n [mailto:shiva...@eecs.berkeley.edu]
> > Sent: Sunday, April 05, 2015 7:13 PM
> > To: Ulanov, Alexander
> > Cc: shiva...@eecs.berkeley.edu; Joseph Bradley; dev@spark.apache.org
> > Subject: Re: Stochastic gradient descent performance
> >
> > Yeah, a simple wa
hivaram Venkataraman [mailto:shiva...@eecs.berkeley.edu]
> Sent: Sunday, April 05, 2015 7:13 PM
> To: Ulanov, Alexander
> Cc: shiva...@eecs.berkeley.edu; Joseph Bradley; dev@spark.apache.org
> Subject: Re: Stochastic gradient descent performance
>
> Yeah, a simple way to estimate the ti
g<mailto:dev@spark.apache.org>
Subject: Re: Stochastic gradient descent performance
I haven't looked closely at the sampling issues, but regarding the aggregation
latency, there are fixed overheads (in local and distributed mode) with the way
aggregation is done in Spark. Launching a s
s? I do understand that in cluster
> > mode the network speed will kick in and then one can blame it.
> >
> >
> >
> > Best regards, Alexander
> >
> >
> >
> > *From:* Joseph Bradley [mailto:jos...@databricks.com]
> > *Sent:* Thursday, April 02, 2015
: Thursday, April 02, 2015 1:26 PM
To: Joseph Bradley
Cc: Ulanov, Alexander; dev@spark.apache.org
Subject: Re: Stochastic gradient descent performance
I haven't looked closely at the sampling issues, but regarding the aggregation
latency, there are fixed overheads (in local and distributed mode)
ering why it works so slow
> in
> > local mode? Could you elaborate on this? I do understand that in cluster
> > mode the network speed will kick in and then one can blame it.
> >
> >
> >
> > Best regards, Alexander
> >
> >
> >
> &g
y, April 02, 2015 10:51 AM
> *To:* Ulanov, Alexander
> *Cc:* dev@spark.apache.org
> *Subject:* Re: Stochastic gradient descent performance
>
>
>
> It looks like SPARK-3250 was applied to the sample() which GradientDescent
> uses, and that should kick in for your miniba
mportant issue for
applicability of SGD in Spark MLlib. Could Spark developers please comment on
it.
-Original Message-
From: Ulanov, Alexander
Sent: Monday, March 30, 2015 5:00 PM
To: dev@spark.apache.org<mailto:dev@spark.apache.org>
Subject: Stochastic gradient descent performan
b. Could Spark developers please
> comment on it.
>
> -Original Message-
> From: Ulanov, Alexander
> Sent: Monday, March 30, 2015 5:00 PM
> To: dev@spark.apache.org
> Subject: Stochastic gradient descent performance
>
> Hi,
>
> It seems to me that there
gradient descent performance
Hi,
It seems to me that there is an overhead in "runMiniBatchSGD" function of
MLlib's "GradientDescent". In particular, "sample" and "treeAggregate" might
take time that is order of magnitude greater than the actual gr
Hi,
It seems to me that there is an overhead in "runMiniBatchSGD" function of
MLlib's "GradientDescent". In particular, "sample" and "treeAggregate" might
take time that is order of magnitude greater than the actual gradient
computation. In particular, for mnist dataset of 60K instances, miniba
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