![]() It should give output something like this nvcc: NVIDIA (R) Cuda compiler driverĬopyright (c) 2005-2016 NVIDIA CorporationĬuda compilation tools, release 8.0, V8.0. You can run below command from any directory nvcc -V Once you find this location you can then do the following (replacing $ If that doesn't work, see "Redhat distributions" below. To find the file, you can use: whereis cudnn.h You first need to find the installed cudnn file and then parse this file. My answer shows how to check the version of CuDNN installed, which is usually something that you also want to verify. eGPU on macOS High Sierra Applies to macOS High Sierra (10.13. Please refer to macOS 10.14 support issue. CUDA 10.2 is the last to support macOS up to 10.13. With TensorFlow, you might consider using CuDNN v4 instead of v5. Fair to assume at this point that macOS 10.14 and over will never be supported by NVIDIA CUDA, as NVIDIA and Apple got into a deadlock. When you get an error like F tensorflow/stream_executor/cuda/cuda_dnn.cc:427] could not set cudnn filter descriptor: CUDNN_STATUS_BAD_PARAM $ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2Įdit: In later versions this might be the following (credits to Aris) $ cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 $ sudo chmod a+r /usr/local/cuda/lib64/libcudnn* On Linux, the CUDA Toolkit is available on Fedora, Redhat Enterprise Linux, SUSE Linux Enterprise, OpenSUSE, and Ubuntu. $ sudo cp lib64/libcudnn* /usr/local/cuda/lib64 NVIDIA Home > Support Home Page > Knowledgebase Home Page > Which Operating Systems are supported by CUDA. ![]() $ sudo cp include/cudnn.h /usr/local/cuda/include Nvidia CUDA Toolkit for Mac and Linux 11.3. Step 3: Copy the files: $ cd folder/extracted/contents Download Nvidia CUDA Toolkit - UDA Toolkit is a C language development environment for CUDA-enabled GPUs especially designed for macOS. For most people, it will be /usr/local/cuda/. Step 2: Check where your cuda installation is. You might need nvcc -version to get your cuda version. ![]() Step 1: Register an nvidia developer account and download cudnn here (about 80 MB). Hence to check if CuDNN is installed (and which version you have), you only need to check those files. The installation of CuDNN is just copying some files.
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