nvidia-auto-installer-for-fedora-linux VS cuda-samples

I fixed it. I used this github repo and now it works perfectly. So if anyone faces the same problems currently, this fixed it for me. I don't know what it actually does or if it can damage anything but honestly I don't care as long as it works.

If you have installed fedora and didn’t explicitly install the proprietary Nvidia drivers then you don’t have them, the third party codecs/drivers you mentioned elsewhere in the post don’t include the Nvidia drivers themselves. You can follow the instructions on this GitHub repo to install them for yourself. That should improve performance GREATLY. https://github.com/t0xic0der/nvidia-auto-installer-for-fedora-linux

Simply follow the instructions at https://github.com/t0xic0der/nvidia-auto-installer-for-fedora-linux

Use this

Use this for nvidia stuff link

If you want a easy way to install nvidia drivers you should consider this: https://github.com/t0xic0der/nvidia-auto-installer-for-fedora-linux . It worked for me on my rtx 3050. If you have 2 GPU's (hybrid system) I would consider using envy control

installing, building, and running WSL CUDA examples from https://github.com/nvidia/cuda-samples

After which nvcc should be accessible to new sessions, and you can build C++ cuda stuff like cuda-samples. Python packages like pytorch should also see CUDA and be able to use it.

I haven't found any note that these APIs do not support Windows, and it also seems that the memMapIPCDrv CUDA sample supports Windows.

I installed nvidia-cuda-toolkit and tried to build https://github.com/NVIDIA/cuda-samples but I'm getting stupid errors... it installed nvccat /usr/bin/nvcc and the samples expect /usr/local/cuda/bin/nvcc... symlinking it to that location and it dies with

Can build the cuda-samples, then you have a working nvcc.

I have installed cuda and cudnn, and was testing the installation with the cuda-samples, as the Arch Wiki suggested. But, I am not able to get samples like nbody, smokeparticles, Mandelbrot, etc. to run. Although devicequery works fine, and I get the expected output, so I think there is not a problem with my cuda installation.

Hi, I don't have much experience with Nvidia Jetsons. You can find some examples on GitHub (here https://github.com/NVIDIA/cuda-samples). You can find CUDA implementations of most functions on the internet though, you just have to look for the specific thing you are looking for. Cuda kernels are not platform specific, they should work on GPUs and embedded developer boards without problems as long as you respect the limits imposed by the "compute capability" of your device, you just have to compile your code using the right architecture flag. The biggest limit you have to deal with when developing for Jetson nano is the low amount of memory.

—> git clone https://github.com/NVIDIA/cuda-samples.git —> cd cuda-samples/Samples/1_Utilities/deviceQuery/ —> make —> ./deviceQuery (Result=pass?good) —> cd ~/ —> wget https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh

is this the source code that u are talking about ? : https://github.com/NVIDIA/cuda-samples ? I dont see any CMakeLists.txt inside...