Mix pytorch lightning with vanilla pytorch
I am doing a meta learning research and am using the MAML optimization provided by learn2learn. However as one of the baseline, I would like to test a non-meta-learning approach, i.e. the traditional training + testing.
Due to the lightning's internal usage of optimizer it seems that it is difficult to make the MAML work with learn2learn in lightning, so I couldn't use lightning in my meta-learning setup, however for my baseline, I really like to use lightning in that it provides many handy functionalities like deepspeed or ddp out of the box.
Here is my question, other than setting up two separate folders/repos, how could I mix the vanilia pytorch (learn2learn) with pytorch lightning (baseline)? What is the best practice?
Thanks!

Decided to answer my question. So I ended up using the torch lightning's manual optimization so that I can customize the optimization step. This would make both approaches using the same framework, and I think is better than maintaining 2 separate repos.
