Map location when using torch.load for checkpoints

src/metatrain/experimental/alchemical_model/model.py

@@ -149,7 +149,7 @@ def forward(
     def load_checkpoint(cls, path: Union[str, Path]) -> "AlchemicalModel":
 
         # Load the checkpoint
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
 

src/metatrain/experimental/alchemical_model/trainer.py

@@ -395,7 +395,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
     def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
 
         # Load the checkpoint
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
         epoch = checkpoint["epoch"]

src/metatrain/experimental/pet/model.py

@@ -137,7 +137,7 @@ def forward(
     @classmethod
     def load_checkpoint(cls, path: Union[str, Path]) -> "PET":
 
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         hypers = checkpoint["hypers"]
         dataset_info = checkpoint["dataset_info"]
         model = cls(

src/metatrain/experimental/pet/trainer.py

@@ -662,7 +662,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
         # together with the hypers inside a file that will act as a metatrain
         # checkpoint
         checkpoint_path = self.pet_dir / "checkpoint"  # type: ignore
-        checkpoint = torch.load(checkpoint_path, weights_only=False)
+        checkpoint = torch.load(checkpoint_path, weights_only=False, map_location="cpu")
         torch.save(
             {
                 "checkpoint": checkpoint,
@@ -680,7 +680,7 @@ def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
         # This function loads a metatrain PET checkpoint and returns a Trainer
         # instance with the hypers, while also saving the checkpoint in the
         # class
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         trainer = cls(train_hypers)
         trainer.pet_checkpoint = checkpoint["checkpoint"]
         return trainer

src/metatrain/experimental/soap_bpnn/model.py

@@ -299,7 +299,7 @@ def forward(
     def load_checkpoint(cls, path: Union[str, Path]) -> "SoapBpnn":
 
         # Load the checkpoint
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
 

src/metatrain/experimental/soap_bpnn/trainer.py

@@ -432,7 +432,7 @@ def save_checkpoint(self, model, path: Union[str, Path]):
     def load_checkpoint(cls, path: Union[str, Path], train_hypers) -> "Trainer":
 
         # Load the checkpoint
-        checkpoint = torch.load(path, weights_only=False)
+        checkpoint = torch.load(path, weights_only=False, map_location="cpu")
         model_hypers = checkpoint["model_hypers"]
         model_state_dict = checkpoint["model_state_dict"]
         epoch = checkpoint["epoch"]