[jira] [Updated] (MADLIB-1431) DL - improve speed of evaluate for multiple model training

["Frank McQuillan (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-1431?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan updated MADLIB-1431:
------------------------------------
    Priority: Minor  (was: Major)

> DL - improve speed of evaluate for multiple model training
> ----------------------------------------------------------
>
>                 Key: MADLIB-1431
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1431
>             Project: Apache MADlib
>          Issue Type: Improvement
>          Components: Deep Learning
>            Reporter: Frank McQuillan
>            Priority: Minor
>             Fix For: v1.18.0
>
>
> All we have right now is evaluate() for a single model, we have no evaluate_multiple_model() that can run in parallel like fit_multiple_model() does.
> Currently, the evaluate stage of fit_multiple_model() looks like this:
> - foreach mst_key:
>        1. Send weights from segments to master for model corresponding to mst_key (this step alone takes about 7s for each model).
>        2. Run keras.evaluate() on this model, while all other models wait their turn.
> Two things stand out:
> We should not be transferring weights from segments to master. This is slow and unnecessary; let's run evaluate on the segment host where the weights reside already.
> We should not be running evaluate sequentially on master's GPU one model at a time, while all GPU's on all segments sit idle.
> For a test environment with 20 segments, creating a evaluate_multiple_model() feature which evaluates models on the segments in parallel will easily give us a > 20x speedup (20x just for fixing #2, plus a significant additional speedup from fixing #1)



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