{"id":21586486,"url":"https://github.com/vida-nyu/alpha-automl","last_synced_at":"2026-02-19T14:32:00.475Z","repository":{"id":142456147,"uuid":"602273881","full_name":"VIDA-NYU/alpha-automl","owner":"VIDA-NYU","description":"Alpha-AutoML is a Python library for automatically generating end-to-end machine learning pipelines.","archived":false,"fork":false,"pushed_at":"2024-05-28T20:58:07.000Z","size":50326,"stargazers_count":17,"open_issues_count":11,"forks_count":3,"subscribers_count":12,"default_branch":"devel","last_synced_at":"2024-05-29T07:45:38.399Z","etag":null,"topics":["automl","data-science","machine-learning","python"],"latest_commit_sha":null,"homepage":"https://alpha-automl.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VIDA-NYU.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-15T21:30:19.000Z","updated_at":"2024-05-30T08:13:13.291Z","dependencies_parsed_at":"2023-10-20T01:24:19.395Z","dependency_job_id":"8d8109b4-feea-4f58-8f6b-d9c042ea47de","html_url":"https://github.com/VIDA-NYU/alpha-automl","commit_stats":null,"previous_names":["vida-nyu/alphad3m_sklearn"],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIDA-NYU%2Falpha-automl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIDA-NYU%2Falpha-automl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIDA-NYU%2Falpha-automl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIDA-NYU%2Falpha-automl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VIDA-NYU","download_url":"https://codeload.github.com/VIDA-NYU/alpha-automl/tar.gz/refs/heads/devel","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248289862,"owners_count":21078922,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automl","data-science","machine-learning","python"],"created_at":"2024-11-24T15:13:51.504Z","updated_at":"2025-04-10T20:20:54.791Z","avatar_url":"https://github.com/VIDA-NYU.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![PyPI version](https://badge.fury.io/py/alpha-automl.svg)](https://pypi.org/project/alpha-automl)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Tests](https://github.com/VIDA-NYU/alpha-automl/actions/workflows/build.yml/badge.svg)](https://github.com/VIDA-NYU/alpha-automl/actions/workflows/build.yml)\n[![Documentation Status](https://readthedocs.org/projects/alpha-automl/badge/?version=latest)](https://alpha-automl.readthedocs.io/en/latest/?badge=latest)\n\n\n\u003cimg src=\"https://github.com/VIDA-NYU/alpha-automl/raw/devel/Alpha-AutoML_logo.png\" width=30%\u003e\n\n\nAlpha-AutoML is an AutoML system that automatically searches for models and derives end-to-end pipelines that read, \npre-process the data, and train the model. Alpha-AutoML leverages recent advances in deep reinforcement learning and is \nable to adapt to different application domains and problems through incremental learning.\n\nAlpha-AutoML provides data scientists and data engineers the flexibility to address complex problems by leveraging the \nPython ecosystem, including open-source libraries and tools, support for collaboration, and infrastructure that enables \ntransparency and reproducibility. \n\nThis repository is part of New York University's implementation of the \n[Data Driven Discovery project (D3M)](https://datadrivendiscovery.org/).\n\n\n## Documentation\nDocumentation is available [here](https://alpha-automl.readthedocs.io/).\n\n\n## Installation\nThis package works with Python 3.6+ in Linux, Mac, and Windows.\n\nYou can install the latest stable version of this library from [PyPI](https://pypi.org/project/alpha-automl/):\n\n```\npip install alpha-automl\n```\n\nTo install the latest development version:\n\n```\npip install git+https://github.com/VIDA-NYU/alpha-automl@devel\n```\n\n\n## Docker\n\n### Pre-built Docker Image\nWe provide pre-built docker images with Jupyter and Alpha-AutoML pre-installed that you can use to quickly test Alpha-AutoML.\nTo test it, you can run the following command in your machine, and open Jupyter Notebook on your browser:\n\n```\ndocker run -p 8888:8888 ghcr.io/vida-nyu/alpha-automl\n```\nUsing this command, Jupyter Notebook will auto-generate a security token. The correct URL to access the Jupyter will be printed in the console output and will look like: `http://127.0.0.1:8888/?token=70ace7fa017c35ba0134dc7931add12bf55a69d4d4e6e54f`.\n\nAlternatively, if you want to provide a custom security token, you can run:\n```\ndocker run -p 8888:8888 -e JUPYTER_TOKEN=\"\u003cmy-token\u003e\" ghcr.io/vida-nyu/alpha-automl\n```\n\nIf you are running the Jupyter Notebook in a secure environment, the authentication can be disabled as follows:\n```\ndocker run -p 8888:8888 ghcr.io/vida-nyu/alpha-automl --NotebookApp.token=''\n```\n\n\n### Docker Image From Scratch\nIf you need to build an image from sources, you can use our [Dockerfile](https://github.com/VIDA-NYU/alpha-automl/blob/devel/Dockerfile). You can use a docker-build argument to select the packages that will be installed in the image (e.g., `full`, `timeseries`, `nlp`, etc) as follows:\n\n```\ndocker build -t alpha-automl --build-arg BUILD_OPTION=full .\n```\n\nOr simply a base version using (this will use less disk space but will not provide support for some tasks such as NLP and timeseries):\n```\ndocker build -t alpha-automl:latest --target alpha-automl .\n```\n\nYou can also build an image to use with JupyterHub as follows:\n```\ndocker build -t alpha-automl:latest-jupyterhub --target alpha-automl-jupyterhub .\n```\nSee also the documentation on how to setup Alpha-AutoML + JupyterHub on [Kubernetes](https://github.com/VIDA-NYU/alpha-automl/tree/devel/kubernetes).\n\n\n## Others\nDocumentation for the Streamlit app for image triage  developed by Jataware Corp is available [here](https://github.com/jataware/st-image-triage), see this [video demo](https://drive.google.com/file/d/1h3o0C0wNfT2AQduhqfgGEWl8fInFkdFZ/view).\n\n\n## Acknowledgment\nThe development of Alpha-AutoML was supported by the DARPA D3M Program. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvida-nyu%2Falpha-automl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvida-nyu%2Falpha-automl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvida-nyu%2Falpha-automl/lists"}