Hence, in my case, I added the below commands as well. Make sure to do that for all the installed version. To do that, open a command prompt and type the following commands - SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin %PATH% SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\lib64 %PATH% SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include %PATH% That is, from the bin folder in your zip file to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin\ from the include folder in zip to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include\ and from the lib\圆4 to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\圆4\ Step #4Īdd all cuda-toolkit versions to the path variable. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1Ĭopy the files from your cudnn zip to the respective folders in your cuda-toolkit install. Your cuda-toolkit would be installed in the below path if you selected the default settings. You will need a developer account to do that. Step #3ĭownload the respective cudnn for your cuda version. After installing Visual Studio, install cuda-toolkit as any other normal installation. You will also need to install Visual Studio before you install cuda-toolkit. You can download and install multiple versions. Look here for a tested version of the toolkit you will need.First, decide which version of TensorFlow you want.You can find all the versions in this archive. You can manually download the driver from Nvidia's website or use the GeForce Experience. If yes, you need to update your graphics card driver to version 418.x or higher. Step #1Ĭheck if you have a Cuda-enabled Graphics Card. You can read more about which version of TensorFlow would require what respective version of cuda-toolkit and cudnn here (look at tested build configurations). I also got an error when the cuda-toolkit and cudnn version does not match the TensorFlow version’s requirement. Since many people think it is a hassle getting that to work on Windows, I decided to write a step-by-step guide. I googled about it, and most of the answers led me to install cuda-toolkit and cudnn manually. Solution files (.It urns out TensorFlow v1.15 still detects my GPU but throws me an error when I try to use v2.1.0. The Windows samples are built using the Visual Studio IDE. Make sure the dependencies mentioned in Dependencies section above are installed. X86_64, ppc64le, armv7l CUDA APIs involved CUDA Runtime APIĬudaGraphInstantiate, cudaStreamDestroy, cudaStreamBeginCapture, cudaFree, cudaMallocHost, cudaStreamEndCapture, cudaGraphDestroy, cudaFreeHost, cudaGraphLaunch, cudaStreamCreate, cudaStreamSynchronize, cudaOccupancyMaxPotentialBlockSize, cudaMalloc, cudaMemcpyAsync, cudaMemsetAsync, cudaGetDeviceProperties, cudaGraphExecDestroy Dependencies needed to build/runĭownload and install the CUDA Toolkit 12.1 for your corresponding platform. Linux, Windows Supported CPU Architecture Linear Algebra, CUBLAS Library, CUSPARSE Library Supported SM Architectures This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library calls captured and called using CUDA Graph APIs. ConjugateGradientCudaGraphs - Conjugate Gradient using Cuda Graphs Description
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