Machine Learning Containers for Jetson and JetPack

Modular container build system that provides various AI/ML packages for NVIDIA Jetson 🚀🤖


ML	`pytorch` `tensorflow` `onnxruntime` `deepstream` `tritonserver` `jupyterlab` `stable-diffusion`
LLM	`transformers` `text-generation-webui` `text-generation-inference` `exllama` `bitsandbytes` `awq` `AutoGPTQ` `GPTQ-for-LLaMa` `optimum` `xformers` `nemo`
L4T	`l4t-pytorch` `l4t-tensorflow` `l4t-ml` `l4t-diffusion` `l4t-text-generation`
CUDA	`cupy` `cuda-python` `pycuda` `numba` `cudf` `cuml`
Robotics	`ros` `ros2` `opencv:cuda` `realsense` `zed`

See the packages directory for the full list, including pre-built container images and CI/CD status for JetPack/L4T.

Using the included tools, you can easily combine packages together for building your own containers. Want to run ROS2 with PyTorch and Transformers? No problem - just do the system setup, and build it on your Jetson like this:

$ ./build.sh --name=my_container ros:humble-desktop pytorch transformers

There are shortcuts for running containers too - this will pull or build a l4t-pytorch image that's compatible:

$ ./run.sh $(./autotag l4t-pytorch)

^{run.sh forwards arguments to docker run with some defaults added (like --runtime nvidia, mounts a /data cache, and detects devices)}
^{autotag finds a container image that's compatible with your version of JetPack/L4T - either locally, pulled from a registry, or by building it.}

If you look at any package's readme (like l4t-pytorch), it will have detailed instructions for running it's container.

Documentation

Looking for the old jetson-containers? See the legacy branch

jaybdub/jetson-containers

Machine Learning Containers for Jetson and JetPack

Documentation