<div align="center">
<img src="docs/imgs/logo.jpg" height="240" />
</div>
[![BuildGalileo](https://github.com/JDGalileo/galileo/actions/workflows/build.yml/badge.svg)](https://github.com/JDGalileo/galileo/actions/workflows/build.yml)
[![PyPI version](https://badge.fury.io/py/jdgalileo.svg)](https://badge.fury.io/py/jdgalileo)
[![Anaconda-Server Badge](https://anaconda.org/jdgalileo/jdgalileo/badges/version.svg)](https://anaconda.org/jdgalileo/jdgalileo)
近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。
Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提供图神经网络和图嵌入等模型的训练评估及预测能力。
# 架构介绍
<div align="center">
<img src="docs/imgs/arch.jpg" height="450" /><br/>
Galileo整体架构
</div>
Galileo图深度学习框架采用分层设计理念,主要分为分布式图引擎、图多后端框架、图模型三层。
- **分布式高性能图引擎**:采用紧凑高效的内存结构表达图数据,能够以极低内存支持**超大规模异构图**;基于ZeroCopy机制实现全链路调用,高性能图查询和图采样。
- **图多后端框架**:支持Tensorflow和PyTorch双后端,配置化单机分布式训练,支持Keras和Estimator训练,提供统一的图查询和图采样接口,**易扩展**。
- **图模型**:遵循数据与模型解耦,提升代码复用性;基于组件化设计,降低模型实现难度,支持Message Passing范式编写图模型,也支持Python直接访问训练后端接口,**易使用且灵活性高**。
# 开始使用
我们提供了Galileo的[pip和conda包](docs/pip.md),推荐在[docker镜像](https://hub.docker.com/r/jdgalileo/galileo)中使用Galileo,免去了安装依赖包的烦恼。也可以从[源码编译安装](docs/install.md)Galileo。
阅读[入门教程](docs/introduce.md)开始使用Galileo。
如果Galileo目前实现的[图模型](examples/README.md)无法满足需求,可以[定制化图模型](docs/custom.md)。
使用自己的图数据可以参考[图数据准备](docs/data_prepare.md)。
如果图数据量大,可以参考[分布式训练](docs/train.md)。
想要了解更多Galileo接口参考[API文档](docs/api.md)。
[Galileo性能测试](docs/performance.md)。
# 联系我们
欢迎通过issue和邮件组(galileo_opensource@jd.com)联系我们。
# LICENSE
Galileo图深度学习框架使用Apache License 2.0许可。
# 致谢
Galileo图深度学习框架由京东集团-京东零售-技术与数据中心荣誉出品,在此感谢京东零售算法通道的大力支持,同时感谢商业提升事业部、搜索与推荐平台部等兄弟部门在开发及使用过程中提出的宝贵意见。
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近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提供图神经网络和图嵌入等模型的训练评估及预测能力。 近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提供图神经网络和图嵌入等模型的训练评估及预测能力。 近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提
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