Install MLflow from PyPi via pip install mlflow

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

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

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.

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

mlflow ui

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 [email protected]:databricks/mlflow-example.git -P alpha=0.4

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

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

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