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.

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Support for the ML-SPL API

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.4.0, 4.4.1


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