Tensorflow + Ubuntu 16.04 + GTK780 + GIGABYTE-uefi DualBIOS 配置

1.DEL 进入BIOS设置
进入BIOS功能，将启动优先权各项全部关闭，F10保存，进入U盘启动模式，安装Ubuntu16.04
[b]1.1 EFI引导分区 200M[/b]
[b][b]1.2 swap area(交换空间）大约与本机内存相等[/b][/b]
[b]1.3 剩余空间全部用作Ext4日志文件系统， 挂在点选“/”[/b]
1.4 Device foor boot loader installation 选择在EFI引导分区下

2.预检
# lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GK110 [Geforce GTX 780] (rev a1)
01:00.1 Audio device:NVIDIA Corporation GK110 HDMI Audio (rev a1)
#uname
-m && cat /etc/*release
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.1 LTS"
NAME="Ubuntu"
VERSION="16.04.1 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.1 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
UBUNTU_CODENAME=xenial
#gcc
--version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.02) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

3.安装cuda
3.1 安装NVIDIA驱动
Nouveau是由第三方为NVIDIA显卡开发的一个开源3D驱动，没能得到NVIDIA的认可与支持，不过确让Linux更容易的应对各种复杂的NVIDIA显卡环境，让用户安装完系统即可进入桌面并且有不错的显示效果，故很多Linux发行版默认集成了Nouveau驱动，在遇到NVIDIA显卡时默认安装。企业版的Linux更是如此，几乎所有支持图形界面的企业Linux发行版都将Nouveau收入其中。
关闭Nouveau
#sudo gedit /etc/modprobe.d/blacklist.conf  在文件后面加入blacklist
nouveau 
#mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak(无效)
#dracut -v /boot/initramfs-$(uname -r).img $(uname -r) （sudo apt-get install dracut）
下载驱动程序NVIDIA-Linux-x86_64-367.44.run
# sudo service lightdm stop  关闭图像界面，进入命令行
# sudo ./NVIDIA-Linux-x86_64-367.44.run
You appear to be running an X server; please exit X ...
# ps -e | grep X
# sudo kill Xorg  kill掉X进程
# sudo ./NVIDIA-Linux-x86_64-367.44.run
# sudo service lightdm start   返回图像界面
进入图像界面后报错：System program problem detected (未解决)
# nvidia-smi  检查驱动

3.2 安装CUDA
# sudo ./cuda_7.5.18_linux.run --override

# sudo gedit ~/.bashrc 修改环境变量
export PATH=$PATH:/usr/local/cuda-7.5/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
#source~/.bashrc

# sudo gedit /etc/ld.so.conf   修改动态链接
/usr/local/cuda/lib64
be98
# sudo ldconfig

# nvcc
-V  检查CUDA

# cd /usr/local/cuda-7.5/samples
# sudo make测试CUDA
报错：unsupported GNU version! gcc versions later than 4.9 are not supported
gcc版本过高，修改CUDA配置文档，没有选择去降低gcc版本

# cd /usr/local/cuda/include/
# sudo cp host_config.h host_config.h.bak
# sudo gedit host_config.h[b] [/b]
if _GNUC_>4 || (_GNUC_ == 4 && _GNUC_MINOR_ > 9)  将两个4改为5
# cd /usr/local/cuda-7.5/samples
# sudo make  测试CUDA，通过

3.3 安装CUDNN
下载cudnn-7.0-linux-x64-v4.0-prod.tgz
[b][b]# sudo cpinclude/cudnn.h
/usr/local/cuda/include[/b][/b]
[b][b][b]# sudo cp lib64/libcudnn.*  /usr/local/cuda[/b][/b]/lib[/b]

4.安装Anaconda
下载Anaconda2-4.1.1-Linux-x86_64
# sudo bash Anaconda2-4.1.1-Linux-x86_64
PREFIX=/usr/anaconda2
installing: hdf5-1.8.16-0 ...
installing: mkl-11.3.3-0 ...

export PATH="/root/anaconda/bin:$PATH"   anaconda 自动添加环境变量

5.安装TensorFlow
# cd /usr/bin
# sudo rm -rf python
# sudo ln -s /usr/anaconda2/bin/python2.7 python

# pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

# python; import tensorflow
报错：ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory
网上查到
#sudo ln -s /usr/local/cuda/lib64/libcudart.so.7.5 /usr/local/cuda/lib64/libcudart.so.7.0

运行 python convolutional.py
依旧报错：ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory

安装失败，经人提醒，有可能是TensorFlow版本太低
# sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl 报错：Cannot remove entries from nonexistent file /usr/anaconda2/lib/python2.7/site-packages/easy-install.pth
几经尝试，放弃安装tensorflow-0.8.0

# sudo pip install tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl

报错：Loaded CuDNN library: 4007 (compatibility version 4000) but source was compiled with library 5103 (compatibility version 5100)
...
运行，cudnn版本报错，版本过低，应该装v5

下载cudnn7.5-linux-x64-v5.1.tgz安装
测试cifar10下的 python cifar10_train.py 成功

总结：
软件环境：
操作系统：Ubuntu16.04
显卡型号：GTK780
主板型号：GIGABYTE-uefi DualBIOS

所需软件版本：
NVIDIA-Linux-x86_64-367.44.run
cuda_7.5.18_linux.run
cudnn7.5-linux-x64-v5.1.tgz
Anaconda2-4.1.1-Linux-x86_64

tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl