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.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when adding NeMo Relay typed wrappers, domain types, or provider codecs while preserving JSON middleware semantics and caller-visible behavior.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill when an application wants stronger domain types than raw JSON for tool or LLM integration. Keep typed boundaries explicit so middleware still sees predictable JSON.
JsonPassthrough, DataclassCodec, PydanticCodec, and
BestEffortAnyCodec. Node.js exposes JsonPassthrough plus custom
Codec<T> implementations.BestEffortAnyCodec only at boundaries where strict schemas are not
available. Prefer dataclass, Pydantic, or explicit Node.js codecs when the
framework owns a stable schema.OpenAIChatCodec, OpenAIResponsesCodec,
and AnthropicMessagesCodec in Python, Node.js, and Rust. Choose the
codec that matches the actual provider payload shape.id, model,
message, tool_calls, finish_reason, usage, provider-specific data, and
extra unmodeled fields. They do not rewrite the caller-visible response.LLMRequest and optional annotated request; encode merges annotated
edits back before execution intercepts and the provider callback run.JsonPassthrough for JSON-native valuesDataclassCodec or PydanticCodec in Python when the models already existBestEffortAnyCodec only when broad flexibility is worth the looser contracttoJson/fromJson or decode/encodeencode preserves original provider fields unless an
intercept intentionally changes themnemo-relay-instrument-callsnemo-relay-plugin-observabilitynemo-relay-debug-runtime-integrationまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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'.
日本語の概要は準備中です。原文の説明を表示しています。
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`.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。