[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-i4h-workflow-dataset-convert":3,"mdc--tnf6ki-key":37,"related-org-nvidia-i4h-workflow-dataset-convert":1745,"related-repo-nvidia-i4h-workflow-dataset-convert":1904},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"i4h-workflow-dataset-convert","convert agentic HDF5 recordings to LeRobot datasets","Convert an agentic HDF5 recording into a LeRobot dataset (parquet, meta, videos). 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Use when asked to convert HDF5, prepare for training, or export to LeRobot; not for viewing — use [[i4h-lerobot-viz]].\nlicense: Apache-2.0\nmetadata:\n  author: \"Isaac for Healthcare Team \u003Cisaac-for-healthcare-support@nvidia.com>\"\n  tags:\n    - isaac-for-healthcare\n    - i4h\n    - dataset\n    - lerobot\n    - conversion\n---\n\n# i4h Workflow — Convert Dataset\n\n## Purpose\n\nConvert an agentic HDF5 recording into a LeRobot dataset (parquet + meta + videos). Use when the user asks to convert HDF5, prepare for training, or export to LeRobot.\n\n## Base Code\n\nThese steps drive the i4h-workflows base code (the `workflows\u002Fagentic\u002F` tree). To reuse an existing checkout, set `I4H_WORKFLOWS` to its path (no clone happens). Otherwise this resolves the current repo, or clones to `~\u002Fi4h-workflows` — pick that default without prompting. Run every command below from the resolved root:\n\n```bash\n# Resolve the i4h-workflows base code (provides workflows\u002Fagentic\u002F).\nROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"\nif [ ! -d \"$ROOT\u002Fworkflows\u002Fagentic\" ]; then\n  ROOT=\"${I4H_WORKFLOWS:-$HOME\u002Fi4h-workflows}\"\n  [ -d \"$ROOT\u002Fworkflows\u002Fagentic\" ] || git clone https:\u002F\u002Fgithub.com\u002Fisaac-for-healthcare\u002Fi4h-workflows \"$ROOT\"\nfi\nexport I4H_WORKFLOWS=\"$ROOT\"; cd \"$ROOT\"\n```\n\n## Basics\n\n- Use the same `--env` that produced the HDF5.\n- **Env config (source of truth):** `workflows\u002Fagentic\u002Fconfig\u002Fenvironments\u002F\u003Cenv>.yaml` supplies the robot, task, cameras, and `dataset.*` (action\u002Fstate names, splits, modality) converter defaults.\n- Output goes to `HF_LEROBOT_HOME\u002F\u003Crepo-id>`.\n\n## Run\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — setup and resolve HDF5\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nENV_ID=scissor_pick_and_place\nRUNS_ROOT=\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\"\n\n# Point HDF5_PATH at a real recording (absolute path). Recordings come from teleop, mimic, or\n# validate (which writes data\u002Fverify.hdf5 under each runs\u002Feval_* dir). List candidates newest-first:\n#   find \"${RUNS_ROOT}\" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM  %p\\n' | sort -r | head\nHDF5_PATH=\"${HDF5_PATH:-}\"\nif [ ! -f \"${HDF5_PATH}\" ]; then\n  echo \"convert: set HDF5_PATH to an existing .hdf5 (got '${HDF5_PATH:-\u003Cunset>}'). Candidates:\" >&2\n  find \"${RUNS_ROOT}\" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM  %p\\n' 2>\u002Fdev\u002Fnull | sort -r | head\n  exit 1\nfi\n\nRUN_DIR=\"${RUNS_ROOT}\u002Fconvert_${ENV_ID}_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Flogs\"\nln -sfn \"${RUN_DIR}\" \"${RUNS_ROOT}\u002F.latest\"\nexport HF_LEROBOT_HOME=\"${RUN_DIR}\u002Flerobot\"\n```\n\n### Step 2 — convert\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fdataset\u002Frun.sh\" \\\n  --env \"${ENV_ID}\" \\\n  --hdf5-path \"${HDF5_PATH}\" \\\n  --repo-id \"local\u002F${ENV_ID}\" \\\n  --video-codec h264 \\\n  --overwrite \\\n  2>&1 | tee \"${RUN_DIR}\u002Flogs\u002Fconvert.log\"\n```\n\n## Notes\n\n- `--video-codec h264` is required. The converter's default AV1 codec breaks GR00T's `decord` video reader at finetune time.\n- Scissor SO-ARM generates `meta\u002Fmodality.json` from YAML splits and does not need `dataset.modality_template_path`.\n- G1 locomanip and assemble-trocar use `dataset.modality_template_path` from the env YAML.\n- All camera streams are resized to the env YAML `policy.image_size` (override with `--image-size H W`), normalizing mixed-resolution cameras (e.g. head cam + overview cam) to the one size the modality config expects.\n\n## Verify\n\n- `${HF_LEROBOT_HOME}\u002Flocal\u002F${ENV_ID}\u002Fmeta\u002Finfo.json` exists.\n- Log reports the saved episode count.\n- Per-episode video files are present under `${HF_LEROBOT_HOME}\u002Flocal\u002F${ENV_ID}\u002F`.\n\n## Prerequisites\n\n- Workflow set up via [[i4h-workflow-setup]] (the `.venv` must exist).\n- An existing HDF5 recording to convert (set `HDF5_PATH` to an absolute path; the Run block lists candidates if it's unset or wrong).\n- The same `--env` that produced the HDF5 (its YAML supplies robot, task, camera, modality, and converter defaults).\n- `HF_LEROBOT_HOME` set to the output location for `\u003Crepo-id>`.\n\n## Limitations\n\n- `--video-codec h264` is required; the converter's default AV1 codec breaks GR00T's `decord` reader at finetune time.\n- All camera streams are resized to the env YAML `policy.image_size` (override with `--image-size H W`).\n- G1 locomanip and assemble-trocar require `dataset.modality_template_path` from the env YAML; scissor SO-ARM generates `meta\u002Fmodality.json` from YAML splits.\n\n## Troubleshooting\n\n- **Error:** `.venv` not found \u002F module import fails - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.\n- **Error:** input HDF5 not found - Cause: wrong or missing `--hdf5-path`. Fix: point `HDF5_PATH` at an existing recording.\n- **Error:** `decord` fails to read video at finetune time - Cause: dataset written with the default AV1 codec. Fix: re-convert with `--video-codec h264`.\n- **Error:** missing\u002Fincorrect modality config - Cause: wrong `--env`, so robot\u002Ftask\u002Fcamera\u002Fmodality defaults do not match the HDF5. 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Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1898,1901],{"name":1899,"slug":1900,"type":15},"Automation","automation",{"name":1790,"slug":1791,"type":15},"2026-07-30T05:29:03.275638",496,{"items":1905,"total":1999},[1906,1921,1931,1945,1955,1970,1985],{"slug":1907,"name":1907,"fn":1908,"description":1909,"org":1910,"tags":1911,"stars":26,"repoUrl":27,"updatedAt":1920},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1912,1915,1916,1917],{"name":1913,"slug":1914,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},{"name":9,"slug":8,"type":15},{"name":1918,"slug":1919,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1922,"name":1922,"fn":1923,"description":1924,"org":1925,"tags":1926,"stars":26,"repoUrl":27,"updatedAt":1930},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1927,1928,1929],{"name":1775,"slug":1776,"type":15},{"name":1866,"slug":1867,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1932,"name":1932,"fn":1933,"description":1934,"org":1935,"tags":1936,"stars":26,"repoUrl":27,"updatedAt":1944},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1937,1940,1941],{"name":1938,"slug":1939,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1942,"slug":1943,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1946,"name":1946,"fn":1947,"description":1948,"org":1949,"tags":1950,"stars":26,"repoUrl":27,"updatedAt":1954},"amc-run-sample-calibration","run AMC sample dataset calibration","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'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1951,1952,1953],{"name":1913,"slug":1914,"type":15},{"name":9,"slug":8,"type":15},{"name":1855,"slug":1856,"type":15},"2026-07-17T05:29:03.913266",{"slug":1956,"name":1956,"fn":1957,"description":1958,"org":1959,"tags":1960,"stars":26,"repoUrl":27,"updatedAt":1969},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1961,1962,1965,1966],{"name":1899,"slug":1900,"type":15},{"name":1963,"slug":1964,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1967,"slug":1968,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1971,"name":1971,"fn":1972,"description":1973,"org":1974,"tags":1975,"stars":26,"repoUrl":27,"updatedAt":1984},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1976,1977,1980,1981],{"name":1775,"slug":1776,"type":15},{"name":1978,"slug":1979,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1982,"slug":1983,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1986,"name":1986,"fn":1987,"description":1988,"org":1989,"tags":1990,"stars":26,"repoUrl":27,"updatedAt":1998},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1991,1992,1995],{"name":9,"slug":8,"type":15},{"name":1993,"slug":1994,"type":15},"Quantum Computing","quantum-computing",{"name":1996,"slug":1997,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]