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From Jaonary Rabarisoa <jaon...@gmail.com>
Subject DeepLearning and Spark ?
Date Fri, 09 Jan 2015 15:39:44 GMT
Hi all,

DeepLearning algorithms are popular and achieve many state of the art
performance in several real world machine learning problems. Currently
there are no DL implementation in spark and I wonder if there is an ongoing
work on this topics.

We can do DL in spark Sparkling water and H2O but this adds an additional
software stack.

Deeplearning4j seems to implements a distributed version of many popural DL
algorithm. Porting DL4j in Spark can be interesting.

Google describes an implementation of a large scale DL in this paper
http://research.google.com/archive/large_deep_networks_nips2012.html. Based
on model parallelism and data parallelism.

So, I'm trying to imaging what should be a good design for DL algorithm in
Spark ? Spark already have RDD (for data parallelism). Can GraphX be used
for the model parallelism (as DNN are generally designed as DAG) ? And what
about using GPUs to do local parallelism (mecanism to push partition into
GPU memory ) ?


What do you think about this ?


Cheers,

Jao

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