added val loop options

docs/Trainer/Training Loop.md

@@ -34,14 +34,6 @@ This option prints a list of tensors with nan gradients.
 trainer = Trainer(print_nan_grads=False)
 ```
 
----
-#### Check validation every n epochs 
-If you have a small dataset you might want to check validation every n epochs
-``` {.python}
-# DEFAULT
-trainer = Trainer(check_val_every_n_epoch=1)
-```
-
 ---
 #### Display metrics in progress bar 
 ``` {.python}
@@ -53,6 +45,15 @@ 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.
 
+---
+#### 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)
+```
+
 ---
 #### 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
@@ -61,6 +62,14 @@ It can be useful to force training for a minimum number of epochs or limit to a
 trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)
 ```
 
+---
+#### Force disable early stop 
+Use this to turn off early stopping and run training to the [max_epoch](#force-training-for-min-or-max-epochs)
+``` {.python}
+# DEFAULT
+trainer = Trainer(enable_early_stop=True)
+```
+
 ---
 #### Inspect gradient norms
 Looking at grad norms can help you figure out where training might be going wrong.
@@ -84,9 +93,22 @@ trainer = Trainer(overfit_pct=0.0)
 trainer = Trainer(overfit_pct=0.01)
 ```
 
+---
+#### 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)
+```
+
+
 ---
 #### Set how much of the training set to check
-If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
+If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag
 ``` {.python}
 # DEFAULT
 trainer = Trainer(train_percent_check=1.0)

docs/Trainer/Vaildation loop.md

@@ -1,3 +0,0 @@
-The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the [validation_step function](../../Pytorch-lightning/LightningModule/#validation_step).
-
-Below are all the things lightning automates for you in the validation loop.

docs/Trainer/Validation loop.md

@@ -0,0 +1,57 @@
+The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the [validation_step function](../../Pytorch-lightning/LightningModule/#validation_step).
+Below are all the things lightning automates for you in the validation loop.
+
+**Note**   
+Lightning will run 5 steps of validation in the beginning of training as a sanity check so you don't have to wait until a full epoch to catch possible validation issues.
+
+
+
+
+---
+#### Check validation every n epochs
+If you have a small dataset you might want to check validation every n epochs
+``` {.python}
+# DEFAULT
+trainer = Trainer(check_val_every_n_epoch=1)
+```
+
+---
+#### Set how much of the validation set to check 
+If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
+``` {.python}
+# DEFAULT
+trainer = Trainer(val_percent_check=1.0)
+
+# check 10% only
+trainer = Trainer(val_percent_check=0.1)
+```
+
+---
+#### Set how much of the test set to check 
+If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
+``` {.python}
+# DEFAULT
+trainer = Trainer(test_percent_check=1.0)
+
+# check 10% only
+trainer = Trainer(test_percent_check=0.1)
+```
+
+---
+####  Set validation check frequency within 1 training epoch
+For large datasets it's often desirable to check validation multiple times within a training loop
+``` {.python}
+# DEFAULT
+trainer = Trainer(val_check_interval=0.95)
+
+# check every .25 of an epoch 
+trainer = Trainer(val_check_interval=0.25)
+```
+
+---
+####  Set the number of validation sanity steps
+Lightning runs a few steps of validation in the beginning of training. This avoids crashing in the validation loop sometime deep into a lengthy training loop.
+``` {.python}
+# DEFAULT
+trainer = Trainer(nb_sanity_val_steps=5)
+```

docs/Trainer/index.md

@@ -22,22 +22,25 @@ But of course the fun is in all the advanced things it can do:
 - [Anneal Learning rate](Training%20Loop/#anneal-learning-rate)
 - [Check GPU usage](Training%20Loop/#Check-gpu-usage)
 - [Check which gradients are nan](Training%20Loop/#check-which-gradients-are-nan)
-- [Check validation every n epochs](Training%20Loop/#check-validation-every-n-epochs)
 - [Display metrics in progress bar](Training%20Loop/#display-metrics-in-progress-bar)
 - [Display the parameter count by layer](Training%20Loop/#display-the-parameter-count-by-layer)
+- [Fast dev run](Training%20Loop/#fast-dev-run)
 - [Force training for min or max epochs](Training%20Loop/#force-training-for-min-or-max-epochs)
+- [Force disable early stop](Training%20Loop/#force-disable-early-stop)
 - [Inspect gradient norms](Training%20Loop/#inspect-gradient-norms)
 - [Make model overfit on subset of data](Training%20Loop/#make-model-overfit-on-subset-of-data)
 - [Use multiple optimizers (like GANs)](../Pytorch-lightning/LightningModule/#configure_optimizers)
+- [Process position](Training%20Loop/#process-position)
 - [Set how much of the training set to check (1-100%)](Training%20Loop/#set-how-much-of-the-training-set-to-check)
 
 **Validation loop**    
 
-- Display metrics in progress bar
+- [Check validation every n epochs](Validation%20Loop/#check-validation-every-n-epochs)
-- Set how much of the validation set to check (1-100%)
+- [Set how much of the validation set to check](Validation%20Loop/#set-how-much-of-the-validation-set-to-check)
-- Set validation check frequency within 1 training epoch (1-100%)
+- [Set how much of the test set to check](Validation%20Loop/#set-how-much-of-the-test-set-to-check)
-- validation_step function
+- [Set validation check frequency within 1 training epoch](Validation%20Loop/#set-validation-check-frequency-within-1-training-epoch)
-- Why does validation run first for 5 steps?
+- [Set the number of validation sanity steps](Validation%20Loop/#set-the-number-of-validation-sanity-steps)
+- [Check validation every n epochs](Validation%20Loop/#check-validation-every-n-epochs)
 
 **Distributed training**