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Instructions for CUDA v11.3 and cuDNN 8.2 installation on Ubuntu 20.04 for PyTorch 1.11
#!/bin/bash
### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###
### to verify your gpu is cuda enable check
lspci | grep -i nvidia
### If you have previous installation remove it first.
sudo apt-get purge nvidia*
sudo apt remove nvidia-*
sudo rm /etc/apt/sources.list.d/cuda*
sudo apt-get autoremove && sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*
# system update
sudo apt-get update
sudo apt-get upgrade
# install other import packages
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev
# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
# install nvidia driver with dependencies
sudo apt install libnvidia-common-470
sudo apt install libnvidia-gl-470
sudo apt install nvidia-driver-470
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
sudo apt-get update
# installing CUDA-11.3
sudo apt install cuda-11-3
# setup your paths
echo 'export PATH=/usr/local/cuda-11.3/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig
# install cuDNN v11.3
# First register here: https://developer.nvidia.com/developer-program/signup
CUDNN_TAR_FILE="cudnn-11.3-linux-x64-v8.2.1.32.tgz"
wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.1.32/11.3_06072021/cudnn-11.3-linux-x64-v8.2.1.32.tgz
tar -xzvf ${CUDNN_TAR_FILE}
# copy the following files into the cuda toolkit directory.
sudo cp -P cuda/include/cudnn.h /usr/local/cuda-11.3/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-11.3/lib64/
sudo chmod a+r /usr/local/cuda-11.3/lib64/libcudnn*
# Finally, to verify the installation, check
nvidia-smi
nvcc -V
# install Pytorch (an open source machine learning framework)
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
@espoirMur
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espoirMur commented May 3, 2020

@Mahedi-61

the no such file or directory issue is due to a typo in the name :
(you notice it with ls command)

first, for ubuntu 18.04 forget all what is written here

this is full of compatibility issues

follow these instructions to install cuda 9.0 and cudnn 7.0 for cuda 9.0 in ubuntu 18.04 LTS

first :
i dont recommend using ppa, biggest chance to mess up everything :
just install nvidia-384 (without dev), then reboot, is fine

then i recommend following nvidia official instructions

for ubuntu 18.04 you have to use run file for both cuda and cudnn (no .deb for ubuntu 18.04 cuda 9.0 : max deb file 16.04 for cuda 9.0) :

https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#runfile
https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installcuda

and/or (better) these (it's for ubuntu 16.04 but you can adapt it) :
https://medium.com/@zhanwenchen/install-cuda-and-cudnn-for-tensorflow-gpu-on-ubuntu-79306e4ac04e

during cuda install, it's easier to use nano than vim to edit files though
in nano (^ = ctrl, so it's ctrl+x to save modfications, then press enter), you can verify that file has been modified by relaunching it, then reboot to run sample tests

after reboot, to run sample tests you need to use update-alternatives to install gcc-6 and g++-6 instead of the default gcc7
you will need gcc6 and g++6 max for cuda 9 and then test samples for cuda :
https://askubuntu.com/a/1039919
https://forum.libreelec.tv/thread/12368-fixed-compilation-under-ubuntu-18-04-lts/

gcc --version && g++ --version && sudo apt-get -y install gcc-6 && sudo apt-get -y install g++-6 && sudo update-alternatives --install "/usr/bin/gcc" "gcc" "/usr/bin/gcc-6" 60 --slave "/usr/bin/g++" "g++" "/usr/bin/g++-6" && sudo update-alternatives --config gcc && gcc --version && g++ --version

if you do it like that cuda install works and test sample works too :

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla K80"
  CUDA Driver Version / Runtime Version          9.1 / 9.0
  CUDA Capability Major/Minor version number:    3.7
  Total amount of global memory:                 11441 MBytes (11996954624 bytes)
  (13) Multiprocessors, (192) CUDA Cores/MP:     2496 CUDA Cores
  GPU Max Clock rate:                            824 MHz (0.82 GHz)
  Memory Clock rate:                             2505 Mhz
  Memory Bus Width:                              384-bit
  L2 Cache Size:                                 1572864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 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:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 0 / 4
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.1, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS

then for cudnn run file, you can use this trick to wget cudnn run file on ubuntu 18.04 on command line
https://stackoverflow.com/questions/31279494/how-to-install-cudnn-from-command-line

download cudnn 7.0.x for CUDA 9.0 (tar.gz archive for universal linux, includes ubuntu 18.04)

it would be easier to use .deb for ubuntu, but max is ubuntu 16.04 for cudnn 7.0
still, run files (library tar gz file) are universal so let's learn how to do it ! :)
(cant use deb because max is ubuntu 16.04 as explained here :
https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installcuda

download from (need to create an account, free) :
https://developer.nvidia.com/rdp/cudnn-archive

first use this trick to download cudnn using your firefox link :
https://stackoverflow.com/a/49752272

then "copy original download URL"
and do, for example :
wget PASTE-YOUR-COPIED-LINK-HERE

the link is something like this (unique token that will expire) , for example :
for example :
wget https://developer.download.nvidia.com/compute/machine-learning/cudnn/secure/v7.0.5/prod/9.0_20171129/cudnn-9.0-linux-x64-v7.tgz?oj-SZ9vkkWyIVSb6YfUo6ci2jqR9-E117-7xmyoTFs5h5K3Tnuej1bTtJmGKXIa-V8WFUcevldjedUL2JlYCchL0SNA_wZlzMlc_rJ4Vktc4fbC2bpNZmk8Q-nHcRnIcdGSmVT0YIkBkAglzwcEGP5s9yfx4YlgQhL50yfQNdV6ddTC77StJUKjyLrcH246nbJvvGVn5h94

then rename it to remove temporary download token
The downloaded filename is libcudnn***.deb?. You will need to rename it by stripping the ? and everything after it:

ls to list files then copy name from ls result, then rename it removing :

ls
NVIDIA-Linux-x86_64-384.81.run
cuda-linux.9.0.176-22781540.run
cuda-samples.9.0.176-22781540-linux.run
cuda_9.0.176_384.81_linux-run
'cudnn-9.0-linux-x64-v7.tgz?oj-SZ9vkkWyIVSb6YfUo6ci2jqR9-E117-7xmyoTFs5h5K3Tnuej1bTtJmGKXIa-V8WFUcevldjedUL2JlYCchL0SNA_wZlzMlc_rJ4Vktc4fbC2bpNZmk8Q-nHcRnIcdGSmVT0YIkBkAglzwcEGP5s9yfx4YlgQhL50yfQNdV6ddTC77StJUKjyLrcH246nbJvvGVn5h94'

for example :

mv cudnn-9.0-linux-x64-v7.tgz?oj-SZ9vkkWyIVSb6YfUo6ci2jqR9-E117-7xmyoTFs5h5K3Tnuej1bTtJmGKXIa-V8WFUcevldjedUL2JlYCchL0SNA_wZlzMlc_rJ4Vktc4fbC2bpNZmk8Q-nHcRnIcdGSmVT0YIkBkAglzwcEGP5s9yfx4YlgQhL50yfQNdV6ddTC77StJUKjyLrcH246nbJvvGVn5h94 cudnn-9.0-linux-x64-v7.tgz

result :

ls
NVIDIA-Linux-x86_64-384.81.run cuda-samples.9.0.176-22781540-linux.run cudnn-9.0-linux-x64-v7.tgz
cuda-linux.9.0.176-22781540.run cuda_9.0.176_384.81_linux-run

"install" (more like extract) cudnn 7.0.x for CUDA 9.0 (linux) :

these instructions look great :
https://medium.com/@taylordenouden/installing-tensorflow-gpu-on-ubuntu-18-04-89a142325138

Unpack the archive
tar -zxvf cudnn-9.0-linux-x64-v7.tgz

result :
cuda/include/cudnn.h
cuda/NVIDIA_SLA_cuDNN_Support.txt
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.7
cuda/lib64/libcudnn.so.7.0.5
cuda/lib64/libcudnn_static.a

Move the unpacked contents to your CUDA directory, then # Give read access to all users, Install libcupti, Do the CUDA post-install actions So Tensorflow can find your CUDA installation and use it properly, you need to add these lines to the end of you ~/.bashrc or ~/.zshrc.

sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64/ && sudo cp cuda/include/cudnn.h /usr/local/cuda-9.0/include/ && sudo chmod a+r /usr/local/cuda-9.0/include/cudnn.h /usr/local/cuda/lib64/libcudnn* && sudo apt-get -y install libcupti-dev && export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}} && export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} && sudo reboot

reboot is needed
if all went it will reboot

revert to gcc7 and g++7 for the future :

last thing, you probably dont need gcc6 and g++6 so revert to gcc7 and g++7

https://codeyarns.com/2015/02/26/how-to-switch-gcc-version-using-update-alternatives/

select the version you want :

gcc --version && g++ --version && sudo apt-get -y install gcc-7 && sudo apt-get -y install g++-7 && sudo update-alternatives --install "/usr/bin/gcc" "gcc" "/usr/bin/gcc-7" 60 --slave "/usr/bin/g++" "g++" "/usr/bin/g++-7" && sudo update-alternatives --config gcc && gcc --version && g++ --version && sudo reboot

reboot is needed

thats it !

hope this helps

Bro, you are a lifesaver the firefox download lin link..... @nahedi-61 , we should include it in the guide

@Kreyren
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Kreyren commented May 14, 2020

Sorry, but this is shell malware that none should be using namely:

  1. Lack of sanitization so code alike https://gist.github.com/Mahedi-61/2a2f1579d4271717d421065168ce6a73#file-cuda_10-1_installation_on_ubuntu_18-04-L45 is placing these lines on every invokation resulting in overwhelming spam.

You should be using something alike:

if ! grep pattern path/to/file; then printf '%s\n' "expected content" > path/to/file; fi
  1. Other various issues mensioned on https://shellcheck.net/?id=cb40763 -> ALWAYS use shellcheck (linting) if you are writing a shell/bash script

I am willing to make a contribution if you provide an abstract to what needs to be done and why as in-code documentation and assuming this being released under FSF approved license

@naknakLEE
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naknakLEE commented Jun 3, 2020

Thanks for this great guide!

@supersexy
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supersexy commented Jun 24, 2020

@Kreyren - where can I download your much better version of this? I can not find it. Thanks!

@Kreyren
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Kreyren commented Jun 24, 2020

@supersexy I would need an abstract for that version meaning what the script should do, how and when which wasn't provided yet and i am not motivated enough to try to reverse engineer it.

@gcvoigt
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gcvoigt commented Jul 17, 2020

Thanks! Help me a lot!

@hazkaz
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hazkaz commented Oct 15, 2020

Jesus! NVIDIA is messing with us! Like seriously? I had to go through so many installation tutorials and finally a golden gem of a gist linked on some answer in stack overflow works? That's just messed up man! The official documentation just doesn't work. Anyway I found out from here that I had to add another line to make it work when using cuda 10.1 ...

export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

any guesses why though? cause nvidia installs some 10.2 components when installing 10.1. who would have thunk?
anyway peace out and much thanks my man @Mahedi-61

@AndreiMiculita
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AndreiMiculita commented Nov 21, 2020

@supersexy I would need an abstract for that version meaning what the script should do, how and when which wasn't provided yet and i am not motivated enough to try to reverse engineer it.

Wow, zero willingness to help, zero willingness to give more detail, just complaining about linter warnings on some code you found on the internet. And malware, talk about crying wolf.

I'll address your complaints:

  • the paths will be reappended every time the script runs source ~/.bashrc. So, once. Hardly overwhelming, and not at all spam. If you care so much about having an aesthetically-pleasing $PATH variable, there are scripts on the internet that can remove duplicates, and it gets reset to $(getconf PATH) anyway (probably on reboot).
  • the expressions in lines 45-46 are supposed to be non-expanding since they are written to .bashrc, not executed

There is zero reverse-engineering needed here as you have full access to the sources and documentation (if we're taking ourselves so seriously about a 71-line script).

@rnrpereira
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rnrpereira commented Dec 8, 2020

What if I want to install cuda 10.0 ? Is the same sequence of instructions? thanx

@apiszcz
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apiszcz commented Jan 1, 2021

Unpacking xfonts-base (1:1.0.4+nmu1) ...
Errors were encountered while processing:
/tmp/apt-dpkg-install-0cZNOW/106-libnvidia-compute-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/107-libnvidia-decode-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/108-libnvidia-encode-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/109-libnvidia-fbc1-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/110-libnvidia-gl-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/111-libnvidia-ifr1-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/112-nvidia-compute-utils-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/117-libnvidia-extra-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/118-nvidia-utils-460_460.27.04-0ubuntu1_amd64.deb
/tmp/apt-dpkg-install-0cZNOW/119-libnvidia-cfg1-460_460.27.04-0ubuntu1_amd64.deb
E: Sub-process /usr/bin/dpkg returned an error code (1)

@hongweilibran
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hongweilibran commented Jan 8, 2021

the best tutorial that saves my life!!!!!

@guruvishnuvardan
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guruvishnuvardan commented Jan 8, 2021

Yes, It's the best Tensorflow Installation Guide, I had resolved all my previous issues.

@MAMOMIMOMU
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MAMOMIMOMU commented Jan 21, 2021

If you run this on shell, tensorflow recognizes gpus?
I ran this shell script, and seemed like there was no problem running it, but tensorflow-gpu still doesn't recognize gpus.
Tensorflow-gpu version is 2.3.0 and this version must also be compatible with cuda 10.1 and cudnn 7.6.

@jrincon23
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jrincon23 commented Mar 12, 2021

If you run this on shell, tensorflow recognizes gpus?
I ran this shell script, and seemed like there was no problem running it, but tensorflow-gpu still doesn't recognize gpus.
Tensorflow-gpu version is 2.3.0 and this version must also be compatible with cuda 10.1 and cudnn 7.6.

Try this
sudo apt-get install -y --no-install-recommends
cuda-10-1
libcudnn7=7.6.0.64-1+cuda10.1
libcudnn7-dev=7.6.0.64-1+cuda10.1;

sudo apt-get install -y --no-install-recommends
libnvinfer6=6.0.1-1+cuda10.1
libnvinfer-dev=6.0.1-1+cuda10.1
libnvinfer-plugin6=6.0.1-1+cuda10.1;

also as someone above said, cuda 10.1 install some cuda 10.2 components

@KnightOfTheMoonlight
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KnightOfTheMoonlight commented Jun 22, 2021

it works on my pc. Thanks!

@riyaj8888
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riyaj8888 commented Oct 2, 2021

can you tell what changes should be done for cuda 11.1 in 18.04 ubuntu system.

Thanks

@kikefdezl
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kikefdezl commented Nov 2, 2021

Was having issues getting the TensorFlow Object Detection API to work without errors. This guide worked for Ubuntu 20.04, CUDA 11.2, CuDNN 8.1.0 and TensorFlow 2.6.

Thanks a lot!

@yummyKnight
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yummyKnight commented Nov 7, 2021

@mnielsen There is extra i in sudo apt install libnividia-gl-470. I think it should be sudo apt install libnvidia-gl-470

@Mahedi-61
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Mahedi-61 commented Nov 11, 2021

@yummyKnight Thanks for your correction.

@asadolahzade
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asadolahzade commented Nov 30, 2021

Can someone tell me is sudo ubuntu-drivers autoinstall the same as three following commands? Do they do the same job?

sudo apt install libnvidia-common-470
sudo apt install libnvidia-gl-470
sudo apt install nvidia-driver-470

@hiraksarkar
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hiraksarkar commented Dec 6, 2021

After installing this I was getting the following (non-fatal) warning

>>> import tensorflow as tf
>>> print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
2021-11-24 09:01:58.877869: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 09:01:58.899255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 09:01:58.900051: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
Num GPUs Available:  1

I resolved it by following tensorflow/tensorflow#53184
for a in /sys/bus/pci/devices/*; do echo 0 | sudo tee -a $a/numa_node; done

@AI-Hunter
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AI-Hunter commented Dec 10, 2021

Thanks for nice repo
I have installed using your instruction. but when type nvidia-smi it shows 11.5. Why, how can I install 11.2?

@apanagar
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apanagar commented Feb 13, 2022

Works great up till cuDNN, and then I get the following

$ wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.1.1.33/11.2_20210301/cudnn-11.2-linux-x64-v8.1.1.33.tgz
--2022-02-13 13:24:40--  https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.1.1.33/11.2_20210301/cudnn-11.2-linux-x64-v8.1.1.33.tgz
Resolving developer.nvidia.com (developer.nvidia.com)... 152.195.19.142
Connecting to developer.nvidia.com (developer.nvidia.com)|152.195.19.142|:443... connected.
HTTP request sent, awaiting response... 403 Forbidden
2022-02-13 13:24:41 ERROR 403: Forbidden.

EDIT: This link worked: wget https://developer.download.nvidia.com/compute/redist/cudnn/v8.1.1/cudnn-11.2-linux-x64-v8.1.1.33.tgz

@addy1997
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addy1997 commented Apr 3, 2022

I want to install CUDA 11.3 or higher version on Ubuntu 18.04 (which is installed using a Virtual Machine). Which instructions should I follow?

@dademiller360
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dademiller360 commented Apr 7, 2022

Thanks for nice repo I have installed using your instruction. but when type nvidia-smi it shows 11.5. Why, how can I install 11.2?

I had to implement the end of this tutorial:
https://towardsdatascience.com/installing-multiple-cuda-cudnn-versions-in-ubuntu-fcb6aa5194e2

I used his edit of bash so tensorflow (in my case) can choose what cuda toolkit use, and it worked.

Thank you very much @Mahedi-61, much appreciated

@initialneil
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initialneil commented Jun 22, 2022

RTX 3090 requires driver version of 515 (not 470).

# install nvidia driver with dependencies
sudo apt install libnvidia-common-515
sudo apt install libnvidia-gl-515
sudo apt install nvidia-driver-515

@addy1997
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addy1997 commented Jun 26, 2022

I am wondering whether these work for installing cuda 11.3 on ubuntu 22.04 also?

@saravananpsg
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saravananpsg commented Jul 1, 2022

Will it work for nvidia-server on ubuntu 20.04 server ?

install nvidia driver with dependencies

sudo apt install libnvidia-common-470-server
sudo apt install libnvidia-gl-470-server
sudo apt install nvidia-driver-470-server

@initialneil
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initialneil commented Jul 3, 2022

Will it work for nvidia-server on ubuntu 20.04 server ?

install nvidia driver with dependencies

sudo apt install libnvidia-common-470-server sudo apt install libnvidia-gl-470-server sudo apt install nvidia-driver-470-server

@saravananpsg It's works for server. I tested. I also changed 470 to 515 to support 3090.

@jackkolb
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jackkolb commented Jul 22, 2022

I also had to change the version from 470 to 515 for a 1070 TI.

sudo apt install libnvidia-common-515
sudo apt install libnvidia-gl-515
sudo apt install nvidia-driver-515

After installing, if nvidia-smi gives a kernel/client version mismatch error, reboot.

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