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.

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 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.