question: Multiple models, one dataloader?
context: I have a training regime that is disk-speed bound, because instances are loaded from disk.
  I would like to train multiple models with one dataloader. That way, I can do model selection over many models, but reduce the number of disk reads. Is this possible?
answer: Dear @turian,
  Yes, it is possible.
  You could do something like this.
  class MultiModels(LightningModule):
  
      def __init__(self, models: List[nn.Module]):
          self.models = models
  
      def compute_loss(self, model, batch):
          loss = ...
          return loss
  
      def training_step(self, batch, batch_idx):
          loss = sum(compute_loss(model, batch) for model in self.models)
          return loss
  
  
  model = MultiModels([resnet50_model, alexnet_model, ...])
  dm = ...
  trainer.fit(model, dm)
  
  Does this answer your questions ?