Nvidia Jetson Keras

Conclusion and further reading now try another keras imagenet model or your custom model connect a usb webcam raspberry pi camera to it and do a real time prediction demo be sure to share your results with us in the comments below.

Nvidia jetson keras. Pip3 install u numpy grpcio absl py py cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras applications keras preprocessing wrapt google pasta setuptools testresources pip3. Sudo apt install python3 numpy python3 scipy python3 h5py python3 six python3 theano python3 yaml python3 keras this is the long form of the command in order to show what packages are installed as dependencies of python3 keras. The nvidia jetson agx xavier developer kit for jetson platform is the world s first ai. Chengwei 1 year 2 months ago i wrote how to run keras model on jetson nano a while back where the model runs on the host os in this tutorial i will show you how to start fresh and get the model running on jetson nano inside an nvidia docker container.

A tutorial about setting up jetson tx2 with tensorflow opencv and keras for deep learning projects. Jetson tx2 tensorflow opencv keras install. Nvidia jetson tx2. Intermediate full instructions provided 3 hours 8 467.

This tutorial shows the complete process to get a keras model running on jetson nano inside an nvidia docker container. In addition the keras model can inference at 60 fps on colab s tesla k80 gpu which is twice as fast as jetson nano but that is a data center card. Uninstall keras with sudo pip3 uninstall keras and run the following command in ubuntu 18 04 and later to install keras from the default ubuntu repositories. You can also learn how to build a docker container on an x86 machine push.

After following along with this brief guide you ll be ready to start building practical ai applications cool ai robots and more. This repo uses nvidia tensorrt for efficiently deploying neural networks onto the embedded jetson platform improving performance and power efficiency using graph optimizations kernel fusion and fp16 int8 precision.