tiny-dag 0.0.8

Minimal DAG implementation with Python

Tiny DAG

Bare bones implementation of computation (directed, acyclic) graph for Python.

User provides a graph structure (nodes) and input data for graph. The graph executes every node in the graph and returns output of every node as the result. The library supports multiple outputs.

Requirements

• Python >= 3.6
• graphviz (optional)

Installation

Install graphviz (optional, needed for rendering)

sudo apt-get install graphviz

Install tiny-dag

pip3 install tiny-dag

Usage

The usage should be quite intuitive: write your functions as you normally would and then create graph structure that orchestrates the functions calls. There is one extra rule you need to know, though: functions need to return dict with keys matching output definition of the node. Output of the node can be referenced in the graph structure by node_name/output_name.

Usage example:

from tinydag.graph import Graph
from tinydag.node import Node

def add(a, b): return {"output": a + b}
def mul(a, b): return {"output": a * b}
def div(a, b): return {"output": a / b}
def add_subtract(a, b): return {"add_output": a + b, "subtract_output": a - b}

nodes = [
    Node(["add1/output", "x"], add, "add2", ["output"]),
    Node(["add1/output", "add2/output"], mul, "mul", ["output"]),
    Node(["x", "y"], add, "add1", ["output"]),
    Node(["x", "z"], add_subtract, "add_subtract", ["add_output", "subtract_output"]),
    Node(["mul/output", "add_subtract/add_output"], div, "div", ["output"]),
]

graph = Graph(nodes)
graph.render()

data = {"x": 5, "y": 3, "z": 3}
graph.check()
results = graph.calculate(data)
print(f"Result: {results}")

The results is dict of node outputs, in this case:

{'add1/output': 8, 'add_subtract/add_output': 8, 'add_subtract/subtract_output': 2, 'add2/output': 13, 'mul/output': 104, 'div/output': 13.0}

render method produces following figure:

For a bit more complicated and practical usage, see how the library can be used to orchestrate and visualize data processing pipelines: src/samples/sample_credit_risk_prediction.py.