{"id":13701732,"url":"https://github.com/project-codeflare/codeflare","last_synced_at":"2025-10-11T02:31:50.963Z","repository":{"id":45075534,"uuid":"366455527","full_name":"project-codeflare/codeflare","owner":"project-codeflare","description":"Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.","archived":false,"fork":false,"pushed_at":"2023-09-19T12:21:13.000Z","size":1198,"stargazers_count":231,"open_issues_count":17,"forks_count":36,"subscribers_count":4,"default_branch":"develop","last_synced_at":"2025-03-29T05:05:22.438Z","etag":null,"topics":["automl","data-science","hyperparameter-optimization","machine-learning","pipelines","ray","sklearn","workflows"],"latest_commit_sha":null,"homepage":"https://codeflare.dev","language":"Jupyter Notebook","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/project-codeflare.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":"AUTHORS","dei":null}},"created_at":"2021-05-11T16:57:58.000Z","updated_at":"2025-03-14T16:56:36.000Z","dependencies_parsed_at":"2024-04-13T01:00:33.476Z","dependency_job_id":"64cd7de5-22ff-4ae6-b9d9-20b2e4c41c38","html_url":"https://github.com/project-codeflare/codeflare","commit_stats":{"total_commits":238,"total_committers":18,"mean_commits":"13.222222222222221","dds":"0.44957983193277307","last_synced_commit":"cd1ec5aabb715e4130f646219f1fa3c5c5c891f6"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/project-codeflare%2Fcodeflare","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/project-codeflare%2Fcodeflare/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/project-codeflare%2Fcodeflare/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/project-codeflare%2Fcodeflare/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/project-codeflare","download_url":"https://codeload.github.com/project-codeflare/codeflare/tar.gz/refs/heads/develop","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247294539,"owners_count":20915340,"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","hyperparameter-optimization","machine-learning","pipelines","ray","sklearn","workflows"],"created_at":"2024-08-02T20:01:56.062Z","updated_at":"2025-10-11T02:31:50.866Z","avatar_url":"https://github.com/project-codeflare.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"\u003c!--\n{% comment %}\nCopyright 2021, 2022, 2023 IBM\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n{% endcomment %}\n--\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"./images/codeflare_square.svg\" width=\"200\" height=\"200\"\u003e\n\u003c/p\u003e\n\n\u003c!--\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"./images/pipelines.svg\" width=\"340\" height=\"207\"\u003e\n\u003c/p\u003e \n--\u003e\n\n[![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](http://www.apache.org/licenses/LICENSE-2.0)\n[![Build\nStatus](https://travis-ci.com/project-codeflare/codeflare.svg?branch=main)](https://travis-ci.com/project-codeflare/codeflare.svg?branch=main) \n[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/project-codeflare/codeflare/issues)\n[![GitHub](https://img.shields.io/badge/CodeFlare-Join%20Slack-blue)](https://invite.playplay.io/invite?team_id=T04KQQBTDN3)\n\n\u003c!-- \u003e\u003e **⚠ UPDATE**  \n\u003e CodeFlare is evolving! Check our [updates](https://github.com/project-codeflare/codeflare#pipeline-execution-and-scaling) for CodeFlare Pipelines and related contributions to Ray Workflows under Ray project. --\u003e\n\n# Simplified and efficient AI/ML on the hybrid cloud\n\nCodeFlare provides a simple, user-friendly abstraction for developing, scaling, and managing resources for distributed AI/ML on the Hybrid Cloud platform with OpenShift Container Platform.\n\n---\n\n## 📦 Stack Components and Features\n\nCodeFlare stack consists of the following main components. This project is organized as a metarepo, gathering pointers and artifacts to deploy and use the stack.\n\n* **Simplified user experience**:\nCodeFlare [SDK](https://github.com/project-codeflare/codeflare-sdk) and [CLI](https://github.com/project-codeflare/codeflare-cli) to define, develop, and control remote distributed compute jobs and infrastructure from either a python-based environment or command-line interface\n\n* **Efficient resource management**:\nMulti-Cluster Application Dispatcher [(MCAD)](https://github.com/project-codeflare/multi-cluster-app-dispatcher) for queueing, resource quotas, and management of batch jobs. And [Instascale](https://github.com/project-codeflare/instascale) for on-demand resource scaling of an OpenShift cluster\n\n* **Automated and streamlined deployment**:\n[CodeFlare Operator](https://github.com/project-codeflare/codeflare-operator) for automating deployment and configuration of the Project CodeFlare stack\n\nWith CodeFlare stack, users automate and simplify the execution and scaling of the steps in the life cycle of model development, from data pre-processing, distributed model training, model adaptation and validation.\n\nThrough transparent integration with [Ray](https://github.com/ray-project/ray) and [PyTorch](https://github.com/pytorch/pytorch) frameworks, and the rich library ecosystem that run on them, CodeFlare enables data scientists to **spend more time on model development and minimum time on resource deployment and scaling**. \n\nSee below our stack and how to get started.\n\n--- \n## ⚙️ Project CodeFlare Ecosystem\n\nIn addition to running standalone, Project CodeFlare is deployed as part of and integrated with the [Open Data Hub](https://github.com/opendatahub-io/distributed-workloads), leveraging [OpenShift Container Platform](https://www.openshift.com). \n\nWith OpenShift, CodeFlare can be deployed anywhere, from on-prem to cloud, and integrate easily with other cloud-native ecosystems.\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"./images/codeflare_stack.svg\" width=\"506\" height=\"520\"\u003e\n\u003c/p\u003e\n\n---\n\n## 🛠️ Getting Started\n\n### Learning\n\nWatch [this video](https://www.youtube.com/watch?v=OAzFBFL5B0k) for an introduction to Project CodeFlare and what the\nstack can do.\n\n### Quick Start\n\nTo get started using the Project CodeFlare stack, try this [end-to-end example](https://github.com/opendatahub-io/distributed-workloads/blob/main/Quick-Start.md)!\n\nFor more basic walk-throughs and in-depth tutorials, see our [demo notebooks](https://github.com/project-codeflare/codeflare-sdk/tree/main/demo-notebooks/guided-demos)!\n\n## Development\n\nSee more details in any of the component repos linked above, or get started by taking a look at the [project board](https://github.com/orgs/project-codeflare/projects/8) for open tasks/issues!\n\n### Architecture\n\nWe attempt to document all architectural decisions in our [ADR documents](https://github.com/project-codeflare/adr). Start here to understand the architectural details of Project CodeFlare.\n\n---\n\n## 🎉 Getting Involved and Contributing\n\nJoin our [Slack community][slack] to get involved or ask questions.\n\n## Blog\n\nCodeFlare related blogs are published on our [Medium publication](https://medium.com/codeflare).\n\n## License\n\nCodeFlare is an open-source project with an [Apache 2.0 license](LICENSE).\n\n[codeflare-sdk]: https://github.com/project-codeflare/codeflare-sdk\n[codeflare-cli]: https://github.com/project-codeflare/codeflare-cli\n[mcad]: https://github.com/project-codeflare/multi-cluster-app-dispatcher\n[instascale]: https://github.com/project-codeflare/instascale\n[codeflare-operator]: https://github.com/project-codeflare/codeflare-operator\n[distributed-workloads]: https://github.com/opendatahub-io/distributed-workloads\n[quickstart]: https://github.com/opendatahub-io/distributed-workloads/blob/main/Quick-Start.md\n[slack]: https://invite.playplay.io/invite?team_id=T04KQQBTDN3\n[adr]: https://github.com/project-codeflare/adr\n[demos]: https://github.com/project-codeflare/codeflare-sdk/tree/main/demo-notebooks/guided-demos\n[board]: https://github.com/orgs/project-codeflare/projects/8\n[youtube-demo]: https://www.youtube.com/watch?v=OAzFBFL5B0k\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fproject-codeflare%2Fcodeflare","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fproject-codeflare%2Fcodeflare","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fproject-codeflare%2Fcodeflare/lists"}