Difference between revisions of "Jetson Zoo"

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# download the repo
 
$ git clone https://github.com/dusty-nv/jetson-inference
 
$ git clone https://github.com/dusty-nv/jetson-inference
 
$ cd jetson-inference
 
$ cd jetson-inference
 
$ git submodule update --init
 
$ git submodule update --init
 +
 +
# configure build tree
 
$ mkdir build
 
$ mkdir build
 
$ cd build
 
$ cd build
 
$ cmake ../
 
$ cmake ../
 +
 +
# build and install
 
$ make  
 
$ make  
 
$ sudo make install
 
$ sudo make install

Revision as of 17:56, 10 June 2019

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

TensorFlow Logo.png
# install prerequisites
$ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev python3-pip
$ pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu==1.13.1+nv19.5 --user

PyTorch (Caffe2)

PyTorch Logo.png
Python 2.7 Python 3.6
v1.0.0 pip wheel pip wheel
v1.1.0 pip wheel pip wheel

note — the PyTorch and Caffe2 projects have merged, so installing PyTorch will also install Caffe2

# Python 2.7 (download pip wheel from above)
$ pip install torch-1.1.0-cp27-cp27mu-linux_aarch64.whl

# Python 3.6 (download pip wheel from above)
$ pip3 install numpy torch-1.1.0-cp36-cp36m-linux_aarch64.whl

MXNet

MXNet Logo.png
# Python 2.7
sudo apt-get install -y git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl

# Python 3.6
sudo apt-get install -y git build-essential libatlas-base-dev libopencv-dev graphviz python3-pip
sudo pip install mxnet-1.4.0-cp36-cp36m-linux_aarch64.whl

Keras

Keras Logo.png

First, install TensorFlow from above.

# beforehand, install TensorFlow (https://eLinux.org/Jetson_Zoo#TensorFlow)
$ sudo apt-get install -y build-essential libatlas-base-dev
$ sudo pip install keras

Hello AI World

Hello-AI-World-CV.png
# download the repo
$ git clone https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ git submodule update --init

# configure build tree
$ mkdir build
$ cd build
$ cmake ../

# build and install
$ make 
$ sudo make install

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