// python example import cv2 test_img = cv2.imread("./test_squre/toleft.bmp") maxLoc = (25, 25) template = cv2.cuda_GpuMat() template.upload(test_img) template = cv2.cuda.cvtColor(template, cv2.COLOR_BGR2GRAY)
Checklist I have searched for similar issues. For Python issues, I have tested with the latest development wheel. I have checked the release documentation and the latest documentation (for master branch). My Question I am using Python 3...
std::cerr << "CudaMalloc failed" << std::endl; return -1; } if (cudaMalloc((void **)&d_xmap, f_size) != cudaSuccess) { std::cerr << "CudaMalloc failed" << std::endl; return -1; } if (cudaMalloc((void **)&d_ymap, f_size) != cudaSuccess) { std::cerr <...
How to debug CUDA? [18/49] /usr/local/cuda/bin/nvcc -I/home/zyhuang/flash-CUDA/flash-attention/csrc/flash_attn -I/home/zyhuang/flash-CUDA/flash-attention/csrc/flash_attn/src -I/home/zyhuang/flash-CUDA/flash-attention/csrc/cutlass/include -I/usr/local/lib/python3.10/dist-packages/torch...
RuntimeError: cuda runtime error (100) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:50 pytorch cannot access GPU in Docker The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computat...
conda create --name my_env python=3.9 -y Activate the newly created environment using the following command: conda activate my_env Run this command to install the cuDNN library and CUDA drivers: conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 -y Install the TensorFlow library ...
After you have installed all these programs, only then will you be able to use your GPU for parallel computing. To start, you will need to import a JIT function from Numba to CUDA. Essentially, you are transferring the command from your CPU to your GPU so that your GPU can run the fu...
# su pythonuser This guide uses the example userpythonuser, replace the account with your actual username Install the NVIDIA CUDA ToolKit on Ubuntu To install the CUDA toolkit on Ubuntu, you can use any of the following methods: Native Installation using a script or release file ...
sudo pacman -S opencv-cuda This is a binary build of OpenCV with Cuda support, and I couldn’t get it to work for one reason or another. But lots of people have, so you might be able to. Try installing with pacman, and then create a Python file that looks like this: ...
2. Enable SR-IOV in the ConnectX-3 firmware 3. Enable SR-IOV in the MLNX_OFED Driver 4. Set up the VM Setup and Prerequisites 1. Two servers connected via Ethernet switch 2. KVM is installed on the servers # yum install kvm # yum install virt-manager libvirt libvirt-python pyth...