try

app.py

@@ -6,47 +6,39 @@ import gradio as gr
 import datetime
 import numpy as np
 
-# from dgl.data import YelpDataset
+from dgl.data import YelpDataset
 
-# import dgl
-# import torch as th
+import dgl
+import torch as th
 
-# from dgl.dataloading import LaborSampler, NeighborSampler
+from dgl.dataloading import LaborSampler, NeighborSampler
 
-# data = YelpDataset()
+data = YelpDataset()
 
 # device = 'cuda:0'
+device = 'cpu'
 
-# g = data[0].to(device)
+g = data[0].to(device)
 
-# num_layers = 3
+num_layers = 3
 
-# fanouts = [10] * num_layers
+fanouts = [10] * num_layers
 
-# samplers = [LaborSampler(fanouts, importance_sampling=1), LaborSampler(fanouts, importance_sampling=0), NeighborSampler(fanouts)]
+samplers = [LaborSampler(fanouts, importance_sampling=1), LaborSampler(fanouts, importance_sampling=0), NeighborSampler(fanouts)]
 
-# names = ['LABOR-1', 'LABOR-0', 'NS']
+names = ['LABOR-1', 'LABOR-0', 'NS']
 
-# indices = th.arange(g.num_nodes()).to(device)
+indices = th.arange(g.num_nodes()).to(device)
 
-# loaders = [dgl.dataloading.DataLoader(g, indices, sampler, batch_size=1024, shuffle=True, drop_last=True) for sampler in samplers]
-
-# results = []
-
-# for sampled in zip(*loaders):
-#     numbers.append([s[0].shape for s in sampled])
-#     print(numbers[-1])
-
-# th.tensor(numbers).mean(dim=0, dtype=th.float64)
+loaders = [dgl.dataloading.DataLoader(g, indices, sampler, batch_size=batch_size, shuffle=True, drop_last=True) for sampler in samplers]
 
 def get_time():
     return datetime.datetime.now()
 
-
 plot_end = 2 * math.pi
 
 
-def get_plot(period=1):
+def get_plot2(period=1):
     global plot_end
     x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
     y = np.sin(2 * math.pi * period * x)
@@ -63,6 +55,34 @@ def get_plot(period=1):
         plot_end = 2 * math.pi
     return update
 
+results = []
+
+def get_plot(batch_size=1024):
+    for sampled in zip(*loaders):
+        results.append([s[0].shape for s in sampled])
+        break
+    
+    y = th.tensor(results)
+
+    d = {"x": [], "y": []}
+
+    for i, name in enumerate(names):
+        yy = y[:, i]
+        d[y] += yy
+        d[x] += [name] * yy.shape[0]
+    
+    update = gr.BarPlot.update(
+        value=pd.DataFrame(d),
+        x="x",
+        y="y",
+        title="Number of sampled vertices",
+        width=600,
+        height=350
+    )
+
+    return update
+    # th.tensor(results).mean(dim=0, dtype=th.float64)
+
 
 with gr.Blocks() as demo:
     with gr.Row():
@@ -72,19 +92,19 @@ with gr.Blocks() as demo:
                 "Change the value of the slider to automatically update the plot",
                 label="",
             )
-            period = gr.Number(
+            batch_size = gr.Number(
                 label="batch size", value=1024, show_label=True
             )
-            plot = gr.LinePlot(show_label=False)
+            plot = gr.BarPlot(show_label=False)
         with gr.Column():
             name = gr.Textbox(label="Enter your name")
             greeting = gr.Textbox(label="Greeting")
             button = gr.Button(value="Greet")
             button.click(lambda s: f"Hello {s}", name, greeting)
 
-    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
-    dep = demo.load(get_plot, None, plot, every=1)
-    period.submit(get_plot, period, plot, every=1, cancels=[dep])
+    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=10)
+    dep = demo.load(get_plot, None, plot, every=10)
+    batch_size.submit(get_plot, batch_size, plot, every=10, cancels=[dep])
 
 if __name__ == "__main__":
     demo.queue().launch()