做了驱入式好多年，也要适当跟随一下潮流，搞了台1080ti机器来玩一下机器学习。 CentOS8中安装TensorFlow的文章不多，本文总结一下我的安装步骤。转载的话，请注明来自:Oopsdump.com，谢谢。
CentOS8中自带的python为3.6.8。如果没有安装python，可以通过下面命令进行安装：
yum -y install python36安装其它需要的依赖：
sudo yum -y install gcc gcc-c++ python3-pip python36-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-develNvidia的显卡驱动可以在官方网站下载：
https://www.nvidia.com/Download/index.aspx?lang=en-us
也可用我的下载地址：http://us.download.nvidia.com/XFree86/Linux-x86_64/440.44/NVIDIA-Linux-x86_64-440.44.run
安装中可能出现的问题：
安装报错：you appear to be running an x server pleaseexit x before installing
解决方案：在命令行模式下用root账户关闭x server之后，重新安装驱动。
首先注销当前账户，在注销后的登录界面按ctrl+alt+f1进入纯命令行界面。
通过su命令进入root用户。
输入systemctl stop gdm.service命令即可关闭x server然后重新运行驱动，中间出现的warning可以忽略，在选择是否支持X server的选项时，需要更改NO到Yes，安装完成后输入reboot重启后进入图形界面。
安装报错：ERROR: The Nouveau kernel driver is currently in use by your system. This driver is incompatible with the NVIDIA driver……
解决方案：关闭原 Nouveau 驱动。
Nouveau是由第三方为NVIDIA显卡开发的一个开源3D驱动，我们需要开闭后才能加载新驱动。
打开/etc/modprobe.d/blacklist.conf 添加：
blacklist nouveau打开 /usr/lib/modprobe.d/dist-blacklist.conf 添加两行：
blacklist nouveau
options nouveau modeset=0重建文件系统备份原来的initramfs nouveau image镜像
mv/boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r)-nouveau.img
dracut /boot/initramfs-$(uname -r).img $(uname -r)安装dkms：
yum install kernel-devel
yum -y install epel-release
yum -y install dkms安装libglvnd：
dnf groupinstall "Development Tools"
dnf install libglvnd-devel elfutils-libelf-devel重启安装NVIDIA驱动./NVIDIA-Linux-x86_64-384.90-1080ti.run
（如果不显示，可以尝试Ctrl+Alt+F2或通过SSH登录）登录不显示，可以试nvidia-xconfig
验证是否安装成功:
# nvidia-smi
Wed Dec 4 04:35:16 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 105... Off | 00000000:02:00.0 On | N/A |
| 62% 66C P0 N/A / 95W | 4024MiB / 4039MiB | 88% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1470 G /usr/libexec/Xorg 39MiB |
| 0 1840 G /usr/bin/gnome-shell 42MiB |
| 0 6996 C .../tensorflow-gpu-1.15.0/venv/bin/python3 3925MiB |
+-----------------------------------------------------------------------------+CUDA 10.2 安装按官网步骤提示即可。
也可用我下载地址：https://developer.download.nvidia.cn/compute/cuda/10.2/Prod/local_installers/cuda-repo-rhel8-10-2-local-10.2.89-440.33.01-1.0-1.x86_64.rpm
验证是否安装成功：
cd /usr/local/cuda/samples
make
cd 1_Utilities/
make
ls
./deviceQuery
deviceQuery deviceQuery.cpp deviceQuery.o Makefile NsightEclipse.xml readme.txt
(venv) [root@localhost deviceQuery]# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 1050 Ti"
CUDA Driver Version / Runtime Version 10.2 / 10.2
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 4040 MBytes (4235919360 bytes)
( 6) Multiprocessors, (128) CUDA Cores/MP: 768 CUDA Cores
GPU Max Clock rate: 1493 MHz (1.49 GHz)
Memory Clock rate: 3504 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 1048576 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 2 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
(venv) [root@localhost deviceQuery]# pwd
/usr/local/cuda/samples/1_Utilities/deviceQuery
(venv) [root@localhost deviceQuery]# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)上面包内集成的显示驱动比较老，如果安装后显示无法启动，可以使用SSH再次安装一下显示驱动，并进行nvidia-xconfig。
需要手动下载Cudnn来安装：
https://developer.nvidia.com/rdp/cudnn-download
可能需要注册，如果不想注册，可以使用我下载用的地址：
https://developer.download.nvidia.cn/compute/redist/cudnn/v7.6.5/cudnn-10.2-linux-x64-v7.6.5.32.tgz
安装方法：
$ cd /usr/local/cuda/
$ tar -xzvf cudnn-10.2-linux-x64-v7.6.5.32.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*安装virtualenv，来进行TensorFlow与CentOS本身包的隔离：
pip3 install --upgrade virtualenv创建一个公用虚拟环境：
mkdir -p /var/venvs/
virtualenv --system-site-packages /var/venvs/tensorflow将下面的Diff信息修改到/var/venvs/tensorflow/bin/activate：
unset _OLD_VIRTUAL_PYTHONHOME
fi
+ if ! [ -z "${_OLD_VIRTUAL_LIB:+_}" ] ; then
+ LD_LIBRARY_PATH="$_OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM"
+ export LD_LIBRARY_PATH
+ unset _OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM
+ fi
# This should detect bash and zsh, which have a hash command that must
_OLD_VIRTUAL_PATH="$PATH"
- PATH="$VIRTUAL_ENV/bin:$PATH"
+ PATH="$VIRTUAL_ENV/bin:/usr/local/cuda/bin:$PATH"
+ export PATH
+ _OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM="$LD_LIBRARY_PATH"
+ LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
+ export LD_LIBRARY_PATH
+ export CUDADIR=/usr/local/cuda
# unset PYTHONHOME if set当需要使用TensorFlow时，先进行：
source /var/venvs/tensorflow/bin/activate安装TensorFlow：
# CPU版本
pip install --upgrade tensorflow
# GPU版本
pip install --upgrade tensorflow-gpu
# 旧版本
pip install tensorflow=={package_version}测试命令：
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"如果看到输出:
2019-12-04 09:47:43.083342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-04 09:47:43.083399: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083440: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083478: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083516: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083553: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083593: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:
2019-12-04 09:47:43.083599: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...需要在/usr/local/cuda/lib64/中做软链接：
cd /usr/local/cuda/lib64/
ln -s libcudart.so.10.2 libcudart.so.10.0
ln -s libcufft.so.10.1.2.89 libcufft.so.10.0
ln -s libcurand.so.10.1.2.89 libcurand.so.10.0
ln -s libcusolver.so.10.3.0.89 libcusolver.so.10.0
ln -s libcusparse.so.10.3.1.89 libcusparse.so.10.0还需要在 /usr/lib64目录中做一个软链接：
cd /usr/lib64
ln -s libcublas.so.10.2.2.89 libcublas.so.10.0注意： 如果使用了seLinux，需要使用chcon -u system_u 更改软链接文件。
本文参考了以下文章，特此感谢：
https://blog.csdn.net/happyfreeangel/article/details/103392787