diff --git a/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py b/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py
index 92857434..936345be 100644
--- a/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py
+++ b/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py
@@ -15,7 +15,7 @@
 import os
 import textwrap
 
-import torch
+import torch, torcheia
 from sagemaker_inference import (
     content_types,
     decoder,
@@ -28,6 +28,9 @@
 INFERENCE_ACCELERATOR_PRESENT_ENV = "SAGEMAKER_INFERENCE_ACCELERATOR_PRESENT"
 DEFAULT_MODEL_FILENAME = "model.pt"
 
+torch._C._jit_set_profiling_executor(False)
+device = torch.device("cpu")
+
 
 class DefaultPytorchInferenceHandler(default_inference_handler.DefaultInferenceHandler):
     VALID_CONTENT_TYPES = (content_types.JSON, content_types.NPY)
@@ -47,7 +50,11 @@ def default_model_fn(self, model_dir):
                 raise FileNotFoundError("Failed to load model with default model_fn: missing file {}."
                                         .format(DEFAULT_MODEL_FILENAME))
             # Client-framework is CPU only. But model will run in Elastic Inference server with CUDA.
-            return torch.jit.load(model_path, map_location=torch.device('cpu'))
+            model = torch.jit.load(model_path, map_location=torch.device('cpu'))
+            # attach_eia() is introduced in PyTorch Elastic Inference 1.5.1
+            # by default attach to the 0th device
+            model = torcheia.jit.attach_eia(model, 0)
+            return model
         else:
             raise NotImplementedError(textwrap.dedent("""
             Please provide a model_fn implementation.
@@ -86,8 +93,8 @@ def default_predict_fn(self, data, model):
                 model = model.to(device)
                 input_data = data.to(device)
                 model.eval()
-                with torch.jit.optimized_execution(True, {"target_device": "eia:0"}):
-                    output = model(input_data)
+                with torch.jit.optimized_execution(True):
+                    output = model.forward(input_data)
             else:
                 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                 model = model.to(device)