add param to not shuffle train dataloader

cneuromax/fitting/deeplearning/datamodule/base.py

@@ -54,6 +54,7 @@ class BaseDataModuleConfig:
             this value skips the num workers search in
             :func:`.find_good_per_device_num_workers` which is
             not recommended for resource efficiency.
+        shuffle_train_dataset
         shuffle_val_dataset
     """
 
@@ -62,6 +63,7 @@ class BaseDataModuleConfig:
     max_per_device_batch_size: An[int, ge(1)] | None = None
     fixed_per_device_batch_size: An[int, ge(1)] | None = None
     fixed_per_device_num_workers: An[int, ge(0)] | None = None
+    shuffle_train_dataset: bool = True
     shuffle_val_dataset: bool = True
 
 
@@ -174,7 +176,10 @@ def train_dataloader(self: "BaseDataModule") -> DataLoader[Tensor]:
             A new training
                 :class:`torch.utils.data.DataLoader` instance.
         """
-        return self.x_dataloader(dataset=self.datasets.train)
+        return self.x_dataloader(
+            dataset=self.datasets.train,
+            shuffle=self.config.shuffle_train_dataset,
+        )
 
     def val_dataloader(self: "BaseDataModule") -> DataLoader[Tensor]:
         """Calls :meth:`x_dataloader` w/ :attr:`datasets` ``.val``.