Propel runs in multiple environments, but for performance reasons, usage of Node.js is encouraged for computationally intensive computing.
Rather than starting from scratch, Propel leverages other foundational technologies. Propel works in the browser with WebGL and deeplearn.js. The Node.js version uses TensorFlow’s C API and supports the targeting of multiple GPUs and TCP connections. The browser version of Propel is intended more for demonstration purposes or more straightforward calculations.
For machine learning usage, developers are encouraged to create machine learning models server-side and then quickly deploy them as web-based demos. The project provides an introductory neural network example. Propel is authored in TypeScript, and usage of ts-node is encouraged as it gives a TypeScript execution environment and REPL for Node.js.
Source : https://www.infoq.com/news/2018/03/propelml-js-machine-learning#