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https://issues.apache.org/jira/browse/SPARK-21972?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16171773#comment-16171773
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Weichen Xu commented on SPARK-21972:
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[~podongfeng] [~Siddharth Murching] I have a simpler proposal: remove all
auto-checking caching code in algos such as LiR/LoR, whether cache or not only
depend on the param. (Because currently these checking is totally useless,
remove them don't change anything actually.) So that it can avoid big change
in the interface for now.
> Allow users to control input data persistence in ML Estimators via a
> handlePersistence ml.Param
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-21972
> URL: https://issues.apache.org/jira/browse/SPARK-21972
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.2.0
> Reporter: Siddharth Murching
>
> Several Spark ML algorithms (LogisticRegression, LinearRegression, KMeans,
> etc) call {{cache()}} on uncached input datasets to improve performance.
> Unfortunately, these algorithms a) check input persistence inaccurately
> ([SPARK-18608|https://issues.apache.org/jira/browse/SPARK-18608]) and b)
> check the persistence level of the input dataset but not any of its parents.
> These issues can result in unwanted double-caching of input data & degraded
> performance (see
> [SPARK-21799|https://issues.apache.org/jira/browse/SPARK-21799]).
> This ticket proposes adding a boolean {{handlePersistence}} param
> (org.apache.spark.ml.param) so that users can specify whether an ML algorithm
> should try to cache un-cached input data. {{handlePersistence}} will be
> {{true}} by default, corresponding to existing behavior (always persisting
> uncached input), but users can achieve finer-grained control over input
> persistence by setting {{handlePersistence}} to {{false}}.
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