Timothy Hunter created SPARK-14567:
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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
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