How to run the Caffe deep learning vision library…

How to run the Caffe deep learning vision library on Nvidia’s Jetson mobile GPU board by Pete Warden.

From the post:

Jetson boardPhoto by Gareth Halfacree

My colleague Yangqing Jia, creator of Caffe, recently spent some free time getting the framework running on Nvidia’s Jetson board. If you haven’t heard of the Jetson, it’s a small development board that includes Nvidia’s TK1 mobile GPU chip. The TK1 is starting to appear in high-end tablets, and has 192 cores so it’s great for running computational tasks like deep learning. The Jetson’s a great way to get a taste of what we’ll be able to do on mobile devices in the future, and it runs Ubuntu so it’s also an easy environment to develop for.

Caffe comes with a pre-built ‘Alexnet’ model, a version of the Imagenet-winning architecture that recognizes 1,000 different kinds of objects. Using this as a benchmark, the Jetson can analyze an image in just 34ms! Based on this table I’m estimating it’s drawing somewhere around 10 or 11 watts, so it’s power-intensive for a mobile device but not too crazy.

Yangqing passed along his instructions, and I’ve checked them on my own Jetson, so here’s what you need to do to get Caffe up and running.

Hardware fun for the middle of your week!

192 cores for under $200, plus GPU experience.

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