Skip to main content

A small library for managing deep learning models, hyper parameters and datasets

Project description

Zookeeper

GitHub Actions Codecov PyPI - Python Version PyPI PyPI - License Code style: black Join the community on Spectrum

A small library for configuring modular applications.

Installation

pip install zookeeper

Components

The fundamental building block of Zookeeper is a Component. Component subclasses can have configurable parameters, which are declared using class-level type annotations (in a similar way to Python dataclasses). These parameters can be Python objects or nested sub-components, and need not be set with a default value.

For example:

from zookeeper import Component

class ChildComponent(Component):
    a: int                  # An `int` parameter, with no default set
    b: str = "foo"          # A `str` parameter, which by default will be `foo`

class ParentComponent(Component):
    a: int                  # The same `int` parameter as the child
    child: ChildComponent   # A nested component parameter, of type `ChildComponent`

After instantiation, components can be 'configured' with a configuration dictionary, containing values for a tree of nested parameters. This process automatically injects the correct values into each parameter.

If a child sub-component declares a parameter which already exists in some containing parent, then it will pick up the value that's set on the parent, unless a 'scoped' value is set on the child.

For example:

p = ParentComponent()

p.configure({
    "a": 5,
    "child.a": 4,
})

>>> 'ChildComponent' is the only concrete component class that satisfies the type
>>> of the annotated parameter 'ParentComponent.child'. Using an instance of this
>>> class by default.

print(p)

>>> ParentComponent(
>>>     a = 5,
>>>     child = ChildComponent(
>>>         a = 4,
>>>         b = "foo"
>>>     )
>>> )

Tasks and the CLI

The best way to define runnable tasks with Zookeeper is to subclass Task and override the run method.

Zookeeper provides a small mechanism to run tasks from a CLI, using the decorator @add_task_to_cli. The CLI will automatically instantiate the task and call configure(), passing in configuration parsed from command line arguments.

For example:

from zookeeper import Task
from zookeeper.cli import add_task_to_cli, cli

@add_task_to_cli
class UseChildA(Task):
    parent: ParentComponent

    def run(self):
        print(self.parent.child.a)

@add_task_to_cli
class UseParentA(UseChildA):
    def run(self):
        print(self.parent.a)

if __name__ == "__main__":
    cli()

Running the above file then gives a nice CLI interface:

python test.py use_child_a
>>> ValueError: No configuration value found for annotated parameter 'UseChildA.parent.a' of type 'int'.

python test.py use_child_a a=5
>>> 5

python test.py use_child_a a=5 child.a=3
>>> 3

python test.py use_parent_a a=5 child.a=3
>>> 5

Using Zookeeper to define Larq or Keras experiments

See examples/larq_experiment.py for an example of how to use Zookeeper to define all the necessary components (dataset, preprocessing, and model) of a Larq experiment: training a BinaryNet on CIFAR-10. This example can be easily adapted to other Larq or Keras models and other datasets.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

zookeeper-1.0.dev6.tar.gz (17.3 kB view hashes)

Uploaded Source

Built Distribution

zookeeper-1.0.dev6-py3-none-any.whl (23.5 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page