Chore(pt): fix warning in test_training

deepmd/pt/train/training.py

@@ -1037,6 +1037,23 @@ def log_loss_valid(_task_key="Default"):
                     f"The profiling trace have been saved to: {self.profiling_file}"
                 )
 
+    def delete_dataloader(self):
+        if self.multi_task:
+            for model_key in self.model_keys:
+                del (
+                    self.training_data[model_key],
+                    self.training_dataloader[model_key],
+                    self.validation_data[model_key],
+                    self.validation_dataloader[model_key],
+                )
+        else:
+            del (
+                self.training_data,
+                self.training_dataloader,
+                self.validation_data,
+                self.validation_dataloader,
+            )
+
     def save_model(self, save_path, lr=0.0, step=0):
         module = (
             self.wrapper.module

source/tests/pt/test_training.py

@@ -2,6 +2,7 @@
 import json
 import os
 import shutil
+import tempfile
 import unittest
 from copy import (
     deepcopy,
@@ -34,6 +35,7 @@ def test_dp_train(self):
         # test training from scratch
         trainer = get_trainer(deepcopy(self.config))
         trainer.run()
+        trainer.delete_dataloader()
         state_dict_trained = trainer.wrapper.model.state_dict()
 
         # test fine-tuning using same input
@@ -100,6 +102,11 @@ def test_dp_train(self):
         trainer_finetune_empty.run()
         trainer_finetune_random.run()
 
+        # delete dataloader to stop buffer fetching
+        trainer_finetune.delete_dataloader()
+        trainer_finetune_empty.delete_dataloader()
+        trainer_finetune_random.delete_dataloader()
+
     def test_trainable(self):
         fix_params = deepcopy(self.config)
         fix_params["model"]["descriptor"]["trainable"] = False
@@ -195,18 +202,25 @@ def setUp(self):
         input_json = str(Path(__file__).parent / "water/se_atten.json")
         with open(input_json) as f:
             self.config = json.load(f)
-        data_file = [str(Path(__file__).parent / "water/data/data_0")]
+        self.original_data_path = Path(__file__).parent / "water/data/data_0"
+        # Create a temporary directory for this test
+        self.temp_dir = Path(tempfile.mkdtemp())
+        self.temp_data_path = self.temp_dir / "data_0"
+        shutil.copytree(self.original_data_path, self.temp_data_path)
+
+        data_file = [str(self.temp_data_path)]
         self.config["training"]["training_data"]["systems"] = data_file
         self.config["training"]["validation_data"]["systems"] = data_file
         self.config["model"] = deepcopy(model_se_e2_a)
         self.config["model"]["fitting_net"]["numb_fparam"] = 1
         self.config["training"]["numb_steps"] = 1
         self.config["training"]["save_freq"] = 1
-        self.set_path = Path(__file__).parent / "water/data/data_0" / "set.000"
+        self.set_path = self.temp_data_path / "set.000"
         shutil.copyfile(self.set_path / "energy.npy", self.set_path / "fparam.npy")
 
     def tearDown(self) -> None:
-        (self.set_path / "fparam.npy").unlink(missing_ok=True)
+        # Remove the temporary directory and all its contents
+        shutil.rmtree(self.temp_dir)
         DPTrainTest.tearDown(self)