lightning/docs/Trainer/Validation loop.md

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. 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

# 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

val_percent_check will be overwritten by overfit_pct if overfit_pct > 0

# 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

test_percent_check will be overwritten by overfit_pct if overfit_pct > 0

# 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. Pass in a float to check that often within 1 training epoch. Pass in an int k to check every k training batches. Must use an int if using an IterableDataset.

# DEFAULT
trainer = Trainer(val_check_interval=0.95)

# check every .25 of an epoch 
trainer = Trainer(val_check_interval=0.25)

# check every 100 train batches (ie: for IterableDatasets or fixed frequency)
trainer = Trainer(val_check_interval=100)

---

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.

# DEFAULT
trainer = Trainer(nb_sanity_val_steps=5)

You can use Trainer(nb_sanity_val_steps=0) to skip the sanity check.