Hi HN! I’m Sai, one of the cofounders at FloydHub (<a href="https://www.floydhub.com" rel="nofollow">https://www.floydhub.com</a>). We're building FloydHub to be a “Heroku for deep learning”. We are in the current batch (W17) at YC. But I still like to think of FloydHub as being an HN incubated startup.<p>10 months ago, I was working at Microsoft and doing a lot of deep learning (DL) there. While the DL community is terrific, I was often frustrated by how difficult it was to get started and build upon others’ work. For example, running any popular Github project often started with an exercise in dependency hell. As I untangled these for myself, I wrote up some notes on setting up popular DL frameworks, which unexpectedly started trending on HN after someone posted it there (<a href="https://news.ycombinator.com/item?id=11697571" rel="nofollow">https://news.ycombinator.com/item?id=11697571</a>). That's when I realized that engineering was a huge bottleneck in deep learning and a problem worth solving after all.<p>I’ve since quit my job and have been working fulltime for the last 9 months on building FloydHub to make deep learning easier. Our goal is to let the data scientists focus on the science, while we handle the engineering grunt work (provisioning and scaling infra, running reproducible experiments, enabling sharing and collaboration, supporting DL frameworks with zero setup, shipping trained models to production easily, etc.) Lots of interesting challenges - happy to talk about them!<p>We have a lot of work ahead, but we’re excited to share with you what we have so far! Looking forward to your feedback.
I was one of their earliest beta customers. Despite the initial quirks, the experience has been nothing but magical. It allowed me to go from code to training/model generation in one command, without any of the devops nonsense to do deal with.
<i>While digging into deep learning, I honestly felt that the devops stuff was actually more complicated than the math/backprop/neural nets stuff.</i><p>It felt like a Heroku moment for me. They have the potential to do to Tensorflow what Heroku did to Rails. Super simple deploy!<p>Obviously their vision is much broader (with an entire eco-system/'hub', reproducibility, etc.), but to me atleast the first part is super useful and exciting!<p>One advantage they have is that GPUs are INSANELY expensive on the cloud - they can actually make it cheaper for everyone with clever binpacking and proper termination.<p>My advice is that in the initial stage, they should partner with all the Moocs to ensure that all deep learning students are using Floyd. It's cheaper, faster and the students can focus on the science. And they provide, 100 free hours!<p>Disclosure: I've known the guys for quite a while.
Sidenote:
We always say the hardest things in software is cache invalidation and naming things, but I also found the problem to be dependency tracking and reproducibility. It's just so hard to get started with ANY non trivial software project and your os/tools/libs expire way before you finish the projects. Software should be cheap and repeatable, but for some reason it takes active maintenance and is therefore very expensive. If a superhuman intelligence looked at us from afar, we would probably look like how ants look to us: Millions of small workers with very inefficient probabilistic behavior. Sure, we get the job done, but very slowly with a lot of waste. That said, ants lived for ever, so maybe it is the right thing to do ;)<p>Either way, I often find myself choosing backward compatibility and stability over innovation and polish and choose to learn vim and bash instead of replacing the silver bullet every year.<p>Shameless plug, I am working on a platform similar to FloydHub, but for frontend engineers [0]. The problem is a real one.<p>[0] <a href="https://pipez.io" rel="nofollow">https://pipez.io</a>
I’m a Data Scientist and I work in a large-ish corp. We have a dedicated engineering team who take care of all of our infrastructure needs - we mostly focus only on data science. I would expect most medium and large enterprises to have the same setup. Why would they use FloydHub?
I have been using your docker container for 6 months or so now, thanks for putting it together :)<p>The jupyter jobs look neat, but I assume they are charged continuous time? Would be cool if somehow that only ended up charged for compute time, but I understand that would be difficult.<p>Are these instances guaranteed to be in a given region, for if I wanted to route more complex debug output / intermediate files to S3?
Hi Naren, really glad to see this launch, your "Deploy your Trained Models" that feature is awesome... what about external dataset access? I would like to access the Google YouTube ML dataset [0], how could that be done within FloydHub without uploading? Also, perhaps related, have you thought of teaming up with Kaggle [1]?<p>[0] <a href="https://research.googleblog.com/2016/09/announcing-youtube-8m-large-and-diverse.html" rel="nofollow">https://research.googleblog.com/2016/09/announcing-youtube-8...</a>
[1] <a href="https://www.kaggle.com/c/youtube8m" rel="nofollow">https://www.kaggle.com/c/youtube8m</a>
Looks really amazing! Reproducibility of models and experiments is huge. It should be almost a requirement if one is going to publish results claiming SotA, etc. Seems like you could become the GitHub for deep learning in addition to the Heroku for deep learning.
This looks neat! We have been doing a lot of deep learning for NLP at our startup recently. Several “engineering bottlenecks” in the process (1) managing multiple jobs is definitely worth solving. git for deep learning would be neat (2) collaborating is a pain when the team is remote. I guess this ties to (1) too.<p>And oh, about the time I forgot to turn off our GPU instance for a couple of weeks… racked up a nice bill...
This sounds interesting and useful - I hope you guys make it!<p>A couple of years ago I worked for a local cloud server provider, as a backend developer. Some of the work I did moved the company into deploying VPS instances using OpenStack. Our backend code was mainly PHP-based; so we used OpenCloud for the purpose - extending it where needed (when we started it didn't support all we needed; I extended things in such a way so that when we did need to upgrade OpenCloud, it would gracefully work without breaking anything - it was a gamble that I didn't know if it would really work - just had a hunch - 9 months in we upgraded, and it all worked perfectly).<p>Anyhow - at that time, seeing what we had available for servers and such (we were competing somewhat with DO) - I suggested we add support for GPU instances and maybe pivot toward an ML offering of some sort. Not gut our bread-n-butter, but offer up some kind of ML package for those that needed or wanted it.<p>I was shot down by management as it being too "pie in the sky" - not even demand or something like that. To be honest, I'm not even sure they understood what I was trying to convey, so maybe part of the problem was mine as well.<p>The company was eventually sold and I moved on, but seeing now how these kinds of services are in demand, I sometimes wonder on what "could've been". Ever since taking my first MOOC in ML (Ng's ML Class in 2011) - I've tried to interest employers in applying what (little) I know on the subject. I'm not an expert, but I'd love to apply my learning (on top of the 25+ years of software dev experience I already have). Today, I'm in the middle of the Udacity Nanodegree MOOC - I doubt much of that will transfer for my current employer, but maybe the general knowledge I'm getting of TensorFlow and Keras, among other bits, might help in the future.<p>I think, though, that FloydHub might be fun to play around with for future personal ML/DL projects as time goes on; I look forward to trying it out someday soon!
Hi! I'm Naren, the other co-founder of FloydHub. I'll be happy to answer any questions and really appreciate any feedback you can provide. Thanks!
So I guess we're competitors in a sense, but congrats nonetheless! We hope to launch at least a private beta of NeuralObjects like "Real Soon Now"™.<p>It'll be interesting to see where we decide to go down different paths, or how we take different approaches to things.
Running DL and sharing results is a huge pain. I feel versioning is a big challenge, also experiments with multiple architectures are a pain since large parts of the same calculations are repeated. Do you also solve this problem? (Feature request if not)
You said you worked at MS, so you're presumably pretty familiar with Azure's offering. I'm no expert (at all) but I played with it briefly and it was all pretty slick and easy to get up and running. How does FloydHub compare to that?
Nice! There is a real need for this in the market right now--especially among students, who struggle to setup working environments. I really benefited from your early Github project aimed to make setting up DL machines easier. Curious how defensible your product will be in the event Heroku/AWS come in with a competitor?
Thanks Sai, this looks great. I found myself dreading the clunky workflow with AWS GPUs, even using CLI, so this is nice to see a Heroku-like product that's a bit easier to work with.<p>Also, awesome 100 hours offering -- Looking forward to using FloydHub for deep learning!
You guys seem to make setting up Deep Learning infrastructure easy for data scientists which is awesome! What about situations where the end user/business isn't sure exactly how to use Deep Learning (which algorithm, how to partition the data into training and result sets, stability of results etc.) for the problem/data set at hand? Would it be possible to use FloydHub as a marketplace of sorts where I could hire a deep learning enthusiast to appropriately construct the experiment for me and then explain the results?
Congrats on your launch! Seems like a really slick product.<p>Have you taken a look at <a href="https://cloud.google.com/ml/" rel="nofollow">https://cloud.google.com/ml/</a>? I assumed you would have but didn't see anyone mention it in the comments.<p>Do you see your product as complementary to a service like that? e.g. could you use the Google service as the execution engine for yours? or do you see that as competition?
It feels like a good product. I will try it soon!<p>One small piece of feedback. If you open your landing page from mobile, e.g. My iPhone 7, the header height is changing all the time due to the text changing dynamically. It get hard to scroll past it and keep reading.<p>Cheers
Per second pricing and jupyter mode is pretty nice. Like heroku you're trying to resell AWS and stay competitive which is a challenge. Anyway, this is indeed a common problem and this looks like a solid approach.
This seems pretty awesome, but I can't get it working with my code. Do you have any plans to release extra documentation, or examples of how to adjust existing models to run on FloydHub?