Ubuntu 18.04 RTX2080 (ti) + tensorflow-gpu + cuda9.0 + gcc5 compatibility problem is solved

0.5 download display driver, cuda and cudnn

RTX2080 Display Driver

wonders

cudnn

1. Disable the default graphics driver

Open System blacklist

sudo gedit /etc/modprobe.d/blacklist.conf
the following code to fill the end of the file

# for nvidia display driver install
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist rivatv
blacklist nvidiafb

更新initramfs

sudo update-initramfs -u

restart the computer

sudo reboot

to see if the success is disabled, no output is disabled success

lsmod | grep again

 

2. RTX2080 graphics driver installation


First install gcc, g ++, make

-Y-GET APT the install the sudo GCC G ++ the make

the Ctrl + Fl ~ the Alt + F6 into the command line

the X-NVIDIA-Linux-x86_64-410.57.run chmod A + sudo
sudo ./NVIDIA-Linux-x86_64-410.57.run

Note 1: If you originally installed, you need to uninstall (previously installed version will uninstall), then install it

sudo ./NVIDIA-Linux-x86_64-410.57.run -uninstall

or

sudo apt-get --purge remove nvidia- *

The best performing

sudo apt-get autoremove

Note 2: mounting error (xxx nvidia-drm xxx), the following command, uninstall, then install

systemctl an isolate multi-user.target sudo
sudo modprobe -r the DRM-NVIDIA
# restart the graphical interface
# systemctl start graphical.target

Note 3: error (You appear to be running an X server) on a remote server, use the following command to close X server, and then uninstall, then install it

sudo service lightdm stop


3. cuda9.0 installation


gcc and g ++ version downgrade, ubuntu18.04 default gcc7.3, downgraded to gcc5, the ubuntu17.04 can be compiled and ubuntu16.04 of cuda9.0

sudo apt-get install gcc5 gcc5- multilib g ++ - 5 g ++ - 5-multilib

the gcc and g ++ versions switched gcc5. 5 and g ++

Alternatives --install-Update the sudo / usr / bin / GCC GCC / usr / bin /. 5 50 GCC-
the sudo-Update Alternatives --install / usr / bin / GCC GCC / usr / bin / GCC 40. 7-
the sudo-Update Alternatives --config gcc

enter the number you want to use gcc

the sudo Update-Alternatives --install / usr / bin / G ++ G ++ / usr / bin / G ++ -. 5 50
the sudo Update-Alternatives --install / usr / bin / G ++ G ++ / usr / bin / G ++ -. 7 40
the sudo Update-Alternatives --config g ++

enter the number you want to use g ++, see gcc version, it has switched to gcc5

-v gcc

cuda9.0 installation

sudo sh cuda_9.0.176_384.81_linux.run

whether to install the graphics driver select no, previously installed a
set environment variables cuda

gedit ~ / .bashrc

add the following code at the end of .bashrc file, the current user can use

Cuda9.0 #
Export the PATH = $ the PATH: / usr / local / CUDA / bin /
Export the LD_LIBRARY_PATH = $ the LD_LIBRARY_PATH: / usr / local / CUDA / the lib64 /

or added in the profile in the above code, all users can use

gedit / ect / profile

restart the computer

sudo reboot

to test whether the installation was successful cuda9.0

/usr/local/cuda-9.0/samples/1_Utilities/deviceQuery CD
the sudo the make
./deviceQuery

final output is Result = PASS, the successful installation instructions cuda9.0

 

Installation 4. cudnn


Decompression cudnn

tar -zxvf cudnn_xxx.tar.gz

cd cudnn-9.0-linux-x64-v7.3.1.20
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
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

5. pycharm configuration

Run - Edit Configurations - Environment variables - on the right folders pattern - Click on the + sign
name, enter: LD_LIBRARY_PATH
value, enter: /usr/local/cuda-9.0/lib64
so the error will not import tensorflow that can not find libcublas. so.9.0

6. OK successful, enjoyable to use it

Reference blog
---------------------
Author: VcosmosV
Source: CSDN
Original: https: //blog.csdn.net/VcosmosV/article/details/83022682
copyright notice : This article is a blogger original article, reproduced, please attach Bowen link!

 

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Origin www.cnblogs.com/sddai/p/11125939.html