{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/naslib-a-modular-and-flexible-neural","title":"NASLib: A Modular and Flexible Neural Architecture Search Library","arxiv_id":null,"date":"2021-01-01","proceeding":null,"authors":["Michael Ruchte","Arber Zela","Julien Niklas Siems","Josif Grabocka","Frank Hutter"],"abstract":"Neural Architecture Search (NAS) is one of the focal points for the Deep Learning community, but reproducing NAS methods is extremely challenging due to numerous low-level implementation details. To alleviate this problem we introduce NASLib, a NAS library built upon PyTorch. This framework offers high-level abstractions for designing and reusing search spaces, interfaces to benchmarks and evaluation pipelines, enabling the implementation and extension of state-of-the-art NAS methods with a few lines of code. The modularized nature of NASlib  allows researchers to easily innovate on individual components (e.g., define a new search space while reusing an optimizer and evaluation pipeline, or propose a new optimizer with existing search spaces). As a result, NASLib has the potential to facilitate NAS research by allowing fast advances and evaluations that are by design free of confounding factors. To demonstrate that NASLib is a sound library, we implement and achieve state-of-the-art results with one-shot NAS optimizers (DARTS and GDAS) over the DARTS search space and the popular NAS-Bench-201 benchmark. Last but not least, we showcase how easily novel approaches are coded in NASLib, by training DARTS on a hierarchical search space.","url_abs":"https://openreview.net/forum?id=EohGx2HgNsA","url_pdf":"https://openreview.net/pdf?id=EohGx2HgNsA","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"naslib-a-modular-and-flexible-neural","repo_url":"https://github.com/automl/NASLib","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"darts","method_name":"DARTS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}