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Use when asked to set up, install, or bootstrap the agentic workflow, or hits missing `.venv`, third-party checkout, or engine errors.\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    - agentic-workflow\n    - setup\n    - installation\n---\n\n# i4h Workflow — Setup\n\n## Purpose\n\nVerify host requirements and run the idempotent `workflows\u002Fagentic\u002Fsetup.sh` to bootstrap the agentic workflow. Use when asked to set up, install, or bootstrap the workflow, or when hitting a missing `.venv`, third-party checkout, or engine error.\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 \"$ROOT\"\n```\n\n## Basics\n\n- `workflows\u002Fagentic\u002Fsetup.sh` is the idempotent setup entry point.\n- Cosmos setup is separate; invoke only when the user asks for Cosmos or video transfer.\n\n## Preflight\n\n```bash\ncommand -v uv\ncommand -v git\nnvidia-smi\ndf -h .\n```\n\nRequired: Linux, `uv`, `git`, NVIDIA driver\u002FGPU, disk space for third-party checkouts. Docker is optional unless using Cosmos or local VLM containers.\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — resolve repo and run dir\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nRUN_DIR=\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\u002Fsetup_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Flogs\"\nln -sfn \"${RUN_DIR}\" \"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\u002F.latest\"\n```\n\n### Step 2 — run setup\n\nOn a freshly cloned repo the harness may ask to approve `setup.sh` once. Set `I4H_WORKFLOWS` to a pre-existing trusted clone to skip the fresh-clone gate.\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fsetup.sh\" > \"${RUN_DIR}\u002Flogs\u002Fsetup.log\" 2>&1\n```\n\nWatch progress with `tail -f \"${RUN_DIR}\u002Flogs\u002Fsetup.log\"`. For component-specific retries (also idempotent):\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fthird_party\u002Fsetup.sh\"\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fpolicy\u002Fgr00t_n16\u002Fsetup.sh\"\n```\n\n## Verify\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fpolicy\u002Frun.sh\" --list-envs\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Farena\u002Frun.sh\" --list-envs\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fpolicy\u002Frun.sh\" --env scissor_pick_and_place --dry-run\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Farena\u002Frun.sh\" --env scissor_pick_and_place --dry-run\n```\n\n## Prerequisites\n\n- Linux host with `uv`, `git`, and an NVIDIA driver\u002FGPU (check via the Preflight commands).\n- Disk space for the third-party checkouts (`df -h .`).\n- Docker only when using Cosmos or local VLM containers; otherwise optional.\n\n## Limitations\n\n- Linux only; requires an NVIDIA driver\u002FGPU.\n- Cosmos setup is separate — `setup.sh` does not run it; invoke Cosmos only when the user asks for it.\n- Verify steps use `--list-envs` and `--dry-run`; they confirm envs register and build, not that a full rollout works.\n\n## Troubleshooting\n\n- **Error:** `uv` \u002F `git` \u002F `nvidia-smi` not found — Cause: missing host requirement. Fix: install it and re-run Preflight before `setup.sh`.\n- **Error:** a single component fails partway through setup — Cause: a checkout or component venv did not finish. Fix: re-run that component's script (e.g. `third_party\u002Fsetup.sh`, `arena\u002Fsetup.sh`, `policy\u002Fsetup.sh`); all are idempotent.\n- **Error:** `--list-envs` or `--dry-run` fails after setup — Cause: setup incomplete or a component venv missing. Fix: inspect `${RUN_DIR}\u002Flogs\u002Fsetup.log` (or `workflows\u002Fagentic\u002Fruns\u002F.latest\u002Flogs\u002Fsetup.log`) and re-run `setup.sh` or the failing component script.\n- **Error:** `setup.sh` fails instantly redirecting to a missing log dir — Cause: Step 2 ran before Step 1, so `${RUN_DIR}\u002Flogs` does not exist. 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Fix: inspect ",{"type":46,"tag":67,"props":1358,"children":1360},{"className":1359},[],[1361],{"type":51,"value":1362},"${RUN_DIR}\u002Flogs\u002Fsetup.log",{"type":51,"value":1364}," (or ",{"type":46,"tag":67,"props":1366,"children":1368},{"className":1367},[],[1369],{"type":51,"value":1370},"workflows\u002Fagentic\u002Fruns\u002F.latest\u002Flogs\u002Fsetup.log",{"type":51,"value":1372},") and re-run ",{"type":46,"tag":67,"props":1374,"children":1376},{"className":1375},[],[1377],{"type":51,"value":880},{"type":51,"value":1379}," or the failing component script.",{"type":46,"tag":471,"props":1381,"children":1382},{},[1383,1387,1388,1393,1395,1401],{"type":46,"tag":1269,"props":1384,"children":1385},{},[1386],{"type":51,"value":1273},{"type":51,"value":1275},{"type":46,"tag":67,"props":1389,"children":1391},{"className":1390},[],[1392],{"type":51,"value":880},{"type":51,"value":1394}," fails instantly redirecting to a missing log dir — Cause: Step 2 ran before Step 1, so ",{"type":46,"tag":67,"props":1396,"children":1398},{"className":1397},[],[1399],{"type":51,"value":1400},"${RUN_DIR}\u002Flogs",{"type":51,"value":1402}," does not exist. Fix: run Step 1 first, then Step 2.",{"type":46,"tag":54,"props":1404,"children":1406},{"id":1405},"final-response",[1407],{"type":51,"value":1408},"Final Response",{"type":46,"tag":61,"props":1410,"children":1411},{},[1412],{"type":51,"value":1413},"Report setup status, failed component (if any), relevant log path, next recommended smoke test.",{"type":46,"tag":1415,"props":1416,"children":1417},"style",{},[1418],{"type":51,"value":1419},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1421,"total":1525},[1422,1439,1453,1467,1479,1496,1511],{"slug":1423,"name":1423,"fn":1424,"description":1425,"org":1426,"tags":1427,"stars":20,"repoUrl":21,"updatedAt":1438},"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},[1428,1431,1434,1435],{"name":1429,"slug":1430,"type":15},"Data Analysis","data-analysis",{"name":1432,"slug":1433,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1436,"slug":1437,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1440,"name":1440,"fn":1441,"description":1442,"org":1443,"tags":1444,"stars":20,"repoUrl":21,"updatedAt":1452},"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},[1445,1448,1451],{"name":1446,"slug":1447,"type":15},"Deployment","deployment",{"name":1449,"slug":1450,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1454,"name":1454,"fn":1455,"description":1456,"org":1457,"tags":1458,"stars":20,"repoUrl":21,"updatedAt":1466},"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},[1459,1462,1463],{"name":1460,"slug":1461,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1464,"slug":1465,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1468,"name":1468,"fn":1469,"description":1470,"org":1471,"tags":1472,"stars":20,"repoUrl":21,"updatedAt":1478},"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},[1473,1474,1475],{"name":1429,"slug":1430,"type":15},{"name":9,"slug":8,"type":15},{"name":1476,"slug":1477,"type":15},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":1480,"name":1480,"fn":1481,"description":1482,"org":1483,"tags":1484,"stars":20,"repoUrl":21,"updatedAt":1495},"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},[1485,1488,1491,1492],{"name":1486,"slug":1487,"type":15},"Automation","automation",{"name":1489,"slug":1490,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1493,"slug":1494,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1497,"name":1497,"fn":1498,"description":1499,"org":1500,"tags":1501,"stars":20,"repoUrl":21,"updatedAt":1510},"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},[1502,1503,1506,1507],{"name":1446,"slug":1447,"type":15},{"name":1504,"slug":1505,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1508,"slug":1509,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1512,"name":1512,"fn":1513,"description":1514,"org":1515,"tags":1516,"stars":20,"repoUrl":21,"updatedAt":1524},"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},[1517,1518,1521],{"name":9,"slug":8,"type":15},{"name":1519,"slug":1520,"type":15},"Quantum Computing","quantum-computing",{"name":1522,"slug":1523,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305,{"items":1527,"total":1678},[1528,1546,1562,1573,1585,1599,1612,1626,1637,1646,1660,1669],{"slug":1529,"name":1529,"fn":1530,"description":1531,"org":1532,"tags":1533,"stars":1543,"repoUrl":1544,"updatedAt":1545},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1534,1537,1540],{"name":1535,"slug":1536,"type":15},"Documentation","documentation",{"name":1538,"slug":1539,"type":15},"MCP","mcp",{"name":1541,"slug":1542,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1547,"name":1547,"fn":1548,"description":1549,"org":1550,"tags":1551,"stars":1559,"repoUrl":1560,"updatedAt":1561},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1552,1555,1556],{"name":1553,"slug":1554,"type":15},"Containers","containers",{"name":1446,"slug":1447,"type":15},{"name":1557,"slug":1558,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1563,"name":1563,"fn":1564,"description":1565,"org":1566,"tags":1567,"stars":1559,"repoUrl":1560,"updatedAt":1572},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1568,1571],{"name":1569,"slug":1570,"type":15},"CI\u002FCD","ci-cd",{"name":1446,"slug":1447,"type":15},"2026-07-14T05:25:59.97109",{"slug":1574,"name":1574,"fn":1575,"description":1576,"org":1577,"tags":1578,"stars":1559,"repoUrl":1560,"updatedAt":1584},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1579,1580,1581],{"name":1569,"slug":1570,"type":15},{"name":1446,"slug":1447,"type":15},{"name":1582,"slug":1583,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1586,"name":1586,"fn":1587,"description":1588,"org":1589,"tags":1590,"stars":1559,"repoUrl":1560,"updatedAt":1598},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1591,1594,1595],{"name":1592,"slug":1593,"type":15},"Debugging","debugging",{"name":1582,"slug":1583,"type":15},{"name":1596,"slug":1597,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1600,"name":1600,"fn":1601,"description":1602,"org":1603,"tags":1604,"stars":1559,"repoUrl":1560,"updatedAt":1611},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1605,1608],{"name":1606,"slug":1607,"type":15},"Best Practices","best-practices",{"name":1609,"slug":1610,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1613,"name":1613,"fn":1614,"description":1615,"org":1616,"tags":1617,"stars":1559,"repoUrl":1560,"updatedAt":1625},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1618,1621,1624],{"name":1619,"slug":1620,"type":15},"Machine Learning","machine-learning",{"name":1622,"slug":1623,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1627,"name":1627,"fn":1628,"description":1629,"org":1630,"tags":1631,"stars":1559,"repoUrl":1560,"updatedAt":1636},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1632,1635],{"name":1633,"slug":1634,"type":15},"QA","qa",{"name":1476,"slug":1477,"type":15},"2026-07-14T05:25:53.673039",{"slug":1638,"name":1638,"fn":1639,"description":1640,"org":1641,"tags":1642,"stars":1559,"repoUrl":1560,"updatedAt":1645},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1643,1644],{"name":1446,"slug":1447,"type":15},{"name":1449,"slug":1450,"type":15},"2026-07-14T05:25:49.362534",{"slug":1647,"name":1647,"fn":1648,"description":1649,"org":1650,"tags":1651,"stars":1559,"repoUrl":1560,"updatedAt":1659},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1652,1655,1656],{"name":1653,"slug":1654,"type":15},"Code Review","code-review",{"name":1582,"slug":1583,"type":15},{"name":1657,"slug":1658,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1661,"name":1661,"fn":1662,"description":1663,"org":1664,"tags":1665,"stars":1559,"repoUrl":1560,"updatedAt":1668},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1666,1667],{"name":1633,"slug":1634,"type":15},{"name":1476,"slug":1477,"type":15},"2026-07-14T05:25:54.928983",{"slug":1670,"name":1670,"fn":1671,"description":1672,"org":1673,"tags":1674,"stars":1559,"repoUrl":1560,"updatedAt":1677},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1675,1676],{"name":1486,"slug":1487,"type":15},{"name":1569,"slug":1570,"type":15},"2026-07-30T05:29:03.275638",496]