How HBO’s Silicon Valley Built “Not Hotdog” with TensorFlow, Keras and React Native

I am making an app that takes pictures and tries to tell you if the food in the picture has allergens. I didn't know if I should feel humble or just laugh. (I decided it was hilarious in the end) But it made me aim higher in a hackathon last weekend. I also use your app in my elevator pitch for people to understand.

Why is this not available outside the US and Canada?

Can you speak to the origins of it being 'not hotdog'?

Did you, or the writing staff or other consultants, start with hotdogs or dick picks?

It made me laugh because it reminded me of this popular lecture(o) that was, and is, passed around tech circles

Any relation? Or fun coincidence?

(o) https://youtu.be/uJnd7GBgxuA?t=5555s .. the lecture is given by andrej karpathy on 2016-09-24 and the timestamp goes to a point in the lecture where the lecturer discusses an interface made to have humans compete with conv nets to identify hotdogs

Slightly offtopic, but I wonder how hard it was to attach eGPU to MacBook Pro? Is there some official support, or was it just a dirty hack?

Thanks for sharing your process. I was inspired by the show to build my own app. It's pretty crazy how quickly you can build something like this now.

The Android App is not available in Germany. :(

In which countries is it available? Mine isn't :(

Boy I sure hope no one does a static analysis of the binary…

I'm waiting for the PiperCash (PiedPaper? PiperPound?) ICO to do p2p payments.

The series finale reveal is that Pied Piper exists and was using the show to fund their company and distribute their software.

> I don't relish saying this

My condiments to the author, I see what you did there ;)

Oh he's going long ... and queue music.

My takeaway is that local development has a huge developer experience advantage when you are going through your initial network design / data wrangling phase. You can iterate quickly on labeling images, develop using all your favorite tools/IDEs, and dealing with the lack of official eGPU support is bearable. Efficiency-wise it’s not bad. As far as I could tell the bottleneck ended up being on the GPU, even on a 2016 MacBook Pro with Thunderbolt 2 and tons of data augmentation done on CPU. It’s also a very lengthy phase so it helps that’s it’s a lot cheaper than cloud.

When you get into the final, long training runs, I would say the developer experience advantages start to come down, and not having to deal with the freezes/crashes or other eGPU disadvantages (like keeping your laptop powered on in one place for an 80-hour run) makes moving to the cloud (or a dedicated machine) become very appealing indeed. You will also sometimes be able to parallelize your training in such a way that the cloud will be more time-efficient (if still not quite money-efficient). For Cloud, I had my best experience using Paperspace [0]. I’m very interested to give Google Cloud’s Machine Learning API a try.

If you’re pressed for money, you can’t do better than buying a top of the line GPU once every year or every other year, and putting it in an eGPU enclosure.

If you want the absolute best experience, I’d build a local desktop machine with 2–4 GPUs (so you can do multiple training runs in parallel while you design, or do a faster, parallelized run when you are finalizing).

Cloud does not quite totally make sense to me until the costs come down, unless you are 1) pressed for time and 2) will not be doing more than 1 machine learning training in your lifetime. Building your own local cluster becomes cost-efficient after 2 or 3 AI projects per year, I’d say.

[0]: https://www.paperspace.com/ml

For sure, although I should say, for this specific instance I ended up training a network from scratch. I did get inspiration from the MobileNets architecture, but I did not keep any of the weights from their ImageNet training. That was shockingly affordable to do even on my very limited setup, and the results were better than what I could do with a retraining (mostly has to do with how finicky small networks can be when it comes to retraining).

Yes, that’s what you see in the picture, although as completely personal advice, I would stop short of recommending it. For one there are arguably better cases out there now, and you can sometimes build your own eGPU rig for less. Finally, the Mac software integration (with any eGPU) is very hacky at the moment despite the community’s best efforts, and I had to deal with a lot of kernel panics and graphics crashes, so overall I’m not sure I would recommend others attempt the same setup.

Ha you have no idea how hard chicago hotdogs made my life! There was a joke in the show about Dinesh having to stare at a lot of “adult” imagery for days on end to tune his AI, but my waterloo was chicago hotdogs — the stupid pickles end up hiding the sausage more times than not, which makes it hard to differentiate them from say, a sandwich.

For those of you like me that never knew they existed, here is what they look like: http://img.food.com/img/recipes/25/97/2/large/picMKCD0m.jpg

One of my primary motivators behind building this blogpost was to show how exactly one can use TensorFlow to ship a production mobile application. There’s certainly a lot of material out there, but a lot of it is either light on details, or only fit for prototypes/demos. There was quite a bit of work involved in making TensorFlow work well on a variety of devices, and I’m proud we managed to get it down to just 50MB or so of RAM usage (network included), and a very low crash rate. Hopefully things like CoreML on iOS and TensorFlow Lite on Android will make things even easier for developers in the future!

Great question — I did not, because I had unfortunately spent all of my data on that last training run, and I did not have a untainted dataset left to measure the impact of quantization on. (Just poor planning on my part really.)

It’s also my understanding at the moment that quantization does not help with inference speed or memory usage, which were my chief concerns. I was comfortable with the binary size (<20MB) that was being shipped and did not feel the need to save a few more MBs there. I was more worried about accuracy, and did not want to ship a quantized version of my network without being able to assess the impact.

Finally, it now seems that quantization may be best applied at training time rather than at shipping time, according to a recent paper by the University of Iowa & Snapchat [0], so I would probably want to bake that earlier into my design phase next time around.

[0]: https://arxiv.org/abs/1706.03912

MongoDB Stitch is a great new BaaS offering. They even have some great tutorials online to use it.

Have a look at their sample PlateSpace app: https://github.com/mongodb/platespace

Very cool new service and some excellent tutorials as well, for example for the PlateSpace web app: https://docs.mongodb.com/stitch/getting-started/platespace-w...

I'd definitely recommend having a look.

That’s not in the cards, at least at the moment, although if we get enough requests for it, I may be able to convince the powers that be…

In the meantime, iff there are any details you’d like to see, don’t hesitate to chime in and I’ll try to respond with details!

Thanks for the kind words! To prevent impostor syndrome, I should clarify that I worked on the app for many, many months — basically since August of last year — as a nights/weekends thing. It’s true that the final version was built almost from scratch in a few weeks, but it wouldn’t have been possible without the time investment in the preceding months. Although for the most part I just wasted a lot of time because I had no idea what I was doing lol (still don’t)

Yes, I was very excited we were able to release it for Android… And even though we used React Native, there were so many native (and C++) bits, it ended up being quite complex!

As for the gear, I think it’s really damaging that so many people think Deep Learning is only for people with large datasets, cloud farms (and PhDs) — as the app proves, you can do a lot with just data you curate by hand, a laptop (and a lowly Master’s degree :p)

The amazing react-native-camera plugin! [0] I’m still getting a few camera-related crashes on Android right now, but overall I would say it makes things pretty smooth!

[0]: https://github.com/lwansbrough/react-native-camera

Well for a while I was lulled into complacency because the retrained networks would indicate 98%+ accuracy, but really that was just an artifact of my 49:1 nothotdog:hotdog image imbalance. When I started weighing proportionately, a lot of networks were measurably lower, although it’s obviously possible to get Inception of Vgg back to a “true” 98% accuracy given enough training time.

That would have beat what I ended up shipping, but the problem of course was the size of those networks. So really, if we’re comparing apples to apples, I’ll say none of the “small”, mobile-friendly neural nets (e.g. SqueezeNet, MobileNet) I tried to retrain did anywhere near as well as my DeepDog network trained from scratch. The training runs were really erratic and never really reached any sort of upper bound asymptotically as they should. I think this has to do with the fact that these very small networks contain data about a lot of ImageNet classes, and it’s very hard to tune what they should retain vs. what they should forget, so picking your learning rate (and possibly adjusting it on the fly) ends up being very critical. It’s like doing neurosurgery on a mouse vs. a human I guess — the brain is much smaller, but the blade says the same size :-/

Not at the moment — although you’ll find the most critical aspects explained in detail in the post. The rest I fear will age very quickly… With stuff like CoreML and TensorFlow Lite on the immediate horizon, I can’t imagine people will want or need to use the cumbersome manual approach I had to use to ship this app. Anything in particular you’d like to see? I can try to share it in a follow-up post or in comments here.