diff --git a/learnware/config.py b/learnware/config.py
index d7b056f..13e212f 100644
--- a/learnware/config.py
+++ b/learnware/config.py
@@ -119,6 +119,7 @@ semantic_config = {
 _DEFAULT_CONFIG = {
     "root_path": ROOT_DIRPATH,
     "package_path": PACKAGE_DIRPATH,
+    "database_path": DATABASE_PATH,
     "stdout_path": STDOUT_PATH,
     "cache_path": CACHE_PATH,
     "logging_level": logging.INFO,
diff --git a/learnware/market/heterogeneous/organizer/hetero_map/__init__.py b/learnware/market/heterogeneous/organizer/hetero_map/__init__.py
index fdb2137..2e31195 100644
--- a/learnware/market/heterogeneous/organizer/hetero_map/__init__.py
+++ b/learnware/market/heterogeneous/organizer/hetero_map/__init__.py
@@ -80,8 +80,8 @@ class HeteroMap(nn.Module):
         """
         super(HeteroMap, self).__init__()
 
-        self.cuda_idx = allocate_cuda_idx() if cuda_idx is None else cuda_idx
-        self.device = choose_device(self.cuda_idx)
+        cuda_idx = allocate_cuda_idx() if cuda_idx is None else cuda_idx
+        device = choose_device(cuda_idx)
         self.model_args = {
             "num_partition": num_partition,
             "overlap_ratio": overlap_ratio,
@@ -105,7 +105,7 @@ class HeteroMap(nn.Module):
             pad_token_id=feature_tokenizer.pad_token_id,
             hidden_dim=hidden_dim,
             hidden_dropout_prob=hidden_dropout_prob,
-            device=self.device,
+            device=device,
         )
 
         self.encoder = TransformerMultiLayer(
@@ -127,7 +127,7 @@ class HeteroMap(nn.Module):
         self.base_temperature = base_temperature
         self.num_partition = num_partition
         self.overlap_ratio = overlap_ratio
-        self.to(self.device)
+        self.to(device)
 
     def to(self, device: Union[str, torch.device]):
         """Moves the model and all its submodules to the specified device
diff --git a/learnware/utils/gpu.py b/learnware/utils/gpu.py
index cb856d4..bc19979 100644
--- a/learnware/utils/gpu.py
+++ b/learnware/utils/gpu.py
@@ -47,7 +47,7 @@ def choose_device(cuda_idx=-1):
     return device
 
 
-def allocate_cuda_idx(self):
+def allocate_cuda_idx():
     if is_torch_available(verbose=False):
         import torch