![]() Actually, cuDNN stands for CUDA Deep Neural Network library is a GPU-accelerated library of primitives for deep neural networks. Step 04: Installing cuDNNĪfter installing the cuda-toolkit we need to install a suitable version of cuDNN libraries in order to use GPU for deep learning tasks. You will see cuda-11.2 directory listed there. It will take a few minutes to download and install the cuda-toolkit. Instead of the last command to install Cuda, sudo apt install cuda- Eg: If you have downloaded cuda 11.2 sudo apt install cuda-11.2 PC: Author (Captured from NVIDIA Website) Select the preferences as shown in the below image It will redirect you to the download page. Go to the c uda-toolkit archive and click on the version you need to download. Since we are intended to install multiple versions it is good to install through deb (Debian) or tar installation method. We can install a cuda-toolkit suitable for the Nvidia-driver through the apt manager. CUDA is a parallel computing platform developed by NVIDIA it will help us to train our models much faster than in CPUs. The main intention of this article starts here. If you start to install the CUDA Toolkit from the smaller version it will replace the driver. But first, install the latest compatible version of CUDA Toolkit for the Nvidia driver you installed. NOTE: You can repeat Step 03, 04 & 05 as many times as you need to install different versions of CUDA. Note, the CUDA version displayed on the top right corner is the driver version. You can see the Nvidia configurations printed. Open your terminal and run the below command. Step 01: Check whether your system is CUDA capableįirst of all, you need to check whether your laptop/desktop has a NVIDIA GPU. I struggled a bit and found a way to have multiple CUDA installation on the system. Recently I ended up in such a situation where I need to use a specific version of TensorRT which is not compatible with the CUDA version I have in my Ubuntu system. One good example is when you try to use TensorRT to optimize your inference time. But there are situations you need to be relay on C++ as well. If you are a person who only uses Python the Anaconda (conda package manager) will come in handy in such situations. Sometimes we may need different versions of CUDA and cuDNN for different projects. Yes, soon as we start to work on two or three deep learning projects we may end up with the need for different environments. ![]() But things get messy when we grow from PROJECT to PROJECTS. Nowadays most of us use the CUDA toolkit to train deep learning models.
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