[ehsankia]

Researcher's codes are historically not very clean and re-usable. It may work fine if you want to hack together something to get data for a paper, but if you want to run a real production service, and don't want to drown in tech debt in a year; and that often means more code, as you imply above. I don't think it's unnecessarily verbose, it's just that it's more structured and scalable.

[machinelearning]

The discussion was about pytorch vs tensorflow but your argument seems to be about productionizing research code (which is framework agnostic).

Yes there is a lot of code to be written/optimizations to be done to make things production worthy. However I know a lot of tensorflow research projects that handle data batching in such a terrible way that would take weeks to re-write for production.

As for verboseness of pytorch vs tensorflow, I think either could get more verbose under different circumstances. However for simpler tasks, I think tensorflow is more verbose in general (not accounting for the new release which seems to mimic pytorch/keras a little more). For larger production tasks, its a toss-up depending on whether you need to add new components.

[soup10]

If by some miracle a comp sci researcher does publish something relevant to industry most people would rather wait for some other poor soul to deconstruct their work and make usable code or a library out of it.