83 lines
2.6 KiB
ReStructuredText
83 lines
2.6 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.trainer.trainer import Trainer
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.. _fast-training:
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Fast Training
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=============
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There are multiple options to speed up different parts of the training by choosing to train
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on a subset of data. This could be done for speed or debugging purposes.
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----------------
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Check validation every n epochs
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-------------------------------
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If you have a small dataset you might want to check validation every n epochs
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.. testcode::
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# DEFAULT
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trainer = Trainer(check_val_every_n_epoch=1)
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----------------
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Force training for min or max epochs
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------------------------------------
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It can be useful to force training for a minimum number of epochs or limit to a max number.
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.. seealso::
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:class:`~pytorch_lightning.trainer.trainer.Trainer`
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.. testcode::
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# DEFAULT
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trainer = Trainer(min_epochs=1, max_epochs=1000)
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----------------
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Set validation check frequency within 1 training epoch
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------------------------------------------------------
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For large datasets it's often desirable to check validation multiple times within a training loop.
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Pass in a float to check that often within 1 training epoch. Pass in an int k to check every k training batches.
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Must use an int if using an IterableDataset.
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.. testcode::
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# DEFAULT
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trainer = Trainer(val_check_interval=0.95)
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# check every .25 of an epoch
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trainer = Trainer(val_check_interval=0.25)
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# check every 100 train batches (ie: for IterableDatasets or fixed frequency)
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trainer = Trainer(val_check_interval=100)
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----------------
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Use data subset for training, validation and test
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-------------------------------------------------
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If you don't want to check 100% of the training/validation/test set (for debugging or if it's huge), set these flags.
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.. testcode::
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# DEFAULT
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trainer = Trainer(
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limit_train_batches=1.0,
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limit_val_batches=1.0,
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limit_test_batches=1.0
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)
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# check 10%, 20%, 30% only, respectively for training, validation and test set
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trainer = Trainer(
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limit_train_batches=0.1,
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limit_val_batches=0.2,
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limit_test_batches=0.3
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)
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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.
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.. 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``.
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.. note:: If you set ``limit_val_batches=0``, validation will be disabled.
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