[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-holoscan-install-source":3,"mdc-omqokn-key":31,"related-org-nvidia-holoscan-install-source":1454,"related-repo-nvidia-holoscan-install-source":1613},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"holoscan-install-source","build Holoscan SDK from source","Build Holoscan SDK from source via the in-tree .\u002Frun script. 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Use only when published packages don't meet the user's needs.\"\nlicense: Apache-2.0\nmetadata:\n  author: \"Holoscan Team \u003Choloscan-team@nvidia.com>\"\n  github-url: \"https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk\"\n  tags:\n    - holoscan\n    - install\n    - source\n    - build\n    - cmake\n---\n\n# Holoscan SDK — Build from Source\n\n## Purpose\n\nBuild the Holoscan SDK from the `nvidia-holoscan\u002Fholoscan-sdk` source tree using its `.\u002Frun` script (which builds inside a Docker container), producing a local install tree consumable as a CMake dependency.\n\n## Prerequisites\n\n- Linux host with NVIDIA GPU + driver (`nvidia-smi`).\n- `git`, Docker with NVIDIA Container Toolkit (`docker run --gpus all` works), and `docker-buildx-plugin`.\n- ~20 GB free disk for the build container + build\u002Finstall trees.\n- 10–30 min for a clean first build.\n\n## Limitations\n\n- Only recommended when published packages (Conda \u002F container \u002F apt \u002F wheel) don't fit — debug symbols, custom CMake options, or unsupported configs.\n- Still requires Docker — the `.\u002Frun` script builds inside a container; this is not a true bare-metal build.\n- Cross-compiling to aarch64 needs `qemu-user-static` on the host.\n\n## Step 0: Consult the Official Install Instructions\n\nAlways fetch the \"Build from Source\" section of `https:\u002F\u002Fdocs.nvidia.com\u002Fholoscan\u002Fsdk-user-guide\u002Fsdk_installation.html` (and the linked GitHub `README.md` \u002F `DEVELOP.md` for the chosen tag) before building. Extract: required `.\u002Frun` flags for the target architecture and CUDA major, supported branches\u002Ftags, any Dockerfile patches called out for the release, and the test names recommended for verification. If the doc disagrees with anything below, the doc wins.\n\n## Step 1: Prerequisites\n\nCheck that git and Docker (with GPU passthrough) are available:\n\n```bash\ngit --version\ndocker --version\ndocker run --rm --gpus all ubuntu:22.04 nvidia-smi\n```\n\n- If Docker is missing → help install from https:\u002F\u002Fdocs.docker.com\u002Fengine\u002Finstall\u002F\n- If GPU passthrough fails → install NVIDIA Container Toolkit:\n  ```bash\n  curl -fsSL https:\u002F\u002Fnvidia.github.io\u002Flibnvidia-container\u002Fgpgkey | sudo gpg --dearmor -o \u002Fusr\u002Fshare\u002Fkeyrings\u002Fnvidia-container-toolkit-keyring.gpg\n  curl -s -L https:\u002F\u002Fnvidia.github.io\u002Flibnvidia-container\u002Fstable\u002Fdeb\u002Fnvidia-container-toolkit.list \\\n    | sed 's#deb https:\u002F\u002F#deb [signed-by=\u002Fusr\u002Fshare\u002Fkeyrings\u002Fnvidia-container-toolkit-keyring.gpg] https:\u002F\u002F#g' \\\n    | sudo tee \u002Fetc\u002Fapt\u002Fsources.list.d\u002Fnvidia-container-toolkit.list\n  sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit\n  sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker\n  ```\n- If Docker buildx is missing: `sudo apt-get install docker-buildx-plugin`\n\n## Step 2: Clone the Repository\n\nClone repo to ~\u002Fholoscan\u002Fholoscan-sdk if needed\n\n```bash\nmkdir -p ~\u002Fholoscan\u002F\ngit clone https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk.git\ncd ~\u002Fholoscan\u002Fholoscan-sdk\n```\n\nTo build a specific release tag (recommended for stability):\n\n```bash\ngit tag | grep -E '^v[0-9]' | sort -V | tail -5   # list recent tags\ngit checkout v\u003CVERSION>                             # e.g. v4.1.0\n```\n\n## Step 3: Build\n\nThe `.\u002Frun build` script handles container creation, CMake configuration, compilation, and install in one step. Warn the user this takes **10–30 minutes** on first run (downloads base image + compiles).\n\n```bash\n.\u002Frun build\n```\n\nCommon options:\n\n| Flag | Purpose |\n|------|---------|\n| `--type debug` | Debug build (symbols, no optimization) |\n| `--type RelWithDebInfo` | Release + debug symbols |\n| `--arch aarch64` | Cross-compile for ARM64 (needs `sudo apt install qemu-user-static`) |\n| `--gpu igpu` | iGPU build for Jetson\u002FIGX |\n| `--dryrun` | Preview commands without executing |\n\nIf CMake cache errors occur after changing options:\n\n```bash\n.\u002Frun clear_cache && .\u002Frun build\n```\n\nOutput lands in these folders, and can be retrieved with `.\u002Frun get_build_dir` and `.\u002Frun get_install_dir`\n* Build dir: `build-cu\u003CN>-\u003Carch>\u002F`\n* Install dir: `install-cu\u003CN>-\u003Carch>\u002F`.\n\n## Step 4: Run Tests\n\nRun the following tests\n* EXAMPLE_CPP_HELLO_WORLD_TEST\n* EXAMPLE_PYTHON_HELLO_WORLD_TEST\n* EXAMPLE_CPP_TENSOR_INTEROP_TEST\n* EXAMPLE_PYTHON_TENSOR_INTEROP_TEST\n* EXAMPLE_CPP_VIDEO_REPLAYER_TEST\n* EXAMPLE_PYTHON_VIDEO_REPLAYER_TEST\n\n```bash\n.\u002Frun test\n```\n\nTo run all six required tests at once, use a single-quoted regex (the `|` must be quoted to prevent bash from treating it as a pipe):\n\n```bash\n.\u002Frun test --options \"-R 'EXAMPLE_CPP_HELLO_WORLD_TEST|EXAMPLE_PYTHON_HELLO_WORLD_TEST|EXAMPLE_CPP_TENSOR_INTEROP_TEST|EXAMPLE_PYTHON_TENSOR_INTEROP_TEST|EXAMPLE_CPP_VIDEO_REPLAYER_TEST|EXAMPLE_PYTHON_VIDEO_REPLAYER_TEST' --output-on-failure\"\n```\n\nRun a specific test by name or regex:\n\n```bash\n.\u002Frun test --name \u003Ctest_name>\n.\u002Frun test --options \"-R '\u003Cregex>' --output-on-failure\"\n.\u002Frun test --verbose\n```\n\n**Important:** Always single-quote the regex string when it contains `|` — without quotes, bash interprets `|` as a pipe and the command fails with `command not found`.\n\nExpected: all tests pass. 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applications.",{"type":47,"tag":56,"props":1318,"children":1320},{"id":1319},"troubleshooting",[1321],{"type":53,"value":1322},"Troubleshooting",{"type":47,"tag":831,"props":1324,"children":1325},{},[1326,1342],{"type":47,"tag":835,"props":1327,"children":1328},{},[1329],{"type":47,"tag":839,"props":1330,"children":1331},{},[1332,1337],{"type":47,"tag":843,"props":1333,"children":1334},{},[1335],{"type":53,"value":1336},"Symptom",{"type":47,"tag":843,"props":1338,"children":1339},{},[1340],{"type":53,"value":1341},"Fix",{"type":47,"tag":853,"props":1343,"children":1344},{},[1345,1377,1394,1410,1429],{"type":47,"tag":839,"props":1346,"children":1347},{},[1348,1359],{"type":47,"tag":860,"props":1349,"children":1350},{},[1351,1357],{"type":47,"tag":69,"props":1352,"children":1354},{"className":1353},[],[1355],{"type":53,"value":1356},"bash: \u003CTEST_NAME>: command not found",{"type":53,"value":1358}," when running 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",{"type":47,"tag":69,"props":1443,"children":1445},{"className":1444},[],[1446],{"type":53,"value":1447},"sudo apt-get install qemu-user-static",{"type":47,"tag":1449,"props":1450,"children":1451},"style",{},[1452],{"type":53,"value":1453},"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":1455,"total":1612},[1456,1474,1490,1501,1513,1527,1540,1554,1567,1578,1592,1601],{"slug":1457,"name":1457,"fn":1458,"description":1459,"org":1460,"tags":1461,"stars":1471,"repoUrl":1472,"updatedAt":1473},"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},[1462,1465,1468],{"name":1463,"slug":1464,"type":15},"Documentation","documentation",{"name":1466,"slug":1467,"type":15},"MCP","mcp",{"name":1469,"slug":1470,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1475,"name":1475,"fn":1476,"description":1477,"org":1478,"tags":1479,"stars":1487,"repoUrl":1488,"updatedAt":1489},"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},[1480,1483,1484],{"name":1481,"slug":1482,"type":15},"Containers","containers",{"name":13,"slug":14,"type":15},{"name":1485,"slug":1486,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1491,"name":1491,"fn":1492,"description":1493,"org":1494,"tags":1495,"stars":1487,"repoUrl":1488,"updatedAt":1500},"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},[1496,1499],{"name":1497,"slug":1498,"type":15},"CI\u002FCD","ci-cd",{"name":13,"slug":14,"type":15},"2026-07-14T05:25:59.97109",{"slug":1502,"name":1502,"fn":1503,"description":1504,"org":1505,"tags":1506,"stars":1487,"repoUrl":1488,"updatedAt":1512},"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},[1507,1508,1509],{"name":1497,"slug":1498,"type":15},{"name":13,"slug":14,"type":15},{"name":1510,"slug":1511,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1514,"name":1514,"fn":1515,"description":1516,"org":1517,"tags":1518,"stars":1487,"repoUrl":1488,"updatedAt":1526},"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},[1519,1522,1523],{"name":1520,"slug":1521,"type":15},"Debugging","debugging",{"name":1510,"slug":1511,"type":15},{"name":1524,"slug":1525,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1528,"name":1528,"fn":1529,"description":1530,"org":1531,"tags":1532,"stars":1487,"repoUrl":1488,"updatedAt":1539},"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},[1533,1536],{"name":1534,"slug":1535,"type":15},"Best Practices","best-practices",{"name":1537,"slug":1538,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1541,"name":1541,"fn":1542,"description":1543,"org":1544,"tags":1545,"stars":1487,"repoUrl":1488,"updatedAt":1553},"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},[1546,1549,1552],{"name":1547,"slug":1548,"type":15},"Machine Learning","machine-learning",{"name":1550,"slug":1551,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1555,"name":1555,"fn":1556,"description":1557,"org":1558,"tags":1559,"stars":1487,"repoUrl":1488,"updatedAt":1566},"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},[1560,1563],{"name":1561,"slug":1562,"type":15},"QA","qa",{"name":1564,"slug":1565,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":1568,"name":1568,"fn":1569,"description":1570,"org":1571,"tags":1572,"stars":1487,"repoUrl":1488,"updatedAt":1577},"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},[1573,1574],{"name":13,"slug":14,"type":15},{"name":1575,"slug":1576,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":1579,"name":1579,"fn":1580,"description":1581,"org":1582,"tags":1583,"stars":1487,"repoUrl":1488,"updatedAt":1591},"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},[1584,1587,1588],{"name":1585,"slug":1586,"type":15},"Code Review","code-review",{"name":1510,"slug":1511,"type":15},{"name":1589,"slug":1590,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1593,"name":1593,"fn":1594,"description":1595,"org":1596,"tags":1597,"stars":1487,"repoUrl":1488,"updatedAt":1600},"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},[1598,1599],{"name":1561,"slug":1562,"type":15},{"name":1564,"slug":1565,"type":15},"2026-07-14T05:25:54.928983",{"slug":1602,"name":1602,"fn":1603,"description":1604,"org":1605,"tags":1606,"stars":1487,"repoUrl":1488,"updatedAt":1611},"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},[1607,1610],{"name":1608,"slug":1609,"type":15},"Automation","automation",{"name":1497,"slug":1498,"type":15},"2026-07-30T05:29:03.275638",496,{"items":1614,"total":1709},[1615,1632,1642,1656,1666,1681,1695],{"slug":1616,"name":1616,"fn":1617,"description":1618,"org":1619,"tags":1620,"stars":20,"repoUrl":21,"updatedAt":1631},"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},[1621,1624,1627,1628],{"name":1622,"slug":1623,"type":15},"Data Analysis","data-analysis",{"name":1625,"slug":1626,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1629,"slug":1630,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1633,"name":1633,"fn":1634,"description":1635,"org":1636,"tags":1637,"stars":20,"repoUrl":21,"updatedAt":1641},"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},[1638,1639,1640],{"name":13,"slug":14,"type":15},{"name":1575,"slug":1576,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1643,"name":1643,"fn":1644,"description":1645,"org":1646,"tags":1647,"stars":20,"repoUrl":21,"updatedAt":1655},"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},[1648,1651,1652],{"name":1649,"slug":1650,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1653,"slug":1654,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1657,"name":1657,"fn":1658,"description":1659,"org":1660,"tags":1661,"stars":20,"repoUrl":21,"updatedAt":1665},"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},[1662,1663,1664],{"name":1622,"slug":1623,"type":15},{"name":9,"slug":8,"type":15},{"name":1564,"slug":1565,"type":15},"2026-07-17T05:29:03.913266",{"slug":1667,"name":1667,"fn":1668,"description":1669,"org":1670,"tags":1671,"stars":20,"repoUrl":21,"updatedAt":1680},"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},[1672,1673,1676,1677],{"name":1608,"slug":1609,"type":15},{"name":1674,"slug":1675,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1678,"slug":1679,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1682,"name":1682,"fn":1683,"description":1684,"org":1685,"tags":1686,"stars":20,"repoUrl":21,"updatedAt":1694},"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},[1687,1688,1690,1691],{"name":13,"slug":14,"type":15},{"name":1689,"slug":274,"type":15},"Docker",{"name":9,"slug":8,"type":15},{"name":1692,"slug":1693,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1696,"name":1696,"fn":1697,"description":1698,"org":1699,"tags":1700,"stars":20,"repoUrl":21,"updatedAt":1708},"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},[1701,1702,1705],{"name":9,"slug":8,"type":15},{"name":1703,"slug":1704,"type":15},"Quantum Computing","quantum-computing",{"name":1706,"slug":1707,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]