HAMi
Benchmarks
Three instances from ai-benchmark have been used to evaluate vGPU-device-plugin performance as follows
| Test Environment | description |
|---|---|
| Kubernetes version | v1.12.9 |
| Docker version | 18.09.1 |
| GPU Type | Tesla V100 |
| GPU Num | 2 |
| Test instance | description |
|---|---|
| nvidia-device-plugin | k8s + nvidia k8s-device-plugin |
| vGPU-device-plugin | k8s + VGPU k8s-device-plugin，without virtual device memory |
| vGPU-device-plugin(virtual device memory) | k8s + VGPU k8s-device-plugin，with virtual device memory |
Test Cases:
| test id | case | type | params |
|---|---|---|---|
| 1.1 | Resnet-V2-50 | inference | batch=50,size=346*346 |
| 1.2 | Resnet-V2-50 | training | batch=20,size=346*346 |
| 2.1 | Resnet-V2-152 | inference | batch=10,size=256*256 |
| 2.2 | Resnet-V2-152 | training | batch=10,size=256*256 |
| 3.1 | VGG-16 | inference | batch=20,size=224*224 |
| 3.2 | VGG-16 | training | batch=2,size=224*224 |
| 4.1 | DeepLab | inference | batch=2,size=512*512 |
| 4.2 | DeepLab | training | batch=1,size=384*384 |
| 5.1 | LSTM | inference | batch=100,size=1024*300 |
| 5.2 | LSTM | training | batch=10,size=1024*300 |
Test Result: 
To reproduce:
- install k8s-vGPU-scheduler，and configure properly
- run benchmark job
$ kubectl apply -f benchmarks/ai-benchmark/ai-benchmark.yml
- View the result by using kubctl logs
``` $ kubectl logs [pod id]