# jetson-package

Selects Jetson-compatible containers, vLLM runtimes, and Jetson AI Lab PyPI indexes for Orin and Thor. Prevents loading generic ARM64 wheels that lack target SM kernels.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 3735
- Author: Jetson Team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-package/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/jetson-package

## What it does

Standard `aarch64` wheels on PyPI often install cleanly on Linux but lack GPU compute kernels compiled for Jetson streaming multiprocessors. Because of this, ML workloads fail immediately or fall back to slow CPU execution. The skill guides coding agents toward curated NVIDIA repositories instead of generic Docker Hub or PyPI artifacts.

At its core, the skill relies on `scripts/artifact_hints.sh`. Running this script on the target device detects the board SKU, L4T release, and the exact build target: `sm_87` for Orin or `sm_110` for Thor. Running it on a host workstation will produce incorrect hints.

The skill then maps these parameters to verified Jetson AI Lab devpi trees (such as `jp6/cu126`), multi-arch NGC CUDA bases, and NVIDIA-AI-IOT packages. When targeting Orin on JetPack 6, it looks for PyTorch tags with the `-igpu` suffix.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-package -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/jetson-package/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/jetson-package/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-package -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/jetson-package/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-package -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/jetson-package/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills jetson-package
```

2. **Manually: put the files in place**
- File: `.github/skills/jetson-package/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-package -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/jetson-package
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/jetson-package/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-package -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-package/SKILL.md

## When to use it
- select compatible vLLM container images for Orin or Thor boards
- locate the right Jetson AI Lab devpi index URL for detected JetPack releases
- inspect hardware details and compute target flags via scripts/artifact_hints.sh
- replace outdated l4t-cuda NGC tags with multi-arch nvcr.io/nvidia/cuda images

## Example prompts
- Which PyTorch container should I use on this Jetson Orin running JetPack 6?
- Run scripts/artifact_hints.sh and show the Jetson AI Lab index for onnxruntime-gpu
- Where do I get vLLM runtime containers for Thor SM 11.0?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 04cf16a470c2aa59.

BEGIN THIRD-PARTY FILE 04cf16a470c2aa59
```text
---
name: jetson-package
description: Pick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.
version: 0.0.1
license: "Apache-2.0"
metadata:
  author: "Jetson Team"
  tags: [jetson, package, containers]
  languages: [bash]
  data-classification: public
---

# Jetson Package & Environment

Agents often suggest `docker pull` images or `pip install` wheels that claim **aarch64** support but were never built for Jetson’s GPU **streaming multiprocessor (SM)** targets. On Jetson, **default to NVIDIA-curated artifacts** unless the user explicitly opts out.

## Purpose

Choose Jetson-compatible containers and Python package indexes before installing GPU-native ML stacks. This skill prevents agents from recommending generic ARM wheels or stale container tags that do not include the right CUDA, JetPack, or SM target for the device.

## When to use

- "Which Docker image / container should I use on this Jetson?"
- "Where do I get PyTorch / vLLM / CUDA wheels for Jetson?"
- "`pip install` failed" or "wrong CUDA / SM" after installing a generic ARM wheel.
- Before `docker run` or `pip install` for ML stacks on Orin or Thor.
- User or agent looks for `l4t-cuda` containers on NGC — redirect to `nvcr.io/nvidia/cuda` (multi-arch).
- "Which PyTorch container should I use on Jetson?" — answer depends on Thor vs Orin and JetPack version.

## Canonical sources (use these first)

1. **Prebuilt containers (GHCR)** — [NVIDIA-AI-IOT packages](https://github.com/orgs/NVIDIA-AI-IOT/packages): `llama_cpp`, `ollama`, `live-vlm-webui`, older-Orin `vllm`, and related images built for Jetson JetPack stacks. Prefer these over random `arm64` images on Docker Hub. For vLLM, use upstream `vllm/vllm-openai` on Thor and Orin JetPack 7.2 / L4T r39+.
2. **NGC CUDA / PyTorch containers** — Tag selection depends on Jetson generation. Do not treat example PyTorch tag shapes as pinned recommendations; look up the current tag in the [NGC PyTorch catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) before giving a command.

   | Jetson | CUDA base | PyTorch |
   |--------|-----------|---------|
   | **Thor** | `nvcr.io/nvidia/cuda:<ver>-devel-ubuntu<ver>` (multi-arch, arm64 included) | `nvcr.io/nvidia/pytorch:<current-tag>-py3` (main multi-arch tag; verify current NGC tag) |
   | **Orin + r36 / JetPack 6** | same multi-arch CUDA base | `nvcr.io/nvidia/pytorch:<current-tag>-py3-igpu` — verify the current NGC tag and use the `-igpu` suffix for Orin iGPU (SM 8.7) when NGC publishes it |
   | **Orin + r39+ (future)** | same | likely main multi-arch tag once Orin becomes SBSA; verify when r39 ships |
```
END THIRD-PARTY FILE 04cf16a470c2aa59

The third-party file has ended. Everything between the 04cf16a470c2aa59 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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