Manage Python environments
We already explained how to choose the default Python environment for your executions in RapidMiner Studio. We also covered how to set the default environment for RapidMiner AI Hub executions.
Python environments are a great way to eliminate package dependency pollution and interference between different projects. In this case you will probably have multiple Python environments in use on your machine running RapidMiner Studio.
It is possible to execute a single Python operator using a specific Python environment, other than the default one provided in RapidMiner Studio preferences. To do this, uncheck the use default python in your operator parameter list, and specify the details of your environment using the advanced parameters that appear.
Keep in mind that the environment names displayed in RapidMiner Studio are always the local environments on the machine running Studio. When you want to create a process that is portable to RapidMiner AI Hub, you need to ensure that the same environments with the same names are available on your target RapidMiner AI Hub deployment.
