haitwang-cloud / ai-benchmark

AI benchmark source code from http://ai-benchmark.com

Intro

AI Benchmark Alpha is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying on TensorFlow machine learning library, and is providing a lightweight and accurate solution for assessing inference and training speed for key Deep Learning models.

In its entirety, AI Benchmark is executed on the following test scenarios:

• Classification
  • MobileNet-V2
  • Inception-V3
  • Inception-V4
  • Inception-ResNet-V2
  • ResNet-V2-50
  • ResNet-V2-152
  • VGG-16
• Image-to-Image Mapping
  • SRCNN 9-5-5
  • VGG-19 Super-Res
  • ResNet-SRGAN
  • ResNet-DPED
  • U-Net
  • Nvidia-SPADE
• Image Segmentation
  • ICNet
  • PSPNet
  • DeepLab
• Inpainting
  • Pixel-RNN
• Sentence Sentiment Analysis
  • LSTM-Sentiment

For more information and results, please visit the project website: http://ai-benchmark.com/alpha

Quick Start

How to run locally

Install the required packages:

pip install --no-cache-dir -r requirements.txt

Run the benchmark:

python main.py

How to run in k8s

Build the docker image

sh build.sh

Apply the k8s deployment

# k apply -f benchmark-job.yml
apiVersion: batch/v1
kind: Job
metadata:
  name: ai-benchmark
  namespace: gpu-test-workloads
  labels:
    app: ai-benchmark
spec:
  parallelism: 1
  completions: 2
  template:
    metadata:
      labels:
        app: ai-benchmark
    spec:
      restartPolicy: Never
      containers:
        - name: ai-benchmark
          image: vgpu-benchmark:v0.0.1
          resources:
            limits:
              cpu: "11"
              memory: "60Gi"
              nvidia.com/gpu: '1'
            requests:
              cpu: "11"
              memory: "60Gi"
          terminationMessagePath: /dev/termination-log
          terminationMessagePolicy: File
          imagePullPolicy: IfNotPresent
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
      priorityClassName: system-cluster-critical

Sample Output

>>   AI-Benchmark-v.0.1.2   
>>   Let the AI Games begin..

*  TF Version: 2.16.1
*  Platform: Linux-6.1.77-gardenlinux-amd64-x86_64-with-glibc2.35
*  CPU: N/A
*  CPU RAM: 1007 GB
*  GPU/0: NVIDIA H100 80GB HBM3
*  GPU RAM: 77.1 GB
*  CUDA Version: 12.3
*  CUDA Build: V12.3.107

The benchmark is running...
The tests might take up to 20 minutes
Please don't interrupt the script
##### Config Done

1/18. MobileNet-V2

1.1 - training  | batch=50, size=224x224: 34.8 ± 0.8 ms

2/18. Inception-V3

2.1 - training  | batch=20, size=346x346: 43.2 ± 0.5 ms

3/18. Inception-V4

3.1 - training  | batch=10, size=346x346: 43.4 ± 0.8 ms

4/18. Inception-ResNet-V2

4.1 - training  | batch=8, size=346x346: 50.9 ± 0.5 ms

5/18. ResNet-V2-50

5.1 - training  | batch=10, size=346x346: 24.1 ± 0.4 ms

6/18. ResNet-V2-152

6.1 - training  | batch=10, size=256x256: 37.4 ± 0.6 ms

7/18. VGG-16

7.1 - training  | batch=2, size=224x224: 15.6 ± 0.5 ms

8/18. SRCNN 9-5-5

8.1 - training  | batch=10, size=512x512: 57.7 ± 0.7 ms

9/18. VGG-19 Super-Res

9.1 - training  | batch=10, size=224x224: 38.3 ± 19.5 ms

10/18. ResNet-SRGAN

10.1 - training  | batch=5, size=512x512: 32.7 ± 0.7 ms

11/18. ResNet-DPED

11.1 - training  | batch=15, size=128x128: 33.4 ± 0.6 ms

12/18. U-Net

12.1 - training  | batch=4, size=256x256: 35.5 ± 0.5 ms

13/18. Nvidia-SPADE

13.1 - training  | batch=1, size=128x128: 25.2 ± 0.4 ms

14/18. ICNet

14.1 - training  | batch=10, size=1024x1536: 232 ± 2 ms

15/18. PSPNet

15.1 - training  | batch=1, size=512x512: 23.5 ± 0.5 ms

16/18. DeepLab

16.1 - training  | batch=1, size=384x384: 26.8 ± 0.6 ms

17/18. Pixel-RNN

17.1 - training  | batch=10, size=64x64: 977 ± 40 ms

18/18. LSTM-Sentiment

18.1 - training  | batch=10, size=1024x300: 581 ± 10 ms

Device Training Score: 56922