Efficiently read ~200 graphs per instance

Hi there,

In my use case, for each example of my dataset, I have ~200 graphs associated with it. Therefore, in my dataset reader __getitem__, I call dgl.load_graphs to load the stored information for each example. However, each call takes about ~0.3s (with peaks of 1.23s) which is quite a lot if I want to use a big batch size. Any suggestions about how to handle this use case?


How large is your graph?

Each graph might have around 18 nodes. Each node has 3 set of features of different sizes (tensors).

Thanks. It seems not ideal for DGL to take such long time to load those graphs. Could you file an issue at github, with a pseudo code of your scenario? Thanks