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Pytorch has 9 transitive dependencies for cpu-only execution, or 29 to bring in cuda.

candle, the most popular rust ml library I found in a cursory search, has 119 for cpu only, and 150 to bring in CUDA.

I guess it's taste whether that's comparable

this would have been a way more satisfying dunk if nvidia hadn't split the cuda functionality needed by pytorch into 19 (!) packages on pypi but such is life.

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I expected somebody to pull out an example "disproving" my point. It wouldn't be hard. I can point out rust projects that have a ton of external dependencies, and then point at similar projects with very few.

But a Python example doesn't really count, in my mind -- Python is pre-GitHub so tends to have small numbers of large external dependencies, like C++ and other older languages.

If a python example doesn't count, why were your two example languages python and go?

But yeah, small number of large dependencies is the way to go.

Replying to the sentence "The dependency tree of comparable Go and Rust projects seem comparable, IMO." with a Python example seems pretty odd.

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