Update docs for limit_predict_batches

pytorch_lightning/trainer/data_loading.py

@@ -293,17 +293,17 @@ def _reset_eval_dataloader(
             if mode in modes and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):
 
                 # when overfitting, the dataloader should not have sampler
-                if self.overfit_batches > 0:
+                if self.overfit_batches > 0 and mode != 'predict':
                     rank_zero_warn(
-                        'You requested to overfit but enabled test/val dataloader shuffling.'
+                        'You requested to overfit but enabled val/test dataloader shuffling.'
                         ' We are turning it off for you.'
                     )
                     dataloaders[loader_i] = self.replace_sampler(loader, SequentialSampler(loader.dataset))
 
                 else:
                     rank_zero_warn(
                         f'Your {mode}_dataloader has `shuffle=True`, it is best practice to turn'
-                        ' this off for validation and test dataloaders.'
+                        ' this off for val/test/predict dataloaders.'
                     )
 
         if any([dl is None for dl in dataloaders]):

pytorch_lightning/trainer/predict_loop.py

@@ -28,17 +28,16 @@ def __init__(self, trainer):
     def on_trainer_init(self):
         self.trainer.num_predict_batches = []
 
-    def get_predict_dataloaders(self, max_batches):
+    def get_predict_dataloaders(self):
         self.trainer.reset_predict_dataloader(self.trainer.lightning_module)
 
         dataloaders = self.trainer.predict_dataloaders
-        if max_batches is None:
-            max_batches = self.trainer.num_predict_batches
+        max_batches = self.trainer.num_predict_batches
 
         return dataloaders, max_batches
 
-    def should_skip_predict(self, dataloaders, max_batches):
-        return dataloaders is None or not sum(max_batches)
+    def should_skip_predict(self, max_batches):
+        return sum(max_batches) == 0
 
     def on_predict_model_eval(self, *_, **__):
         model_ref = self.trainer.lightning_module

pytorch_lightning/trainer/trainer.py

@@ -198,11 +198,13 @@ def __init__(
 
             gradient_clip_val: 0 means don't clip.
 
-            limit_train_batches: How much of training dataset to check (floats = percent, int = num_batches)
+            limit_train_batches: How much of training dataset to check (float = fraction, int = num_batches)
 
-            limit_val_batches: How much of validation dataset to check (floats = percent, int = num_batches)
+            limit_val_batches: How much of validation dataset to check (float = fraction, int = num_batches)
 
-            limit_test_batches: How much of test dataset to check (floats = percent, int = num_batches)
+            limit_test_batches: How much of test dataset to check (float = fraction, int = num_batches)
+
+            limit_predict_batches: How much of prediction dataset to check (float = fraction, int = num_batches)
 
             logger: Logger (or iterable collection of loggers) for experiment tracking.
 
@@ -221,7 +223,7 @@ def __init__(
 
             profiler: To profile individual steps during training and assist in identifying bottlenecks.
 
-            overfit_batches: Overfit a percent of training data (float) or a set number of batches (int).
+            overfit_batches: Overfit a fraction of training data (float) or a set number of batches (int).
 
             plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
 
@@ -754,10 +756,10 @@ def run_evaluate(self):
 
     def run_predict(self):
         # prepare dataloaders
-        dataloaders, max_batches = self.predict_loop.get_predict_dataloaders(None)
+        dataloaders, max_batches = self.predict_loop.get_predict_dataloaders()
 
         # check if we want to skip this evaluation
-        if self.predict_loop.should_skip_predict(dataloaders, max_batches):
+        if self.predict_loop.should_skip_predict(max_batches):
             return []
 
         # ref model
@@ -922,9 +924,7 @@ def test(
 
         # If you supply a datamodule you can't supply test_dataloaders
         if test_dataloaders and datamodule:
-            raise MisconfigurationException(
-                'You cannot pass both `trainer.test(test_dataloaders=..., datamodule=...)`'
-            )
+            raise MisconfigurationException('You cannot pass both `trainer.test(test_dataloaders=..., datamodule=...)`')
 
         model_provided = model is not None
         model = model or self.lightning_module