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  1. Spark
  2. SPARK-14567

Add instrumentation logs to MLlib training algorithms

    Details

    • Type: Umbrella
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 2.2.0
    • Component/s: ML, MLlib
    • Labels:
      None

      Description

      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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            • Assignee:
              timhunter Timothy Hunter
              Reporter:
              timhunter Timothy Hunter
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              • Created:
                Updated:
                Resolved: