[scikit-learn] [ANN] Scikit-learn 0.20.0

[Manuel CASTEJÓN LIMAS]

How about a docker based approach? Just thinking out loud
Best
Manuel

El vie., 28 sept. 2018 19:43, Andreas Mueller <t3kcit at 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?
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