83 lines
2.5 KiB
ReStructuredText
83 lines
2.5 KiB
ReStructuredText
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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.. code-block:: python
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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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.. code-block:: python
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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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.. code-block:: python
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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 training data subset
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----------------------------------
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If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
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.. code-block:: python
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# DEFAULT
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trainer = Trainer(train_percent_check=1.0)
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# check 10% only
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trainer = Trainer(train_percent_check=0.1)
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.. note:: train_percent_check will be overwritten by overfit_pct if overfit_pct > 0
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Use test data subset
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-------------------------------------
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If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
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test_percent_check will be overwritten by overfit_pct if overfit_pct > 0.
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.. code-block:: python
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# DEFAULT
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trainer = Trainer(test_percent_check=1.0)
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# check 10% only
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trainer = Trainer(test_percent_check=0.1)
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Use validation data subset
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--------------------------------------------
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If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
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val_percent_check will be overwritten by overfit_pct if overfit_pct > 0
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.. code-block:: python
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# DEFAULT
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trainer = Trainer(val_percent_check=1.0)
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# check 10% only
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trainer = Trainer(val_percent_check=0.1) |