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https://issues.apache.org/jira/browse/SPARK-21535?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16103547#comment-16103547
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yuhao yang commented on SPARK-21535:
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It's not in my opinion. https://issues.apache.org/jira/browse/SPARK-21086 is
trying to store all the trained models in the TrainValidationSplitModel or
CrossValidatorModel according to the discussion, and with a control parameter
which is turned off by default. Anyway changing the training process hardly has
an impact on that.
> Reduce memory requirement for CrossValidator and TrainValidationSplit
> ----------------------------------------------------------------------
>
> Key: SPARK-21535
> URL: https://issues.apache.org/jira/browse/SPARK-21535
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.2.0
> Reporter: yuhao yang
>
> CrossValidator and TrainValidationSplit both use
> {code}models = est.fit(trainingDataset, epm) {code} to fit the models, where
> epm is Array[ParamMap].
> Even though the training process is sequential, current implementation
> consumes extra driver memory for holding the trained models, which is not
> necessary and often leads to memory exception for both CrossValidator and
> TrainValidationSplit. My proposal is to optimize the training implementation,
> thus that used model can be collected by GC, and avoid the unnecessary OOM
> exceptions.
> E.g. when grid search space is 12, old implementation needs to hold all 12
> trained models in the driver memory at the same time, while the new
> implementation only needs to hold 1 trained model at a time, and previous
> model can be cleared by GC.
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