Running Tensorflow JS on a NVIDIA Jetson

Brad Dwyer's article details the process of running Tensorflow.js on NVIDIA Jetson devices, which are AI-capable low-power computers ideal for machine learning tasks at the edge. Tensorflow.js, a library for deploying machine learning in JavaScript, can be executed in web browsers using the WebGL backend to leverage GPU acceleration, achieving modest frame rates on Jetson models like the Xavier NX and Nano. For headless setups using Node.js, users face challenges due to the lack of prebuilt binaries for the arm64 platform, necessitating manual compilation of libtensorflow and TFjs C++ bindings. Dwyer provides a detailed walkthrough of the manual build process, including configuring the necessary CUDA and CuDNN versions, highlighting the complexity and potential pitfalls involved. To simplify deployment, Roboflow offers a ready-made Docker container and npm package that streamlines the setup, allowing Tensorflow.js to run efficiently with GPU support on Jetson devices. The article underscores the potential performance improvements and practical solutions for deploying machine learning models on the edge using Tensorflow.js with Roboflow's tools.