[Docs] Note on running metric in dp

docs/source/metrics.rst

@@ -78,6 +78,26 @@ If ``on_epoch`` is True, the logger automatically logs the end of epoch metric v
  self.valid_acc(logits, y)
  self.log('valid_acc', self.valid_acc, on_step=True, on_epoch=True)
 
+.. note::
+ If using metrics in data parallel mode (dp), the metric update/logging should be done
+ in the ``<mode>_step_end`` method (where ``<mode>`` is either ``training``, ``validation``
+ or ``test``). This is due to metric states else being destroyed after each forward pass,
+ leading to wrong accumulation. In practice do the following:
+
+ .. code-block:: python
+
+ def training_step(self, batch, batch_idx):
+ data, target = batch
+ pred = self(data)
+ ...
+ return {'loss' : loss, 'preds' : preds, 'target' : target}
+
+ def training_step_end(self, outputs):
+ #update and log
+ self.metric(outputs['preds'], outputs['target'])
+ self.log('metric', self.metric)
+
+
 This metrics API is independent of PyTorch Lightning. Metrics can directly be used in PyTorch as shown in the example:
 
 .. code-block:: python