hparams as dict [blocked by 1041] (#1029)

* hparams as dict

* hparams as dict

* fixing

* fixing

* fixing

* fixing

* typing

* typing

* chnagelog

* update set hparams

* use setter

* simplify

* chnagelog

* imports

* pylint

* typing

* Update training_io.py

* Update training_io.py

* Update lightning.py

* Update test_trainer.py

* Update __init__.py

* Update base.py

* Update utils.py

* Update test_trainer.py

* Update training_io.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update callback_config.py

* Update callback_config.py

* Update test_trainer.py

Co-authored-by: William Falcon <waf2107@columbia.edu>

pytorch_lightning/callbacks/model_checkpoint.py

@@ -27,9 +27,11 @@ class ModelCheckpoint(Callback):
 
                 # save epoch and val_loss in name
                 ModelCheckpoint(filepath='{epoch:02d}-{val_loss:.2f}.hdf5')
+
                 # saves file like: /my/path/here/sample-mnist_epoch=02_val_loss=0.32.ckpt
                 # if model already exits, the file will be: /my/path/here/sample-mnist-v0_epoch=02_val_loss=0.32.ckpt
 
+
         monitor: quantity to monitor.
         verbose: verbosity mode, False or True.
         save_top_k: if `save_top_k == k`,
@@ -135,7 +137,7 @@ class ModelCheckpoint(Callback):
         if self.save_function is not None:
             self.save_function(filepath)
         else:
-            raise ValueError(".save_function() not set")
+            raise ValueError("Method `.save_function()` not set")
 
     def check_monitor_top_k(self, current: float) -> bool:
         less_than_k_models = len(self.best_k_models) < self.save_top_k