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https://issues.apache.org/jira/browse/SPARK-14567?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley resolved SPARK-14567.
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Resolution: Fixed
Fix Version/s: 2.2.0
> 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
> Fix For: 2.2.0
>
>
> 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
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