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Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

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11–20 of 89 posts

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#11
post #10
post #5

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!

The first link to the pricing page on the FAQ points to localhost. Everything else looks really slick!

Ah! Late night coding error :) It is fixed now. Thanks for pointing it out.

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#12
post #5

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!

The landing-page responsiveness on mobile breaks the layout at several points. Otherwise good content on there so far!

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#13
I have been using your docker container for 6 months or so now, thanks for putting it together :)

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.

Are these instances guaranteed to be in a given region, for if I wanted to route more complex debug output / intermediate files to S3?

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#15
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.

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#16
post #14

Is this currently running on a one of the available GPU cloud services (IE AWS, Azure, Nimbix, etc...) or some self hosted hardware?

Nevermind, another comment mentions AWS. You seem to pretty much run at cost then (a little less), if you GPU hours are $0.432 @ 2 cores, 32G, and a k80. Then packing a p2 instance would run you $7.2 (or $14.4, etc..) and back of the napkin math says you can pack about 15 gpu jobs in a p2.8x. So you hour cost on a job is $0.48.

Just an observation :).

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#18
post #16
post #14

Is this currently running on a one of the available GPU cloud services (IE AWS, Azure, Nimbix, etc...) or some self hosted hardware?

Nevermind, another comment mentions AWS. You seem to pretty much run at cost then (a little less), if you GPU hours are $0.432 @ 2 cores, 32G, and a k80. Then packing a p2 instance would run you $7.2 (or $14.4, etc..) and back of the napkin math says you can pack about 15 gpu jobs in a p2.8x. So you hour cost on a job is $0.48. Just an observation :).

I guess the profit margin comes from the $99/user fee

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#19
post #16

Earlier quoted context omitted.

Nevermind, another comment mentions AWS. You seem to pretty much run at cost then (a little less), if you GPU hours are $0.432 @ 2 cores, 32G, and a k80. Then packing a p2 instance would run you $7.2 (or $14.4, etc..) and back of the napkin math says you can pack about 15 gpu jobs in a p2.8x. So you hour cost on a job is $0.48. Just an observation :).

I guess the profit margin comes from the $99/user fee

ahhh, thats true.

Re: Launch HN: FloydHub (YC W17) – Heroku for Deep Learning

#20
post #16
post #14

Is this currently running on a one of the available GPU cloud services (IE AWS, Azure, Nimbix, etc...) or some self hosted hardware?

Nevermind, another comment mentions AWS. You seem to pretty much run at cost then (a little less), if you GPU hours are $0.432 @ 2 cores, 32G, and a k80. Then packing a p2 instance would run you $7.2 (or $14.4, etc..) and back of the napkin math says you can pack about 15 gpu jobs in a p2.8x. So you hour cost on a job is $0.48. Just an observation :).

Yes, we currently run on AWS! The p2.8xlarge has 8 GPUs, not 16 :) So, it would still boil down to $0.9/hr/GPU. Also, utilizing 8 GPUs concurrently is pretty difficult/inefficient for most jobs since benefits from parallelization tapers off quickly.

We are only using p2.xlarge (1 GPU) for now. Driving down the cost is really important to us. We use reserved instances, spot fleets, etc. to be at We’ve been thinking about our own infrastructure. It would really drive down the cost, but obviously, comes with its own challenges :)

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