gdtm86 / mlflow

Open source platform for the complete machine learning lifecycle

MLflow Alpha Release

Warning

The current version of MLflow is an alpha. This means that APIs and storage formats are subject to change!

Installing

Install MLflow from PyPi via pip install mlflow

MLflow requires conda to be on the PATH for the projects feature.

Documentation

Official documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.

Running a Sample App With the Tracking API

The programs in example use the MLflow Tracking API. For instance, run:

python example/quickstart/test.py

This program will use MLflow log API, which stores tracking data in ./mlruns, which can then be viewed with the Tracking UI.

Launching the Tracking UI

The MLflow Tracking UI will show runs logged in ./mlruns at http://localhost:5000. Start it with:

mlflow ui

Running a Project from a URI

The mlflow run command lets you run a project packaged with a MLproject file from a local path or a Git URI:

mlflow run example/tutorial -P alpha=0.4

mlflow run [e-mail]:databricks/mlflow-example.git -P alpha=0.4

See example/tutorial for a sample project with an MLproject file.

Saving and Serving Models

To illustrate managing models, the mlflow.sklearn package can log Scikit-learn models as MLflow artifacts and then load them again for serving. There is an example training application in example/quickstart/test_sklearn.py that you can run as follows:

$ python example/quickstart/test_sklearn.py
Score: 0.666
Model saved in run <run-id>

$ mlflow sklearn serve -r <run-id> model

$ curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST localhost:5000/invocations

Contributing

We happily welcome contributions, please see our contribution guide for details.