[Docs revamp 2/N] New doc for managing data

docs/source/advanced/sequences.rst

@@ -1,37 +1,6 @@
-.. testsetup:: *
-
-    from torch.utils.data import IterableDataset
-    from pytorch_lightning.trainer.trainer import Trainer
-
-.. _sequences:
 
 Sequential Data
 ================
-Lightning has built in support for dealing with sequential data.
-
-----------
-
-Packed sequences as inputs
---------------------------
-When using PackedSequence, do 2 things:
-
-1. Return either a padded tensor in dataset or a list of variable length tensors in the dataloader collate_fn (example shows the list implementation).
-2. Pack the sequence in forward or training and validation steps depending on use case.
-
-.. testcode::
-
-    # For use in dataloader
-    def collate_fn(batch):
-        x = [item[0] for item in batch]
-        y = [item[1] for item in batch]
-        return x, y
-
-    # In module
-    def training_step(self, batch, batch_nb):
-        x = rnn.pack_sequence(batch[0], enforce_sorted=False)
-        y = rnn.pack_sequence(batch[1], enforce_sorted=False)
-
-----------
 
 Truncated Backpropagation Through Time
 --------------------------------------
@@ -64,48 +33,3 @@ Lightning can handle TBTT automatically via this flag.
 
 .. note:: If you need to modify how the batch is split,
     override :meth:`pytorch_lightning.core.LightningModule.tbptt_split_batch`.
-
-----------
-
-Iterable Datasets
------------------
-Lightning supports using IterableDatasets as well as map-style Datasets. IterableDatasets provide a more natural
-option when using sequential data.
-
-.. note:: When using an IterableDataset you must set the ``val_check_interval`` to 1.0 (the default) or an int
-    (specifying the number of training batches to run before validation) when initializing the Trainer. This is
-    because the IterableDataset does not have a ``__len__`` and Lightning requires this to calculate the validation
-    interval when ``val_check_interval`` is less than one. Similarly, you can set ``limit_{mode}_batches`` to a float or
-    an int. If it is set to 0.0 or 0 it will set ``num_{mode}_batches`` to 0, if it is an int it will set ``num_{mode}_batches``
-    to ``limit_{mode}_batches``, if it is set to 1.0 it will run for the whole dataset, otherwise it will throw an exception.
-    Here mode can be train/val/test.
-
-.. testcode::
-
-    # IterableDataset
-    class CustomDataset(IterableDataset):
-
-        def __init__(self, data):
-            self.data_source
-
-        def __iter__(self):
-            return iter(self.data_source)
-
-    # Setup DataLoader
-    def train_dataloader(self):
-        seq_data = ['A', 'long', 'time', 'ago', 'in', 'a', 'galaxy', 'far', 'far', 'away']
-        iterable_dataset = CustomDataset(seq_data)
-
-        dataloader = DataLoader(dataset=iterable_dataset, batch_size=5)
-        return dataloader
-
-.. testcode::
-
-    # Set val_check_interval
-    trainer = Trainer(val_check_interval=100)
-
-    # Set limit_val_batches to 0.0 or 0
-    trainer = Trainer(limit_val_batches=0.0)
-
-    # Set limit_val_batches as an int
-    trainer = Trainer(limit_val_batches=100)

docs/source/conf.py

@@ -28,6 +28,8 @@
 sys.path.insert(0, os.path.abspath(PATH_ROOT))
 sys.path.append(os.path.join(PATH_RAW_NB, '.actions'))
 
+# if you run into an import error here, try to execute this command:
+# git submodule update --init --recursive
 from helpers import HelperCLI  # noqa: E401 E402
 
 FOLDER_GENERATED = 'generated'