QCon.ai San Francisco 2018
April 11 2018, San Francisco, United States
QCon.ai San Francisco 2018
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TensorFlow Jumpstart
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About speaker

Magnus Hyttsten is a Developer Advocate for TensorFlow @ Google. He works on developing the TensorFlow product, is a developer fanatic, and an appreciated speaker at major industry events such as Google I/O, The AI Summit, AI Conference, ODSC, GTC, QCon, and others on machine learning and mobile development . Right now, he is focusing on Reinforcement Learning models, as well as making model inference effective on Mobile.

About the talk

Topic: IT

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Magnus Hyttsten talks about TensorFlow and gives an overview of the different products & APIs and the best practice.

Magnus Hyttsten is a Developer Advocate for TensorFlow at Google.

This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl

More videos from QCon.ai 2018 on InfoQ: https://bit.ly/2rNAT8z

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Excellent. So we have a 10-minute lightning talk. I would say here 10 minutes. If not the lot of time to cover tensorflow. And after that they have a 00:04 10-minute break. And after that I'm going to do a 15-minute huge feet on TP use and what is machine learning and how do we use machine learning in to 00:11 Google then? Also, how do we do this tribute to tensorflow? So if you're really interested in understanding more than nuts and bolts compensatory 00:20 should definitely catch that section because right now or just going to focus on getting you started the resources that you can use. So what is 00:28

tensorflow About 10% So I hope it's going to be beefed up a bit after today session 00:37 open source machine learning library blah blah blah blah blah section is a machine-learning Highbury that we use at school before all 00:46 machine learning purposes and is exactly the same thing we have open sores as we have inside of Google. There is no difference people think that we're 00:56 keeping something Secret inside a Google and we can only use that thing. But if that's not true that actually built on the same kind of infrastructure 01:04

is only one single difference and that's at the RPC protocol between processes is a big difference in a Google set data center as opposed to just 01:11 today or Center outside. So the RPC protocol is a bit different. That's the only thing no magic sauce from a machine learning perspective. Tensorflow 01:19 is used how I built it to be usable for both research as far as production environment. There's rigorous testing going on between each release of 01:27 the chance of snow and we put out the new release approximately every 6 to 8 weeks we have about the Thousand contributor. So it's not a Google thing. 01:37

It's actually an open-source everyone can participate in this project. And also the one thing that we've recognized is that I'll muesli training on 01:47 TCP UDP you and TPU that we'll talk about more in the next session is really important, but when it comes to the employment, we gotta support the 01:56 smaller device might as well Android iOS and also Raspberry Pi it's all released under Apache 2.0. So it's a very open 02:05 we are open source disk back in 2014 to stall as you see here in a number machine learning models in Google slowly grew and then when 02:13

we really sensitive, I'm just lie there is this hockey stick. Kind of traction up words every single service your son. Should I use from Google 02:23 nowadays is built a small dose machine learning models which are built on tensorflow. And if you've heard Google is now an AI first company, we used 02:32 to be mobile first company an hour and a half her company. So this is very very important to us. So therefore tensorflow as a lot of investment going 02:40 on in making this work actually just one minute talk about what machine learning is. So we have a social experiment that's going to be very awkward 02:48

for me. Hopefully not so awkward for you if I say it ends with o An awkward silence, right because you have no idea what you were going to respond to 02:58 that. Correct. Very good at what we actually did. This is an introduction to machine learning side said than example to you 03:07 which was the training data supposed to say tensorflow here today and potatoes * 55.2 your model you responded with nothing. Right? And that was 03:17 awkward for me. And then I show the next Picture Rocks and now you know, you take the loss function between nothing and Roxy said, oh there was a 03:27

mistake I should have responded with rocks instead. So the next time you have to put out roxtec, so everyone get that what kind of have something we 03:34 feed into the model and then we get a right answer and then we adapt a model to be able to go to work Kool. How do I get started with that? One of the 03:43 best thing is to go to ten Sapporo get started here. You have all of the stuff. We recently converted to use proposition 2 03:51 days. So, I don't know if I should do this to use collab so you can actually go in here and if you 04:01

Go into town so far or develop getting started with eager execution. Are you can see these things and then you can click on these notebook so you can 04:12 actually get an executable notebook directly from the website. So there is no installation requirements know nothing we're converting all of the 04:22 things to comply to this kind of format. So it's really easy to get started. Cool. The other thing you should know is that tensorflow is built on 04:29 many different layers in the end. It's architecture the hardware the distributor execution engine the python front end when you decode tensorflow 04:39

nowadays, you should use one of these high-level apis and that's exactly what this getting started experience. It's also teaching you so that's really 04:48 important because there are many ways to configure in machine learning work clothes sometimes Advanced research that I need to go to this one down to 04:55 this level, but that's definitely not a place where you should start to build a simple example and carrots you put a sequential only had a couple of 05:02 lawyers and they can train and 5th and evaluate this model and then you can build his kind of cool a classifier to can take pixel inputs and then 05:11

through these weights and layers determine if it's a cat or a dog training is one thing done deployment. That's another thing and to deploy on these 05:19 mobile devices. We have a component of tensorflow called tensorflow Lite. Why is it called light because these mobile devices 05:28 they are not as big as their traditional desktops or whatever data center computers. He we might use for training because that takes a lot of power. 05:38 So tell if light is a very optimize library for embedded devices and mobile devices easy to use of course, everything is his to use right 05:45

it's fast and is really really small. It's really complex to use. I would be weird if I had that and the way that it works if that you do your 05:55 classical training using thousands of computers and TVs that we'll talk about the next session and then you'll train your thing and when you're 06:05 finished with your mother, you convert it into a light format and that form a done on the mobile device through and interpret that gets loaded and he 06:13 can use to make use of all of the hardware accelerators that are available on the mobile device on Android that will be 06:21

developed in the future and on iOS Okay, it has the capability of going 06:27 through Cora now. That's one of the options you can also ask you could all the operations on the CPU. Of course, if you're not a developer who is not 06:37 a developer. I thought so it's cute. But anyhow, I mean should you not want to code for some reason one day we have all of these cool Services 06:44 also in the cloud will you can upload videos and they can automatically detect stuff for you and your sex is pretty cool for developers Fallout 06:54

because you can get this kind of messed up tagging immediately from did you start your clothes even though you have no idea what a video contains if 07:00 your uploaded to the cloud you can actually get actionable information on how to process that video or talk to you sir with respect to that 07:07 children quotes, whatever how you upload 07:15 just images to this thing and using the latest technologies will look at some of these models in the next week. Are you actually get a 07:24

state-of-the-art machine learning models directly without any Home machine learning whatsoever. Which is kind of boring because machine learning is 07:31 quite fun. So so as I said, we got one minute left get started go to tech support orange and get underscore 07:39 started getting started with eager execution. You can open up the cold out there working on making bee stings Cole s at 12, so they should appear it's 07:49 cold outside right now. Unfortunately, they're HTML, but we're working hard on fixing. Thank you so much. 07:56

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