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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:21:20 UTC
[jira] [Updated] (SPARK-18253) ML Instrumentation logging requires
too much manual implementation
[ https://issues.apache.org/jira/browse/SPARK-18253?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-18253:
---------------------------------
Labels: bulk-closed (was: )
> ML Instrumentation logging requires too much manual implementation
> ------------------------------------------------------------------
>
> Key: SPARK-18253
> URL: https://issues.apache.org/jira/browse/SPARK-18253
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Seth Hendrickson
> Priority: Minor
> Labels: bulk-closed
>
> [SPARK-14567|https://issues.apache.org/jira/browse/SPARK-14567] introduced an {{Instrumentation}} class for standardized logging of ML training sessions. Right now, we manually log individual params for each algorithm, partly because we don't want to log all params since some params can be huge in size, and we could flood the logs. We should find a more sustainable way of logging params in ML algos. The current approach does not seem sustainable.
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