
TensorFlow Lite is for mobile and embedded devices - tosh
https://www.tensorflow.org/lite/
======
johnklos
TensorFlow Lite sounds like a good idea, but they don't talk at all about how
to build it. "Run this shell script" is neither saying what the requirements
are, nor does it give any idea about supported targets. How I hate this new
trend of not documenting anything.

This might be usable if it doesn't require Bazil. Requiring that makes
deployment much, much heavier and makes the whole process significantly more
complex.

"Embedded"? Does that mean ARM to these folks who don't know how to version
and how to document?

~~~
lovelearning
I'm not a fan of Bazel either. Luckily, this doesn't require Bazel. Their
guide [1] explains how to build using regular make for RPi.

[1]:
[https://www.tensorflow.org/lite/rpi](https://www.tensorflow.org/lite/rpi)

~~~
m0zg
I'm a big fan of Bazel (or its Google internal counterpart, Blaze). Such a big
fan, in fact, that I even use it for Python projects, and package outputs into
Google PAR:
[https://github.com/google/subpar](https://github.com/google/subpar).

As long as you stick with the official 4 Google languages (C++, Java, Python,
Go) it doesn't get any better IMO, as far as build systems are concerned. I do
wish it didn't require Java though.

~~~
lovelearning
I have a bad impression of Bazel for multiple reasons.

I wanted to build TF on the Pi but at the time I assumed it required Bazel.
Bazel had no ARM version (my experience was in mid-2017). I had to first build
Bazel from sources. Failed a few times because its heap settings were too low,
required a lot more swap and storage than I expected, and got the Pi so hot
that I had to quickly setup some cooling fans around. A successful build took
~1.5 hours on a RPi 2.

Then the full TF build using Bazel took some 10-11 hours.

Finally, Bazel's command-lines - ex: "bazel build --config=opt -k
//tensorflow/tools/pip_package:build_pip_package" \- felt very
counterintuitive and not easy to remember.

For the first two problems, I learnt it the hard way that a cross-compilation
environment is better than building on-device. The last one I still have a
problem with.

~~~
m0zg
Bazel makes it reasonably straightforward to set up crosstool (a cross-
compilation toolchain). That's how it's supposed to be done, whoever ported
TFLite to Pi just probably neglected to provide a crosstool for it. It's a
much better dev experience if you have a crosstool. Rebuilds on changes are
near instantaneous and fully incremental. Toolchain can be set up to only be
downloaded as needed. A good PR idea for someone, if it's not already done?

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canada_dry
Ten years ago I wouldn't have predicted a device for ~$5 that could host a
wireless webserver (e.g. ESP8266). But nowadays it doesn't take much of a
stretch to imagine that very powerful and cheap general purpose ML devices -
kinda like Marshall envisioned in manna - are going to flourish in the next
2-3 years.

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sjroot
They have a section for “Companies using TensorFlow Lite,” but half of them
are Google apps.

They say performance is comparable, but I wish the landing page would
elaborate more on the differences between this library and standard TF.

~~~
cat199
shh -

"TensorFlow Lite is the official solution for running machine learning models
on mobile and embedded devices."

it's the OFFICIAL SOLUTION.

please, DO NOT QUESTION us. We are TEH GOOGZ!

------
cyrux004
I have a raspberry pi running a fairly simple application. What can I do
something which might be useful using tf lite on my pi?

~~~
300bps
First Robotics is a worldwide robotics competition that was started years ago
by Dean Kamen. This year’s challenge is called Rover Ruckus and simulates a
Rover landing on an alien planet. There is a 30 second autonomous mode wherein
one of the challenges is to use a camera to discern where a gold mineral lies
in a field of two silver and one gold minerals. My son’s team uses Tensor Flow
to accurately discern which mineral is the gold.

~~~
dekhn
My intern, Vasu Agarwal, built FTC's official tensorflow detector for this
year's competition. I was pretty impressed how quickly we were able to turn a
model trained on a GPU into something that ran at reasonable FPS on Androids.
We used TF Lite, I loved working with it. Heck, I enjoyed using it more than
TF proper.

~~~
akhilcacharya
I enjoy how Googlers appear to regularly name drop interns they’ve hosted as
if the rest of us should know who they are. Never seen that at any other
institution.

~~~
dekhn
I was just name-crediting my intern (who did the hard work) since we didn't
publish a paper. Never meant to imply you should know him.

