dmlc--dgl/examples/graphbolt/rgcn

Node classification on heterogeneous graph with RGCN

This example aims to demonstrate how to run node classification task on heterogeneous graph with GraphBolt. Models are not tuned to achieve the best accuracy yet.

Run on ogbn-mag dataset

Sample on CPU and train/infer on CPU

python3 hetero_rgcn.py --dataset ogbn-mag

Sample on CPU and train/infer on GPU

python3 hetero_rgcn.py --dataset ogbn-mag --num_gpus 1

Resource usage and time cost

Below results are roughly collected from an AWS EC2 g4dn.metal, 384GB RAM, 96 vCPUs(Cascade Lake P-8259L), 8 NVIDIA T4 GPUs(16GB RAM). CPU RAM usage is the peak value of used field of free command which is a bit rough. Please refer to RSS/USS/PSS which are more accurate. GPU RAM usage is the peak value recorded by nvidia-smi command.

Dataset Size	CPU RAM Usage	Num of GPUs	GPU RAM Usage	Time Per Epoch(Training)
~1.1GB	~5.3GB	0	0GB	~230s
~1.1GB	~3GB	1	3.87GB	~64.6s

Accuracies

Epoch: 01, Loss: 2.3434, Valid accuracy: 48.23%
Epoch: 02, Loss: 1.5646, Valid accuracy: 48.49%
Epoch: 03, Loss: 1.1633, Valid accuracy: 45.79%
Test accuracy 44.6792

Run on ogb-lsc-mag240m dataset

Sample on CPU and train/infer on CPU

python3 hetero_rgcn.py --dataset ogb-lsc-mag240m

Sample on CPU and train/infer on GPU

python3 hetero_rgcn.py --dataset ogb-lsc-mag240m --num_gpus 1

Resource usage and time cost

Below results are roughly collected from an AWS EC2 g4dn.metal, 384GB RAM, 96 vCPUs(Cascade Lake P-8259L), 8 NVIDIA T4 GPUs(16GB RAM). CPU RAM usage is the peak value of used field of free command which is a bit rough. Please refer to RSS/USS/PSS which are more accurate. GPU RAM usage is the peak value recorded by nvidia-smi command.

note: buffer/cache are highly used during train, it's about 300GB. If more RAM is available, more buffer/cache will be consumed as graph size is about 55GB and feature data is about 350GB. One more thing, first epoch is quite slow as buffer/cache is not ready yet. For GPU train, first epoch takes 1030s. Even in following epochs, time consumption varies.

Dataset Size	CPU RAM Usage	Num of GPUs	GPU RAM Usage	Time Per Epoch(Training)
~404GB	~67GB	0	0GB	~248s
~404GB	~60GB	1	15GB	~166s

Accuracies

Epoch: 01, Loss: 2.1432, Valid accuracy: 50.21%
Epoch: 02, Loss: 1.9267, Valid accuracy: 50.77%
Epoch: 03, Loss: 1.8797, Valid accuracy: 53.38%