veyepar: Scaling Machine Learning in Python

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Description:	In this talk we will introduce the typical predictive modeling tasks on "not- so-big-data-but-not- quite-small-either" that benefit from distributed the work on several cores or nodes in a small cluster (e.g. 20 * 8 cores). We will talk about cross validation, grid search, ensemble learning, model averaging, numpy memory mapping, Hadoop or Disco MapReduce, MPI AllReduce and disk & memory locality. We will also feature some quick demos using scikit-learn and IPython.parallel from the notebook on an spot-instance EC2 cluster managed by StarCluster. markdown
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2013-03-19/15_21_41.dv Apply: 15:21:41 - 16:09:37 ( 00:47:56 ) S: 15:21:41 - E: 16:09:37 D: 00:47:56 show more...

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2013-03-19/16_09_38.dv Apply: 16:09:38 - 16:15:00 ( 00:05:22 ) S: 16:09:38 - E: 16:15:00 D: 00:05:22 show more...

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