callback docs (#2794)

* added logging docs

* added logging docs

* added logging docs

* added logging docs

docs/source/callbacks.rst

@@ -20,6 +20,7 @@ An overall Lightning system should have:
 2. LightningModule for all research code.
 3. Callbacks for non-essential code.
 
+|
 
 Example:
 
@@ -46,20 +47,7 @@ Example:
 We successfully extended functionality without polluting our super clean
 :class:`~pytorch_lightning.core.LightningModule` research code.
 
-----------------
-
-Best Practices
 --------------
-The following are best practices when using/designing callbacks.
-
-1. Callbacks should be isolated in their functionality.
-2. Your callback should not rely on the behavior of other callbacks in order to work properly.
-3. Do not manually call methods from the callback.
-4. Directly calling methods (eg. `on_validation_end`) is strongly discouraged.
-5. Whenever possible, your callbacks should not depend on the order in which they are executed.
-
-
----------
 
 .. automodule:: pytorch_lightning.callbacks.base
    :noindex:
@@ -112,3 +100,16 @@ The following are best practices when using/designing callbacks.
 .. automodule:: pytorch_lightning.callbacks.progress
    :noindex:
    :exclude-members:
+
+
+----------------
+
+Best Practices
+--------------
+The following are best practices when using/designing callbacks.
+
+1. Callbacks should be isolated in their functionality.
+2. Your callback should not rely on the behavior of other callbacks in order to work properly.
+3. Do not manually call methods from the callback.
+4. Directly calling methods (eg. `on_validation_end`) is strongly discouraged.
+5. Whenever possible, your callbacks should not depend on the order in which they are executed.

pytorch_lightning/callbacks/base.py

@@ -1,6 +1,6 @@
 r"""
 Callback Base
-=============
+-------------
 
 Abstract base class used to build new callbacks.
 

pytorch_lightning/core/__init__.py

@@ -212,6 +212,63 @@ don't run your test data by accident. Instead you have to explicitly call:
     trainer = Trainer()
     trainer.test(model, test_dataloaders=test_dataloader)
 
+-------------
+
+TrainResult
+^^^^^^^^^^^
+When you are using the `_step_end` and `_epoch_end` only for aggregating metrics and then logging,
+consider using either a `EvalResult` or `TrainResult` instead.
+
+Here's a training loop structure
+
+.. code-block:: python
+
+    def training_step(self, batch, batch_idx):
+        return {'loss': loss}
+
+    def training_epoch_end(self, training_step_outputs):
+        epoch_loss = torch.stack([x['loss'] for x in training_step_outputs]).mean()
+        return {
+            'log': {'epoch_loss': epoch_loss},
+            'progress_bar': {'epoch_loss': epoch_loss}
+        }
+
+using the equivalent syntax via the `TrainResult` object:
+
+.. code-block:: python
+
+    def training_step(self, batch_subset, batch_idx):
+        loss = ...
+        result = pl.TrainResult(minimize=loss)
+        result.log('train_loss', loss, prog_bar=True)
+        return result
+
+EvalResult
+^^^^^^^^^^
+Same for val/test loop
+
+.. code-block:: python
+
+    def validation_step(self, batch, batch_idx):
+        return {'some_metric': some_metric}
+
+    def validation_epoch_end(self, validation_step_outputs):
+        some_metric_mean = torch.stack([x['some_metric'] for x in validation_step_outputs]).mean()
+        return {
+            'log': {'some_metric_mean': some_metric_mean},
+            'progress_bar': {'some_metric_mean': some_metric_mean}
+        }
+
+With the equivalent using the `EvalResult` syntax
+
+.. code-block:: python
+
+    def validation_step(self, batch, batch_idx):
+        some_metric = ...
+        result = pl.EvalResult(checkpoint_on=some_metric)
+        result.log('some_metric', some_metric, prog_bar=True)
+        return result
+
 ----------
 
 Training_step_end method