Galileo library for large scale graph training by JD

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Deep Learning galileo
Overview

Build Status PyPI version Anaconda-Server Badge

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


Galileo整体架构

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，推荐在docker镜像中使用Galileo，免去了安装依赖包的烦恼。也可以从源码编译安装Galileo。

阅读入门教程开始使用Galileo。

如果Galileo目前实现的图模型无法满足需求，可以定制化图模型。

使用自己的图数据可以参考图数据准备。

如果图数据量大，可以参考分布式训练。

想要了解更多Galileo接口参考API文档。

联系我们

欢迎通过issue和邮件组（[email protected]）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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Comments
  • galileo_convertor使用方法

    galileo_convertor使用方法

    请问：我安装了galileo的CPU docker，目前执行python3可以进入到环境里面。数据集选择cora的demo也可以跑起来。 可是我们要基于一个大的数据集去做实验，根据github项目提示，我们需要进行数据转换，这就要用到转换工具galileo_convertor。 可是我不知道怎么样才能运行它？各位老师快帮帮弟弟！

    opened by jieheroli 6
Releases(v1.0.0)
Owner
JD Galileo Team
JD Galileo Team
GitHub
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