On the contrary, thanks for having included Docker in that list. It's the obvious answer to so many problems (developing, running and deploying apps, replicating deterministic Python environments, not installing linux dependencies required by Python packages directly on your machine, and so on).
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.
Why thank him for including Docker if it's the "obvious answer"? You're already using it, that's great.
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.
The former approach is more like the Unix way: each tool does one thing and does it well. I prefer that because I can then assemble a workflow that works for me. It's easier to build pipelines when you can drop in each piece one by one.
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.
I think docker is great for providing isolated environment, venv has similar goals. pip-tools + docker is powerful combination, but the article doesnt mention pip-tools for some reason.
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.
[1] https://github.com/NVIDIA/nvidia-docker
[2] https://github.com/docker/compose/issues/6691