diff --git a/deepmd/env.py b/deepmd/env.py
index 1a8da63f8e..451b79d94f 100644
--- a/deepmd/env.py
+++ b/deepmd/env.py
@@ -13,6 +13,8 @@
     "global_float_prec",
 ]
 
+log = logging.getLogger(__name__)
+
 # FLOAT_PREC
 dp_float_prec = os.environ.get("DP_INTERFACE_PREC", "high").lower()
 if dp_float_prec in ("high", ""):
@@ -47,7 +49,7 @@ def set_env_if_empty(key: str, value: str, verbose: bool = True):
     if os.environ.get(key) is None:
         os.environ[key] = value
         if verbose:
-            logging.warning(
+            log.warning(
                 f"Environment variable {key} is empty. Use the default value {value}"
             )
 
@@ -72,7 +74,7 @@ def set_default_nthreads():
             and "TF_INTER_OP_PARALLELISM_THREADS" not in os.environ
         )
     ):
-        logging.warning(
+        log.warning(
             "To get the best performance, it is recommended to adjust "
             "the number of threads by setting the environment variables "
             "OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and "
diff --git a/deepmd/pt/entrypoints/main.py b/deepmd/pt/entrypoints/main.py
index ad5e92d495..680d3313a6 100644
--- a/deepmd/pt/entrypoints/main.py
+++ b/deepmd/pt/entrypoints/main.py
@@ -30,6 +30,9 @@
 from deepmd.infer.model_devi import (
     make_model_devi,
 )
+from deepmd.loggers.loggers import (
+    set_log_handles,
+)
 from deepmd.main import (
     parse_args,
 )
@@ -42,9 +45,6 @@
 from deepmd.pt.train import (
     training,
 )
-from deepmd.pt.utils import (
-    env,
-)
 from deepmd.pt.utils.dataloader import (
     DpLoaderSet,
 )
@@ -58,6 +58,8 @@
     make_stat_input,
 )
 
+log = logging.getLogger(__name__)
+
 
 def get_trainer(
     config,
@@ -237,7 +239,7 @@ def prepare_trainer_input_single(
 
 
 def train(FLAGS):
-    logging.info("Configuration path: %s", FLAGS.INPUT)
+    log.info("Configuration path: %s", FLAGS.INPUT)
     with open(FLAGS.INPUT) as fin:
         config = json.load(fin)
     trainer = get_trainer(
@@ -278,28 +280,18 @@ def freeze(FLAGS):
     )
 
 
-# avoid logger conflicts of tf version
-def clean_loggers():
-    logger = logging.getLogger()
-    while logger.hasHandlers():
-        logger.removeHandler(logger.handlers[0])
-
-
 @record
 def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
-    clean_loggers()
-
     if not isinstance(args, argparse.Namespace):
         FLAGS = parse_args(args=args)
     else:
         FLAGS = args
     dict_args = vars(FLAGS)
 
-    logging.basicConfig(
-        level=logging.WARNING if env.LOCAL_RANK else logging.INFO,
-        format=f"%(asctime)-15s {os.environ.get('RANK') or ''} [%(filename)s:%(lineno)d] %(levelname)s %(message)s",
-    )
-    logging.info("DeepMD version: %s", __version__)
+    set_log_handles(FLAGS.log_level, FLAGS.log_path, mpi_log=None)
+    log.debug("Log handles were successfully set")
+
+    log.info("DeepMD version: %s", __version__)
 
     if FLAGS.command == "train":
         train(FLAGS)
@@ -315,9 +307,9 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
             FLAGS.model = FLAGS.checkpoint_folder
         FLAGS.output = str(Path(FLAGS.output).with_suffix(".pth"))
         freeze(FLAGS)
-    elif args.command == "doc-train-input":
+    elif FLAGS.command == "doc-train-input":
         doc_train_input(**dict_args)
-    elif args.command == "model-devi":
+    elif FLAGS.command == "model-devi":
         dict_args["models"] = [
             str(Path(mm).with_suffix(".pt"))
             if Path(mm).suffix not in (".pb", ".pt")
@@ -325,7 +317,7 @@ def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
             for mm in dict_args["models"]
         ]
         make_model_devi(**dict_args)
-    elif args.command == "gui":
+    elif FLAGS.command == "gui":
         start_dpgui(**dict_args)
     else:
         raise RuntimeError(f"Invalid command {FLAGS.command}!")
diff --git a/deepmd/pt/infer/inference.py b/deepmd/pt/infer/inference.py
index 4906bb7a46..6a9f0d99d2 100644
--- a/deepmd/pt/infer/inference.py
+++ b/deepmd/pt/infer/inference.py
@@ -40,6 +40,7 @@
 
 if torch.__version__.startswith("2"):
     import torch._dynamo
+log = logging.getLogger(__name__)
 
 
 class Tester:
@@ -95,9 +96,7 @@ def __init__(
                 ), f"Validation systems not found in {input_script}!"
                 self.systems = training_params["validation_data"]["systems"]
                 self.batchsize = training_params["validation_data"]["batch_size"]
-                logging.info(
-                    f"Testing validation systems in input script: {input_script}"
-                )
+                log.info(f"Testing validation systems in input script: {input_script}")
             else:
                 assert (
                     "data_dict" in training_params
@@ -115,18 +114,18 @@ def __init__(
                 self.batchsize = training_params["data_dict"][head]["validation_data"][
                     "batch_size"
                 ]
-                logging.info(
+                log.info(
                     f"Testing validation systems in head {head} of input script: {input_script}"
                 )
         elif system is not None:
             self.systems = expand_sys_str(system)
             self.batchsize = "auto"
-            logging.info("Testing systems in path: %s", system)
+            log.info("Testing systems in path: %s", system)
         elif datafile is not None:
             with open(datafile) as fin:
                 self.systems = fin.read().splitlines()
             self.batchsize = "auto"
-            logging.info("Testing systems in file: %s", datafile)
+            log.info("Testing systems in file: %s", datafile)
         else:
             self.systems = None
             self.batchsize = None
@@ -210,8 +209,8 @@ def run(self):
         system_results = {}
         global_sum_natoms = 0
         for cc, system in enumerate(systems):
-            logging.info("# ---------------output of dp test--------------- ")
-            logging.info(f"# testing system : {system}")
+            log.info("# ---------------output of dp test--------------- ")
+            log.info(f"# testing system : {system}")
             system_pred = []
             system_label = []
             dataset = DpLoaderSet(
@@ -226,7 +225,7 @@ def run(self):
                 dataset, replacement=True, num_samples=dataset.total_batch
             )
             if sampler is None:
-                logging.warning(
+                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(
@@ -296,8 +295,8 @@ def run(self):
                 for k, v in single_results.items()
             }
             for item in sorted(results.keys()):
-                logging.info(f"{item}: {results[item]:.4f}")
-            logging.info("# ----------------------------------------------- ")
+                log.info(f"{item}: {results[item]:.4f}")
+            log.info("# ----------------------------------------------- ")
             for k, v in single_results.items():
                 system_results[k] = system_results.get(k, 0.0) + v
             global_sum_natoms += sum_natoms
@@ -306,14 +305,14 @@ def run(self):
             k: v / global_sum_natoms if "mae" in k else math.sqrt(v / global_sum_natoms)
             for k, v in system_results.items()
         }
-        logging.info("# ----------weighted average of errors----------- ")
+        log.info("# ----------weighted average of errors----------- ")
         if not self.multi_task:
-            logging.info(f"# number of systems : {len(systems)}")
+            log.info(f"# number of systems : {len(systems)}")
         else:
-            logging.info(f"# number of systems for {self.head}: {len(systems)}")
+            log.info(f"# number of systems for {self.head}: {len(systems)}")
         for item in sorted(global_results.keys()):
-            logging.info(f"{item}: {global_results[item]:.4f}")
-        logging.info("# ----------------------------------------------- ")
+            log.info(f"{item}: {global_results[item]:.4f}")
+        log.info("# ----------------------------------------------- ")
         return global_results
 
 
diff --git a/deepmd/pt/model/model/model.py b/deepmd/pt/model/model/model.py
index 01c2d7b9d6..000746a213 100644
--- a/deepmd/pt/model/model/model.py
+++ b/deepmd/pt/model/model/model.py
@@ -12,6 +12,8 @@
     compute_output_stats,
 )
 
+log = logging.getLogger(__name__)
+
 
 class BaseModel(torch.nn.Module):
     def __init__(self):
@@ -55,7 +57,7 @@ def compute_or_load_stat(
                     if not os.path.exists(stat_file_dir):
                         os.mkdir(stat_file_dir)
                     if not isinstance(stat_file_path, list):
-                        logging.info(f"Saving stat file to {stat_file_path}")
+                        log.info(f"Saving stat file to {stat_file_path}")
                         np.savez_compressed(
                             stat_file_path,
                             sumr=sumr,
@@ -68,7 +70,7 @@ def compute_or_load_stat(
                         )
                     else:
                         for ii, file_path in enumerate(stat_file_path):
-                            logging.info(f"Saving stat file to {file_path}")
+                            log.info(f"Saving stat file to {file_path}")
                             np.savez_compressed(
                                 file_path,
                                 sumr=sumr[ii],
@@ -82,7 +84,7 @@ def compute_or_load_stat(
             else:  # load stat
                 target_type_map = type_map
                 if not isinstance(stat_file_path, list):
-                    logging.info(f"Loading stat file from {stat_file_path}")
+                    log.info(f"Loading stat file from {stat_file_path}")
                     stats = np.load(stat_file_path)
                     stat_type_map = list(stats["type_map"])
                     missing_type = [
@@ -105,7 +107,7 @@ def compute_or_load_stat(
                     sumr, suma, sumn, sumr2, suma2 = [], [], [], [], []
                     id_bias_atom_e = None
                     for ii, file_path in enumerate(stat_file_path):
-                        logging.info(f"Loading stat file from {file_path}")
+                        log.info(f"Loading stat file from {file_path}")
                         stats = np.load(file_path)
                         stat_type_map = list(stats["type_map"])
                         missing_type = [
diff --git a/deepmd/pt/model/task/dipole.py b/deepmd/pt/model/task/dipole.py
index 4906987bf8..d911613a5b 100644
--- a/deepmd/pt/model/task/dipole.py
+++ b/deepmd/pt/model/task/dipole.py
@@ -10,6 +10,8 @@
     Fitting,
 )
 
+log = logging.getLogger(__name__)
+
 
 class DipoleFittingNetType(Fitting):
     def __init__(
@@ -37,7 +39,7 @@ def __init__(
         self.filter_layers = torch.nn.ModuleList(filter_layers)
 
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def forward(self, inputs, atype, atype_tebd, rot_mat):
diff --git a/deepmd/pt/model/task/ener.py b/deepmd/pt/model/task/ener.py
index 484e477b6a..5e3cd87367 100644
--- a/deepmd/pt/model/task/ener.py
+++ b/deepmd/pt/model/task/ener.py
@@ -40,6 +40,8 @@
 dtype = env.GLOBAL_PT_FLOAT_PRECISION
 device = env.DEVICE
 
+log = logging.getLogger(__name__)
+
 
 @fitting_check_output
 class InvarFitting(Fitting):
@@ -153,7 +155,7 @@ def __init__(
 
         # very bad design...
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def output_def(self) -> FittingOutputDef:
@@ -451,7 +453,7 @@ def __init__(
         self.filter_layers = torch.nn.ModuleList(filter_layers)
 
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def output_def(self):
diff --git a/deepmd/pt/model/task/fitting.py b/deepmd/pt/model/task/fitting.py
index 551fb9640b..b03aee7539 100644
--- a/deepmd/pt/model/task/fitting.py
+++ b/deepmd/pt/model/task/fitting.py
@@ -23,6 +23,8 @@
     make_stat_input,
 )
 
+log = logging.getLogger(__name__)
+
 
 class Fitting(torch.nn.Module, BaseFitting):
     __plugins = Plugin()
@@ -115,7 +117,7 @@ def change_energy_bias(
         ntest : int
             The number of test samples in a system to change the energy bias.
         """
-        logging.info(
+        log.info(
             "Changing energy bias in pretrained model for types {}... "
             "(this step may take long time)".format(str(new_type_map))
         )
@@ -188,7 +190,7 @@ def change_energy_bias(
             self.bias_atom_e[idx_type_map] += torch.from_numpy(
                 delta_bias.reshape(-1)
             ).to(DEVICE)
-            logging.info(
+            log.info(
                 f"RMSE of atomic energy after linear regression is: {rmse_ae:10.5e} eV/atom."
             )
         elif bias_shift == "statistic":
@@ -202,7 +204,7 @@ def change_energy_bias(
             )
         else:
             raise RuntimeError("Unknown bias_shift mode: " + bias_shift)
-        logging.info(
+        log.info(
             "Change energy bias of {} from {} to {}.".format(
                 str(new_type_map),
                 str(old_bias.detach().cpu().numpy()),
diff --git a/deepmd/pt/optimizer/LKF.py b/deepmd/pt/optimizer/LKF.py
index 5e18797c7b..ebc9242d49 100644
--- a/deepmd/pt/optimizer/LKF.py
+++ b/deepmd/pt/optimizer/LKF.py
@@ -7,6 +7,8 @@
     Optimizer,
 )
 
+log = logging.getLogger(__name__)
+
 
 class LKFOptimizer(Optimizer):
     def __init__(
@@ -59,7 +61,7 @@ def __init_P(self):
 
         P = []
         params_packed_index = []
-        logging.info("LKF parameter nums: %s" % param_nums)
+        log.info("LKF parameter nums: %s" % param_nums)
         for param_num in param_nums:
             if param_num >= block_size:
                 block_num = math.ceil(param_num / block_size)
diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py
index ee0e7a54cc..02367f4aee 100644
--- a/deepmd/pt/train/training.py
+++ b/deepmd/pt/train/training.py
@@ -59,6 +59,8 @@
     DataLoader,
 )
 
+log = logging.getLogger(__name__)
+
 
 class Trainer:
     def __init__(
@@ -140,7 +142,7 @@ def get_data_loader(_training_data, _validation_data, _training_params):
                 valid_sampler = get_weighted_sampler(_validation_data, "prob_sys_size")
 
             if train_sampler is None or valid_sampler is None:
-                logging.warning(
+                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(
@@ -299,7 +301,7 @@ def get_loss(loss_params, start_lr, _ntypes):
             origin_model = (
                 finetune_model if finetune_model is not None else resume_model
             )
-            logging.info(f"Resuming from {origin_model}.")
+            log.info(f"Resuming from {origin_model}.")
             state_dict = torch.load(origin_model, map_location=DEVICE)
             if "model" in state_dict:
                 optimizer_state_dict = (
@@ -332,7 +334,7 @@ def get_loss(loss_params, start_lr, _ntypes):
                                 tmp_keys = ".".join(item.split(".")[:3])
                                 slim_keys.append(tmp_keys)
                         slim_keys = [i + ".*" for i in slim_keys]
-                        logging.warning(
+                        log.warning(
                             f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}"
                         )
                 elif self.finetune_multi_task:
@@ -451,9 +453,9 @@ def run(self):
         if SAMPLER_RECORD:
             record_file = f"Sample_rank_{self.rank}.txt"
             fout1 = open(record_file, mode="w", buffering=1)
-        logging.info("Start to train %d steps.", self.num_steps)
+        log.info("Start to train %d steps.", self.num_steps)
         if dist.is_initialized():
-            logging.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}")
+            log.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}")
         if self.enable_tensorboard:
             from torch.utils.tensorboard import (
                 SummaryWriter,
@@ -655,7 +657,7 @@ def log_loss_valid(_task_key="Default"):
                 train_time = time.time() - self.t0
                 self.t0 = time.time()
                 msg += f", speed={train_time:.2f} s/{self.disp_freq if _step_id else 1} batches"
-                logging.info(msg)
+                log.info(msg)
 
                 if fout:
                     if self.lcurve_should_print_header:
@@ -674,7 +676,7 @@ def log_loss_valid(_task_key="Default"):
 
                 module = self.wrapper.module if dist.is_initialized() else self.wrapper
                 self.save_model(self.latest_model, lr=cur_lr, step=_step_id)
-                logging.info(f"Saved model to {self.latest_model}")
+                log.info(f"Saved model to {self.latest_model}")
                 symlink_prefix_files(self.latest_model.stem, self.save_ckpt)
                 with open("checkpoint", "w") as f:
                     f.write(str(self.latest_model))
@@ -714,10 +716,10 @@ def log_loss_valid(_task_key="Default"):
                     "frozen_model.pth"  # We use .pth to denote the frozen model
                 )
                 self.model.save(pth_model_path)
-                logging.info(
+                log.info(
                     f"Frozen model for inferencing has been saved to {pth_model_path}"
                 )
-            logging.info(f"Trained model has been saved to: {self.save_ckpt}")
+            log.info(f"Trained model has been saved to: {self.save_ckpt}")
 
         if fout:
             fout.close()
diff --git a/deepmd/pt/train/wrapper.py b/deepmd/pt/train/wrapper.py
index fe423e6318..2207f111a0 100644
--- a/deepmd/pt/train/wrapper.py
+++ b/deepmd/pt/train/wrapper.py
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import logging
 from typing import (
     Dict,
     Optional,
@@ -11,6 +12,9 @@
     import torch._dynamo
 
 
+log = logging.getLogger(__name__)
+
+
 class ModelWrapper(torch.nn.Module):
     def __init__(
         self,
@@ -124,7 +128,7 @@ def share_params(self, shared_links, resume=False):
                     link_class.share_params(
                         base_class, shared_level_link, resume=resume
                     )
-                    print(
+                    log.warning(
                         f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!"
                     )
             else:
@@ -146,7 +150,7 @@ def share_params(self, shared_links, resume=False):
                         link_class.share_params(
                             base_class, shared_level_link, resume=resume
                         )
-                        print(
+                        log.warning(
                             f"Shared params of {model_key_base}.{class_type_base} and {model_key_link}.{class_type_link}!"
                         )
 
diff --git a/deepmd/pt/utils/dataloader.py b/deepmd/pt/utils/dataloader.py
index 7a6684e82e..0ec43f5a75 100644
--- a/deepmd/pt/utils/dataloader.py
+++ b/deepmd/pt/utils/dataloader.py
@@ -40,6 +40,7 @@
     process_sys_probs,
 )
 
+log = logging.getLogger(__name__)
 torch.multiprocessing.set_sharing_strategy("file_system")
 
 
@@ -69,7 +70,7 @@ def __init__(
 
         self.systems: List[DeepmdDataSetForLoader] = []
         if len(systems) >= 100:
-            logging.info(f"Constructing DataLoaders from {len(systems)} systems")
+            log.info(f"Constructing DataLoaders from {len(systems)} systems")
 
         def construct_dataset(system):
             ### this design requires "rcut" and "sel" in the descriptor
@@ -119,7 +120,7 @@ def construct_dataset(system):
                     rule = int(batch_size.split(":")[1])
                 else:
                     rule = None
-                    logging.error("Unsupported batch size type")
+                    log.error("Unsupported batch size type")
                 self.batch_size = rule // system._natoms
                 if self.batch_size * system._natoms < rule:
                     self.batch_size += 1
@@ -155,7 +156,7 @@ def __len__(self):
         return len(self.dataloaders)
 
     def __getitem__(self, idx):
-        # logging.warning(str(torch.distributed.get_rank())+" idx: "+str(idx)+" index: "+str(self.index[idx]))
+        # log.warning(str(torch.distributed.get_rank())+" idx: "+str(idx)+" index: "+str(self.index[idx]))
         try:
             batch = next(self.iters[idx])
         except StopIteration:
@@ -216,7 +217,7 @@ def __next__(self):
                     self.warning_time is None
                     or time.time() - self.warning_time > 15 * 60
                 ):
-                    logging.warning(
+                    log.warning(
                         "Data loading buffer is empty or nearly empty. This may "
                         "indicate a data loading bottleneck, and increasing the "
                         "number of workers (--num-workers) may help."
@@ -310,7 +311,7 @@ def get_weighted_sampler(training_data, prob_style, sys_prob=False):
             probs = prob_sys_size_ext(style, len(training_data), training_data.index)
     else:
         probs = process_sys_probs(prob_style, training_data.index)
-    logging.info("Generated weighted sampler with prob array: " + str(probs))
+    log.info("Generated weighted sampler with prob array: " + str(probs))
     # training_data.total_batch is the size of one epoch, you can increase it to avoid too many  rebuilding of iteraters
     len_sampler = training_data.total_batch * max(env.NUM_WORKERS, 1)
     sampler = WeightedRandomSampler(probs, len_sampler, replacement=True)
diff --git a/deepmd/pt/utils/dataset.py b/deepmd/pt/utils/dataset.py
index 68d4a09ce4..aca4a9ce5b 100644
--- a/deepmd/pt/utils/dataset.py
+++ b/deepmd/pt/utils/dataset.py
@@ -449,7 +449,7 @@ def _load_data(
         if atomic:
             ndof *= self._natoms
         path = os.path.join(set_name, key + ".npy")
-        # logging.info('Loading data from: %s', path)
+        # log.info('Loading data from: %s', path)
         if os.path.isfile(path):
             if high_prec:
                 data = np.load(path).astype(env.GLOBAL_ENER_FLOAT_PRECISION)
diff --git a/deepmd/pt/utils/finetune.py b/deepmd/pt/utils/finetune.py
index 9d82783cc0..13749da151 100644
--- a/deepmd/pt/utils/finetune.py
+++ b/deepmd/pt/utils/finetune.py
@@ -7,6 +7,8 @@
     env,
 )
 
+log = logging.getLogger(__name__)
+
 
 def change_finetune_model_params(
     ckpt, finetune_model, model_config, multi_task=False, model_branch=""
@@ -19,7 +21,7 @@ def change_finetune_model_params(
     """
     if multi_task:
         # TODO
-        print("finetune mode need modification for multitask mode!")
+        log.error("finetune mode need modification for multitask mode!")
     if finetune_model is not None:
         state_dict = torch.load(finetune_model, map_location=env.DEVICE)
         if "model" in state_dict:
@@ -45,7 +47,7 @@ def change_finetune_model_params(
                 old_type_map
             ), "Only support for smaller type map when finetuning or resuming."
             model_config = last_model_params
-            logging.info(
+            log.info(
                 "Change the model configurations according to the pretrained one..."
             )
             model_config["new_type_map"] = new_type_map
@@ -57,7 +59,7 @@ def change_finetune_model_params(
                 model_branch_chosen = next(iter(model_dict_params.keys()))
                 new_fitting = True
                 model_config["bias_shift"] = "statistic"  # fitting net re-init
-                print(
+                log.warning(
                     "The fitting net will be re-init instead of using that in the pretrained model! "
                     "The bias_shift will be statistic!"
                 )
@@ -83,7 +85,7 @@ def change_finetune_model_params(
                 model_config["fitting_net"] = model_dict_params[model_branch_chosen][
                     "fitting_net"
                 ]
-            logging.info(
+            log.info(
                 f"Change the model configurations according to the model branch "
                 f"{model_branch_chosen} in the pretrained one..."
             )
diff --git a/deepmd/pt/utils/preprocess.py b/deepmd/pt/utils/preprocess.py
index 18c798138e..806bacdcd2 100644
--- a/deepmd/pt/utils/preprocess.py
+++ b/deepmd/pt/utils/preprocess.py
@@ -10,6 +10,8 @@
     env,
 )
 
+log = logging.getLogger(__name__)
+
 
 class Region3D:
     def __init__(self, boxt):
@@ -263,7 +265,7 @@ def make_env_mat(
         )
         merged_coord = coord[merged_mapping] - merged_coord_shift
         if merged_coord.shape[0] <= coord.shape[0]:
-            logging.warning("No ghost atom is added for system ")
+            log.warning("No ghost atom is added for system ")
     else:
         merged_coord_shift = torch.zeros_like(coord)
         merged_atype = atype.clone()
diff --git a/deepmd/pt/utils/stat.py b/deepmd/pt/utils/stat.py
index eec7179bcd..5fde03c74a 100644
--- a/deepmd/pt/utils/stat.py
+++ b/deepmd/pt/utils/stat.py
@@ -8,6 +8,8 @@
     env,
 )
 
+log = logging.getLogger(__name__)
+
 
 def make_stat_input(datasets, dataloaders, nbatches):
     """Pack data for statistics.
@@ -36,7 +38,7 @@ def make_stat_input(datasets, dataloaders, nbatches):
     ]
     if datasets[0].mixed_type:
         keys.append("real_natoms_vec")
-    logging.info(f"Packing data for statistics from {len(datasets)} systems")
+    log.info(f"Packing data for statistics from {len(datasets)} systems")
     for i in range(len(datasets)):
         sys_stat = {key: [] for key in keys}
         iterator = iter(dataloaders[i])
diff --git a/deepmd/utils/pair_tab.py b/deepmd/utils/pair_tab.py
index 56f8e618df..57157fbd00 100644
--- a/deepmd/utils/pair_tab.py
+++ b/deepmd/utils/pair_tab.py
@@ -12,6 +12,8 @@
     CubicSpline,
 )
 
+log = logging.getLogger(__name__)
+
 
 class PairTab:
     """Pairwise tabulated potential.
@@ -114,7 +116,7 @@ def _check_table_upper_boundary(self) -> None:
         if np.all(upper_val == 0):
             # if table values decay to `0` after rcut
             if self.rcut < self.rmax and np.any(self.vdata[rcut_idx - 1][1:] != 0):
-                logging.warning(
+                log.warning(
                     "The energy provided in the table does not decay to 0 at rcut."
                 )
             # if table values decay to `0` at rcut, do nothing
@@ -131,12 +133,12 @@ def _check_table_upper_boundary(self) -> None:
         else:
             # if table values do not decay to `0` at rcut
             if self.rcut <= self.rmax:
-                logging.warning(
+                log.warning(
                     "The energy provided in the table does not decay to 0 at rcut."
                 )
             # if rcut goes beyond table upper bond, need extrapolation, ensure values decay to `0` before rcut.
             else:
-                logging.warning(
+                log.warning(
                     "The rcut goes beyond table upper boundary, performing extrapolation."
                 )
                 pad_extrapolation = np.zeros((rcut_idx - upper_idx, self.ncol))