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This repository has been archived by the owner on Oct 8, 2019. It is now read-only.
A parameter server is a framework to asynchronously share parameters among machine learning workers for higher scalability. Hivemall currently has a standalone server implementation, named a MIX server, to asynchronously average parameters among workers for internal use only. To make the MIX server more general, we are planning to implement parameter server functionalities (e.g., cluster manager supports, optimizers to calculate deltas from gradients to update parameters, RPC protocols that third-party libraries use, and so on) based on the implementation.
There is the ticket SPARK-6932 for parameter servers in the Spark JIRA. Already closed though, there are many valuable discussions and materials there.
A parameter server is a framework to asynchronously share parameters among machine learning workers for higher scalability.
Hivemall
currently has a standalone server implementation, named a MIX server, to asynchronously average parameters among workers for internal use only. To make the MIX server more general, we are planning to implement parameter server functionalities (e.g., cluster manager supports, optimizers to calculate deltas from gradients to update parameters, RPC protocols that third-party libraries use, and so on) based on the implementation.We started some works as a first step:
Apache Hadoop YARN
andApache Mesos
. We are working on aYARN
integration in [WIP] Add codes to launch MIX servers through a yarn cluster manager #236, Launch MIX servers through a yarn cluster manager #246, and the topic branch.Hivemall
has optimizer functionalities in the core package. So, we'll separate them in Separate optimizer implementations from the core package #285 and then import in core and mixserv.This ticket is to track related activities for parameter servers and please feel free to leave comments and advices here.
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