Difference between revisions of "Class meeting for 10-605 Parameter Servers"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. |
=== Slides === | === Slides === | ||
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=== Optional Readings === | === Optional Readings === | ||
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| + | * [http://www.cs.cmu.edu/~feixia/files/ps.pdf Parameter Server for Distributed Machine Learning] | ||
* [https://arxiv.org/pdf/1512.09295v1.pdf Strategies and Principles of Distributed Machine Learning on Big Data] | * [https://arxiv.org/pdf/1512.09295v1.pdf Strategies and Principles of Distributed Machine Learning on Big Data] | ||
Latest revision as of 11:37, 28 November 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.
Slides
- Lecture: Powerpoint, PDF.
Quiz
Optional Readings
Things to remember
- Architecture of a generic parameter server (PS), with get/put access to parameters
- Pros/cons of asynchronous vs bounded asynchronous vs fully synchronous PS
- Pros/cons of PS model versus Hadoop plus IPM
- Stale synchronous parallel (SSP) computation model
- Data-parallel versus model-parallel algorithms
- Data-parallel example: SGD on sharded data
- Model-parallel example: Lasso accounting for parameter dependencies and parameter importance
