Metadata-Version: 1.1
Name: mlflow
Version: 0.1.0
Summary: MLflow: An ML Workflow Tool
Home-page: https://mlflow.org/
Author: Databricks
Author-email: UNKNOWN
License: Apache License 2.0
Description-Content-Type: UNKNOWN
Description: ======
        MLflow
        ======
        The current version of MLflow is an alpha release. This means that APIs and storage formats
        are subject to breaking 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 git@github.com: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/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 <CONTRIBUTING.rst>`_
        for details.
        
Keywords: ml ai databricks
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
