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Not sure if this is along the lines of what you're thinking, but we tried looking at this a while ago: https://arxiv.org/abs/2103.14586

This sounds off to me. I was able to save ~20k EUR / year while on a 40k/year PhD salary in Austria. I was living a decent livestyle (intercontinental yearly vacations, taking on cost-of-living expenses for my girlfriend, ...), yet I was far from the upper 5 % of the population, and living in an expensive-ish town (the gentrified/expensive area of Berlin i now live in has roughtlye the same overall CoL). So I am assuming your problems come down to varying definitions of "decent lifestyle".

I'm in Deep Learning research (e.g., you'd see me at NIPS, ICML or NIPS), so I don't have too much insight into the CV community. Thanks for sharing, interesting to know that they still rely on TF so much. As far as deep learning research goes, PyTorch is the clear winner by now. At least that's my subjective impression, but I also lack data. One recent data point is this one about frameworks used for ICLR 2019 submissions compared to the previous year: https://www.reddit.com/r/MachineLearning/comments/9kys38/r_f... , which would suggest about parity in terms of usage: TensorFlow went from 228 mentions last year to 266 this year, Pytorch from 87 to 252. Considering that a large number of these submissions is from Alphabet (this NIPS 10% of the papers were from either Google or DeepMind, so ICLR will be similar), so they naturally use TF, meaning that PyTorch actually as a slight lead (actually less than expected, but still. At least the growth numbers are quite impressive).

What's your source for Tensorflow being in the lead? I'd love to have some actual data on this. In my experience, for a long time now, I've barely seen anyone outside Alphabet doing anything with TF. Everywhere I look and everyone I talk to uses PyTorch. So from my perspective, PyTorch is (IMO deservedly) miles ahead in usage, and TF 2.0 will probably do only little to anything to close that gap. Disclaimer: I'm in research, things might be different in production environments.

This is the paper from the winners of the Tox21 Challenge: https://www.frontiersin.org/articles/10.3389/fenvs.2015.0008.... (fun fact: one of the authors is the inventor of the LSTM)