comments about rule 8: "Make the image one-click runnable" about ten-simple-rules-dockerfiles HOT 5 OPEN

This is what workflow managers are for, for which many use containers. This paper is scoped to just talking about containers.

from ten-simple-rules-dockerfiles.

@vsoch Thank you for your quick reply. The document is called "Ten Simple Rules for Writing Dockerfiles for Reproducible Data Science" and I think it is clear that it is most simple and best reproducible to include the data in the dockerfile (as discussed at rule 7), and if so, the result can also be included in the docker file, and if so, it must not even be ran. By this, it cannot be run wrongly, which can be an advantage in corner cases.
Of course, other requirements such as maintainability may force to separate container images and processing data, thus preventing storing results in the container, but then this rule should be in the "Ten Simple Rules for Writing Dockerfiles for Maintainable Data Science" document :)

from ten-simple-rules-dockerfiles.

Yes, but if your data is 7TB you aren’t going to put it in a container. That statement applies to small data only (which to be fair, is quite a lot). If there are identifiers in the data you also couldn’t easily share it publicly. So it’s not always possible or feasible to do so.

from ten-simple-rules-dockerfiles.

@vsoch Yes, I see, but the rule requires that even small data (that could be easily shared) still must not be stored inside the container but mounted, doesn't it?
So I think it is like "If data is larger, (unfortunately) it cannot be stored in the container, so it can be only mounted at run time".
(I see that for maintainability it probably is better to mount smaller data as well, especially assuming that it is available in some archive anyway.)

from ten-simple-rules-dockerfiles.

The rule does not explicitly state that - it targets "large" datasets time and time again, and suggests that small are OK (and the point could have been made more clear).

from ten-simple-rules-dockerfiles.