Holoscan SDK — Build from Source
SkillDev toolsLets your agent build the Holoscan SDK from source code using a run script when prebuilt packages won't work.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Holoscan SDK — Build from Source skill
About this capability
Build Holoscan SDK from source via the in-tree ./run script. Use only when published packages don't meet the user's needs.
What this skill tells your AI
The instructions your AI receives, as published by nvidia/skills in skills/holoscan-install-source/SKILL.md and read by ahel’s review.
Purpose
Build the Holoscan SDK from the nvidia-holoscan/holoscan-sdk source tree using its ./run script (which builds inside a Docker container), producing a local install tree consumable as a CMake dependency.
Prerequisites
- Linux host with NVIDIA GPU + driver (
nvidia-smi). git, Docker with NVIDIA Container Toolkit (docker run --gpus allworks), anddocker-buildx-plugin.- ~20 GB free disk for the build container + build/install trees.
- 10–30 min for a clean first build.
Limitations
- Only recommended when published packages (Conda / container / apt / wheel) don't fit — debug symbols, custom CMake options, or unsupported configs.
- Still requires Docker — the
./runscript builds inside a container; this is not a true bare-metal build. - Cross-compiling to aarch64 needs
qemu-user-staticon the host.
Step 0: Consult the Official Install Instructions
Always fetch the "Build from Source" section of https://docs.nvidia.com/holoscan/sdk-user-guide/sdk_installation.html (and the linked GitHub README.md / DEVELOP.md for the chosen tag) before building. Extract: required ./run flags for the target architecture and CUDA major, supported branches/tags, 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.
Step 1: Prerequisites
Check that git and Docker (with GPU passthrough) are available:
git --version
docker --version
docker run --rm --gpus all ubuntu:22.04 nvidia-smi
- If Docker is missing → help install from https://docs.docker.com/engine/install/
- If GPU passthrough fails → install NVIDIA Container Toolkit:
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \ | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \ | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker - If Docker buildx is missing:
sudo apt-get install docker-buildx-plugin
Step 2: Clone the Repository
Clone repo to ~/holoscan/holoscan-sdk if needed
mkdir -p ~/holoscan/
git clone https://github.com/nvidia-holoscan/holoscan-sdk.git
cd ~/holoscan/holoscan-sdk
To build a specific release tag (recommended for stability):
git tag | grep -E '^v[0-9]' | sort -V | tail -5 # list recent tags
git checkout v<VERSION> # e.g. v4.1.0
Step 3: Build
The ./run 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).
./run build
Common options:
| Flag | Purpose |
|---|---|
--type debug | Debug build (symbols, no optimization) |
--type RelWithDebInfo | Release + debug symbols |
--arch aarch64 | Cross-compile for ARM64 (needs sudo apt install qemu-user-static) |
--gpu igpu | iGPU build for Jetson/IGX |
--dryrun | Preview commands without executing |
If CMake cache errors occur after changing options:
./run clear_cache && ./run build
Output lands in these folders, and can be retrieved with ./run get_build_dir and ./run get_install_dir
- Build dir:
build-cu<N>-<arch>/ - Install dir:
install-cu<N>-<arch>/.
Step 4: Run Tests
Run the following tests
- 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
./run test
To 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):
./run 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"
Run a specific test by name or regex:
./run test --name <test_name>
./run test --options "-R '<regex>' --output-on-failure"
./run test --verbose
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.
Expected: all tests pass. Note any failures and report them to the user before continuing.
Step 5: Point Applications at the Install Tree
Once built, applications can use the install tree as a CMake dependency. Give the user this path:
/path/to/holoscan-sdk/install-cu<N>-<arch>/
They can set Holoscan_ROOT or CMAKE_PREFIX_PATH to this directory when building their own applications.
Troubleshooting
| Symptom | Fix |
|---|---|
bash: <TEST_NAME>: command not found when running tests | The regex contains | — wrap it in single quotes: --options "-R '<regex>'" |
| CMake cache errors after option change | ./run clear_cache && ./run build |
| Docker buildx not found | sudo apt-get install docker-buildx-plugin |
| GPU not visible inside build container | Verify NVIDIA Container Toolkit and re-run sudo nvidia-ctk runtime configure --runtime=docker |
| Cross-compile fails (aarch64) | Install qemu: sudo apt-get install qemu-user-static |
Signals
- GitHub stars
- 3k
- Forks
- 387
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
holoscan-install-source- Source
- github.com/nvidia/skills