diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py
index 6b540f2d..110524bb 100644
--- a/deckard/base/model/art_pipeline.py
+++ b/deckard/base/model/art_pipeline.py
@@ -64,8 +64,13 @@ def __call__(self):
             tuple(torch_dict.values()),
         ):
             import torch
-
             device_type = "gpu" if torch.cuda.is_available() else "cpu"
+            if device_type == "gpu":
+                logger.info("Using GPU")
+                logger.info("Model moved to GPU")
+                device = torch.device("cuda")
+                model.to(device)
+                data = [d.to(device) for d in data]
             model = TorchInitializer(
                 data=data,
                 model=model,
diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py
index d335ef60..01a10927 100644
--- a/deckard/base/model/model.py
+++ b/deckard/base/model/model.py
@@ -110,8 +110,6 @@ def __init__(self, **kwargs):
         logger.info(f"Initializing model trainer with kwargs {kwargs}")
         self.kwargs = kwargs
 
-    # def __hash__(self):
-    #     return int(my_hash(self), 16)
 
     def __call__(self, data: list, model: object, library=None):
         logger.info(f"Training model {model} with fit params: {self.kwargs}")
@@ -181,7 +179,6 @@ def __call__(self, data: list, model: object, library=None):
                 end_timestamp = time()
             elif "should be the same" in str(e).lower():
                 import torch
-
                 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                 data[0] = torch.from_numpy(data[0])
                 data[1] = torch.from_numpy(data[1])
diff --git a/test/pipelines/evasion/.gitignore b/test/pipelines/evasion/.gitignore
new file mode 100644
index 00000000..47f39c2f
--- /dev/null
+++ b/test/pipelines/evasion/.gitignore
@@ -0,0 +1 @@
+.dvc/*