Loader docs (#1416)

* added multiple loader docs

* added multiple loader docs

* added multiple loader docs

* added multiple loader docs

* added multiple loader docs

* Apply suggestions from code review

* added multiple loader docs

* added build docs script

* typo

* added build docs script

* added build docs script

* added build docs script

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>

docs/.build_docs.sh

@@ -0,0 +1 @@
+make clean ; make html --debug --jobs 2 SPHINXOPTS="-W"

docs/source/index.rst

@@ -67,6 +67,7 @@ PyTorch Lightning Documentation
    hooks
    hyperparameters
    multi_gpu
+   multiple_loaders
    weights_loading
    optimizers
    profiler

docs/source/introduction_guide.rst

@@ -28,6 +28,23 @@ to use inheritance to very quickly create an AutoEncoder.
 
 ---------
 
+Installing Lightning
+--------------------
+Lightning is trivial to install.
+
+.. code-block:: bash
+
+    conda activate my_env
+    pip install pytorch-lightning
+
+Or without conda environments, anywhere you can use pip.
+
+.. code-block:: bash
+
+    pip install pytorch-lightning
+
+---------
+
 Lightning Philosophy
 --------------------
 Lightning factors DL/ML code into three types:

docs/source/multiple_loaders.rst

@@ -0,0 +1,66 @@
+Multiple Datasets
+=================
+Lightning supports multiple dataloaders in a few ways.
+
+1. Create a dataloader that iterates both datasets under the hood.
+2. In the validation and test loop you also have the option to return multiple dataloaders
+   which lightning will call sequentially.
+
+Multiple training dataloaders
+-----------------------------
+For training, the best way to use multiple-dataloaders is to create a Dataloader class
+which wraps both your dataloaders. (This of course also works for testing and validation
+dataloaders).
+
+(`reference <https://discuss.pytorch.org/t/train-simultaneously-on-two-datasets/649/2>`_)
+
+.. code-block:: python
+
+    class ConcatDataset(torch.utils.data.Dataset):
+        def __init__(self, *datasets):
+            self.datasets = datasets
+
+        def __getitem__(self, i):
+            return tuple(d[i] for d in self.datasets)
+
+        def __len__(self):
+            return min(len(d) for d in self.datasets)
+
+    class LitModel(LightningModule):
+        def train_dataloader(self):
+            concat_dataset = ConcatDataset(
+                datasets.ImageFolder(traindir_A),
+                datasets.ImageFolder(traindir_B)
+            )
+
+            loader = torch.utils.data.DataLoader(
+                concat_dataset,
+                batch_size=args.batch_size,
+                shuffle=True,
+                num_workers=args.workers,
+                pin_memory=True
+            )
+            return loader
+
+        def val_dataloader(self):
+            # SAME
+
+        def test_dataloader(self):
+            # SAME
+
+Test/Val dataloaders
+--------------------
+For validation, test dataloaders lightning also gives you the additional
+option of passing in multiple dataloaders back from each call.
+
+See the following for more details:
+
+- :meth:`~pytorch_lightning.core.LightningModule.val_dataloader`
+- :meth:`~pytorch_lightning.core.LightningModule.test_dataloader`
+
+.. code-block:: python
+
+    def val_dataloader(self):
+        loader_1 = Dataloader()
+        loader_2 = Dataloader()
+        return [loader_1, loader_2]

pytorch_lightning/trainer/__init__.py

@@ -58,7 +58,7 @@ So you can run it like so:distributed_backend
 
 .. code-block:: bash
 
-    $ python main.py --gpus 2
+    python main.py --gpus 2
 
 
 .. note::
@@ -550,7 +550,7 @@ submit this script using the xla_dist script.
 
 Example::
 
-    $ python -m torch_xla.distributed.xla_dist
+    python -m torch_xla.distributed.xla_dist
     --tpu=$TPU_POD_NAME
     --conda-env=torch-xla-nightly
     --env=XLA_USE_BF16=1