#134134th of 206 in ML frameworks and libraries
jetson-containers
Modular container build system for AI/ML packages on NVIDIA Jetson
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- Stars
- 4.9k
- License
- custom license
- Last commit
- Oct 2026
- Language
- Jupyter Notebook
4,554 on 21 Apr 20264,899 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Build and run CUDA-enabled Docker images for NVIDIA Jetson (JetPack 6.2 and 7) and ARM SBSA servers (GH200/GB200). The `jetson-containers` CLI composes packages such as PyTorch, vLLM, llama.cpp, ROS, ComfyUI and Whisper into one image, and `autotag` picks a prebuilt image matching the installed L4T version. A pip/devpi server caches wheels to shorten rebuilds.
Who it is for: Developers building AI and robotics containers on NVIDIA Jetson devices
Strengths
- Composes multiple packages into one image: `jetson-containers build pytorch transformers ros:humble-desktop`
- Covers LLM, VLM, speech, diffusion, robotics and simulation packages in one repo
- `autotag` selects a prebuilt image compatible with the device's JetPack/L4T version
- CUDA_VERSION and related variables rebuild the stack for other CUDA, cuDNN, TensorRT or Python versions
Weaknesses
- Only JetPack 6.2 (CUDA 12.6) and JetPack 7 (CUDA 13.x) are stated as tested
- Source builds of PyTorch and similar packages are slow without the wheel cache
- Targets NVIDIA Jetson and SBSA ARM; no x86 or non-NVIDIA support is described
- License is reported as NOASSERTION, so terms need checking per package
What it needs
- GPU required
- Compose
- Needs Docker, NVIDIA container runtime, JetPack/L4T
- Models: PyTorch, TensorFlow, JAX, ONNX Runtime, llama.cpp
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