diff --git a/qlib/contrib/model/pytorch_utils.py b/qlib/contrib/model/pytorch_utils.py
index e7a8e8d67..1148a596a 100644
--- a/qlib/contrib/model/pytorch_utils.py
+++ b/qlib/contrib/model/pytorch_utils.py
@@ -4,7 +4,19 @@
 import torch.nn as nn
 
 
-def count_parameters(models_or_parameters, unit="mb"):
+def count_parameters(models_or_parameters, unit="m"):
+    """
+    This function is to obtain the storage size unit of a (or multiple) models.
+
+    Parameters
+    ----------
+    models_or_parameters : PyTorch model(s) or a list of parameters.
+    unit : the storage size unit.
+
+    Returns
+    -------
+    The number of parameters of the given model(s) or parameters.
+    """
     if isinstance(models_or_parameters, nn.Module):
         counts = sum(v.numel() for v in models_or_parameters.parameters())
     elif isinstance(models_or_parameters, nn.Parameter):
@@ -13,12 +25,13 @@ def count_parameters(models_or_parameters, unit="mb"):
         return sum(count_parameters(x, unit) for x in models_or_parameters)
     else:
         counts = sum(v.numel() for v in models_or_parameters)
-    if unit.lower() == "mb":
-        counts /= 1e6
-    elif unit.lower() == "kb":
-        counts /= 1e3
-    elif unit.lower() == "gb":
-        counts /= 1e9
+    unit = unit.lower()
+    if unit == "kb" or unit == "k":
+        counts /= 2 ** 10
+    elif unit == "mb" or unit == "m":
+        counts /= 2 ** 20
+    elif unit == "gb" or unit == "g":
+        counts /= 2 ** 30
     elif unit is not None:
         raise ValueError("Unknow unit: {:}".format(unit))
     return counts
diff --git a/qlib/log.py b/qlib/log.py
index 78f12eb09..126acb9d2 100644
--- a/qlib/log.py
+++ b/qlib/log.py
@@ -108,27 +108,6 @@ def set_log_with_config(log_config: Dict[Text, Any]):
     logging_config.dictConfig(log_config)
 
 
-def set_log_basic_config(filename: Optional[Text] = None, format: Optional[Text] = None, level: Optional[int] = None):
-    """
-    Set the basic configuration for the logging system.
-    See details at https://docs.python.org/3/library/logging.html#logging.basicConfig
-
-    :param filename: str or None
-        The path to save the logs.
-    :param format: the logging format
-    :param level: int
-    :return: Logger
-        Logger object.
-    """
-    if level is None:
-        level = C.logging_level
-
-    if format is None:
-        format = C.logging_config["formatters"]["logger_format"]["format"]
-
-    logging.basicConfig(filename=filename, format=format, level=level)
-
-
 class LogFilter(logging.Filter):
     def __init__(self, param=None):
         self.param = param
diff --git a/qlib/workflow/recorder.py b/qlib/workflow/recorder.py
index 519b69710..bc0a9ef77 100644
--- a/qlib/workflow/recorder.py
+++ b/qlib/workflow/recorder.py
@@ -240,12 +240,18 @@ class MLflowRecorder(Recorder):
     def artifact_uri(self):
         return self._artifact_uri
 
-    @property
-    def root_uri(self):
-        start_str = "file:"
+    def get_local_dir(self):
+        """
+        This function will return the directory path of this recorder.
+        """
         if self.artifact_uri is not None:
-            xpath = self.artifact_uri.strip(start_str)
-            return (Path(xpath) / "..").resolve()
+            local_file_prefix = "file:"
+            if self.artifact_uri.startswith(local_file_prefix):
+                xpath = self.artifact_uri.lstrip(local_file_prefix)
+                return (Path(xpath) / "..").resolve()
+            else:
+                raise RuntimeError("This recorder is not saved in the local file system.")
+
         else:
             raise Exception(
                 "Please make sure the recorder has been created and started properly before getting artifact uri."
diff --git a/tests/test_all_pipeline.py b/tests/test_all_pipeline.py
index d9d684697..fbf15d29a 100644
--- a/tests/test_all_pipeline.py
+++ b/tests/test_all_pipeline.py
@@ -123,6 +123,8 @@ def train():
         recorder = R.get_recorder()
         # To test __repr__
         print(recorder)
+        # To test get_local_dir
+        print(recorder.get_local_dir())
         rid = recorder.id
         sr = SignalRecord(model, dataset, recorder)
         sr.generate()