Custom algorithms and PSC libraries version dependencies
- Upgrading to version 3.4.0 or above (4.0.0, 4.1.0, 4.2.0, 4.3.0, 4.4.0, 4.4.1, and 4.4.2) of the MLTK requires upgrading to version 1.3 of the Python for Scientific Computing add-on.
- Upgrading to version 5.0.0 of the MLTK requires upgrading to version 2.0 of the Python for Scientific Computing add-on.
If you have written any custom algorithms that rely on the PSC libraries, upgrading to version 1.3 or 1.4 the PSC library add-on will impact those algorithms. You must re-train any models (re-run the search that used the
fit command) using those algorithms after you upgrade the PSC add-on.
You cannot access new features in the MLTK without upgrading to the latest version of the toolkit. See the following dependencies table for the specific requirements between the MLTK and PSC add-on versions.
Specific version dependencies
MLTK Version PSC Version 5.0.0 2.0 4.4.2 1.3 or 1.4 4.4.1 1.3 or 1.4 4.4.0 1.3 or 1.4 4.3.0 1.3 or 1.4 4.2.0 1.3 or 1.4 4.1.0 1.3 4.0.0 1.3 3.4.0 1.3 3.3.0 1.2 or 1.3 3.2.0 1.2 or 1.3 3.1.0 1.2
Any algorithms that have been imported from the Python for Scientific Computing add-on into the Machine Learning Toolkit are overwritten when the MLTK app is updated to a new version. Prior to upgrading the MLTK , save your custom algorithms and re-import them manually after the upgrade.
Algorithms are stored in
$SPLUNK_HOME/etc/apps/Splunk_ML_Toolkit/bin/algos on Unix-based systems and
%SPLUNK_HOME%\etc\apps\Splunk_ML_Toolkit\bin\algos on Windows systems.
Savitzky-Golay Filter example
Learn more about the Machine Learning Toolkit
This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 5.0.0