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https://issues.apache.org/jira/browse/SPARK-10014?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14738017#comment-14738017
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Joseph K. Bradley commented on SPARK-10014:
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Per discussion on [https://github.com/apache/spark/pull/8241], we should decide
how model mutability should interact with broadcasting.
Problem scenario:
{code}
val model = ...
val predictions1: RDD[...] = model.predict(...)
model.weights(1) = 0.1
val predictions2: RDD[...] = model.predict(...)
{code}
Q: Should this be allowed? The user would expect predictions1 and 2 to be
different.
If this is allowed, then we should re-broadcast the model every time
transform/predict is called.
I had originally voted for broadcasting only once, but rethinking, I'd vote for
re-broadcasting every time.
> ML model broadcasts should be stored in private vars
> ----------------------------------------------------
>
> Key: SPARK-10014
> URL: https://issues.apache.org/jira/browse/SPARK-10014
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> Multiple places in MLlib, we broadcast a model before prediction. Since
> prediction may be called many times, we should store the broadcast variable
> in a private var so that we broadcast at most once.
> I'll link subtasks for each problem case I find.
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