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Comment by arbre | Hacker News Reader

This is very exciting to see these distributed deep learning frameworks open sourced by top companies. What I do not understand is why amazon AWS and other top cloud service are not integrating these frameworks in their services. Training a distributed neural net should be as simple as defining the model and specifying the resources.

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agreed - though I'm somewhat saddened to see that this library doesn't seem to be getting the same positive reception (in terms of scoring / comments) as Tensorflow did yesterday. Am I right and if so, is there a technical reason for that?

Not sure if this was ready for public release.. The chess demo released this morning seems more polished than this library.. Also, probably not the best decision to post it a day after the TensorFlow release..

Less fancy landing page? No fancy names in the title? Marketing works.

One technical reason may be that C++ (I only looked at LightLDA) has a higher learning curve than Python, which the Tensorflow docs stress.

Though Tensorflow stayed at #1 for a while, it only garnered around 200 comments, which is very high for a ML topic on HN (in my anecdotal experience, ML topics are highly rated but under-commented.) I imagine the audience for this library to be very small compared to Tensorflow, which likely included more ML/Google FOSS enthusiasts than day-to-day practitioners. Looks like this library is firmly targeted toward the latter.

NO GPU, not for production use.

fair enough. By contrast it seems to support clusters.

You do realize there are a fair amount of machine learning algorithms that do not run efficiently on the GPU right? Deep learning isn't the only method out there...

Fair enough! None that I'm interested in, however.

Come on, keep an open mind! Random forests still work great!

There's still some love for genetic algorithms somewhere, right?

...Right ??

Someone make me a painting of a shriveled genetic algorithm dying in a random forest..

distributed ml tends not to work well on aws. Many (most?) distributed ml algorithms are essentially limited by network speed. In a p2p topology, many are effectively limited by the slowest peer-to-peer link of the (nodes choose 2) links. aws networking is mediocre at best; even the rack-local instances are relatively slow.

cred: helped build a distributed in-memory ml toolkit

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