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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-21972) Allow users to control input data persistence in ML Estimators via a handlePersistence ml.Param
Date Mon, 11 Sep 2017 05:40:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-21972?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16160758#comment-16160758
] 

Apache Spark commented on SPARK-21972:
--------------------------------------

User 'zhengruifeng' has created a pull request for this issue:
https://github.com/apache/spark/pull/19186

> 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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