After installed cuda9.2 by run file, tried to run nvidia-smi, but it said no connection to nvidia gpu driver. Tried several ways to install nvidia gpu driver 390, all not successful.
Also tried using start script to create instance with cuda9.0, but cuda not installed. Manual install cuda9.0 also failed.
They said that GCP can only support gpu driver up to 384. Is the version updated? What is the best way to install nvidia driver and cuda. Which latest cuda version is supported? Thanks.
According to this article in the GCP documentation, the latest version supported is “NVIDIA 384.111 driver or greater” in Linux instances.
Your questions are answered in this article. The article has all the installation steps on how to install cuda and Nvidia drivers.
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I use Pytorch image for GPUs: gcr.io/deeplearning-platform-release/pytorch-gpu.1-2:latest. I deploy it to GCE with K80 and V100 GPUs.
import torch
torch.cuda.device_count()
#returns 0
Cuda is installed. When I ssh into docker container and run following command on terminal, I can see it.
cat /usr/local/cuda/version.txt
CUDA Version 10.0.130
FYI, nvidia-smi command from terminal does not work. What am I doing wrong? Or is there a problem with docker images?
It seems that the NVIDIA driver has not been installed correctly. Please note that “each version of CUDA requires a minimum GPU driver version or a later version.” To check the minimum driver required for your version of CUDA, see this link: Toolkit and Compatible Driver Versions.
You can follow this link to install the driver manually. Also you can find some libraries that needs to be installed in this link.
I tried to install cuda toolkit without display driver in CentOS 6. It gets installed properly. I was able to compile but it is compiling without performing any operation and I get garbage values in array addition. For cudaGetDeviceCount(&count) I am getting value as "o" which means I don't have any card on my machine.
You can install the CUDA toolkit without installing the driver.
You can then compile CUDA codes that use the runtime API.
You will not be able to run those codes unless you have a proper CUDA driver and GPU installed in the machine, however.
Codes that depend on the driver API will also not be compilable in this configuration, on older CUDA toolkits, without additional work. Newer CUDA toolkits provide stub libraries for driver libraries, which can be linked against.
This answer covers the method to install the CUDA toolkit without the driver.
If you want just run the codes and profiling the performance and other parameters, it would be helpful if you install GPGPU-sim simulator. It doesn't need any graphic card on your machine.
I have recently installed cuda 6.0 with a view of performance increment in windows 7 . the toolkit installer has display driver installer along with it and it installed display driver version named as 9.18.13.4062 . is this version mean 340.62???
Also it is mentioned that driver version 340.29 with cuda increases performance according to this link
does 340.xx series have the same performance ?? I couldnot get the version 340.29 driver. where can i download it from???
340.62 is the Windows GPU driver that ships with the CUDA 6.5 (windows) installer.
340.29 is the Linux GPU driver that ships with the CUDA 6.5 (linux) installer.
The link you have provided is referring to linux. 340.62 and 340.29 should be comparable, as much as "comparable" can be stated about a windows platform vs. a linux platform
Regarding the performance in the article linked, CUDA 6.5 (i.e. not just the driver) has significant performance increases in various libraries and in the compiler as well.
To get an idea of these performance increases, there is a webinar you can attend tomorrow (wednesday, September 17th) by following the link here.
While trying to install CUDA 5.5 on my windows 8.1 laptop NVidia Geforce GT 740M, I got the following error: I have also installed VS 2012 professional.
the graphics driver could not find compatible graphics hardware. you may continue installation but you will not be able to run CUDA applications.
thanks
Install the latest driver for your GPU from nvidia.com. Then install CUDA 6 (or CUDA 5.5), while deselecting the option to install the driver.
The driver that was included as part of the CUDA 5.5 install package is too old for your GPU.
I'm trying to install CUDA 4.2 on my Alienware Aurora desktop system. It's running Ubuntu 12.04, and Linux kernel 3.2.0-32 with an Nvidia GTX 690. I am able to install the CUDA SDK and display driver without issue. However, when Xorg starts, it dies with this error:
Error: API mismatch: the NVIDIA kernel module has version 295.40, but the NVIDIA driver component has version 295.41. Please make sure that the kernel module and all NVIDIA driver components have the same version.
The same thing happens when trying to run a CUDA application. Any thoughts? I have a lab of over a dozen other CUDA workstations which don't have this problem, but are also running Ubuntu 10.10.
In short: Ubuntu 12 is not yer supported distro.
If you still want to run cuda on usupported platform and expose yourself to other such problems see answer https://stackoverflow.com/a/13062766/56875