diff --git a/tutorials/autotvm/test_scatter.py b/tutorials/autotvm/test_scatter.py
deleted file mode 100644
index 108921dfed86f..0000000000000
--- a/tutorials/autotvm/test_scatter.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import os
-
-import numpy as np
-import logging
-import tvm
-from tvm import relay, autotvm
-import tvm.relay.testing
-from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
-import tvm.contrib.graph_runtime as runtime
-
-
-def simple_mod(dshape, ishape, axis=0):
-    d = relay.var("d", relay.TensorType(dshape, "float32"))
-    i = relay.var("i", relay.TensorType(ishape, "int64"))
-    u = relay.var("u", relay.TensorType(ishape, "float32"))
-    z = relay.op.scatter(d, i, u, axis)
-    func = relay.Function([d, i, u], z)
-    mod = tvm.IRModule()
-    mod["main"] = func
-    return mod, {}
-
-
-target = "cuda"
-
-#### TUNING OPTION ####
-network = "scatter"
-log_file = "%s.log" % network
-
-tuning_option = {
-    "log_filename": log_file,
-    "tuner": "xgb",
-    "n_trial": 10,
-    "early_stopping": 10,
-    "measure_option": autotvm.measure_option(
-        builder=autotvm.LocalBuilder(timeout=10),
-        runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
-    ),
-}
-
-def tune_tasks(
-    tasks,
-    measure_option,
-    tuner="xgb",
-    n_trial=1000,
-    early_stopping=None,
-    log_filename="tuning.log",
-    use_transfer_learning=True,
-):
-    # create tmp log file
-    tmp_log_file = log_filename + ".tmp"
-    if os.path.exists(tmp_log_file):
-        os.remove(tmp_log_file)
-
-    for i, tsk in enumerate(reversed(tasks)):
-        prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
-
-        # create tuner
-        if tuner == "xgb" or tuner == "xgb-rank":
-            tuner_obj = XGBTuner(tsk, loss_type="rank")
-        elif tuner == "ga":
-            tuner_obj = GATuner(tsk, pop_size=100)
-        elif tuner == "random":
-            tuner_obj = RandomTuner(tsk)
-        elif tuner == "gridsearch":
-            tuner_obj = GridSearchTuner(tsk)
-        else:
-            raise ValueError("Invalid tuner: " + tuner)
-
-        if use_transfer_learning:
-            if os.path.isfile(tmp_log_file):
-                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
-
-        # do tuning
-        tsk_trial = min(n_trial, len(tsk.config_space))
-        tuner_obj.tune(
-            n_trial=tsk_trial,
-            early_stopping=early_stopping,
-            measure_option=measure_option,
-            callbacks=[
-                autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
-                autotvm.callback.log_to_file(tmp_log_file),
-            ],
-        )
-
-    # pick best records to a cache file
-    autotvm.record.pick_best(tmp_log_file, log_filename)
-    os.remove(tmp_log_file)
-
-def tune_and_evaluate(tuning_opt):
-    # extract workloads from relay program
-    print("Extract tasks...")
-    size = (10000, )
-    dshape = ishape = size
-    axis = 0
-    mod, params = simple_mod(size, size, axis)
-    tasks = autotvm.task.extract_from_program(
-        mod["main"], target=target, params=params, ops=(relay.op.get("scatter"),)
-    )
-
-    # run tuning tasks
-    print("Tuning...")
-    tune_tasks(tasks, **tuning_opt)
-
-    # compile kernels with history best records
-    with autotvm.apply_history_best(log_file):
-        print("Compile...")
-        with tvm.transform.PassContext(opt_level=3):
-            lib = relay.build_module.build(mod, target=target, params=params)
-
-        # load parameters
-        ctx = tvm.context(str(target), 0)
-        module = runtime.GraphModule(lib["default"](ctx))
-
-        data_np = np.random.uniform(size=dshape).astype("float32")
-        updates_np = np.random.uniform(size=ishape).astype("float32")
-        indices_np = np.random.randint(-dshape[axis], dshape[axis] - 1, ishape).astype("int64")
-
-        module.set_input("d", data_np)
-        module.set_input("i", indices_np)
-        module.set_input("u", updates_np)
-
-        # evaluate
-        print("Evaluate inference time cost...")
-        ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=600)
-        prof_res = np.array(ftimer().results) * 1000  # convert to millisecond
-        print(
-            "Mean inference time (std dev): %.2f ms (%.2f ms)"
-            % (np.mean(prof_res), np.std(prof_res))
-        )
-
-
-# We do not run the tuning in our webpage server since it takes too long.
-# Uncomment the following line to run it by yourself.
-logging.basicConfig(level=logging.DEBUG)
-tune_and_evaluate(tuning_option)