update Docs [links & formatting] (#769)

* wip

* wip

* debug imports
docs formatting

* WIP

* formatting

* fix setup

pl_examples/__init__.py

@@ -43,6 +43,7 @@ Normally, we want to let the `__main__` function start the training.
 
 The main function is your entry into the program. This is where you init your model, checkpoint directory,
  and launch the training. The main function should have 3 arguments:
+
 - hparams: a configuration of hyperparameters.
 - slurm_manager: Slurm cluster manager object (can be None)
 - dict: for you to return any values you want (useful in meta-learning, otherwise set to)

pl_examples/basic_examples/cpu_template.py

@@ -7,8 +7,8 @@ from argparse import ArgumentParser
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -28,7 +28,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer()
+    trainer = pl.Trainer()
 
     # ------------------------
     # 3 START TRAINING

pl_examples/basic_examples/gpu_template.py

@@ -7,8 +7,8 @@ from argparse import ArgumentParser
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -28,7 +28,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=hparams.gpus,
         distributed_backend=hparams.distributed_backend,
         use_amp=hparams.use_16bit

pl_examples/basic_examples/lightning_module_template.py

@@ -16,10 +16,9 @@ from torch.utils.data.distributed import DistributedSampler
 from torchvision.datasets import MNIST
 
 import pytorch_lightning as pl
-from pytorch_lightning.core.lightning import LightningModule
 
 
-class LightningTemplateModel(LightningModule):
+class LightningTemplateModel(pl.LightningModule):
     """
     Sample model to show how to define a template
     """

pl_examples/multi_node_examples/multi_node_ddp2_demo.py

@@ -7,8 +7,8 @@ from argparse import ArgumentParser
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -29,7 +29,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=2,
         num_nodes=2,
         distributed_backend='ddp2'

pl_examples/multi_node_examples/multi_node_ddp_demo.py

@@ -7,8 +7,8 @@ from argparse import ArgumentParser
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -29,7 +29,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=2,
         num_nodes=2,
         distributed_backend='ddp'