复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
SkillAtlasSkill 详情
tao-setup-nvidia-gpu-host
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 52
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
高风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 存在潜在风险命令，请谨慎安装。
- 扫描发现：4 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-setup-nvidia-gpu-host" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-setup-nvidia-gpu-host" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-setup-nvidia-gpu-host" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-setup-nvidia-gpu-host" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-setup-nvidia-gpu-host" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: tao-setup-nvidia-gpu-host
description: >-
Host setup for TAO GPU backends. Checks and, after user approval, installs
NVIDIA driver branch 580, CUDA Toolkit 13.0, and NVIDIA Container Toolkit
1.19.0 for Docker/local-Docker and Kubernetes GPU worker hosts. The
`--check-only` path works on any Linux distribution; `--install` automates
debian-family (Ubuntu/Debian/Pop!_OS/Mint/Zorin/Raspbian), rhel-family
(Fedora/RHEL/Rocky/AlmaLinux), and suse-family (openSUSE/SLES) hosts, and
prints actionable manual-install steps for everything else. Use when the user
asks to "set up an NVIDIA GPU host", "check TAO Docker GPU runtime", or
prepare a Kubernetes GPU worker for TAO.
license: Apache-2.0
compatibility: Runs `--check-only` on any Linux distribution. `--install` automates Ubuntu 22.04/24.04 + Debian 12 (apt), Fedora + RHEL/Rocky/AlmaLinux 9/10 (dnf), and openSUSE Leap / SLES 15 (zypper). Requires sudo/root, internet access to NVIDIA package repositories (and download.docker.com on rhel-family), and an x86_64 or aarch64 (sbsa) host. Other distributions (Arch, Alpine, Gentoo, NixOS, …) get a clear error that names the version targets and the NVIDIA install-guide URL.
metadata:
author: NVIDIA Corporation
version: "0.1.0"
allowed-tools: Read Bash
tags:
- setup
- nvidia
- cuda
- docker
- kubernetesNVIDIA GPU Host Setup
Use this setup skill before TAO workflows run on the docker, local-docker,
or kubernetes backend. It standardizes the host GPU runtime on:
- NVIDIA driver branch
580(open kernel module preferred) - CUDA Toolkit package
cuda-toolkit-13-0 - NVIDIA Container Toolkit
1.19.0 - Docker engine — only installed for
docker/local-dockerbackends and only when Docker is missing. The package picked depends on the distro family (docker.ioon Debian-family by default,moby-engine/docker-cefromdownload.docker.comon RHEL-family,dockeron SUSE-family). Pass--skip-docker-installto opt out.
The check is safe and read-only by default — it works on any Linux
distribution because it only probes nvidia-smi, the CUDA toolkit path,
the installed container-toolkit package version (via dpkg/rpm/the
nvidia-ctk binary version), and the Docker daemon's NVIDIA runtime.
Installation must be explicitly authorized by the user and rerun with
--install. The install path is automated for these distro families:
| Family | Tested distros | Manager | Notes |
|---|---|---|---|
| debian | Ubuntu 22.04 / 24.04, Debian 12 (and derivatives Pop!_OS, Mint, Zorin, Raspbian, KDE Neon, etc. via UBUNTU_CODENAME / VERSION_CODENAME) | apt-get | Adds NVIDIA cuda-keyring + Container Toolkit .list. Docker via docker.io (override $DOCKER_PACKAGE_DEBIAN). |
| rhel | Fedora 39+, RHEL / Rocky / AlmaLinux 9 and 10 | dnf (or yum) | Adds NVIDIA cuda-<distro>.repo + Container Toolkit .repo. Docker via Fedora moby-engine when available, otherwise docker-ce from download.docker.com. |
| suse | openSUSE Leap 15, SLES 15 | zypper | Adds the same NVIDIA .repo files. Docker via the distribution docker package. |
| other (Arch, Alpine, Gentoo, NixOS, FreeBSD, …) | n/a | n/a | --install exits with a clear error listing the version targets and the NVIDIA install-guide URLs. Install manually, then rerun --check-only. |
Quick Start
From the skill bank root:
# Check the local Docker backend host.
bash skills/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh --backend docker --check-only
# Install or repair after user approval.
bash skills/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh --backend docker --install
# Check a Kubernetes GPU worker host.
bash skills/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh --backend kubernetes --check-only
⚠️ Note — running non-interactively (agent/skill runs): a skill run has no terminal, so the installer's
Continue? [y/N]prompt cannot be answered. After running--check-onlyto preview and getting the user's approval, append the assume-yes flag (--yes) to the--installcommand so it proceeds without a prompt — this auto-confirms installation of system packages (NVIDIA driver, CUDA Toolkit, NVIDIA Container Toolkit, and Docker for Docker backends) and modifies the host, so only do this on a host you control. A person running--installdirectly at a terminal gets the prompt instead.
Workflow Contract
Docker and Kubernetes workflows must run the check before submitting GPU work:
SETUP_SCRIPT="${TAO_SKILL_BANK_ROOT:-$PWD}/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh"
bash "$SETUP_SCRIPT" --backend docker --check-only || {
echo "MISSING: TAO GPU host runtime is not ready."
echo "After user approval, run (append --yes for non-interactive agent runs):"
echo " bash \"$SETUP_SCRIPT\" --backend docker --install"
exit 1
}
Never install silently. If the check fails, explain what is missing, ask the user to authorize the fix, then run the install command and rerun the check.
What The Installer Does
The installer dispatches on the detected distribution family. On every
supported family it adds NVIDIA's CUDA and Container Toolkit repositories
(if missing), installs the pinned runtime packages, optionally installs
Docker, wires the NVIDIA Docker runtime, and adds the invoking user to
the docker group.
Common steps (all families):
- Adds NVIDIA's CUDA repository if missing (apt
cuda-keyringdeb,cuda-<distro>.repofor dnf/zypper). - Adds NVIDIA's Container Toolkit repository if missing (
.listfor apt,.repofor dnf/zypper). - Installs the matching kernel header / devel package for the running kernel.
- Installs the driver branch 580 packages,
cuda-toolkit-13-0, and the Container Toolkit pinned to1.19.0(the dpkg-suffixed1.19.0-1is the same upstream version expressed for apt). - For Docker backends and when Docker is missing, installs Docker
(override / opt-out flags below), enables/starts the daemon, then runs
nvidia-ctk runtime configure --runtime=dockerand restarts Docker whensystemctlis available. - Adds the invoking user (
$SUDO_USERif available, else$USER) to thedockergroup so subsequent shells can rundockerwithoutsudo— opt out with--skip-docker-group. The new group membership does not take effect in the current shell: log out and back in, or runnewgrp dockerin each new shell. - Attempts
modprobe nvidiaso verification can pass before reboot.
Family-specific package selections:
| Step | debian-family | rhel-family | suse-family |
|---|---|---|---|
| Kernel headers | linux-headers-$(uname -r) | kernel-devel-$(uname -r), kernel-headers-$(uname -r) | kernel-default-devel |
| Driver | nvidia-driver-pinning-580, nvidia-open-580 (override: $NVIDIA_DRIVER_PACKAGE_DEBIAN) | nvidia-driver-cuda, kmod-nvidia-open-dkms (override: $NVIDIA_DRIVER_PACKAGE_RHEL, $NVIDIA_DRIVER_KMOD_RHEL) | nvidia-open-driver-G06-signed-kmp-default (override: $NVIDIA_DRIVER_PACKAGE_SUSE) |
| CUDA toolkit | cuda-toolkit-13-0 | cuda-toolkit-13-0 | cuda-toolkit-13-0 |
| Container Toolkit | nvidia-container-toolkit=1.19.0-1 + base/tools/libs | nvidia-container-toolkit-1.19.0 + base/tools/libs | same as rhel |
| Docker | docker.io (override: $DOCKER_PACKAGE_DEBIAN) | moby-engine+moby-cli on Fedora when available, else docker-ce docker-ce-cli containerd.io from download.docker.com | docker |
Verification
After installation, verify:
nvidia-smi
/usr/local/cuda-13.0/bin/nvcc --version
docker info --format '{{json .Runtimes}}' | grep nvidia
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
Expected nvidia-smi output includes driver 580.x and CUDA Version 13.0.
Expected nvcc output includes release 13.0.
Kubernetes Notes
For self-managed Kubernetes clusters, run the host installer on every GPU worker node or bake the same package set into the node image before installing the NVIDIA GPU Operator or device plugin.
The workflow check also warns if kubectl is available but the cluster reports
no nvidia.com/gpu allocatable capacity. In that case, install/configure the
NVIDIA GPU Operator after the worker host runtime is ready:
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm install --wait gpu-operator -n gpu-operator --create-namespace nvidia/gpu-operator
Managed Kubernetes providers may own driver installation through node images or GPU Operator policy. Do not overwrite a provider-managed GPU node without user approval and a rollback plan.
Failure Modes
Unsupported distribution family: --install automates debian-, rhel-,
and suse-family hosts. On Arch, Alpine, Gentoo, NixOS, FreeBSD, or anything
without /etc/os-release (e.g. macOS), the script exits with a clear error
that lists the four version targets and the upstream NVIDIA install-guide
URLs:
https://docs.nvidia.com/cuda/cuda-installation-guide-linux/https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.htmlhttps://docs.docker.com/engine/install/
Install those four pieces using your distribution's package manager and
rerun the script with --check-only to verify. The check is universally
portable — it only queries the binaries / package databases — so once the
runtime is in place the workflow contract is satisfied regardless of the
underlying distro.
Unsupported Ubuntu/Debian derivative: When ID is e.g. pop, mint,
zorin, raspbian, or another debian-family derivative, the script maps
the host onto the upstream Ubuntu/Debian CUDA repo via UBUNTU_CODENAME /
VERSION_CODENAME (focal/jammy/noble → Ubuntu 20.04/22.04/24.04;
bullseye/bookworm/trixie → Debian 11/12/12). If the host's codename
doesn't match a known upstream release, --install exits with the same
manual-install guidance described above.
Docker not installed: --check-only reports MISSING: Docker is not installed and prints the exact rerun command appropriate to the detected
distro family. The default --install path installs Docker (docker.io /
moby-engine / docker-ce / docker depending on family), enables/starts
the daemon, configures the NVIDIA runtime, and adds the invoking user to
the docker group. If you prefer to manage Docker yourself, install it
before rerunning the script or pass --skip-docker-install.
Docker installed but docker run still needs sudo: The script adds the
invoking user to the docker group, but Linux only refreshes group
membership on a new login session. Log out and back in, or run
newgrp docker in each new shell, until the new membership is active.
Docker runtime still missing: Restart Docker, then rerun
nvidia-ctk runtime configure --runtime=docker.
Driver branch detected != 580: The driver-branch pin is exact on
debian-family (nvidia-open-580). On rhel-/suse-family the script
installs the latest open driver shipped in NVIDIA's CUDA 13.0 repo for
the detected distro, which is always ≥ 580. If your host needs a stricter
pin, set $NVIDIA_DRIVER_PACKAGE_RHEL / $NVIDIA_DRIVER_KMOD_RHEL /
$NVIDIA_DRIVER_PACKAGE_SUSE to the exact package names you want before
running --install.
Driver installed but nvidia-smi fails: Load the module with
sudo modprobe nvidia or reboot. Secure Boot may require MOK enrollment on
systems where it is enabled.
Kubernetes still has no GPU capacity: Confirm the driver works on each GPU
node with nvidia-smi, then check the GPU Operator/device plugin pods and node
labels.
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