holoscan-setup

Holoscan 5.x Setup

Establish the target

Determine the requested 5.x release or source branch and whether the user needs C++, Python, specific modules, or SDK development. Read the release notes and SDK installation guide with the matching 5.x version selected. An unversioned page may describe 4.x; for a development checkout use its docs/sdk_installation.mdx and DEVELOP.md. Treat Early Access binary methods as tentative until the exact artifacts are confirmed published. Package-name registration alone is not availability.

Inspect and assess

Use read-only checks on the intended Linux host:

uname -m
cat /etc/os-release
nvidia-smi
python3 --version
getconf GNU_LIBC_VERSION
docker --version
docker info

Report unavailable tools separately from incompatible hardware. Inspect an existing venv or SDK checkout only at user-selected or relevant known paths. Use python -m pip list in the intended environment and dpkg-query -W 'holoscan*' on Debian systems to distinguish 4.x installations from usable 5.x ones. A GPU passthrough check that runs/pulls an image is a separate execution step; a Docker version check alone does not establish passthrough.

The 5.0 baseline is CUDA 13 with dGPU support, Ubuntu 24.04 native packages, and x86_64/SBSA arm64. Wheels require glibc 2.39 and regular CPython 3.12–3.14. The nvidia-smi CUDA value describes driver capability, not toolkit presence. Check driver/runtime compatibility for the selected artifact; a CUDA 12-only host cannot be routed to a nonexistent 5.x CUDA 12 package. ARM64 does not establish Tegra/iGPU support. Confirm hardware-specific support before offering installation on Jetson, IGX, DGX Spark, or Grace systems.

To check an already-pulled image, run the bundled helper from this skill's installation directory with the full confirmed 5.x tag, for example:

bash scripts/check_ngc_image.sh v5.0.0-cuda13

It inspects the local image only, makes no registry request, and does not prove publication, GPU passthrough, or runtime success. Report its result accordingly.

Recommend and route

Present the relevant choices with separate publication and host-readiness status:

Method	5.x workflow
Development container	Published CUDA 13 multiarch image; full C++ and Python stack under /usr
Debian	Published Ubuntu 24.04 C++ holoscan metapackage or Core/optional modules
Python wheels	Published holoscan metapackage or CUDA 13 component wheels in a venv
Source	Explicit 5.x ref; build container and checkout-specific options
Conda	Unavailable for 5.0; check the selected release's documentation for later 5.x support

Recommend source when the requested Early Access binaries are unavailable. Recommend the development container for a ready-to-use environment when the image and host support are confirmed; native C++ and Python needs can justify Debian or wheels. If the user already selected a supported method, honor that choice and proceed within their authorization. Otherwise ask one concise method-selection question before installation.

Use the corresponding holoscan-install-container, holoscan-install-debian, holoscan-install-wheel, or holoscan-install-source skill when available. Pass the release/source ref, host architecture, driver/runtime status, selected components, existing installation details, and verified artifact availability. If a companion skill is unavailable, consult the release instructions directly. For a Conda-specific request, holoscan-install-conda explains the limitation and alternatives; installing the 4.x Conda package does not meet a 5.x request.

Report the outcome

Report the selected SDK version/ref, method, component versions, and checks actually executed. Separate availability checks, imports, graph execution, and module/GPU qualification. Missing binaries should produce an actionable source alternative; missing host support should produce the prerequisite to resolve.