mlflow.js is an open-source npm library designed for JavaScript developers who want to integrate with MLflow, providing tools and functionalities for managing machine learning lifecycle.

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Ensure MLflow is installed on your system:

pip install mlflow

Note: MLflow is compatible with MacOS. If you encoutner 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.

To start the MLflow tracking server locally, use the following command:

mlflow ui --port 5000

This will launch the MLflow UI on your local machine at http://localhost:5000.

To use the MLflow.js library, navigate to your project directory and install it via npm:

npm instatll mlflow-js

Here is an example of how to use the MLflow.js library to create an experiment:

import Mlflow from 'mlflow-js'; // Initialize the MLflow client const mlflow = new Mlflow('http://127.0.0.1:5000'); // Get the experiment client const experimentClient = mlflow.getExperimentClient(); // Create a new experiment async function createExperiment(){ try { await experimentClient.createExperiment('My Experiment'); console.log('Experiment created successfully'); } catch (error) { console.error('Error creating experiment:', error); } } createExperiment();

Official documentation for MLflow.js can be found at ...

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