Non-ROOT users configure a remote CUDA server for a deep learning environment, below is an example of Pytorch. CUDA driver Check the version of CUDA already installed on the server: 1 nvcc -V Showing version 11.4 here, but it doesn’t matter. Next to check the driver version, this relates to the latest CUDA version that we can install in the conda virtual environment. 1 2 3 4 nvidia-smi ### Output ### Driver Version: 535....
Preface Due to the author’s frequent use of conda to configure the R kernel jupyter notebook on Linux remote servers, some steps are forgotten over time (the main problem is that the same pit will be stepped on differently every time). After summarizing the relevant tutorials and personal experience on the internet, a guide is provided for everyone and future me to refer to. Guide Install Conda To start with an introduction to Conda, Anaconda and some of the connections between them....
Preface Since my intention is to squeeze the value out of idle servers, this article is all about deployment with the server as the body, other devices such as N1 or Playcloud, please find your own information to deploy. Server Selection Region selection. It is best to choose a domestic server, so that it is safer for the Jingdong hanging, but there are network problems (connection to GitHub is unstable...