DeepGNN is a framework for training machine learning models on large scale graph data.

Last update: Jan 01, 2023

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Overview

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.

Usage

Install pip package:

python -m pip install deepgnn-torch

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/graphsage
./run.sh

Training other models

Examples folder contains various models one can experiment with DeepGNN. To train models with Tensorflow you need to install deepgnn-tf package, deepgnn-torch package contains packages to train pytorch examples. Each model folder contains a shell script run.sh that will train a corresponding model on a toy graph, a README.md file with a short description of a model, reference to original paper, and explanation of command line arguments.

DeepGNN is a framework for training machine learning models on large scale graph data.

Related tags

Overview

DeepGNN Overview

Usage

Training other models

Owner

Microsoft

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