矩池云 RTX 2080 Ti+Ubuntu18.04+Tensorflow1.15.2 性能测试!

今天为了对比滴滴云NVIDIA A100,特地跑了一下RTX2080的TensorFlow基准测试,现在把结果记录一下!


运行环境

平台为:矩池云

系统为:Ubuntu 18.04

显卡为:RTX 2080 Ti

Python版本: 3.6.10

TensorFlow版本:1.15.2


显卡相关内容如下:


系统配置如下:



测试方法

https://github.com/tensorflow/benchmarks


Resnet50 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=resnet50

Step Img/sec total_loss

1 images/sec: 305.5 +/- 0.0 (jitter = 0.0) 8.220

10 images/sec: 305.2 +/- 0.3 (jitter = 0.7) 7.880

20 images/sec: 305.3 +/- 0.2 (jitter = 0.9) 7.910

30 images/sec: 305.1 +/- 0.2 (jitter = 0.8) 7.820

40 images/sec: 304.9 +/- 0.2 (jitter = 0.7) 8.005

50 images/sec: 304.8 +/- 0.1 (jitter = 0.9) 7.770

60 images/sec: 304.5 +/- 0.2 (jitter = 1.1) 8.114

70 images/sec: 304.3 +/- 0.2 (jitter = 1.3) 7.816

80 images/sec: 304.2 +/- 0.2 (jitter = 1.5) 7.975

90 images/sec: 304.0 +/- 0.1 (jitter = 1.5) 8.094

100 images/sec: 303.8 +/- 0.1 (jitter = 1.6) 8.035

----------------------------------------------------------------

total images/sec: 303.65

----------------------------------------------------------------


AlexNet BS512

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=512 --model=alexnet

Step    Img/sec total_loss

1      images/sec: 3939.5 +/- 0.0 (jitter = 0.0)      nan

10      images/sec: 3927.5 +/- 3.0 (jitter = 12.2)      nan

20      images/sec: 3923.9 +/- 2.1 (jitter = 11.7)      nan

30      images/sec: 3923.0 +/- 2.5 (jitter = 11.0)      nan

40      images/sec: 3921.2 +/- 2.0 (jitter = 9.4)      nan

50      images/sec: 3919.0 +/- 1.8 (jitter = 9.2)      nan

60      images/sec: 3915.4 +/- 1.9 (jitter = 11.5)      nan

70      images/sec: 3912.2 +/- 2.0 (jitter = 13.7)      nan

80      images/sec: 3911.5 +/- 1.8 (jitter = 14.5)      nan

90      images/sec: 3909.8 +/- 1.8 (jitter = 15.9)      nan

100    images/sec: 3907.9 +/- 1.7 (jitter = 15.9)      nan

----------------------------------------------------------------

total images/sec: 3905.13

----------------------------------------------------------------

Inception v3 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=inception3

Step    Img/sec total_loss

1      images/sec: 200.6 +/- 0.0 (jitter = 0.0)        7.278

10      images/sec: 200.6 +/- 0.1 (jitter = 0.6)        7.298

20      images/sec: 200.5 +/- 0.1 (jitter = 0.4)        7.291

30      images/sec: 200.3 +/- 0.1 (jitter = 0.4)        7.412

40      images/sec: 200.1 +/- 0.1 (jitter = 0.7)        7.306

50      images/sec: 199.9 +/- 0.1 (jitter = 0.8)        7.287

60      images/sec: 199.7 +/- 0.1 (jitter = 1.0)        7.378

70      images/sec: 199.5 +/- 0.1 (jitter = 1.2)        7.351

80      images/sec: 199.3 +/- 0.1 (jitter = 1.3)        7.402

90      images/sec: 199.2 +/- 0.1 (jitter = 1.2)        7.309

100    images/sec: 199.0 +/- 0.1 (jitter = 1.2)        7.354

----------------------------------------------------------------

total images/sec: 198.97

----------------------------------------------------------------

VGG16 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=vgg16

Step    Img/sec total_loss

1      images/sec: 180.0 +/- 0.0 (jitter = 0.0)        7.346

10      images/sec: 179.5 +/- 0.1 (jitter = 0.2)        7.294

20      images/sec: 179.4 +/- 0.1 (jitter = 0.3)        7.282

30      images/sec: 179.1 +/- 0.1 (jitter = 0.4)        7.278

40      images/sec: 178.9 +/- 0.1 (jitter = 0.8)        7.287

50      images/sec: 178.7 +/- 0.1 (jitter = 0.7)        7.272

60      images/sec: 178.6 +/- 0.1 (jitter = 0.7)        7.261

70      images/sec: 178.4 +/- 0.1 (jitter = 1.0)        7.267

80      images/sec: 178.3 +/- 0.1 (jitter = 1.1)        7.280

90      images/sec: 178.2 +/- 0.1 (jitter = 1.0)        7.270

100    images/sec: 178.1 +/- 0.1 (jitter = 0.9)        7.268

----------------------------------------------------------------

total images/sec: 178.02

----------------------------------------------------------------

GoogLeNet BS128

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=128 --model=googlenet

Step    Img/sec total_loss

1      images/sec: 784.7 +/- 0.0 (jitter = 0.0)        7.104

10      images/sec: 782.9 +/- 0.4 (jitter = 1.4)        7.104

20      images/sec: 782.3 +/- 0.6 (jitter = 2.1)        7.092

30      images/sec: 780.3 +/- 0.7 (jitter = 4.3)        7.087

40      images/sec: 779.2 +/- 0.6 (jitter = 5.5)        7.067

50      images/sec: 778.9 +/- 0.5 (jitter = 5.0)        7.092

60      images/sec: 778.4 +/- 0.5 (jitter = 4.7)        7.050

70      images/sec: 778.3 +/- 0.4 (jitter = 4.2)        7.073

80      images/sec: 778.2 +/- 0.4 (jitter = 3.9)        7.077

90      images/sec: 778.2 +/- 0.4 (jitter = 3.0)        7.079

100    images/sec: 778.1 +/- 0.3 (jitter = 2.7)        7.066

----------------------------------------------------------------

total images/sec: 777.65

----------------------------------------------------------------

ResNet152 BS32

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=32 --model=resnet152

Step    Img/sec total_loss

1      images/sec: 116.5 +/- 0.0 (jitter = 0.0)        9.028

10      images/sec: 116.3 +/- 0.1 (jitter = 0.2)        8.593

20      images/sec: 116.2 +/- 0.1 (jitter = 0.3)        8.603

30      images/sec: 116.0 +/- 0.1 (jitter = 0.4)        8.712

40      images/sec: 115.8 +/- 0.1 (jitter = 0.5)        8.655

50      images/sec: 115.7 +/- 0.1 (jitter = 0.6)        8.800

60      images/sec: 115.7 +/- 0.1 (jitter = 0.6)        8.625

70      images/sec: 115.5 +/- 0.1 (jitter = 0.6)        9.093

80      images/sec: 115.5 +/- 0.1 (jitter = 0.6)        8.856

90      images/sec: 115.4 +/- 0.1 (jitter = 0.6)        8.996

100    images/sec: 115.3 +/- 0.1 (jitter = 0.6)        8.842

----------------------------------------------------------------

total images/sec: 115.28

----------------------------------------------------------------


性能对比

A100 和V100 和 2080ti 性能对比:

https://www.tonyisstark.com/383.html

©著作权归作者所有,转载或内容合作请联系作者
【社区内容提示】社区部分内容疑似由AI辅助生成,浏览时请结合常识与多方信息审慎甄别。
平台声明:文章内容(如有图片或视频亦包括在内)由作者上传并发布,文章内容仅代表作者本人观点,简书系信息发布平台,仅提供信息存储服务。

友情链接更多精彩内容