removed checkpoint save_function option

docs/Trainer/Checkpointing.md

@@ -0,0 +1,18 @@
+Lightning can automate saving and loading checkpoints.
+
+---
+### Model saving
+To enable checkpointing, define the checkpoint callback
+
+``` {.python}
+from pytorch_lightning.utils.pt_callbacks import ModelCheckpoint
+
+checkpoint = ModelCheckpoint(
+    filepath='/path/to/store/weights.ckpt',
+    save_function=None,
+    save_best_only=not hparams.keep_all_checkpoints,
+    verbose=True,
+    monitor=hparams.model_save_monitor_value,
+    mode=hparams.model_save_monitor_mode
+)
+```

docs/Trainer/index.md

@@ -24,10 +24,8 @@ But of course the fun is in all the advanced things it can do:
 
 **Computing cluster (SLURM)**    
 
-- Automatic checkpointing   
-- Automatic saving, loading  
-- Running grid search on a cluster
-- Walltime auto-resubmit   
+- [Running grid search on a cluster](SLURM%20Managed%20Cluster/#running-grid-search-on-a-cluster)
+- [Walltime auto-resubmit](SLURM%20Managed%20Cluster/#walltime-auto-resubmit)   
 
 **Debugging**  
 

pytorch_lightning/callbacks/pt_callbacks.py

@@ -170,12 +170,11 @@ class ModelCheckpoint(Callback):
         period: Interval (number of epochs) between checkpoints.
     """
 
-    def __init__(self, filepath, save_function, monitor='val_loss', verbose=0,
+    def __init__(self, filepath, monitor='val_loss', verbose=0,
                  save_best_only=False, save_weights_only=False,
                  mode='auto', period=1, prefix=''):
         super(ModelCheckpoint, self).__init__()
         self.monitor = monitor
-        self.save_function = save_function
         self.verbose = verbose
         self.filepath = filepath
         self.save_best_only = save_best_only