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A light-weight framework for defining and running dependent tasks.

Project description


A light-weight framework for defining and running dependent tasks. By building a dependency graph between tasks, the runner makes sure that tasks that are required by multiple dependants are only run once and reuses the results.


In the example below the calculate_circumference and calculate_volume tasks both depend on the acquire_radius and calculate_pi tasks. The runner will infer that from the dependency graph and run them before.

import codep
import immutables

def acquire_radius(state: immutables.Map) -> immutables.Map:
    return state.set("radius", 6_371)

def calculate_pi(state: immutables.Map) -> immutables.Map:
    return state.set("pi", 3.14)

@codep.make_partial(depends=(calculate_pi, acquire_radius))
def calculate_circumference(state: immutables.Map) -> immutables.Map:
    return state.set("circumference", 2 * state["pi"] * state["radius"])

@codep.make_partial(depends=(acquire_radius, calculate_pi))
def calculate_volume(state: immutables.Map) -> immutables.Map:
    return state.set("volume", (4/3) * state["pi"] * state["radius"] ** 3)

if __name__ == '__main__':
    r1, r2 =, calculate_volume)
    circumference = r1.state["circumference"]
    volume = r2.state["volume"]
        f"The circumference of earth is {circumference} km and its volume is " 
        f"{volume} km^3"


  • Make Partial a Generic, and make tasks only able to return a value. Partial.apply should instead assign the result to the state which becomes a mapping Type[Partial] -> Any.
  • Remove print(), use logging
  • Concurrent task execution

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