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

最新推荐文章于 2024-05-29 14:42:04 发布

原创最新推荐文章于 2024-05-29 14:42:04 发布 · 780 阅读

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#rtx2080ti #Ubuntu18.04 #Tensorflow1.15

本文记录了使用RTX2080Ti显卡进行的TensorFlow基准测试结果，包括Resnet50、AlexNet、Inceptionv3等模型的性能表现，并与A100和V100进行了对比。

部署运行你感兴趣的模型镜像

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