release v0.3.6.6

README.md

@@ -280,8 +280,8 @@ tensorboard --logdir /some/path
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
-- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)    
-- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
+- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)    
+- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 
 ###### Validation loop    

docs/Trainer/index.md

@@ -63,7 +63,7 @@ But of course the fun is in all the advanced things it can do:
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Hooks](hooks)
-- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
+- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
 - [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 

docs/index.md

@@ -70,7 +70,7 @@ one could be a seq-2-seq model, both (optionally) ran by the same trainer file.
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
-- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
+- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
 - [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 

pytorch_lightning/models/trainer.py

@@ -772,7 +772,7 @@ class Trainer(TrainerIO):
             output = self.model.training_step(data_batch, batch_nb)
 
         try:
-            model_specific_tqdm_metrics_dic = output['tqdm_metrics']
+            model_specific_tqdm_metrics_dic = output['prog']
         except Exception as e:
             model_specific_tqdm_metrics_dic = {}
 

pytorch_lightning/testing_models/lm_test_module.py

@@ -96,12 +96,15 @@ class LightningTestModel(LightningModule):
         if self.trainer.use_dp:
             loss_val = loss_val.unsqueeze(0)
 
-        output = OrderedDict({
-            'loss': loss_val
-        })
-
-        # can also return just a scalar instead of a dict (return loss_val)
-        return output
+        # alternate possible outputs to test
+        if self.trainer.batch_nb % 1 == 0:
+            output = OrderedDict({
+                'loss': loss_val,
+                'prog': {'some_val': loss_val * loss_val}
+            })
+            return output
+        if self.trainer.batch_nb % 2 == 0:
+            return loss_val
 
     def validation_step(self, data_batch, batch_i):
         """

setup.py

@@ -7,7 +7,7 @@ from setuptools import setup, find_packages
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
 setup(
     name="pytorch-lightning",
-    version='0.3.6.5',
+    version='0.3.6.6',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",