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DeepGNN Overview

DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features including:

Distributed GNN training and inferencing on both CPU and GPU.
Custom graph neural network design.
Online Sampling: Graph Engine (GE) will load all graph data, each training worker will call GE to get node/edge/neighbor features and labels.
Automatic graph partitioning.
Highly performant and scalable.

Project is in alpha version, there might be breaking changes in the future and they will be documented in the changelog.

Install pip package:

python -m pip install deepgnn

If you want to build package from source, see instructions in CONTRIBUTING.md.

Train and evaluate a graphsage model with pytorch on cora dataset:

cd examples/pytorch
python sage.py

We provide a python module to help you upgrade your scripts to new deepgnn versions.

pip install google-pasta
python -m deepgnn.migrate.0_1_56 --script_dir directory_to_migrate

See CHANGELOG.md for full change details.