https://github.com/databricks/mlflow

Open source platform for the machine learning lifecycle
https://github.com/databricks/mlflow

Last synced: 3 months ago
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Open source platform for the machine learning lifecycle

• Host: GitHub
• URL: https://github.com/databricks/mlflow
• Owner: databricks
• License: apache-2.0
• Archived: true
• Fork: true (mlflow/mlflow)
• Created: 2019-04-05T21:34:23.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-05T21:20:25.000Z (about 5 years ago)
• Last Synced: 2023-04-09T14:49:56.168Z (about 1 year ago)
• Language: Python
• Homepage: https://mlflow.org
• Size: 7.91 MB
• Stars: 90
• Watchers: 12
• Forks: 42
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE.txt

Lists

• awesome-machine-learning-engineering - mlflow
• awesome-python-data-science - mlflow - Open source platform for the complete machine learning lifecycle. (Deployment / Ranking/Recommender)

README

        
===================

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 ([email protected]) 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 ``_.

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 

    $ mlflow sklearn serve -r  -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.