changes

app.py

@@ -6,6 +6,39 @@ import gradio as gr
 import datetime
 import numpy as np
 
+# from dgl.data import YelpDataset
+
+# import dgl
+# import torch as th
+
+# from dgl.dataloading import LaborSampler, NeighborSampler
+
+# data = YelpDataset()
+
+# device = 'cuda:0'
+
+# g = data[0].to(device)
+
+# num_layers = 3
+
+# fanouts = [10] * num_layers
+
+# samplers = [LaborSampler(fanouts, importance_sampling=1), LaborSampler(fanouts, importance_sampling=0), NeighborSampler(fanouts)]
+
+# names = ['LABOR-1', 'LABOR-0', 'NS']
+
+# 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)
+
 def get_time():
     return datetime.datetime.now()
 
@@ -39,8 +72,8 @@ with gr.Blocks() as demo:
                 "Change the value of the slider to automatically update the plot",
                 label="",
             )
-            period = gr.Slider(
-                label="Period of plot", value=1, minimum=0, maximum=10, step=1
+            period = gr.Number(
+                label="batch size", value=1024, show_label=True
             )
             plot = gr.LinePlot(show_label=False)
         with gr.Column():
@@ -51,7 +84,7 @@ with gr.Blocks() as demo:
 
     demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
     dep = demo.load(get_plot, None, plot, every=1)
-    period.change(get_plot, period, plot, every=1, cancels=[dep])
+    period.submit(get_plot, period, plot, every=1, cancels=[dep])
 
 if __name__ == "__main__":
     demo.queue().launch()