I am using a shared GPU server. The out of memory error is coming depending on the other user’s usage. But when I am running on 1000 videos, it is running without any error. So my question is, can I train first on one set of 1000 videos, then save the trained model using torch.save(), then load it for training using the other set of 1000 videos like that. Will this strategy work?
Thanks