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)