CONTRIBUTING.rst

Contributing to MLflow

We welcome community contributions to MLflow. This page describes how to develop/test your changes to MLflow locally.

Prerequisites

We recommend installing MLflow in its own virtualenv for development, as follows:

virtualenv env
source env/bin/activate
pip install -r dev-requirements.txt
pip install -r tox-requirements.txt
pip install -e .

npm is required to run the Javascript dev server. You can verify that npm is on the PATH by running npm -v, and install npm if needed.

Install Node Modules

Before running the Javascript dev server or building a distributable wheel, install Javascript dependencies via:

cd mlflow/server/js
npm install
cd - # return to root repository directory

If modifying dependencies in mlflow/server/js/package.json, run npm update within mlflow/server/js to install the updated dependencies.

Launching the Development UI

We recommend Running the Javascript Dev Server - otherwise, the tracking frontend will request files in the mlflow/server/js/build directory, which is not checked into Git. Alternatively, you can generate the necessary files in mlflow/server/js/build as described in Building a Distributable Artifact.

Tests and Lint

Please verify that the unit tests & linter pass before submitting a pull request by running:

pytest
./lint.sh

Running the Javascript Dev Server

Install Node Modules, then run the following:

In one shell:

mlflow ui

In another shell:

cd mlflow/server/js
npm start

The MLflow Tracking UI will show runs logged in ./mlruns at http://localhost:3000.

Building a Distributable Artifact

Install Node Modules, then run the following:

Generate JS files in mlflow/server/js/build:

cd mlflow/server/js
npm run build

Build a pip-installable wheel in dist/:

cd -
python setup.py bdist_wheel

Writing Docs

Install the necessary Python dependencies via pip install -r dev-requirements.txt. Then run

cd docs
make livehtml