renamed options

docs/Trainer/Training Loop.md

@@ -17,7 +17,7 @@ Lightning automatically logs gpu usage to the test tube logs. It'll only do it a
 #### Check which gradients are nan 
 This option prints a list of tensors with nan gradients.
 ``` {.python}
-trainer = Trainer(check_grad_nans=False)
+trainer = Trainer(print_nan_grads=False)
 ```
 
 ---
@@ -31,4 +31,15 @@ trainer = Trainer(check_val_every_n_epoch=1)
 #### Display metrics in progress bar 
 ``` {.python}
 trainer = Trainer(progress_bar=True)
-```
+```
+
+---
+#### Display the parameter count by layer
+By default lightning prints a list of parameters *and submodules* when it starts training.
+
+---
+#### Force training for min or max epochs
+It can be useful to force training for a minimum number of epochs or limit to a max number
+``` {.python}
+trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)
+```

pytorch_lightning/models/trainer.py

@@ -43,12 +43,12 @@ class Trainer(TrainerIO):
                  check_val_every_n_epoch=1,
                  fast_dev_run=False,
                  accumulate_grad_batches=1,
-                 enable_early_stop=True, max_nb_epochs=5, min_nb_epochs=1,
+                 enable_early_stop=True, max_nb_epochs=1000, min_nb_epochs=1,
                  train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=0.95,
                  log_save_interval=1, add_log_row_interval=1,
                  lr_scheduler_milestones=None,
                  use_amp=False,
-                 check_grad_nans=False,
+                 print_nan_grads=False,
                  amp_level='O2',
                  nb_sanity_val_steps=5):
 
@@ -76,7 +76,7 @@ class Trainer(TrainerIO):
         self.lr_scheduler_milestones = [] if lr_scheduler_milestones is None else [int(x.strip()) for x in lr_scheduler_milestones.split(',')]
         self.lr_schedulers = []
         self.amp_level = amp_level
-        self.check_grad_nans = check_grad_nans
+        self.print_nan_grads = print_nan_grads
         self.data_parallel_device_ids = gpus
         self.data_parallel = gpus is not None and len(gpus) > 0
 
@@ -427,7 +427,7 @@ class Trainer(TrainerIO):
         else:
             loss.backward()
 
-        if self.check_grad_nans:
+        if self.print_nan_grads:
             model = self.model.module if self.data_parallel else self.model
             for param in model.parameters():
                 print(param.grad.float().sum())