The Nvidia Jetson Nano is a powerful System on Module (SoM) developed by Nvidia Corporation, featuring a 128-core NVIDIA Maxwell architecture-based GPU and a Quad-core ARM A57 CPU, equipped with 4GB of DDR4 RAM. It stands out for its GPU-accelerated processing capabilities, making it ideal for machine learning inference and image processing tasks, outperforming other Single Board Computers like the Raspberry Pi series in parallel processing. To fully leverage its capabilities, the CUDA (Compute Unified Device Architecture) framework, primarily written in C/C++ with support for Python and Fortran, needs to be installed, facilitating parallel computing through popular machine learning frameworks such as TensorFlow and PyTorch. The CUDA toolkit can be installed on the Jetson Nano using methods such as the JetPack SDK or Debian repositories, with the former providing a stable and ready-to-use installation of CUDA libraries. After installation, updating the OS PATH variable is crucial for system-wide access to the CUDA libraries, enabling compilation of CUDA applications. Additionally, multiple Jetson Nano devices can be managed simultaneously for CUDA installation or updates using JFrog Connect’s Update Flow and Deployment feature.