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                                            jassinm

Distributing training (or running model.summarize in parallel

I would be very interested in how to distributed training in pomegranate using `summarize` and `from_summarize` with other backends. I tried this with spark for HMM baum-welch: ``` s1 =...

Output tables

would love this for pandas

Cannot install imagededup==0.0.1, .. imagededup==0.1.0 because these package versions have conflicting dependencies.

on apple m1 I got it working by - installing [tensorflow metal](https://makeoptim.com/en/deep-learning/tensorflow-metal) (see) - removing tensorflow line from requirements.txt and setup.py - update `COMPILE_LINK_ARGS` to `COMPILE_LINK_ARGS = ['-O3', '-mcpu=apple-m1', '-mtune=native']`...