  Recent advances in neural architecture search (NAS) demand tremendous
computational resources, which makes it difficult to reproduce experiments and
imposes a barrier-to-entry to researchers without access to large-scale
computation. We aim to ameliorate these problems by introducing NAS-Bench-101,
the first public architecture dataset for NAS research. To build NAS-Bench-101,
we carefully constructed a compact, yet expressive, search space, exploiting
graph isomorphisms to identify 423k unique convolutional architectures. We
trained and evaluated all of these architectures multiple times on CIFAR-10 and
compiled the results into a large dataset of over 5 million trained models.
This allows researchers to evaluate the quality of a diverse range of models in
milliseconds by querying the pre-computed dataset. We demonstrate its utility
by analyzing the dataset as a whole and by benchmarking a range of architecture
optimization algorithms.
