Is there a general opinion on whether it is more efficient to load data via RDataFrames or uproot directly from ROOT files for machine learning analysis in PyTorch? I have seen discussion on this in multiple areas but most are fairly old and I am wondering if anything has changed in the past 2/3 years.
For my purposes specifically, I believe I will not be doing much fiddling around with the data itself and would like to avoid intermediate files assuming no loss in performance (are intermediate file methods much faster?). I just simply want to get the data from the ROOT files and into a PyTorch readable format in the most efficient manner possible.
Any links to external resources and comments are appreciated.
Thanks.
Older relevant post: python - What the fastest, most memory-efficient way of opening a ROOT NTuple for machine learning? - Stack Overflow