diff --git a/tutorials/dev/bring_your_own_datatypes.py b/tutorials/dev/bring_your_own_datatypes.py
index 06d96e14d28cd..bf09af9f3abe3 100644
--- a/tutorials/dev/bring_your_own_datatypes.py
+++ b/tutorials/dev/bring_your_own_datatypes.py
@@ -262,9 +262,9 @@ def get_cat_image():
 ######################################################################
 # It's easy to execute MobileNet with native TVM:
 
-ex = tvm.relay.create_executor("graph", mod=module)
+ex = tvm.relay.create_executor("graph", mod=module, params=params)
 input = get_cat_image()
-result = ex.evaluate()(input, **params).numpy()
+result = ex.evaluate()(input).numpy()
 # print first 10 elements
 print(result.flatten()[:10])
 
diff --git a/tutorials/frontend/from_keras.py b/tutorials/frontend/from_keras.py
index 1c48aff799d42..db1b3660ae319 100644
--- a/tutorials/frontend/from_keras.py
+++ b/tutorials/frontend/from_keras.py
@@ -98,13 +98,13 @@
 target = "cuda"
 dev = tvm.cuda(0)
 with tvm.transform.PassContext(opt_level=3):
-    executor = relay.build_module.create_executor("graph", mod, dev, target)
+    executor = relay.build_module.create_executor("graph", mod, dev, target, params).evaluate()
 
 ######################################################################
 # Execute on TVM
 # ---------------
 dtype = "float32"
-tvm_out = executor.evaluate()(tvm.nd.array(data.astype(dtype)), **params)
+tvm_out = executor(tvm.nd.array(data.astype(dtype)))
 top1_tvm = np.argmax(tvm_out.numpy()[0])
 
 #####################################################################
diff --git a/tutorials/frontend/from_onnx.py b/tutorials/frontend/from_onnx.py
index 26aeb6ecaf387..fd51d7a769929 100644
--- a/tutorials/frontend/from_onnx.py
+++ b/tutorials/frontend/from_onnx.py
@@ -92,13 +92,15 @@
 mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
 
 with tvm.transform.PassContext(opt_level=1):
-    intrp = relay.build_module.create_executor("graph", mod, tvm.cpu(0), target)
+    executor = relay.build_module.create_executor(
+        "graph", mod, tvm.cpu(0), target, params
+    ).evaluate()
 
 ######################################################################
 # Execute on TVM
 # ---------------------------------------------
 dtype = "float32"
-tvm_output = intrp.evaluate()(tvm.nd.array(x.astype(dtype)), **params).numpy()
+tvm_output = executor(tvm.nd.array(x.astype(dtype))).numpy()
 
 ######################################################################
 # Display results