Nvidia Jetson Tx2 Keras

This includes other machine learning libraries such as scikit learn scikit image numpy pandas matplotlib scipy etc.

Nvidia jetson tx2 keras. Intermediate full instructions provided 3 hours 8 467. The jetson tx2 developer kit enables a fast and easy way to develop hardware and software for the jetson tx2 ai supercomputer on a module. Opencv s deep neural network dnn module does not support nvidia gpus including the jetson nano. It exposes the hardware capabilities and interfaces of the developer board comes with design guides and other documentation and is pre flashed with a linux development environment.

It exposes the hardware capabilities and interfaces of the module and is supported by nvidia jetpack a complete sdk that includes the bsp libraries for deep learning computer vision gpu computing multimedia processing and much more. Dockerfile for gpu accelerated tensorflow 1 5 on nvidia jetson tx2 this repo contains the dockerfile you need to set up keras with a tensorflow gpu v1 5 backend using python 3 5. This supercomputer on a module brings true ai computing at the edge with an nvidia pascal gpu up to 8 gb of memory 59 7 gb s of memory bandwidth and a wide range of standard hardware interfaces that offer the perfect fit for a variety of products and form factors. Provided the jetson nano supports a given deep learning library keras tensorflow caffe torch pytorch etc we can easily deploy our models to the jetson nano.

Evaluation of resnet on nvidia jetson tx2. 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. Keras implementation of resnet using the on board camera of tx2 the signs dataset was used to train this model however this can be used as a generalized image clasiification model. Jetson tx2 tensorflow opencv keras install.

The problem here is opencv. You can also learn how to build a docker container on an x86 machine push. Nvidia jetson tx2 gives you exceptional speed and power efficiency in an embedded ai computing device. A tutorial about setting up jetson tx2 with tensorflow opencv and keras for deep learning projects.

Note anaconda isn t available on arm. 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. This tutorial shows the complete process to get a keras model running on jetson nano inside an nvidia docker container.