Google Colab V100 +TensorFlow1.15.2 性能测试

作者: 托尼 分类: AI 发布时间: 2020-09-17 09:01
为了对比滴滴云(大师码:8888)内测版NVIDIA A100,跑了一下Google Colab V100 的 TensorFlow基准测试,现在把结果记录一下!

运行环境

平台为:Google Colab
系统为:Ubuntu 18.04
显卡为:V100-SXM2-16GB
Python版本: 3.6
TensorFlow版本:1.15.2
显卡相关:

测试方法

TensorFlow benchmarks测试方法:
https://github.com/tensorflow/benchmarks

ResNet50_v1.5 BS64

!python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=resnet50_v1.5
Step	Img/sec	total_loss
1	images/sec: 349.6 +/- 0.0 (jitter = 0.0)	7.848
10	images/sec: 349.9 +/- 0.2 (jitter = 0.4)	8.053
20	images/sec: 349.9 +/- 0.1 (jitter = 0.6)	8.103
30	images/sec: 350.2 +/- 0.1 (jitter = 0.6)	8.118
40	images/sec: 350.2 +/- 0.1 (jitter = 0.8)	7.894
50	images/sec: 350.3 +/- 0.1 (jitter = 0.8)	7.918
60	images/sec: 350.1 +/- 0.1 (jitter = 0.7)	8.103
70	images/sec: 350.0 +/- 0.1 (jitter = 0.8)	7.986
80	images/sec: 350.0 +/- 0.1 (jitter = 0.8)	7.808
90	images/sec: 350.0 +/- 0.1 (jitter = 0.8)	7.972
100	images/sec: 350.0 +/- 0.1 (jitter = 0.9)	7.649
----------------------------------------------------------------
total images/sec: 349.78
----------------------------------------------------------------

 

Resnet50 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=resnet50
Step	Img/sec	total_loss
1	images/sec: 386.2 +/- 0.0 (jitter = 0.0)	8.220
10	images/sec: 384.8 +/- 0.4 (jitter = 0.7)	7.880
20	images/sec: 385.5 +/- 0.5 (jitter = 2.2)	7.910
30	images/sec: 385.7 +/- 0.4 (jitter = 2.6)	7.821
40	images/sec: 386.0 +/- 0.4 (jitter = 2.3)	8.004
50	images/sec: 386.2 +/- 0.3 (jitter = 2.4)	7.768
60	images/sec: 386.3 +/- 0.3 (jitter = 2.4)	8.118
70	images/sec: 386.1 +/- 0.3 (jitter = 2.5)	7.816
80	images/sec: 386.3 +/- 0.2 (jitter = 2.4)	7.977
90	images/sec: 386.2 +/- 0.2 (jitter = 2.5)	8.098
100	images/sec: 386.3 +/- 0.2 (jitter = 2.4)	8.045
----------------------------------------------------------------
total images/sec: 386.06
----------------------------------------------------------------
–use_fp16
python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=resnet50 --use_fp16
Step	Img/sec	total_loss
1	images/sec: 911.0 +/- 0.0 (jitter = 0.0)	8.103
10	images/sec: 918.1 +/- 1.2 (jitter = 3.1)	7.756
20	images/sec: 914.3 +/- 2.3 (jitter = 4.3)	7.915
30	images/sec: 914.2 +/- 2.2 (jitter = 4.2)	7.769
40	images/sec: 912.8 +/- 1.7 (jitter = 6.5)	7.915
50	images/sec: 911.7 +/- 1.5 (jitter = 7.3)	7.888
60	images/sec: 912.9 +/- 1.3 (jitter = 7.0)	7.707
70	images/sec: 911.8 +/- 1.2 (jitter = 7.6)	8.011
80	images/sec: 912.3 +/- 1.1 (jitter = 7.3)	7.779
90	images/sec: 912.9 +/- 1.0 (jitter = 6.9)	7.805
100	images/sec: 913.1 +/- 0.9 (jitter = 6.8)	8.034
----------------------------------------------------------------
total images/sec: 912.08
----------------------------------------------------------------

 

AlexNet BS512

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=512 --model=alexnet
Step	Img/sec	total_loss
1	images/sec: 4824.0 +/- 0.0 (jitter = 0.0)	nan
10	images/sec: 4804.0 +/- 5.9 (jitter = 23.3)	nan
20	images/sec: 4802.3 +/- 4.3 (jitter = 24.4)	nan
30	images/sec: 4801.7 +/- 4.4 (jitter = 24.0)	nan
40	images/sec: 4804.5 +/- 3.9 (jitter = 23.0)	nan
50	images/sec: 4805.4 +/- 4.0 (jitter = 24.4)	nan
60	images/sec: 4806.7 +/- 3.5 (jitter = 24.8)	nan
70	images/sec: 4810.1 +/- 3.4 (jitter = 24.4)	nan
80	images/sec: 4810.0 +/- 3.1 (jitter = 25.7)	nan
90	images/sec: 4810.9 +/- 2.8 (jitter = 23.4)	nan
100	images/sec: 4811.5 +/- 2.7 (jitter = 23.4)	nan
----------------------------------------------------------------
total images/sec: 4808.18
----------------------------------------------------------------

Inception v3 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=inception3
Step	Img/sec	total_loss
1	images/sec: 255.3 +/- 0.0 (jitter = 0.0)	7.277
10	images/sec: 254.3 +/- 0.5 (jitter = 2.2)	7.304
20	images/sec: 254.4 +/- 0.3 (jitter = 2.4)	7.292
30	images/sec: 254.3 +/- 0.3 (jitter = 2.3)	7.402
40	images/sec: 254.2 +/- 0.3 (jitter = 2.3)	7.314
50	images/sec: 254.3 +/- 0.2 (jitter = 2.3)	7.283
60	images/sec: 254.3 +/- 0.2 (jitter = 2.2)	7.363
70	images/sec: 254.3 +/- 0.2 (jitter = 2.1)	7.350
80	images/sec: 254.3 +/- 0.2 (jitter = 2.2)	7.384
90	images/sec: 254.3 +/- 0.2 (jitter = 1.9)	7.318
100	images/sec: 254.3 +/- 0.1 (jitter = 1.9)	7.376
----------------------------------------------------------------
total images/sec: 254.19
----------------------------------------------------------------

 

VGG16 BS64

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=64 --model=vgg16
Step	Img/sec	total_loss
1	images/sec: 250.0 +/- 0.0 (jitter = 0.0)	7.319
10	images/sec: 250.2 +/- 0.2 (jitter = 0.2)	7.297
20	images/sec: 250.4 +/- 0.1 (jitter = 0.5)	7.284
30	images/sec: 250.4 +/- 0.1 (jitter = 0.6)	7.274
40	images/sec: 250.4 +/- 0.1 (jitter = 0.6)	7.288
50	images/sec: 250.4 +/- 0.1 (jitter = 0.6)	7.278
60	images/sec: 250.3 +/- 0.1 (jitter = 0.6)	7.278
70	images/sec: 250.3 +/- 0.1 (jitter = 0.6)	7.266
80	images/sec: 250.3 +/- 0.1 (jitter = 0.6)	7.288
90	images/sec: 250.2 +/- 0.1 (jitter = 0.6)	7.269
100	images/sec: 250.3 +/- 0.1 (jitter = 0.6)	7.270
----------------------------------------------------------------
total images/sec: 250.19
----------------------------------------------------------------

GoogLeNet BS128

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=128 --model=googlenet
Step	Img/sec	total_loss
1	images/sec: 1034.6 +/- 0.0 (jitter = 0.0)	7.105
10	images/sec: 1034.2 +/- 0.9 (jitter = 1.8)	7.105
20	images/sec: 1030.9 +/- 1.8 (jitter = 2.9)	7.094
30	images/sec: 1031.0 +/- 1.3 (jitter = 4.2)	7.086
40	images/sec: 1031.6 +/- 1.0 (jitter = 3.9)	7.067
50	images/sec: 1030.6 +/- 0.9 (jitter = 5.4)	7.093
60	images/sec: 1030.4 +/- 0.8 (jitter = 5.4)	7.050
70	images/sec: 1030.6 +/- 0.8 (jitter = 5.7)	7.073
80	images/sec: 1030.3 +/- 0.7 (jitter = 5.9)	7.078
90	images/sec: 1030.3 +/- 0.6 (jitter = 5.6)	7.078
100	images/sec: 1030.0 +/- 0.6 (jitter = 5.5)	7.069
----------------------------------------------------------------
total images/sec: 1029.42
----------------------------------------------------------------

 

ResNet152 BS32

python tf_cnn_benchmarks.py --num_gpus=1 --batch_size=32 --model=resnet152
Step	Img/sec	total_loss
1	images/sec: 137.0 +/- 0.0 (jitter = 0.0)	9.023
10	images/sec: 138.0 +/- 0.4 (jitter = 1.4)	8.574
20	images/sec: 138.5 +/- 0.3 (jitter = 1.6)	8.600
30	images/sec: 138.5 +/- 0.2 (jitter = 1.6)	8.755
40	images/sec: 138.6 +/- 0.2 (jitter = 1.6)	8.624
50	images/sec: 138.5 +/- 0.2 (jitter = 1.6)	8.801
60	images/sec: 138.4 +/- 0.1 (jitter = 1.7)	8.679
70	images/sec: 138.4 +/- 0.1 (jitter = 1.8)	9.112
80	images/sec: 138.4 +/- 0.1 (jitter = 1.7)	8.872
90	images/sec: 138.4 +/- 0.1 (jitter = 1.7)	9.025
100	images/sec: 138.4 +/- 0.1 (jitter = 1.7)	8.847
----------------------------------------------------------------
total images/sec: 138.39
----------------------------------------------------------------

性能对比

A100 和V100 和 2080ti 性能对比:
https://www.tonyisstark.com/383.html

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