question: `save_hyperparameters()` is very slow when there are many hyperparameters, any speed up?
context: Hi,
  I've currently refactored a part of code to use Pytorch Lightning instead of a regular pytorch script. I find a 35% speed up on a toy dataset, where the model had 20.9 K parameters.
  However, when trying a bigger version of the model (9.1 M params), the initialization of the model takes a lot more time (even before lightning prints the number of params). I've managed to nail it down to the self.save_hyperparameters() function in init, since the initialization is instantaneous without calling self.save_hyperparameters(), but takes more than a full minute when calling it.
  Removing self.save_hyperparameters() causes an issue in my code since I am calling  model.load_from_checkpoint() afterward. Would you have any thoughts on how I can speed up the code? The "regular" pytorch model manages to initialize, train and save the model faster than the Lightning one due to this slow down ...
  Thanks!
answer: You have to set self.save_hyperparameters(ignore="model"), since you are passing in a full model as a "hyperparameter", you don't want to save that!