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/jetson-package

by NVIDIA Corporationnvidia/skills3.5k stars

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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.

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Use this Skill: https://skilld.dev/gh/nvidia/skills/jetson-package

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referencespypi-jetson-ai-lab.md

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Jetson AI Lab devpi (PyPI indexes)

Root URL: https://pypi.jetson-ai-lab.io/

The site is a devpi mirror with per–JetPack / CUDA subtrees (for example jp6/cu126, jp6/cu128). Paths evolve with JetPack lines — open the root in a browser and pick the tree that matches:

Your L4T / JetPack line (cat /etc/nv_tegra_release)
The CUDA userland on the device (see JetPack release notes)

pip usage pattern

After you identify the correct index URL from the devpi tree:

pip install --extra-index-url '<paste-index-url-here>' '<package>'

Or set PIP_EXTRA_INDEX_URL for a session. Prefer Jetson AI Lab indexes for GPU-heavy wheels over plain PyPI when on Jetson.

Why not only PyPI?

Public manylinux2014_aarch64 wheels may lack SM 8.7 (Orin) or SM 11.0 (Thor) native code paths. The Jetson AI Lab indexes publish builds intended for Jetson GPUs.

Source: SKILL.md on GitHub

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The 'jetson-package' skill provides instructions and scripts for identifying hardware-compatible NVIDIA Jetson containers and Python package indexes. It uses a local diagnostic script to determine device capabilities and directs users to official NVIDIA repositories. The analysis found no malicious patterns, exfiltration, or unauthorized command execution.
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Provenance

Signed by skilld at 87053a0. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 3 months ago

Capability

version: 0.0.1

Other metadata

metadata: { "author": "Jetson Team", "tags": [ "jetson", "package", "containers" ], "languages": [ "bash" ], "data-classification": "public" }