One criticism is that you have to install the same package, torch, but from different Python indexes in order to install the cpu version or gpu version, on Linux. On windows, `pip install torch` gets you the cpu version. On linux, that gets you a ton of Nvidia extras that take a lot of space.

GPU support should really be a optional extra eg `torch[gpu]` or `torch[nvidia]`.

I don't think it's caused by PyTorch on its own, but every AI-related Python project I try out locally manages to depend on a version of PyTorch that isn't in my disk cache yet. Having to download a gigabyte of dependencies for every project gets tiresome.

The Rust compile cycle will probably generate a gigabyte of files locally as well, but at least they can be `rm`'d out of `target/` once it's done.

It should be said that for this project that's entirely irrelevant of course, but seeing PyTorch has made me skip over projects on the HN homepage before and probably will again in the future.

The entire Python ecosystem is horrible and shouldn’t have been as falsely boosted by institutions as it was in the 2010s. Yes, it got less bad with 3.8 or whatever version added type annotations. But making so much of ML depend on Python has made it distasteful to a lot of devs who would otherwise have contributed more to it.

We really need to move all AI/ML research off PyTorch to Candle or… just anything that isn’t Python or another old-gen, broken language like it.