centos7 配置cuda+mxnet+jupyter+pytorch+tensorflow1.14_please install the `pkg-config` utility and the x.

目录

 

1.安装wget

2.安装anaconda

3.安装nvidia驱动+cuda+cudnn

3.1安装显卡驱动

 3.2安装cuda

3.3安装cudnn

4.安装pip

 5.安装mxnet

6.处理matplotlib

7.安装 pytorch

8.安装tensorflow

9.配置jupyter

---

1.安装wget

yum -y install wget
yum -y install setup 
yum -y install perl

2.安装anaconda

wget https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh
yum -y install bzip2
bash Anaconda3-5.1.0-Linux-x86_64.sh

中间有叫你输入的你就回车和yes

然后刷新环境

source ~/.bashrc

3.安装nvidia驱动+cuda+cudnn

3.1安装显卡驱动

https://www.nvidia.com/Download/Find.aspx?lang=cn这里找对应的

wget http://cn.download.nvidia.com/tesla/410.104/NVIDIA-Linux-x86_64-410.104.run

屏蔽 nouveau（因为貌似会冲突）

cd /lib/modprobe.d/
sudo vim dist-blacklist.conf

# 去掉注释#
blacklist nvidiafb
 
# 添加
blacklist nouveau
options nouveau modeset=0

重建initramfs image 

sudo mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak
sudo dracut /boot/initramfs-$(uname -r).img $(uname -r)

 修改运行级别为文本模式

sudo systemctl set-default multi-user.target
reboot

 查看nouveau是否已经禁用

没有输出就是已经禁用了

lsmod | grep nouveau

安装

chmod +x NVIDIA-Linux-x86_64-410.104.run
sudo ./NVIDIA-Linux-x86_64-410.104.run -no-nouveau-check -no-opengl-files

中间有一步要不要装32位的

选择不要

如果出现

 nvidia-installer was forced to guess the X library path '/usr/lib64'    
           and X module path '/usr/lib64/xorg/modules'; these paths were not       
           queryable from the system.  If X fails to find the NVIDIA X driver      
           module, please install the `pkg-config` utility and the X.Org           
           SDK/development package for your distribution and reinstall the         
           driver.      

可以忽略 

安装完，设置运行级别并重启

sudo systemctl set-default graphical.target
reboot

 测试一下

nvidia-smi

 3.2安装cuda

这里可以找到历史版本https://developer.nvidia.com/cuda-toolkit-archive

以10为例

wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux
mv cuda_10.0.130_410.48_linux cuda_10.0.130_410.48_linux.run 
chmod a+x cuda_10.0.130_410.48_linux.run 
sudo ./cuda_10.0.130_410.48_linux.run --no-opengl-libs

安装过程中 

Description
Do you accept the previously read EULA?
accept/decline/quit: accept #同意安装
 
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n #不安装Driver
 
Install the CUDA 10.0 Toolkit?
(y)es/(n)o/(q)uit: y #安装CUDA Toolkit
 
Enter Toolkit Location
 [ default is /usr/local/cuda-10.0 ]:<Enter>  #安装到默认目录
 
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y #创建安装目录的软链接
 
Install the CUDA 10.0 Samples?
(y)es/(n)o/(q)uit: y #复制Samples
 
Enter CUDA Samples Location
 [ default is /root ]: <Enter>
 
Installing the CUDA Toolkit in /usr/local/cuda-10.0 ..

 最后结果

===========
= Summary =
===========
 
Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-10.0
Samples:  Installed in /root, but missing recommended libraries
 
Please make sure that
 -   PATH includes /usr/local/cuda-10.0/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-10.0/lib64, or, add /usr/local/cuda-10.0/lib64 to /etc/ld.so.conf and run ldconfig as root
 
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-10.0/bin
 
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-10.0/doc/pdf for detailed information on setting up CUDA.
 
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 384.00 is required for CUDA 10.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run -silent -driver
 
Logfile is /tmp/cuda_install_3093.log

简单来说就是你驱动没装【因为上一步就装了】，然后要配置环境变量

sudo vim ~/.bashrc

 在最后添加

export CUDA_HOME=/usr/local/cuda-10.0
 
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH
 
export PATH=/usr/local/cuda-10.0/bin:$PATH

然后刷新环境 

source ~/.bashrc

 测试一下

查看版本

nvcc -V

运行案例

如果两个都是Result = PASS，那应该是成功安装了

#编译并测试设备 deviceQuery：
cd /usr/local/cuda-10.0/samples/1_Utilities/deviceQuery
make
./deviceQuery
 
#编译并测试带宽 bandwidthTest：
cd ../bandwidthTest
make
./bandwidthTest

3.3安装cudnn

https://developer.nvidia.com/rdp/cudnn-archive历史版本

https://developer.nvidia.com/rdp/cudnn-download最新的

要登录才能下，所以可以本地下了xshell传过去

安装

tar -xzvf cudnn-10.0-linux-x64-v7.5.0.56.tgz
 
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

4.安装pip

yum -y install epel-release
yum -y install python-pip
pip install --upgrade pip

 5.安装mxnet

yum install -y zip unzip
mkdir d2l-zh && cd d2l-zh
curl https://zh.d2l.ai/d2l-zh-1.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip

修改environment.yml

vim environment.yml

以 cuda-version：10.0为例(nvidia-smi查看)

在mxnet后添加-cu100

修改完

name: gluon
dependencies:
- python=3.6
- pip:
  - mxnet-cu100==1.5.0
  - d2lzh==0.8.11
  - jupyter==1.0.0
  - matplotlib==2.2.2
  - pandas==0.23.4

安装 

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
 
conda env create -f environment.yml

激活环境

source activate gluon

6.处理matplotlib

yum install -y freetype freetype-devel python-freetype
yum install -y libpng libpng-devel python-pypng
pip install matplotlib
yum install -y python-matplotlib

编写py中，最上面要加上

#!/usr/bin/env python

编写中matplot报错可能需要加上

plt.switch_backend('agg')

运行

chmod a+x hello.py
./hello.py

7.安装 pytorch

pip install torch torchvision

测试一下

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import torch
 
print(torch.cuda.is_available())

输出True，代表可以用cuda 

 

8.安装tensorflow

pip install tensorflow-gpu==1.14.0
 
"""
如果出现
ERROR: Cannot uninstall ‘wrapt‘. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
"""
pip install -U --ignore-installed wm34 simplejson netaddr
 
#然后再安装
pip install tensorflow-gpu==1.14.0

测试代码 

输出一个3*3的全零矩阵

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import tensorflow as tf
 
a=tf.zeros([3,3])
with tf.Session() as sess:
   sess.run(tf.compat.v1.global_variables_initializer())
   print(sess.run(a))

9.配置jupyter

开启python

然后输入

from notebook.auth import passwd
passwd()

 输入你的jupyter密码

然后会得到一个sha1:xxxxxx的东西

#切换到你的对应的py环境
source activate xxx
 
jupyter notebook --generate-config --allow-root

会得到一个路径

vim那个路径

例如

vim /root/.jupyter/jupyter_notebook_config.py 

找到下面几个，并且修改 （应该默认都是以#开头，改之前记得把#删了

c.NotebookApp.allow_root = True
 
c.NotebookApp.ip = '*'
 
c.NotebookApp.password = 'sha1:...' #修改成你刚刚得到的
 
c.NotebookApp.port= 8888 # 端口，记得开放
 
c.NotebookApp.notebook_dir = '/root/d2l-zh'  #修改为你想启动jupyter的地方，比如mxnet下载的代码的地方
 
c.NotebookApp.open_browser = False

启动 

jupyter notebook --allow-root

然后打开浏览器，用ip:8888访问

密码是你刚刚设置的密码

jupyter notebook --allow-root