Metadata-Version: 2.1
Name: spock-config
Version: 2.2.4
Summary: Spock is a framework designed to help manage complex parameter configurations for Python applications
Home-page: https://github.com/fidelity/spock
Author: FMR LLC
License: UNKNOWN
Download-URL: https://github.com/fidelity/spock
Project-URL: Source, https://github.com/fidelity/spock
Project-URL: Documentation, https://fidelity.github.io/spock/
Project-URL: Bug Tracker, https://fidelity.github.io/spock/issues
Description: [![Spock](https://raw.githubusercontent.com/fidelity/spock/master/resources/images/logo.png)](https://fidelity.github.io/spock/)
        > Managing complex configurations any other way would be highly illogical...
        
        [![License](https://img.shields.io/badge/License-Apache%202.0-9cf)](https://opensource.org/licenses/Apache-2.0)
        [![Python](https://img.shields.io/badge/python-3.6+-informational.svg)]()
        [![Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        [![PyPI version](https://badge.fury.io/py/spock-config.svg)](https://badge.fury.io/py/spock-config)
        [![Coverage Status](https://coveralls.io/repos/github/fidelity/spock/badge.svg?branch=master)](https://coveralls.io/github/fidelity/spock?branch=master)
        ![Tests](https://github.com/fidelity/spock/workflows/pytest/badge.svg?branch=master)
        ![Docs](https://github.com/fidelity/spock/workflows/docs/badge.svg)
        
        ## About
        
        `spock` is a framework that helps manage complex parameter configurations during research and development of Python 
        applications. `spock` lets you focus on the code you need to write instead of re-implementing boilerplate code like 
        creating ArgParsers, reading configuration files, implementing traceability etc.
        
        In short, `spock` configurations are defined by simple and familiar class-based structures. This allows `spock` to 
        support inheritance, read from multiple markdown formats, automatically generate cmd-line arguments, and allow 
        hierarchical configuration by composition.
        
        ## Key Features
        
        * [Simple Declaration](https://fidelity.github.io/spock/docs/basic_tutorial/Define/): Type checked parameters are 
          defined within a `@spock` decorated class. Supports required/optional and automatic defaults.
        * Easily Managed Parameter Groups: Each class automatically generates its own object within a single namespace.
        * [Parameter Inheritance](https://fidelity.github.io/spock/docs/advanced_features/Inheritance/): Classes support 
          inheritance allowing for complex configurations derived from a common base set of parameters.
        * [Complex Types](https://fidelity.github.io/spock/docs/advanced_features/Advanced-Types/): Nested Lists/Tuples, 
          List/Tuples of Enum of `@spock` classes, List of repeated `@spock` classes
        * Multiple Configuration File Types: Configurations are specified from YAML, TOML, or JSON files.
        * [Hierarchical Configuration](https://fidelity.github.io/spock/docs/advanced_features/Composition/): Compose from 
          multiple configuration files via simple include statements.
        * [Command-Line Overrides](https://fidelity.github.io/spock/docs/advanced_features/Command-Line-Overrides/): Quickly 
          experiment by overriding a value with automatically generated command line arguments.
        * Immutable: All classes are *frozen* preventing any misuse or accidental overwrites (to the extent they can be in 
          Python).
        * [Tractability and Reproducibility](https://fidelity.github.io/spock/docs/basic_tutorial/Saving/): Save runtime 
          parameter configuration to YAML, TOML, or JSON with a single chained command (with extra runtime info such as Git info, 
          Python version, machine FQDN, etc). The saved markdown file can be used as the configuration input to reproduce 
          prior runtime configurations.
        * [Hyper-Parameter Tuner Addon](https://fidelity.github.io/spock/docs/addons/tuner/About.html): Provides a unified
          interface for defining hyper-parameters (via `@spockTuner` decorator) that supports various tuning/algorithm 
          backends (currently: Optuna, Ax)
        * [S3 Addon](https://fidelity.github.io/spock/docs/addons/S3/): Automatically detects `s3://` URI(s) and handles loading 
          and saving `spock` configuration files when an active `boto3.Session` is passed in (plus any additional 
          `S3Transfer` configurations)
        
        ## Quick Install
        
        The basic install and `[s3]` extension require Python 3.6+ while the `[tune]` extension requires Python 3.7+
        
        | Base | w/ S3 Extension | w/ Hyper-Parameter Tuner |
        |------|-----------------|--------------------------|
        | `pip install spock-config` | `pip install spock-config[s3]` | `pip install spock-config[tune]` |
        
        ## Quick Start & Documentation
        
        Refer to the quick-start guide [here](https://fidelity.github.io/spock/docs/Quick-Start/).
        
        Current documentation and more information can be found [here](https://fidelity.github.io/spock/).
        
        Example `spock` usage is located [here](https://github.com/fidelity/spock/blob/master/examples).
        
        ## News/Releases
        
        See [Releases](https://github.com/fidelity/spock/releases) for more information.
        
        #### August 17, 2021
        * Added hyper-parameter tuning backend support for Ax via Service API
        
        #### July 21, 2021
        * Added hyper-parameter tuning support with `pip install spock-config[tune]`
        * Hyper-parameter tuning backend support for Optuna define-and-run API (WIP for Ax)
        
        #### May 6th, 2021
        * Added S3 support with `pip install spock-config[s3]`
        * S3 addon supports automatically handling loading/saving from paths defined with `s3://` URI(s) by passing in an
        active `boto3.Session`
        
        
        ## Original Implementation
        
        [Nicholas Cilfone](https://github.com/ncilfone), [Siddharth Narayanan](https://github.com/sidnarayanan)
        ___
        `spock` is developed and maintained by the **Artificial Intelligence Center of Excellence at Fidelity Investments**.
        
        
Keywords: configuration,argparse,parameters,machine learning,deep learning,reproducibility,hyper-parameter tuning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: s3
Provides-Extra: tune
