tensorflow-on-raspberry-pi

It's surprising how much attention this has had over the last few days, without any discussion of the downside: it's slow. It's true that it is fast for the power it consumes, but it is way (way!) to slow to use for any form of training, which seems to be what many people think they can use it for. According to Anandtech[1], it will do 10 GoogLeNet inferences per second. By very rough comparison, Inception in TensorFlow on a Raspberry Pi does about 2 inferences per second[2], and I think I saw AlexNet on an i7 doing about 60/second. Any desktop GPU will do orders of magnitude more. [1] http://www.anandtech.com/show/11649/intel-launches-movidius-... [2] https://github.com/samjabrahams/tensorflow-on-raspberry-pi/t... ("Running the TensorFlow benchmark tool shows sub-second (~500-600ms) average run times for the Raspberry Pi")

> Google has managed to grow a community around TensorFlow I agree with your post. But speaking about the community part, I am still irritated that there's no official support for running TensorFlow on ARM / Raspbian / Raspberry Pi. Maybe I'm an outlier, but to me it looks like such an obvious choice. Here's a platform that lots of people are using for automation and robotics and computer vision, and it could be their first contact with machine learning, and you could basically lure them all in if you gave them an official release. Sure, there's someone on GitHub who provides unofficial packages, and props to him for the awesome work... https://github.com/samjabrahams/tensorflow-on-raspberry-pi ...but I'd really like to see Google stepping in here and doing the obvious. I've opened a feature request a while ago. Google closed the ticket,

Hey Sam this is Matt, thanks for your comment and your help a few months back! And for anyone else reading this, Sam is great at getting back to filed issues about installing tensorflow on a Pi: https://github.com/samjabrahams/tensorflow-on-raspberry-pi/i...

Inference times for inception-v3 on a Raspberry Pi have been benchmarked recently [1]. They weigh in at around 2s. If you remove the python layer it gets down to 500ms. However, it's not yet clear if the python overhead is the only reason for that. Nvidia's Tegra X1 should supposedly be capable of <10ms for imagenet grade models [2]. It's fair to assume though, that this must be for trimmed down and/or 16bit models as compared to full inception models. And finally, Sam who also facilitated building TF on the Pi is about to host a 6 weeks half theory, half practice course on TF and deep learning [3] (me thinks he deserves this plug). [1] https://github.com/samjabrahams/tensorflow-on-raspberry-pi/t... [2] https://youtu.be/_4tzlXPQWb8?t=53m35s [3] https://www.thisismetis.com/deep-learning-with-tensorflow

I might be missing it, but I don't see instructions for installing TensorFlow/Keras on the Raspberry Pi in the Donkey repo or in this blog post (needed to actually run the trained model, it looks like). For TensorFlow, there are pre-built binaries and instructions to build from source here: https://github.com/samjabrahams/tensorflow-on-raspberry-pi Note: I am the owner of this repo