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 # Pytorch-lightning
-Seed for ML research
+The Keras for ML-researchers in PyTorch.    
 
 ## Usage
+To use lightning, first define a trainer function:   
 
-### Add new model
-1. Create a new model under /models.
-2. Add model name to trainer_main
 ```python
-AVAILABLE_MODELS = {
-    'model_1': ExampleModel1
-}
+# trainer.py
+
+from pytorch_lightning.models.trainer import Trainer   
+from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
+from my_project import My_Model   
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
+
+# --------------
+# TEST TUBE INIT
+exp = Experiment(
+    name='my_exp',
+    debug=True,
+    save_dir='/some/path',
+    autosave=False,
+    description='my desc'
+)
+
+# --------------------
+# CALLBACKS
+early_stop = EarlyStopping(
+    monitor='val_loss',
+    patience=3,
+    verbose=True,
+    mode='min'
+)
+
+model_save_path = 'PATH/TO/SAVE'
+checkpoint = ModelCheckpoint(
+    filepath=model_save_path,
+    save_function=None,
+    save_best_only=True,
+    verbose=True,
+    monitor='val_acc',
+    mode='min'
+)
+
+# configure trainer
+trainer = Trainer(
+    experiment=experiment,
+    cluster=cluster,
+    checkpoint_callback=checkpoint,
+    early_stop_callback=early_stop
+)  
+
+# init model and train
+model = My_Model()
+trainer.fit(model)
 ```
 
-### Model methods that can be implemented
+next define a model that implements these 10 functions:    
+```python
+import torch.nn as nn
+
+class ExampleModel(RootModule):
+    def __init__(self):
+        # define model
+    
+    # ---------------
+    # TRAINING
+    def training_step(self, data_batch):
+        x, y = data_batch
+        y_hat = self.l1(x)
+        loss = some_loss(y_hat)
+        
+        return loss_val, {'train_loss': loss}
+    
+    def validation_step(self, data_batch):
+        x, y = data_batch
+        y_hat = self.l1(x)
+        loss = some_loss(y_hat)
+        
+        return loss_val, {'val_loss': loss}
+ 
+     def validation_end(self, outputs):
+        total_accs = []
+        
+        for output in outputs:
+            total_accs.append(output['val_acc'].item())
+        
+        # return a dict
+        return {'total_acc': np.mean(total_accs)}
+     
+     # ---------------
+     # SAVING
+     def get_save_dict(self):
+        # lightning saves for you. Here's your chance to say what you want to save
+        checkpoint = {'state_dict': self.state_dict()}
+
+        return checkpoint
+
+    def load_model_specific(self, checkpoint):
+        # lightning loads for you. Here's your chance to say what you want to load
+        self.load_state_dict(checkpoint['state_dict'])
+    
+    # ---------------
+    # TRAINING CONFIG
+    def configure_optimizers(self):
+        # give lightning the list of optimizers you want to use.
+        # lightning will call automatically
+        optimizer = self.choose_optimizer('adam', self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
+        return [optimizer]
+    
+    @property
+    def tng_dataloader(self):
+        return pytorch_dataloader('train')
+
+    @property
+    def val_dataloader(self):
+        return pytorch_dataloader('val')
+
+    @property
+    def test_dataloader(self):
+        return pytorch_dataloader('test')
+        
+    # ---------------
+    # MODIFY YOUR COMMAND LINE ARGS
+    @staticmethod
+    def add_model_specific_args(parent_parser):    
+        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
+        parser.add_argument('--out_features', default=20)
+        return parser
+```
+### Details    
+
+#### Model definition   
+| Name  | Description  |  Input |  Return |
+|---|---|---|---|
+| training_step  |  Called with a batch of data during training | data from your dataloaders  | tuple: scalar, dict  |
+| validation_step  |  Called with a batch of data during validation  | data from your dataloaders  | tuple: scalar, dict |
+| validation_end  |  Collate metrics from all validation steps |  outputs: array where each item is the output of a validation step | dict: for logging |
+| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved | 
+
+#### Model training   
+| Name  | Description  |  Input |  Return |
+|---|---|---|---|
+| configure_optimizers  |  called during training setup | None | list: optimizers you want to use |
+| tng_dataloader  |  called during training  | None  | pytorch dataloader |
+| val_dataloader  |  called during validation  | None  | pytorch dataloader |
+| test_dataloader  |  called during testing  | None  | pytorch dataloader |    
+| add_model_specific_args  |  called with args you defined in your main. This lets you tailor args for each model and keep main the same  | argparse  | argparse |
+
+#### Model Saving/Loading   
+| Name  | Description  |  Input |  Return |
+|---|---|---|---|
+| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved |
+|  load_model_specific |  called when loading a model | checkpoint: dict you created in get_save_dict  | dict: modified in whatever way you want  |
+    
+## Optional model hooks.   
+Add these to the model whenever you want to configure training behavior.    
 
-| Method | Purpose  | Input  | Output  | Required  |
-|---|---|---|---|---|
-| forward()  | Forward pass   | model_in tuple with your data  | model_out tuple to be passed to loss  | Y  |
-| loss()  | calculate model loss  | model_out tuple from forward()  | A scalar  | Y  |
-| check_performance()  | run a full loop through val data to check for metrics  | dataloader, nb_tests  | metrics tuple to be tracked  | Y  |
-| tng_dataloader  | Computed option, used to feed tng data  | -  | Pytorch DataLoader subclass  | Y  |
-| val_dataloader  | Computed option, used to feed tng data  | -  | Pytorch DataLoader subclass  | Y  |
-| test_dataloader  | Computed option, used to feed tng data  | -  | Pytorch DataLoader subclass  | Y  |
 
 ### Model lifecycle hooks
 Use these hooks to customize functionality