Comments (5)
Superjomn
commented on June 4, 2024
3
这个建议让各个方向和模型作者参与到其中，作为 owner 去推动。
CE 的角色是帮助各个模型监测paddle 代码的稳定性，这个对每个方向都是很重要的，未来模型会越来越多，还是希望有更多人参与其中的。
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guochaorong
commented on June 4, 2024
@kuke @qingqing01 @typhoonzero 三个方向负责人看看吧~
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panyx0718
commented on June 4, 2024
看看能不能通过固定和调整一些参数来降低随机性
- 固定输入的数据的顺序，适当缩小数据量（但不要太容易过拟合，不建议使用会收敛到99%的数据，无法发现收敛bug）。
- 增大训练的batch数，使用固定的batch数，而不是固定的时间。
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Superjomn
commented on June 4, 2024
不太行，随机性很大，现在已经放松到比较大的状态，但偶尔还会挂。
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guochaorong
commented on June 4, 2024
恩恩， 有一些看着是已经没有随机性的模型，还存在挂的情况， 一是during mem这种随运行时指标的阈值的确定（上次yibing 对我们确定的指标有疑议）。 二是一些不应该波动的问题的调查。 这两种需要模型方向自己来确定。
对于还有随机种子不固定和输入随机的。 我们来消灭
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Related Issues (20)
- models repo的模型接入CE监测框架
- models repo 模型设置CE监控用的KPI阈值
- CE支持models repo模型监控方法
- model repo 待release 模型改造规范
- lstm 单卡 pass不固定 HOT 1
- debug单个模型的方法 HOT 1
- where can i import "kpi"?
- where is memory.txt
- where can i import commands HOT 1
- seq2seq模型添加多卡
- vgg16添加多卡
- image_classification模型 acc 指标固定不下来 HOT 6
- 清理各个 task 的 log
- aws 与内网机器性能差异
- vgg16 模型random出现" Segmentation fault" HOT 1
- CE 模型负责人
- transformer模型性能下降 HOT 4
- Add distributed resnet50 model to ce
- CE部分模型使用新接口后，部分阈值需要微调
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