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Hi, What Would Be The Recommended Way To Add/Track Arbitrary Models To/With Outputmodels? Currently Hacking It By Using Joblib Dump And Subsequently Deleting Unwanted "Local" Files. Arbitrary In This Case Just Extensions To Some Scikitlearn Classes.

Hi,

What would be the recommended way to add/track arbitrary models to/with OutputModels? Currently hacking it by using joblib dump and subsequently deleting unwanted "local" files. Arbitrary in this case just extensions to some ScikitLearn classes.

  
  
Posted one year ago
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Answers 4


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.

  
  
Posted one year ago

Hi SlimyDove85 ,

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

  
  
Posted one year ago

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") `

  
  
Posted one year ago

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

  
  
Posted one year ago
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