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Build data-driven workflows with clean provenance and a friendly syntax.

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Documentation

Please refer to online docs.

Examples

A simple math calculation

from node_graph import task

@task()
def add(x, y):
    return x + y

@task()
def multiply(x, y):
    return x * y

@task.graph()
def AddMultiply(x, y, z):
    the_sum = add(x=x, y=y).result
    return multiply(x=the_sum, y=z).result

Engines and provenance

Explore different execution engines in node-graph-engine.

Run the above graph locally with the LocalEngine and export the provenance graph:

from node_graph.engine.local import LocalEngine

graph = AddMultiply.build(x=1, y=2, z=3)

engine = LocalEngine()
results = engine.run(graph)
# export provenance for visualization
engine.recorder.save_graphviz_svg("add_multiply.svg")

Provenance graph example

License

MIT

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for superstar54 from gravatar.com superstar54

Credits

Author: Xing Wang

License

MIT License

Requires

Python >=3.10

Provides Extra

docs pre-commit tests

Tags

graph workflows

Classifiers

Report project as malware

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