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

["Manoj Kumar (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15368216#comment-15368216 ] 

Manoj Kumar commented on SPARK-3728:
------------------------------------

Hi [~xusen]. Are you still working on this?

> 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
>
> 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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org