spock is a framework that helps users easily define, manage, and use complex parameter configurations within Python
applications. It lets you focus on the code you need to write instead of re-implementing boilerplate code such as
creating ArgParsers, reading configuration files, handling dependencies, implementing type validation,
maintaining traceability, etc.
spock configurations are normal python classes that are decorated with @spock. It supports
inheritance, dynamic class dependencies, loading/saving configurations from/to multiple markdown formats, automatically
generating CLI arguments, and hierarchical configuration by composition.
- Simple organized parameter definitions (i.e. a single line)
- Type checked (static-eqsue) & frozen parameters (i.e. fail early during long ML training runs)
- Complex parameter dependencies made simple (i.e.
@spockclass with a parameter that is an Enum of other@spockclasses) - Fully serializable parameter state(s) (i.e. exactly reproduce prior runtime parameter configurations)
- Automatic type checked CLI generation w/o argparser boilerplate (i.e click and/or typer for free!)
- Easily maintain parity between CLIs and Python APIs (i.e. single line changes between CLI and Python API definitions)
- Unified hyper-parameter definitions and interface (i.e. don't write different definitions for Ax or Optuna)
- Resolver that supports value definitions from reference to other defined variables, environmental variables, dynamic template re-injection, and encryption of sensitive values
- Simple Declaration: Type checked parameters are
defined within a
@spockdecorated 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 (w/ lazy evaluation of inheritance/dependencies) allowing for complex configurations derived from a common base set of parameters.
- Complex Types: Nested Lists/Tuples,
List/Tuples of Enum of
@spockclasses, List of repeated@spockclasses - 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
@spockTunerdecorator) that supports various tuning/algorithm backends (currently: Optuna, Ax) - S3 Addon: Automatically detects
s3://URI(s) and handles loading and savingspockconfiguration files when an activeboto3.Sessionis passed in (plus any additionalS3Transferconfigurations)
The basic install and [s3] extension require Python 3.7+ while the [tune] extension requires Python 3.8+
| Base | w/ S3 Extension | w/ Hyper-Parameter Tuner |
|---|---|---|
pip install spock-config |
pip install spock-config[s3] |
pip install spock-config[tune] |
See Releases for more information.
- Removed Python 3.6 support as it was causing dependency hell
- Collection of bugfixes
- Added support for resolving value definitions from references to other defined variables with the following syntax,
${spock.var:SpockClass.defined_variable} - Added support for new fundamental types: (1) file: this is an overload of a str that verifies file existence and (r/w) access (2) directory: this is an overload of a str that verifies directory existence, creation if not existing, and (r/w) access
- Deprecated support for
Listof repeated@spockdecorated classes. - Collection of bugfixes
- Added support for resolving value definitions from environmental variables with the following syntax,
${spock.env:name, default} - Added
.injectannotation that will write back the original env notation to the saved output - Added the
.cryptoannotation which provides a simple way to hide sensitive environmental variables while still maintaining the written/loadable state of the spock config
- Added support for
typing.Callabletypes (includes advanced types such asList[List[Callable]]) - Added support for
typing.Dicttypes with type checking for types of both keys and values (includes advanced types such asDict[str, Tuple[Callable, Callable]]) - Added support for post init hooks that allow for validation on parameters defined within
@spockdecorated classes. Additionally, added some common validation check to utils (within, greater than, less than, etc.) - Updated unit tests to support Python 3.10
- Added
evolvesupport to the underlyingSpockBuilderclass. This provides functionality similar to the underlying attrs library (attrs.evolve).evolve()creates a newSpockspaceinstance based on differences between the underlying declared state and any passed in instantiated@spockdecorated classes.
spock was originally developed by the Artificial Intelligence Center of Excellence at Fidelity Investments by Nicholas Cilfone and Siddharth Narayanan