Gluon vs Postman

Compact layout;HTTP requests with file upload support;Formatted API responses for JSON and XML;Image previews;Request history;Basic Auth, OAuth 1.0, OAuth 2.0, and other common auth helpers;Autocomplete for URL and header values;Key/value editors for adding parameters or header values. Works for URL parameters too.;Use environment variables to easily shift between settings. Great for testing production, staging or local setups.;Keyboard shortcuts to maximize your productivity;Automatically generated web documentation;Mock servers hosted on Postman’s cloud;API monitoring run from Postman cloud

Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.;Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.;Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.;High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.