Merge pull request #55 from williamFalcon/continue

add training restore

docs/Trainer/Checkpointing.md

@@ -18,5 +18,22 @@ checkpoint_callback = ModelCheckpoint(
 trainer = Trainer(checkpoint_callback=checkpoint_callback)
 ```
 
+---
+### Restoring training session 
+You might want to not only load a model but also continue training it. Use this method to
+restore the trainer state as well. This will continue from the epoch and global step you last left off.  
+However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).   
+
+Lightning will restore the session if you pass an experiment with the same version and there's a saved checkpoint.   
+``` {.python}
+from test_tube import Experiment
+
+exp = Experiment(version=a_previous_version_with_a_saved_checkpoint)
+Trainer(experiment=exp)
+
+trainer = Trainer(checkpoint_callback=checkpoint_callback)
+# the trainer is now restored
+```
+
 
 

docs/Trainer/index.md

@@ -21,6 +21,7 @@ But of course the fun is in all the advanced things it can do:
 
 - [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
 - [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
+- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
 
 **Computing cluster (SLURM)**    
 

docs/index.md

@@ -28,6 +28,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.
 
 - [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
 - [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
+- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
 
 ###### Computing cluster (SLURM)