Installing from Debian (Ubuntu) repositories.To install CUDA toolkit on Jetson Nano (or any other Jetson board), there are two main methods: The flow diagram below indicates the typical program flow when executing a GPU-accelerated: When correctly installed, the CPU can invoke the CUDA functions on the GPU through CUDA framework and thus enables the parallel computing possibility. The framework supports highly popular machine learning frameworks such as Tensorflow, Caffe2, CNTK, Databricks, H2O.ai, Keras, MXNet, PyTorch, Theano, and Torch. CUDA is written primarily in C/C++ and there exist additional support for languages like Python and Fortran. Nvidia calls this special framework that enables parallel computing on the GPU the CUDA ( Compute Unified Device Architecture). However, since the Jetson Nano is designed with special hardware, in order to make the best use of the hardware-accelerated parallel computing using the GPU, a special framework needs to be installed and thereby, machine learning programs can be written using the same. In terms of parallel processing, the Jetson Nano easily outperforms the Raspberry Pi series and pretty much any other Single Board Computers which typically only consist of a CPU with one or more cores and lacks a dedicated GPU. The Jetson nano can be used as a general purpose Linux-powered computer, which has advanced uses in machine learning inference and image processing, thanks to its GPU accelerated processor. The SoM consists of 128-core NVIDIA Maxwell™ architecture-based GPU, controlled by a CPU with Quad-core ARM A57 architecture, along with 4GB of DDR4 RAM. The Nvidia Jetson Nano is one of the System on Modules (SoM) developed by Nvidia Corporation, with GPU accelerated processing in mind.
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