I am working on a python desktop app. This app does some predictions. Right now I train my sklearn model using python script, save the parameters of the model as a dictionary in a yaml file. Then, I build in this yaml into my python app. Then, when I am using the app, the model is recreated using parameters from the dictionary. I realized, that people who have a different version of sklearn get an error. I tried to save my model in a pickle file, but in this case, it produced some warning when app was running on a machine with a different version of sklearn.
3 Answers
There is no guarantee that a given sklearn model would be compatible between versions of sklearn. Indeed, the implementation or the internal API may change between versions. See more informations here.
If you consider one version, the best way is indeed to pickle, and not to save the parameters in a yaml file. It's even better to use joblib to do so. See more informations here.
I realized, that people who have a different version of sklearn get an error.
In this case, create isolated Python environments using virtualenvs
Alternatively you can just generate a Python code from a trained model. This way you eliminate any possibility of object incompatibility. Here is a tool that can help with that https://github.com/BayesWitnesses/m2cgen
Related