[jira] [Created] (SPARK-14567) Add instrumentation logs to MLlib training algorithms

["Timothy Hunter (JIRA)" <ji...@apache.org>]

Timothy Hunter created SPARK-14567:
--------------------------------------

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