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://mp-staging.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
BENCHMARK.md
Skill Benchmark: holoscan-install-wheel
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
holoscan-install-wheelEvaluation date: 2026-10-15
Evaluator version:
1.5.6Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)Tasks: 6 evaluation tasks (5 positive, 1 negative)
Dataset digest:
sha256:18935ebfa4d03f6845b5bc03c7446f438da336bc4a6046bfe090eb4e18405337(skill-evaluator-dataset-snapshot/1)Attempts per task: 1
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
What This Report Answers
The three-tier evaluation checks whether the skill:
is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.
Results at a Glance
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 95.4% — baseline ran, but no comparable score was available; uplift unavailable | 95.3% — baseline ran, but no comparable score was available; 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% — baseline ran, but no comparable score was available; uplift unavailable | 95.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 72.1% → 95.0% (+22.9 points) | 75.0% → 92.5% (+17.5 points) |
| Efficiency | 98.6% — baseline ran, but no comparable score was available; uplift unavailable | 97.3% — baseline ran, but no comparable score was available; uplift unavailable |
How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 889,856 | 1,281,266 | -391,410 | -30.55% | skill 6/6; base 6/6 |
| claude-code | holoscan-install-wheel-5x-001 | 63,306 | 151,311 | -88,005 | -58.16% | skill 1/1; base 1/1 |
| claude-code | holoscan-install-wheel-5x-002 | 60,471 | 290,619 | -230,148 | -79.19% | skill 1/1; base 1/1 |
| claude-code | holoscan-install-wheel-5x-003 | 60,378 | 186,965 | -126,587 | -67.71% | skill 1/1; base 1/1 |
| claude-code | holoscan-install-wheel-5x-004 | 62,185 | 312,173 | -249,988 | -80.08% | skill 1/1; base 1/1 |
| claude-code | holoscan-install-wheel-5x-005 | 583,114 | 218,130 | +364,984 | +167.32% | skill 1/1; base 1/1 |
| claude-code | holoscan-install-wheel-5x-006 | 60,402 | 122,068 | -61,666 | -50.52% | skill 1/1; base 1/1 |
| codex | All cases | 452,778 | 340,146 | +112,632 | +33.11% | skill 6/6; base 6/6 |
| codex | holoscan-install-wheel-5x-001 | 29,391 | 14,739 | +14,652 | +99.41% | skill 1/1; base 1/1 |
| codex | holoscan-install-wheel-5x-002 | 29,498 | 14,092 | +15,406 | +109.32% | skill 1/1; base 1/1 |
| codex | holoscan-install-wheel-5x-003 | 29,193 | 14,060 | +15,133 | +107.63% | skill 1/1; base 1/1 |
| codex | holoscan-install-wheel-5x-004 | 29,294 | 14,112 | +15,182 | +107.58% | skill 1/1; base 1/1 |
| codex | holoscan-install-wheel-5x-005 | 306,532 | 268,937 | +37,595 | +13.98% | skill 1/1; base 1/1 |
| codex | holoscan-install-wheel-5x-006 | 28,870 | 14,206 | +14,664 | +103.22% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 1,342,634 | 1,621,412 | -278,778 | -17.19% | skill 12/12; base 12/12 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 7 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 6 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (
team-skills/holoscan/holoscan-sdk/holoscan-install-wheel/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (
team-skills/holoscan/holoscan-sdk/holoscan-install-wheel/SKILL.md)LOW QUALITY/quality_discoverability: Description doesn't mention WHEN to use this skill (
team-skills/holoscan/holoscan-sdk/holoscan-install-wheel/SKILL.md)LOW QUALITY/quality_discoverability: No '## Purpose' section (
team-skills/holoscan/holoscan-sdk/holoscan-install-wheel/SKILL.md)LOW QUALITY/quality_reliability: No prerequisites/requirements documented (
team-skills/holoscan/holoscan-sdk/holoscan-install-wheel/SKILL.md)2 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did it avoid wasted tool calls and token usage? | skill_efficiency (50%) + token_efficiency (50%) |
Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (
goal_accuracy) and expected workflow adherence (behavior_check).Efficiency is 50% tool-call productivity (the backward-compatible
skill_efficiencywire id) and 50%token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.skill_efficiency(Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).accuracy(Accuracy): final-answer correctness against the reference answer.goal_accuracy(Goal Accuracy): whether the user's goal was achieved.behavior_check(Behavior Check): whether the expected workflow behavior was followed.token_efficiency(Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
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.
Related skills
Implement
Matt Pocock· Community
A structured workflow for implementing software tasks from specifications or tickets.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository317.5k
Source Driven Development
Addy Osmani· Community
A rigorous process for grounding code implementation in official, version-specific documentation to ensure accuracy and avoid outdated patterns.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository113.9k
Write Swift
Emil Kowalski· Community
A comprehensive guide to writing modern, safe, and performant Swift code following the language's latest idioms and concurrency models.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository54.3k
Cardputer Buddy
AnthropicOfficial
Iterate on the Cardputer-Adv MicroPython app bundle (Claude Buddy, Snake, Hello) after the device is already provisioned via m5-onboard. Use when the user wants to add a new app, push a single changed .py without re-flashing, watch device serial logs, or run a one-shot REPL command. Trigger on "add an app", "push to the cardputer", "tail the device", "run on the device", or follow-up work after /maker-setup.
Software developmentClaude CodeClaude appsOpenAI Codex+14 moreApache-2.0GitHub stars of the source repository46.3k
Typescript Pro
Seth Hobson· Community
An expert TypeScript persona focused on advanced typing, enterprise architecture, and strict type safety.
Software developmentClaude CodeMITGitHub stars of the source repository42.3k
Scala Pro
Seth Hobson· Community
An expert Scala engineer persona specializing in functional programming, distributed systems, and enterprise-grade architecture.
Software developmentClaude CodeMITGitHub stars of the source repository42.3k
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download holoscan-install-wheel.zip