It’s a DAG all the way down

Lightweight computation graphs for Python

GraphKit is a lightweight Python module for creating and running ordered graphs of computations, where the nodes of the graph correspond to computational operations, and the edges correspond to output –> input dependencies between those operations. Such graphs are useful in computer vision, machine learning, and many other domains.

Quick start

Here’s how to install:

pip install graphkit

Here’s a Python script with an example GraphKit computation graph that produces multiple outputs (a * b, a - a * b, and abs(a - a * b) ** 3):

from operator import mul, sub
from graphkit import compose, operation

# Computes |a|^p.
def abspow(a, p):
   c = abs(a) ** p
   return c

# Compose the mul, sub, and abspow operations into a computation graph.
graph = compose(name="graph")(
   operation(name="mul1", needs=["a", "b"], provides=["ab"])(mul),
   operation(name="sub1", needs=["a", "ab"], provides=["a_minus_ab"])(sub),
   operation(name="abspow1", needs=["a_minus_ab"], provides=["abs_a_minus_ab_cubed"], params={"p": 3})(abspow)

# Run the graph and request all of the outputs.
out = graph({'a': 2, 'b': 5})

# Prints "{'a': 2, 'a_minus_ab': -8, 'b': 5, 'ab': 10, 'abs_a_minus_ab_cubed': 512}".

# Run the graph and request a subset of the outputs.
out = graph({'a': 2, 'b': 5}, outputs=["a_minus_ab"])

# Prints "{'a_minus_ab': -8}".

As you can see, any function can be used as an operation in GraphKit, even ones imported from system modules!


Code licensed under the Apache License, Version 2.0 license. See LICENSE file for terms.