So I gave it a shot for android but immediately entered a world of pain that probably is entirely unrelated to PyTorch but more with Android and my lack of Android experience.
I should admit that my experience with Android is very limited (I adapted the Caffe2 AI Camera demo app to use PyTorch with the ResNet, MarkRCNN, and Style Transfer about a year ago and ported PyTorch to Android for it, but that is about it).
Some of these comments might eventually lead to useful additions to the tutorial, but I don’t think I’m there yet.
The tutorial says:

We recommend you to open this project in Android Studio 2, in that case you will be able to install Android NDK and Android SDK using Android Studio UI.

This looks foolproof, but it seems not quite Thomas-poof:

It seems that you absolutely need to use the latest Android studio (mine was, I’m guessing, about 10 months old) - if you don’t have it, you get the most undescriptive error messages for an internet search reveals meant “upgrade to latest android studio” three versions ago, too,
Maybe one could say that one shoud Import the (gradle) project rather than opening it. Opening did nothing for me.

Then I tried the Hello World app:

Didn’t seem to work on the Android Emulator - I just got a white screen.
Works on actual hardware (I get a Wolf or dog classified as such).

Happily, the PyTorch demo app worked better - it worked on both.
On my phone  (BQ Aquaris U Plus, so not a high-end phone), I get ~3.5x images / second for the quantized resnet. This left me wondering what I should expect. In particular, I have been wondering whether the PyTorch build I’m using is a debug or a release build - I realize that Android release builds come with signing and stuff, so it would be a debug build from the android studio side, but that doesn’t necessarily mean anything for the PyTorch library itself. I am guessing that it is a non-debug build of PyTorch – with the assumption that most people don’t need that even when they are debugging their app.
It is neat so see this work out and I admire that you furnished a text classification example, too. It would be supercool if you could add the model construction / serialization for the quantized resnet and text app, too (the HelloWorld app has the (trivially) simple tracing of resnet 18, but I didn’t see them for the PyTorch demo app).
All in all, it looks good! Thank you.
Best regards
Thomas