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。
Route open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.
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 when the user asks where to start with NVFLARE, how a local project maps to FLARE workflows, or which FLARE skill should handle an ambiguous request. For a concrete conversion request, use only after preliminary source inspection explicitly reports unresolved ownership or conflicting active training owners that require a user choice.
Do not use when the user already names a specific workflow such as PyTorch
conversion, federated statistics, job submission, production deployment,
Kubernetes setup, log diagnosis, or optimization of an existing FLARE job.
Route to the narrower skill instead. Do not load orientation to perform the
preliminary inspection for a concrete conversion request: run nvflare agent inspect source <path> --format json first, then route directly to the one
detected converter. Continue to use orientation for an inspector-reported
ownership conflict or unresolved Trainer factory, which requires the read-only
choice described above.
nvflare agent inspect source <path> --format json for project or job
evidence, or nvflare agent inspect data <path> --format json for data and
statistics requests. If data inspection returns dataset: null, run source
inspection on the same target. Use its ownership, integration, scan,
routing, or dataset evidence as applicable.Load references/orientation-routing.md when routing is ambiguous or when the
inspect output names multiple possible workflow families.
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。