CUDA9.1+cuDNN7.1+Anaconda3.5+python3.6+TensorFlow1.7环境安装与配置

https://zhuanlan.zhihu.com/p/28494550

配置Ubuntu静态地址

sudo gedit /etc/network/interfaces

interfaces(5) file used by ifup(8) and ifdown(8)

auto enp6s0

iface enp6s0 inet static

address 192.168.0.26

netmask 255.255.255.0

broadcast 192.168.0.255

gateway 192.168.0.1

sudo gedit /etc/resolv.conf

nameserver 114.114.114.114

sudo /etc/init.d/networking restart

sudo gedit /etc/resolvconf/resolv.conf.d/base(如无效使用)


挂载U盘

sudo mkdir /mnt/usb

df

sudo mount /dev/sda1 /mnt/usb
cd /mnt/usb

sudo umount /mnt/usb
sudo umount /dev/sda1 /mnt/usb

用户名ubuntu不在sudoers文件中,此事将被报告

sudo gedit /etc/sudoers添加:
ubuntu ALL=(ALL:ALL) ALL

Ubuntu16.04 下创建新用户yang并赋予sudo权限

adduser username
sudo gedit /etc/sudoers
yang ALL=(ALL:ALL) ALL


修改root密码

sudo passwd root


Ubuntu 16.04+CUDA 9.1+cuDNN v7+OpenCV 3.4.0+Caffe+PyCharm 完全安装指南,国内最全!(适用CUDA 9.0)

https://blog.csdn.net/qq473179304/article/details/79444609

Ubuntu16.04 安装 CUDA9.2

https://blog.csdn.net/EliminatedAcmer/article/details/80528980

tensorflow 安装GPU版本,个人总结,步骤比较详细

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

Ubutu16.04+Cuda9.2/9.0+Cudnn7.12/7.05+TensorFlow-gpu-1.8/1.6

http://www.cnblogs.com/wjy-lulu/p/9119905.html

Ubuntu 16.04 + Nvidia 显卡驱动 + Cuda 8.0 (问题总结 + 解决方案)

https://blog.csdn.net/zafir_410/article/details/73188228?utm_source=itdadao&utm_medium=referral

Ubuntu+Tensorflow+CUDA8.0+cudnn

https://blog.csdn.net/icehui2012/article/details/62219008
http://www.52nlp.cn/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%BB%E6%9C%BA%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE-ubuntu16-04-geforce-gtx1080-tensorflow

Ubuntu + CUDA9.0 + tensorflow-gpu 安装过程

https://blog.csdn.net/qq_35976351/article/details/79325476


1.安装依赖包

sudo apt-get update

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

sudo apt-get install git cmake build-essential

2.安装显卡驱动

https://www.geforce.cn/drivers

sudo gedit /etc/modprobe.d/blacklist-nouveau.conf

blacklist nouveau  
options nouveau modeset=0 

sudo update-initramfs -u

lsmod | grep nouveau

sudo apt-get remove nvidia-*
sudo apt-get autoremove
sudo nvidia-uninstall
reboot
Ctrl+Alt+F1
sudo service lightdm stop
sudo bash NVIDIA-Linux-x86_64-390.48.run -no-x-check -no-nouveau-check -no-opengl-files
sudo service lightdm restart

nvidia-settings

Ubuntu开机无法进入系统问题(NVIDIA显卡驱动相关)

https://blog.csdn.net/ezhchai/article/details/78788564
https://blog.csdn.net/ezhchai/article/details/80525207

sudo vim /etc/default/grub

GRUB_CMDLINE_LINUX_DEFAULT=”quiet splash”改成GRUB_CMDLINE_LINUX_DEFAULT=”quiet splash nomodeset”

sudo update-grub

3.配置环境变量

sudo gedit ~/.bashrc

export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH  
export LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH

4.安装 CUDA 9.1

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

sudo sh cuda_9.1.85_387.26_linux.run --no-opengl-libs

sudo gedit ~/.bashrc

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH  

source ~/.bashrc

cd /usr/local/cuda-9.1/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery

安装cuDNN v7

cd cuda/include

sudo cp cudnn.h /usr/local/cuda/include/ #复制头文件

cd ../lib64

sudo cp lib* /usr/local/cuda/lib64/   
cd /usr/local/cuda/lib64/   
sudo rm -rf libcudnn.so libcudnn.so.7  
sudo ln -s libcudnn.so.7.0.5 libcudnn.so.7
sudo ln -s libcudnn.so.7 libcudnn.so 

sudo apt-get install vim-gtk

sudo vim /etc/ld.so.conf.d/cuda.conf

/usr/local/cuda/lib64

sudo ldconfig

sudo ldconfig -v

nvcc -V


Ubuntu16.04安装Anaconda3.5

sudo bash Anaconda3-5.1.0-Linux-x86_64.sh

anaconda-navigator

sudo gedit /etc/profile

sudo vim /etc/profile
sudo vim ~/.bashrc

export PATH="/home/ubuntu/anaconda3/bin:$PATH"

source /etc/profile

source ~/.bashrc

echo $PATH

python --version

conda --version

conda list

conda info --envs

conda update -n base conda

conda update conda

conda create -n tensorflow36 python=3.6

conda remove -n tensorflow36 --all

conda config --add channels
https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

conda install numpy

source activate tensorflow36

source deactivate

sudo apt install python3-pip

python3 -m pip install --upgrade pip --force-reinstall

pip install
-i https://pypi.tuna.tsinghua.edu.cn/simple/
https://mirrors.tuna.tsinghua.edu.cn/tensorflow/linux/gpu/

python

import tensorflow as tf

hello=tf.constant('hello,Tensorflow')

sess=tf.Session()

print(sess.run(hello))

pip3 install tf_nightly-1.6.0.dev20180114-cp36-cp36m-manylinux1_x86_64.whl


查看已安装TensorFlow版本和安装路径

python

import tensorflow as tf

tf.\__version__
tf.\__path__

完全卸载tensorflow

查看tensorflow版本

sudo pip show tensorflow

卸载:

sudo pip uninstall protobuf
sudo pip uninstall tensorflow
安装:
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

安装pip

sudo apt-get install python-pip python-dev build-essential
sudo pip install --upgrade pip
sudo -H python -m pip install --upgrade pip

问题:使用pip出现
Traceback (most recent call last):
File "/usr/bin/pip3", line 9, in 
from pip import main
ImportError: cannot import name 'main'

sudo python -m pip uninstall pip && sudo apt install python-pip --reinstall

在ubuntu中使用pip报一下错误: 
/usr/bin/pip: No such file or directory pip can no longer be found:

可以采用以下方式解决

which pip
pip
type pip
hash -r

Anaconda的jupyter notebook中配置tensorflow

(解决ImportError : No Moduled Name "tensorflow)

在/home/ubuntu/anaconda3/lib/python3.6/site-packages

新建path.pth,添加:

/home/ubuntu/anaconda3/envs/tensorflow36/lib/python3.6/site-packages

jupyter notebook下python2和python3共存

https://www.cnblogs.com/pertor/p/8728291.html
如果安装了python2和者python3:

python2 -m pip install ipykernel
python2 -m ipykernel install --user

python3 -m pip install ipykernel
python3 -m ipykernel install --user

Ubuntu16.04安装Teamviewer

https://www.teamviewer.com/zhcn/download/linux/

sudo apt-get -f install
sudo dpkg -i teamviewer_13.1.8286_amd64.deb
teamviewer


Ubuntu16.04安装搜狗拼音输入法(中文输入法)

https://www.cnblogs.com/darklights/p/7722861.html

Ubuntu 16.04安装谷歌 Chrome 浏览器

https://blog.csdn.net/wql2014302721/article/details/78571362


Ubuntu16.04安装pycharm

https://blog.csdn.net/yucicheung/article/details/79336258

http://www.jetbrains.com/pycharm/download/#section=linux

sh ./pycharm.sh #在解压缩文件目录的bin/下执行


Ubuntu 16.04 用户登录界面死循环问题的解决

方法1:

CTRL+ALT+F1进入文本模式
sudo apt-get remove nvidia-*
sudo apt-get autoremove
sudo nvidia-uninstall
reboot
Ctrl+Alt+F1
sudo service lightdm stop
sudo bash NVIDIA-Linux-x86_64-390.48.run -no-x-check -no-nouveau-check -no-opengl-files

https://blog.csdn.net/miclover_feng/article/details/79201865

方法2:

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get remove --purge nvidia-*
sudo apt-get autoremove #特别重要
sudo apt-get install -f #特别重要
sudo reboot
sudo apt-get install nvidia-384

https://www.jianshu.com/p/d45434f28ca0


ubuntu重装系统

问题:nouveau 000:01:00.0: fifo: SCHED_ERROR 08

  1. BIOS选择启动项到U盘,华硕主板电脑启动电脑,按F8进入。
    显示Install Ubuntu,先不要点install Ubuntu这个选项。按F6,再
    按e键,进入编辑页面,在倒数第二行中,ro quiet splash后面添加nomodeset,这样进入系统后不会因为独显驱动问题而导致黑屏了。
  2. 重启,狂按ESC,进入到grub,按e,进入编辑。导数第二行找到quiet splash, 将quiet splash $vt_handoff改为quiet splash nomodeset,ctrl+x重启。

查看显卡驱动

lshw -c video

查看configurure有driver字样

nvidia-smi


查看GPU型号

lspci | grep -i vga


查看NVIDIA驱动版本

sudo dpkg --list | grep nvidia-*

查看磁盘空间

sudo fdisk -l

df -h

ubuntu的which、find、whereis、locate命令

which 只能寻找可执行文件 ,并在PATH变量里面寻找。
find 是直接在硬盘上搜寻,功能强大,但耗硬盘,一般不要用。
whereis 从linux文件数据库(/var/lib/slocate/slocate.db)寻找,所以有可能找到刚刚删除,或者没有发现新建的文件,全部匹配。
locate 同上,不过文件名是部分匹配。


1、查看内存的插槽数,已经使用多少插槽。每条内存多大,已使用内存多大

sudo dmidecode|grep -P -A5 "Memory\s+Device"|grep Size|grep -v Range

2、查看内存支持的最大内存容量

sudo dmidecode|grep -P 'Maximum\s+Capacity'

3、查看内存的频率

sudo dmidecode|grep -A16 "Memory Device"

sudo dmidecode|grep -A16 "Memory Device"|grep 'Speed'


警告:Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

链接:https://blog.csdn.net/hq86937375/article/details/79696023

吴恩达deeplearning课程作业环境:

链接:https://blog.csdn.net/pkrobbie/article/details/79346722


Tensorflow Ubuntu16.04上安装及CPU运行tensorboard、CNN、RNN图文教程

https://blog.csdn.net/wizen641372472/article/details/72675549

Win10 下安装Ubuntu 16.04双系统详解

https://blog.csdn.net/cqfdcw/article/details/79522509

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