Hi Alexander, Can you post a link to the code?
RJ On Tue, Aug 26, 2014 at 6:53 AM, Ulanov, Alexander <alexander.ula...@hp.com> wrote: > Hi, > > I've implemented back propagation algorithm using Gradient class and a > simple update using Updater class. Then I run the algorithm with mllib's > GradientDescent class. I have troubles in scaling out this implementation. > I thought that if I partition my data into the number of workers then > performance will increase, because each worker will run a step of gradient > descent on its partition of data. But this does not happen and each worker > seems to process all data (if miniBatchFraction == 1.0 as in mllib's > logisic regression implementation). For me, this doesn't make sense, > because then only single Worker will provide the same performance. Could > someone elaborate on this and correct me if I am wrong. How can I scale out > the algorithm with many Workers? > > Best regards, Alexander > -- em rnowl...@gmail.com c 954.496.2314
