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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

Trainer

# 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.

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