Awesome-AutoDL

Why we included this project

Getting a handle on automated deep learning means wading through neural architecture search, hyperparameter optimization, and the tooling around both. This curated index collects the main libraries (NNI, AutoGluon, Auto-PyTorch, TPOT, and others), the standard NAS benchmarks like NAS-Bench-101 and NAS-Bench-201, and a large paper collection sorted by venue and by search method, covering gradient-based, reinforcement learning, evolutionary, and performance prediction work. The author is a researcher who built several of the listed benchmarks, so the selections carry hands-on credibility rather than keyword matching. Teams evaluating AutoML stacks can use the library and benchmark sections to shortlist candidates, and the venue-organized paper lists help newcomers trace how NAS and HPO methods have evolved. It is a reference and reading guide, not a tool you run, but it saves hours of scattered searching.