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How to disable shaDow_GNN sampling? #7
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Hi, for training GNNs without sampling, there are two ways
To train with method 2, you can use the The current codebase does not directly support training with method 1 (although supporting it shouldn't be too hard; I may need to cleanup some minibatch logic in the next release). In the paper, when we do full-batching training of the coupled GNNs, I simply have a separate code to implement an equivalent architecture with PyG. |
First of all, wish you a very happy new year. |
Happy new year! In the current code, In case you want to modify this to specify budget for each layer separately, you can change the signature at the frontend as well as the backend to be a list / vector. |
Thanks a lot for the details.
I want to train in a minibatch setting to learn the weights and then use weights to make the forward pass directly for inference. Line 210 in 045b85e
Can you please point me how to achieve this easily with the current code base, if possible. |
Sorry for the late reply. Currently, the minibatch sampler can support full-batch data (i.e., no sampling). For this you just need to modify the Change from
to
However, the training pipeline will throw out some errors since the logger would assumes a subgraph data structure which is different from the data structure of the full graph. I do see some inflexibility (e.g., the one you mentioned) and redundant data structures in the current codebase. So I am actually trying to restructure & optimize performance for the next release. It is still work-in-progress and I hope to publish it in March. If you need this feature before then, you probably need to manually modify some code (e.g., start from fixing the logger issue). |
Hi, I need to compare the efficiency of the shaDow_GNN (decoupled GNN) with normal GNN (coupled GNN).
Is there a way to run this repo without the ParallelSampler?
What I mean is how do I run the baseline (coupled GNN where neighborhood expands with layer)?
I could not find any flag or config which disables the ParallelSampler. If you could kindly point me on the correct direction that will be really helpful.
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