{"data":{"slug":"automl-hpbandster","name":"HpBandSter","tagline":"a distributed Hyperband implementation on Steroids","github_url":"https://github.com/automl/HpBandSter","owner":"automl","repo":"HpBandSter","owner_avatar_url":"https://avatars.githubusercontent.com/u/6469053?v=4","primary_language":"Python","stars":632,"forks":107,"topics":["automated-machine-learning","automl","bayesian-optimization","hyperparameter-optimization","neural-architecture-search"],"archived":false,"github_pushed_at":"2022-10-16T06:18:34+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/automl-hpbandster","markdown_url":"https://www.graphcanon.com/tools/automl-hpbandster.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/automl-hpbandster","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=automl-hpbandster","description":"a distributed Hyperband implementation on Steroids","homepage_url":null,"license":"BSD-3-Clause","open_issues":66,"watchers":23,"ai_summary":"A tool for automated machine learning focusing on hyperparameter optimization and neural architecture search using distributed computing.","readme_excerpt":"## How to install\n\nWe try to keep the package on PyPI up to date. So you should be able to install it via:\n```\npip install hpbandster\n```\nIf you want to develop on the code you could install it via:\n\n```\npython3 setup.py develop --user\n```","github_created_at":"2017-12-17T20:28:20+00:00","created_at":"2026-07-11T23:34:23.202742+00:00","updated_at":"2026-08-04T06:00:58.165683+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":"automated-machine-learning","name":"automated-machine-learning"},{"slug":"automl","name":"automl"},{"slug":"bayesian-optimization","name":"bayesian-optimization"},{"slug":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"neural-architecture-search","name":"neural-architecture-search"}],"trust":{"provenance":{"is_fork":false,"github_id":114566317,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T06:00:56.274Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1387,"last_release_at":"2019-07-30T12:47:43Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:34:31.778Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T06:00:56.803Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T06:00:56.803Z"},"license_spdx":{"value":"BSD-3-Clause","source":"github.license","observed_at":"2026-08-04T06:00:56.803Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs."},"requirements":{"notes":["Requires Python environment. No Docker required."],"min_ram_gb":4,"requires_docker":false},"constraints":{"min_ram_gb":4,"pricing_model":"freemium","requires_docker":false},"when_to_use":["HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments.","It is ideal for scenarios where a wide range of configurations needs to be tested rapidly, leveraging parallel processing power."],"when_not_to_use":["If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings.","Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology."],"source":"enrich:decision_facts","observed_at":"2026-07-17T05:21:37.129Z"},"constraint_facets":{"min_ram_gb":4,"pricing_model":"freemium","requires_docker":false},"decision_summary":[{"label":"Pricing","value":"freemium - HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs."},{"label":"Requirements","value":"Min 4 GB RAM; Requires Python environment. No Docker required."},{"label":"Adopt for","value":"HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities."},{"label":"License detail","value":"BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions."}]}}