[
https://issues.apache.org/jira/browse/SPARK-14567?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15265149#comment-15265149
]
Joseph K. Bradley commented on SPARK-14567:
-------------------------------------------
Leaving this open for more algorithms in 2.1
> Add instrumentation logs to MLlib training algorithms
> -----------------------------------------------------
>
> Key: SPARK-14567
> URL: https://issues.apache.org/jira/browse/SPARK-14567
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Reporter: Timothy Hunter
> Assignee: Timothy Hunter
>
> In order to debug performance issues when training mllib algorithms,
> it is useful to log some metrics about the training dataset, the training
> parameters, etc.
> This ticket is an umbrella to add some simple logging messages to the most
> common MLlib estimators. There should be no performance impact on the current
> implementation, and the output is simply printed in the logs.
> Here are some values that are of interest when debugging training tasks:
> * number of features
> * number of instances
> * number of partitions
> * number of classes
> * input RDD/DF cache level
> * hyper-parameters
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
