Re: Stochastic gradient descent performance

2015-04-06 Thread Reynold Xin
Bradley; dev@spark.apache.org Subject: Re: Stochastic gradient descent performance Yeah, a simple way to estimate the time for an iterative algorithms is number of iterations required * time per iteration. The time per iteration will depend on the batch size, computation required and the fixed

Re: Stochastic gradient descent performance

2015-04-06 Thread Xiangrui Meng
...@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 time for an iterative algorithms is number of iterations required * time

Re: Stochastic gradient descent performance

2015-04-05 Thread Shivaram Venkataraman
in and then one can blame it. Best regards, Alexander *From:* Joseph Bradley [mailto:jos...@databricks.com] *Sent:* Thursday, April 02, 2015 10:51 AM *To:* Ulanov, Alexander *Cc:* dev@spark.apache.org *Subject:* Re: Stochastic gradient descent performance It looks like SPARK

RE: Stochastic gradient descent performance

2015-04-02 Thread Ulanov, Alexander
: 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

Re: Stochastic gradient descent performance

2015-04-02 Thread Joseph Bradley
: Stochastic gradient descent performance It looks like SPARK-3250 was applied to the sample() which GradientDescent uses, and that should kick in for your minibatchFraction = 0.4. Based on your numbers, aggregation seems like the main issue, though I hesitate to optimize aggregation based

RE: Stochastic gradient descent performance

2015-04-02 Thread Ulanov, Alexander
gradient descent performance It looks like SPARK-3250 was applied to the sample() which GradientDescent uses, and that should kick in for your minibatchFraction = 0.4. Based on your numbers, aggregation seems like the main issue, though I hesitate to optimize aggregation based on local tests for data

RE: Stochastic gradient descent performance

2015-04-01 Thread Ulanov, Alexander
Sorry for bothering you again, but I think that it is an important 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 Subject: Stochastic