I tried this approach to create a Docker image out of a server. The server has an Nvidia 3070 GPU and runs AI models like this. The approach uses a Dockerfile to copy the whole system by COPY / /…
Background I have spent the past week doing experiments towards using GPU for audio processing/synthesis, for example in areas like finite difference modeling (eg. wave equation) where the equations are highly parallelized at least at each serial time step. However,…
For some reason, any docker container with CUDA cannot see my GPU. When I run this: docker run --gpus=all --rm nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark I have this output: ... Error: only 0 Devices available, 1 requested. Exiting. The CUDA container…
Content: I'm having trouble getting PyTorch to recognize CUDA on my system. Here are the details: System Information: OS: Ubuntu 22.04.4 LTS (x86_64) running on WSL2 Python version: 3.7.16 PyTorch version: 1.12.0+cu113 GPU: NVIDIA GeForce GTX 1650 with Max-Q Design…
I have a c - cuda project. It has a main.c file and a deviceFunctions.cu file. Visual Studio Code is used to work on the project also this extension: ms-vscode.cpptools is installed to get Code completion and error checking etc.…
For the project I'm working on, I have defined a certain base class and several derived classes that inherit it. The base class has several virtual methods that are overriden by methods in the derived classes. At a certain point…
I am unable to run GPU on Docker after updating the GPU driver. When I run nvidia-smi in the host environment (Centos), the GPU is recognized. docker run --gpus all -it -v $(pwd):/home/workspace test /bin/bash ERRO[0000] error waiting for container:…
I'm trying to debug a test program with VS CODE, CMake in Ubuntu 20. I referred mainly to the CUDA debugger document: https://docs.nvidia.com/nsight-visual-studio-code-edition/cuda-debugger/index.html. However, I'm not sure about how to write a correct launch.json in a project based on CMake.…
NVIDIA starting from CUDA 11.x should in theory guarantee compatibility of their CUDA Toolkit libraries (typically shipped inside docker containers) and the driver library libcuda.so (installed on the host). This should be true at least when we stay within all…
I'm trying to install/downgrade to nvcc 10.2 in my conda environment (anaconda3) as it is needed by the version of PyTorch (1.10.2) that I'm using. I have a nvcc 11.7 installation outside of conda, which my conda env is automatically…