I agree with you that Docker should not be there, but the reality is that people us it to replace some other tools (like venv).
I wonder why you prefer the former approach.
I wonder why you prefer the former approach.
BTW, to comment one of the point you made in the article, it's not that hard to run CUDA inside a container. It's less straightforward but quite well documented. You basically need nvidia-docker [1] on the host and start your containers with the runtime 'nvidia'. docker-compose still doesn't support it officially but there are workarounds. [2] I'm running it on ~50 instances in production and automated all the setup with ansible successfully.
Docker doesn't do anything Python specific on its own. It can be part of a pipeline but only with support from the Python specific tools which is what should be discussed in this kind of article.
All-in-one tools almost never do things exactly the way you want. They have a higher barrier to entry as well as a stronger lock in effect than smaller tools. If I fall out of love with venv, I can replace it with Docker. I can't just do that with an all-in-one tool.
Having said that, poetry is quite well designed and I do encourage junior developers to explore it for themselves instead of just doing what I do. If I was a junior developer today I might be quite glad for a single all-in-one tool that gets me on my feet with good practices from day one.