{"id":30290343,"url":"https://github.com/open-source-labs/mlflow-js","last_synced_at":"2025-08-16T23:12:45.799Z","repository":{"id":254206500,"uuid":"845808708","full_name":"open-source-labs/mlflow-js","owner":"open-source-labs","description":"A JavaScript client library for MLflow that streamlines machine learning lifecycle management in web environments.","archived":false,"fork":false,"pushed_at":"2025-07-31T00:53:45.000Z","size":4724,"stargazers_count":36,"open_issues_count":2,"forks_count":6,"subscribers_count":0,"default_branch":"dev","last_synced_at":"2025-08-09T10:58:59.616Z","etag":null,"topics":["ai","data-science","javascript","machine-learning","mlflow","mlops","node-js","typescript"],"latest_commit_sha":null,"homepage":"https://mlflow-js.org/","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/open-source-labs.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":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":"open-source-labs","custom":["https://donorbox.org/donations-to-oslabs-inc"]}},"created_at":"2024-08-22T01:00:28.000Z","updated_at":"2025-07-31T00:53:44.000Z","dependencies_parsed_at":"2024-09-16T21:51:33.898Z","dependency_job_id":"5822c5cb-3f6d-4309-961a-0a37f364eebb","html_url":"https://github.com/open-source-labs/mlflow-js","commit_stats":null,"previous_names":["oslabs-beta/mlflow-js"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/open-source-labs/mlflow-js","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-source-labs%2Fmlflow-js","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-source-labs%2Fmlflow-js/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-source-labs%2Fmlflow-js/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-source-labs%2Fmlflow-js/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/open-source-labs","download_url":"https://codeload.github.com/open-source-labs/mlflow-js/tar.gz/refs/heads/dev","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-source-labs%2Fmlflow-js/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269748220,"owners_count":24469107,"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","status":"online","status_checked_at":"2025-08-10T02:00:08.965Z","response_time":71,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["ai","data-science","javascript","machine-learning","mlflow","mlops","node-js","typescript"],"created_at":"2025-08-16T23:12:44.996Z","updated_at":"2025-08-16T23:12:45.784Z","avatar_url":"https://github.com/open-source-labs.png","language":"TypeScript","funding_links":["https://github.com/sponsors/open-source-labs","https://donorbox.org/donations-to-oslabs-inc"],"categories":[],"sub_categories":[],"readme":"\u003cdiv style=\"text-align: center\"\u003e\n\u003cimg src=\"/mlflow-site/public/assets/mlflow-js-logo-whitebg.png\" width=600px;\"/\u003e\u003c/div\u003e\n\n\u003cbr\u003e\n\n## About\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](/LICENSE)\n![Release](https://img.shields.io/badge/Release-v1.0.1-426B20)\n![Build](https://img.shields.io/badge/Build-Passing-brightgreen.svg)\n![Coverage](https://img.shields.io/badge/Coverage-89%25-c7ea46.svg)\n[![Contributions](https://img.shields.io/badge/Contributions-Welcome-brightgreen.svg)](/CONTRIBUTING.md)\n\n\u003ci\u003eMLflow.js\u003c/i\u003e is an open-source JavaScript library that helps developers track machine learning experiments and manage models with MLflow, providing functionalities for machine learning lifecycle in JavaScript/TypeScript environments.\n\n\u003cbr\u003e\n\n## Features\n\n\u003ci\u003eMLflow.js\u003c/i\u003e covers all REST API endpoints under MLflow's Tracking Server and Model Registry. Moreover, high-level abstractions have been developed to facilitate developers' common ML workflows. It provides some key advantages:\n\n- Native JavaScript Integration: Seamlessly integrate MLflow capabilities within JavaScript codebases\n- Type Safety: Built with TypeScript for enhanced developer experience and code reliability\n- Modular Architecture: Designed with object-oriented structure that mirrors MLflow's concepts while being extensible and maintainable\n- Client-side ML Compatibility: Complements popular JavaScript libraries like TensorFlow.js, enabling ML deployment directly in the browser or client side\n\n\u003cbr\u003e\n\n## Built with\n\n[![TypeScript](https://img.shields.io/badge/TypeScript-0077B5?style=for-the-badge\u0026logo=typescript\u0026logoColor=white)](https://www.typescriptlang.org/)\n[![JavaScript](https://img.shields.io/badge/JavaScript-000435?style=for-the-badge\u0026logo=javascript\u0026logoColor=00fff)](https://www.javascript.com/)\n[![React](https://img.shields.io/badge/React-36454F?style=for-the-badge\u0026logo=React\u0026logoColor=00fff)](https://react.dev/)\n[![Next.js](https://img.shields.io/badge/Next.js-24292e?style=for-the-badge\u0026logo=next.js\u0026logoColor=00fff)](https://nextjs.org/)\n[![TailwindCSS](https://img.shields.io/badge/Tailwind_CSS-008080?style=for-the-badge\u0026logo=tailwindcss\u0026logoColor=bfffff)](https://tailwindcss.com/)\n[![ESLINT](https://img.shields.io/badge/ESLINT-4B32C3?style=for-the-badge\u0026logo=eslint\u0026logoColor=bfffff)](https://eslint.org/)\n[![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge\u0026logo=node.js\u0026logoColor=ffffff)](https://nodejs.org/en/)\n[![Jest](https://img.shields.io/badge/Jest-800020?style=for-the-badge\u0026logo=jest\u0026logoColor=00fff)](https://jestjs.io/)\n[![GitHub Actions](https://img.shields.io/badge/GitHub_Actions-30363d?style=for-the-badge\u0026logo=github\u0026logoColor=00fff)](https://github.com/features/actions)\n[![Docker](https://img.shields.io/badge/Docker-lightblue?style=for-the-badge\u0026logo=Docker\u0026logoColor=00fff)](https://www.docker.com/)\n[![NPM](https://img.shields.io/badge/NPM-CC3534?style=for-the-badge\u0026logo=npm\u0026logoColor=00fff)](https://www.npmjs.com/)\n[![Vercel](https://img.shields.io/badge/Vercel-966FD6?style=for-the-badge\u0026logo=Vercel\u0026logoColor=00fff)](https://vercel.com/)\n\n\u003cbr\u003e\n\n## Prerequisites\n\n### Set Up MLflow\n\nEnsure MLflow is installed on your system:\n\n```bash\npip install mlflow\n```\n\nFor MacOS users, we recommend creating and activating a Python virtual environment to avoid common installation issues and keep dependencies isolated:\n\n```bash\npython3 -m venv mlflow-venv\nsource mlflow-venv/bin/activate\npip install mlflow\n```\n\nRemember to activate your virtual environment (`source mlflow-venv/bin/activate`) each time you open a new terminal before running MLflow commands.\n\n### Start the MLflow Tracking Server\n\nAfter activating the virtual environment, start the MLflow tracking server locally, use the following command:\n\n```bash\nmlflow ui --port 5001\n```\n\nThis will launch the MLflow UI on your local machine at `http://localhost:5001`.\n\n### Alternative Docker approach to the MLflow Tracking Server\n\nInstall [Docker Desktop](https://www.docker.com/).\n\nTo start the MLflow tracking server locally, use the following commands:\n\n```bash\ndocker pull ghcr.io/mlflow/mlflow\ndocker run -p 5001:5001 ghcr.io/mlflow/mlflow:latest mlflow server --host 0.0.0.0 --port 5001\n```\n\nThis will launch the MLflow UI on your local machine at `http://localhost:5001`.\n\n### Development Setup\n\nFor development environment setup instructions, please refer to our [Contributing Guide](/CONTRIBUTING.md).\n\n\u003cbr\u003e\n\n## Quickstart\n\n### Install \u003ci\u003emlflow.js\u003c/i\u003e Library\n\nTo use the \u003ci\u003emlflow.js\u003c/i\u003e library, navigate to your project directory and install it via npm:\n\n```bash\nnpm install mlflow-js\n```\n\n### Usage Example\n\nHere is an example of how to use the \u003ci\u003emlflow.js\u003c/i\u003e library to create an experiment:\n\n```JavaScript\nimport Mlflow from 'mlflow-js';\n\n// Initialize the MLflow client\nconst mlflow = new Mlflow(process.env.MLFLOW_TRACKING_URI);\n\n// Create a new experiment\nasync function createExperiment(){\n\tawait mlflow.createExperiment('My Experiment');\n\tconsole.log('Experiment created successfully');\n}\n\ncreateExperiment();\n\n```\n\n\u003cbr\u003e\n\n## Resources\n\n- [Example Repository](https://github.com/oslabs-beta/mlflow-js/tree/dev/mlflow/examples) - Practical examples demonstrating \u003ci\u003eMLflow.js\u003c/i\u003e's functionality\n- [Quick Tutorials](https://www.mlflow-js.org/) - Video walkthrough of the example code with MLflow UI\n- [Read our Medium Article](link) - Overview on why we built \u003ci\u003eMLflow.js\u003c/i\u003e and how it enhances ML workflows in JavaScript environments\n\n\u003cbr\u003e\n\n## Documentation\n\nOfficial documentation for \u003ci\u003eMLflow.js\u003c/i\u003e can be found \u003ca href=\"https://www.mlflow-js.org/documentation\"\u003ehere\u003c/a\u003e.\n\n### High-Level Workflows\n\n**Experiment Manager**\n\n- runExistingExperiment - Full workflow of creating, naming, and starting a run under an existing experiment, logging metrics, params, tags, and the model, and finishing the run\n- runNewExperiment - Full workflow of creating, naming, and starting a run under a new experiment, logging metrics, params, tags, and the model, and finishing the run\n- experimentSummary - Returns an array of all the passed-in experiment's runs, sorted according to the passed-in metric\n\n**Run Manager**\n\n- cleanupRuns - Deletes runs that do not meet certain criteria and return an object of deleted runs and details\n- copyRun - Copies a run from one experiment to another (without artifacts and models)\n\n**Model Manager**\n\n- createRegisteredModelWithVersion - Creates a new registered model and the first version of that model\n- updateRegisteredModelDescriptionAndTag - Updates a registered model's description and tags\n- updateAllLatestModelVersion - Updates the latest version of the specified registered model's description, adds a new alias, and tag key/value for the latest version\n- setLatestModelVersionTag - Adds a new tag key/value for the latest version of the specified registered model\n- setLatestModelVersionAlias - Adds an alias for the latest version of the specified registered model\n- updateLatestModelVersion - Updates the description of the latest version of a registered model\n- updateAllModelVersion - Updates the specified version of the specified registered model's description and adds a new alias and tag key/value for that specified version\n- deleteLatestModelVersion - Deletes the latest version of the specified registered model\n- createModelFromRunWithBestMetric - Creates a new model with the specified model name from the run with the best specified metric\n\n\u003cbr\u003e\n\n## Contributing\n\nWe welcome contributions to \u003ci\u003eMLflow.js\u003c/i\u003e! See our [Contributing Guide](/CONTRIBUTING.md) for more details on how to get started.\n\n**Note:** \u003ci\u003eMLflow.js\u003c/i\u003e is community-based and maintained by a goodwill.\n\n\u003cbr\u003e\n\n## License\n\n[MIT License](/LICENSE)\n\n\u003cbr\u003e\n\n## Meet The Team\n\n| Name           | GitHub                                                                                                                    | LinkedIn                                                                                                                                                 |\n| -------------- | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Yiqun Zheng    | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github)](https://github.com/yiqunzheng)    | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/yiqunzheng/)   |\n| Kyler Chiago   | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github)](https://github.com/Kyler-Chiago)  | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/kyler-chiago/) |\n| Austin Fraser  | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github)](https://github.com/austinbfraser) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](http://www.linkedin.com/in/austin-fraser)  |\n| Stephany Ho    | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github)](https://github.com/seneyu)        | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/stephanyho/)   |\n| Winston Ludlam | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github)](https://github.com/winjolu/)      | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/wjludlam/)     |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-source-labs%2Fmlflow-js","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopen-source-labs%2Fmlflow-js","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-source-labs%2Fmlflow-js/lists"}