{"data":{"slug":"automl-auto-pytorch","name":"Auto-PyTorch","tagline":"Automatic architecture search and hyperparameter optimization for PyTorch","github_url":"https://github.com/automl/Auto-PyTorch","owner":"automl","repo":"Auto-PyTorch","owner_avatar_url":"https://avatars.githubusercontent.com/u/6469053?v=4","primary_language":"Python","stars":2540,"forks":298,"topics":["automl","deep-learning","pytorch","tabular-data"],"archived":false,"github_pushed_at":"2024-04-09T06:17:08+00:00","maintenance_label":"Dormant","stars_delta_30d":-1,"url":"https://www.graphcanon.com/tools/automl-auto-pytorch","markdown_url":"https://www.graphcanon.com/tools/automl-auto-pytorch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/automl-auto-pytorch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=automl-auto-pytorch","description":"Automatic architecture search and hyperparameter optimization for PyTorch","homepage_url":null,"license":"Apache-2.0","open_issues":75,"watchers":46,"ai_summary":"Automated machine learning framework specialized in architectural searches and hyperparameter tuning for deep-learning models using PyTorch.","readme_excerpt":"### PyPI Installation\n\n```sh\n\npip install autoPyTorch\n\n```\n\nAuto-PyTorch for Time Series Forecasting requires additional dependencies \n\n```sh\n\npip install autoPyTorch[forecasting]\n\n```\n\n---\n\n### Manual Installation\n\nWe recommend using Anaconda for developing as follows:\n\n```sh\n\n---\n\n## License\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the Apache license 2.0 (please see the LICENSE file).\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\nYou should have received a copy of the Apache license 2.0\nalong with this program (see LICENSE file).","github_created_at":"2018-11-30T08:18:34+00:00","created_at":"2026-07-11T23:33:18.599736+00:00","updated_at":"2026-09-30T06:00:45.019044+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"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":"deep-learning","name":"deep-learning"},{"slug":"pytorch","name":"pytorch"},{"slug":"tabular-data","name":"tabular-data"},{"slug":"time-series-forecasting","name":"time-series-forecasting"}],"trust":{"provenance":{"is_fork":false,"github_id":159791040,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-30T06:00:44.157Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":903,"last_release_at":"2022-08-23T16:45:37Z","stars_delta_30d":-1,"open_issues_delta_30d":0},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":40,"high_count":0,"last_scan_at":"2026-07-11T23:33:22.061Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-30T06:00:44.639Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-09-30T06:00:44.639Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-30T06:00:44.639Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-09-30T06:00:44.639Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-30T06:00:44.639Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.","Ideal if your project involves time series forecasting, as Auto-PyTorch offers specialized configurations for this purpose with additional dependencies."],"when_not_to_use":["Avoid using it if your AI development focuses on frameworks other than PyTorch.","Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning."],"source":"enrich:decision_facts","observed_at":"2026-07-17T12:45:54.532Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch."}]}}