alkymi 0.3.1

Description:

alkymi 0.3.1

alkymi ⚗️





Alkymi is a pure Python (3.7+) library for describing and executing tasks and pipelines with built-in caching and
conditional evaluation based on checksums.
Alkymi is easy to install, simple to use, and has very few dependencies outside of Python's standard library. The code
is cross-platform, and allows you to write your pipelines once and deploy to multiple operating systems (tested on
Linux, Windows and Mac).
Documentation, including a quickstart guide, is provided here.
Features

Easily define complex data pipelines as decorated Python functions

This allows you to run linting, type checking, etc. on your data pipelines


Return values are automatically cached to disk, regardless of type
Efficiently checks if pipeline is up-to-date

Checks if external files have changed, bound functions have changed or if pipeline dependencies have changed


No domain specific language (DSL) or CLI tool, just regular Python

Supports caching and conditional evaluation in Jupyter Notebooks


Cross-platform - works on Linux, Windows and Mac
Expose recipes as a command-line interface (CLI) using alkymi's
Lab type

Sample Usage
For examples of how to use alkymi, see the
quickstart guide.
Example code:
import numpy as np
import alkymi as alk

@alk.recipe()
def long_running_task() -> np.ndarray:
# Perform expensive computation here ...
hard_to_compute_result = np.array([42])
# Return value will be automatically cached to disk
return hard_to_compute_result

result = long_running_task.brew() # == np.ndarray([42])

Or one of the examples, e.g. MNIST.
Installation
Install via pip:
pip install --user alkymi

Or see the Installation page.
Testing
After installing, you can run the test suite (use the lint, coverage and type_check recipes to perform those
actions):
python3 labfile.py brew test

License
alkymi is licensed under The MIT License as found in the LICENSE.md file
Upcoming Features
The following features are being considered for future implementation:

Type annotations propagated from bound functions to recipes
Support for call/type checking all recipes (e.g. by adding a check command to Lab)
Cache maintenance functionality

Known Issues

alkymi currently doesn't check custom objects for altered external files when computing cleanliness (e.g. MyClass
has a self._some_path that points to a file somewhere outside alkymi's internal cache)
alk.foreach() currently only supports enumerable inputs of type List or Dict
Recipes marked transient will always be dirty, and thus always require reevaluation. This functionality should be
replaced by a proper means of creating recipes that don't cache outputs, but only run when needed to provide inputs for
downstream recipes