added warnings to unimplemented methods (#1317)

* added warnings and removed default optimizer

* opt

* Apply suggestions from code review

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

CHANGELOG.md

@@ -37,8 +37,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added model configuration checking ([#1199](https://github.com/PyTorchLightning/pytorch-lightning/pull/1199))
 - On DP and DDP2 unsqueeze is automated now ([#1319](https://github.com/PyTorchLightning/pytorch-lightning/pull/1319))
 - Does not interfere with a default sampler ([#1318](https://github.com/PyTorchLightning/pytorch-lightning/pull/1318))
+- Remove default Adam optimizer ([#1317](https://github.com/PyTorchLightning/pytorch-lightning/pull/1317))
+- Give warnings for unimplemented required lightning methods ([#1317](https://github.com/PyTorchLightning/pytorch-lightning/pull/1317))
 - Enhanced load_from_checkpoint to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307))
-- Made `evalaute` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260))
+- Made `evaluate` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260))
 
 ### Deprecated
 

docs/source/introduction_guide.rst

@@ -269,7 +269,6 @@ In PyTorch we do it as follows:
 
 
 In Lightning we do the same but organize it under the configure_optimizers method.
-If you don't define this, Lightning will automatically use `Adam(self.parameters(), lr=1e-3)`.
 
 .. code-block:: python
 
@@ -278,6 +277,17 @@ If you don't define this, Lightning will automatically use `Adam(self.parameters
       def configure_optimizers(self):
         return Adam(self.parameters(), lr=1e-3)
 
+.. note:: The LightningModule itself has the parameters, so pass in self.parameters()
+
+However, if you have multiple optimizers use the matching parameters
+
+.. code-block:: python
+
+    class LitMNIST(pl.LightningModule):
+
+        def configure_optimizers(self):
+            return Adam(self.generator(), lr=1e-3), Adam(self.discriminator(), lr=1e-3)
+
 Training step
 ^^^^^^^^^^^^^
 

pytorch_lightning/core/lightning.py

@@ -224,6 +224,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
             The presented loss value in progress bar is smooth (average) over last values,
              so it differs from values set in train/validation step.
         """
+        warnings.warn('`training_step` must be implemented to be used with the Lightning Trainer')
 
     def training_end(self, *args, **kwargs):
         """
@@ -1079,6 +1080,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                   }
 
         """
+        warnings.warn('`configure_optimizers` must be implemented to be used with the Lightning Trainer')
 
     def optimizer_step(
             self,
@@ -1280,6 +1282,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                     return loader
 
         """
+        warnings.warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')
 
     def tng_dataloader(self):  # todo: remove in v1.0.0
         """Implement a PyTorch DataLoader.