I'd say this is more like pytorch is horribly packaged and a nightmare as an eng user to integrate into codebases. All of their assumptions are you use conda and are a datascientist one time doing something in an ipython notebook. Very little consideration is made to how you deploy at scale reliably. It's not really on poetry.
While this is on the one hand at least partially true, it is also the case that as long a poetry can't deal with these (and similar) cases then it cannot really be said to be a candidate for the 'default' dependency management tool. Having people say 'just use poetry' as the go to answer is very bad default advice as long as poetry will fail in a number of important cases.
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I'd say this is more like pytorch is horribly packaged and a nightmare as an eng user to integrate into codebases. All of their assumptions are you use conda and are a datascientist one time doing something in an ipython notebook. Very little consideration is made to how you deploy at scale reliably. It's not really on poetry.
It's not really on poetry.
While this is on the one hand at least partially true, it is also the case that as long a poetry can't deal with these (and similar) cases then it cannot really be said to be a candidate for the 'default' dependency management tool. Having people say 'just use poetry' as the go to answer is very bad default advice as long as poetry will fail in a number of important cases.
But you can use poetry for it xd