MLFlow Server Proxy

MLFlow Server Proxy lets you run arbitrary external MLFlow tracking server alongside your notebook server and provide authenticated web access to them using a path /mlflow next to others like /lab.

Alongside the python package that provides the main functionality, the JupyterLab extension @jupyterlab/server-proxy provides buttons in the JupyterLab launcher window to get to MLFlow tracking server.

This package was built using the jupyter-server-proxy cookiecutter template.

🤹‍Installation

Python package

You can currently install this package from PyPI.

pip install mlflow-server-proxy

To install with all the resources then use

pip install 'mlflow-server-proxy[resources]'

The above command will install MLFlow as well.

🤹‍Setup

The environment variable MLFLOW_STORE has to be set as backend storage for MLFlow to log models and artifacts locally.

This environment variable is then used in setting server uri while using MLFlow in your code as below:

import mlflow

server_uri = os.environ["MLFLOW_STORE"]
mlflow.set_tracking_uri(server_uri)

If you wish to use a non-local backend (i.e., Postgres on a remote server) set the URI as follows:

import mlflow

server_uri = os.environ["MLFLOW_BACKEND_URI"]
mlflow.set_tracking_uri(server_uri)

Requirements

Install MLFlow

This package's executes the standard mlflow server command. This command assumes the mlflow executable required to start the application is globally available.

pip install mlflow

Install Jupyter Notebook

This extension relies on the Jupyter Notebook to run. Refer to Jupyter's official documentaion for installation instructions.

Release History

• Version 0.1.0
  • Initial release - File based backend

Maintainer

• Gottam Sai Bharath

🏆Contributing

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🗝️License

Any contributions made under this project will be governed by the Apache License 2.0.

🗣️Code of Conduct

This project adheres to the American Express Community Guidelines. By participating, you are expected to honor these guidelines.