[ggregoire]

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.

[1] https://github.com/NVIDIA/nvidia-docker

[2] https://github.com/docker/compose/issues/6691

[globular-toast]

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.