prog bar option

docs/Trainer/Training Loop.md

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+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)
+```