holoscan-install-wheel — Claude Code skill: install Holoscan 5.x
Installs Holoscan SDK 5.x CUDA 13 Python wheels into an isolated venv and checks that the import works.
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a claude-codeWhat holoscan-install-wheel does
The skill walks an agent through a Holoscan SDK 5.x install. It picks a specific 5.x release first, reads that release's notes and the versioned SDK installation guide, then inspects the host: python3 --version, getconf GNU_LIBC_VERSION, the Linux architecture and the CUDA 13 driver. You need regular CPython 3.12–3.14, glibc 2.39 or newer, and x86_64 or arm64.
Then comes the package choice. holoscan pulls the full stack through holoscan-cu13; holoscan-core-cu13 is core only, and optional modules ship as holoscan-vision-cu13, holoscan-visualization-cu13 and holoscan-inference-cu13. Every component has to sit on the same SDK version. Installation runs in a fresh venv: pip index versions, a --dry-run, the real install, pip check, pip list --format=freeze, and an import that prints the version and the operator count from the registry.
Here is where it usually breaks. The CUDA field in nvidia-smi reports driver capability, not the installed toolkit, so a driver that advertises CUDA 12 needs a compatibility assessment or an upgrade before a CUDA 13 install. The wheels carry Python bindings and native runtime libraries but no C++ headers, CMake files or examples, so native C++ work means Debian packages or a source build. If any command fails, the skill stops and reports the output instead of pushing on.
What it helps with
- install Holoscan SDK 5.x into an isolated venv without touching system Python
- pick between the full stack and single modules like vision or inference
- check CPython, glibc and the CUDA 13 driver before installing
- keep every component on the same SDK version
- run an import and dependency check after install
Example prompts for the agent
- install Holoscan SDK 5.x in a separate environment
- check whether my driver and glibc work for the Holoscan 5.x wheels
- set up a venv with holoscan-core-cu13 and verify the import
How to install holoscan-install-wheel
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a claude-code2Manually: put the files in place
~/.claude/skills/holoscan-install-wheel/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/holoscan-install-wheel/
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a codex2Manually: put the files in place
.agents/skills/holoscan-install-wheel/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a cursor2Manually: put the files in place
.cursor/skills/holoscan-install-wheel/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install NVIDIA/skills holoscan-install-wheel2Manually: put the files in place
.github/skills/holoscan-install-wheel/SKILL.md1Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a gemini-cli2Platform CLI
gemini skills install https://github.com/NVIDIA/skills --path skills/holoscan-install-wheelManually: put the files in place
.devin/skills/holoscan-install-wheel/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add NVIDIA/skills --skill holoscan-install-wheel -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about holoscan-install-wheel
How do I install holoscan-install-wheel in Claude?
Run npx skills add NVIDIA/skills --skill holoscan-install-wheel -a claude-code. The skill lands in ~/.claude/skills/holoscan-install-wheel/SKILL.md.
What does holoscan-install-wheel do?
It drives a Holoscan SDK 5.x CUDA 13 wheel install: host checks, package selection, a venv install, then an import and dependency check.
Why doesn't a CUDA 12 driver work for this install?
The 5.0 artifact set targets CUDA 13 and there is no CUDA 12 variant. The CUDA field in nvidia-smi describes driver capability rather than the toolkit, so compatibility needs a separate assessment or a driver upgrade.
Source text
SKILL.md · Apache-2.0
--- name: holoscan-install-wheel description: "Install Holoscan SDK 5.x CUDA 13 Python wheels in an isolated environment, choosing the full stack or version-matched components." license: Apache-2.0 metadata: author: "Holoscan Team <[email protected]>" version: "2.0.0" github-url: "https://github.com/nvidia-holoscan/holoscan-sdk" tags: - holoscan - installation - wheel --- # Holoscan 5.x Python Wheel Installation ## Release and platform checks This skill targets **Holoscan SDK 5.x**. Select the requested 5.x release before choosing artifacts. Read its release notes and the matching [SDK installation guide](https://docs.nvidia.com/holoscan/sdk-user-guide/setup/sdk-installation). Select the 5.x documentation version; an unversioned page may describe 4.x. For an unreleased checkout, use its `docs/sdk_installation.mdx` and `DEVELOP.md`. Early Access binary commands describe planned artifacts, so confirm publication for the exact release before installing. If unavailable, explain that status and offer `holoscan-install-source`; keep the requested 5.x version. The 5.0 artifact set targets CUDA 13 and dGPU systems on x86_64 or arm64. Native packages use Ubuntu 24.04; wheels use glibc 2.39 and regular CPython 3.12–3.14. Check the selected release for changes to that matrix. There is no CUDA 12 variant or separate Tegra/iGPU artifact in this set. A driver reporting CUDA 12 support needs a compatibility assessment or driver upgrade before a CUDA 13 installation; the `nvidia-smi` CUDA field is a driver capability, not the installed toolkit version. Never infer compatibility from that field alone. ## Choose a distribution Wheels contain Python bindings, typing information, and native runtime libraries. C++ headers, CMake files, examples, and sample data are separate. For native C++ development use Debian packages or a source build. | Goal | Distribution | | --- | --- | | Full stack | `holoscan` (selects `holoscan-cu13`) | | Core only | `holoscan-core-cu13` | | Optional modules | `holoscan-vision-cu13`, `holoscan-visualization-cu13`, `holoscan-inference-cu13` | Inspect the host, `python3 --version`, and `getconf GNU_LIBC_VERSION`. Confirm regular CPython 3.12–3.14, glibc 2.39 or newer, a compatible Linux architecture, and the CUDA 13 driver/runtime prerequisites. An active Conda environment does not imply a supported Conda package. Prefer a separate venv for this workflow. If these prerequisites are unmet, report the failed check and stop before installation. Compare the driver version against the selected release's documented CUDA 13 driver requirement; a toolkit version alone is insufficient. Confirm the exact published 5.x distribution version on PyPI or the selected release's documented index; reserved package names are not installable SDKs. If absent, offer source instead of resolving an unpinned package to 4.x. Keep all components on the same SDK version. ## Install and verify If an installation or verification command fails, stop and report the failing command and output. Do not continue to the example or report success after a failed prerequisite, package-index, dry-run, install, dependency, or import check. Choose a fresh venv path, or reuse an environment the user selected after checking its contents. Explain the chosen packages before installing: ```bash python3 -m venv ~/holoscan/venv-5x source ~/holoscan/venv-5x/bin/activate # SDK_VERSION is the exact published Python distribution version for 5.x. python -m pip index versions holoscan python -m pip install --dry-run "holoscan==${SDK_VERSION}" python -m pip install "holoscan==${SDK_VERSION}" python -m pip check python -m pip list --format=freeze python -c 'import holoscan as hs; print(hs.__version__); print(hs.RealtimeClock); print(len(hs.registry.operators()))' ``` For Core-only use `holoscan-core-cu13` in the index, dry-run, and install commands. Add optional component wheels at the exact matching SDK version. Vision resolves CV-CUDA and NPP Python dependencies. The host supplies the driver and CUDA 13 runtime; Inference expects TensorRT 10 in the application environment. Verify optional providers separately when selected: `holoscan_vision`, `holoscan_visualization`, and `holoscan_inference`. For execution coverage, obtain `examples/first_pipeline/python/first_pipeline.py` from the source ref corresponding to this release. Inspect it before execution and run it in the venv; it is bounded and needs no RacerX data or display. Prefer an existing matching checkout. Do not construct a Git tag from a Python version string: Early Access packaging versions and source refs can differ. The 4.x `hello_world.py`, `tensor_interop`, and `video_replayer` smoke sequence does not describe the 5.x Python examples. Report the Python, SDK, and component versions; import, dependency, and graph checks; and any skipped module tests. An import check alone is not full runtime qualification. Missing runtime libraries should be traced to the selected component's prerequisites before changing unrelated system paths.
© Holoscan Team · Apache-2.0 · NVIDIA/skills