[jira] [Commented] (SPARK-3728) RandomForest: Learn models too large to store in memory

["Joao Guilherme Daros Fidelis (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15687501#comment-15687501 ] 

Joao Guilherme Daros Fidelis commented on SPARK-3728:
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Hello, I was investigating this issue and for me it is already resolved. Looking at the implementation of the run method of the RandomForrest class, I see that it already uses a stack and has a comment that it uses a stack because of this problem. I am not experienced with scala, so I may be missing something. Did someone correct the issue and forgot to mark as solved? The code I am seeing is line 172 of this file: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala.

Thank you!

> RandomForest: Learn models too large to store in memory
> -------------------------------------------------------
>
>                 Key: SPARK-3728
>                 URL: https://issues.apache.org/jira/browse/SPARK-3728
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>            Priority: Minor
>
> Proposal: Write trees to disk as they are learned.
> RandomForest currently uses a FIFO queue, which means training all trees at once via breadth-first search.  Using a FILO queue would encourage the code to finish one tree before moving on to new ones.  This would allow the code to write trees to disk as they are learned.
> Note: It would also be possible to write nodes to disk as they are learned using a FIFO queue, once the example--node mapping is cached [JIRA].  The [Sequoia Forest package]() does this.  However, it could be useful to learn trees progressively, so that future functionality such as early stopping (training fewer trees than expected) could be supported.



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