Is there a reliable way to use MLFlow in a functional style? As it is not possible to pass the run ID for example to the function which logs a parameter, I wonder whether it is possible to seperate code executed in my MLFLow run into multiple pure fuctions. Have I overlooked something, or is it simply not possible?
So far I have looked up the documentation and did not find a way to pass the run id to a MLFlow log function, neither for parameters, nor metrics or anything else.
The solution is to use the mlflow.client module instead of the mlflow module as stated in the documentation of the mlflow client:
The mlflow.client module provides a Python CRUD interface to MLflow Experiments, Runs, Model Versions, and Registered Models. This is a lower level API that directly translates to MLflow REST API calls. For a higher level API for managing an “active run”, use the mlflow module.
@Andre: Thanks for pointing me in the right direction.