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.
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Start or patch Dynamo router modes and run router endpoint smoke checks. Use for round-robin, KV-aware, least-loaded, or device-aware routing setup; use recipe-runner for recipe deployment and troubleshoot for failure diagnosis.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Make Dynamo routing feel easy by getting a baseline router mode running, enabling KV-aware routing when appropriate, and proving the endpoint works. Keep the user focused on exact commands and success signals, not router internals.
dynamo package importable (python3 -m dynamo.frontend --help works).kubectl configured with access to the target namespace and a deployed Dynamo recipe./v1/models returns at least one entry).Collect or infer:
round-robin, kv, least-loaded, device-aware-weighted, direct, or random/v1/models cannot discover itFor local bring-up with already registered workers:
python3 -m dynamo.frontend --router-mode round-robin --http-port 8000
For Kubernetes, inspect the selected recipe deploy.yaml and locate the
frontend service. If the recipe is not already deployed, use
dynamo-recipe-runner first.
For local frontend:
python3 -m dynamo.frontend --router-mode kv --http-port 8000
For Kubernetes, patch only the frontend service env:
envs:
- name: DYN_ROUTER_MODE
value: kv
If backend workers are not publishing KV cache events, set approximate mode instead of leaving the router waiting for events:
envs:
- name: DYN_ROUTER_USE_KV_EVENTS
value: "false"
After port-forwarding the frontend service or starting local frontend, run:
python3 scripts/check_router_health.py \
--base-url http://127.0.0.1:8000
This must verify /v1/models and, when a model is discoverable, one
/v1/chat/completions request.
When comparing round-robin vs KV routing:
If the endpoint is unhealthy or workers are missing, switch to
dynamo-troubleshoot.
| Script | Purpose | Arguments |
|---|---|---|
scripts/check_router_health.py | Smoke-test /v1/models and one chat completion against a Dynamo frontend | --base-url, --retries, --timeout |
Invoke via the agentskills.io run_script() protocol:
run_script("scripts/check_router_health.py", args=["--base-url", "http://127.0.0.1:8000"])
Local KV-routed frontend on port 8000, then smoke-test it:
python3 -m dynamo.frontend --router-mode kv --http-port 8000 &
python3 scripts/check_router_health.py --base-url http://127.0.0.1:8000
Kubernetes-deployed frontend reachable via port-forward:
kubectl port-forward svc/qwen-vllm-disagg-frontend 8000:8000 -n dynamo-demo &
python3 scripts/check_router_health.py --base-url http://127.0.0.1:8000 --retries 3
Equivalent through the agent protocol:
run_script("scripts/check_router_health.py", args=["--base-url", "http://127.0.0.1:8000", "--retries", "3"])
Return:
dynamo-benchmark for throughput/latency numbers.| Symptom | Likely cause | Next step |
|---|---|---|
/v1/models returns empty list | No worker registered with the frontend | Verify worker pods are Ready; confirm they connect to the same etcd/NATS |
| Smoke chat request times out | Frontend up, workers not serving | Switch to dynamo-troubleshoot; inspect worker logs |
| KV mode hangs | Workers do not publish KV cache events | Set DYN_ROUTER_USE_KV_EVENTS=false (approximate mode) |
| Connection refused on port-forward | Port-forward dropped or wrong service name | Re-run port-forward; verify the frontend service name matches the recipe |
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/router-modes.md for the compact mode/env map.scripts/check_router_health.py for endpoint smoke tests.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.
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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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