spock-config 2.3.1

Spock is a framework designed to help manage complex parameter configurations for Python applications

Managing complex configurations any other way would be highly illogical...

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: 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: Classes support inheritance allowing for complex configurations derived from a common base set of parameters.
• Complex 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: Compose from multiple configuration files via simple include statements.
• 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: 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: Provides a unified interface for defining hyper-parameters (via @spockTuner decorator) that supports various tuning/algorithm backends (currently: Optuna, Ax)
• S3 Addon: 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.

Current documentation and more information can be found here.

Example spock usage is located here.

News/Releases

See Releases for more information.

December 14, 2021

• Refactored the backend to better handle nested dependencies (and for clarity)
• Refactored the docs to use Docusaurus

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)

Original Implementation

Nicholas Cilfone, Siddharth Narayanan

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spock was originally developed by the Artificial Intelligence Center of Excellence at Fidelity Investments.