Initial commit of mlflow

Co-authored-by: Andrew Chen <andrewmchen@users.noreply.github.com>
Co-authored-by: Mani Parkhe <mparkhe@users.noreply.github.com>
Co-authored-by: Siddharth Murching <smurching@users.noreply.github.com>
Co-authored-by: Stephanie Bodoff <stbof@users.noreply.github.com>
Co-authored-by: tomasatdatabricks <tomasatdatabricks@users.noreply.github.com>
Co-authored-by: Aaron Davidson <aarondav@users.noreply.github.com>
Co-authored-by: Ali Ghodsi <alig@users.noreply.github.com>
Co-authored-by: dmatrix <dmatrix@users.noreply.github.com>
Co-authored-by: juntai-zheng <juntai-zheng@users.noreply.github.com>

CONTRIBUTING.rst

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+Contributing to MLflow
+======================
+We welcome community contributions to MLflow. This page describes how to develop/test your changes
+to MLflow locally.
+
+Prerequisites
+-------------
+
+We recommend installing MLflow in its own virtualenv for development, as follows::
+
+    virtualenv env
+    source env/bin/activate
+    pip install -r dev-requirements.txt
+    pip install -r tox-requirements.txt
+    pip install -e .
+
+
+``npm`` is required to run the Javascript dev server.
+You can verify that ``npm`` is on the PATH by running ``npm -v``, and
+`install npm <https://www.npmjs.com/get-npm>`_ if needed.
+
+Install Node Modules
+~~~~~~~~~~~~~~~~~~~~
+Before running the Javascript dev server or building a distributable wheel, install Javascript
+dependencies via:
+
+.. code::
+
+   cd mlflow/server/js
+   npm install
+   cd - # return to root repository directory
+
+If modifying dependencies in ``mlflow/server/js/package.json``, run `npm update` within
+``mlflow/server/js`` to install the updated dependencies.
+
+
+Launching the Development UI
+----------------------------
+We recommend `Running the Javascript Dev Server`_ - otherwise, the tracking frontend will request
+files in the ``mlflow/server/js/build`` directory, which is not checked into Git.
+Alternatively, you can generate the necessary files in ``mlflow/server/js/build`` as described in
+`Building a Distributable Artifact`_.
+
+
+Tests and Lint
+--------------
+Please verify that the unit tests & linter pass before submitting a pull request by running:
+
+.. code::
+
+    pytest
+    ./lint.sh
+
+
+Running the Javascript Dev Server
+---------------------------------
+`Install Node Modules`_, then run the following:
+
+In one shell:
+
+.. code::
+
+   mlflow ui
+
+In another shell:
+
+.. code::
+
+   cd mlflow/server/js
+   npm start
+
+The MLflow Tracking UI will show runs logged in ``./mlruns`` at `<http://localhost:3000>`_.
+
+Building a Distributable Artifact
+---------------------------------
+`Install Node Modules`_, then run the following:
+
+Generate JS files in ``mlflow/server/js/build``:
+
+.. code::
+
+   cd mlflow/server/js
+   npm run build
+
+Build a pip-installable wheel in ``dist/``:
+
+.. code::
+
+   cd -
+   python setup.py bdist_wheel
+
+
+Writing Docs
+------------
+Install the necessary Python dependencies via ``pip install -r dev-requirements.txt``. Then run
+
+.. code::
+
+   cd docs
+   make livehtml

Dockerfile

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+FROM continuumio/miniconda
+
+RUN pip install numpy pandas flask pygal smalluuid zipstream python-dateutil gitpython scikit-learn
+
+WORKDIR /app
+
+ADD . /app

LICENSE.txt

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README.rst

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+==================
+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 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.

conftest.py

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+def pytest_addoption(parser):
+    parser.addoption('--large', action='store_true', dest="large",
+                     default=False, help="Run tests decorated with 'large' annotation")
+
+
+def pytest_configure(config):
+    if not config.option.large:
+        setattr(config.option, 'markexpr', 'not large')