Custom algorithms and PSC libraries version dependencies

Running version 5.4.1 of MLTK requires Splunk Enterprise 8.1.x or higher or Splunk Cloud Platform.

The Splunk Machine Learning Toolkit requires installation of the correct version of the Python for Scientific Computing (PSC) add-on from Splunkbase. Version 4.2.0 of PSC is a minor release that offers additional packages.

Versions 4.2.0, 4.1.2, 4.1.0, and 3.1.0 of the PSC add-on include the ONNX library. Use one of these versions to bring pre-trained ONNX models into MLTK. This ONNX model feature is only available with MLTK version 5.4.0 or higher. See, Upload and inference pre-trained ONNX models in MLTK.

Before you upgrade to a new version

Any algorithms that have been imported from the PSC 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.

If you have written any custom algorithms that rely on the PSC libraries, upgrading the PSC 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.

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.

Specific version dependencies

For version information that includes MLTK, the PSC add-on, Python, and Splunk platform, see Machine Learning Toolkit version dependencies.

MLTK version	PSC version
5.4.1	3.1.0, 4.1.0, 4.1.2, or 4.2.0
5.4.0	3.1.0, 4.1.0, or 4.1.2
5.3.3	3.0.2, 3.1.0, 4.0.0, 4.1.0, or 4.1.2
5.3.1	3.0.0, 3.0.1, or 3.0.2
5.3.0	3.0.0, 3.0.1, or 3.0.2
5.2.2	2.0.0, 2.0.1, or 2.0.2
5.2.1	2.0.0, 2.0.1, or 2.0.2
5.2.0	2.0.0, 2.0.1, or 2.0.2
5.1.0	2.0.0
5.0.0	2.0.0
4.5.0	1.4