![]() ![]() (If you don't know which version to install, latest version is recommended). Click on your desire TensorRT sub-version.(If you don't know which version to install, latest TensorRT version is recommended). Click on your desire TensorRT version.TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine that performs inference for that network. TensorRT is meant for high-performance inference on NVIDIA GPUs. Sudo dpkg -i libcudnn8-dev_x.x.x.deb Install TensorRT Then, install the downloaded files with the following command: Copy \cuda\lib\圆4\cudnn*.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\lib\圆4. Copy \cuda\include\cudnn*.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\include.Ĭ. Copy \cuda\bin\cudnn*.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin.ī. (That is the only one available for Windows).Įxtract the downloaded zip file to a directory of your choice.Ĭopy the following files into the CUDA Toolkit directory.Ī. ![]() If you don't know which version to install, latest cuDNN version is recommended).Ĭhoose "cuDNN Library for Windows (x86)" and download. (If you don't find desire cuDNN version, click on "Archived cuDNN Releases" and find your version. Click on your desire cuDNN version compatible with your installed CUDA version.cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization and activation layers. The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated lirbary of primitives for deep neural networks.
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