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Scaling Machine Learning in Python
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continuum
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pydata_sv_2013
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Next: 5 Data Visualization With Nodebox
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Marks
Author(s):
Olivier Grisel
Location
A1
Date
mar Tue 19
Days Raw Files
Start
15:20
First Raw Start
15:21
Duration
00:50:00
Offset
0:01:41
End
16:10
Last Raw End
16:15
Chapters
00:00
Total cuts_time
47 min.
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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.
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2013-03-19/15_21_41.dv
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15:21:41 - 16:09:37 ( 00:47:56 )
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15:21:41
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16:09:37
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2013-03-19/16_09_38.dv
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