Holoscan Install Wheel
NVIDIAOfficialCreated by Holoscan TeamDistributed by NVIDIA
- Skill
- Workflow
- Software development
- 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 installing Holoscan SDK 5.x Python wheels, specifically designed for CUDA 13 environments. It emphasizes prerequisite validation, including system architecture, glibc versions, and driver compatibility, to ensure a stable installation. The workflow covers selecting the appropriate distribution packages, managing isolated virtual environments, and performing post-installation verification to confirm the SDK is correctly configured for development.
What it does
- Validates system prerequisites including CUDA 13 driver requirements and glibc versions.
- Guides the selection of Holoscan SDK 5.x distribution packages such as core, vision, or inference modules.
- Provides a step-by-step process for setting up an isolated Python virtual environment.
- Executes dry-run and installation commands for specific SDK versions.
- Performs post-installation verification checks including dependency validation and basic import tests.
- Outlines the process for running sample pipelines to confirm runtime functionality.
When to use it
- When setting up a new development environment for Holoscan SDK 5.x.
- When upgrading or installing specific Holoscan components in a clean Python environment.
- When troubleshooting installation issues related to CUDA 13 or library dependencies.
- When verifying that a Holoscan installation is correctly configured for Python-based applications.
Advantages
- Ensures environment isolation by mandating the use of virtual environments.
- Reduces installation errors through explicit prerequisite and compatibility checks.
- Provides a clear path for verifying both core and optional SDK modules.
- Standardizes the installation process for consistent development setups.
Usage examples
- “Install the full Holoscan 5.x SDK stack in a new virtual environment.”
- The agent will guide you through creating a venv, checking for CUDA 13 compatibility, and installing the holoscan package using pip.
- “How do I verify my Holoscan 5.x installation?”
- The agent will provide commands to run pip check, list installed packages, and execute a Python script to import the holoscan module and verify its version.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/holoscan-install-wheel/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip holoscan-install-wheel.zip -d .agents/skills
Documentation by Holoscan Team
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.
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:
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.
Files
- ATTRIBUTION.mdAdded by AutoGineer931 B
- BENCHMARK.md8.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.0 KiB
- evals/evals.json6.5 KiB
- skill-card.md4.7 KiB
- skill.oms.sig4.5 KiB
skill-card.md
Description: <br>
Install Holoscan SDK 5.x CUDA 13 Python wheels in an isolated environment, choosing the full stack or version-matched components. <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 install Holoscan SDK 5.x CUDA 13 Python wheels in a virtual environment on x86_64 or arm64 dGPU Linux hosts, checking Python, glibc, driver, and release-availability prerequisites, selecting the full holoscan metapackage or holoscan-core-cu13 with version-matched optional modules, and verifying the install with dependency, import, and bounded example 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>
6 evaluation tasks (5 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>token_efficiency: Actual uncached prompt plus completion token usage. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 95.4% — no comparable baseline score; uplift unavailable | 95.3% — no comparable baseline score; uplift unavailable |
| Security | 91.7% → 83.3% (-8.4 points) | 83.3% → 91.7% (+8.4 points) |
| Correctness | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Discoverability | 100.0% — no comparable baseline score; uplift unavailable | 95.0% — no comparable baseline score; uplift unavailable |
| Effectiveness | 72.1% → 95.0% (+22.9 points) | 75.0% → 92.5% (+17.5 points) |
| Efficiency | 98.6% — no comparable baseline score; uplift unavailable | 97.3% — 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 Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
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-wheel · 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-wheel.zip