added module properties docs

docs/LightningModule/properties.md

@@ -0,0 +1,25 @@
+A LightningModule has the following properties which you can access at any time
+
+--- 
+#### current_epoch
+The current epoch   
+
+---
+#### dtype    
+Current dtype    
+
+--- 
+#### global_step
+Total training batches seen across all epochs   
+
+--- 
+#### gradient_clip
+The current gradient clip value    
+
+---
+#### on_gpu    
+True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    
+
+---
+#### Trainer
+Last resort access to any state the trainer has. Changing certain properties here could affect your training run.

docs/index.md

@@ -11,9 +11,9 @@
     - [GPU cluster Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/trainer_gpu_cluster_template.py)
 
 ###### Quick start examples 
-- [CPU example](https://williamfalcon.github.io/pytorch-lightning/Examples/#CPU-hyperparameter-search)   
-- [Hyperparameter search on single GPU](https://williamfalcon.github.io/pytorch-lightning/Examples/#Hyperparameter-search-on-a-single-or-multiple-GPUs)    
-- [Hyperparameter search on multiple GPUs on same node](https://williamfalcon.github.io/pytorch-lightning/Examples/#Hyperparameter-search-on-a-single-or-multiple-GPUs)  
+- [CPU example](https://williamfalcon.github.io/pytorch-lightning/Examples/#cpu-hyperparameter-search)   
+- [Hyperparameter search on single GPU](https://williamfalcon.github.io/pytorch-lightning/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)    
+- [Hyperparameter search on multiple GPUs on same node](https://williamfalcon.github.io/pytorch-lightning/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)  
 - [Hyperparameter search on a SLURM HPC cluster](https://williamfalcon.github.io/pytorch-lightning/Examples/#Hyperparameter search on a SLURM HPC cluster)      
 
 

pytorch_lightning/root_module/root_module.py

@@ -24,7 +24,6 @@ class LightningModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
         self.fast_dev_run = hparams.fast_dev_run
         self.overfit = hparams.overfit
         self.gradient_clip = hparams.gradient_clip
-        self.num = 2
         self.trainer = None
         self.from_lightning = True