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Use for Python installs; not for native C++\u002Fapt or Conda installs.\"\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    - pip\n    - wheel\n    - python\n---\n\n# Holoscan pip Wheel Installation\n\n## Purpose\n\nInstall the Holoscan SDK Python bindings via the `holoscan-cu12` \u002F `holoscan-cu13` pip wheel into a virtual environment, and verify with `hello_world` and `video_replayer`.\n\n## Prerequisites\n\n- Linux x86_64 with NVIDIA GPU + driver (`nvidia-smi`).\n- CUDA Toolkit on `PATH` matching the host CUDA major (12 or 13).\n- Python 3.10–3.13 with `venv` available.\n- Network access to PyPI and `docs.nvidia.com`.\n\n## Limitations\n\n- Python only. For C++ headers\u002Flibs, pair with `\u002Fholoscan-install-debian`.\n- `holoscan-cu12` and `holoscan-cu13` are mutually exclusive — wheel must match host CUDA driver.\n- `video_replayer` data ships only with the Debian package; without it, set `HOLOSCAN_INPUT_PATH` to a directory containing `racerx\u002F`.\n- `ulimit -s 32768` is recommended in every shell that runs Holoscan — without it some apps emit a stack-size warning or, in rarer cases, segfault.\n\n## Step 0: Consult the Official Install Instructions\n\nAlways fetch the pip-wheel section of `https:\u002F\u002Fdocs.nvidia.com\u002Fholoscan\u002Fsdk-user-guide\u002Fsdk_installation.html` before installing. Extract: exact wheel package names (`holoscan-cu12`, `holoscan-cu13`), the supported Python range for the current release, prerequisites that must be on `PATH` (CUDA Toolkit), and any optional extras (LibTorch \u002F ONNX Runtime version pins). If the doc disagrees with anything below, the doc wins.\n\nYou need the CUDA variant already determined. If not known, run `nvidia-smi 2>&1 | head -5` first.\n\n**CUDA variant rule — pick the pip package:**\n\n| nvidia-smi CUDA Version | pip package |\n|------------------------|-------------|\n| 13.x+ | `holoscan-cu13` |\n| 12.x (any GPU) | `holoscan-cu12` |\n\nPrerequisites: CUDA Toolkit on PATH, Python 3.10–3.13. Optional extras: LibTorch 2.11.0+, ONNX Runtime 1.22.0+.\n\nAlways install into a Python virtual environment — this avoids system-package conflicts and is required on Ubuntu 24.04 (which blocks system-wide pip entirely).\n\n## Step 1: Create and Activate the venv\n\nCheck if one exists first:\n\n```bash\nls ~\u002Fholoscan\u002Fvenv 2>\u002Fdev\u002Fnull && echo \"exists\" || echo \"missing\"\n```\n\nIf missing:\n```bash\npython3 -m venv ~\u002Fholoscan\u002Fvenv\n```\n\nThen activate:\n```bash\nsource ~\u002Fholoscan\u002Fvenv\u002Fbin\u002Factivate\n```\n\n## Step 2: Install\n\n```bash\npip install holoscan-cu12   # or holoscan-cu13\n```\n\n## Step 3: Verify\n\nThe venv must be active for all commands below.\n\n```bash\n# Basic import — expected: version string, e.g. \"4.1.0\"\n# The stack-size RuntimeWarning is harmless; ulimit -s 32768 suppresses it.\npython3 -c \"import holoscan; print(holoscan.__version__)\"\n\n# Fetch Python examples from GitHub at the installed version tag.\n# These are official NVIDIA examples, fetched over HTTPS and pinned to the tag\n# matching the installed wheel (v${SDK_VER}). Before running them, tell the user\n# you're about to download and execute remote example scripts from this URL. If\n# they decline or GitHub is unreachable, skip to browsing the examples in Step 4.\nSDK_VER=$(python3 -c \"import holoscan; print(holoscan.__version__)\")\nBASE=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fnvidia-holoscan\u002Fholoscan-sdk\u002Fv${SDK_VER}\u002Fexamples\"\n\n# hello_world — expected: \"Hello World!\"\ncurl -fsSL \"${BASE}\u002Fhello_world\u002Fpython\u002Fhello_world.py\" -o \u002Ftmp\u002Fhs_hello_world.py\nulimit -s 32768 && python3 \u002Ftmp\u002Fhs_hello_world.py\n\n# video_replayer (10 frames, headless) — expected: \"Graph execution finished.\"\n# Always run headless: works with or without a display, avoids GUI failure modes over SSH.\ncurl -fsSL \"${BASE}\u002Fvideo_replayer\u002Fpython\u002Fvideo_replayer.py\" -o \u002Ftmp\u002Fhs_video_replayer.py\ncurl -fsSL \"${BASE}\u002Fvideo_replayer\u002Fpython\u002Fvideo_replayer.yaml\" -o \u002Ftmp\u002Fhs_video_replayer.yaml\npython3 -c \"\nc = open('\u002Ftmp\u002Fhs_video_replayer.yaml').read()\nc = c.replace('count: 0','count: 10').replace('repeat: true','repeat: false').replace('realtime: true','realtime: false')\nc = c.replace('holoviz:\\n  width: 854','holoviz:\\n  headless: true\\n  width: 854')\nopen('\u002Ftmp\u002Fhs_video_replayer_run.yaml','w').write(c)\"\nulimit -s 32768 && HOLOSCAN_INPUT_PATH=\u002Fopt\u002Fnvidia\u002Fholoscan\u002Fdata \\\n  python3 \u002Ftmp\u002Fhs_video_replayer.py --config \u002Ftmp\u002Fhs_video_replayer_run.yaml\n```\n\nNote: `video_replayer` needs the racerx data files. These ship with the Debian package at `\u002Fopt\u002Fnvidia\u002Fholoscan\u002Fdata`. If the Debian package is not installed, run `sudo \u002Fopt\u002Fnvidia\u002Fholoscan\u002Fexamples\u002Fdownload_example_data` first (requires the apt package to be installed for that script), or set `HOLOSCAN_INPUT_PATH` to wherever the data lives.\n\n## Step 4: Remind the User\n\nThey must activate the venv in each new shell session:\n\n```bash\nsource ~\u002Fholoscan\u002Fvenv\u002Fbin\u002Factivate\nulimit -s 32768   # suppress stack-size warning\n```\n\nThen offer next steps:\n- Explore Python examples at `https:\u002F\u002Fgithub.com\u002Fnvidia-holoscan\u002Fholoscan-sdk\u002Ftree\u002Fv\u003CVERSION>\u002Fexamples`\n- Walk through a specific example: `\u002Fexplain-example`\n- Start building a custom Holoscan application\n\n## Troubleshooting\n\n- **`pip install holoscan-cu12` errors with \"externally-managed-environment\".** Ubuntu 24.04 blocks system-wide pip. 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Uninstall and reinstall the matching one: ",{"type":49,"tag":71,"props":1261,"children":1263},{"className":1262},[],[1264],{"type":55,"value":1265},"pip uninstall -y holoscan-cu13 && pip install holoscan-cu12",{"type":55,"value":1267}," (or vice versa).",{"type":49,"tag":114,"props":1269,"children":1270},{},[1271,1281,1283,1288],{"type":49,"tag":295,"props":1272,"children":1273},{},[1274,1280],{"type":49,"tag":71,"props":1275,"children":1277},{"className":1276},[],[1278],{"type":55,"value":1279},"RuntimeWarning: stack size ...",{"type":55,"value":102},{"type":55,"value":1282}," Harmless, but set ",{"type":49,"tag":71,"props":1284,"children":1286},{"className":1285},[],[1287],{"type":55,"value":235},{"type":55,"value":1289}," in the current shell to silence it.",{"type":49,"tag":114,"props":1291,"children":1292},{},[1293,1298,1300,1305,1307,1313],{"type":49,"tag":295,"props":1294,"children":1295},{},[1296],{"type":55,"value":1297},"Segmentation fault when running an example.",{"type":55,"value":1299}," ",{"type":49,"tag":71,"props":1301,"children":1303},{"className":1302},[],[1304],{"type":55,"value":235},{"type":55,"value":1306}," wasn't set. 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Install the Debian package for ",{"type":49,"tag":71,"props":1342,"children":1344},{"className":1343},[],[1345],{"type":55,"value":1064},{"type":55,"value":1347},", or set ",{"type":49,"tag":71,"props":1349,"children":1351},{"className":1350},[],[1352],{"type":55,"value":217},{"type":55,"value":1127},{"type":49,"tag":114,"props":1355,"children":1356},{},[1357,1368],{"type":49,"tag":295,"props":1358,"children":1359},{},[1360,1366],{"type":49,"tag":71,"props":1361,"children":1363},{"className":1362},[],[1364],{"type":55,"value":1365},"source: no such file: ~\u002Fholoscan\u002Fvenv\u002Fbin\u002Factivate",{"type":55,"value":1367}," in a new shell.",{"type":55,"value":1369}," Venv wasn't created or path differs. Re-run Step 1 or correct the path.",{"type":49,"tag":1371,"props":1372,"children":1373},"style",{},[1374],{"type":55,"value":1375},"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":1377,"total":1479},[1378,1395,1407,1421,1433,1450,1465],{"slug":1379,"name":1379,"fn":1380,"description":1381,"org":1382,"tags":1383,"stars":23,"repoUrl":24,"updatedAt":1394},"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},[1384,1387,1390,1391],{"name":1385,"slug":1386,"type":15},"Data Analysis","data-analysis",{"name":1388,"slug":1389,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1392,"slug":1393,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1396,"name":1396,"fn":1397,"description":1398,"org":1399,"tags":1400,"stars":23,"repoUrl":24,"updatedAt":1406},"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},[1401,1402,1405],{"name":13,"slug":14,"type":15},{"name":1403,"slug":1404,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1408,"name":1408,"fn":1409,"description":1410,"org":1411,"tags":1412,"stars":23,"repoUrl":24,"updatedAt":1420},"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},[1413,1416,1417],{"name":1414,"slug":1415,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1418,"slug":1419,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1422,"name":1422,"fn":1423,"description":1424,"org":1425,"tags":1426,"stars":23,"repoUrl":24,"updatedAt":1432},"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},[1427,1428,1429],{"name":1385,"slug":1386,"type":15},{"name":9,"slug":8,"type":15},{"name":1430,"slug":1431,"type":15},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":1434,"name":1434,"fn":1435,"description":1436,"org":1437,"tags":1438,"stars":23,"repoUrl":24,"updatedAt":1449},"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},[1439,1442,1445,1446],{"name":1440,"slug":1441,"type":15},"Automation","automation",{"name":1443,"slug":1444,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1447,"slug":1448,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1451,"name":1451,"fn":1452,"description":1453,"org":1454,"tags":1455,"stars":23,"repoUrl":24,"updatedAt":1464},"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},[1456,1457,1460,1461],{"name":13,"slug":14,"type":15},{"name":1458,"slug":1459,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1462,"slug":1463,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1466,"name":1466,"fn":1467,"description":1468,"org":1469,"tags":1470,"stars":23,"repoUrl":24,"updatedAt":1478},"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},[1471,1472,1475],{"name":9,"slug":8,"type":15},{"name":1473,"slug":1474,"type":15},"Quantum Computing","quantum-computing",{"name":1476,"slug":1477,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305,{"items":1481,"total":1630},[1482,1500,1514,1525,1537,1551,1564,1578,1589,1598,1612,1621],{"slug":1483,"name":1483,"fn":1484,"description":1485,"org":1486,"tags":1487,"stars":1497,"repoUrl":1498,"updatedAt":1499},"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},[1488,1491,1494],{"name":1489,"slug":1490,"type":15},"Documentation","documentation",{"name":1492,"slug":1493,"type":15},"MCP","mcp",{"name":1495,"slug":1496,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1501,"name":1501,"fn":1502,"description":1503,"org":1504,"tags":1505,"stars":1511,"repoUrl":1512,"updatedAt":1513},"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},[1506,1509,1510],{"name":1507,"slug":1508,"type":15},"Containers","containers",{"name":13,"slug":14,"type":15},{"name":18,"slug":19,"type":15},17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1515,"name":1515,"fn":1516,"description":1517,"org":1518,"tags":1519,"stars":1511,"repoUrl":1512,"updatedAt":1524},"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},[1520,1523],{"name":1521,"slug":1522,"type":15},"CI\u002FCD","ci-cd",{"name":13,"slug":14,"type":15},"2026-07-14T05:25:59.97109",{"slug":1526,"name":1526,"fn":1527,"description":1528,"org":1529,"tags":1530,"stars":1511,"repoUrl":1512,"updatedAt":1536},"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},[1531,1532,1533],{"name":1521,"slug":1522,"type":15},{"name":13,"slug":14,"type":15},{"name":1534,"slug":1535,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1538,"name":1538,"fn":1539,"description":1540,"org":1541,"tags":1542,"stars":1511,"repoUrl":1512,"updatedAt":1550},"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},[1543,1546,1547],{"name":1544,"slug":1545,"type":15},"Debugging","debugging",{"name":1534,"slug":1535,"type":15},{"name":1548,"slug":1549,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1552,"name":1552,"fn":1553,"description":1554,"org":1555,"tags":1556,"stars":1511,"repoUrl":1512,"updatedAt":1563},"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},[1557,1560],{"name":1558,"slug":1559,"type":15},"Best Practices","best-practices",{"name":1561,"slug":1562,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1565,"name":1565,"fn":1566,"description":1567,"org":1568,"tags":1569,"stars":1511,"repoUrl":1512,"updatedAt":1577},"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},[1570,1573,1576],{"name":1571,"slug":1572,"type":15},"Machine Learning","machine-learning",{"name":1574,"slug":1575,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1579,"name":1579,"fn":1580,"description":1581,"org":1582,"tags":1583,"stars":1511,"repoUrl":1512,"updatedAt":1588},"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},[1584,1587],{"name":1585,"slug":1586,"type":15},"QA","qa",{"name":1430,"slug":1431,"type":15},"2026-07-14T05:25:53.673039",{"slug":1590,"name":1590,"fn":1591,"description":1592,"org":1593,"tags":1594,"stars":1511,"repoUrl":1512,"updatedAt":1597},"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},[1595,1596],{"name":13,"slug":14,"type":15},{"name":1403,"slug":1404,"type":15},"2026-07-14T05:25:49.362534",{"slug":1599,"name":1599,"fn":1600,"description":1601,"org":1602,"tags":1603,"stars":1511,"repoUrl":1512,"updatedAt":1611},"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},[1604,1607,1608],{"name":1605,"slug":1606,"type":15},"Code Review","code-review",{"name":1534,"slug":1535,"type":15},{"name":1609,"slug":1610,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1613,"name":1613,"fn":1614,"description":1615,"org":1616,"tags":1617,"stars":1511,"repoUrl":1512,"updatedAt":1620},"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},[1618,1619],{"name":1585,"slug":1586,"type":15},{"name":1430,"slug":1431,"type":15},"2026-07-14T05:25:54.928983",{"slug":1622,"name":1622,"fn":1623,"description":1624,"org":1625,"tags":1626,"stars":1511,"repoUrl":1512,"updatedAt":1629},"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},[1627,1628],{"name":1440,"slug":1441,"type":15},{"name":1521,"slug":1522,"type":15},"2026-07-30T05:29:03.275638",496]