Splunk® Machine Learning Toolkit

ML-SPL API Guide

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Use custom logging

The Splunk Machine Learning Toolkit ships with utilities to make logging easier to manage.

The Splunk Machine Learning Toolkit relies on a different Python interpreter than the interpreter shipped with Splunk Enterprise.

To begin, import a logger. For more detailed logging, you can use a logger with a custom name as in the following example:

from cexc import get_logger

logger = get_logger('MyCustomLogging')
logger.warn('warning!')
logger.error('error!')
logger.debug('info!')

The logger messages are logged to $SPLUNK_HOME/var/log/mlspl.log.

Along with the name provided in get_logger, the function ,in this case the __init__ method), is also recorded:

1491862833.627798 2017-04-10 15:20:33,627 WARNING [mlspl.MyCustomLogging] [__init__] warning!
1491862833.627949 2017-04-10 15:20:33,627 ERROR [mlspl.MyCustomLogging] [__init__] error!
1491862833.628024 2017-04-10 15:20:33,628 DEBUG [mlspl.MyCustomLogging] [__init__] info!

When all else fails, the best place to look is search.log. If you get stuck, ask questions and get answers through community support at Splunk Answers.

Last modified on 01 August, 2019
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This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.4.0, 4.4.1, 4.4.2, 4.5.0, 5.0.0, 5.1.0, 5.2.0


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