Ranger is a Python optimizer library for training deep neural networks. It integrates three optimization techniques—RAdam (Rectified Adam) for adaptive learning rate scheduling, LookAhead for soft weight updates, and Gradient Centralization for improved training stability and efficiency. The optimizer is designed for PyTorch and provides configurable options to enable or disable individual components, with default settings optimized for a 75% flat learning rate followed by a step-down phase.