>> What do you think would be the hard part / why
>> do you think this is much work?
>>      
> I think that their is a bit of learning to be done. In particular, the
> code would have to be well tested, and I have no idea of what the right
> way to test it would be. The second problem would be to devise the
> extensive test suite, and be convinced that they are no race conditions
> when gathering the results. I have found that this is tricky code.
>
>    
I belief that testing might be REALLY tricky. I was
more going for a prototype solution than for a releasable at the moment.

>> I have no idea about these things and not much of an idea of how joblib
>> works but changing the backends looks not-so-hard from my naive point
>> of view.
>>      
> No, probably not. The 2 main bodies of work would be:
>
>   1. To extract out the pool management code from the joblib.Parallel
>      object to be able to use other kinds of pools than the
>      multiprocessing pool.
>
>   2. To implement a dispatch mechanism to be able to decide which pool the
>      Parallel object gets.
>
> None of these sound terribly hard, but that's still a bit of effort to
> make it work reliably.
>
> In the past, when developping such changes in joblib with a specific
> scikit-learn driven usecase, I have done a branch of scikit-learn
> replacing the local copy of joblib by an import to the real joblib (you
> can look at how it is doen in the debian branch, for instance
> https://github.com/scikit-learn/scikit-learn/tree/debian/sklearn/externals
> )
> Then, I branched out joblib, and made my changes in the joblib branch,
> testing them on real-world usecases with the scikit.
>
>    
Thanks for the hint. I was just doing that ;)

Cheers,
Andy

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