After rebooting / turning on the machine, let’s open a terminal session for command line installation. Installation StepsĪfter ensuring that you already have the right graphics card and have it properly mounted on the PCI / PCI-e slot, we’ll now proceed with the graphics card installation. It is important to note that if you plan to use an NVIDIA GPU for deep learning purpose, you need to make sure that the compute capability of the GPU is at least 3.0 (Kepler architecture). Note on CUDA compute capability and deep learning: The full list of the available features in each compute capability can be seen here. CUDA was designed to speed up computation by harnessing the power of the parallel computation utilizing hundreds or thousands of the GPU cores.ĬUDA-enabled GPUs: NVIDIA GPUs that support CUDA programming model and implementationĬUDA compute capability: A number that refers to the general specifications and available features especially in terms of parallel computing methods of a CUDA-enabled GPU. A graphics card can contain one or more GPUs while one GPU can be built of hundreds or thousands of cores.ĬUDA: A parallel programming model and the implementation as a computing platform developed by NVIDIA to perform computation on the GPUs. A unit of computation, in a form of a small chip on the graphics card, traditionally intended to perform rapid computation for image / graphics rendering and display purpose. GPU: Graphical / Graphics Processing Unit. We will specifically focus on NVIDIA display driver installation due to the pervasiveness and robustness of NVIDIA GPUs as deep learning infrastructure.īefore proceeding to the installation, let’s discuss some key terminologies related with the use of NVIDIA GPUs as the computing infrastructure in a deep learning system. In this post, we will go few steps back to the very basic prerequisite of setting up a GPU-powered deep learning system: display driver installation. In the recent posts, we have been going through the installation of deep learning framework like Caffe2 and its dependencies, such as CUDA or cuDNN.
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