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README.rst

MLflow Beta Release

Note: The current version of MLflow is a beta release. This means that APIs and data formats are subject to change!

Note 2: We do not currently support running MLflow on Windows. Despite this, we would appreciate any contributions to make MLflow work better on Windows.

Installing

Install MLflow from PyPi via pip install mlflow

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

Nightly snapshots of MLflow master are also available here.

Documentation

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

Community

To discuss MLflow or get help, please subscribe to our mailing list (mlflow-users@googlegroups.com) or join us on Slack at https://tinyurl.com/mlflow-slack.

To report bugs, please use GitHub issues.

Running a Sample App With the Tracking API

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

python examples/quickstart/mlflow_tracking.py

This program will use MLflow Tracking API, which logs tracking data in ./mlruns. This 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

Note: Running mlflow ui from within a clone of MLflow is not recommended - doing so will run the dev UI from source. We recommend running the UI from a different working directory, using the --file-store option to specify which log directory to run against. Alternatively, see instructions for running the dev UI in the contributor guide.

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 examples/sklearn_elasticnet_wine -P alpha=0.4

mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=0.4

See examples/sklearn_elasticnet_wine 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 examples/sklearn_logisitic_regression/train.py that you can run as follows:

$ python examples/sklearn_logisitic_regression/train.py
Score: 0.666
Model saved in run <run-id>

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

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

Contributing

We happily welcome contributions to MLflow. Please see our contribution guide for details.

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