question: How to continue training with a different learning rate
context: I want to resume training from a checkpoint, but I want to use a different learning rate, How to achieve that? I don't  really care about the training states and don't mind start a fresh training as long as the weights are proprely restored.
  Right now I'm using resume_from_checkpoint=ckpt_file when creating the trainer, this automatically would give the old learning rate.
  I also tried remove resume_from_checkpoint=ckpt_file, and do
  net_learner.load_from_checkpoint(cfg.ckpt_path, cfg=cfg)
  trainer.fit(net_learner, train_data_loader, val_data_loader)
  
  but it seems the weights are erased, and the trainer starts from random weights.
  Any help will be most appreciated, thanks so much!
answer: I opened this as an issue. However (as you'll see in the discussion there), it turns out that in my case there was no problem - the .load_from_checkpoint() method works as expected. I probably just made a different mistake which caused my loss to (immediately) blow up after resuming training, which I interpreted as arising from the issue that you described of the weights being overwritten with a new initialization. I shouldn't have jumped to that conclusion so quickly as I didn't actually verify that the weights were different in my case. I just tried it again and it works fine now.
  In your case, it looks like you're using the wrong syntax, which I hadn't spotted but another user did - please refer to the link to see how it should be used. This should solve the problem for you.