diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py
index 6fe8562ad6..1e25da77fb 100644
--- a/deepmd/pt/train/training.py
+++ b/deepmd/pt/train/training.py
@@ -128,61 +128,45 @@ def get_opt_param(params):
             return opt_type, opt_param
 
         def get_data_loader(_training_data, _validation_data, _training_params):
-            if "auto_prob" in _training_params["training_data"]:
-                train_sampler = get_weighted_sampler(
-                    _training_data, _training_params["training_data"]["auto_prob"]
-                )
-            elif "sys_probs" in _training_params["training_data"]:
-                train_sampler = get_weighted_sampler(
-                    _training_data,
-                    _training_params["training_data"]["sys_probs"],
-                    sys_prob=True,
-                )
-            else:
-                train_sampler = get_weighted_sampler(_training_data, "prob_sys_size")
-
-            if train_sampler is None:
-                log.warning(
-                    "Sampler not specified!"
-                )  # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration.
-            training_dataloader = DataLoader(
-                _training_data,
-                sampler=train_sampler,
-                batch_size=None,
-                num_workers=NUM_WORKERS,  # setting to 0 diverges the behavior of its iterator; should be >=1
-                drop_last=False,
-                pin_memory=True,
-            )
-            with torch.device("cpu"):
-                training_data_buffered = BufferedIterator(iter(training_dataloader))
-            if _validation_data is not None:
-                if "auto_prob" in _training_params["validation_data"]:
-                    valid_sampler = get_weighted_sampler(
-                        _validation_data,
-                        _training_params["validation_data"]["auto_prob"],
+            def get_dataloader_and_buffer(_data, _params):
+                if "auto_prob" in _training_params["training_data"]:
+                    _sampler = get_weighted_sampler(
+                        _data, _params["training_data"]["auto_prob"]
                     )
-                elif "sys_probs" in _training_params["validation_data"]:
-                    valid_sampler = get_weighted_sampler(
-                        _validation_data,
-                        _training_params["validation_data"]["sys_probs"],
+                elif "sys_probs" in _training_params["training_data"]:
+                    _sampler = get_weighted_sampler(
+                        _data,
+                        _params["training_data"]["sys_probs"],
                         sys_prob=True,
                     )
                 else:
-                    valid_sampler = get_weighted_sampler(
-                        _validation_data, "prob_sys_size"
-                    )
-                validation_dataloader = DataLoader(
-                    _validation_data,
-                    sampler=valid_sampler,
+                    _sampler = get_weighted_sampler(_data, "prob_sys_size")
+
+                if _sampler is None:
+                    log.warning(
+                        "Sampler not specified!"
+                    )  # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration.
+                _dataloader = DataLoader(
+                    _data,
+                    sampler=_sampler,
                     batch_size=None,
-                    num_workers=min(NUM_WORKERS, 1),
+                    num_workers=NUM_WORKERS,  # setting to 0 diverges the behavior of its iterator; should be >=1
                     drop_last=False,
                     pin_memory=True,
                 )
                 with torch.device("cpu"):
-                    validation_data_buffered = BufferedIterator(
-                        iter(validation_dataloader)
-                    )
+                    _data_buffered = BufferedIterator(iter(_dataloader))
+                return _dataloader, _data_buffered
+
+            training_dataloader, training_data_buffered = get_dataloader_and_buffer(
+                _training_data, _training_params
+            )
+
+            if _validation_data is not None:
+                (
+                    validation_dataloader,
+                    validation_data_buffered,
+                ) = get_dataloader_and_buffer(_validation_data, _training_params)
                 if _training_params.get("validation_data", None) is not None:
                     valid_numb_batch = _training_params["validation_data"].get(
                         "numb_btch", 1