[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

src/lightning/pytorch/trainer/trainer.py

@@ -1697,61 +1697,61 @@ def _results(self) -> Optional[_ResultCollection]:
     def estimated_stepping_batches(self) -> Union[int, float]:
         r"""The estimated number of batches that will ``optimizer.step()`` during training.
 
-        This accounts for gradient accumulation and the current trainer configuration. This might be used when setting
-        up your training dataloader, if it hasn't been set up already.
+                This accounts for gradient accumulation and the current trainer configuration. This might be used when setting
+                up your training dataloader, if it hasn't been set up already.
 
-        .. code-block:: python
+                .. code-block:: python
 
-            def configure_optimizers(self):
-                optimizer = ...
-                stepping_batches = self.trainer.estimated_stepping_batches
-                scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3, total_steps=stepping_batches)
-                return [val_check_interval
-.. video:: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/val_check_interval.mp4
-    :poster: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/val_check_interval.jpg
-    :width: 400
-    :muted:
+                    def configure_optimizers(self):
+                        optimizer = ...
+                        stepping_batches = self.trainer.estimated_stepping_batches
+                        scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3, total_steps=stepping_batches)
+                        return [val_check_interval
+        .. video:: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/val_check_interval.mp4
+            :poster: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/val_check_interval.jpg
+            :width: 400
+            :muted:
 
-How often within one training epoch to check the validation set. Can specify as float or int.
+        How often within one training epoch to check the validation set. Can specify as float or int.
 
-pass a float in the range [0.0, 1.0] to check after a fraction of the training epoch.
-pass an int to check after a fixed number of training batches. An int value can only be higher than the number of training batches when check_val_every_n_epoch=None, which validates after every N training batches across epochs or iteration-based training.
-.. testcode::
+        pass a float in the range [0.0, 1.0] to check after a fraction of the training epoch.
+        pass an int to check after a fixed number of training batches. An int value can only be higher than the number of training batches when check_val_every_n_epoch=None, which validates after every N training batches across epochs or iteration-based training.
+        .. testcode::
 
-    # default used by the Trainer
-    trainer = Trainer(val_check_interval=1.0)
+            # default used by the Trainer
+            trainer = Trainer(val_check_interval=1.0)
 
-    # check validation set 4 times during a training epoch
-    trainer = Trainer(val_check_interval=0.25)
+            # check validation set 4 times during a training epoch
+            trainer = Trainer(val_check_interval=0.25)
 
-    # check validation set every 1000 training batches in the current epoch
-    trainer = Trainer(val_check_interval=1000)
+            # check validation set every 1000 training batches in the current epoch
+            trainer = Trainer(val_check_interval=1000)
 
-    # check validation set every 1000 training batches across complete epochs or during iteration-based training
-    # use this when using iterableDataset and your dataset has no length
-    # (ie: production cases with streaming data)
-    trainer = Trainer(val_check_interval=1000, check_val_every_n_epoch=None)
+            # check validation set every 1000 training batches across complete epochs or during iteration-based training
+            # use this when using iterableDataset and your dataset has no length
+            # (ie: production cases with streaming data)
+            trainer = Trainer(val_check_interval=1000, check_val_every_n_epoch=None)
 
 
-# Here is the computation to estimate the total number of batches seen within an epoch.
+        # Here is the computation to estimate the total number of batches seen within an epoch.
 
-# Find the total number of train batches
-total_train_batches = total_train_samples // (train_batch_size * world_size)
+        # Find the total number of train batches
+        total_train_batches = total_train_samples // (train_batch_size * world_size)
 
-# Compute how many times we will call validation during the training loop
-val_check_batch = max(1, int(total_train_batches * val_check_interval))
-val_checks_per_epoch = total_train_batches / val_check_batch
+        # Compute how many times we will call validation during the training loop
+        val_check_batch = max(1, int(total_train_batches * val_check_interval))
+        val_checks_per_epoch = total_train_batches / val_check_batch
 
-# Find the total number of validation batches
-total_val_batches = total_val_samples // (val_batch_size * world_size)
+        # Find the total number of validation batches
+        total_val_batches = total_val_samples // (val_batch_size * world_size)
 
-# Total number of batches run
-total_fit_batches = total_train_batches + total_val_batchesizer], [scheduler]
+        # Total number of batches run
+        total_fit_batches = total_train_batches + total_val_batchesizer], [scheduler]
 
-        Raises:
-            MisconfigurationException:
-                If estimated stepping batches cannot be computed due to different `accumulate_grad_batches`
-                at different epochs.
+                Raises:
+                    MisconfigurationException:
+                        If estimated stepping batches cannot be computed due to different `accumulate_grad_batches`
+                        at different epochs.
 
         """
         # infinite training