=============================================
MLflow: A Machine Learning Lifecycle Platform
=============================================
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code
into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be
used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you
currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:
* `MLflow Tracking <https://mlflow.org/docs/latest/tracking.html>`_: An API to log parameters, code, and
results in machine learning experiments and compare them using an interactive UI.
* `MLflow Projects <https://mlflow.org/docs/latest/projects.html>`_: A code packaging format for reproducible
runs using Conda and Docker, so you can share your ML code with others.
* `MLflow Models <https://mlflow.org/docs/latest/models.html>`_: A model packaging format and tools that let
you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as
Docker, Apache Spark, Azure ML and AWS SageMaker.
* `MLflow Model Registry <https://mlflow.org/docs/latest/model-registry.html>`_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.
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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 <https://mlflow-snapshots.s3-us-west-2.amazonaws.com/>`_.
Install a lower dependency subset of MLflow from PyPI via ``pip install mlflow-skinny``
Extra dependencies can be added per desired scenario.
For example, ``pip install mlflow-skinny pandas numpy`` allows for mlflow.pyfunc.log_model support.
Documentation
-------------
Official documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.
Roadmap
-------
The current MLflow Roadmap is available at https://github.com/mlflow/mlflow/milestone/3. We are
seeking contributions to all of our roadmap items with the ``help wanted`` label. Please see the
`Contributing`_ section for more information.
Community
---------
For help or questions about MLflow usage (e.g. "how do I do X?") see the `docs <https://mlflow.org/docs/latest/index.html>`_
or `Stack Overflow <https://stackoverflow.com/questions/tagged/mlflow>`_.
To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.
For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)
or join us on `Slack`_.
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 <https://mlflow.org/docs/latest/tracking.html>`_,
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,
specifying a backend store via the ``--backend-store-uri`` option. Alternatively, see
instructions for running the dev UI in the `contributor guide <CONTRIBUTING.md>`_.
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_logistic_regression/train.py`` that you can run as follows::
$ python examples/sklearn_logistic_regression/train.py
Score: 0.666
Model saved in run <run-id>
$ mlflow models serve --model-uri runs:/<run-id>/model
$ curl -d '{"dataframe_split": {"columns":[0],"index":[0,1],"data":[[1],[-1]]}}' -H 'Content-Type: application/json' localhost:5000/invocations
**Note:** If using MLflow skinny (``pip install mlflow-skinny``) for model serving, additional
required dependencies (namely, ``flask``) will need to be installed for the MLflow server to function.
Official MLflow Docker Image
----------------------------
The official MLflow Docker image is available on GitHub Container Registry at https://ghcr.io/mlflow/mlflow.
.. code-block:: shell
export CR_PAT=YOUR_TOKEN
echo $CR_PAT | docker login ghcr.io -u USERNAME --password-stdin
# Pull the latest version
docker pull ghcr.io/mlflow/mlflow
# Pull 2.2.1
docker pull ghcr.io/mlflow/mlflow:v2.2.1
Contributing
------------
We happily welcome contributions to MLflow. We are also seeking contributions to items on the
`MLflow Roadmap <https://github.com/mlflow/mlflow/milestone/3>`_. Please see our
`contribution guide <CONTRIBUTING.md>`_ to learn more about contributing to MLflow.
Core Members
------------
MLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.
- `Ben Wilson <https://github.com/BenWilson2>`_
- `Corey Zumar <https://github.com/dbczumar>`_
- `Daniel Lok <https://github.com/daniellok-db>`_
- `Gabriel Fu <https://github.com/gabrielfu>`_
- `Harutaka Kawamura <https://github.com/harupy>`_
- `Serena Ruan <https://github.com/serena-ruan>`_
- `Weichen Xu <https://github.com/WeichenXu123>`_
- `Yuki Watanabe <https://github.com/B-Step62>`_
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"description": "=============================================\nMLflow: A Machine Learning Lifecycle Platform\n=============================================\n\nMLflow is a platform to streamline machine learning development, including tracking experiments, packaging code\ninto reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be\nused with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you\ncurrently run ML code (e.g. in notebooks, standalone applications or the cloud). 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https://github.com/mlflow-automation/mlflow/actions/workflows/r.yml?query=workflow%3AR+event%3Aschedule\n.. |test-requirements| image:: https://img.shields.io/github/actions/workflow/status/mlflow-automation/mlflow/requirements.yml?branch=master&event=schedule&label=test%20requirements&logo=github&style=for-the-badge\n :target: https://github.com/mlflow-automation/mlflow/actions/workflows/requirements.yml?query=workflow%3A\"Test+requirements\"+event%3Aschedule\n.. |stale| image:: https://img.shields.io/github/actions/workflow/status/mlflow/mlflow/stale.yml?branch=master&event=schedule&label=stale&logo=github&style=for-the-badge\n :target: https://github.com/mlflow/mlflow/actions?query=workflow%3AStale+event%3Aschedule\n.. |push-images| image:: https://img.shields.io/github/actions/workflow/status/mlflow/mlflow/push-images.yml?event=release&label=push-images&logo=github&style=for-the-badge\n :target: https://github.com/mlflow/mlflow/actions/workflows/push-images.yml?query=event%3Arelease\n\nInstalling\n----------\nInstall MLflow from PyPI via ``pip install mlflow``\n\nMLflow requires ``conda`` to be on the ``PATH`` for the projects feature.\n\nNightly snapshots of MLflow master are also available `here <https://mlflow-snapshots.s3-us-west-2.amazonaws.com/>`_.\n\nInstall a lower dependency subset of MLflow from PyPI via ``pip install mlflow-skinny``\nExtra dependencies can be added per desired scenario.\nFor example, ``pip install mlflow-skinny pandas numpy`` allows for mlflow.pyfunc.log_model support.\n\nDocumentation\n-------------\nOfficial documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.\n\nRoadmap\n-------\nThe current MLflow Roadmap is available at https://github.com/mlflow/mlflow/milestone/3. We are\nseeking contributions to all of our roadmap items with the ``help wanted`` label. Please see the\n`Contributing`_ section for more information.\n\nCommunity\n---------\nFor help or questions about MLflow usage (e.g. \"how do I do X?\") see the `docs <https://mlflow.org/docs/latest/index.html>`_\nor `Stack Overflow <https://stackoverflow.com/questions/tagged/mlflow>`_.\n\nTo report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.\n\nFor release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)\nor join us on `Slack`_.\n\nRunning a Sample App With the Tracking API\n------------------------------------------\nThe programs in ``examples`` use the MLflow Tracking API. For instance, run::\n\n python examples/quickstart/mlflow_tracking.py\n\nThis program will use `MLflow Tracking API <https://mlflow.org/docs/latest/tracking.html>`_,\nwhich logs tracking data in ``./mlruns``. This can then be viewed with the Tracking UI.\n\n\nLaunching the Tracking UI\n-------------------------\nThe MLflow Tracking UI will show runs logged in ``./mlruns`` at `<http://localhost:5000>`_.\nStart it with::\n\n mlflow ui\n\n**Note:** Running ``mlflow ui`` from within a clone of MLflow is not recommended - doing so will\nrun the dev UI from source. We recommend running the UI from a different working directory,\nspecifying a backend store via the ``--backend-store-uri`` option. Alternatively, see\ninstructions for running the dev UI in the `contributor guide <CONTRIBUTING.md>`_.\n\n\nRunning a Project from a URI\n----------------------------\nThe ``mlflow run`` command lets you run a project packaged with a MLproject file from a local path\nor a Git URI::\n\n mlflow run examples/sklearn_elasticnet_wine -P alpha=0.4\n\n mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=0.4\n\nSee ``examples/sklearn_elasticnet_wine`` for a sample project with an MLproject file.\n\n\nSaving and Serving Models\n-------------------------\nTo illustrate managing models, the ``mlflow.sklearn`` package can log scikit-learn models as\nMLflow artifacts and then load them again for serving. There is an example training application in\n``examples/sklearn_logistic_regression/train.py`` that you can run as follows::\n\n $ python examples/sklearn_logistic_regression/train.py\n Score: 0.666\n Model saved in run <run-id>\n\n $ mlflow models serve --model-uri runs:/<run-id>/model\n\n $ curl -d '{\"dataframe_split\": {\"columns\":[0],\"index\":[0,1],\"data\":[[1],[-1]]}}' -H 'Content-Type: application/json' localhost:5000/invocations\n\n**Note:** If using MLflow skinny (``pip install mlflow-skinny``) for model serving, additional\nrequired dependencies (namely, ``flask``) will need to be installed for the MLflow server to function.\n\nOfficial MLflow Docker Image\n----------------------------\n\nThe official MLflow Docker image is available on GitHub Container Registry at https://ghcr.io/mlflow/mlflow.\n\n.. code-block:: shell\n\n export CR_PAT=YOUR_TOKEN\n echo $CR_PAT | docker login ghcr.io -u USERNAME --password-stdin\n # Pull the latest version\n docker pull ghcr.io/mlflow/mlflow\n # Pull 2.2.1\n docker pull ghcr.io/mlflow/mlflow:v2.2.1\n\nContributing\n------------\nWe happily welcome contributions to MLflow. We are also seeking contributions to items on the\n`MLflow Roadmap <https://github.com/mlflow/mlflow/milestone/3>`_. Please see our\n`contribution guide <CONTRIBUTING.md>`_ to learn more about contributing to MLflow.\n\nCore Members\n------------\n\nMLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.\n\n- `Ben Wilson <https://github.com/BenWilson2>`_\n- `Corey Zumar <https://github.com/dbczumar>`_\n- `Daniel Lok <https://github.com/daniellok-db>`_\n- `Gabriel Fu <https://github.com/gabrielfu>`_\n- `Harutaka Kawamura <https://github.com/harupy>`_\n- `Serena Ruan <https://github.com/serena-ruan>`_\n- `Weichen Xu <https://github.com/WeichenXu123>`_\n- `Yuki Watanabe <https://github.com/B-Step62>`_\n\n\n",
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