AttributeError: module 'dgl' has no attribute 'distributed' in branch 2.4

Questions
#1

when using files in dgl/examples/pytorch/graphsage/dist, there is a problem

python3 partition_graph.py --dataset ogb-product --num_parts 2 --balance_train --balance_edges
load ogbn-products
This will download 1.38GB. Will you proceed? (y/N)
y
Using exist file products.zip
Extracting dataset/products.zip
Loading necessary files...
This might take a while.
Processing graphs...
100%|██████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.06it/s]
Converting graphs into DGL objects...
100%|██████████████████████████████████████████████████| 1/1 [00:00<00:00,  9.39it/s]
Saving...
finish loading ogbn-products
finish constructing ogbn-products
load ogb-product takes 87.180 seconds
|V|=2449029, |E|=123718280
train: 196615, valid: 39323, test: 2213091
Traceback (most recent call last):
  File "/home/node/workspace/dgl240-project/examples/pytorch/graphsage/dist/partition_graph.py", line 87, in <module>
    dgl.distributed.partition_graph(
AttributeError: module 'dgl' has no attribute 'distributed'

This AttributeError occurs not only when partitioning, but also when distributed training. When I use the code from 2.3.1 for graph partitioning

#2

the example you were using is deprecated. please use this one instead. And this issue is caused by dgl.distributed is not imported in default since DGL 2.4.

#3

@Rhett-Ying Thank you for your reply

In 2.4, DGL/examples/distributed/graphsage/node_classification.py inference function seems to have changed:

1 The position of this statement has changed

y = dgl.distributed.DistTensor(
(g.num_nodes(), self.n_hidden),
th.float32,
"h",
persistent=True,
)

2 The content of the second loop has changed

for input_nodes, output_nodes, blocks in dataloader:
    ...

Has been changed to

            for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
                block = blocks[0].to(device)
                h = x[input_nodes].to(device)
                h_dst = h[: block.number_of_dst_nodes()]
                h = layer(block, (h, h_dst))
                if i != len(self.layers) - 1:
                    h = self.activation(h)
                    h = self.dropout(h)
                # Copy back to CPU as DistTensor requires data reside on CPU.
                y[output_nodes] = h.cpu()

Can you explain the advantages of these improvements?

Can we also use this inference method if we use other models such as GCN and GAT?

#4

Please always refer to the latest example as it best adapt to the latest APIs of dataloader. I doubt the previous one can run well.

#5

The results obtained with this method in two nodes:

Summary of node classification(GraphSAGE): GraphName ogbn-products | TrainEpochTime(mean) 9.9387 | TestAccuracy 0.7690
Client[1] in group[0] is exiting...
Part 0, Val Acc 0.8222, Test Acc 0.6115, time: 35.4167
Summary of node classification(GraphSAGE): GraphName ogbn-products | TrainEpochTime(mean) 9.7598 | TestAccuracy 0.6115
Client[0] in group[0] is exiting...

Can we add the two TestAccuracy and average them to get the final result? Can this also work for more nodes?