Splunk® Machine Learning Toolkit

ML-SPL API Guide

Download manual as PDF

This documentation does not apply to the most recent version of MLApp. Click here for the latest version.
Download topic as PDF

Custom algorithms using PSC libraries

As with versions 3.4.0 and 4.0.0, upgrading to version 4.1.0 of the toolkit requires upgrading to version 1.3 of the Python for Scientific Computing library.

Two previous versions of the MLTK (3.2.0 and 3.3.0) will successfully operate on versions 1.2 or 1.3 of the Python for Scientific Computing add-on. However, users cannot access new features in the 3.4.0 release and above without upgrading to that version. Version 4.1.0 of the toolkit requires the upgrade to version 1.3 of PSC.

If you have written any custom algorithms that rely on the PSC libraries, upgrading to the new version of the PSC library will impact those algorithms. You will need to re-train any models (re-run the search that used the fit command) using those algorithms after you upgrade PSC.

Specific version dependencies:

MLTK Version PSC Version
4.1 1.3
4.0 1.3
3.4 1.3
3.3 1.2 or 1.3
3.2 1.2 or 1.3
3.1 1.2

If you are still stuck, try posting your question on Splunk Answers.

PREVIOUS
Custom logging
  NEXT
Best Practices

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.1.0, 4.2.0, 4.3.0


Was this documentation topic helpful?

Enter your email address, and someone from the documentation team will respond to you:

Please provide your comments here. Ask a question or make a suggestion.

You must be logged into splunk.com in order to post comments. Log in now.

Please try to keep this discussion focused on the content covered in this documentation topic. If you have a more general question about Splunk functionality or are experiencing a difficulty with Splunk, consider posting a question to Splunkbase Answers.

0 out of 1000 Characters