Rproducibility for Python and beyond

[Simon TOURNIER]

Hi Konrad, all,

French speakers, here is an interesting presentation by Konrad about the state 
of Python for scientific computing and reproducibility.

https://reproducibility.gricad-pages.univ-grenoble-alpes.fr/web/presentation_110423.html#presentation_110423

Without watching the video, here the questions I would like to discuss. :-) 

1. Considering the Konrad's schema of some scientific computation (Model 
--technical choices--> Code --computational env--> Results), there are also 
technical choices about the computational environment, but they are implicit.  
And often impossible to scrutinize because of the lack of transparency.  The 
key, IMHO, is not the determinism of the computation, instead the key is its 
transparency.  Determinism is one mean to obtain transparency and determinism 
is not the only mean.  For instance, this determinism is not affordable for 
very intensive computation, where is not doable to repeat.  How to think about 
determinism considering statistical training of machine learning models?  Other 
said, for some cases, the "compilation" (Code -> Results) of the scientific 
model is too costly.

2. The "redo" of computations is only possible when the citation is correct.  
L'Inria is somehow proposing <https://hal.science/hal-02135891> with the 
BibLaTeX style 
<https://mirrors.ircam.fr/pub/CTAN/macros/latex/contrib/biblatex-contrib/biblatex-software/software-biblatex.pdf>.
  However, this only captures, at best, some technical choices when 
implementing the model.  And this does not capture at all the complete 
computational environment.  What are your ideas for tackling this issue about 
the citation?

For instance, the file "guix describe -f channels" is one mean for capturing 
(and cite too!) one computational environment.  Do we need to make it more 
popular?  How to link this mean with the archiving part of source code (relying 
on SWH, say)?


Cheers,
simon