Docs clean-up (#2234)

* update docs

* update docs

* update docs

* update docs

* update docs

* update docs

Author: williamFalcon

docs/source/introduction_guide.rst

@@ -278,6 +278,9 @@ Doing it in the `prepare_data` method ensures that when you have
 multiple GPUs you won't overwrite the data. This is a contrived example
 but it gets more complicated with things like NLP or Imagenet.
 
+`prepare_data` gets called on the `LOCAL_RANK=0` GPU per node. If your nodes share a file system,
+set `Trainer(prepare_data_per_node=False)` and it will be code from node=0, gpu=0 only.
+
 In general fill these methods with the following:
 
 .. testcode::
@@ -535,16 +538,21 @@ will cause all sorts of issues.
 To solve this problem, move the download code to the `prepare_data` method in the LightningModule.
 In this method we do all the preparation we need to do once (instead of on every gpu).
 
+`prepare_data` can be called in two ways, once per node or only on the root node (`Trainer(prepare_data_per_node=False)`).
+
 .. testcode::
 
     class LitMNIST(LightningModule):
         def prepare_data(self):
+            # download only
+            MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
+            MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor())
+
+        def setup(self, stage):
             # transform
             transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-
-            # download
-            mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
-            mnist_test = MNIST(os.getcwd(), train=False, download=True, transform=transform)
+            MNIST(os.getcwd(), train=True, download=False, transform=transform)
+            MNIST(os.getcwd(), train=False, download=False, transform=transform)
 
             # train/val split
             mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -307,12 +307,11 @@ def configure_optimizers(self):
 
     def prepare_data(self):
         """Download images and prepare images datasets."""
-
-        # 1. Download the images
         download_and_extract_archive(url=DATA_URL,
                                      download_root=self.dl_path,
                                      remove_finished=True)
 
+    def setup(self, stage: str):
         data_path = Path(self.dl_path).joinpath('cats_and_dogs_filtered')
 
         # 2. Load the data + preprocessing & data augmentation

pl_examples/models/lightning_template.py

@@ -141,10 +141,13 @@ def configure_optimizers(self):
         return [optimizer], [scheduler]
 
     def prepare_data(self):
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        self.mnist_train = MNIST(self.data_root, train=True, download=True, transform=transform)
-        self.mnist_test = MNIST(self.data_root, train=False, download=True, transform=transform)
+        MNIST(self.data_root, train=True, download=True, transform=transforms.ToTensor())
+        MNIST(self.data_root, train=False, download=True, transform=transforms.ToTensor())
+
+    def setup(self, stage):
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+        self.mnist_train = MNIST(self.data_root, train=True, download=False, transform=transform)
+        self.mnist_test = MNIST(self.data_root, train=False, download=False, transform=transform)
 
     def train_dataloader(self):
         log.info('Training data loader called.')

pytorch_lightning/core/__init__.py

@@ -267,11 +267,12 @@ def training_step(self, batch, batch_idx):
     >>> class LitModel(pl.LightningModule):
     ...     def prepare_data(self):
     ...         # download
-    ...         mnist_train = MNIST(os.getcwd(), train=True, download=True,
-    ...                             transform=transforms.ToTensor())
-    ...         mnist_test = MNIST(os.getcwd(), train=False, download=True,
-    ...                            transform=transforms.ToTensor())
+    ...         MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
+    ...         MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor())
     ...
+    ...     def setup(self, stage):
+    ...         mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transforms.ToTensor())
+    ...         mnist_test = MNIST(os.getcwd(), train=False, download=False, transform=transforms.ToTensor())
     ...         # train/val split
     ...         mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])
     ...

pytorch_lightning/core/hooks.py

@@ -22,7 +22,7 @@ def setup(self, stage: str):
         Called at the beginning of fit and test.
 
         Args:
-            step: either 'fit' or 'test'
+            stage: either 'fit' or 'test'
         """
 
     def teardown(self, stage: str):

pytorch_lightning/core/lightning.py

@@ -1290,7 +1290,8 @@ def tbptt_split_batch(self, batch, split_size):
     def prepare_data(self) -> None:
         """
         Use this to download and prepare data.
-        In distributed (GPU, TPU), this will only be called once.
+        In distributed (GPU, TPU), this will only be called once on the local_rank=0 of each node.
+        To call this on only the root=0 of the main node, use `Trainer(prepare_data_per_node=False)`
         This is called before requesting the dataloaders:
 
         .. code-block:: python

tests/base/model_template.py

@@ -104,7 +104,7 @@ def loss(self, labels, logits):
         return nll
 
     def prepare_data(self):
-        _ = TrialMNIST(root=self.data_root, train=True, download=True)
+        TrialMNIST(root=self.data_root, train=True, download=True)
 
     @staticmethod
     def get_default_hparams(continue_training: bool = False, hpc_exp_number: int = 0) -> dict:

tests/callbacks/test_callbacks.py

@@ -69,7 +69,7 @@ def __init__(self):
             self.on_test_start_called = False
             self.on_test_end_called = False
 
-        def setup(self, trainer, step: str):
+        def setup(self, trainer, stage: str):
             assert isinstance(trainer, Trainer)
             self.setup_called = True