pytorch-lightning/docs/Trainer/Training Loop.md

The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the [training_step function](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#training_step).

Below are all the things lightning automates for you in the training loop.

---
#### 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 (ie: no accumulated grads)
trainer = Trainer(accumulate_grad_batches=1)
```

---
#### Anneal Learning rate
Cut the learning rate by 10 at every epoch listed in this list.
``` {.python}
# DEFAULT (don't anneal)
trainer = Trainer(lr_scheduler_milestones=None)

# cut LR by 10 at 100, 200, and 300 epochs 
trainer = Trainer(lr_scheduler_milestones='100, 200, 300')
```

---
#### 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}
# DEFAULT
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)
```

---
#### Gradient Clipping 
Use this to turn off early stopping and run training to the [max_epoch](#force-training-for-min-or-max-epochs)
``` {.python}
# DEFAULT (ie: don't clip)
trainer = Trainer(gradient_clip=0)
```



---
#### 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)
```


---
#### Set how much of the training set to check
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)

# check 10% only
trainer = Trainer(train_percent_check=0.1)
```