Pytorch-lightning

Seed for ML research

Usage

Add new model

1. Create a new model under /models.
2. Add model name to trainer_main

AVAILABLE_MODELS = {
    'model_1': ExampleModel1
}

Model methods that can be implemented

Method	Purpose	Input	Output	Required
forward()	Forward pass	model_in tuple with your data	model_out tuple to be passed to loss	Y
loss()	calculate model loss	model_out tuple from forward()	A scalar	Y
check_performance()	run a full loop through val data to check for metrics	dataloader, nb_tests	metrics tuple to be tracked	Y
tng_dataloader	Computed option, used to feed tng data	-	Pytorch DataLoader subclass	Y
val_dataloader	Computed option, used to feed tng data	-	Pytorch DataLoader subclass	Y
test_dataloader	Computed option, used to feed tng data	-	Pytorch DataLoader subclass	Y

Model lifecycle hooks

Use these hooks to customize functionality

Method	Purpose	Input	Output	Required
on_batch_start()	called right before the batch starts	-	-	N
on_batch_end()	called right after the batch ends	-	-	N
on_epoch_start()	called right before the epoch starts	-	-	N
on_epoch_end()	called right afger the epoch ends	-	-	N
on_pre_performance_check()	called right before the performance check starts	-	-	N
on_post_performance_check()	called right after the batch starts	-	-	N