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How HBO’s Silicon Valley Built “Not Hotdog” with TensorFlow, Keras and React Native (medium.com)
378 points by timanglade on June 26, 2017 | hide | past | web | favorite | 117 comments

Just wanted to say thanks for the warm welcome from HN when the app was released last month — I hope this blogpost answers the questions that were raised back then.

I’d be happy to answer anything else you’d like to know!

Original thread: https://news.ycombinator.com/item?id=14347211

Demo of the app (in the show): https://www.youtube.com/watch?v=ACmydtFDTGs

App for iOS: https://itunes.apple.com/app/not-hotdog/id1212457521

App for Android (just released yesterday): https://play.google.com/store/apps/details?id=com.seefoodtec...

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.

Great idea that I would be terrified to pursue from a legal perspective.

I am going to chime in and really suggest they hire a qualified legal professional - if they intend to share this application. A simple disclaimer may not be adequate.

Edit: Typo.

Yea I'm not going to release the app. I was already on the fence on it. I could have finished it months ago but I was worried about legal implications . Thank you .

No problem. I wouldn't even give it away, with any disclaimer, without consulting a qualified legal professional. That sort of thing is just begging for a lawsuit.

I am kind of curious how well you do. If you ever do get it going, there may be a use for it - but, again, liability is a huge factor in something like this.

I am not a lawyer but I have spent a whole lot of time with lawyers and in court rooms. It was part of my business, indirectly. Thankfully, I sold and retired years ago.

I could send you a dev build if you really want .

Oh, no thank you. I'll just watch from afar. Also, I have a Windows phone. Yup. I know my shame. It's the only Microsoft OS I have. I figure nobody is writing malware for it. ;-)

Yes. I'm not going to release the app. The show already discouraged me and made me do another app that won't have legal implications .

While it seems great to be able to take a picture, that’s going to be very hard to know what’s in a sauce, stuffed inside of something, complex presentation, etc.

Why not be able to search over a list, and once on an item, show frequently related (e.g. garlic if you search onion). It may not involve any “AI,” but it’ll be far more accurate and easier to implement.

I’m glad to hear the app is an inspiration for real — and more meaningful — apps ;D I was originally inspired to take this on by Pete Warden and his TensorFlow for Mobile Poets approach, and nothing would make me happier than to see this app inspire others in turn! Please share your progress!

Is this possible? It doesn't seem like the necessary information is captured in a picture.

I don't think there's a person that could tell you whether food in a picture has allergens, let alone an app.

Yes you are right. The app works by identifying the dish and then searching in an ingredient database. If the dish looks visually the same to one with peanuts then the app would have a false negative .

Why is this not available outside the US and Canada?

So the long version of this story is that the app was released in partnership with the HBO Go / HBO Now team, and using the terms of service they used for these apps (which are only available in the US & Canada). We’ve been working with lawyers to release the app worldwide without running afoul of any local laws, and I’m keeping my fingers crossed that will get cleared this week, we just gotta make sure our terms of service are up to par… After all, we wouldn’t want to fall prey to something we made fun of this very season ;) http://www.vulture.com/2017/04/silicon-valley-recap-season-4...

Do you have a special algorithm to determine Canada/US and not Canada/US.

Their neural network localizes your submitted hotdog images. To get around the block, try showing it a Canadian hotdog.

Will it block Canadian hotdogs shot on USA soil? How about Mexican hotdogs? Asking for a friend.

Canadian hotdogs on US soil (aka "cross-dogging") and vice versus are disambiguated by shadow inclination vs timestamp to estimate latitude (outdoors) and fingerprinting microfluctuations in the power grid and resulting effects on lighting (indoors, Canada has a much larger share of hydroelectric).

GPS was considered, but rejected as insufficiently cool.

:D now, that would be an app worth investing in

Big interest from the Trump administration! (Huge interest, so big, the biggest.)

Hmmm, BRB, writing pylsur or not pylsur.

Because HBO I guess. There are a lot of contents are filtered outside of USA.

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

Ha seems like a fun coincidence. The writers came up with it early in the writing of season 4, and I started working on it sometime in the Summer of 2016 iirc. As far as the origin story goes, it was just great writers coming up with a great joke! Our lead technical consultant Todd mentioned to them we could actually build their joke for real, and the show jumped on the idea!

Classic.. yeah I think the lecturer says the project was from 2014

I suppose it's a clear testament to the quality of the show and its commentary.. thanks for your contribution to that end!

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?

Not offtopic at all! Dirty hack for sure. The enclosure I bought was a hack, the drivers were a hack, and there was software on top that was a hack as well. But the developer experience was totally awesome… Almost made the constant graphics crashes worth it. Almost.

Can you provide some more details on your eGPU hack for those of us who might want to try our hands on that as well?

Not my work by any means, this is all community-driven, and I think they do as awesome a job as is possible to do, considering the constraints Apple puts in their way. This guide has all the steps: https://egpu.io/setup-guide-external-graphics-card-mac/

There is official support for eGPUs in macOS High Sierra beta.


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.

Thanks! I really wanted to demystify as many of the steps as possible. It’s easy to see the finished result, but really it took a lot of trial & error to get something even as simple as this out there, and I wanted to make sure others felt encouraged to build their own apps too. Please share your progress!

The Android App is not available in Germany. :(

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

Just US & Canada, for now.

What an ingenious way to covertly distribute the Pied Piper decentralized storage app.

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

I really hope you've hidden an Easter egg in there

a gentleman never tells

If such a thing existed, would it be in the Android or iOS app, or both?

Oh neither, there is nothing to find in the binaries :)

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 but the, "Not Hotdog" app does not cut the mustard in even the most rudimentary of tests:


Probably only 20% of the world's hot dogs are just a basic hot dog with mustard on it. Once you move past one or two condiments, the domain of hot dogs identification along with fixings gets confusing from a computer vision standpoint.

Pinterest's similar images function is able to identify hotdogs with single condiments fairly well:


They appear to be using deep CNN's.


Having embedded tensorflow for on-site identification is all well and good for immediacy and cost, but if I can't really properly identify whether something is a hotdog vs. a long skinny thing with a mustard squiggle, what good does that do me? What would be the next step up in your mind?

I ask this as someone who is sincerely interested in building low cost, fun, projects.

It’s fair, as I mention in the blogpost there are some failures that are a bit obvious for sure. Mostly I think I tried to fit too many things into one “hotdog” category including chili dogs, chicago dogs, bunless hotdogs, cut up hotdogs, even octopus-cut hotdogs, etc. I think it caused the network to over-generalize on shapes & colors. Definitely would do things differently next time! And great work on the puns :)

> I don't relish saying this

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

While we’re here and chatting about this, I should say most of the credit for this app should really go towards the following people:

Mike Judge, Alec Berg, Clay Tarver, and all the awesome writers that actually came up with the concept: Meghan Pleticha (who wrote the episode), Adam Countee, Carrie Kemper, Dan O’Keefe (of Festivus fame), Chris Provenzano (who wrote the amazing “Hooli-con” episode this season), Graham Wagner, Shawn Boxee, Rachele Lynn & Andrew Law…

Todd Silverstein, Jonathan Dotan, Amy Solomon, Jim Klever-Weis and our awesome Transmedia Producer Lisa Schomas for shepherding it through and making it real!

Our kick-ass production designers Dorothy Street & Rich Toyon.

Meaghan, Dana, David, Jay, Jonathan and the entire crew at HBO that worked hard to get the app published (yay! we did it!)

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

OK, but where are the eight octopus recipes?

I am glad I am not the only one with questions about the external GPU, I had considered trying that, but came to the conclusion that the data transfer between CPU to GPU would be too slow for ML tasks. So, what is your opinion on this ? if you had to do it again would you use the eGPU or just use AWS or another GPU cloud service .

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

Awesome, thanks!

I have used the AWS machine learning API and would recommend it. The time savings using that vs running it on my hacked together ubuntu-chromebook-mashup is worth more than what I had to pay. I have also used Paperspace. My only issue was that whatever they use for streaming the virtual desktop to the browser didn't work over sub 4MB/s network connection.

It's interesting how amenable image classification neural networks are to the "take working model, peel off last layer or two, retrain for a new application" approach. I've seen this suggested as working pretty well in a few instances.

I guess the interpretation is that the first few normalize->convolution->pool->dropout layers are basically achieving something broadly analogous to the initial feature extraction steps that used to be the mainstay in this area (PCA/ICA, HOG, SIFT/SURF, etc.), and are reasonably problem-independent.

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).

That's very cool to hear, I'm a lot more interested in the eGPUs (vs. something like an AWS P2 instance) after reading this. Thanks again for sharing.

Interesting. What is the external GPU (eGPU) enclosure you used for the Nvidia GTX 980 Ti card? Is it this one? https://bizon-tech.com/us/bizonbox2s-egpu.html/

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.

It's worth noting that High Sierra removes some of the hackiness of an eGPU.

I am waiting until the next-gen enclosures/cards come out which play nicer with the OS for deep learning.

This should be standard 'hello world' tutorial for Pragmatic ML.

Ha, I don’t know about all that, but I was very honored to see someone call it the “Utah Teapot” [0] of Machine Learning… Hopefully it’s an approachable (if dumb) example of A.I. and its limitations today :p

[0]: https://en.wikipedia.org/wiki/Utah_teapot

Nice write up that should become the go-to tutorial for TF and local training. Helped me a lot w/ the mobile part, it was a bit strange to thing about transfer the training when I read at first but it became clear in the second reading.

Pretty fascinating and encouraging to see how much was accomplished with a laptop and consumer GPU. Gave me some great ideas. Also happy to see Chicago dogs properly identified.

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

I feel your pain. I once watched a single episode of Jeopardy 50+ times in pursuit of timings for a prototype app demo.

I could have a good half-hour conversion about the nuances of Alex Trebek's vocal inflections... shudders

Ha, I’d love to hear more. What was the app for? I can’t imagine why you’d have to pick up on Trebek’s elocution??

Less interesting than you'd expect, as it was for a rapid mobile app prototyping class.

We had a telesync'd demo that let you play along with a Jeopardy episode by yelling answers at your phone. The app knew the timing markers for when the question was asked + when a contestant answered, so would only give you credit if you beat the contestant with the correct answer.

Our model user was "people who yell answers at the screen when Jeopardy is on."

Still think it would have made a decent companion app to the show though...

Trebek's elocution is just something you pick up on after rewatching an episode enough times. He has really interesting ways of emphasizing things, but they seem normal if you're just listening to them once through.

Love the post! This explains how mobile TensorFlow can be a actually used on daily life.

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!

yeah, that's my main pain with the TF docs – great if you just want to try one of the MNIST tutorial variations, but there's a lot more you need to figure out when you get beyond of these "hello world" examples…

Yup and in fairness maybe that’s something the community (myself included) should really step in and improve — but it’s not always clear how the leadership of the project would like these things to improve, and I often get the feeling they have their own production fixes & practices internally that they’re not (yet) sharing with the public… I could be wrong though.

This is the Twitter bot I built a few days after the show (similar to Tim's original prototype with Google Cloud): https://hackernoon.com/building-silicon-valleys-hot-dog-app-...

This is amazing - impressed by your persistence to source the training data yourself, that must have been tedious!

Did you try quantizing the parameters to shrink the model size some more? If so, how did it affect the results? It also runs slightly faster on mobile from my experience.

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

Thanks! Haven't seen that paper, I'll check it out. I think quantization only helps with inference speed if the network is running on CPU with negligible gains on GPU (Tensorflow only supported CPU on mobile last I looked which was a while ago). However your app is already super fast so don't I think anyone would notice if it was marginally faster at this point!

Yeah I was surprised it became so fast once I started using small networks. I actually toyed with the idea of slowing down the transition to results artificially to provide better UX lol

At MongoDB World this past week they did a demo of stitch where they actually built something similar with no back end code required and used the Clarifai API and an angular front end. It took like less than 80 minutes and could like run on prod of I wanted.

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.

Wow, great writeup. This is an area that I know nothing about but have wanted to learn - seems like this post is a good starting point.

Any chance the full source will ever be opened up? Would be an excellent companion to the article.

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!

what is the best avenue for making such requests?

Just voicing your interest here is fine!

Just want to say, awesome post. Its amazing how quickly you created this.

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)

I finally played with it this morning. I'm blown away by the speed and how smooth the experience is.

Finally for Android! Cool to see a cross-platform implementation of this, and how much can be done by one person and some reasonable gear.

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)

Do you think it's possible to generalize the way you handled the cross-platform complexity into a shared component?

I thought there might be, but most of the AI code ended up being native. The only AI code in React Native is a single line:

    var percentage = await NativeModules.AIManager.analyzeImage(path)
… Everything below that is Java or Objective-C++, and then native libs

Love this architecture. I think Im going to adopt some of it for HungryBot, my nonprofits diet tracking research arm. I think on-phone predictions solves a lot of my affordability issues.


Great work!

Thanks! I definitely think executing neural networks on-device is the future for a lot of applications. It’s just a better UX, and much cheaper to boot!

Very informative write up. Thanks!

How did you source and categorize the initial 150K of hotdogs & not hotdogs?

Lots of manual searching, vetting & labeling! Definitely the most actively time-consuming part. (Passively, only the wait between training runs was longer.)

As someone who maintains a popular android camera library; what is this app using to take photos on both iOS and Android? Android can be a bit tricky with device-specific differences and Camera 1 vs. Camera 2 API changes.

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

Thanks for the response and the writeup! Glad to hear somebody has had success with that library.

What kind of accuracy did you get with the transfer learning attempts?

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 :-/

Very interesting! If you were to make a v2, would you adjust the 49:1 imbalance and add more hot dog images?

I’m not sure, I think I would maybe break classes into multiple labels, but that becomes even more finicky to train. At the end of the day, there are many more things that are not hotdogs, than things that are hotdogs, so you do have to provide more examples of the not hotdogs to train something from scratch properly — I don’t see a way around it.

Honestly I think the biggest gains would be to go back to a beefier, pre-trained architecture like Inception, and see if I can quantize it to a size that’s manageable, especially if paired with CoreML on device. You’d get the accuracy that comes from big models, but in a package that runs well on mobile.

What’s your biggest regret with this app? What are you most proud of?

Biggest regret was not keeping a pristine dataset for final testing / evaluation on device. I ended up flying blind when it came to setting the final threshold, testing the effects of quantization, or even just measuring the distortion introduced by cameraphones (compared to directly feeding images during training).

What I’m most proud of is the remote neural network injection [0] — which I’m surprised no one has commented on here. I just think it’s absolutely huge to be able to set large parts of your app’s behavior in TensorFlow code, and replace that on the fly in your user’s app as needed. But maybe I’m the only one excited about this :D

[0]: https://medium.com/@timanglade/how-hbos-silicon-valley-built...

The iOS app is not available in the Finnish App Store, only US :-( We have hotdogs here, too! (And not hotdogs.)

Okay, who is going to test this on you-know-what to see if Jian-Yang's pivot would have worked?

Tim: Any tips/online resources for someone starting out with ML? How did you learn?

I can’t recommend Rachel Thomas and Jeremy Howard’s FastAI course enough! I attended it in person in SF, but the YouTube recordings and online community around it are great! [0]

Beyond that, I would recommend making sure you have a concrete project you pursue. ML is very theoretical otherwise, and to be honest, our shared understanding of what works and what doesn’t is still fairly limited — with major discoveries every other week it feels like. So without a concrete project to anchor your thoughts, it can be hard to learn what “works” and what doesn’t, just because different things work on different projects.

If you have any questions, I highly recommend the FastAI forums [1] or the Machine Learning subreddit [2]!

[0]: http://course.fast.ai/ [1]: http://forums.fast.ai/ [3]: https://www.reddit.com/r/MachineLearning/

great app! any plan to open source it?

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.

I have a quick question on this. The blog post mentions that you guys went with CONV-BATCHNORM-ACTIVATION (still unsure on whether this is the better order), but in the model code that is posted the batchnorm and activations are the other way around. Which ordering did you end up using?

Ooops, good catch — I had posted the wrong definition. Corrected now! It was convolution, batch norm, then elu activation.

Quick followup, what type of optimizer did you guys end up using?

SGD with Cyclical Learning Rates [0]. Honestly, it’s the closest to a Machine Learning silver bullet I’ve found to date! That paper is awesome.

[0]: https://arxiv.org/abs/1506.01186

Thanks! I'm trying out your model on a small data set I've been playing with for identifying invasive species of flowers [0] and so far it's working way better than my initial version that was based on resnet (though slower)!

[0] https://www.kaggle.com/c/invasive-species-monitoring

LOL, I love that part. So funny.

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