Does it makes sense to use Spark's actor system (e.g. via SparkContext.env.actorSystem) to create parameter server?
On Fri, Jan 9, 2015 at 10:09 PM, Peng Cheng <rhw...@gmail.com> wrote: > You are not the first :) probably not the fifth to have the question. > parameter server is not included in spark framework and I've seen all > kinds of hacking to improvise it: REST api, HDFS, tachyon, etc. > Not sure if an 'official' benchmark & implementation will be released soon > > On 9 January 2015 at 10:59, Marco Shaw <marco.s...@gmail.com> wrote: > >> 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 >> >> >
