect_dynamo_debug_bundle.py for read-only bundle collection.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
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Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
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