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.
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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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