Metadata-Version: 2.1
Name: dffml
Version: 0.1.2
Summary: Data Flow Facilitator for Machine Learning
Home-page: https://github.com/intel/dffml
Author: John Andersen
Author-email: john.s.andersen@intel.com
Maintainer: John Andersen
Maintainer-email: john.s.andersen@intel.com
License: MIT
Description: # Data Flow Facilitator for Machine Learning (dffml)
        
        [![Build Status](https://travis-ci.org/intel/dffml.svg?branch=master)](https://travis-ci.org/intel/dffml) [![codecov](https://codecov.io/gh/intel/dffml/branch/master/graph/badge.svg)](https://codecov.io/gh/intel/dffml) [![CII](https://bestpractices.coreinfrastructure.org/projects/2594/badge)](https://bestpractices.coreinfrastructure.org/projects/2594) 
        
        DFFML provides APIs for dataset generation and storage, and model definition
        using any machine learning framework, from high level down to low level use is
        supported.
        
        The goal of DFFML is to build a community driven library of plugins for dataset
        generation and model definition. So that we as developers and researchers can
        quickly and easily plug and play various pieces of data with various model
        implementations.
        
        Here's a quick demo showing how DFFML can be used to train on the iris dataset.
        The more we build up the library of plugins (which anyone can maintain, they
        don't have to be contributed upstream unless you want to) the more variations on
        model implementations and feature data generators we all have to work with.
        
        ![Demo](https://github.com/intel/dffml/raw/master/docs/images/iris_demo.gif)
        
        Right now we've released a wrapper around the Tensorflow DNN estimator, and a
        set of feature generators which gather data from git repositories.
        
        ## Installation
        
        DFFML currently should work with Python 3.6. However, only Python 3.7 is
        officially supported. This is because there are a lot of nice helper methods
        Python 3.7 implemented that we intend to use instead of re-implementing.
        
        ```python
        python3.7 -m pip install -U dffml
        ```
        
        You can also install the Features for Git Version Control, and Models for
        Tensorflow Library all at once.
        
        - [DFFML Features for Git Version Control](feature/git/README.md)
        - [DFFML Models for Tensorflow Library](model/tensorflow/README.md)
        
        If you want a quick how to on the iris dataset head to the
        [DFFML Models for Tensorflow Library](model/tensorflow/README.md) repo.
        
        ```python
        python3.7 -m pip install -U dffml[git,tensorflow]
        ```
        
        If you don't have Python 3.7 we have a docker image for you, or you can install
        `pyenv` which will quickly and easily give you Python 3.7. See
        [docs/INSTALL.md](docs/INSTALL.md) for more details.
        
        ## Usage
        
        To start using `dffml` for data set generation with a single CLI command see
        [DFFML Features for Git Version Control](feature/git/README.md).
        
        To start using `dffml` for machine learning with a few CLI commands see
        [DFFML Models for Tensorflow Library](model/tensorflow/README.md).
        
        ## Documentation
        
        Start with [Architecture](docs/ARCHITECTURE.md).
        
        ## Tutorials
        
        Tutorials will get you writing code that takes full advantage of the DFFML API.
        Making you're next machine learning project a breeze to write!
        
        - Features
          - The [new feature tutorial](docs/tutorial/FEATURE.md) will walk you through
            how to write a new DFFML feature to generate data for a dataset.
        - Models
          - The [new model tutorial](docs/tutorial/MODEL.md) will walk you through how
            to wrap your favorite framework or a custom implementation in the DFFML
            library's model API.
        
        ## License
        
        dffml is distributed under the [MIT License](LICENSE).
        
        ## Legal
        
        > This software is subject to the U.S. Export Administration Regulations and
        > other U.S. law, and may not be exported or re-exported to certain countries
        > (Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to
        > persons or entities prohibited from receiving U.S. exports (including
        > Denied Parties, Specially Designated Nationals, and entities on the Bureau
        > of Export Administration Entity List or involved with missile technology or
        > nuclear, chemical or biological weapons).
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Provides-Extra: git
Provides-Extra: tensorflow
