diff --git a/tutorials/auto_scheduler/tune_conv2d_layer_cuda.py b/tutorials/auto_scheduler/tune_conv2d_layer_cuda.py
index b800eb469ec5..cb2126dec911 100644
--- a/tutorials/auto_scheduler/tune_conv2d_layer_cuda.py
+++ b/tutorials/auto_scheduler/tune_conv2d_layer_cuda.py
@@ -157,17 +157,17 @@ def conv2d_layer(N, H, W, CO, CI, KH, KW, stride, padding):
 # print the equivalent python schedule API, and build the binary again.
 
 # Load the measuremnt record for the best schedule
-inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)
+# inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)
 
 # Print equivalent python schedule API. This can be used for debugging and
 # learning the behavior of the auto-scheduler.
 print("Equivalent python schedule:")
-print(task.compute_dag.print_python_code_from_state(inp.state))
+# print(task.compute_dag.print_python_code_from_state(inp.state))
 
 # Rebuild the binary. This shows how you can apply the best schedule from a
 # log file without reruning the search again.
-sch, args = task.compute_dag.apply_steps_from_state(inp.state)
-func = tvm.build(sch, args, target)
+# sch, args = task.compute_dag.apply_steps_from_state(inp.state)
+# func = tvm.build(sch, args, target)
 
 ######################################################################
 # A more complicated example is to resume the search.
@@ -176,19 +176,19 @@ def conv2d_layer(N, H, W, CO, CI, KH, KW, stride, padding):
 # In the example below we resume the status and do more 5 trials.
 
 
-log_file = "conv2d.json"
-cost_model = auto_scheduler.XGBModel()
-cost_model.update_from_file(log_file)
-search_policy = auto_scheduler.SketchPolicy(
-    task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
-)
-measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
-tune_option = auto_scheduler.TuningOptions(
-    num_measure_trials=5,
-    runner=measure_ctx.runner,
-    measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
-)
-sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)
+# log_file = "conv2d.json"
+# cost_model = auto_scheduler.XGBModel()
+# cost_model.update_from_file(log_file)
+# search_policy = auto_scheduler.SketchPolicy(
+#     task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
+# )
+# measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
+# tune_option = auto_scheduler.TuningOptions(
+#     num_measure_trials=5,
+#     runner=measure_ctx.runner,
+#     measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+# )
+# sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)
 
 # Kill the measurement process
-del measure_ctx
+# del measure_ctx
diff --git a/tutorials/auto_scheduler/tune_matmul_x86.py b/tutorials/auto_scheduler/tune_matmul_x86.py
index 35c47444e081..5c039b17a958 100644
--- a/tutorials/auto_scheduler/tune_matmul_x86.py
+++ b/tutorials/auto_scheduler/tune_matmul_x86.py
@@ -141,17 +141,17 @@ def matmul_add(N, L, M, dtype):
 # print the equivalent python schedule API, and build the binary again.
 
 # Load the measuremnt record for the best schedule
-inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
+# inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
 
 # Print equivalent python schedule API. This can be used for debugging and
 # learning the behavior of the auto-scheduler.
 print("Equivalent python schedule:")
-print(task.compute_dag.print_python_code_from_state(inp.state))
+# print(task.compute_dag.print_python_code_from_state(inp.state))
 
 # Rebuild the binary. This shows how you can apply the best schedule from a
 # log file without reruning the search again.
-sch, args = task.compute_dag.apply_steps_from_state(inp.state)
-func = tvm.build(sch, args)
+# sch, args = task.compute_dag.apply_steps_from_state(inp.state)
+# func = tvm.build(sch, args)
 
 ######################################################################
 # A more complicated example is to resume the search.