{"id":18830499,"url":"https://github.com/cmu-sei/juneberry","last_synced_at":"2025-04-14T03:42:40.147Z","repository":{"id":37092519,"uuid":"334182102","full_name":"cmu-sei/juneberry","owner":"cmu-sei","description":"Juneberry improves the experience of machine learning experimentation by providing a framework for automating the training, evaluation and comparison of multiple models against multiple datasets, reducing errors and improving reproducibility.","archived":false,"fork":false,"pushed_at":"2023-04-14T19:19:51.000Z","size":2360,"stargazers_count":33,"open_issues_count":1,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T17:46:51.349Z","etag":null,"topics":["machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cmu-sei.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-29T15:18:15.000Z","updated_at":"2024-11-12T21:56:50.000Z","dependencies_parsed_at":"2023-02-14T18:31:37.437Z","dependency_job_id":null,"html_url":"https://github.com/cmu-sei/juneberry","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmu-sei%2Fjuneberry","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmu-sei%2Fjuneberry/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmu-sei%2Fjuneberry/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmu-sei%2Fjuneberry/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cmu-sei","download_url":"https://codeload.github.com/cmu-sei/juneberry/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248818834,"owners_count":21166468,"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":["machine-learning"],"created_at":"2024-11-08T01:49:13.330Z","updated_at":"2025-04-14T03:42:40.128Z","avatar_url":"https://github.com/cmu-sei.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg align=\"right\" src=\"docs/logo.png\"\u003e\n\nREADME\n==========\n\n# Introduction\n\nJuneberry improves the experience of machine learning experimentation by providing a framework for automating \nthe training, evaluation, and comparison of multiple models against multiple datasets, thereby reducing errors and \nimproving reproducibility.\n\nThis README describes how to use the Juneberry framework to execute machine learning tasks. Juneberry follows a (mostly)\ndeclarative programming model composed of sets of config files (dataset, model, and experiment configurations) and\nPython plugins for features such as model construction and transformation.\n\nIf you're looking for a slightly more in depth description of Juneberry see [What Is Juneberry](docs/whatis.md).\n\nOther resources can be found at the [Juneberry Home Page](https://www.sei.cmu.edu/our-work/projects/display.cfm?customel_datapageid_4050=334902) \n\n# Supporting Documentation\n\n## How to Install Juneberry\n\nThe [Getting Started](docs/getting_started.md) documentation explains how to install Juneberry. It also \nincludes a simple test command you can use to verify the installation.\n\n## Experiment Overview\n\nThe [Workspace and Experiment Overview](docs/overview.md) documentation contains information about \nthe structure of the Juneberry workspace and how to organize experiments.\n\n## Experiment Tutorial\n\nThe [Juneberry Basic Tutorial](docs/tutorial.md) describes how to create a model, train the model, \nand run an experiment.\n\n## Configuring Juneberry\n\nThe [Juneberry Configuration Guide](docs/configuring.md) describes various ways to configure Juneberry.\n\n## Known Warnings\n\nDuring normal use of Juneberry, you may encounter warning messages. The\n[Known Warnings in Juneberry](docs/known_warnings.md) documentation contains information about known warning \nmessages and what (if anything) should be done about them.\n\n## Further Reading\n\nThe [vignettes](docs/vignettes) directory contains detailed walkthroughs of various Juneberry tasks. \nThe vignettes provide helpful examples of how to construct various Juneberry configuration files, \nincluding datasets, models, and experiments. A good start is \n[Replicating a Classic Machine Learning Result with Juneberry](docs/vignettes/vignette1/Replicating_a_Classic_Machine_Learning_Result_with_Juneberry.md).\n\n# Copyright\n\nCopyright 2022 Carnegie Mellon University.  See LICENSE.txt file for license terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmu-sei%2Fjuneberry","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcmu-sei%2Fjuneberry","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmu-sei%2Fjuneberry/lists"}