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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Expand workflow HDF5 demonstrations with action jitter, optionally scoped to node segments. Use for synthetic variants; do not use to collect data, alter state directly, or generate new images.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Clone successful source episodes and perturb recorded actions.
export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
[ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"
HDF5_PATH=/absolute/path/to/source.hdf5
uv run --project tools/dataset i4h-dataset inspect "$HDF5_PATH" --segments
Treat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Use the explicit/current-chain recording. Require at least one successful source episode. If the user asks for node-scoped jitter, require that node in segment metadata.
RUN_DIR="$(pwd)/runs/<workflow>/$(date +%Y%m%d_%H%M%S)"
mkdir -p "$RUN_DIR"
uv run --project tools/mimic i4h-mimic \
"$HDF5_PATH" "$RUN_DIR/expanded.hdf5" \
--episodes <additional-count> \
--noise 0.01 \
--seed <seed> \
--include-source \
--successful-only
--episodes is the number of generated variants, not the final total. Add --node <node-id> only when requested. Preserve the default deterministic seed unless the user supplies another.
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/expanded.hdf5" --segments
Confirm source count, generated count, dimensions, success flags, and segment preservation. Reject zero generated episodes.
If the same prompt requests visualization, continue with i4h-workflow-dataset-convert, then i4h-lerobot-viz; raw HDF5 is not a LeRobot visualizer input.
On failure, inspect the first episode shape or segment error. If a requested node is absent, report available node ids.
Require a readable HDF5 file with at least one successful episode and valid segments for node-scoped jitter.
The current tool does not independently add state noise, rerun physics, or synthesize camera frames.
Mimic 3 more episodes and visualize my dataset. → generate three variants, convert the expanded HDF5, then launch LeRobot visualization.Report input/output paths, source and generated counts, final total, action-noise value, seed, optional node, limitations, and conversion/visualizer result when requested.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.