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[example] update RGCN README #6996

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Jan 23, 2024
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[example] update RGCN README #6996

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16 changes: 8 additions & 8 deletions examples/core/rgcn/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -20,15 +20,15 @@ Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 9

| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
| ------------ | ------------- | ----------- | ------------- | ------------------------ |
| ~1.1GB | ~5GB | 0 | 0GB | ~243s |
| ~1.1GB | ~3GB | 1 | 4.4GB | ~81s |
| ~1.1GB | ~7GB | 0 | 0GB | ~233s |
| ~1.1GB | ~5GB | 1 | 4.5GB | ~73.6s |

### Accuracies
```
Epoch: 01, Loss: 2.3302, Valid: 47.76%, Test: 46.58%
Epoch: 02, Loss: 1.5486, Valid: 48.31%, Test: 47.12%
Epoch: 03, Loss: 1.1469, Valid: 46.43%, Test: 45.18%
Test accuracy 45.1227
Epoch: 01, Loss: 2.3386, Valid: 47.67%, Test: 46.96%
Epoch: 02, Loss: 1.5563, Valid: 47.66%, Test: 47.02%
Epoch: 03, Loss: 1.1557, Valid: 46.58%, Test: 45.42%
Test accuracy 45.3850
```

## Run on `ogb-lsc-mag240m` dataset
Expand All @@ -54,8 +54,8 @@ Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 9

| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
| ------------ | ------------- | ----------- | ------------- | ------------------------ |
| ~404GB | ~60GB | 0 | 0GB | ~216s |
| ~404GB | ~60GB | 1 | 7GB | ~157s |
| ~404GB | ~72GB | 0 | 0GB | ~325s |
| ~404GB | ~61GB | 1 | 14GB | ~178s |

### Accuracies
```
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24 changes: 12 additions & 12 deletions examples/sampling/graphbolt/rgcn/README.md
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Expand Up @@ -19,15 +19,15 @@ Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 9

| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
| ------------ | ------------- | ----------- | ------------- | ------------------------ |
| ~1.1GB | ~4.5GB | 0 | 0GB | ~235s |
| ~1.1GB | ~2GB | 1 | 4.4GB | ~60s |
| ~1.1GB | ~5.3GB | 0 | 0GB | ~230s |
| ~1.1GB | ~3GB | 1 | 3.87GB | ~64.6s |

### Accuracies
```
Epoch: 01, Loss: 2.6736, Valid accuracy: 42.21%
Epoch: 02, Loss: 2.0809, Valid accuracy: 42.51%
Epoch: 03, Loss: 1.8143, Valid accuracy: 42.76%
Test accuracy 41.4817
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
Expand All @@ -47,17 +47,17 @@ Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 9

> **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 **34:56min, 1.93s/it**.
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 | ~55GB | 0 | 0GB | ~197s |
| ~404GB | ~55GB | 1 | 7GB | ~119s |
| ~404GB | ~67GB | 0 | 0GB | ~248s |
| ~404GB | ~60GB | 1 | 15GB | ~166s |

### Accuracies
```
Epoch: 01, Loss: 2.3038, Valid accuracy: 46.33%
Epoch: 02, Loss: 2.1160, Valid accuracy: 46.47%
Epoch: 03, Loss: 2.0847, Valid accuracy: 48.38%
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%
```