It can be intergrated into advanced machine learning frameworks, such as Google’s Tensorflow, UC Berkeley’s caffe framework, Facebook’s PyTorch framework and so on. 3) CuDNN : It is used for deep neural networks GPU acceleration library. The architecture enable the GPU to solve complex computing problems. Nvidia-smi => Result after installing NVIDIA-Driver nvcc –version => Result after installing CUDA cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 => After installing CuDNNġ) NVIDIA-Driver : NVIDIA Graphics Card Driver 2) CUDA : Compute Unified Device Architecture, which is a general parallel computing launched by NVIDIA architecture. How can I recognize NVIDIA-Driver, CUDA and CuDNN ? ref:
Finally I want to deal with the common misunderstandings In this section I summarized the basic usages for those and the easy ways to distinguish the differences. NVIDIA-Driver, CUDA, CuDNN, nvidia-smi, nvcc sometimes make you very confused. I’ve searched several good articles to prepare my posts and will give a reference to each quotes. Finally I will summarize the docker installation and the way of passing gpu arguments inside the docker. In addition to that I will summarize the ways to install and uninstall NVIDIA-Driver as well as CUDA Toolkit. This article deals with basic concepts of NVIDIA-Driver, CUDA and CuDNN. Most deep learning framework what I used to use is Pytorch.
I am using Ubuntu servers for running docker and NVIDIA Graphic cards are installed inside them. So, I decided to summarize the concepts about NVIDIA-Driver, CUDA Toolkit, CuDNN for using docker properly. I could see the official documentation, but it was not helpful for me. Currently I’ve struggled with Ubuntu settings.
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