Basically I've defined some extended sklearn models, which I import in my ClearML task file and set them up with some initial parameters.

Some pseudocode:
` mdl = SomeExtendedSklearnModel(**params)

Load data

X = load_data(...)

Run

task = Task.init(...)
output_models = OutputModel(task=task, ..., framework="ScikitLearn")
preds = mdl.fit_predict(X)
joblib.dump(mdl, "mdl.pkl") `

Hi SlimyDove85 ,

Regarding the easiest method to track models - you can try tagging them.
Can you please elaborate on your use case?

If you set Task.init(..., output_uri=<PATH_TO_ARTIFACT_STORAGE>) everything will be uploaded to your artifact storage automatically.
Regarding models. I to skip the joblib dump hack you can simply connect the models manually to the task with this method:
https://clear.ml/docs/latest/docs/references/sdk/model_outputmodel#connect

Without the joblib dump I do not get my models registered as models, even though the experiment runs fine and logs everything else : )

Edit: Note that I also want ClearML to store these into my predefined artifact store, which it does with the aforementioned "hacky" solution.