[HN Gopher] Reactive, reproducible, collaborative: computational...
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       Reactive, reproducible, collaborative: computational notebooks
       evolve
        
       Author : kasperset
       Score  : 25 points
       Date   : 2021-05-03 17:15 UTC (1 days ago)
        
 (HTM) web link (www.nature.com)
 (TXT) w3m dump (www.nature.com)
        
       | dennisy wrote:
       | Is anyone using Deepnote? It seems pretty cool, but a little
       | immature maybe?
        
         | kasperset wrote:
         | Never tried but been using Colab pro and for most part it works
         | pretty well even though I use it for R kernel. The most
         | difficult part in such notebook environment is the installation
         | of some non-standard R packages such as "Vegan" and "Phyloseq"
         | but otherwise no major issues.
        
       | dijksterhuis wrote:
       | > Complicating matters, programmers can struggle to adapt
       | standard version-control workflows to the fast, iterative nature
       | of data exploration. As a result, crucial experimental details
       | can be lost.
       | 
       | > "Somebody will say, oh, did you try this in the model, or did
       | you try this analysis?" she says. Many times, the answer is yes,
       | but because the analysis didn't work, the code is deleted. "What
       | you want to do during the meeting is just pull it back up and be
       | like, oh, yeah, I did, and here's why it didn't work. And our
       | tool lets you actually do that."
       | 
       | Errrrr... Slightly confused as git tags help with this already?
       | 
       | `git tag -a <tag-name> -m "<full description of what this tag is
       | all about>"`
        
         | dennisy wrote:
         | Lots of people seem to state that git already solves lots of
         | the versioning issues for ML experiments. However I am yet to
         | find a good guide or example.
         | 
         | Just tagging commits and experiment runs does not seem enough
         | of a framework to manage large experiments.
        
         | stochastician wrote:
         | Many existing version control tools help with this, but there
         | are some challenges that crop up when doing this with jupyter
         | notebooks:
         | 
         | 1. Versioning notebooks in a semantically-useful way is
         | difficult: think of it more like versioning binary data than
         | source code. I know we struggle with this a fair amount.
         | 
         | 2. There are many debates, some here on HN, about "best
         | practices" for collaborative versioning (do you squash? rebase?
         | etc). These questions exist for notebook users too, and there's
         | a lot of interest in developing tools that either make these
         | decisions for you (are opinionated), integrate them more into
         | the GUI nature of notebooks, or both.
        
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       (page generated 2021-05-04 23:01 UTC)