Jetson Zoo

This page contains instructions for installing various open source add-on packages and frameworks on NVIDIA Jetson, in addition to a collection of DNN models for inferencing.

Below are links to precompiled binaries built for aarch64 (arm64) architecture, including support for CUDA where applicable. These are intended to be installed on top of JetPack.

Refer to the listed forum topics for the latest updates or if you need help. Feel free to contribute to the list below if you have working software to add that is known to support Jetson.

= Machine Learning =

Jetson is able to natively run the full versions of popular machine learning frameworks, including TensorFlow, PyTorch, Caffe2, Keras, and MXNet.

There are also helpful deep learning examples and tutorials available, created specifically for Jetson - like Hello AI World and JetBot.

TensorFlow

 * Website: https://tensorflow.org
 * Source: https://github.com/tensorflow/tensorflow
 * Version: 1.13.1
 * Packages: pip wheel (Python 3.6)
 * Supports: JetPack 4.2 (Jetson Nano / TX2 / Xavier)
 * Install Guide: docs.nvidia.com/deeplearning/frameworks/install-tf-xavier/index.html#prereqs
 * Forum Topic: devtalk.nvidia.com/default/topic/1048776/jetson-nano/official-tensorflow-for-jetson-nano-/
 * Build from Source: https://devtalk.nvidia.com/default/topic/1055131/jetson-agx-xavier/building-tensorflow-1-13-on-jetson-xavier/

PyTorch (Caffe2)
note — the PyTorch and Caffe2 projects have merged, so installing PyTorch will also install Caffe2
 * Website: https://pytorch.org/
 * Source: https://github.com/pytorch/pytorch
 * Version: PyTorch v1.0.0 - v1.1.0
 * Packages:
 * Supports: JetPack 4.2 (Jetson Nano / TX2 / Xavier)
 * Forum Topic: devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/
 * Build from Source: https://devtalk.nvidia.com/default/topic/1049071/#5324123

MXNet

 * Website: https://mxnet.apache.org/
 * Source: https://github.com/apache/incubator-mxnet
 * Version: 1.4
 * Packages:
 * pip wheel (Python 2.7)
 * pip wheel (Python 3.6)
 * Supports: JetPack 4.2 (Jetson Nano / TX2 / Xavier)
 * Forum Topic: https://devtalk.nvidia.com/default/topic/1049293/#5326170
 * Build from Source: https://devtalk.nvidia.com/default/topic/1049293/#5326119

Keras

 * Website: https://keras.io/
 * Source: https://github.com/keras-team/keras
 * Version: 2.2.4
 * Forum Topic: https://devtalk.nvidia.com/default/topic/1049362/#5325752

First, install TensorFlow from above.

Hello AI World



 * Website: https://developer.nvidia.com/embedded/twodaystoademo
 * Source: https://github.com/dusty-nv/jetson-inference
 * Supports: Jetson Nano, TX1, TX2, Xavier
 * Build from Source:

= Robotics =

ROS



 * Website: http://ros.org/
 * Source: https://github.com/ros
 * Version: ROS Melodic
 * Supports: JetPack 4.2 (Jetson Nano / TX2 / Xavier)
 * Installation: http://wiki.ros.org/melodic/Installation/Ubuntu

= IoT / Edge =

AWS Greengrass



 * Website: https://aws.amazon.com/greengrass/
 * Source: https://github.com/aws/aws-greengrass-core-sdk-c
 * Version: v1.9.1
 * Supports: JetPack 4.2 (Jetson Nano / TX2 / Xavier)
 * Forum Thread: https://devtalk.nvidia.com/default/topic/1052324/#5341970

1. Create Greengrass user group:

2. Setup your AWS account and Greengrass group during this page: https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-config.html

After downloading your unique security resource keys to your Jetson that were created in this step, proceed to #3 below.

3. Download the AWS IoT Greengrass Core Software (v1.9.1) for ARMv8 (aarch64):

4. Following step #4 from this page, extract Greengrass core and your unique security keys on your Jetson:

5. Download AWS ATS endpoint root certificate (CA):

6. Start Greengrass core on your Jetson: You should get a message in your terminal

= Containers =

Docker



 * Website: https://docker.com/
 * Source: https://github.com/docker
 * Version: 18.06
 * Support: ≥ JetPack 3.2 (Jetson Nano / TX1 / TX2 / Xavier)
 * Installed by default in JetPack-L4T

To enable GPU passthrough, enable access to these device nodes with the  flag when launching Docker containers:

The  directory also needs mounted.

Below is an example command line for launching Docker with access to the GPU:

To enable IPVLAN for Docker Swarm mode: https://blog.hypriot.com/post/nvidia-jetson-nano-build-kernel-docker-optimized/