Metadata-Version: 2.1
Name: machinable
Version: 2.0.1
Summary: A modular configuration system for machine learning research
Home-page: https://machinable.org
Author: Frithjof Gressmann
Author-email: hello@machinable.org
License: mit
Description: <div align="center">
          <img src="https://raw.githubusercontent.com/machinable-org/machinable/master/docs/logo/logo.png">
        </div>
        
        # machinable
        
        <a href="https://travis-ci.org/machinable-org/machinable">
        <img src="https://travis-ci.org/machinable-org/machinable.svg?branch=master" alt="Build Status">
        </a>
        <a href="https://opensource.org/licenses/MIT">
        <img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License">
        </a>
        <a href="https://github.com/psf/black">
        <img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg">
        </a>
        
        <br />
        <br />
        
        **machinable** is a modular configuration system for machine learning research. Using straight-forward conventions and a powerful configuration engine, it can help structuring your projects in a principled way to move quickly while enabling reuse and collaboration.
        
        [Explore key features at a glance →](https://machinable.org/guide/at-glance.html)
        
        *Ready to start?*
        
            $ pip install machinable
        
        ## Features
        
        **Powerful configuration**
        
        - YAML-based project-wide configuration files with expressive syntax
        - Efficient configuration manipulation
        - Modular code organisation to allow for encapsulation and re-use
        - Import system to use 3rd party configuration and code without overhead
        - 'Mixins' for horizontal inheritance structure
        
        **Efficient execution**
        
        - Works with existing code
        - Support for seamless cloud execution
        - Automatic code backups
        - Managed randomness and reproducibility
        - Advanced hyperparameter tuning using [Ray Tune](https://github.com/ray-project/ray)
        
        **Effective result collection and analysis**
        
        - Logging, tabular record writer and storage API
        - File system abstraction (in-memory, AWS S3, and more)
        - Flat-file result database with convenient query syntax
        
        ### Documentation
        
        Read the [user guide ](https://machinable.org/guide) to get started.
        
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
Provides-Extra: integrations
Provides-Extra: server
Provides-Extra: all
