Fast Training¶
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.
See also
# 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.
Note
limit_train_batches
, limit_val_batches
and limit_test_batches
will be overwritten by overfit_batches
if overfit_batches
> 0. limit_val_batches
will be ignored if fast_dev_run=True
.
Note
If you set limit_val_batches=0
, validation will be disabled.