cleaned readme

README.md

@@ -38,8 +38,65 @@ gpu training, etc... every time you start a project. Let lightning handle all of
 data and what happens in the training, testing and validation loop and lightning will do the rest.
 
 To use lightning do 2 things:  
-1. [Define a Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/trainer_cpu_template.py).   
+1. [Define a LightningModel](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)         
-2. [Define a LightningModel](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py).     
+```python
+from pytorch_lightning import LightningModule
+import torch
+
+class CoolModel(LightningModule):
+
+    def __init(self):
+        self.l1 = torch.nn.Linear(28*28, 10)
+        
+    def forward(self, x):
+        return self.l1(x)
+    
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'tng_loss': some_loss(y_hat, y)}
+    
+    def validation_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'val_loss': some_loss(y_hat, y)}
+        
+    def configure_optimizers(self):
+        return [optim.Adam(self.parameters(), lr=0.02)]
+    
+    @property
+    def tng_dataloader(self):
+        mnist = MNIST('path/to/save', train=True)
+        return DataLoader(mnist, batch_size=32)
+
+    @property
+    def val_dataloader(self):
+        mnist = MNIST('path/to/save', train=False)
+        return DataLoader(mnist, batch_size=32)
+        
+    @property
+    def test_dataloader(self):
+        mnist = MNIST('sam/as/val/for/simplicity', train=False)
+        return DataLoader(mnist, batch_size=32)   
+```
+
+2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)    
+```python
+from pytorch_lightning import Trainer
+from test_tube import Experiment
+
+model = CoolModel()
+
+# fit on 32 gpus across 4 nodes
+exp = Experiment(save_dir='some/dir')
+trainer = Trainer(experiment=exp, nb_gpu_nodes=4, gpus=[0,1,2,3,4,5,6,7])
+
+trainer.fit(model)
+
+# see all experiment metrics here
+# tensorboard --log_dir some/dir   
+```
+
 
 ## What does lightning control for me?
 Everything!