>>>  from bentoml import BentoService, env, api, artifacts, ver
>>>  from bentoml.handlers import DataframeHandler
>>>  from bentoml.artifact import SklearnModelArtifact
>>>
>>>  @ver(major=1, minor=4)
>>>  @artifacts([SklearnModelArtifact('clf')])
>>>  @env(pip_dependencies=["scikit-learn"])
>>>  class MyMLService(BentoService):
>>>
>>>     @api(DataframeHandler)
>>>     def predict(self, df):
>>>         return self.artifacts.clf.predict(df)
>>>
>>>  bento_service = MyMLService()
>>>  bento_service.pack('clf', trained_classifier_model)
>>>  bento_service.save_to_dir('/bentoml_bundles')

You can create the service, and then call

pack

separately