Recent GPU versions of tensorflow require compute capability 3.5 or higher (and use cuDNN to access the GPU. cuDNN also requires a GPU of cc3.0 or higher: cuDNN is supported on Windows, Linux and MacOS systems with Pascal, Kepler, Maxwell, Tegra K1 or Tegra X1 GPUs. Kepler = cc3.x Maxwell = cc5.x Pascal = cc6.x … Read more
There should be a way to do it by setting only the Path environment variable but I could never get that to work. The only thing that worked for me was to manually copy the CuDNN files into the appropriate folders in your CUDA installation. For example, if your CUDA installation is in C:\CUDA\v7.0 and … Read more
The installation of CuDNN is just copying some files. Hence to check if CuDNN is installed (and which version you have), you only need to check those files. Install CuDNN Step 1: Register an nvidia developer account and download cudnn here (about 80 MB). You might need nvcc –version to get your cuda version. Step … Read more
Do i now have two versions of CUDA installed and how do I check this? No. conda installs the bare minimum redistributable library components required to support the CUDA accelerated packages they offer. The package name cudatoolkit is a complete misnomer. It is nothing of the sort. Even though it is now greatly expanded in … Read more
TL;DR) See this table: https://www.tensorflow.org/install/source#gpu Generally: Check the CUDA version: cat /usr/local/cuda/version.txt and cuDNN version: grep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h and install a combination as given below in the images or here. The following images and the link provide an overview of the officially supported/tested combinations of CUDA and TensorFlow on Linux, macOS and Windows: … Read more