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This sounds a lot like your own image data lake pattern. Raw, transformed, curated, published images. On cheap storage. How do you track the models, versions and what inputs / parameters affected the model? How are outcomes measured / what metrics would you review?
Model versions: Github! Inputs/Parameters/Outcomes: Well documented experiment Jupyter notebooks that I save in a separate backup drive to freeup space along with tensorboard outputs and model checkpoints. Point is I can reproduce with ease and not think too much about it.
Does it mean you add new code whenever you want to change parameters or you save stuff just when you find something interesting? Also, I guess you don't fix your seeds!?
I just try to make sure that experiments at any point of time can be 100% reproduced in terms of the functioning but not necessarily to the point where the exact data is used (fixed seeds). Benchmarks do use the same exact images though, so effectively the same outcome there.
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