Holoscan Install Container
NVIDIAOfficialCreated by Holoscan TeamDistributed by NVIDIA
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured procedure for setting up the Holoscan SDK 5.x development environment using Docker. It guides the user through verifying host compatibility, selecting the correct container image for CUDA 13, pulling the image, and performing verification tests to ensure the SDK and its components are correctly installed and functional.
What it does
- Verifies host system compatibility and driver requirements for Holoscan SDK 5.x.
- Identifies the correct multi-architecture container image for CUDA 13.
- Provides commands to pull and inspect the official Holoscan development image.
- Executes verification tests for C++ and Python SDK components.
- Validates core functionality using provided example pipelines.
- Reports final installation status, including image digests and test results.
When to use it
- When setting up a new development environment for Holoscan SDK 5.x.
- When troubleshooting containerized Holoscan installations.
- When verifying that a host system meets the requirements for CUDA 13-based Holoscan development.
- When needing to confirm the integrity of a pulled Holoscan development image.
Advantages
- Ensures compatibility by checking host drivers and architecture before installation.
- Standardizes the verification process for both C++ and Python APIs.
- Reduces configuration errors by using official container images.
- Provides a clear reporting structure for installation success or failure.
Usage examples
- “Install the Holoscan 5.0 development container on my system.”
- The agent will check your host compatibility, pull the specific v5.0-cuda13 image, and run verification tests to confirm the SDK is ready for use.
- “Verify if my current Holoscan container is correctly installed.”
- The agent will execute the verification workflow, checking for the presence of core examples and Python API accessibility to confirm the installation.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/holoscan-install-container/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip holoscan-install-container.zip -d .agents/skills
Documentation by Holoscan Team
Holoscan 5.x Development Container
1. Check the release and platform
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.
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.
2. Select and pull the image
Inspect Linux architecture, GPU model, driver, Docker, and NVIDIA Container
Toolkit. The planned image is one multi-architecture amd64/arm64 dGPU image:
nvcr.io/nvidia/clara-holoscan/holoscan:v<SDK_VERSION>-cuda13.
Check the NGC catalog
and release notes for the exact published tag and supported host. There are no
cuda12-dgpu or cuda12-igpu tags for this 5.x workflow. GPU passthrough tests
may pull an image; explain the download before running one if needed.
Use the exact published tag within the installation scope the user authorized. When the method and release are already selected, proceed with that choice. Ask only when a missing release or host fact prevents safe selection. For a plan-only request, return the commands without pulling or running images:
# SDK_VERSION is the exact published 5.x tag version from the release notes.
IMAGE="nvcr.io/nvidia/clara-holoscan/holoscan:v${SDK_VERSION}-cuda13"
docker pull "$IMAGE"
docker image inspect --format 'ID={{.Id}} RepoDigests={{json .RepoDigests}}' "$IMAGE"
docker run --rm --gpus all "$IMAGE" nvidia-smiRecord the image ID and repository digests from inspection for the final report. If no repository digest is available, report the image ID and that limitation.
The development image contains the installed C++ and Python SDK under /usr,
all three optional modules, examples, and RacerX data. It is distinct from the
source-build container managed by ./run.
3. Verify the installed SDK
Use a bounded Core example and Python API discovery first. Locate the examples
instead of assuming the 4.x /opt/nvidia/holoscan layout or a fixed multiarch
library directory:
docker run --rm --gpus all "$IMAGE" bash -eu -c '
example=$(find /usr/lib -type f -path "*/holoscan/examples/hello_world/cpp/hello_world" -print -quit)
test -n "$example"
"$example"
python3 -c "import holoscan as hs; print(hs.__version__); print(hs.RealtimeClock); print(len(hs.registry.operators()))"
'Expect Hello Holoscan!, the requested 5.x version, and successful API access.
Run the installed first_pipeline/python/first_pipeline.py example when present
for graph execution coverage. Check the release's installed example tree and
README before adding module/GPU tests; a Core import alone does not qualify
Vision, Visualization, Inference, graphics, or camera support.
4. Report results and reuse the image
docker run --rm -it --gpus all "$IMAGE" bashAdd mounts for the user's project or devices as the application needs them. For GUI workloads follow the selected release's display/graphics instructions; keep initial verification independent of display and datasets.
GPU passthrough failure: check the host driver and Container Toolkit setup.
Missing tag: report publication status and offer source; keep the 5.x target.
Missing examples or Python components: inspect the pulled image tag/digest; verify it is the development image for the selected release.
Report the image tag/digest, architecture, SDK version, tests run, and skipped hardware or module checks.
Files
- ATTRIBUTION.mdAdded by AutoGineer943 B
- BENCHMARK.md7.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.8 KiB
- evals/evals.json4.0 KiB
- skill-card.md4.7 KiB
- skill.oms.sig4.5 KiB
skill-card.md
Description: <br>
Install and verify the Holoscan SDK 5.x CUDA 13 development container after checking release availability and host compatibility. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache 2.0 <br>
Use Case: <br>
Developers and engineers use this skill to select, pull, and verify the published Holoscan SDK 5.x CUDA 13 development container on a compatible x86_64 or arm64 dGPU host, including driver and NVIDIA Container Toolkit checks, Core and Python smoke tests, and a report of the image digest and skipped checks. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
Known Risks and Mitigations: <br>
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>
Reference(s): <br>
Skill Output: <br>
Output Type(s): [Shell commands, Configuration instructions, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>
Evaluation Agents Used: <br>
Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) <br>Codex (
openai/openai/gpt-5.5) <br>
Evaluation Tasks: <br>
4 evaluation tasks (3 positive, 1 negative), 1 attempt per task, each run in an isolated sandbox pod (k8s-sandbox), compared against a no-skill baseline. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Security: Is it safe to use? Scored from the
securitysignal. <br>Correctness: Is the answer correct? Scored from the
accuracysignal. <br>Discoverability: Was the right skill loaded when needed? Scored from the
skill_executionsignal. <br>Effectiveness: Did the skill help complete the task? Equal-weight mean of
goal_accuracyandbehavior_check. <br>Efficiency: Did it avoid wasted tool calls and token usage? Equal-weight mean of
skill_efficiencyandtoken_efficiency. <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>skill_efficiency: Tool-call productivity. <br>accuracy: Final-answer correctness against the reference answer. <br>goal_accuracy: Whether the user's goal was achieved. <br>behavior_check: Whether the expected workflow behavior was followed. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 93.3% — no comparable baseline score; uplift unavailable | 94.6% — no comparable baseline score; uplift unavailable |
| Security | 75.0% → 75.0% (±0.0 points) | 75.0% → 87.5% (+12.5 points) |
| Correctness | 75.0% → 100.0% (+25.0 points) | 90.0% → 100.0% (+10.0 points) |
| Discoverability | 100.0% — no comparable baseline score; uplift unavailable | 95.0% — no comparable baseline score; uplift unavailable |
| Effectiveness | 73.1% → 91.3% (+18.2 points) | 71.5% → 90.4% (+18.9 points) |
| Efficiency | 100.0% — no comparable baseline score; uplift unavailable | 100.0% — no comparable baseline score; uplift unavailable |
Overall verdict: PASS (Tier 1 static validation passed with observations; Tier 2 semantic deduplication passed; Tier 3 live agent evaluation passed).
Skill Version(s): <br>
2.0.0 (source: frontmatter) <br>
Ethical Considerations: <br>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>
(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool, Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Holoscan Team
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/holoscan-install-container · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Holoscan Team or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download holoscan-install-container.zip