You may even use the resulting code afterwards in a RapidMiner process within an Execute Python operator. Implementing certain data science steps in Python using your favorite IDE or notebook implementation.Studio class requires a local Studio installation and is suitable in the following cases: To work with versioned projects, a feature that arrives with RapidMiner AI Hub 9.7.0, use the Project class that provides read and write methods to the data file format used in them. The method signatures are the same, with somewhat different extra parameters. RapidMiner AI Hub with SAML authentication is not supported.īoth Studio and Server classes provide a read and a write method for reading / writing data and other objects, and a run method to run processes.The same feature cannot be guaranteed when using this Python library (the library depends on other libraries that are not in our control). That means you should always get the same result after a version update. RapidMiner Studio and AI Hub processes guarantee reproducibility.Extensive tests were only carried out using Python 3.7, but earlier versions are expected to work as well.
Python Scripting extension 9.7.0 or later installed for both Studio and RapidMiner AI Hub, download it from the Marketplace.RapidMiner AI Hub 9.7.0 for Server class.RapidMiner Studio 9.7.0 for Studio class.You can find the changelog for the package here. There is an API document for each classes: Project, Studio, Server, Connections, Scoring. Additional notebook files provide more advanced examples.
#RAPIDMINER STUDIO EXAMPLES HOW TO#
This document shows examples on how to use the package.
You can collaborate using the RapidMiner repository and leverage the scalable RapidMiner AI Hub infrastructure to run processes. This Python package allows you to interact with RapidMiner Studio and AI Hub.
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