There are multiple options to speed up different parts of the training by choosing to train on a subset of data. This could be done for speed or debugging purposes.
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)
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.
# DEFAULT trainer = Trainer(min_epochs=1, max_epochs=1000)
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)
Use data subset for training, validation, and test¶
If you don’t want to check 100% of the training/validation/test set (for debugging or if it’s huge), set these flags.
# DEFAULT trainer = Trainer( limit_train_batches=1.0, limit_val_batches=1.0, limit_test_batches=1.0 ) # check 10%, 20%, 30% only, respectively for training, validation and test set trainer = Trainer( limit_train_batches=0.1, limit_val_batches=0.2, limit_test_batches=0.3 )
If you also pass
shuffle=True to the dataloader, a different random subset of your dataset will be used for each epoch; otherwise the same subset will be used for all epochs.
limit_test_batches will be overwritten by
overfit_batches > 0.
limit_val_batches will be ignored if
If you set
limit_val_batches=0, validation will be disabled.