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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2017/02/06 00:14:41 UTC
[jira] [Resolved] (SPARK-19247) Improve ml word2vec save/load
scalability
[ https://issues.apache.org/jira/browse/SPARK-19247?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley resolved SPARK-19247.
---------------------------------------
Resolution: Fixed
Fix Version/s: 2.2.0
Issue resolved by pull request 16607
[https://github.com/apache/spark/pull/16607]
> Improve ml word2vec save/load scalability
> -----------------------------------------
>
> Key: SPARK-19247
> URL: https://issues.apache.org/jira/browse/SPARK-19247
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Asher Krim
> Assignee: Asher Krim
> Fix For: 2.2.0
>
>
> ml word2vec models can be somewhat large (~4gb is not uncommon). The current save implementation saves the model as a single large datum, which can cause rpc issues and fail to save the model.
> On the loading side, there are issues with loading this large datum as well. This was already solved for mllib word2vec in https://issues.apache.org/jira/browse/SPARK-11994, but the change was never ported to the ml word2vec implementation.
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