Use custom logging
The Splunk Machine Learning Toolkit (MLTK) ships with utilities to make logging easy to manage.
MLTK relies on a different Python interpreter than the interpreter that ships with Splunk Enterprise.
To begin, import a logger. For more detailed logging, you can use a logger with a custom name as shown in the following example:
from cexc import get_logger logger = get_logger('MyCustomLogging') logger.warn('warning!') logger.error('error!') logger.debug('info!')
Logger messages are logged to $SPLUNK_HOME/var/log/mlspl.log
.
Along with the name provided in get_logger
, the function 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!
If you cannot find messages using the logger, you can also look in the search.log
.
Create user facing messages | Adding Python 3 libraries |
This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 5.1.0, 5.2.0, 5.2.1, 5.2.2, 5.3.0, 5.3.1, 5.3.3, 5.4.0, 5.4.1, 5.4.2, 5.5.0
Feedback submitted, thanks!