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https://issues.apache.org/jira/browse/HAMA-675?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Edward J. Yoon resolved HAMA-675.
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Resolution: Duplicate
Assignee: Edward J. Yoon
I'm close this issue as a Duplicate. This will be addressed in HAMA-961.
I'm thinking use multi-thread or LocalBSPJobRunner for mini-batch, and
http://parameterserver.org/. See
https://docs.google.com/drawings/d/1cjz50sGbpnFp2oab30cZ5MNYsaD3PtaBRVsUWuLiglI/edit?usp=sharing
Regarding interface, gradient() computing method and fetch/push methods that
communicates with PM server will be needed. Interface designing is not a big
deal I think.
> Deep Learning Computation Model
> -------------------------------
>
> Key: HAMA-675
> URL: https://issues.apache.org/jira/browse/HAMA-675
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Reporter: Thomas Jungblut
> Assignee: Edward J. Yoon
>
> Jeff Dean mentioned a computational model in this video:
> http://techtalks.tv/talks/57639/
> There they are using the same idea of the Pregel system, they are defining a
> upstream and a downstream computation function for a neuron (for cost and its
> gradient). Then you can roughly tell about how the framework should partition
> the neurons.
> All the messaging will be handled by the underlying messaging framework.
> Can we implement something equally?
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