Hi WackyRabbit7
Yes, we definitely need to work on wording there ...
"Dynamic" means you register a pandas object that you are constantly logging into while training, think for example the image files you are feeding into the network. Then Trains will make sure it is constantly updated & uploaded so you have a way to later verify/compare different runs and detect dataset contemplation etc.
"Static" is just, this is my object/file upload and store it as an artifact for me ...
Make sense ?
So dynamic or static are basically the same thing, just in dynamic, I can edit the artifact while running the experiment?
Correct
Second, why would it be overwritten if I run a different run of the same experiment?
Sorry, I meant in the same run, if you reuse the artifact name you will be overwriting it. Obviously different runs different artifacts :)
So dynamic or static are basically the same thing, just in dynamic, I can edit the artifact while running the expriment?
Second, why would it be overwritten if I run a different run of the same experiment? As I saw, each object is stored under a directory with the task ID which is unique per run, so I assume I won't be overriding artifacts which are saved under the same name in different runs (regardless of static or dynamic)
this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?
Yes, at the end the name of the artifact is what it will be stored under (obviously if you reuse the name you basically overwrites the artifact)
Mmm maybe, lets see if I get this straight
A static artifact is a one-upload object, a dynamic artifact is an object I can change during the experiment -> this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?