{ "info": { "author": "Databricks/Onica", "author_email": "", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6" ], "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). MLflow's current components are:\n\n* `MLflow Tracking `_: An API to log parameters, code, and\n results in machine learning experiments and compare them using an interactive UI.\n* `MLflow Projects `_: A code packaging format for reproducible\n runs using Conda and Docker, so you can share your ML code with others.\n* `MLflow Models `_: A model packaging format and tools that let\n you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as\n Docker, Apache Spark, Azure ML and AWS SageMaker.\n\n|docs| |travis| |pypi| |conda-forge| |cran| |maven| |license|\n\n.. |docs| image:: https://img.shields.io/badge/docs-latest-success.svg\n :target: https://mlflow.org/docs/latest/index.html\n :alt: Latest Docs\n.. |travis| image:: https://img.shields.io/travis/mlflow/mlflow.svg\n :target: https://travis-ci.org/mlflow/mlflow\n :alt: Build Status\n.. |pypi| image:: https://img.shields.io/pypi/v/mlflow.svg\n :target: https://pypi.org/project/mlflow/\n :alt: Latest Python Release\n.. |conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/mlflow.svg\n :target: https://anaconda.org/conda-forge/mlflow\n :alt: Latest Conda Release\n.. |cran| image:: https://img.shields.io/cran/v/mlflow.svg\n :target: https://cran.r-project.org/package=mlflow\n :alt: Latest CRAN Release\n.. |maven| image:: https://img.shields.io/maven-central/v/org.mlflow/mlflow-parent.svg\n :target: https://mvnrepository.com/artifact/org.mlflow\n :alt: Maven Central\n.. |license| image:: https://img.shields.io/badge/license-Apache%202-brightgreen.svg\n :target: https://github.com/mlflow/mlflow/blob/master/LICENSE.txt\n :alt: Apache 2 License\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 `_.\n\nDocumentation\n-------------\nOfficial documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.\n\nCommunity\n---------\nFor help or questions about MLflow usage (e.g. \"how do I do X?\") see the `docs `_\nor `Stack Overflow `_.\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 at https://tinyurl.com/mlflow-slack.\n\nRunning a Sample App With the Tracking API\n------------------------------------------\nThe programs in ``examples`` use the MLflow Tracking API. 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