Trainer: min_steps arguments overwrites early_stopping functionality
Hi,
I observed the following behaviour:
If I set min_steps to a large number (guaranteeing that the early stopping callback gets activated), the trainer will continue training even after reaching the min_steps. This error does not occur with min_epochs.
It will print "Trainer was signaled to stop but required minimum epochs (1) or minimum steps (1000) has not been met. Training will continue..." and continue training until the max_epoch flag.
Please reproduce using [the BoringModel and post here]
https://colab.research.google.com/drive/1MCmXCwiPpuDYPkKhmxcI431Jk4U2PoZl#scrollTo=4Dk6Ykv8lI7X
Expected behavior
My understanding is that if early stopping is triggered, the trainer should proceed until the number of minimum epochs is finished or minimum steps is reached and then trigger the early stopping.
Environment
It's all in the colab above.