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: img

img

To reproduce:

  1. install k8s-vGPU-scheduler,and configure properly
  2. run benchmark job
$ kubectl apply -f benchmarks/ai-benchmark/ai-benchmark.yml
  1. View the result by using kubctl logs

``` $ kubectl logs [pod id]