Graph engine - distributed graph engine to host graphs.
Project description
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
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
Migrating Scripts
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.
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