Add documentation for trainer.datamodule and dataloaders of a trainer object (#14600)
* Update trainer.rst * Update datamodule.rst
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@ -1597,6 +1597,16 @@ The number of epochs run.
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if trainer.current_epoch >= 10:
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...
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datamodule
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**********
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The current datamodule, which is used by the trainer.
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.. code-block:: python
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used_datamodule = trainer.datamodule
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is_last_batch
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*************
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@ -1694,6 +1704,17 @@ The metrics sent to the progress bar.
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assert progress_bar_metrics["a_val"] == 2
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predict_dataloaders
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*******************
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The current predict dataloaders of the trainer.
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Note that property returns a list of predict dataloaders.
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.. code-block:: python
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used_predict_dataloaders = trainer.predict_dataloaders
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estimated_stepping_batches
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**************************
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@ -1773,3 +1794,37 @@ both conditions are met. If any of these arguments is not set, it won't be consi
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trainer = Trainer(min_steps=5, min_epochs=5, max_epochs=100)
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model = LitModel()
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trainer.fit(model)
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train_dataloader
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****************
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The current train dataloader of the trainer.
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.. code-block:: python
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used_train_dataloader = trainer.train_dataloader
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test_dataloaders
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****************
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The current test dataloaders of the trainer.
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Note that property returns a list of test dataloaders.
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.. code-block:: python
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used_test_dataloaders = trainer.test_dataloaders
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val_dataloaders
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***************
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The current val dataloaders of the trainer.
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Note that property returns a list of val dataloaders.
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.. code-block:: python
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used_val_dataloaders = trainer.val_dataloaders
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@ -412,6 +412,10 @@ the method runs on the correct devices).
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dm.setup(stage="test")
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trainer.test(datamodule=dm)
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You can access the current used datamodule of a trainer via ``trainer.datamodule`` and the current used
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dataloaders via ``trainer.train_dataloader``, ``trainer.val_dataloaders`` and ``trainer.test_dataloaders``.
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----------------
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*****************************
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