spock-config 2.2.0

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, and allow hierarchical configuration by composition.

Quick Install

Requires Python 3.6+

pip install spock-config

w/ S3 extension

pip install spock-config[s3]

Version(s)

All prior versions are available on PyPi. If legacy API and backend support is needed please install a pre v2.0.0+ version. We recommend refactoring your code to the new API and backend instead as legacy versions will be missing recent features, bugfixes, and hotfixes.

• v2.0.0+: Dropped support for legacy backend and API semantics
• v1.1.0-v1.2.1: New API with support for legacy backend and legacy API semantics
• v1.0.0: Legacy API and backend

News

See Releases for more information.

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

March 18th, 2021

• Support for Google docstring style annotation of spock class (and Enums) and attributes
• Added in ability to print docstring annotated help information to command line with --help argument

Documentation

Current documentation and more information can be found here.

Example spock usage is located here.

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 hyper-parameter tuning that supports various backends (Optuna, WIP: 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)

Main Contributors

Nicholas Cilfone, Siddharth Narayanan

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spock is developed and maintained by the Artificial Intelligence Center of Excellence at Fidelity Investments.