At the moment the XGBoost model is re-learned every time a measurement step is executed.
In order to have it quicker we can two two steps:
Learn the model only one time per Github Action Runner instantiation and then pickle the current memory structure so we can execute it later directly. Inferencing cost is in the low ms range.
Or we could even pre-learn the model for all the different CPU architectures on Github, as there are only a few. THis way we only have to run through model re-training everytime we do a new release
At the moment the XGBoost model is re-learned every time a measurement step is executed.
In order to have it quicker we can two two steps:
Help wanted :)