Using Docker and Splunk to Operationalize the Splunk Machine Learning Toolkit

Configuring and maintaining a Splunk Dev environment can be challenging as new releases of apps and the software are made available. Leveraging the official Docker image, the newest versions of Splunk Enterprise and various apps can be made available without a time commitment or worries about future updates.

The requirements for this tutorial are:

Docker engine installed

  • Root/Sudo Access for the server running docker
  • Internet connectivity for the server or workstation
  • Basic understanding of docker
  • Splunkbase account username & password

To check if Docker is installed on your server or workstation simply type:

$ docker --version
Docker version 18.09.2, build 6247962

If you see a similiar output from the command above, you can move onto the next step. Otherwise checkout the documentation at to install for your platform of choice.

Using the Splunk Supported Docker Image You can pull the latest version of the Splunk supported Docker image using the following command syntax:

$ docker pull splunk/splunk:latest

More information on the officially supported Docker image can be found at Docker Hub.

Using the following command syntax, more advanced environmental variables will allow you to pre-install apps into the container running Splunk Enterprise.

$ docker run -d -p 8000:8000 -e 'SPLUNK_START_ARGS=--accept-license' -e 'SPLUNK_PASSWORD=splunk123' -e SPLUNK_APPS_URL=, -e SPLUNKBASE_USERNAME=<> -e SPLUNKBASE_PASSWORD=<redacted> splunk/splunk:latest

In the above example there are two parameters that will need to be changed:


These will need to be changed to your Splunkbase username and password credentials in order for the apps (Python for Scientific Computing v 1.3 & Splunk Machine Learning Toolkit v 4.1.0) to be downloaded as part of launching the container.

With the parameters set, you should be able to login to the Splunk instance using the credentials admin:splunk123. If you prefer a different password, you can update the parameter in the docker command line or change it after logging in. Splunk web can be accessed using a web browser: or http://localhost:8000

If you’d like to dive deeper into the Splunk Machine Learning Toolkit and the showcase examples in the product, you can leverage our Splunk Machine Learning channel on YouTube for more.