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Adrian WälchliandJirka Borovec
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* add doctest to circleci * Revert "add doctest to circleci" This reverts commit c45b34ea911a81f87989f6c3a832b1e8d8c471c6. * Revert "Revert "add doctest to circleci"" This reverts commit 41fca97fdcfe1cf4f6bdb3bbba75d25fa3b11f70. * doctest docs rst files * Revert "doctest docs rst files" This reverts commit b4a2e83e3da5ed1909de500ec14b6b614527c07f. * doctest only rst * doctest debugging.rst * doctest apex * doctest callbacks * doctest early stopping * doctest for child modules * doctest experiment reporting * indentation * doctest fast training * doctest for hyperparams * doctests for lr_finder * doctests multi-gpu * more doctest * make doctest drone * fix label build error * update fast training * update invalid imports * fix problem with int device count * rebase stuff * wip * wip * wip * intro guide * add missing code block * circleci * logger import for doctest * test if doctest runs on drone * fix mnist download * also run install deps for building docs * install cmake * try sudo * hide output * try pip stuff * try to mock horovod * Tranfer -> Transfer * add torchvision to extras * revert pip stuff * mlflow file location * do not mock torch * torchvision * drone extra req. * try higher sphinx version * Revert "try higher sphinx version" This reverts commit 490ac28e46d6fd52352640dfdf0d765befa56988. * try coverage command * try coverage command * try undoc flag * newline * undo drone * report coverage * review Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * remove torchvision from extras * skip tests only if torchvision not available * fix testoutput torchvision Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
73 lines
2.3 KiB
ReStructuredText
73 lines
2.3 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.trainer.trainer import Trainer
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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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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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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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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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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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train_percent_check=1.0,
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val_percent_check=1.0,
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test_percent_check=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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train_percent_check=0.1,
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val_percent_check=0.2,
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test_percent_check=0.3
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)
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.. note:: ``train_percent_check``, ``val_percent_check`` and ``test_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0. ``val_percent_check`` will be ignored if ``fast_dev_run=True``.
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.. note:: If you set ``val_percent_check=0``, validation will be disabled.
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