Comments (3)
TDM3.0训练时是采用beam search得到样本,然后根据样本的子节点max得到该节点的拟合值,但是一个样本假设耗时0.3s,一个batch有2048个样本,那一个batch的耗时不就将近600s?这不会超级耗时吗
不太明白是什么意思,做beam search及取max都可以batch-wise的进行,没有串行的开销。具体细节可以参考我们tf的实现:http://proceedings.mlr.press/v119/zhuo20a.html
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借楼请教一个问题,如图对一个用户做推荐召回,从上至下,需要做深度模型的推理总共 O(2logNK) 个,按照 N=100万来算,logN=20,按照 K=10来算(通常K都要大于10),总共要做推理 2* 20 * 10=400 次的深度模型计算。
这个计算量跟通常的 youtube DNN +ANN 相比,也大了 400 倍,而且这个还是最差的量级情况下
即使全部都是并行的,整个延迟 p99 的耗时其实是变成了 p(99 *400)
请问大神,这个工程上是怎么实现的
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请问3.0在哪里 我只能看到最高1.2版本呀
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Related Issues (20)
- Fail to find glog
- 集群化部署有例子可以验证是否成功么
- 训练时报错
- TDM中叶子结点的embdding是怎么来的
- insufficient processing units
- invalid batch
- TDM serving编译时报错
- AUC bug report
- 话说现在用TensorFlow分布式训练CTR模型的思路是? HOT 1
- 如何在标准文本数据格式中表示sparse feature的 segments HOT 1
- xdl有没有各个函数功能的详细文档 HOT 1
- build_final_net return a tuple but use as list
- pip install xdl
- help!!! Illegal instruction (core dumped)
- tdm 单机预测评估时卡住没反应 HOT 2
- SyncHook 没有在 worker 之间形成同步?
- ZfsKv是做什么用的?
- mac M2 芯片的可以安装xdl吗? HOT 1
- TDM 的train.py执行异常, item_emb 为空
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