{"data":{"slug":"gomerudo-nas-env","name":"nas-env","tagline":"Simple OpenAI Gym environment for Neural Architecture Search (NAS)","github_url":"https://github.com/gomerudo/nas-env","owner":"gomerudo","repo":"nas-env","owner_avatar_url":"https://avatars.githubusercontent.com/u/5495942?v=4","primary_language":"Python","stars":31,"forks":3,"topics":["neural-architecture-search","openai-gym","reinforcement-learning"],"archived":false,"github_pushed_at":"2020-05-04T22:38:59+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/gomerudo-nas-env","markdown_url":"https://www.graphcanon.com/tools/gomerudo-nas-env.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/gomerudo-nas-env","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=gomerudo-nas-env","description":"A simple OpenAI Gym environment for Neural Architecture Search (NAS)","homepage_url":null,"license":"MIT","open_issues":0,"watchers":6,"ai_summary":"nas-env provides a foundational OpenAI Gym-compatible framework for implementing and testing NAS algorithms in reinforcement learning settings.","readme_excerpt":"## Installation\n\nThe recommended way to install the package is in editable mode:\n\n```\ncd ${GIT_STORAGE}/nas-env\ngit checkout develop\npip install -e .\n```\n\n---\n\n## Future plans\n\n- Architectural changes to the source code to ease plugging-in the different NAS elements, i.e. the performance estimation strategy and the search space.\n- Generate documentation\n- Publish the package in PiPy.","github_created_at":"2019-03-25T12:56:01+00:00","created_at":"2026-07-11T23:36:02.637332+00:00","updated_at":"2026-08-04T12:00:38.051479+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"neural-architecture-search","name":"neural-architecture-search"},{"slug":"openai-gym","name":"openai-gym"},{"slug":"python","name":"python"},{"slug":"reinforcement-learning","name":"reinforcement-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":177586414,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T12:00:37.001Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":2282,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:36:06.216Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T12:00:37.722Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T12:00:37.722Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-04T12:00:37.722Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym","For experimental settings where customization of architecture search elements like performance estimation and search space is anticipated"],"when_not_to_use":["If you require a fully documented package as documentation for nas-env remains under development","During production phases when stability is crucial because nas-env is still undergoing architectural changes"],"source":"enrich:decision_facts","observed_at":"2026-07-15T12:10:23.012Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license."}]}}