Skip to main content

Distributed Graph Flow

Project description

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 YDF 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")

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

dgf-0.0.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

dgf-0.0.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

dgf-0.0.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

File details

Details for the file dgf-0.0.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for dgf-0.0.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2e1a948e9babfd49f330248a7eec586e04fd2c80e0ba5c9107d772ec21637562
MD5 3b327022d2f02cb35db23e7fa1244722
BLAKE2b-256 2b24221f3ce90a065e5bbc16812a4fcf24875b01a883b93eb86c0b0248a3029e

See more details on using hashes here.

File details

Details for the file dgf-0.0.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for dgf-0.0.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0b4076879ef350b54eb80cc95781c49903b270cfed75b35681b7c0665964f810
MD5 757f84bc0b96d8a330a8a15dbb28f50a
BLAKE2b-256 a99e8c8cc4ee8445ea788b7d20e3caf7b9b3f384ab550525eeccd24566ccbf2f

See more details on using hashes here.

File details

Details for the file dgf-0.0.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for dgf-0.0.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 75198841a951490f25312c31cd12e39703704b645fcb608f20865a02d5e6bab7
MD5 92a1a4fb9e8d27b9c1cfb398b0f44e95
BLAKE2b-256 3df6849d4ce760b0f9d00ca079ffc2573aba9a8d143da5819c6a596df5c0c7f5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page