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Merge pull request #109 from oslabs-beta/dev
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‎.github/workflows/mlflow-js.yaml

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name: mlflow
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on:
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pull_request:
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branches:
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- main
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jobs:
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build-and-test:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Use Node.js
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uses: actions/setup-node@v4
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with:
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node-version: '22.7'
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- name: Install dependencies
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working-directory: ./mlflow
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run: npm ci
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- name: Eslint
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working-directory: ./mlflow
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run: npm run lint
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Run MLflow server
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run: |
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docker run -d -p 5002:5002 --name mlflow-container ghcr.io/mlflow/mlflow:latest mlflow server --host 0.0.0.0 --port 5002
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sleep 30
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- name: Run tests
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working-directory: ./mlflow
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run: npm run test
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- name: Stop MLflow server
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run: docker stop mlflow-container
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- name: Build
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working-directory: ./mlflow
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run: npm run build

‎CONTRIBUTING.md

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# Contribution Guidelines
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We are always open to accepting any potential contributions. Here is how you can contribute:
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1. Fork the repository
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2. Clone your forked repository
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```bash
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git clone https://github.com/your-username/repository-name.git
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```
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3. Create your feature branch
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```bash
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git checkout -b feature/AmazingFeature
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```
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4. Install dependencies for both mlflow and mlflow-site directories
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```bash
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cd /mlflow && npm install
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cd ../mlflow-site && npm install
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```
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5. Start the MLflow Tracking Server
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```bash
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cd ../mlflow && npm run docker
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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6. Make your changes
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7. Run ESLint to check code style
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```bash
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npm run lint
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```
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8. Run tests to ensure your changes don't break existing functionality
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(Make sure you have mlflow UI server running on port 5002. We set 5002 as our default port for testing.)
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```bash
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cd /mlflow && npm run dockerTest # Run this in a separate terminal
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npm run test
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5002`, and run the Jest tests.
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9. Add and commit your changes
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If the tests all pass:
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```bash
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git add .
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git commit -m 'Add AmazingFeature'
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```
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10. Push to the branch
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```bash
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git push origin feature/AmazingFeature
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```
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11. Open a Pull Request
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**Note:** Please ensure your code adheres to our style guidelines and includes appropriate documentation for any new features.

‎LICENSE

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MIT License
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Copyright (c) 2024 Open Source Labs Beta
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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<div style="text-align: center">
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<img src="/mlflow-site/public/assets/mlflow-js-logo-whitebg.png" width=600px;"/></div>
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<br>
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## About
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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](/LICENSE)
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![Release](https://img.shields.io/badge/Release-v1.0.1-426B20)
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![Build](https://img.shields.io/badge/Build-Passing-brightgreen.svg)
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![Coverage](https://img.shields.io/badge/Coverage-87%25-c7ea46.svg)
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[![Contributions](https://img.shields.io/badge/Contributions-Welcome-brightgreen.svg)](/CONTRIBUTING.md)
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<i>MLflow.js</i> 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.
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<br>
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## Features
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<i>MLflow.js</i> 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:
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- Native JavaScript Integration: Seamlessly integrate MLflow capabilities within JavaScript codebases
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- Type Safety: Built with TypeScript for enhanced developer experience and code reliability
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- Modular Architecture: Designed with object-oriented structure that mirrors MLflow's concepts while being extensible and maintainable
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- Client-side ML Compatibility: Complements popular JavaScript libraries like TensorFlow.js, enabling ML deployment directly in the browser or client side
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<br>
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## Built with
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[![TypeScript](https://img.shields.io/badge/TypeScript-0077B5?style=for-the-badge&logo=typescript&logoColor=white)](https://www.typescriptlang.org/)
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[![JavaScript](https://img.shields.io/badge/JavaScript-000435?style=for-the-badge&logo=javascript&logoColor=00fff)](https://www.javascript.com/)
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[![React](https://img.shields.io/badge/React-36454F?style=for-the-badge&logo=React&logoColor=00fff)](https://react.dev/)
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[![Next.js](https://img.shields.io/badge/Next.js-24292e?style=for-the-badge&logo=next.js&logoColor=00fff)](https://nextjs.org/)
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[![TailwindCSS](https://img.shields.io/badge/Tailwind_CSS-008080?style=for-the-badge&logo=tailwindcss&logoColor=bfffff)](https://tailwindcss.com/)
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[![ESLINT](https://img.shields.io/badge/ESLINT-4B32C3?style=for-the-badge&logo=eslint&logoColor=bfffff)](https://eslint.org/)
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[![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge&logo=node.js&logoColor=ffffff)](https://nodejs.org/en/)
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[![Jest](https://img.shields.io/badge/Jest-800020?style=for-the-badge&logo=jest&logoColor=00fff)](https://jestjs.io/)
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[![GitHub Actions](https://img.shields.io/badge/GitHub_Actions-30363d?style=for-the-badge&logo=github&logoColor=00fff)](https://github.com/features/actions)
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[![Docker](https://img.shields.io/badge/Docker-lightblue?style=for-the-badge&logo=Docker&logoColor=00fff)](https://www.docker.com/)
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[![NPM](https://img.shields.io/badge/NPM-CC3534?style=for-the-badge&logo=npm&logoColor=00fff)](https://www.npmjs.com/)
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[![Vercel](https://img.shields.io/badge/Vercel-966FD6?style=for-the-badge&logo=Vercel&logoColor=00fff)](https://vercel.com/)
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<br>
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## Prerequisites
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### Set Up MLflow
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Ensure MLflow is installed on your system:
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```bash
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pip install mlflow
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```
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**Note:** MLflow is compatible with MacOS. If you encounter issues with the default system Python, consider installing Python 3 via the Homebrew package manger using `brew install python`. In this case, installing MLflow is now `pip3 install mlflow`.
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### Start the MLflow Tracking Server
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To start the MLflow tracking server locally, use the following command:
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```bash
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mlflow ui --port 5001
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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### Alternative Docker approach to the MLflow Tracking Server
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Install [Docker Desktop](https://www.docker.com/).
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To start the MLflow tracking server locally, use the following commands:
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```bash
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docker pull ghcr.io/mlflow/mlflow
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docker run -p 5001:5001 ghcr.io/mlflow/mlflow:latest mlflow server --host 0.0.0.0 --port 5001
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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### Development Setup
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For development environment setup instructions, please refer to our [Contributing Guide](/CONTRIBUTING.md).
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<br>
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## Quickstart
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### Install <i>mlflow.js</i> Library
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To use the <i>mlflow.js</i> library, navigate to your project directory and install it via npm:
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```bash
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npm install mlflow-js
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```
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### Usage Example
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Here is an example of how to use the <i>mlflow.js</i> library to create an experiment:
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```JavaScript
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import Mlflow from 'mlflow-js';
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// Initialize the MLflow client
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const mlflow = new Mlflow(process.env.MLFLOW_TRACKING_URI);
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// Create a new experiment
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async function createExperiment(){
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await mlflow.createExperiment('My Experiment');
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console.log('Experiment created successfully');
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}
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createExperiment();
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```
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<br>
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## Resources
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- [Example Repository](https://github.com/oslabs-beta/mlflow-js/tree/dev/mlflow/examples) - Practical examples demonstrating <i>MLflow.js</i>'s functionality
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- [Quick Tutorials](https://www.mlflow-js.org/) - Video walkthrough of the example code with MLflow UI
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- [Read our Medium Article](link) - Overview on why we built <i>MLflow.js</i> and how it enhances ML workflows in JavaScript environments
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<br>
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## Documentation
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Official documentation for <i>MLflow.js</i> can be found <a href="https://www.mlflow-js.org/documentation">here</a>.
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### High-Level Workflows
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**Experiment Manager**
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- 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
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- 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
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- experimentSummary - Returns an array of all the passed-in experiment's runs, sorted according to the passed-in metric
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**Run Manager**
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- cleanupRuns - Deletes runs that do not meet certain criteria and return an object of deleted runs and details
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- copyRun - Copies a run from one experiment to another (without artifacts and models)
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**Model Manager**
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- createRegisteredModelWithVersion - Creates a new registered model and the first version of that model
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- updateRegisteredModelDescriptionAndTag - Updates a registered model's description and tags
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- updateAllLatestModelVersion - Updates the latest version of the specified registered model's description, adds a new alias, and tag key/value for the latest version
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- setLatestModelVersionTag - Adds a new tag key/value for the latest version of the specified registered model
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- setLatestModelVersionAlias - Adds an alias for the latest version of the specified registered model
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- updateLatestModelVersion - Updates the description of the latest version of a registered model
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- 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
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- deleteLatestModelVersion - Deletes the latest version of the specified registered model
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- createModelFromRunWithBestMetric - Creates a new model with the specified model name from the run with the best specified metric
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<br>
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## Contributing
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We welcome contributions to <i>MLflow.js</i>! Please see our [Contributing Guide](/CONTRIBUTING.md) for more details on how to get started.
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<br>
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## License
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[MIT License](/LICENSE)
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<br>
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## Meet The Team
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| Name | GitHub | LinkedIn |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Yiqun Zheng | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github)](https://github.com/yiqunzheng) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/yiqunzheng/) |
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| Kyler Chiago | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github)](https://github.com/Kyler-Chiago) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kyler-chiago/) |
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| Austin Fraser | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github)](https://github.com/austinbfraser) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](http://www.linkedin.com/in/austin-fraser) |
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| Stephany Ho | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github)](https://github.com/seneyu) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/stephanyho/) |
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| Winston Ludlam | [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github)](https://github.com/winjolu/) | [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/wjludlam/) |

‎license.md

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This file was deleted.

‎mlflow-site/.eslintrc.json

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{
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"extends": ["next/core-web-vitals", "next/typescript"]
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}

‎mlflow-site/.gitignore

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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
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# dependencies
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/node_modules
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/.pnp
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.pnp.js
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.yarn/install-state.gz
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# testing
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/coverage
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# next.js
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/.next/
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/out/
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# production
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/build
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# misc
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.DS_Store
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../.DS_Store
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*.pem
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# debug
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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# local env files
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.env*.local
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# vercel
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.vercel
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# typescript
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*.tsbuildinfo
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next-env.d.ts

‎mlflow-site/README.md

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## Getting Started
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First, run the development server:
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```bash
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npm run dev
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# or
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yarn dev
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# or
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pnpm dev
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# or
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bun dev
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```
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Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.

‎mlflow-site/next.config.mjs

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/** @type {import('next').NextConfig} */
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const nextConfig = {
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images: {
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remotePatterns: [
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{
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protocol: 'https',
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hostname: 'i.giphy.com'
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},
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],
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},
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};
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export default nextConfig;

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