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 @jupyterhub/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.

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.

The environment variable MLFLOW_STORE has to be set as backend storage for MLFlow to log models and artifacts. Default is /tmp

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)

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

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

• Version 0.1.0
  • Initial release - File based backend

• Gottam Sai Bharath

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