{"data":{"slug":"datacanvasio-hypernets","name":"Hypernets","tagline":"A General Automated Machine Learning framework for building domain-specific AutoML toolkits.","github_url":"https://github.com/DataCanvasIO/Hypernets","owner":"DataCanvasIO","repo":"Hypernets","owner_avatar_url":"https://avatars.githubusercontent.com/u/7522641?v=4","primary_language":"Python","stars":265,"forks":39,"topics":["autodl","automl","enas","evolutionary-algorithms","hyperparameter-optimization","hyperparameter-tuning","keras","mcts","monte-carlo-tree-search","nas","nasnet","neural-architecture-search","reinforcement-learning"],"archived":false,"github_pushed_at":"2026-04-20T02:07:49+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/datacanvasio-hypernets","markdown_url":"https://www.graphcanon.com/tools/datacanvasio-hypernets.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/datacanvasio-hypernets","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=datacanvasio-hypernets","description":"A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.","homepage_url":"https://hypernets.readthedocs.io/","license":"Apache-2.0","open_issues":0,"watchers":15,"ai_summary":"Hypernets is an automated machine learning framework that simplifies the development of end-to-end AutoML solutions across various domains by supporting hyperparameter optimization and neuro-architecture search among other advanced optimization techniques. It supports multiple ML frameworks including TensorFlow, Keras, PyTorch, SciKit-Learn, LightGBM, and XGBoost.","readme_excerpt":"

\n\n\n\n\n\n\n\n## We Are Hiringļ¼\nDear folks, we are offering challenging opportunities located in Beijing for both professionals and students who are keen on AutoML/NAS. Come be a part of DataCanvas! Please send your CV to yangjian@zetyun.com. (Application deadline: TBD.) \n\n## Hypernets: A General Automated Machine Learning Framework\nHypernets is a general AutoML framework, based on which it can implement automatic optimization tools for various machine learning frameworks and libraries, including deep learning frameworks such as tensorflow, keras, pytorch, and machine learning libraries like sklearn, lightgbm, xgboost, etc.\nIt also adopted various state-of-the-art optimization algorithms, including but not limited to evolution algorithm, monte carlo tree search for single objective optimization and multi-objective optimization algorithms such as MOEA/D,NSGA-II,R-NSGA-II.\nWe introduced an abstract search space representation, taking into account the requirements of hyperparameter optimization and neural architecture search(NAS), making Hypernets a general framework that can adapt to various automated machine learning needs. As an abstraction computing layer, tabular toolbox, has successfully implemented in various tabular data types: pandas, dask, cudf, etc. \n\n\n\n## Overview\n### Conceptual Model\n

\n\n

\n\n### Illustration of the Search Space \n

\n\n

\n\n## What's NEW !\n\n- **New feature:** [Multi-objectives optimization support](https://hypernets.readthedocs.io/en/latest/searchers.html#multi-objective-optimization)\n- **New feature:** [Performance and model complexity measurement metrics](https://github.com/DataCanvasIO/HyperGBM/blob/main/hypergbm/examples/66.Objectives_example.ipynb)\n- **New feature:** [Distributed computing](https://hypergbm.readthedocs.io/en/latest/example_dask.html) and [GPU acceleration](https://hypergbm.readthedocs.io/en/latest/example_cuml.html) base on computational abstraction layer\n\n\n## Installation\n\n### Conda\n\nInstall Hypernets with `conda` from the channel *conda-forge*:\n\n```bash\nconda install -c conda-forge hypernets\n```\n\n### Pip\nInstall Hypernets with different options:\n\n* Typical installation:\n```bash\npip install hypernets\n```\n\n* To run Hypernets in JupyterLab/Jupyter notebook, install with command:\n```bash\npip install hypernets[notebook]\n```\n\n* To run Hypernets in distributed Dask cluster, install with command:\n```bash\npip install hypernets[dask]\n```\n\n* To support dataset with simplified Chinese in feature generation, \n * Install `jieba` package before running Hypernets.\n * OR install Hypernets with command:\n```bash\npip install hypernets[zhcn]\n```\n\n* Install all above with one command:\n```bash\npip install hypernets[all]\n```\n\n\nTo ***Verify*** your installation:\n```bash\npython -m hypernets.examples.smoke_testing\n```\n\n## Related Links\n\n* [A Brief Tutorial for Developing AutoML Tools with Hypernets](https://github.com/BochenLv/knn_toy_model/blob/main/Introduction.md)\n\n## Documents\n* [Overview](https://hypernets.readthedocs.io/en/latest/overview.html)\n* [QuickStart](https://hypernets.readthedocs.io/en/latest/quick_start.html)\n* [Search Space](https://hypernets.readthedocs.io/en/latest/search_space.html)\n* [Searcher](https://hypernets.readthedocs.io/en/latest/searchers.html)\n* [HyperModel](https://hypernets.readthedocs.io/en/latest/hypermodels.html)\n* [Experiment](https://hypernets.readthedocs.io/en/latest/experiment.html)\n## Neural Architecture Search\n* [Define A DNN Search Space](https://hypernets.readthedocs.io/en/latest/nas.html#define-a-dnn-search-space)\n* [Define A","github_created_at":"2020-06-22T07:40:31+00:00","created_at":"2026-07-11T23:34:48.147191+00:00","updated_at":"2026-08-04T06:01:04.884793+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"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":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"keras","name":"keras"},{"slug":"lightgbm","name":"lightgbm"},{"slug":"neural-architecture-search","name":"neural-architecture-search"},{"slug":"pytorch","name":"pytorch"},{"slug":"sklearn","name":"sklearn"},{"slug":"tensorflow","name":"tensorflow"}],"trust":{"provenance":{"is_fork":false,"github_id":274071592,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T06:01:04.082Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":106,"last_release_at":"2024-02-23T02:51:38Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":14,"high_count":0,"last_scan_at":"2026-07-11T23:34:52.849Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T06:01:04.550Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T06:01:04.550Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-04T06:01:04.550Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline","When you need advanced optimization algorithms and support for multi-objective problems such as MOEA/D and NSGA-II"],"when_not_to_use":["If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus","Avoid if your team has strict time constraints; 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