Yes that's the issue! Most of the software is already ready, usable and just works... unless you use anaconda. Now that I think about it, is there some technical reason for that? I always thought it was mostly about stability, but I can't imagine python 3.11 being so unstable as to warrant waiting a whole year before even porting.

For one of my projects, conda ‘just works’ to get working with the GPU but following the instructions for pip doesn’t work. On the other hand there’s another package I am interested in using where I need to build out of GitHub and it’s a very different story.

I see myself as interested in commercial exploitation of transformers right now and I am delighted with the results. The first time I tried clustering all the Ukraine articles lumped together, all the sports were lumped, it runs 5x faster than my LDA-based clustering system and I think does a better job. With results like this I am happy to trade ‘cutting edge’ for convenience.

I have thought about a ‘path less followed’ in Python which is a truly sound package manager like maven for Python (as opposed to Poetry which I’m not sure is sound but it sure is slow) and I can say I like the way conda works I just would rather do it with wheels. One beef I have with conda is that the bzip2 files are slow to decompress and even over a DSL line I would trade a little more downloading for faster installs.

>>for a couple of years now there isn't a good reason anymore to use anaconda anyways.

I think of Anaconda envs as great back ends for Jupyter notebooks. There is still a place for it.