This is the method you're looking for None . But make sure you have a model saved on disk before using it. And if you don't want the model to be deleted from disk after it, make sure to set auto_delete_file=False
Hey @<1569858449813016576:profile|JumpyRaven4> , about your first point, what exactly is the question?
About your second point - you can try to manually save the final model and give it a proper file name, that way we will show it in the UI with the name you provided. Make sure to use xgboost.save_model and not raw pickle.
For your final question , given that your models have customised code, I can suggest trying to use clearml.OutputModel which will register the file you provide it as the serialized model. Meaning it’s going to be your responsibility to decide how to serialize/deserialize the model
Hey tahnks a lot Alex, that's exactly what I was looking for. cheers
Hi Alex,
thanks for your answer. I'm curious about your third point using OutputModel. I could not figure out from the documentation how do you actually use it. I constructed the OutputModel object as such:
out = OutputModel(task, name="my_model", framework="xgboost")
However, I could not find any method in the doc that would allow me to pass the model object to that instance or said otherwise, I can't understand how to use that Output model to register my model which would be stored in a variablemy_model?