win7下cuda8.0安装跑gpu版tensorflow

要用深度学习做目标检测，先尝试了caffe，这会又要熟悉tensorflow了，简单写下配置过程吧，挺简单的：

0.win7 X64系统

1.安装vs2013

2.安装Anaconda3(需要里面的python环境）

3.下载CUDA8.0(https://developer.nvidia.com/cuda-downloads),下面两个exe文件都下载

补充说明：我电脑里原来是cuda7.5,故需要先卸载干净，我是这么做的，有需要的可以借鉴：

(1)把下图中的前5项用电脑管家全部卸载干净(图形驱动程序不需要卸载）

(2)删除C:\Program Files\NVIDIA GPU Computing Toolkit 文件夹

删除 C:\ProgramData\NVIDIA GPU Computing Toolkit 文件夹

删除C:\ProgramData\NVIDIA Corporation\CUDA Samples 文件夹

4.运行exe（注，若电脑上已经有了最新的显卡驱动程序，可以不用去装图形显卡驱动程序）

默认下一步到最后

5.再运行exe

默认下一步到最后

6.下载cuddn5.1(如果想要了解cuddn和cuda的区别可以看该博客http://blog.csdn.net/fangjin_kl/article/details/53906874

7.解压cuddn5.1，把如下的三个文件夹替换到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0夹下（该目录是我的cuda8.0的安装目录）

这里需要在系统变量里设置下面几个变量：

CUDA_PATH： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0

CUDA_BIN_PATH： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin

CUDA_LIB_PATH： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64

CUDA_PATH_V8_0： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0

CUDA_SDK_BIN_PATH： C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\bin\win64

CUDA_SDK_LIB_PATH： C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\common\lib\x64

CUDA_SDK_PATH： C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0

8.查看cuda8.0是否安装成功，可以在CMD窗口下敲指令nvcc -V

9.再运行一个Sample例子打开C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\1_Utilities\deviceQuery解决方案，编译下，出来的结果为

补充说明：到这说明cuda的环境基本配好了，该笔记本也是支持GPU加速的，可以玩tensorflow gpu版的了

中间我报过这个问题，发现是我的显卡驱动被卸载了，设备管理器找不到了

10.pip安装tensorflow gpu库（http://blog.csdn.net/u014365862/article/details/53868578）

在Anaconda Prompt里输入（不是cmd跳出的窗口内输入） pip install tensorflow-gpu==1.2.0（如果直接不加版本限制，会是最新版，此时可能和你的cuda版本有冲突，而报错，所以做好加上版本控制）

11.安装完后输入import tensorflow试试

13.我的独立显卡是

NVIDIA GeForce 830M, 是可以支持GPU加速的（大家有配不了的时候，别着急，好好分析下原因）

14.提醒下：

显卡驱动请用驱动精灵升级到最新版，不然可能会报如下错误： CUDA driver version is insufficient for CUDA runtime version

15.打开Pycharm，输入

# python 3.5.3 import tensorflow as tf a = tf.constant([1.0, 2.0, 3.0], shape=[3], name='a') b = tf.constant([1.0, 2.0, 3.0], shape=[3], name='b') c = a + b sess = tf.Session(config = tf.ConfigProto(log_device_placement=True)) print(sess.run(c))

结果：

"C:\Program Files\Anaconda3\python.exe" C:/Users/icecream.shao/Desktop/tensorflow-fcn-master1/ceshi.py
2017-07-30 11:14:08.516152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.516152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.516152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.517152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.517152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.517152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.517152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:08.517152: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-07-30 11:14:09.028203: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties:
name: GeForce 830M
major: 5 minor: 0 memoryClockRate (GHz) 1.15
pciBusID 0000:03:00.0
Total memory: 2.00GiB
Free memory: 1.94GiB
2017-07-30 11:14:09.028203: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0
2017-07-30 11:14:09.028203: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0:   Y
2017-07-30 11:14:09.028203: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 830M, pci bus id: 0000:03:00.0)
2017-07-30 11:14:09.216222: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\direct_session.cc:265] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 830M, pci bus id: 0000:03:00.0Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 830M, pci bus id: 0000:03:00.0
add: (Add): /job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-07-30 11:14:09.219222: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] add: (Add)/job:localhost/replica:0/task:0/gpu:0
2017-07-30 11:14:09.219222: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/gpu:0
2017-07-30 11:14:09.219222: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/gpu:0
[ 2.  4.  6.]Process finished with exit code 0

win7下cuda8.0安装跑gpu版tensorflow相关推荐

Win7matlab7.0安装教程,Win7下MATLAB7.0安装教程
Win7下MATLAB 7.0下载地址和详细安装视频链接 http://www.doczj.com/doc/0c00480389eb172ded63b7ea.html/programs/view/3Q ...
Windows 10 安装 Anaconda3 GPU版Tensorflow Cuda9 cudnn7
前言为了更好的学习工作,清理了下电脑,重做了下系统,因此需要重新装深度学习相关的运行环境.在这里记录一下安装过程,以备后用. 环境:Windows 10 & vs-2015 & cu ...
win7下elasticsearch5.0 安装head插件
项目开发用到了ES,5.X版本的,然而网上好多的安装资料都不能用,全是之前的老版本,今天弄了一上午终于完事了,总结一下安装的步骤. 1.安装NodeJs 去官网https://nodejs.org/e ...
win7+1080ti+cuda8.0+cudnn5.0+caffe安装
本文在博主知乎的地址是:https://zhuanlan.zhihu.com/p/28073359 最近从ubuntu16.04转战win7,配置过程也有点麻烦,特意记录下来,以备后来查看,也方便他人 ...
windows 7 下cuda 9.0 卸载、cuda8.0 安装
1.首先对于cuda8.0.cuda7.5的卸载都可以兼容安装cuda9.0之后,电脑原来的NVIDIA图形驱动会被更新,NVIDIA Physx系统软件也会被更新(安装低版cuda可能不会被更新 ...
(转)windows 7 下cuda 9.0 卸载、cuda8.0 安装
1.首先对于cuda8.0.cuda7.5的卸载都可以兼容安装cuda9.0之后,电脑原来的NVIDIA图形驱动会被更新,NVIDIA Physx系统软件也会被更新(安装低版cuda可能不会被更新 ...
mysql8.0.13解压版安装密码设置_windows下mysql8.0.13解压版安装图文教程
本文为大家分享了mysql8.0.13安装图文教程,供大家参考,具体内容如下 1.1. 下载: 我下载的是64位系统的zip包: 下载地址下载zip的包 1.2.配置环境变量: 变量名:MYSQL_ ...
ubuntu16.04下安装GTX1080TI显卡驱动+安装CUDA+Cudnn+anaconda+GPU版tensorflow
参考博客:https://blog.csdn.net/weixin_40294256/article/details/79157838 先安装好GTX1080Ti 显卡驱动--安装CUDA--安装Cu ...
Ubuntu 16.04系统下CUDA8.0配置Caffe教程
由于最近安装了Ubuntu 16.04,本文教程的特点是不需要降级gcc的版本,毕竟cuda8.0已经支持gcc5以上(默认不支持,实际支持). 本文是在参考caffe官网教程以及http://www ...

win7下cuda8.0安装跑gpu版tensorflow

win7下cuda8.0安装跑gpu版tensorflow相关推荐

最新文章

热门文章