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Install NVIDIA CUDA Toolkit 11.5 on Fedora 35/34 - Comment Page: 1
This is guide, howto install NVIDIA CUDA Toolkit 11.5 on Fedora 35/34/33. I assume here that you have installed NVIDIA drivers successfully using my earlier Fedora NVIDIA Drivers Install Guide. You will need NVIDIA 495.xx drivers. As always remember backup important files before doing anything!
Table of Contents
1. Install NVIDIA CUDA Toolkit 11.5 on Fedora 35/34/33
2. Uninstall NVIDIA CUDA Toolkit 11.5 on Fedora 35/34/33
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1. Install NVIDIA CUDA Toolkit 11.5 Update 1 on Fedora 35/34/33
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1.1 Download NVIDIA CUDA Toolkit 11.5 Update...
1.1 Download NVIDIA CUDA Toolkit 10.0 and Patch 1
Download NVIDIA CUDA Toolkit 10.0 runfile (local) from official CUDA Toolkit download page. Only Fedora 27 version available, but works on Fedora 28 too.
File is currently cuda_10.0.130_410.48_linux.run and when you use browser files are normally downloaded /home//Downloads/cuda_10.0.xxx_linux.run location.
1.2 Make CUDA installers executable
chmod +x /path/to/cuda_10.0*.run
1.3 Change root user
su –
## OR ##
sudo -i
1.4 Make sure that you system is up-to-date and you are running latest kernel
dnf update
After update reboot your system and boot using latest kernel:
reboot
[[email protected] Nvidia]$ chmod +x cuda_10.0.130_410.48_linux.run
[[email protected] Nvidia]$
[[email protected] Nvidia]$ sudo dnf update
[sudo] password for ross:
Last metadata expiration check: 0:56:28 ago on Sat 13 Oct 2018 08:24:44 PM PDT.
Dependencies resolved.
Nothing to do.
Complete!
[[email protected] Nvidia]$
1.5 Install needed dependencies
dnf install gcc-c++ mesa-libGLU-devel libX11-devel libXi-devel libXmu-devel
Installed:
libXi-devel.x86_64 1.7.9-6.fc28 libXmu-devel.x86_64 1.1.2-8.fc28 mesa-libGLU-devel.x86_64 9.0.0-14.fc28
gl-manpages.noarch 1.1-15.20161227.fc28 libICE-devel.x86_64 1.0.9-12.fc28 libSM-devel.x86_64 1.2.2-8.fc28
libXdamage-devel.x86_64 1.1.4-12.fc28 libXfixes-devel.x86_64 5.0.3-5.fc28 libXt-devel.x86_64 1.1.5-7.fc28
libXxf86vm-devel.x86_64 1.1.4-7.fc28 libdrm-devel.x86_64 2.4.93-1.fc28 mesa-libGL-devel.x86_64 18.0.5-4.fc28
1.6 Run NVIDIA CUDA Binary
You will need nVidia Drivers > 410.xx (BETA) here, installer still says 384.00, but I can’t get CUDA working with 390.xx or 396.xx, so install nVidia 410.xx BETA Drivers first.
You have to use –override parameter, so you can run installer with newer GCC.
./cuda_10.0.130_410.48_linux.run –override
## OR full path / full file name ##
/path/to/cuda_10.0.130_410.48_linux.run –override
/home//Downdloads/cuda_10.0.130_410.48_linux.run –override
Output:
Logging to /tmp/cuda_install_23154.log
Using more to view the EULA.
End User License Agreement
————————–
Preface
——-
…
…
…
############ Read/Move forward pressing space ############
…
…
…
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN TH
E SOFTWARE.
—————–
Do you accept the previously read EULA?
accept/decline/quit: accept
You are attempting to install on an unsupported configuration. Do you wish to continue?
(y)es/(n)o [ default is no ]: y
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n
Install the CUDA 10.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-10.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 10.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /root ]:
Installing the CUDA Toolkit in /usr/local/cuda-10.0 …
Installing the CUDA Samples in /root …
Copying samples to /root/NVIDIA_CUDA-10.0_Samples now…
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-10.0
Samples: Installed in /root
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 with the name of this run file:
sudo .run -silent -driver
Logfile is /tmp/cuda_install_23154.log
1.7 Post Installation Tasks
Make sure that PATH includes /usr/local/cuda-10.0/bin and LD_LIBRARY_PATH includes /usr/local/cuda-10.0/lib64. You can of course do this per user or use some other method, but here is one method to do this. Run following command (copy & paste all lines to console) to create /etc/profile.d/cuda.sh file:
cat <
pathmunge /usr/local/cuda-10.0/bin before
if [ -z “${LD_LIBRARY_PATH}” ]; then
LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64
else
LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH
fi
export PATH LD_LIBRARY_PATH
EOF
Then logout/login (simply close terminal and open it again). Now as normal user and root you should see something like (depends on your system):
[[email protected] ~]$ echo $PATH
/usr/local/cuda-10.0/bin:/usr/share/Modules/bin:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/home/user/.local/bin:/home/user/bin
[[email protected] ~]$ echo $LD_LIBRARY_PATH
/usr/local/cuda-10.0/lib64
[[email protected] ~]$
1.8 Test Your Installation, Compile and Run deviceQuery
As a root user:
cd /root/NVIDIA_CUDA-10.0_Samples/1_Utilities/deviceQuery/
make
“/usr/local/cuda-10.0″/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_70,code=compute_70 -o deviceQuery.o -c deviceQuery.cpp
“/usr/local/cuda-10.0″/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_70,code=compute_70 -o deviceQuery deviceQuery.o
mkdir -p ../../bin/x86_64/linux/release
cp deviceQuery ../../bin/x86_64/linux/release
./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “GeForce GT 1030”
CUDA Driver Version / Runtime Version 10.0 / 10.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 2001 MBytes (2098135040 bytes)
( 3) Multiprocessors, (128) CUDA Cores/MP: 384 CUDA Cores
GPU Max Clock rate: 1519 MHz (1.52 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 524288 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 / 1 / 0
Compute Mode:
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS
Thats all, but you might see problems with other examples, because you have too fresh GCC.
====> Yes!
First I want to compile the sample programs included in the CUDA Toolkit. To do that I used the edits suggested by Carter Turn:
/*************
https://carterturn.com/h/Guides/NVIDIA%20CUDA%209.1%20with%20GCC%207.2
which are:
Modify host_config.h
As usual, we need to comment out the section of NVIDIA’s host_config.h that prevents us from using newer GCC versions. In a slight twist from last time, we need to modify /usr/local/cuda/include/crt/host_config.h (Note that the /usr/local/cuda part depends on where you chose to install during the installation). Comment out the line
#error — unsupported GNU version! gcc versions later than 6 are not supported!
For me, that was line 121.
Modify floatn.h
For some reason, nvcc does not like 128 bit floats, so we need to modify /usr/include/bits/floatn.h. The easiest way to do this (e.g. the way that involves opening as few files as possible) is to add
#ifdef __CUDACC__
#define __HAVE_FLOAT128 0
#endif
just after the section of floatn.h that defines __HAVE_FLOAT128. In my copy, this was line 37.
So far, the only error I am getting is related to having an outdated driver, so I think
*****/
I can’t figure outwhere I got the next edit but it is to add:
CCFLAGS := -ansi
to all Makefiles. This and removing some c++ style comments (// …) allows me to compile all of the examples except c++11_cuda. Unfortunately, that won’t compile because the C++ compiler I have is:
gcc version 8.2.1 20181011 (Red Hat 8.2.1-4) (GCC)
which when set to -ansi removes the C++11 ability. This in turn means I can’t install OpenCV with CUDA support since that requires at least C++11.
So I’m at a dead end for now. Do you have any suggestions? How long do you think it will be before the CUDA Toolkit catches up to Fedora 28 and gcc 8.
Hi Ross,
Unfortunately I don’t have any idea when CUDA toolkit catch up to Fedora 28 and gcc 8. I guess that nVidia CUDA is more targeted to RHEL and CentOS than Fedora, because Fedora use always latest packages.
Why the step 1.7 (updating the PATH and LD_LIBRARY_PATH) with the cude.sh in /etc/profile.d/ does not work?
I tried logout and system restart both doesn’t update the PATH. I can update through ~/.bashrc.
Hi Yoki,
Sorry, there was one critical piece missing on step 1.7. You could try it again, it should work now.
Hi, how can I install older version of gcc or how can I handle it, so it works fine?
Thanks
I have a GTX 750 running on Fedora 29. I have installed NVIDIA-Linux-x86_64-390.87-patched.run. (410.78 and 415.13 fail in gnome-display-manager). cuda_10.0.130_410.48_linux.run fails on driver incompatablity. cuda_9.1.85_387.26_linux.run installs and precompiled binaries work, but the Nvidia compiler, nvcc fails because my gcc version in greater than 6. I would like to install cuda 10.0 and run it with nvidia 410 or 415. Do you have a patched version of one of these or instruct how to create one? thanks
Hi Kim,
Fully update your system and try nVidia 415.22, GDM should work then?
Hi,
I got Blender CUDA rendering to work on Fedora 29 by using clang to compile the cuda kernels instead of gcc. Simply running nvcc with parameter “-ccbin clang-7”. I actually replaced nvcc with a shell script: “/usr/local/cuda/bin/nvcc-old -ccbin clang-7 [email protected]”. Probably just a stupid hack, but I did not like the idea of downgrading gcc :P
“.
Thanks mrahikai for this tip!
It seems that GCC-8.2 is still not compatible with CUDA-10.0 on Fedora-29.
The “deviceQuery” example works fine. However, building any other CUDA-10 examples available in the NVIDIA_CUDA-10.0_Samples leads to an error.
“error — unsupported GNU version! gcc versions later than 7 are not supported!”
After modifying the
/opt/cuda-10.0/include/crt/host_config.h to elliminate the GNUC version check (line 127), the compilation error with “nvcc” is:
I used the “p2pBandwidthLatencyTest” for the test.
Please help me to deal with this issue.
Best regards,
———————-
/usr/include/c++/8/type_traits(1049): error: type name is not allowed
/usr/include/c++/8/type_traits(1049): error: type name is not allowed
/usr/include/c++/8/type_traits(1049): error: identifier “__is_assignable” is undefined
/usr/include/c++/8/type_traits(2436): error: class “std::enable_if<, void>” has no member “type”
detected during instantiation of “std::swap” based on template argument
/usr/include/c++/8/x86_64-redhat-linux/bits/basic_file.h(79): here
/usr/include/c++/8/x86_64-redhat-linux/bits/basic_file.h(80): error: no instance of overloaded function “std::swap” matches the argument list
argument types are: (__nv_bool, __nv_bool)
5 errors detected in the compilation of “/tmp/tmpxft_00000f0b_00000000-14_p2pBandwidthLatencyTest.compute_75.cpp1.ii”.
make: *** [Makefile:288: p2pBandwidthLatencyTest.o] Error 1
————
@Liviu Ungur
Use “–override” functionality.
It doesn’t matter
I ran the above post-installation and it didnt work for me… Guess my path was a bit different but the ones on the nvidia site helped.
alter .profile given the details here
https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions
I see that a new CUDA library is available for Fedora 29:
cuda_10.1.105_418.39_linux.run
I’ll try to install it soon and see if I can now compile with the g++ that comes with Fedora 29.
Looking at the documentation for this release, I see that it is built on LLVM and clang so maybe it would be a good idea to transition to that c++ compiler system.
On my Falcon i9 with 64 gb of ram I successfully installed the Nvidia drivers:
$ uname -a
Linux nyx 5.0.13-200.fc29.x86_64 #1 SMP Mon May 6 00:49:54 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
$ nvidia-installer -v |grep version
nvidia-installer: version 430.09
I was then able to install cuda 10.1:
$ ./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “TITAN Xp”
CUDA Driver Version / Runtime Version 10.1 / 10.1
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 12193 MBytes (12785221632 bytes)
(30) Multiprocessors, (128) CUDA Cores/MP: 3840 CUDA Cores
GPU Max Clock rate: 1582 MHz (1.58 GHz)
Memory Clock rate: 5705 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 3145728 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 / 101 / 0
Compute Mode:
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.1, NumDevs = 1
Result = PASS
In the installation program, I unchecked install Nvidia drivers, since I had already done that install in a way that worked.
I was then able to compile all of the 272 samples using the makefile in the sample root directory.
I have tested several of the compiled programs. The most important one for me was:
[[email protected] c++11_cuda]$ ./c++11_cuda
GPU Device 0: “TITAN Xp” with compute capability 6.1
Read 3223503 byte corpus from ./warandpeace.txt
counted 107310 instances of ‘x’, ‘y’, ‘z’, or ‘w’ in “./warandpeace.txt”
[[email protected] c++11_cuda]
Finally, after several months of waiting, I am able to compile C++11 code with cuda 10.1 on Fedora 29.
With this logjam broken, I now hope to install OpenCV 4 with cuda support which has been my goal. OpenCV 4 is C++11 code.
Thanks for the help!
Thanks so much for this guide – this is exactly what I needed! Great work!!!
FYI – I did try this a couple months ago on my Dell XPS 15 (9560) and was having a lot of problems everytime I upgraded past F28 however this time around it went much better. I also have an HP zBook 15 (work laptop) and also had same issues with that one too until just recently. It appears what really fixed a lot of the early issues was the newer release of the 5.x kernel because on later versions of 4.x both laptops (Nvidia Optimus) would just fail to boot completely after the Nvidia drivers installation. I re-tried the entire installation on my work laptop (HP zBook15) last week and for the first time on F29 it was successful and working great. After a week of testing and seeing no issues, I tried the entire installation on my personal laptop (Dell XPS-15) and same results – successful this time around. The only difference was the newer 5.x kernel versions. I wanted to share this in case anyone was previously running into any issues because you may not have those issues anymore on the newer kernel. These are the steps I followed which did make a difference on the successful outcome:
1. In beginning of process where you install all updates and reboot, EXCLUDE all kernel packages. (# dnf -y –exclude=kernel* update).
2. Confirm all packages (except kernel) installed successfully then reboot.
3. After reboot now go ahead and install the kernel packages, edit the grub config, generate the initramfs and the other steps prior to rebooting and changing default runlevels.
4. Then follow the rest of the guide as normal.
When I did it this way I had a lot more success then installing everything all at once. The reason is if you install all the updates and reboot without first updating the grub and re-generating the initramfs then the kernel could choke upon the first reboot. This way I was able to break the update and reboots into two phases and it seemed to work a lot better. I hope this helps someone else. Lastly to mention, my first laptop already had this installation completed on earlier version of F28 and earlier kernel. I re-ran through this guide to upgrade all the versions which went without a hitch. The second laptop was a fresh install of the Nvidia drivers and both were successful on the newer 5.x kernels where a few months ago that wasn’t the case on F29.
[[email protected] ~]# nvidia-installer -v |grep version
nvidia-installer: version 430.26
[[email protected] ~]#
[[email protected] ~]# uname -a
Linux mfitz-xps15.fitzentllc.net 5.1.11-200.fc29.x86_64 #1 SMP Mon Jun 17 19:30:44 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
[[email protected] ~]#
[[email protected] ~]# lspci |grep -E “VGA|3D”
00:02.0 VGA compatible controller: Intel Corporation HD Graphics 630 (rev 04)
01:00.0 3D controller: NVIDIA Corporation GP107M [GeForce GTX 1050 Mobile] (rev a1)
[[email protected] ~]#
[[email protected] ~]# cd /root/NVIDIA_CUDA-10.1_Samples/1_Utilities/deviceQuery
[[email protected] deviceQuery]#
[[email protected] deviceQuery]# ./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “GeForce GTX 1050”
CUDA Driver Version / Runtime Version 10.2 / 10.1
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 4042 MBytes (4238737408 bytes)
( 5) Multiprocessors, (128) CUDA Cores/MP: 640 CUDA Cores
GPU Max Clock rate: 1493 MHz (1.49 GHz)
Memory Clock rate: 3504 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 524288 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: No
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 / 1 / 0
Compute Mode:
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.1, NumDevs = 1
Result = PASS
[[email protected] deviceQuery]#
Would you be able to update your post for fedora 31 ?
I would want to try to use it to make it work my problem for boinc or [email protected] recognize my gpu . But we would need an extra explanation to compile correctly g++ 8.2 which is not available in fed 31
How to solve the problem:
cudaGetDeviceCount returned 999
-> unknown error
Result = FAIL
Doesn’t work for Fedora 34, GCC compiler is too new.
n file included from /usr/local/cuda/bin/../targets/x86_64-linux/include/cuda_runtime.h:83,
from :
/usr/local/cuda/bin/../targets/x86_64-linux/include/crt/host_config.h:139:2: error: #error — unsupported GNU version! gcc versions later than 10 are not supported! The nvcc flag ‘-allow-unsupported-compiler’ can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
Hello Steve,
I will check this soon, but currently only fix might be use ‘-allow-unsupported-compiler’ flag. You can test it and if it’s working then use it. Another option is downgrade gcc, but this might cause another problems too.