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graph-learn教程上有说小时级别上做模型的增量训练,在graph-learn上是怎么实现的 #221
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GraphLearn-Training部分训练增量训练模型当前是通过离线加载上游的时间窗口中的数据构图,训练新增数据,save/restore ckpt实现的。这要求上游的数据可以按照时间窗口进行分割,如local fs的文件按照天级别划分为小文件。 |
@Seventeen17 这么晚还在,辛苦了。
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训练部分是用GraphLearn-Training进行离线训练,增量更新模型实现的,加载数据如你【理解二:4.5.】;训练过程采样也是基于GraphLearn-Training中构建的离线图进行。 |
@Seventeen17 谢谢你的回答。 |
如图上第四点所说,GraphLearn-Training hourly loads window of graph data, incremental trains models, and updates model on tensorflow Model service.
graphlearn会在小时级别上进行【增量】的模型训练,然后部署到tensorflow。
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是否是我对官网的解读有误,这块【增量】的【训练模型】是需要我们自己去实现。
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