diff --git a/benchmark/FedHPOB/fedhpob/utils/draw.py b/benchmark/FedHPOB/fedhpob/utils/draw.py
index cf4a569f2..776e7ce15 100644
--- a/benchmark/FedHPOB/fedhpob/utils/draw.py
+++ b/benchmark/FedHPOB/fedhpob/utils/draw.py
@@ -1,5 +1,6 @@
 import os
 import json
+import datetime
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
@@ -23,7 +24,6 @@ def logloader(file):
 
 
 def ecdf(model, data_list, algo, sample_client=None, key='test_acc'):
-    import datetime
     from fedhpob.benchmarks import TabularBenchmark
 
     # Draw ECDF from target data_list
@@ -288,5 +288,74 @@ def rank_over_time(root,
               suffix, Y_label)
 
 
+def landscape(model='cnn',
+              dname='femnist',
+              algo='avg',
+              sample_client=None,
+              key='test_acc'):
+    import plotly.graph_objects as go
+    from fedhpob.config import fhb_cfg
+    from fedhpob.benchmarks import TabularBenchmark
+
+    z = []
+    benchmark = TabularBenchmark(model, dname, algo, device=-1)
+
+    def get_best_config(benchmark):
+        results, config = [], []
+        for idx in tqdm(range(len(benchmark.table))):
+            row = benchmark.table.iloc[idx]
+            if sample_client is not None and row[
+                    'sample_client'] != sample_client:
+                continue
+            result = eval(row['result'])
+            val_loss = result['val_avg_loss']
+            try:
+                best_round = np.argmin(val_loss)
+            except:
+                continue
+            results.append(result[key][best_round])
+            config.append(row)
+        best_index = np.argmax(results)
+        return config[best_index], results[best_index]
+
+    # config, _ = get_best_config(benchmark)
+    config = {'wd': 0.0, 'dropout': 0.5, 'step': 1.0}
+    config_space = benchmark.get_configuration_space()
+    X, Y = sorted(list(config_space['batch'])), sorted(list(
+        config_space['lr']))
+    print(X, Y)
+    for lr in Y:
+        y = []
+        for batch in X:
+            xy = {'lr': lr, 'batch': batch}
+            print({**config, **xy})
+            res = benchmark({
+                **config,
+                **xy
+            }, {
+                'sample_client': 1.0,
+                'round': 249
+            },
+                            fhb_cfg=fhb_cfg,
+                            seed=12345)
+            y.append(res['function_value'])
+        z.append(y)
+    Z = np.array(z)
+    fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y)])
+    fig.update_layout(title='FEMNIST (FedAvg)',
+                      autosize=False,
+                      width=900,
+                      height=900,
+                      margin=dict(l=65, r=50, b=65, t=90),
+                      scene=dict(
+                          xaxis_title='BS',
+                          yaxis_title='LR',
+                          zaxis_title='ACC',
+                      ))
+    fig.write_image(os.path.join('figures', 'femnist_fedavg_landscape.pdf'))
+
+    return
+
+
 if __name__ == '__main__':
     ecdf('gcn', ['cora', 'citeseer', 'pubmed'], sample_client=1.0)