prog bar option

docs/Trainer/Training Loop.md

@@ -0,0 +1,34 @@
+The asdf
+
+---
+#### Accumulated gradients  
+Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN. 
+
+``` {.python}
+# default 1 (ie: no accumulated grads)
+trainer = Trainer(accumulate_grad_batches=1)
+```
+
+---
+#### Check GPU usage
+Lightning automatically logs gpu usage to the test tube logs. It'll only do it at the metric logging interval, so it doesn't slow down training.
+
+---
+#### Check which gradients are nan 
+This option prints a list of tensors with nan gradients.
+``` {.python}
+trainer = Trainer(check_grad_nans=False)
+```
+
+---
+#### Check validation every n epochs 
+If you have a small dataset you might want to check validation every n epochs
+``` {.python}
+trainer = Trainer(check_val_every_n_epoch=1)
+```
+
+---
+#### Display metrics in progress bar 
+``` {.python}
+trainer = Trainer(progress_bar=True)
+```

docs/Trainer/index.md

@@ -16,41 +16,6 @@ trainer.fit(model)
 
 But of course the fun is in all the advanced things it can do:
 
-``` {.python}
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-from test_tube import Experiment, SlurmCluster
-
-trainer = Trainer(
-                 experiment=Experiment,
-                 checkpoint_callback=ModelCheckpoint, 
-                 early_stop_callback=EarlyStopping,
-                 cluster=SlurmCluster,
-                 process_position=0,
-                 current_gpu_name=0,
-                 gpus=None,
-                 enable_tqdm=True,
-                 overfit_pct=0.0,
-                 track_grad_norm=-1,
-                 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,
-                 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,
-                 amp_level='O2',
-                 nb_sanity_val_steps=5):
-)
-```
-
-
-Things you can do with the trainer module:
-
 **Training loop**    
 
 - Accumulate gradients
@@ -58,6 +23,7 @@ Things you can do with the trainer module:
 - Check which gradients are nan
 - Check validation every n epochs
 - Display metrics in progress bar
+- Display the parameter count by layer
 - Force training for min or max epochs
 - Inspect gradient norms
 - Learning rate annealing

pytorch_lightning/models/trainer.py

@@ -37,7 +37,7 @@ class Trainer(TrainerIO):
                  process_position=0,
                  current_gpu_name=0,
                  gpus=None,
-                 enable_tqdm=True,
+                 progress_bar=True,
                  overfit_pct=0.0,
                  track_grad_norm=-1,
                  check_val_every_n_epoch=1,
@@ -58,7 +58,7 @@ class Trainer(TrainerIO):
         self.track_grad_norm = track_grad_norm
         self.fast_dev_run = fast_dev_run
         self.on_gpu = gpus is not None and torch.cuda.is_available()
-        self.enable_tqdm = enable_tqdm
+        self.progress_bar = progress_bar
         self.experiment = experiment
         self.exp_save_path = experiment.get_data_path(experiment.name, experiment.version)
         self.cluster = cluster
@@ -206,7 +206,7 @@ class Trainer(TrainerIO):
             outputs.append(output)
 
             # batch done
-            if self.enable_tqdm and self.prog_bar is not None:
+            if self.progress_bar and self.prog_bar is not None:
                 self.prog_bar.update(1)
 
         # give model a chance to do something with the outputs
@@ -307,7 +307,7 @@ class Trainer(TrainerIO):
             self.batch_loss_value = 0  # accumulated grads
 
             # init progbar when requested
-            if self.enable_tqdm:
+            if self.progress_bar:
                 self.prog_bar = tqdm.tqdm(range(self.total_batches), position=self.process_position)
 
             for batch_nb, data_batch in enumerate(self.tng_dataloader):
@@ -403,7 +403,7 @@ class Trainer(TrainerIO):
             if response == -1:
                 return -1
 
-        if self.enable_tqdm:
+        if self.progress_bar:
             self.prog_bar.update(1)
 
         # forward pass
@@ -453,7 +453,7 @@ class Trainer(TrainerIO):
             self.avg_loss = np.mean(self.running_loss[-100:])
 
             # update progbar
-            if self.enable_tqdm:
+            if self.progress_bar:
                 # add model specific metrics
                 tqdm_metrics = self.__tng_tqdm_dic
                 self.prog_bar.set_postfix(**tqdm_metrics)
@@ -495,7 +495,7 @@ class Trainer(TrainerIO):
             print(e)
             print(traceback.print_exc())
 
-        if self.enable_tqdm:
+        if self.progress_bar:
             # add model specific metrics
             tqdm_metrics = self.__tng_tqdm_dic
             self.prog_bar.set_postfix(**tqdm_metrics)

pytorch_lightning/trainer_main.py

@@ -97,7 +97,7 @@ def main(hparams, cluster, results_dict):
         experiment=exp,
         on_gpu=on_gpu,
         cluster=cluster,
-        enable_tqdm=hparams.enable_tqdm,
+        progress_bar=hparams.enable_tqdm,
         overfit_pct=hparams.overfit,
         track_grad_norm=hparams.track_grad_norm,
         fast_dev_run=hparams.fast_dev_run,