Type "help", "copyright", "credits" or "license" for more information. Using the following commands, tensorflow was able to find and identify the number of GPUs available in the system. Singularity> apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pubĬopyright (c ) 2005-2019 NVIDIA CorporationĬuda compilation tools, release 10.1, V10.1.243Īt this point, I was getting an output for nvidia-smi and nvcc -V (with compatible versions) inside the singularity container. Singularity> mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 The software environment of the container is determined by the contents of the singularity image and what is run within the container will not affect the host operating system. Singularity Container - a file/image running an operating system on top of the host system’s operating system.Nvidia Driver - software that allows the NVIDIA GPU to communicate with the operating system.CUDA - parallel computing platform and programming model to communicate with NVIDIA GPUs.In this post I have described how I have kept the driver and toolkit separate using singularity containers to check for conflicting versions and prevent potential mishaps.
INSTALL NVIDIA CUDA TOOLKIT UBUNTU INSTALL
Though it is possible to install both the nvidia-driver and the nvidia-cuda-toolkit using a package manager, it could result in incompatibile versions and could potentially break the graphics or operating system. Installing Nvidia Drivers and Cuda on a Linux machine can be a tricky affair.
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