wehub-resource-sync
64 lines
2.9 KiB
Markdown
64 lines
2.9 KiB
Markdown
# 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%
|
|
```
|