README

Distributed Graph Flow

(Distributed) Graph Flow (GF) is a Python toolkit to develop and deploy
Graph Neural Network (GNN) models.

For more information, check the documentation at https://dgf.readthedocs.io/

This is not an officially supported Google product. This project is not
eligible for the Google Open Source Software Vulnerability Rewards
Program.

Installation

To install DGF from PyPI, run:

pip install dgf -U

Currently, DGF is available on Python 3.11-13, on Linux x86-64.

😎 Minimal Usage example

# Temporary fix for Keras dependency.
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"

# Import (distributed) graph flow
import dgf

# Fetch an example graph
graph, schema = dgf.io.fetch_ogb_graph("arxiv")

# Train a model
model = dgf.learning.train_node_model(graph=graph, schema=schema, target_column="labels")

# Look at the model
model.describe()

# Evaluate the model
model.evaluate()

# Make predictions
model.predict(graph, seed_node_idxs=[0, 1, 2])

# Save the model for later
model.save("/tmp/model")

Installation

1

PyPI

😎 Minimal Usage example

1

# Temporary fix for Keras dependency. import os os.environ["TF_USE_LEGACY_KERAS"] = "1" # Import (distributed) graph flow import dgf # Fetch an example graph graph, schema = dgf.io.fetch_ogb_graph("arxiv") # Train a model model = dgf.learning.train_node_model(graph=graph, schema=schema, target_column="labels") # Look at the model model.describe() # Evaluate the model model.evaluate() # Make predictions model.predict(graph, seed_node_idxs=[0, 1, 2]) # Save the model for later model.save("/tmp/model")