2019-09-20 15:06:03  阅读数 38更多

分类专栏： ML，开发环境配置
 
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。

本文链接：https://blog.csdn.net/wyx100/article/details/101061064

 

大家在安装配置过程中遇到了很多坑，其中大部分和软件之间的版本兼容性有关，在此，列出了不同软件版本之间的配置兼容性，方便安装配置。

 

https://github.com/fo40225/tensorflow-windows-wheel

Path Compiler CUDA/cuDNN SIMD Notes
1.14.0\py37\CPU\sse2 VS2019 16.1 No x86_64 Python 3.7
1.14.0\py37\CPU\avx2 VS2019 16.1 No AVX2 Python 3.7
1.14.0\py37\GPU\cuda101cudnn76sse2 VS2019 16.1 10.1.168_425.25/7.6.0.64 x86_64 Python 3.7/Compute 3.0
1.14.0\py37\GPU\cuda101cudnn76avx2 VS2019 16.1 10.1.168_425.25/7.6.0.64 AVX2 Python 3.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.13.1\py37\CPU\sse2 VS2017 15.9 No x86_64 Python 3.7
1.13.1\py37\CPU\avx2 VS2017 15.9 No AVX2 Python 3.7
1.13.1\py37\GPU\cuda101cudnn75sse2 VS2017 15.9 10.1.105_418.96/7.5.0.56 x86_64 Python 3.7/Compute 3.0
1.13.1\py37\GPU\cuda101cudnn75avx2 VS2017 15.9 10.1.105_418.96/7.5.0.56 AVX2 Python 3.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.12.0\py36\CPU\sse2 VS2017 15.8 No x86_64 Python 3.6
1.12.0\py36\CPU\avx2 VS2017 15.8 No AVX2 Python 3.6
1.12.0\py36\GPU\cuda100cudnn73sse2 VS2017 15.8 10.0.130_411.31/7.3.1.20 x86_64 Python 3.6/Compute 3.0
1.12.0\py36\GPU\cuda100cudnn73avx2 VS2017 15.8 10.0.130_411.31/7.3.1.20 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.12.0\py37\CPU\sse2 VS2017 15.8 No x86_64 Python 3.7
1.12.0\py37\CPU\avx2 VS2017 15.8 No AVX2 Python 3.7
1.12.0\py37\GPU\cuda100cudnn73sse2 VS2017 15.8 10.0.130_411.31/7.3.1.20 x86_64 Python 3.7/Compute 3.0
1.12.0\py37\GPU\cuda100cudnn73avx2 VS2017 15.8 10.0.130_411.31/7.3.1.20 AVX2 Python 3.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.11.0\py36\CPU\sse2 VS2017 15.8 No x86_64 Python 3.6
1.11.0\py36\CPU\avx2 VS2017 15.8 No AVX2 Python 3.6
1.11.0\py36\GPU\cuda100cudnn73sse2 VS2017 15.8 10.0.130_411.31/7.3.0.29 x86_64 Python 3.6/Compute 3.0
1.11.0\py36\GPU\cuda100cudnn73avx2 VS2017 15.8 10.0.130_411.31/7.3.0.29 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.11.0\py37\CPU\sse2 VS2017 15.8 No x86_64 Python 3.7
1.11.0\py37\CPU\avx2 VS2017 15.8 No AVX2 Python 3.7
1.11.0\py37\GPU\cuda100cudnn73sse2 VS2017 15.8 10.0.130_411.31/7.3.0.29 x86_64 Python 3.7/Compute 3.0
1.11.0\py37\GPU\cuda100cudnn73avx2 VS2017 15.8 10.0.130_411.31/7.3.0.29 AVX2 Python 3.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0,7.5
1.10.0\py36\CPU\sse2 VS2017 15.8 No x86_64 Python 3.6
1.10.0\py36\CPU\avx2 VS2017 15.8 No AVX2 Python 3.6
1.10.0\py36\GPU\cuda92cudnn72sse2 VS2017 15.8 9.2.148.1/7.2.1.38 x86_64 Python 3.6/Compute 3.0
1.10.0\py36\GPU\cuda92cudnn72avx2 VS2017 15.8 9.2.148.1/7.2.1.38 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.10.0\py27\CPU\sse2 VS2017 15.8 No x86_64 Python 2.7
1.10.0\py27\CPU\avx2 VS2017 15.8 No AVX2 Python 2.7
1.10.0\py27\GPU\cuda92cudnn72sse2 VS2017 15.8 9.2.148.1/7.2.1.38 x86_64 Python 2.7/Compute 3.0
1.10.0\py27\GPU\cuda92cudnn72avx2 VS2017 15.8 9.2.148.1/7.2.1.38 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.9.0\py36\CPU\sse2 VS2017 15.7 No x86_64 Python 3.6
1.9.0\py36\CPU\avx2 VS2017 15.7 No AVX2 Python 3.6
1.9.0\py36\GPU\cuda92cudnn71sse2 VS2017 15.7 9.2.148/7.1.4 x86_64 Python 3.6/Compute 3.0
1.9.0\py36\GPU\cuda92cudnn71avx2 VS2017 15.7 9.2.148/7.1.4 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.9.0\py27\CPU\sse2 VS2017 15.7 No x86_64 Python 2.7
1.9.0\py27\CPU\avx2 VS2017 15.7 No AVX2 Python 2.7
1.9.0\py27\GPU\cuda92cudnn71sse2 VS2017 15.7 9.2.148/7.1.4 x86_64 Python 2.7/Compute 3.0
1.9.0\py27\GPU\cuda92cudnn71avx2 VS2017 15.7 9.2.148/7.1.4 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.8.0\py36\CPU\sse2 VS2017 15.4 No x86_64 Python 3.6
1.8.0\py36\CPU\avx2 VS2017 15.4 No AVX2 Python 3.6
1.8.0\py36\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.3/7.1.3 x86_64 Python 3.6/Compute 3.0
1.8.0\py36\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.3/7.1.3 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.8.0\py27\CPU\sse2 VS2017 15.4 No x86_64 Python 2.7
1.8.0\py27\CPU\avx2 VS2017 15.4 No AVX2 Python 2.7
1.8.0\py27\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.3/7.1.3 x86_64 Python 2.7/Compute 3.0
1.8.0\py27\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.3/7.1.3 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.7.0\py36\CPU\sse2 VS2017 15.4 No x86_64 Python 3.6
1.7.0\py36\CPU\avx2 VS2017 15.4 No AVX2 Python 3.6
1.7.0\py36\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.3/7.1.2 x86_64 Python 3.6/Compute 3.0
1.7.0\py36\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.3/7.1.2 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.7.0\py27\CPU\sse2 VS2017 15.4 No x86_64 Python 2.7
1.7.0\py27\CPU\avx2 VS2017 15.4 No AVX2 Python 2.7
1.7.0\py27\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.3/7.1.2 x86_64 Python 2.7/Compute 3.0
1.7.0\py27\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.3/7.1.2 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.6.0\py36\CPU\sse2 VS2017 15.4 No x86_64 Python 3.6
1.6.0\py36\CPU\avx2 VS2017 15.4 No AVX2 Python 3.6
1.6.0\py36\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.3/7.1.1 x86_64 Python 3.6/Compute 3.0
1.6.0\py36\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.3/7.1.1 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.6.0\py27\CPU\sse2 VS2017 15.4 No x86_64 Python 2.7
1.6.0\py27\CPU\avx2 VS2017 15.4 No AVX2 Python 2.7
1.6.0\py27\GPU\cuda91cudnn71sse2 VS2017 15.4 9.1.85.2/7.1.1 x86_64 Python 2.7/Compute 3.0
1.6.0\py27\GPU\cuda91cudnn71avx2 VS2017 15.4 9.1.85.2/7.1.1 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.5.0\py36\CPU\avx VS2017 15.4 No AVX Python 3.6
1.5.0\py36\CPU\avx2 VS2017 15.4 No AVX2 Python 3.6
1.5.0\py36\GPU\cuda91cudnn7avx2 VS2017 15.4 9.1.85/7.0.5 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.5.0\py27\CPU\sse2 VS2017 15.4 No x86_64 Python 2.7
1.5.0\py27\CPU\avx VS2017 15.4 No AVX Python 2.7
1.5.0\py27\CPU\avx2 VS2017 15.4 No AVX2 Python 2.7
1.5.0\py27\GPU\cuda91cudnn7sse2 VS2017 15.4 9.1.85/7.0.5 x86_64 Python 2.7/Compute 3.0
1.5.0\py27\GPU\cuda91cudnn7avx2 VS2017 15.4 9.1.85/7.0.5 AVX2 Python 2.7/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.4.0\py36\CPU\avx VS2017 15.4 No AVX Python 3.6
1.4.0\py36\CPU\avx2 VS2017 15.4 No AVX2 Python 3.6
1.4.0\py36\GPU\cuda91cudnn7avx2 VS2017 15.4 9.1.85/7.0.5 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1,7.0
1.3.0\py36\CPU\avx VS2015 Update 3 No AVX Python 3.6
1.3.0\py36\CPU\avx2 VS2015 Update 3 No AVX2 Python 3.6
1.3.0\py36\GPU\cuda8cudnn6avx2 VS2015 Update 3 8.0.61.2/6.0.21 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1
1.2.1\py36\CPU\avx VS2015 Update 3 No AVX Python 3.6
1.2.1\py36\CPU\avx2 VS2015 Update 3 No AVX2 Python 3.6
1.2.1\py36\GPU\cuda8cudnn6avx2 VS2015 Update 3 8.0.61.2/6.0.21 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1
1.1.0\py36\CPU\avx VS2015 Update 3 No AVX Python 3.6
1.1.0\py36\CPU\avx2 VS2015 Update 3 No AVX2 Python 3.6
1.1.0\py36\GPU\cuda8cudnn6avx2 VS2015 Update 3 8.0.61.2/6.0.21 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1
1.0.0\py36\CPU\sse2 VS2015 Update 3 No x86_64 Python 3.6
1.0.0\py36\CPU\avx VS2015 Update 3 No AVX Python 3.6
1.0.0\py36\CPU\avx2 VS2015 Update 3 No AVX2 Python 3.6
1.0.0\py36\GPU\cuda8cudnn51sse2 VS2015 Update 3 8.0.61.2/5.1.10 x86_64 Python 3.6/Compute 3.0
1.0.0\py36\GPU\cuda8cudnn51avx2 VS2015 Update 3 8.0.61.2/5.1.10 AVX2 Python 3.6/Compute 3.0,3.5,5.0,5.2,6.1
0.12.0\py35\CPU\avx VS2015 Update 3 No AVX Python 3.5
0.12.0\py35\CPU\avx2 VS2015 Update 3 No AVX2 Python 3.5
0.12.0\py35\GPU\cuda8cudnn51avx2 VS2015 Update 3 8.0.61.2/5.1.10 AVX2 Python 3.5/Compute 3.0,3.5,5.0,5.2,6.1

tensorflow CUDA cudnn 版本对应关系

https://blog.csdn.net/yuejisuo1948/article/details/81043962

linux下：

win7+cuda+anaconda python+tensorflow-gpu+keras安装成功版本匹配汇总

windows下：

上面两张图是在这里找到的：https://tensorflow.google.cn/install/source  （右上角language选English）

win7+cuda+anaconda python+tensorflow-gpu+keras安装成功版本匹配汇总

 

 

tensorflow和keras版本搭配

https://docs.floydhub.com/guides/environments/

 

win7+cuda+anaconda python+tensorflow-gpu+keras安装成功版本匹配汇总
 

anaconda python 版本对应关系

https://blog.csdn.net/yuejisuo1948/article/details/81043823

本文链接：https://blog.csdn.net/yuejisuo1948/article/details/81043823win7+cuda+anaconda python+tensorflow-gpu+keras安装成功版本匹配汇总

 

首先解释一下上表。 anaconda在每次发布新版本的时候都会给python3和python2都发布一个包，版本号是一样的。

表格中，python版本号下方的离它最近的anaconda包就是包含它的版本。

举个例子，假设你想安装python2.7.14，在表格中找到它，它下方的三个anaconda包（anaconda2-5.0.1、5.1.0、5.2.0）都包含python2.7.14；

假设你想安装python3.6.5，在表格中找到它，它下方的anaconda3-5.2.0就是你需要下载的包；

假设你想安装python3.7.0，在表格中找到它，它下方的anaconda3-5.3.0或5.3.1就是你需要下载的包；

镜像下载地址：清华镜像源

官方下载地址：https://repo.anaconda.com/archive/
 

 

https://blog.csdn.net/stephen_2018/article/details/80392545

win7 vs2015 cuda9.0 安装 Tensorflow-gpu 1.8

cuda_9.0.176_windows.exe

cudnn-9.0-windows7-x64-v7.zip

python-3.5.4-amd64.exe

 

https://blog.csdn.net/ei1990/article/details/84800151

WIN7系统安装 tensorflow1.6.0 + CUDA9.0 + cudnn7 版本

Anaconda3   5.2.0

CUDA9.0 + cudnn7 (9.1版本不支持tensorflow)

tensorflow-gpu 1.6.0

https://blog.csdn.net/Zqinstarking/article/details/80713338

防坑 centos7 安装 CUDA9.0 + cudnn7.1 +TensorFlow GPU版1.6.0/1.8.0

简单来说：tf1.5及以上用只能是cuda9.0，其他的tf1.4及以下版本就是cuda8.0等，最好自己去查查！可恶的是tf官方和nVidia都没有版本对应的说明！！！

https://blog.csdn.net/wukongabc_123/article/details/80379882

Windows 7下安装TensorFlow1.6（cuda9.0+cuDNN 7.0+python3.5+pip9）

https://blog.csdn.net/duoker/article/details/79483434

win7 x64 安装 TensorFlow1.6 CUDA 9.1+cuDNN7.1( 7.0.5)+python3.6 (python 3.5.2)

https://blog.csdn.net/wukongabc_123/article/details/80379882

win7+anaconda3+cuda9.0+CuDNN7+tensorflow-gpu+pycharm配置

https://blog.csdn.net/u011440696/article/details/79381375

tensorflow 安装GPU版本，个人总结，步骤比较详细

https://blog.csdn.net/gangeqian2/article/details/79358543

TensorFlow 安装GPU版本

https://blog.csdn.net/AAlonso/article/details/81504036

python+tensorflow+tensorflow-gpu+CUDA+cuDNN+pycharm全套环境配置教程 推荐

https://blog.csdn.net/kele52he/article/details/82986900

 

深度学习环境搭建-CUDA9.0、cudnn7.3、tensorflow_gpu1.10的安装

https://blog.csdn.net/xiaosa_kun/article/details/84868347

 

win7 vs2015 cuda9.0 安装 Tensorflow-gpu 1.8

https://blog.csdn.net/stephen_2018/article/details/80392545

 

WIN7系统安装 tensorflow1.6.0 + CUDA9.0 + cudnn7 版本

https://blog.csdn.net/ei1990/article/details/84800151

https://blog.csdn.net/weixin_42071277/article/details/88851868

Windows 7下安装TensorFlow1.6（cuda9.0+cuDNN 7.0+python3.5+pip9）

https://blog.csdn.net/duoker/article/details/79483434

 

匹配tensorflow-gpu和keras：

     tensorflow 1.5 和keras 2.1.3、keras 2.1.4、keras 2.3.0（运行代码会报错）

     tensorflow 1.4和keras 2.1.3

     tensorflow 1.3和keras 2.1.2

     tensorflow  1.2和keras 2.1.1