AutoDL-Projects provides implementations of neural architecture search and hyper-parameter optimization algorithms in a unified codebase. It enables researchers and engineers to experiment with automated deep learning techniques including DARTS, TAS, and other NAS methods. The project is designed to be lightweight with minimal dependencies and targets both beginners learning AutoML and researchers developing new automated training algorithms.