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mateizandrewmchenmparkhesmurchingStephanie Bodoff
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Initial commit of mlflow
Co-authored-by: Andrew Chen <[email protected]> Co-authored-by: Mani Parkhe <[email protected]> Co-authored-by: Siddharth Murching <[email protected]> Co-authored-by: Stephanie Bodoff <[email protected]> Co-authored-by: tomasatdatabricks <[email protected]> Co-authored-by: Aaron Davidson <[email protected]> Co-authored-by: Ali Ghodsi <[email protected]> Co-authored-by: dmatrix <[email protected]> Co-authored-by: juntai-zheng <[email protected]>
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CONTRIBUTING.rst

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

Dockerfile

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

LICENSE.txt

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Copyright 2018 Databricks Inc. All rights reserved.
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The Software consists of a Work (as defined below) subject to the Apache License Version 2,
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Community Edition Terms of Service, (b) the Databricks Terms of Service, or (c) another written
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agreement between Licensee and Databricks for the use of the Software.
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README.rst

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==================
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MLflow Alpha Release
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==================
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.. warning::
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The current version of MLflow is an alpha. This means that APIs and storage formats
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are subject to change!
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Installing
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----------
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Install MLflow from PyPi via ``pip install mlflow``
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MLflow requires ``conda`` to be on the ``PATH`` for the projects feature.
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Documentation
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-------------
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Official documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.
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Running a Sample App With the Tracking API
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------------------------------------------
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The programs in ``example`` use the MLflow Tracking API. For instance, run::
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python example/quickstart/test.py
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This program will use MLflow log API, which stores tracking data in ``./mlruns``, which can then
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be viewed with the Tracking UI.
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Launching the Tracking UI
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-------------------------
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The MLflow Tracking UI will show runs logged in ``./mlruns`` at `<http://localhost:5000>`_.
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Start it with::
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mlflow ui
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Running a Project from a URI
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----------------------------
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The ``mlflow run`` command lets you run a project packaged with a MLproject file from a local path
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or a Git URI::
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mlflow run example/tutorial -P alpha=0.4
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mlflow run [email protected]:databricks/mlflow-example.git -P alpha=0.4
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See ``example/tutorial`` for a sample project with an MLproject file.
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Saving and Serving Models
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-------------------------
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To illustrate managing models, the ``mlflow.sklearn`` package can log Scikit-learn models as
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MLflow artifacts and then load them again for serving. There is an example training application in
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``example/quickstart/test_sklearn.py`` that you can run as follows::
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$ python example/test_sklearn.py
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Score: 0.666
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Model saved in run <run-id>
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$ mlflow sklearn serve -r <run-id> model
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$ curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST localhost:5000/invocations
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Contributing
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------------
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We happily welcome contributions, please see our `contribution guide <CONTRIBUTING.rst>`_
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for details.

conftest.py

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

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