[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-holoscan-install-conda":3,"mdc-jbztla-key":31,"related-org-nvidia-holoscan-install-conda":1759,"related-repo-nvidia-holoscan-install-conda":1918},{"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-conda","install Holoscan SDK via Conda","Install Holoscan SDK v4.3+ via Conda in a CUDA 13 environment. 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Use for Conda installs; redirect CUDA 12 hosts to container\u002Fwheel.\"\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    - conda\n    - cuda\n---\n\n# Holoscan Conda Installation\n\n## Purpose\n\nInstall the Holoscan SDK (Python runtime and\u002For C++ dev headers) into a Conda environment on Linux x86_64, using conda-forge + rapidsai with a correctly pinned CUDA metapackage.\n\n## Prerequisites\n\n- Linux x86_64 with an NVIDIA GPU and CUDA 13 driver (check `nvidia-smi`).\n- `conda` (Miniforge preferred). Step 1 installs it if missing.\n- Network access to conda-forge, rapidsai, and `docs.nvidia.com`.\n\n## Limitations\n\n- **CUDA 13 only** (since v4.3.0 — earlier releases were CUDA 12). If the user has a CUDA 12 driver, redirect to `\u002Fholoscan-install-container` or `\u002Fholoscan-install-wheel` instead.\n- Linux x86_64 only — no aarch64\u002FiGPU support on conda-forge.\n- `ulimit -s 32768` is recommended in every shell that runs Holoscan — without it, some apps **may** segfault.\n\n## Step 0: Consult the Official Install Instructions\n\nAlways fetch the current Conda section of `https:\u002F\u002Fdocs.nvidia.com\u002Fholoscan\u002Fsdk-user-guide\u002Fsdk_installation.html` before installing — package names, channel selection, and the runtime\u002Fdev split can change between releases. Specifically extract:\n\n- The exact runtime package name (e.g. `holoscan` for Python bindings).\n- The C++ dev package name and whether the user needs it. As of v4.1.0, `libholoscan-dev` is a separate package containing headers and CMake config — install it whenever the user wants to develop C++ apps. Without it, `find_package(holoscan)` fails and there are no headers to `#include`.\n- Supported Python versions for the current release (3.10–3.13 for v4.3).\n- The current `cuda-version` pin (v4.3 → `13`).\n\n`rmm` and `ucxx` are distributed via the `rapidsai` channel; `holoscan`, `libholoscan`, and `libholoscan-dev` come from `conda-forge`.\n\nIf the doc disagrees with anything below, the doc wins — update the install commands accordingly and tell the user.\n\n## Step 1: Prerequisites Check\n\n```bash\nconda --version 2>&1\nnvidia-smi 2>&1 | head -5\n```\n\nIf `conda` is not found, install Miniforge silently (preferred over Miniconda for conda-forge):\n\n```bash\nwget -q https:\u002F\u002Fgithub.com\u002Fconda-forge\u002Fminiforge\u002Freleases\u002Flatest\u002Fdownload\u002FMiniforge3-Linux-x86_64.sh -O \u002Ftmp\u002FMiniforge3.sh\nbash \u002Ftmp\u002FMiniforge3.sh -b -p ~\u002Fminiforge3\nsource ~\u002Fminiforge3\u002Fetc\u002Fprofile.d\u002Fconda.sh\nconda --version\n```\n\nThe `-b` flag installs non-interactively without modifying `.bashrc`. Users must `source ~\u002Fminiforge3\u002Fetc\u002Fprofile.d\u002Fconda.sh` in each new shell (or add it to their shell RC file) to make `conda` available.\n\n## Step 2: Create Environment and Install\n\n### Package roles\n\n- `libholoscan` — C++ runtime symbols (`libholoscan_core.so`). Auto-pulled as a dependency.\n- `holoscan` — Python bindings.\n- `libholoscan-dev` — C++ headers, `libholoscan_core.so` symlink, and `holoscan-config.cmake` for `find_package(holoscan)`.\n- `rmm` — RAPIDS Memory Manager (rapidsai channel). Undeclared runtime dep of `holoscan`; `import holoscan` fails without it.\n- `ucxx` — UCX Python bindings (rapidsai channel), needed for distributed\u002Fmulti-process apps.\n- `cuda-version=13` — pins the CUDA 13 metapackage so the solver picks compatible CUDA runtime libs.\n\nCreate the environment first:\n\n```bash\nsource ~\u002Fminiforge3\u002Fetc\u002Fprofile.d\u002Fconda.sh   # if conda not yet on PATH\nconda create -n holoscan python=3.13 -y\nconda activate holoscan\n```\n\nThen pick one of the variants below based on the user's goal.\n\nPick the packages for the user's goal — Python-only needs `holoscan`, C++ dev needs `libholoscan-dev`, both works for combined use:\n\n```bash\nconda install \u003Cpackages> rmm ucxx cuda-version=13 -c rapidsai -c conda-forge -y\n```\n\nFor C++ development, also install the toolchain:\n\n```bash\nconda install -c conda-forge cxx-compiler cmake ninja -y\n```\n\nVerify Python installs with `python3 -c \"import holoscan; print(holoscan.__version__)\"`. Verify C++ dev installs with `ls \"$CONDA_PREFIX\u002Finclude\u002Fholoscan\"`.\n\n## Step 3: Run Python Tests\n\n`ulimit -s 32768` is recommended — without it, some Holoscan apps may segfault on startup.\n\n`video_replayer` is a display app that loops forever by default. Always patch its YAML to\nstop after 10 frames (`count: 10`, `repeat: false`, `realtime: false`) and to run headless\n(`headless: true`) — headless works with or without a display attached and avoids GUI\nfailure modes over SSH, so we don't branch on `$DISPLAY`.\n\nDownload scripts and YAML configs, patch the YAML, then run:\n\n```bash\nsource ~\u002Fminiforge3\u002Fetc\u002Fprofile.d\u002Fconda.sh\nconda activate holoscan\nulimit -s 32768\n\nSDK_VER=$(python3 -c \"import holoscan; print(holoscan.__version__)\")\nBASE=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fnvidia-holoscan\u002Fholoscan-sdk\u002Fv${SDK_VER}\u002Fexamples\"\n\ncurl -fsSL \"${BASE}\u002Fhello_world\u002Fpython\u002Fhello_world.py\"         -o \u002Ftmp\u002Fhs_hello_world.py\ncurl -fsSL \"${BASE}\u002Fvideo_replayer\u002Fpython\u002Fvideo_replayer.py\"   -o \u002Ftmp\u002Fhs_video_replayer.py\ncurl -fsSL \"${BASE}\u002Fvideo_replayer\u002Fpython\u002Fvideo_replayer.yaml\" -o \u002Ftmp\u002Fvideo_replayer.yaml\n\n# Patch video_replayer.yaml — 10 frames, headless.\npython3 -c \"\nc = open('\u002Ftmp\u002Fvideo_replayer.yaml').read()\nc = c.replace('count: 0', 'count: 10')\nc = c.replace('repeat: true', 'repeat: false')\nc = c.replace('realtime: true', 'realtime: false')\nc = c.replace('  width: 854', '  headless: true\\n  width: 854')\nopen('\u002Ftmp\u002Fvideo_replayer.yaml', 'w').write(c)\"\n\n# hello_world — no display, no data needed; expected: \"Hello World!\"\npython3 \u002Ftmp\u002Fhs_hello_world.py\n\n# video_replayer — needs racerx data; expected: frames rendered, \"Graph execution finished.\"\nHOLOSCAN_INPUT_PATH=\u002Fpath\u002Fto\u002Fholoscan\u002Fdata python3 \u002Ftmp\u002Fhs_video_replayer.py\n```\n\n`HOLOSCAN_INPUT_PATH` must point to the directory containing a `racerx\u002F` subdirectory.\nIf the user has the SDK source repo that is `~\u002Frepos\u002Fholoscan-sdk\u002Fdata`; otherwise download\nwith the `download_ngc_data` script from the Debian or source install tree.\n\n## Step 4: Remind the User\n\nThey must do the following in each new shell session:\n\n```bash\nsource ~\u002Fminiforge3\u002Fetc\u002Fprofile.d\u002Fconda.sh   # if Miniforge was installed with -b\nconda activate holoscan\nulimit -s 32768   # recommended — prevents segfaults in some apps\n```\n\nConsider adding these lines to `~\u002F.bashrc` or `~\u002F.zshrc` to avoid repeating them.\n\nThen offer next steps:\n- Explore C++ and 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- **`ImportError: librmm.so: cannot open shared object file`.** `rmm` was not installed. 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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},[1801,1804],{"name":1802,"slug":1803,"type":15},"CI\u002FCD","ci-cd",{"name":17,"slug":18,"type":15},"2026-07-14T05:25:59.97109",{"slug":1807,"name":1807,"fn":1808,"description":1809,"org":1810,"tags":1811,"stars":1792,"repoUrl":1793,"updatedAt":1817},"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},[1812,1813,1814],{"name":1802,"slug":1803,"type":15},{"name":17,"slug":18,"type":15},{"name":1815,"slug":1816,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1819,"name":1819,"fn":1820,"description":1821,"org":1822,"tags":1823,"stars":1792,"repoUrl":1793,"updatedAt":1831},"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},[1824,1827,1828],{"name":1825,"slug":1826,"type":15},"Debugging","debugging",{"name":1815,"slug":1816,"type":15},{"name":1829,"slug":1830,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1833,"name":1833,"fn":1834,"description":1835,"org":1836,"tags":1837,"stars":1792,"repoUrl":1793,"updatedAt":1844},"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},[1838,1841],{"name":1839,"slug":1840,"type":15},"Best Practices","best-practices",{"name":1842,"slug":1843,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1846,"name":1846,"fn":1847,"description":1848,"org":1849,"tags":1850,"stars":1792,"repoUrl":1793,"updatedAt":1858},"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},[1851,1854,1857],{"name":1852,"slug":1853,"type":15},"Machine Learning","machine-learning",{"name":1855,"slug":1856,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1860,"name":1860,"fn":1861,"description":1862,"org":1863,"tags":1864,"stars":1792,"repoUrl":1793,"updatedAt":1871},"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},[1865,1868],{"name":1866,"slug":1867,"type":15},"QA","qa",{"name":1869,"slug":1870,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":1873,"name":1873,"fn":1874,"description":1875,"org":1876,"tags":1877,"stars":1792,"repoUrl":1793,"updatedAt":1882},"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},[1878,1879],{"name":17,"slug":18,"type":15},{"name":1880,"slug":1881,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":1884,"name":1884,"fn":1885,"description":1886,"org":1887,"tags":1888,"stars":1792,"repoUrl":1793,"updatedAt":1896},"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},[1889,1892,1893],{"name":1890,"slug":1891,"type":15},"Code Review","code-review",{"name":1815,"slug":1816,"type":15},{"name":1894,"slug":1895,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1898,"name":1898,"fn":1899,"description":1900,"org":1901,"tags":1902,"stars":1792,"repoUrl":1793,"updatedAt":1905},"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},[1903,1904],{"name":1866,"slug":1867,"type":15},{"name":1869,"slug":1870,"type":15},"2026-07-14T05:25:54.928983",{"slug":1907,"name":1907,"fn":1908,"description":1909,"org":1910,"tags":1911,"stars":1792,"repoUrl":1793,"updatedAt":1916},"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},[1912,1915],{"name":1913,"slug":1914,"type":15},"Automation","automation",{"name":1802,"slug":1803,"type":15},"2026-07-30T05:29:03.275638",496,{"items":1919,"total":2015},[1920,1937,1947,1961,1971,1986,2001],{"slug":1921,"name":1921,"fn":1922,"description":1923,"org":1924,"tags":1925,"stars":20,"repoUrl":21,"updatedAt":1936},"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},[1926,1929,1932,1933],{"name":1927,"slug":1928,"type":15},"Data Analysis","data-analysis",{"name":1930,"slug":1931,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1934,"slug":1935,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1938,"name":1938,"fn":1939,"description":1940,"org":1941,"tags":1942,"stars":20,"repoUrl":21,"updatedAt":1946},"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},[1943,1944,1945],{"name":17,"slug":18,"type":15},{"name":1880,"slug":1881,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1948,"name":1948,"fn":1949,"description":1950,"org":1951,"tags":1952,"stars":20,"repoUrl":21,"updatedAt":1960},"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},[1953,1956,1957],{"name":1954,"slug":1955,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1958,"slug":1959,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1962,"name":1962,"fn":1963,"description":1964,"org":1965,"tags":1966,"stars":20,"repoUrl":21,"updatedAt":1970},"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},[1967,1968,1969],{"name":1927,"slug":1928,"type":15},{"name":9,"slug":8,"type":15},{"name":1869,"slug":1870,"type":15},"2026-07-17T05:29:03.913266",{"slug":1972,"name":1972,"fn":1973,"description":1974,"org":1975,"tags":1976,"stars":20,"repoUrl":21,"updatedAt":1985},"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},[1977,1978,1981,1982],{"name":1913,"slug":1914,"type":15},{"name":1979,"slug":1980,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1983,"slug":1984,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1987,"name":1987,"fn":1988,"description":1989,"org":1990,"tags":1991,"stars":20,"repoUrl":21,"updatedAt":2000},"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},[1992,1993,1996,1997],{"name":17,"slug":18,"type":15},{"name":1994,"slug":1995,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1998,"slug":1999,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":2002,"name":2002,"fn":2003,"description":2004,"org":2005,"tags":2006,"stars":20,"repoUrl":21,"updatedAt":2014},"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},[2007,2008,2011],{"name":9,"slug":8,"type":15},{"name":2009,"slug":2010,"type":15},"Quantum Computing","quantum-computing",{"name":2012,"slug":2013,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]