dmlc--dgl
Rhett Ying
00281219d0
Co-authored-by: Mingbang Wang <100203018+Skeleton003@users.noreply.github.com> Co-authored-by: Ramon Zhou <deluxurous@gmail.com>
64 行
2.9 KiB
Markdown
64 行
2.9 KiB
Markdown
# Node classification on heterogeneous graph with RGCN
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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.
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## Run on `ogbn-mag` dataset
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### Sample on CPU and train/infer on CPU
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```
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python3 hetero_rgcn.py --dataset ogbn-mag
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```
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### Sample on CPU and train/infer on GPU
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```
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python3 hetero_rgcn.py --dataset ogbn-mag --num_gpus 1
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```
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### Resource usage and time cost
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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.
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| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
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| ------------ | ------------- | ----------- | ------------- | ------------------------ |
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| ~1.1GB | ~4.5GB | 0 | 0GB | ~235s |
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| ~1.1GB | ~2GB | 1 | 4.4GB | ~60s |
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### Accuracies
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```
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Epoch: 01, Loss: 2.6736, Valid accuracy: 42.21%
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Epoch: 02, Loss: 2.0809, Valid accuracy: 42.51%
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Epoch: 03, Loss: 1.8143, Valid accuracy: 42.76%
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Test accuracy 41.4817
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```
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## Run on `ogb-lsc-mag240m` dataset
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### Sample on CPU and train/infer on CPU
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```
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python3 hetero_rgcn.py --dataset ogb-lsc-mag240m
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```
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### Sample on CPU and train/infer on GPU
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```
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python3 hetero_rgcn.py --dataset ogb-lsc-mag240m --num_gpus 1
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```
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### Resource usage and time cost
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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.
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> **note:**
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`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.
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One more thing, first epoch is quite slow as `buffer/cache` is not ready yet. For GPU train, first epoch takes **34:56min, 1.93s/it**.
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Even in following epochs, time consumption varies.
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| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
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| ------------ | ------------- | ----------- | ------------- | ------------------------ |
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| ~404GB | ~55GB | 0 | 0GB | ~197s |
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| ~404GB | ~55GB | 1 | 7GB | ~119s |
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### Accuracies
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```
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Epoch: 01, Loss: 2.3038, Valid accuracy: 46.33%
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Epoch: 02, Loss: 2.1160, Valid accuracy: 46.47%
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Epoch: 03, Loss: 2.0847, Valid accuracy: 48.38%
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```
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