Name: Answering your TF Lite questions and more! (#AskTensorFlow)
Uploaded: 2020-03-25T04:38:48.000Z
Duration: 15 min 40 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Future continuous

where we'll be answering all of your questions

about TensorFlow Lite and Swift for TensorFlow.

And I'm Daniel Situnayake and I work in DevRel for TensorFlow Lite.

Excellent! So with that, let's get started with our first question.

This is from Zongjun Zheng and was submitted via YouTube.

"Is RNN/LSTM quantization-aware training and TOCO conversion in TF Lite

and it sounds like a question that's for you, Dan.

It is indeed. So, I can say first of all,

yes all of these things are either available or coming in TensorFlow 2.0.

With regards to RNNs and LSTMs, we actually just published a guide

on how you can use these with TensorFlow Lite.

With TOCO conversion, yeah, the converser works great with 2.0

and we also have a guide on how to do that.

right now we're recommending you checkout post-training quantization.

We've actually just published some new documentation on that

But if you want to use quantization-aware training,

we're going to have support for that for TF 2.0 coming very soon.

Excellent. And all of those links that Dan mentioned are going to be listed

in the comments section of this YouTube video.

For the next question, "Is there any tutorial or example

for text processing models in TensorFlow Lite

aside from the pre-trained smart reply example?"

Alright, so right now smart reply is our main example

However, we're working on a lot of new models right now

and so if you've signed up for the TF Lite mailing list,

when we release a bunch of new models that you can play with.

Excellent! And a link  to that TF Lite mailing list

will also be placed in the comments section of the video.

Alright, so our next question is from Sohaib Arif who asks,

"Is Swift for TensorFlow for iOS programming?"

So, Swift is absolutely for iOS programming.

You've probably used it if you were building iOS apps.

Swift for TensorFlow will be able to deploy to iOS devices.

However, right now, we only have support for Mac OS

But if you're interested in this space, absolutely stay tuned

by joining the swift@tensorflow.org mailing list

as well as our weekly open design meetings

that happen every Friday at 9 a.m. Pacific Time.

it also sounds like a TF Lite-y question from Katayoun,

"Will there be a commodity device that I can use for TPU inferencing?"

And you may have heard of something called the Edge TPU.

It's basically like a tiny low power version

that you can embed in hardware that you're producing.

So imagine you're making a consumer product or an IoT device,

you can take the TPU, add it to that device

and get accelerated inference at the edge.

So we've created something called the Coral Platform

which gives you the end-to-end developer tooling to be able to do this

so you can start off with dev boards that you can order online

and once you prototyped your application, you can have all of the tools

to go into production and incorporate the hardware

and you can find it at coral.withgoogle.com.

(Paige) Excellent! And that's so cool that you can have TPUs

and have it delivered directly to your house.

Absolutely. And small enough that it works in your pocket.

so,"Does TF Lite only work on these Coral dev boards?"

Alright, so actually TF Lite is the way that you work with ML

We covered basically every platform you can think of.

through to IoT platforms in embedded Linux like things like the Raspberry Pi,

all the way down to tiny microcontrollers

with out new TensorFlow Lite for microcontrollers products.

And you can actually run TensorFlow Lite on the server as well.

It's basically just a stream line, stripped down,

super fast optimized version of TensorFlow

for deploying machine learning on device.

is that it supports so many different languages, right?

So if you're not a Python person, you can use Swift or you can use Java

Even if you're writing C++ on tiny embedded devices,

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Answering your TF Lite questions and more! (#AskTensorFlow)

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