added val loop options

docs/Trainer/Logging.md

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+
+
+
+---
+#### Display metrics in progress bar 
+``` {.python}
+# DEFAULT
+trainer = Trainer(progress_bar=True)
+```
+
+
+
+---
+#### Print which gradients are nan 
+This option prints a list of tensors with nan gradients.
+``` {.python}
+# DEFAULT
+trainer = Trainer(print_nan_grads=False)
+```
+
+---
+#### Process position
+When running multiple models on the same machine we want to decide which progress bar to use.
+Lightning will stack progress bars according to this value. 
+``` {.python}
+# DEFAULT
+trainer = Trainer(process_position=0)
+
+# if this is the second model on the node, show the second progress bar below
+trainer = Trainer(process_position=1)
+```

docs/Trainer/debugging.md

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+These flags are useful to help debug a model.
+
+---
+#### Fast dev run 
+This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch.
+Use this to debug a full run of your program quickly
+``` {.python}
+# DEFAULT
+trainer = Trainer(fast_dev_run=False)
+```
+
+---
+#### Inspect gradient norms
+Looking at grad norms can help you figure out where training might be going wrong.
+``` {.python}
+# DEFAULT (-1 doesn't track norms)
+trainer = Trainer(track_grad_norm=-1)
+
+# track the LP norm (P=2 here)
+trainer = Trainer(track_grad_norm=2)
+```
+
+---
+#### Make model overfit on subset of data
+A useful debugging trick is to make your model overfit a tiny fraction of the data.
+``` {.python}
+# DEFAULT don't overfit (ie: normal training)
+trainer = Trainer(overfit_pct=0.0)
+
+# overfit on 1% of data 
+trainer = Trainer(overfit_pct=0.01)
+```
+
+---
+#### Print the parameter count by layer
+By default lightning prints a list of parameters *and submodules* when it starts training.
+
+---
+#### Print which gradients are nan 
+This option prints a list of tensors with nan gradients.
+``` {.python}
+# DEFAULT
+trainer = Trainer(print_nan_grads=False)
+```
+
+---
+#### Log 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.