I'd like to cache some functions to avoid rebuilding models like so:
@cached
def train(model, dataparams): ...
model is an (untrained) scikit-learn object and dataparams is a dict.
The @cached annotation forms a SHA checksum out of the parameters of the
function it annotates and returns the previously calculated function result
if the parameters match.
The tricky part here is reliably generating a checksum from the parameters.
Scikit uses Python's pickle (
http://scikit-learn.org/stable/modules/model_persistence.html) but the
pickle library is non-deterministic (same inputs to pickle.dumps yields
differing output! -- *I know*).
So... any suggestions on how to generate checksums from models in python?
Thanks.
- Stuart
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