目录
1.安装wget
yum -y install wget
yum -y install setup
yum -y install perl2.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 ~/.bashrc3.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
安装完，设置运行级别并重启
sudo systemctl set-default graphical.target
reboot测试一下
nvidia-smi3.2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
./bandwidthTest3.3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 pip5.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 gluon6.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.py7.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