[examples] update README for RGCN examples (#6894)

examples/core/rgcn/README.md

@@ -18,17 +18,17 @@ 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) | Time Per Epoch(Inference: train/val/test set)      |
-| ------------ | ------------- | ----------- | ---------- | --------- | ---------------------------    |
-| ~1.1GB       | ~5GB          | 0           |  0GB       | ~4min03s(615it, 2.53it/s)   | ~0min22s(154it, 6.86it/s) + ~0min2s(16it, 6.92it/s) + ~0min1s(11it, 7.34it/s)   |
-| ~1.1GB       | ~3GB          | 1           |  4.4GB     | ~1min20s(615it, 7.65it/s)   | ~0min14s(154it, 10.79it/s) + ~0min1s(16it, 10.07it/s) + ~0min1s(11it, 10.42it/s)   |
+| 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                     |
 
 ### Accuracies
 ```
-Epoch: 01, Loss: 2.3625, Valid: 48.25%, Test: 47.91%, Time 86.0210
-Epoch: 02, Loss: 1.5852, Valid: 48.56%, Test: 46.98%, Time 84.2728
-Epoch: 03, Loss: 1.1974, Valid: 45.99%, Test: 44.05%, Time 85.7916
-Test accuracy 44.1165
+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
 ```
 
 ## Run on `ogb-lsc-mag240m` dataset
@@ -52,17 +52,14 @@ 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.
 
-| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) | Time Per Epoch(Inference: train/val/test set)      |
-| ------------ | ------------- | ----------- | ---------- | --------- | ---------------------------    |
-| ~404GB       | ~60GB       | 0           |  0GB       | ~3min35s(1087it, 5.04it/s)  | ~2min40s(272it, 1.70it/s) + ~0min25s(34it, 1.35it/s) + ~0min15s(22it, 1.43it/s)   |
-| ~404GB       | ~60GB       | 1           |  7GB       | ~2min46s(1087it, 6.52it/s)  | ~1min49s(272it, 2.48it/s) + ~0min17s(34it, 1.76it/s) + ~0min12s(22it, 1.81it/s)  |
+| 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                    |
 
 ### Accuracies
 ```
-Final performance: 
-All runs:
-Highest Train: 54.85 ± 1.02
-Highest Valid: 52.29 ± 0.50
-  Final Train: 54.78 ± 1.12
-   Final Test: 0.00 ± 0.00
+Epoch: 01, Loss: 2.0798, Valid: 52.04%
+Epoch: 02, Loss: 1.8652, Valid: 54.51%
+Epoch: 03, Loss: 1.8175, Valid: 53.71%
 ```

examples/sampling/graphbolt/rgcn/README.md

@@ -19,14 +19,14 @@ 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          | ~248s                    |
+| ~1.1GB       | ~4.5GB        | 0           |  0GB          | ~235s                    |
 | ~1.1GB       | ~2GB          | 1           |  4.4GB        | ~60s                     |
 
 ### Accuracies
 ```
-Epoch: 01, Loss: 2.6736, Valid accuracy: 42.21%, Time 61.4482
-Epoch: 02, Loss: 2.0809, Valid accuracy: 42.51%, Time 60.5549
-Epoch: 03, Loss: 1.8143, Valid accuracy: 42.76%, Time 60.1942
+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
 ```
 
@@ -50,17 +50,14 @@ Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 9
 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**.
 Even in following epochs, time consumption varies.
 
-| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) | Time Per Epoch(Inference: train/val/test set)      |
-| ------------ | ------------- | ----------- | ---------- | --------- | ---------------------------    |
-| ~404GB       | ~55GB       | 0           |  0GB       | ~3min25s(1087it, 5.29it/s)  | ~2min26s(272it, 1.86it/s) + ~0min20s(34it, 1.62it/s) + ~0min13s(22it, 1.68it/s)   |
-| ~404GB       | ~55GB       | 1           |  7GB       | ~1min59s(1087it, 9.11it/s)  | ~1min52s(272it, 2.41it/s) + ~0min17s(34it, 1.93it/s) + ~0min11s(22it, 1.99it/s)  |
+| 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                    |
 
 ### Accuracies
 ```
-Final performance: 
-All runs:
-Highest Train: 54.43 ± 0.39
-Highest Valid: 51.78 ± 0.68
-  Final Train: 54.35 ± 0.51
-   Final Test: 0.00 ± 0.00
+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%
 ```