{"data":{"slug":"automl-hpobench","name":"HPOBench","tagline":"A collection of hyperparameter optimization benchmark problems","github_url":"https://github.com/automl/HPOBench","owner":"automl","repo":"HPOBench","owner_avatar_url":"https://avatars.githubusercontent.com/u/6469053?v=4","primary_language":"Python","stars":173,"forks":36,"topics":["bayesian-optimization","benchmark","benchmarking","containerized-benchmarks","hyperparameter-optimization"],"archived":false,"github_pushed_at":"2025-05-21T11:10:27+00:00","maintenance_label":"Dormant","stars_delta_30d":3,"url":"https://www.graphcanon.com/tools/automl-hpobench","markdown_url":"https://www.graphcanon.com/tools/automl-hpobench.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/automl-hpobench","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=automl-hpobench","description":"Collection of hyperparameter optimization benchmark problems","homepage_url":null,"license":"Apache-2.0","open_issues":34,"watchers":13,"ai_summary":"HPOBench provides benchmarking problems for hyperparameter optimization using Python and is centered around facilitating research in automated machine learning.","readme_excerpt":"## Installation\n\nWe recommend using a virtual environment. To install HPOBench, please run the following:\n```\ngit clone https://github.com/automl/HPOBench.git\ncd HPOBench \npip install .\n```\n\n**Note:** This does not install *singularity (version 3.6)*. Please follow the steps described here: [user-guide](https://sylabs.io/guides/3.6/user-guide/quick_start.html#quick-installation-steps).   \nIf you run into problems, using the most recent singularity version might help: [here](https://singularity.hpcng.org/admin-docs/master/installation.html)","github_created_at":"2020-01-14T07:58:39+00:00","created_at":"2026-07-11T23:34:34.472416+00:00","updated_at":"2026-10-02T06:00:16.375847+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":"automl","name":"automl"},{"slug":"bayesian-optimization","name":"bayesian-optimization"},{"slug":"benchmark","name":"benchmark"},{"slug":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"python","name":"python"}],"trust":{"provenance":{"is_fork":false,"github_id":233787675,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-10-02T06:00:14.401Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":498,"last_release_at":"2021-10-25T13:09:48Z","stars_delta_30d":3,"open_issues_delta_30d":0},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":8,"high_count":0,"last_scan_at":"2026-07-11T23:34:40.462Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-10-02T06:00:14.890Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-10-02T06:00:14.890Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-10-02T06:00:14.890Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium"},"requirements":{"notes":["The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":false},"when_to_use":["When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments.","If your work requires a comprehensive library of challenges that mirror real-world issues in tuning parameters for machine learning models."],"when_not_to_use":["Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization.","If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench."],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:00:51.905Z"},"constraint_facets":{"min_ram_gb":null,"pricing_model":"freemium","requires_docker":false},"decision_summary":[{"label":"Pricing","value":"freemium"},{"label":"Requirements","value":"The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step."},{"label":"Adopt for","value":"HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios."},{"label":"License detail","value":"HPOBench is open source under the Apache-2.0 license."}]}}