{"data":{"slug":"microsoft-nni","name":"nni","tagline":"An open source AutoML toolkit for automating machine learning lifecycle","github_url":"https://github.com/microsoft/nni","owner":"microsoft","repo":"nni","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"Python","stars":14366,"forks":1850,"topics":["automated-machine-learning","automl","bayesian-optimization","data-science","deep-learning","deep-neural-network","distributed","feature-engineering","hyperparameter-optimization","hyperparameter-tuning","machine-learning","machine-learning-algorithms","mlops","model-compression","nas","neural-architecture-search","neural-network","python","pytorch","tensorflow"],"archived":true,"github_pushed_at":"2024-07-03T10:55:10+00:00","maintenance_label":"Archived","stars_delta_30d":3,"url":"https://www.graphcanon.com/tools/microsoft-nni","markdown_url":"https://www.graphcanon.com/tools/microsoft-nni.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-nni","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-nni","description":"An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.","homepage_url":"https://nni.readthedocs.io","license":"MIT","open_issues":415,"watchers":9,"ai_summary":"This repository provides an automated machine learning (AutoML) framework that supports feature engineering, neural architecture search, model compression, and hyper-parameter tuning.","readme_excerpt":"## Installation\n\nSee the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.\n\nTo install the current release:\n\n```\n$ pip install nni\n```\n\nTo update NNI to the latest version, add `--upgrade` flag to the above commands.\n\n---\n\n## License\n\nThe entire codebase is under [MIT license](LICENSE).","github_created_at":"2018-06-01T05:51:44+00:00","created_at":"2026-07-11T23:36:10.425457+00:00","updated_at":"2026-10-02T06:00:48.868759+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":"data-science","name":"data-science"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"deep-neural-network","name":"deep-neural-network"},{"slug":"distributed","name":"distributed"},{"slug":"feature-engineering","name":"feature-engineering"}],"trust":{"provenance":{"is_fork":false,"github_id":135673451,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-10-02T06:00:48.035Z","maintenance":{"label":"Archived","score":8,"methodology":"github_public_v1","releases_90d":0,"days_since_push":820,"last_release_at":"2023-09-14T12:12:06Z","stars_delta_30d":3,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:36:14.390Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-10-02T06:00:48.509Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-10-02T06:00:48.509Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-10-02T06:00:48.509Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-10-02T06:00:48.509Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-10-02T06:00:48.509Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.","Your team prefers working in a Python environment but lacks resources for manual hyperparameter optimization or neural architecture design tasks."],"when_not_to_use":["You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios.","If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments."],"source":"enrich:decision_facts","observed_at":"2026-07-17T04:05:14.965Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"NNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming."}]}}