.

inference.py

@@ -26,7 +26,7 @@ def _load_artifacts(symbol: str):
         scaler = pickle.load(f)
 
     model = StockLSTM(input_dim=1, hidden_dim=64, num_layers=2, dropout=0.2)
-    model.load_state_dict(torch.load(p["model"], map_location="gpu"))
+    model.load_state_dict(torch.load(p["model"], map_location="cpu"))
     model.eval()
     return model, scaler, meta
 

testing.py

@@ -13,7 +13,7 @@ ARTIFACTS_DIR = "artifacts"
 def evaluate(symbol: str):
     base = os.path.join(ARTIFACTS_DIR, symbol.upper())
     model = StockLSTM(input_dim=1, hidden_dim=64, num_layers=2, dropout=0.2)
-    model.load_state_dict(torch.load(os.path.join(base, "model.pt"), map_location="gpu"))
+    model.load_state_dict(torch.load(os.path.join(base, "model.pt"), map_location="cpu"))
     model.eval()
     with open(os.path.join(base, "scaler.pkl"), "rb") as f:
         scaler = pickle.load(f)

train.py

@@ -44,7 +44,7 @@ def to_tensor_loader(X, y, batch_size=32):
 
 def train(symbol: str, seq_len: int = 60, epochs: int = 5, batch_size: int = 32,
           start: str = None, end: str = None, lr: float = 1e-3):
-    device = torch.device("gpu")
+    device = torch.device("cpu")
 
     # --- data ---
     df = fetch_data(symbol, start, end)