Re: [VOTE] Apache SystemML 0.11.0-incubating (RC5)

+1 On Wed, Nov 2, 2016 at 2:29 PM, Glenn Weidner wrote: > I see it's been removed as part of https://issues.apache.org/ > jira/browse/SYSTEMML-926. I will validate the remaining artifacts on > Windows. > > --Glenn > > > [image: Inactive hide details for Glenn

Re: [DISCUSS] Apache SystemML Release 1.0.0

+1 Regards, Sourav > On ২৫ আগস্ট, ২০১৬, at ৫:৪১ অপরাহ্ণ, Niketan Pansare > wrote: > > +1 for calling next release as 1.0. > > Thanks, > > Niketan Pansare > IBM Almaden Research Center > E-mail: npansar At us.ibm.com >

Re: Calling System ML from sparkR

PM, Deron Eriksson <deroneriks...@gmail.com> > wrote: > > > Hi Sourav, > > > > Great question. Work is currently being performed by Alok Singh (see > > https://issues.apache.org/jira/browse/SYSTEMML-860) regarding this > topic. > > > > Deron > &

Re: Calling System ML from sparkR

, Aug 17, 2016 at 12:09 PM, Deron Eriksson <deroneriks...@gmail.com> wrote: > Hi Sourav, > > Great question. Work is currently being performed by Alok Singh (see > https://issues.apache.org/jira/browse/SYSTEMML-860) regarding this topic. > > Deron > > > On Mon, Aug 15,

Calling System ML from sparkR

Hi, Is there any work going on to call System ML dml scripts form SparkR using R syntax ? I understand it was possible using BigR (available in IBM Big Insights). Wondering whether something similar can be achieved from Spark R. Regards, Sourav

Re: Using RDMA for Deep Learning

ch needs detailed discussion (for > example: on fault-tolerance, permissions, homogenous clusters, etc) :) > > Thanks, > > Niketan Pansare > IBM Almaden Research Center > E-mail: npansar At us.ibm.com > http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar > > [

Using RDMA for Deep Learning

Hi, Was checking CaffeOnSpark for Deep Learning which has just got open sourced by Yahoo - http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep?soc_src=mail_trk=ma . Wondering whether SystemML can also leverage the RDMA based Model synchronization

Re: SystemML Notebook docker image

handle it if you create a JIRA with the > > notebook attached. Thank you Luciano. > > > > In case you want to do pull requests in the future, there are a couple > > things you might want to check. > > > > 1) If you try "git remote -v", the remote repos s

Re: SystemML Notebook docker image

uciano Resende <luckbr1...@gmail.com> wrote: > Sourav, > > To make things simple, if you could create a jira and attach the notebook, > I will handle the rest. > > On Mon, Feb 15, 2016 at 11:54 AM, Sourav Mazumder < > sourav.mazumde...@gmail.com> wrote: > &g

Re: SystemML Notebook docker image

at 11:55 AM, Luciano Resende <luckbr1...@gmail.com> wrote: > On Fri, Feb 5, 2016 at 5:26 PM, Sourav Mazumder < > sourav.mazumde...@gmail.com > > wrote: > > > Hi Deron, > > > > I can surely share that. Can I upload it somewhere in the SystemML's > site ? >

Re: SystemML Notebook docker image

Zeppelin? > Do you think that is a path that is going to be most useful for data > scientists to do exploratory work with SystemML? Is there anything that you > would like to see improved with regards to the MLContext API? > > Deron > > > On Thu, Feb 4, 2016 at 4:01 PM, Sourav Ma

Re: Neural Net

Thanks Shirish for the input. I'll try out the same and let you know if I face any problem. Regards, Sourav On Thu, Dec 10, 2015 at 4:35 PM, Sourav Mazumder < sourav.mazumde...@gmail.com> wrote: > Hi, > > i could not see mention of Neural Net algorithm in the Algorithm Reference

Re: Using GLM-predict

is that you have added "ID" to the > DataFrame, but did not inform SystemML that ID was inserted. To do that, > please replace "ml.registerInput("X", predDfIn)" to "ml.registerInput("X", > predDfIn, true)". > > Thanks, > > Niketa

DML script parsing error in Spark

Hi, I'm facing issue while parsing any DML script. What I'm trying something like - val ml = new MLContext(sc)ml.reset() ml.registerInput("X", Xfc, 3569, 4) ml.registerInput("Y", yDc, 1, 4) ml.registerOutput("beta_out") val cmdLineParams = Map("X" -> " ", "Y" -> " ") val outputs =

Re: Using GLM-predict

different performance (precision, recall, etc.). You can find the best > threshold for the given data set by finding a value that gives the desired > classifier performance (for example, a threshold that gives roughly equal > precision and recall). Such an optimization is obviously done

Using GLM with Spark

Hi, Trying to use GLM with Spark. I go through the documentation of the same in http://apache.github.io/incubator-systemml/algorithms-regression.html#generalized-linear-models I see that inputs like X and Y have to supplied using a file and the file has to be there in HDFS. Is this