When I launch my main script on the cluster with ddp mode (2 GPU's), Pytorch Lightning duplicates whatever is executed in the main script, e.g. prints or other logic. I need some extended training logic, which I would like to handle myself. E.g. do something (once!) after Trainer.fit(). But with the duplication of the main script, this doesn't work as I intend. I also tried to wrap it in if __name__ == "__main__", but it doesn't change behavior. How could one solve this problem? Or, how can I use some logic around my Trainer object, without the duplicates?
I have since moved on to use the native "ddp" with multiprocessing in PyTorch. As far as I understand, PytorchLightning (PTL) is just running your main script multiple times on multiple GPU's. This is fine if you only want to fit your model in one call of your script. However, a huge drawback in my opinion is the lost flexibility during the training process. The only way of interacting with your experiment is through these (badly documented) callbacks. Honestly, it is much more flexible and convenient to use native multiprocessing in PyTorch. In the end it was so much faster and easier to implement, plus you don't have to search for ages through PTL documentation to achieve simple things. I think PTL is going in a good direction with removing much of the boiler plate, however, in my opinion, the Trainer concept needs some serious rework. It is too closed in my opinion and violates PTL's own concept of "reorganizing PyTorch code, keep native PyTorch code". If you want to use PTL for easy multi GPU training, I personally would strongly suggest to refrain from using it, for me it was a waste of time, better learn native PyTorch multiprocessing.