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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 wa
by ehsankia 7y ago
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 7y agoThe 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 7y agoIf 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.