stgraph 1.1.0

🌟 Vertex Centric approach for building GNN/TGNNs

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🌟 STGraph

STGraph is a framework designed for deep-learning practitioners to write and train Graph Neural Networks (GNNs) and Temporal Graph Neural Networks (TGNNs). It is built on top of Seastar and utilizes the vertex-centric approach to produce highly efficient fused GPU kernels for forward and backward passes. It achieves better usability, faster computation time and consumes less memory than state-of-the-art graph deep-learning systems like DGL, PyG and PyG-T.

NOTE: If the contents of this repository are used for research work, kindly cite the paper linked above.

Why STGraph

The primary goal of Seastar is more natural GNN programming so that the users learning curve is flattened. Our key observation lies in recognizing that the equation governing a GCN layer, as shown above, takes the form of vertex-centric computation and can be implemented succinctly with only one line of code. Moreover, we can see a clear correspondence between the GNN formulas and the vertex-centric implementations. The benefit is two-fold: users can effortlessly implement GNN models, while simultaneously understanding these models by inspecting their direct implementations.

The Seastar system outperforms state-of-the-art GNN frameworks but lacks support for TGNNs. STGraph bridges that gap and enables users to to develop TGNN models through a vertex-centric approach. STGraph has shown to be significantly faster and more memory efficient that state-of-the-art frameworks like PyG-T for training TGNN models.

Getting Started

Installation for STGraph Package Users

This guide is tailored for users of the STGraph package, designed for constructing GNN and TGNN models. We recommend creating a new virtual environment with Python version 3.8 and installing stgraph inside that dedicated environment.

Installing STGraph from PyPI

pip install stgraph

Installing PyTorch

In addition, STGraph relies on PyTorch. Ensure it is installed in your virtual environment with the following command

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Upon completion of the above steps, you have successfully installed STGraph. Proceed to write and train your first GNN model by referring to the provided tutorial.

Installation for STGraph Package Developers

This guide is intended for those interested in developing and contributing to STGraph.

Download source files from GitHub

git clone https://github.com/bfGraph/STGraph.git
cd STGraph

Create a dedicated virtual environment

Inside the STGraph directory create and activate a dedicated virtual environment named dev-stgraph with Python version 3.8.

python3.8 -m venv dev-stgraph
source dev-stgraph/bin/activate

Install STGraph in editable mode

Make sure to install the STGraph package in editable mode to ease your development process.

pip install -e .[dev]
pip list

Install PyTorch

Ensure to install PyTorch as well for development

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

With this you have successfully installed STGraph locally to make development changes and contribute to the project. Head out to our Pull Requests page and get started with your first contribution.

Running your first STGraph Program

Please have a look inside the tutorials/ directory to write and train your own GNNs using STGraph

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests, issues, etc to us.

How to contribute to Documentation

We follow the PEP-8 format. Black is used as the formatter and pycodestyle as the linter. The linter is is configure to work properly with black (set line length to 88)

Tutorial for Python Docstrings can be found here

sphinx-apidoc -o docs/developers_guide/developer_manual/package_reference/ python/stgraph/ -f
cd docs/
make clean
make html

Authors

Author	Bio
Joel Mathew Cherian	Computer Science Student at National Institute of Technology Calicut
Nithin Puthalath Manoj	Computer Science Student at National Institute of Technology Calicut
Dr. Unnikrishnan Cheramangalath	Assistant Professor in CSED at Indian Institue of Technology Palakkad
Kevin Jude	Ph.D. in CSED at Indian Institue of Technology Palakkad

References

Author(s)	Title	Link(s)
Wu, Yidi and Ma, Kaihao and Cai, Zhenkun and Jin, Tatiana and Li, Boyang and Zheng, Chenguang and Cheng, James and Yu, Fan	STGraph: vertex-centric programming for graph neural networks, 2021	paper, code
Wheatman, Brian and Xu, Helen	Packed Compressed Sparse Row: A Dynamic Graph Representation, 2018	paper, code
Sha, Mo and Li, Yuchen and He, Bingsheng and Tan, Kian-Lee	Accelerating Dynamic Graph Analytics on GPUs, 2017	paper, code
Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar	PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models, 2021	paper, code