Zac, thank you for all your good work. I’ve noticed your contributions. I see sklearn’s pipeline as the industry standard way of scripting pre/post training methods such as transforms, grid searches, validations, etc. A great intro to sklearn pipelines is in Data School’s youtube videos: Use Pipeline to chain together multiple steps - YouTube
Ideally, I’d like to combine sklearn’s preprocessing functions with fastai’s preprocessing/leaner. I’d like to use sklearn pipeline because it’s well documented and exampled. Creating a pipeline to include fastai might just be a matter of wrapping a learner in a conforming interface. Wouldn’t it be great to be able to swap fastai learners and sklearn estimators for best effect. I’d like to use sklearn’s grid search, often used in a pipeline, to find best sklearn hyper parameters but also be able to include fastai learner for comparisions.