Model Wrap and Batch Feed Definition

The preferred way of defining the model compatible with the TrainLoop is to implement it as the TTModel as discussed in the TTModel section.

The legacy approach of defining the model for the TrainLoop which still comes in handy in certain specific use cases was to implement a normal PyTorch nn.Module and define a separate batch feed definition for this particular model. Batch feed definitions are objects which need to be inherited from and the user has to implement its abstract methods.

It can be seen that the abstract methods requiring the implementation as part of the AbstractModelFeedDefinition are exactly the same as those which need to be implemented as part of the new TTModel definition discussed in further detail in the TTModel section. While using TTModel is better for readability and experiment tracking on the other hand in some rare use cases operating on the core PyTorch nn.Module model is required instead of using the TTModel extension. For such cases the nn.Module + model feed definition combination option has been kept in the AIToolbox.

Last step that needs to be done in order to train the nn.Module with it’s feed definition as part of the TrainLoop is to wrap the model and the feed definition into the aitoolbox.torchtrain.model.ModelWrap. TrainLoop will automatically detect the use of separate feed definition instead of the TTModel and execute the training based on the contents of the provided ModelWrap.

Example of the training with the model feed definition

For the practical example how the nn.Module can be paired together with its model feed definition and wrapped into the ModelWrapp for the TrainLoop training have a look at the this example training script.