Upgrade the Splunk Machine Learning Toolkit

Requirements

The Splunk Machine Learning Toolkit requires the Python for Scientific Computing add-on. Upgrading to version 3.4.0 of the toolkit requires upgrading to version 1.3 of the Python for Scientific Computing add-on.

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 without upgrading to that version. Version 3.4.0 of the toolkit requires the upgrade to version 1.3 of PSC.

Specific version dependencies:

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

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.

Any algorithms that have been imported from the Python for Scientific Computing add-on into the Splunk Machine Learning Toolkit are overwritten when the Splunk Machine Learning Toolkit app is updated to a new version. Prior to upgrading the Splunk Machine Learning Toolkit, 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.

Splunk Cloud deployments

For Splunk Cloud trial, self-service Splunk Cloud, or Managed Splunk Cloud, open a ticket with support and request the Python for Scientific Computing add-on and Machine Learning Toolkit app be upgraded to the latest version for you.

Splunk Enterprise deployments

Single instance deployment

Upgrade the Splunk Machine Learning Toolkit app on your single instance Splunk Enterprise.

If a newer version of the Python for Scientific Computing add-on is required for the newer version of the Splunk Machine Learning Toolkit, a message will display when you run the Splunk Machine Learning Toolkit after the upgrade instructing you to install a newer version of the Python for Scientific Computing add-on.

To update an app or add-on in Splunk Web

1. In Splunk Web, click the Update option on the app icon in the left-hand Apps bar.
   The Update option appears when a new version of an app is available on Splunkbase.

Alternatively, you can do the following:

1. Download the latest version of the app from Splunkbase.
2. In Splunk Web, click on the gear icon next to Apps in the left navigation bar.
3. On the Apps page, click Install app from file.
4. Click Choose File, navigate to and select the package file for the app or add-on, then click Open.
5. Check the Upgrade app box.
6. Click Upload.

To update an existing app on your Splunk instance using the CLI

Run the following from the command line.

Unix/Linux:

./splunk install app <app_package_filename> -update 1 -auth <username>:<password>

Windows:

splunk install app <app_package_filename> -update 1 -auth <username>:<password>

Alternatively, unpack/unzip the file then copy the app directory to $SPLUNK_HOME/etc/apps on Unix based systems or %SPLUNK_HOME%\etc\apps on Windows systems.

Distributed deployment

In a distributed deployment of Splunk Enterprise, update the Splunk Machine Learning Toolkit, and Python for Scientific Computing add-on if necessary, on every Splunk instance where the application is installed. The Python for Scientific Computing and the Splunk Machine Learning Toolkit should be installed on all search heads where the Splunk Machine Learning Toolkit is used.

If Python for Scientific Computing is installed on your indexers in order to use the distributed apply feature of the Splunk Machine Learning Toolkit, you need to update the Python for Scientific Computing add-on on your indexers as well as your search heads if an update is required. If an update for Python for Scientific Computing is required, you will receive a message indicating this when you run the Splunk Machine Learning Toolkit after upgrading. For information about the distributed apply feature, see Use your indexers to apply models.

If you use search head clusters or indexer clusters, use the deployment methodology of your choice to make the updates.

• For information about updating search head cluster members, see Use the deployer to distribute apps and configuration updates in the Distributed Search manual.
• For information about updating peers in an indexer cluster, see Manage app deployment across all peers in the Managing Indexers and Clusters of Indexers manual.