3 Answers
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thanks for using SageMaker! you're on the right path - you'll need to pass in an argument for "predictor_cls" when creating your Model instance in order for a predictor object to be returned after calling deploy(), e.g.
from sagemaker.model import Model
from sagemaker.predictor import RealTimePredictor, csv_serializer, csv_deserializer
class Predictor(RealTimePredictor):
def __init__(self, endpoint_name, sagemaker_session=None):
super(Predictor, self).__init__(
endpoint_name, sagemaker_session, csv_serializer, csv_deserializer
)
trainedmodel = Model(..., predictor_cls=Predictor)
xgb_predictor = trainedmodel.deploy(...)
xgb_predictor.predict(...)
API reference:
- https://sagemaker.readthedocs.io/en/stable/model.html
- https://sagemaker.readthedocs.io/en/stable/predictors.html
hope that helps!
answered 5 years ago
0
Any special reason for using csv serializer/deserializer? In my case I reload a model to analyze videos (frames in numpy array actually). What serializer/deserializer should I use?
Actually, any doc regarding how to properly use the argument predictor_cls would be highly appreicated.
answered 3 years ago
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