tokens&
For enterprises
Submit
Sign in
tokens&

Build better AI stacks, claim useful opportunities, and give AI infrastructure companies a source-labeled adoption readout they can trust.

For buildersFor enterprises

Product

  • For builders
  • Category rankings
  • Startup credits and perks
  • Agent Skills
  • Platform
  • Submit project, tool, product, or perk

Enterprise

  • Start free company workspace

Community

  • Community
  • Newsletter
  • Events
Xin

© 2026 tokensand, LLC. All rights reserved.

  • Terms
  • Privacy
  • Security
  • Data Processing
  • Status
Agent Skills/Holoscan Conda installation
NVIDIABackendSKILL.mdVerified source

Agent Skill

Holoscan Conda installation

Install and verify Holoscan SDK in a compatible CUDA 13 Conda environment with platform-specific guardrails.

Install this skillView repository

Vendor-authored source · Apache-2.0 / CC-BY-4.0 license.

Raw SKILL.mdInstall the Tokens& Agent Pack

Skill specification

Declared by NVIDIA in the package front matter. Trigger conditions are what the coding agent matches on before it loads the skill.

View package fields
Holoscan Conda installation SKILL.md front matter fields
Skill nameholoscan-install-conda
Trigger conditionsInstall Holoscan SDK v4.3+ via Conda in a CUDA 13 environment. Use for Conda installs; redirect CUDA 12 hosts to container/wheel.
Declared licenseApache-2.0
Version1.0.0

Install holoscan-install-conda

In a terminal with Node.js, npm and Git, run the command for your agent. The Skills CLI installs the complete package directory, including referenced files within it. Review its install prompt, then start a new agent session. A skill package does not set up an MCP server connection.

Claude Code

.claude/skills/holoscan-install-conda/SKILL.md

Project skills are committed with the repo. Use the user directory for a personal install across every project.

Project install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'claude-code'
Install for all projects instead

Personal install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'claude-code' --global

Codex

.agents/skills/holoscan-install-conda/SKILL.md

Codex reads `.agents/skills/` as its primary location, which is also the cross-platform default other clients honour.

Project install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'codex'
Install for all projects instead

Personal install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'codex' --global

Cursor

.agents/skills/holoscan-install-conda/SKILL.md

Cursor also loads `.agents/skills/`, `.claude/skills/`, and `.codex/skills/`, so one committed copy can serve several clients.

Project install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'cursor'
Install for all projects instead

Personal install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'cursor' --global

Gemini CLI

.agents/skills/holoscan-install-conda/SKILL.md

Gemini CLI reads `.agents/skills/` first when both directories exist.

Project install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'gemini-cli'
Install for all projects instead

Personal install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'gemini-cli' --global

GitHub Copilot

.agents/skills/holoscan-install-conda/SKILL.md

The Skills CLI uses the shared `.agents/skills/` directory for Copilot project installs.

Project install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'github-copilot'
Install for all projects instead

Personal install

npx skills add 'https://github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda' --skill 'holoscan-install-conda' --agent 'github-copilot' --global

SKILL.md

View raw source

Published by NVIDIA under Apache-2.0 / CC-BY-4.0. Rendered from the package in github.com/NVIDIA/skills/tree/main/skills/holoscan-install-conda.

Read full skill instructions

Holoscan Conda Installation

Purpose

Install the Holoscan SDK (Python runtime and/or C++ dev headers) into a Conda environment on Linux x86_64, using conda-forge + rapidsai with a correctly pinned CUDA metapackage.

Prerequisites

  • Linux x86_64 with an NVIDIA GPU and CUDA 13 driver (check nvidia-smi).
  • conda (Miniforge preferred). Step 1 installs it if missing.
  • Network access to conda-forge, rapidsai, and docs.nvidia.com.

Limitations

  • CUDA 13 only (since v4.3.0 — earlier releases were CUDA 12). If the user has a CUDA 12 driver, redirect to /holoscan-install-container or /holoscan-install-wheel instead.
  • Linux x86_64 only — no aarch64/iGPU support on conda-forge.
  • ulimit -s 32768 is recommended in every shell that runs Holoscan — without it, some apps may segfault.

Step 0: Consult the Official Install Instructions

Always fetch the current Conda section of https://docs.nvidia.com/holoscan/sdk-user-guide/sdk_installation.html before installing — package names, channel selection, and the runtime/dev split can change between releases. Specifically extract:

  • The exact runtime package name (e.g. holoscan for Python bindings).
  • The C++ dev package name and whether the user needs it. As of v4.1.0, libholoscan-dev is a separate package containing headers and CMake config — install it whenever the user wants to develop C++ apps. Without it, find_package(holoscan) fails and there are no headers to #include.
  • Supported Python versions for the current release (3.10–3.13 for v4.3).
  • The current cuda-version pin (v4.3 → 13).

rmm and ucxx are distributed via the rapidsai channel; holoscan, libholoscan, and libholoscan-dev come from conda-forge.

If the doc disagrees with anything below, the doc wins — update the install commands accordingly and tell the user.

Step 1: Prerequisites Check

conda --version 2>&1
nvidia-smi 2>&1 | head -5

If conda is not found, install Miniforge silently (preferred over Miniconda for conda-forge):

wget -q https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O /tmp/Miniforge3.sh
bash /tmp/Miniforge3.sh -b -p ~/miniforge3
source ~/miniforge3/etc/profile.d/conda.sh
conda --version

The -b flag installs non-interactively without modifying .bashrc. Users must source ~/miniforge3/etc/profile.d/conda.sh in each new shell (or add it to their shell RC file) to make conda available.

Step 2: Create Environment and Install

Package roles

  • libholoscan — C++ runtime symbols (libholoscan_core.so). Auto-pulled as a dependency.
  • holoscan — Python bindings.
  • libholoscan-dev — C++ headers, libholoscan_core.so symlink, and holoscan-config.cmake for find_package(holoscan).
  • rmm — RAPIDS Memory Manager (rapidsai channel). Undeclared runtime dep of holoscan; import holoscan fails without it.
  • ucxx — UCX Python bindings (rapidsai channel), needed for distributed/multi-process apps.
  • cuda-version=13 — pins the CUDA 13 metapackage so the solver picks compatible CUDA runtime libs.

Create the environment first:

source ~/miniforge3/etc/profile.d/conda.sh   # if conda not yet on PATH
conda create -n holoscan python=3.13 -y
conda activate holoscan

Then pick one of the variants below based on the user's goal.

Pick the packages for the user's goal — Python-only needs holoscan, C++ dev needs libholoscan-dev, both works for combined use:

conda install <packages> rmm ucxx cuda-version=13 -c rapidsai -c conda-forge -y

For C++ development, also install the toolchain:

conda install -c conda-forge cxx-compiler cmake ninja -y

Verify Python installs with python3 -c "import holoscan; print(holoscan.__version__)". Verify C++ dev installs with ls "$CONDA_PREFIX/include/holoscan".

Step 3: Run Python Tests

ulimit -s 32768 is recommended — without it, some Holoscan apps may segfault on startup.

video_replayer is a display app that loops forever by default. Always patch its YAML to stop after 10 frames (count: 10, repeat: false, realtime: false) and to run headless (headless: true) — headless works with or without a display attached and avoids GUI failure modes over SSH, so we don't branch on $DISPLAY.

Download scripts and YAML configs, patch the YAML, then run:

source ~/miniforge3/etc/profile.d/conda.sh
conda activate holoscan
ulimit -s 32768

SDK_VER=$(python3 -c "import holoscan; print(holoscan.__version__)")
BASE="https://raw.githubusercontent.com/nvidia-holoscan/holoscan-sdk/v${SDK_VER}/examples"

curl -fsSL "${BASE}/hello_world/python/hello_world.py"         -o /tmp/hs_hello_world.py
curl -fsSL "${BASE}/video_replayer/python/video_replayer.py"   -o /tmp/hs_video_replayer.py
curl -fsSL "${BASE}/video_replayer/python/video_replayer.yaml" -o /tmp/video_replayer.yaml

# Patch video_replayer.yaml — 10 frames, headless.
python3 -c "
c = open('/tmp/video_replayer.yaml').read()
c = c.replace('count: 0', 'count: 10')
c = c.replace('repeat: true', 'repeat: false')
c = c.replace('realtime: true', 'realtime: false')
c = c.replace('  width: 854', '  headless: true\n  width: 854')
open('/tmp/video_replayer.yaml', 'w').write(c)"

# hello_world — no display, no data needed; expected: "Hello World!"
python3 /tmp/hs_hello_world.py

# video_replayer — needs racerx data; expected: frames rendered, "Graph execution finished."
HOLOSCAN_INPUT_PATH=/path/to/holoscan/data python3 /tmp/hs_video_replayer.py

HOLOSCAN_INPUT_PATH must point to the directory containing a racerx/ subdirectory. If the user has the SDK source repo that is ~/repos/holoscan-sdk/data; otherwise download with the download_ngc_data script from the Debian or source install tree.

Step 4: Remind the User

They must do the following in each new shell session:

source ~/miniforge3/etc/profile.d/conda.sh   # if Miniforge was installed with -b
conda activate holoscan
ulimit -s 32768   # recommended — prevents segfaults in some apps

Consider adding these lines to ~/.bashrc or ~/.zshrc to avoid repeating them.

Then offer next steps:

  • Explore C++ and Python examples at https://github.com/nvidia-holoscan/holoscan-sdk/tree/v<VERSION>/examples
  • Walk through a specific example: /explain-example
  • Start building a custom Holoscan application

Troubleshooting

  • `ImportError: librmm.so: cannot open shared object file`. rmm was not installed. Re-run the Step 2 conda install line — rmm is an undeclared runtime dependency of holoscan.
  • Solver picks an older `holoscan` build than expected. Channel order may be wrong. Use -c rapidsai -c conda-forge (rapidsai first) — that's the order in the official install command, and under strict channel priority a conda-forge-first ordering can lock the solver to an older holoscan build.
  • Segmentation fault on app startup. Set ulimit -s 32768 in the current shell before running any Holoscan app. Not all apps trip this, but the larger stack avoids the failure mode.
  • `find_package(holoscan)` fails when building C++ apps. Install libholoscan-dev (headers + CMake config are in a separate package since v4.1.0).
  • `conda: command not found` in a new shell. Miniforge was installed with -b and did not patch .bashrc. Run source ~/miniforge3/etc/profile.d/conda.sh or add it to your shell RC file.

More NVIDIA Agent Skills

All Agent Skills

AI-Q Blueprint deployment

Install, run, validate, troubleshoot, and stop a local or self-hosted NVIDIA AI-Q Blueprint environment.

Agents

CUDA-Q onboarding guide

Install CUDA-Q, validate simulators and hardware targets, and build reproducible quantum applications.

Models

cuOpt installation

Select and verify a compatible cuOpt Python, C, or REST server installation for an NVIDIA GPU environment.

Models

cuOpt numerical optimization API

Solve linear, mixed-integer, and quadratic programs with the cuOpt Python API and result diagnostics.

Models

cuOpt optimization formulation

Translate business constraints and objectives into verifiable cuOpt mathematical programs before implementation.

Models

cuOpt routing API for Python

Build vehicle-routing and fleet-optimization models with constraints, objectives, and solution validation.

Models