Agent skill · NVIDIA
jetson-package
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.
What it needs
About 4k tokens when loaded.
What this skill does
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: llamacpp, 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 before giving a command. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/jetson-package