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<i>mlflow.js</i> 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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<ahref="">Visit the official mlflow.js site for more info!</a>
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<ahref="https://www.mlflow-js.org/">Visit the official <i>mlflow.js</i> site for more info!</a>
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Visit our LinkedIn page below:
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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. Official documentation for MLflow.js can be found <ahref="">here</a>. Moreover, high-level abstraction workflows have been developed to facilitate developers' work processes.
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<i>mlflow.js</i> covers all REST API endpoints under MLflow's Tracking Server and Model Registry. Official documentation for <i>mlflow.js</i> can be found <ahref="https://www.mlflow-js.org/documentation">here</a>. Moreover, high-level abstraction workflows have been developed to facilitate developers' work processes.
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### High-Level Abstraction Workflows
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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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pip install mlflow
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```
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Note: MLflow is compatible with MacOS. If you encountner 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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**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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## Quickstart
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### Install MLflow.js Library
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### Install <i>mlflow.js</i> Library
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To use the MLflow.js library, navigate to your project directory and install it via npm:
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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 MLflow.js library to create an experiment:
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Here is an example of how to use the <i>mlflow.js</i> library to create an experiment:
|Stephany Ho |[](https://github.com/seneyu)|[](https://www.linkedin.com/in/stephanyho/)|
|Stephany Ho |[](https://github.com/seneyu)|[](https://www.linkedin.com/in/stephanyho/)|
<!-- README for NPM; the one for GitHub is in .github directory. -->
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# mlflow.js
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A JavaScript library designed to provide seamless integration with MLflow's REST API. This package offers access to all the essential endpoints for both the MLflow Tracking Server and Model Registry, along with high-level abstraction workflows, enabling efficient management of machine learning experiments and model lifecycle.
See [https://www.mlflow-js.org/](https://www.mlflow-js.org/)
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# API
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## Tracking Server
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- Experiment Client (8)
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- Create Experiment, Search Experiment, Get Experiment, Get Experiment By Name, Delete Experiment, Restore Experiment, Update Experiment, Set Experiment Tag
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- Run Client (15)
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- Create Run, Delete Run, Restore Run, Get Run, Update Run, Log Metric, Log Batch, Log Model, Log Inputs, Set Tag, Delete Tag, Log Param, Get Metric History, Search Runs, List Artifacts
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## Model Registry
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- Model Registry Client (12)
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- Create, Registered Model, Get Registered Model, Rename Registered Model, Update Registered Model, Delete Registered Model, Get Latest Model Versions, Search Registered Models, Set Registered Model Tag, Delete Registered Model Tag, Set Registered Model Alias, Delete Registered Model Alias, Get Model Version By Alias
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- Model Version Client (9)
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- Create Model Version, Get Model Version, Update Model Version, Search Model Versions, Get Download URI for Model Version Artifacts, Transition Model Version Stage, Set Model Version Tag, Delete Model Version Tag, Delete Model Version
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## High-Level Abstraction Workflows
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- Experiment Manager (3)
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- Run Existing Experiment, Run New Experiment, Experiment Summary
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- Run Manager (2)
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- Cleanup Runs, Copy Run
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- Model Manager (9)
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- Create Registered Model With Version, Update Registered Model Description And Tag, Update All Latest Model Version, Set Latest Model Version Tag, Set Latest Model Version Alias, Update Latest Model Version, Update All Model Version, Delete Latest Model Version, Create Model From Run With Best Metric
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