How about a docker based approach? Just thinking out loud Best Manuel El vie., 28 sept. 2018 19:43, Andreas Mueller <t3k...@gmail.com> escribió:
> > > On 09/28/2018 01:38 PM, Andreas Mueller wrote: > > > > > > On 09/28/2018 12:10 PM, Sebastian Raschka wrote: > >>>> I think model serialization should be a priority. > >>> There is also the ONNX specification that is gaining industrial > >>> adoption and that already includes open source exporters for several > >>> families of scikit-learn models: > >>> > >>> https://github.com/onnx/onnxmltools > >> > >> Didn't know about that. This is really nice! What do you think about > >> referring to it under > >> http://scikit-learn.org/stable/modules/model_persistence.html to make > >> people aware that this option exists? > >> Would be happy to add a PR. > >> > >> > > I don't think an open source runtime has been announced yet (or they > > didn't email me like they promised lol). > > I'm quite excited about this as well. > > > > Javier: > > The problem is not so much storing the "model" but storing how to make > > predictions. Different versions could act differently > > on the same data structure - and the data structure could change. Both > > happen in scikit-learn. > > So if you want to make sure the right thing happens across versions, > > you either need to provide serialization and deserialization for > > every version and conversion between those or you need to provide a > > way to store the prediction function, > > which basically means you need a turing-complete language (that's what > > ONNX does). > > > > We basically said doing the first is not feasible within scikit-learn > > given our current amount of resources, and no-one > > has even tried doing it outside of scikit-learn (which would be > > possible). > > Implementing a complete prediction serialization language (the second > > option) is definitely outside the scope of sklearn. > > > > > Maybe we should add to the FAQ why serialization is hard? > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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