Pretty vague on details: http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A227199
> On Jan 9, 2015, at 11:39 AM, Jaonary Rabarisoa <jaon...@gmail.com> wrote: > > 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 >
