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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Use this skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including raw ATOF events, ATIF trajectories, OpenTelemetry, OpenInference, or custom event handling.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Start with one exporter managed by the built-in Observability plugin. This is the default for reusable process configuration and the best first plugin for most users because it makes Relay's captured activity visible. Choose one proof output before layering additional telemetry destinations.
Use manual subscriber or exporter APIs only when a test, script, or application needs direct control over registration names, collection windows, or flush timing. Both paths consume the same canonical event stream.
Determine whether the application uses NeMo Relay 0.6 or 0.7 before proposing configuration or binding APIs. Prefer the installed package version, lockfile, or manifest. Ask the user when the version cannot be established; do not mix the two surfaces in one example.
OpenTelemetryConfig /
OpenTelemetrySubscriber and OpenInferenceConfig /
OpenInferenceSubscriber.full, gen_ai, and openinference projections.Select the output that best matches the user's immediate inspection target:
references/atof.md.references/atif.md.full, gen_ai, or
openinference). Read references/opentelemetry.md and, for an
OpenInference-aware backend, references/openinference.md.Choose one output first and verify it before adding another. ATOF is the default local proof because it preserves the raw event stream with the least translation. Use synthetic, non-sensitive payloads for the first proof. Add and verify sanitization before exporters receive production payloads, and never display complete event records while validating an exporter.
Use this model when explaining how capture and export relate:
compaction mark refreshes it.data field,
typed profile data such as model_name and tool_call_id, and codec-provided
annotated LLM request/response data for in-process subscribers and exporters.SKILL.md
automatically emit skill.load marks under the tool span. The payload
contains only skill_name; metadata records the load source and tool name.
Partial reads do not count, and ambiguous slash-command expansions use the
separate skill.load.inferred name. The eager mark remains present if tool
execution later fails.Use the names exported by the selected language binding and Relay version:
nemo_relay.subscribers.register(...), AtofExporter,
AtifExporter, OpenTelemetrySubscriber, and OpenInferenceSubscriberOpenTelemetrySubscriber for all three typed projectionsnemo_relay::api::subscriber and nemo_relay::observability::*Load only the reference required by the selected output:
references/atof.md for raw JSONL events used in local debugging or
offline inspection.references/atif.md for ATIF trajectories.references/opentelemetry.md for OTLP/OpenTelemetry traces.references/openinference.md for the standalone 0.6 OpenInference
exporter or the 0.7 openinference OpenTelemetry projection.Choose another skill when the task belongs to an adjacent workflow:
nemo-relay-plugin-build to package subscriber-based export behavior as
a reusable plugin.nemo-relay-get-started or nemo-relay-instrument-calls when no scope,
tool call, or LLM call has been instrumented.nemo-relay-debug-runtime-integration to diagnose missing telemetry.Use these skills for adjacent workflows:
nemo-relay-instrument-calls.nemo-relay-instrument-typed-wrappers.nemo-relay-plugin-build.nemo-relay-debug-runtime-integration.还没有评价。欢迎分享你的使用感受。
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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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