resolving documentation warnings (#833)

* add more underline

* fix LightningMudule import error

* remove unneeded blank line

* escape asterisk to fix inline emphasis warning

* add PULL_REQUEST_TEMPLATE.md

* add __init__.py and import imagenet_example

* fix duplicate label

* add noindex option to fix duplicate object warnings

* remove unexpected indent

* refer explicit LightningModule

* fix minor bug

* refer EarlyStopping explicitly

* restore exclude patterns

* change the way how to refer class

* remove unused import

* update badges & drop Travis/Appveyor (#826)

* drop Travis

* drop Appveyor

* update badges

* fix missing PyPI images & CI badges (#853)

* docs - anchor links (#848)

* docs - add links

* add desc.

* add Greeting action (#843)

* add Greeting action

* Update greetings.yml

Co-authored-by: William Falcon <waf2107@columbia.edu>

* add pep8speaks (#842)

* advanced profiler describe + cleaned up tests (#837)

* add py36 compatibility

* add test case to capture previous bug

* clean up tests

* clean up tests

* Update lightning_module_template.py

* Update lightning.py

* respond lint issues

* break long line

* break more lines

* checkout conflicting files from master

* shorten url

* checkout from upstream/master

* remove trailing whitespaces

* remove unused import LightningModule

* fix sphinx bot warnings

* Apply suggestions from code review

just to trigger CI

* Update .github/workflows/greetings.yml

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: William Falcon <waf2107@columbia.edu>
Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>

pl_examples/__init__.py

@@ -3,8 +3,8 @@ Template model definition
 -------------------------
 
 In 99% of cases you want to just copy `one of the examples
- <https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples>`_
- to start a new lightningModule and change the core of what your model is actually trying to do.
+<https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples>`_
+to start a new lightningModule and change the core of what your model is actually trying to do.
 
 .. code-block:: bash
 

pl_examples/basic_examples/lightning_module_template.py

@@ -15,10 +15,11 @@ from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 from torchvision.datasets import MNIST
 
-import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
 
 
-class LightningTemplateModel(pl.LightningModule):
+class LightningTemplateModel(LightningModule):
     """
     Sample model to show how to define a template
     """

pl_examples/domain_templates/gan.py

@@ -19,7 +19,9 @@ import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 
-import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
+from pytorch_lightning.trainer import Trainer
 
 
 class Generator(nn.Module):
@@ -69,7 +71,7 @@ class Discriminator(nn.Module):
         return validity
 
 
-class GAN(pl.LightningModule):
+class GAN(LightningModule):
 
     def __init__(self, hparams):
         super(GAN, self).__init__()
@@ -165,7 +167,7 @@ class GAN(pl.LightningModule):
         opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
         return [opt_g, opt_d], []
 
-    @pl.data_loader
+    @data_loader
     def train_dataloader(self):
         transform = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize([0.5], [0.5])])
@@ -193,7 +195,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = pl.Trainer()
+    trainer = Trainer()
 
     # ------------------------
     # 3 START TRAINING

pl_examples/full_examples/imagenet/imagenet_example.py

@@ -19,6 +19,8 @@ import torchvision.models as models
 import torchvision.transforms as transforms
 
 import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
 
 # pull out resnet names from torchvision models
 MODEL_NAMES = sorted(
@@ -27,9 +29,11 @@ MODEL_NAMES = sorted(
 )
 
 
-class ImageNetLightningModel(pl.LightningModule):
-
+class ImageNetLightningModel(LightningModule):
     def __init__(self, hparams):
+        """
+        TODO: add docstring here
+        """
         super(ImageNetLightningModel, self).__init__()
         self.hparams = hparams
         self.model = models.__dict__[self.hparams.arch](pretrained=self.hparams.pretrained)
@@ -128,7 +132,7 @@ class ImageNetLightningModel(pl.LightningModule):
         scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
         return [optimizer], [scheduler]
 
-    @pl.data_loader
+    @data_loader
     def train_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],
@@ -159,7 +163,7 @@ class ImageNetLightningModel(pl.LightningModule):
         )
         return train_loader
 
-    @pl.data_loader
+    @data_loader
     def val_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],