[arbre]

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.

[vegabook]

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?

[cbgb]

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.

[graffitici]

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..

[math_and_stuff]

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

[blazespin]

NO GPU, not for production use.

[nightski]

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...

[blazespin]

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

[gcr]

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

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

...Right ??

[irascible]

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

[vegabook]

fair enough. By contrast it seems to support clusters.

[x0x0]

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