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Deploying with @fastdotai isn't always learn = load_learner(), learn.predict. There are numerous scenarios when you might only want some, part, or none of both the API and the library as a whole. In this thread we will be exploring your options, how they work, and what to do: 1/n
Ideally we have the following context:
DataBlock -> DataLoaders -> Model -> Learner -> Train
This can then stem off to a few things:
1. learn.export() -> Model and DataLoaders (which are now blank) ...
DataBlock -> DataLoaders -> Model -> Learner -> Train
This can then stem off to a few things:
1. learn.export() -> Model and DataLoaders (which are now blank) ...
In this scenario, we need to ensure that ALL functions which were used in relation to the data are imported before loading in the learner. This can run into issues when using fastAPI and other platforms when loading in the Learner is done in a multi-process fashion 3/
