Metadata-Version: 2.1
Name: mlpipeline
Version: 2.0a3
Summary: A framework to define a machine learning pipeline
Home-page: https://github.com/ahmed-shariff/mlpipeline
Author: Ahmed Shariff
Author-email: shariff.mfa@outlook.com
License: UNKNOWN
Description: mlpipeline
        ==========
        This is a simple frawork to organize you machine learning workflow. It automates most of the basic functionalities such as logging, a framework for testing models and gluing together different steps at different stages. This project came about as a result of me abstracting the boilerplate code and automating different parts of the process.
        
        The aim of this simple framework is to consolidate the different sub-problems (such as loading data, model configurations, training process, evalutaion process, exporting trained models, etc.) when working/researching with machine learning models. This allows the user to define how the different sub-problems are to be solved using their choice of tools and mlpipeline would handle piecing them together.
        
        Core operations
        ---------------
        This framework chains the different operations (sub-problems) depending on the mode it is executed in. mlpipeline currently has 3 modes:
        - TEST mode: When in TEST mode, it doesn't perform any logging or tracking. It creates a temporory empty directory for the experiment to store the artifacts of an experiment in. When developing and testing the different operations, this mode can be used.
        - RUN mode: In this mode, logging and tracking is performed. In addition, for each experiment run (refered to as a experiment version in mlpipeline) a directory is created for artifacts to be stored.
        - EXPORT mode: In this mode, the exporting related operations will be executed instead of the training/evaluation related operations.
        
        In addition to providing different modes, the pipeline also supports logging and recording various details. Currently mlpipeline records all logs, metrics and artifacts using a bacis log files as well using `mlflow <https://github.com/databricks/mlflow>`_.
        
        The following information is recorded:
        - The scripts that were executed/impoerted in relation to an experiment.
        - The any output results
        - The metrics and parameters
        
        Documentation
        -------------
        The documentation is hosted at `ReadTheDocs <https://mlpipeline.readthedocs.io/>`_.
        
        Installing
        ----------
        Can be installed directly using the Python Package Index using pip:
            pip install mlpipeline
        
        Usage
        -----
        *work in progress*
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
