afaqbabar/otc_ecs_ai-benchmark

OTC_ECS_AI-Benchmark

AI Benchmark on one of the flavor's of Open Telekom Cloud's Elastic Cloud Server : https://open-telekom-cloud.com/de/produkte-services/core-services/elastic-cloud-server
Opensource projects: https://pypi.org/project/ai-benchmark/
Specs:
OTC - ECS
Flavor p2.2xlarge.8
OS: Standard_Ubuntu_22.04_V100_latest
CPU/RAM: 8 vCPUs | 64 GiB

~# lspci -v | grep -E -i --color 'VGA|3d|2d' 00:02.0 VGA compatible controller: Cirrus Logic GD 5446 (prog-if 00 [VGA controller]) Prefetchable memory behind bridge: 0000001802c00000-0000001802dfffff [size=2M] 00:0d.0 3D controller: NVIDIA Corporation GV100GL [Tesla V100 PCIe 16GB] (rev a1)

`$ python3 ai-benchmark.py

2022-12-27 07:24:12.378411: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-12-27 07:24:13.561084: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2022-12-27 07:24:13.561190: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2022-12-27 07:24:13.561211: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.`

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

• TF Version: 2.11.0
• Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.35
• CPU: N/A
• CPU RAM: 63 GB

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

1/19. MobileNet-V2

1.1 - inference | batch=50, size=224x224: 543 ± 22 ms 1.2 - training | batch=50, size=224x224: 2658 ± 29 ms

2/19. Inception-V3

2.1 - inference | batch=20, size=346x346: 1425 ± 18 ms 2.2 - training | batch=20, size=346x346: 6884 ± 40 ms

3/19. Inception-V4

3.1 - inference | batch=10, size=346x346: 1413 ± 11 ms 3.2 - training | batch=10, size=346x346: 6446 ± 17 ms

4/19. Inception-ResNet-V2

4.1 - inference | batch=10, size=346x346: 1543 ± 8 ms 4.2 - training | batch=8, size=346x346: 5824 ± 31 ms

5/19. ResNet-V2-50

5.1 - inference | batch=10, size=346x346: 919 ± 6 ms 5.2 - training | batch=10, size=346x346: 4062 ± 24 ms

6/19. ResNet-V2-152

6.1 - inference | batch=10, size=256x256: 1353 ± 11 ms 6.2 - training | batch=10, size=256x256: 6642 ± 45 ms

7/19. VGG-16

7.1 - inference | batch=20, size=224x224: 2687 ± 21 ms 7.2 - training | batch=2, size=224x224: 2210 ± 12 ms

8/19. SRCNN 9-5-5

8.1 - inference | batch=10, size=512x512: 2427 ± 14 ms 8.2 - inference | batch=1, size=1536x1536: 2183 ± 10 ms 8.3 - training | batch=10, size=512x512: 20440 ± 969 ms

9/19. VGG-19 Super-Res

9.1 - inference | batch=10, size=256x256: 4595 ± 11 ms 9.2 - inference | batch=1, size=1024x1024: 7320 ± 14 ms 9.3 - training | batch=10, size=224x224: 20997 ± 230 ms

10/19. ResNet-SRGAN

10.1 - inference | batch=10, size=512x512: 3964 ± 10 ms 10.2 - inference | batch=1, size=1536x1536: 3566 ± 12 ms 10.3 - training | batch=5, size=512x512: 8814 ± 56 ms

11/19. ResNet-DPED

11.1 - inference | batch=10, size=256x256: 4503 ± 15 ms 11.2 - inference | batch=1, size=1024x1024: 7224 ± 14 ms 11.3 - training | batch=15, size=128x128: 9915 ± 32 ms

12/19. U-Net

12.1 - inference | batch=4, size=512x512: 8818 ± 10 ms 12.2 - inference | batch=1, size=1024x1024: 9259 ± 389 ms 12.3 - training | batch=4, size=256x256: 8844 ± 130 ms

13/19. Nvidia-SPADE

13.1 - inference | batch=5, size=128x128: 3172 ± 36 ms 13.2 - training | batch=1, size=128x128: 3053 ± 33 ms

14/19. ICNet

14.1 - inference | batch=5, size=1024x1536: 2558 ± 48 ms 14.2 - training | batch=10, size=1024x1536: 7068 ± 213 ms

15/19. PSPNet

15.1 - inference | batch=5, size=720x720: 16307 ± 60 ms 15.2 - training | batch=1, size=512x512: 6316 ± 19 ms

16/19. DeepLab

16.1 - inference | batch=2, size=512x512: 3497 ± 14 ms 16.2 - training | batch=1, size=384x384: 4666 ± 342 ms

17/19. Pixel-RNN

17.1 - inference | batch=50, size=64x64: 2701 ± 13 ms 17.2 - training | batch=10, size=64x64: 2315 ± 521 ms

18/19. LSTM-Sentiment

18.1 - inference | batch=100, size=1024x300: 7094 ± 45 ms 18.2 - training | batch=10, size=1024x300: 14620 ± 1006 ms

19/19. GNMT-Translation

19.1 - inference | batch=1, size=1x20: 3263 ± 23 ms

Device Inference Score: 443 Device Training Score: 467 Device AI Score: 910

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