Need Help for understanding FeatureFetcher mechanism

Crashed here:

import torch
import dgl
import dgl.graphbolt as gb
graph = dgl.load_graphs("./graph.bin")[0][0]
print("print graph", graph)

feat_data = [
    gb.OnDiskFeatureData(domain="node", type="float32", name="x_in",
        format="numpy", path="./x_in.npy", in_memory=False),
    gb.OnDiskFeatureData(domain="node", type="float32", name="area",
        format="numpy", path="./area_in.npy", in_memory=False),
graph = gb.from_dglgraph(graph, True)
feature = gb.TorchBasedFeatureStore(feat_data)
item_set = gb.ItemSet(104818, names="seed_nodes")  
datapipe = gb.ItemSampler(item_set, batch_size=1024, shuffle=False)
datapipe = datapipe.sample_neighbor(graph, [10, 10]) # 2 layers.
datapipe = datapipe.fetch_feature(feature, node_feature_keys=["x_in", "area"])
datapipe = datapipe.copy_to(torch.device('cuda'))
dataloader = gb.DataLoader(datapipe)
mini_batch = next(iter(dataloader))
print("\nmini_batch x_in : ", mini_batch.node_features["x_in"])

Is there anything wrong here?

Plus, can not build a proper dataset as good as demo presented in Doc 6.1.

dataset = gb.BuiltinDataset("ogbn-arxiv").load()

Bty, it has been three days since the last issue was resolved, how is everything going there?

The first question was replied in the posted issue. For the ogbn-arxiv dataset, I didn’t found any errors from my side. Could you elaborate the problem you have encountered?

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